Journal articles on the topic 'Yolo v 3 model'
To see the other types of publications on this topic, follow the link: Yolo v 3 model.
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Yolo v 3 model.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Acharya, M., J. M. Burke, C. Hansen, and R. W. Rorie. "21 EVALUATION OF SEMEN EXTENDERS FOR SHORT-TERM STORAGE OF RAM SEMEN AT 4°C." Reproduction, Fertility and Development 29, no. 1 (2017): 118. http://dx.doi.org/10.1071/rdv29n1ab21.
Full textAbstract:
Preliminary studies found that progressive motility of ram sperm declined ~75% when stored at 4°C for 24 h, and continued to decline over time when using extenders supplemented with 5% egg yolk. The current study evaluated the effects of different combinations of extenders, ethylene glycol (EG), egg yolk, and penicillamine, hypotaurine, and epinephrine on ram sperm progressive motility during storage. Semen collected from 3 Katahdin and 2 Suffolk rams by electroejaculation was distributed across treatment combinations consisting of either TRIS citrate or milk extender supplemented with 5 or 20% (v/v) egg yolk, ± 1% ethylene glycol (EG) and ± 20 µM penicillamine, 10 µM hypotaurine and 2 µM epinephrine (PHE). For each semen collection, TRIS citrate extender was prepared from a 4× solution so that the TRIS, citric acid and fructose concentration were constant at 300, 94.7, 27.8 mM, respectively, regardless of semen dilution factor. A 4× milk extender was also used so that the extender contained 10% (w/v) milk powder, regardless of semen dilution factor. Both extenders were supplemented with 50 µg mL−1 of gentamicin. Semen was diluted in extender to a final concentration of 300 million sperm/mL in 1.5-mL tubes, and cooled to 4°C over a 2- to 3-h period. Semen was evaluated initially and daily for 3 days, using computer-assisted sperm analysis. Repeated-measures data were analysed using the mixed model (JMP 12.0 software; SAS Institute Inc., Cary, NC, USA) for main effects of extender, supplements, and their interactions. Nonsignificant interactions were removed from the model before reanalysis. Data are presented as LSMeans ± standard errors. Initially, sperm progressive motility averaged 41 ± 6.2% across treatments. After an initial decline, overall progressive motility did not change (P > 0.05) significantly (mean of 22.3 ± 1.6 and 23.05 ± 1.3% at 48 and 72 h, respectively). Over time and across treatment combinations, mean progressive motility was maintained to a greater extent (P < 0.01) by milk than TRIS-based extender (28.2 ± 1.1 v. 18.9 ± 1.1%, respectively). Across extenders, progressive motility of sperm was similar (P = 0.50) for 5 and 20% egg yolk (22.2 ± 1.4 v. 24.4 ± 1.4). Addition of 1% EG increased (P < 0.01) progressive motility (25.8 ± 1.05 v. 21.3 ± 1.05). Addition of PHE also increased (P < 0.01) progressive motility from 20.9 ± 1.04 to 26.3 ± 1.04%. There was an interaction between EG and % egg yolk, primarily due to an effect on sperm stored in TRIS citrate extender. Addition of 1% EG to extender containing 5% egg yolk improved (P < 0.01) progressive motility from 18.5 ± 1.5 to 26.9 ± 1.5%). Addition of 1% EG to TRIS citrate extender also increased (P < 0.05) progressive motility, from 14.6 ± 1.5 to 23.2 ± 1.5%. Results indicate that milk extender supplemented with 1% EG, PHE, and either 5 or 20% egg yolk is capable of maintaining progressive motility of ram semen at ~60% of its initial value when stored at 4°C for up to 72 h. Additional studies are needed to evaluate pregnancy rate after insemination of ewes with stored semen.
2
Han, Xu, Lining Zhao, Yue Ning, and Jingfeng Hu. "ShipYOLO: An Enhanced Model for Ship Detection." Journal of Advanced Transportation 2021 (June 23, 2021): 1–11. http://dx.doi.org/10.1155/2021/1060182.
Full textAbstract:
The application of ship detection for assistant intelligent ship navigation has stringent requirements for the model’s detection speed and accuracy. In response to this problem, this study uses an improved YOLO-V4 detection model (ShipYOLO) to detect ships. Compared to YOLO-V4, the model has three main improvements. Firstly, the backbone network (CSPDarknet) of YOLO-V4 is optimized. In the training process, the 3 × 3 convolution, 1 × 1 convolution, and identity parallel mode are used to replace the original feature extraction component (ResUnit) and more features are extracted. In the inference process, the branch parameters are combined to form a new backbone network named RCSPDarknet, which improves the inference speed of the model while improving the accuracy. Secondly, in order to solve the problem of missed detection of the small-scale ships, we designed a new amplified receptive field module named DSPP with dilated convolution and Max-Pooling, which improves the model’s acquisition of small-scale ship spatial information and robustness of ship target space displacement. Finally, we use the attention mechanism and Resnet’s shortcut idea to improve the feature pyramid structure (PAFPN) of YOLO-V4 and get a new feature pyramid structure named AtFPN. The structure effectively improves the model’s feature extraction effect for ships of different scales and reduces the number of model parameters, further improving the model’s inference speed and detection accuracy. In addition, we have created a ship dataset with a total of 2238 images, which is a single-category dataset. The experimental results show that ShipYOLO has the advantage of faster speed and higher accuracy even in different input sizes. Considering the input size of 320 × 320 on the PC equipped with NVIDIA 1080Ti GPU, the FPS and mAP@5 : 5:95 (mAP90) of ShipYOLO are increased by 23.7% and 13.6% (10.6%), respectively, with an input size of 320 × 320, ShipYOLO, compared to YOLO-V4.
3
Zhang, Xiaolong, Bin Qian, Haitao Si, Liuying Zeng, and Hui Wang. "Research on the computer intelligent recognition of electric appliance by Yolo V5 algorithm." Journal of Physics: Conference Series 2083, no. 3 (November 1, 2021): 032078. http://dx.doi.org/10.1088/1742-6596/2083/3/032078.
Full textAbstract:
Abstract In the process of power operation, the correct identification of tools can lay a foundation for the detection of violations in power operation. In order to realize the recognition of power instruments, based on the current Yolo V5 algorithm, a detection algorithm for power instruments is proposed by improving Yolo V5 algorithm. Firstly, the model of Yolo V5 algorithm is introduced. Then the establishment of the power tool database and the process of model training are analysed. Finally, the test results are analysed and evaluated. The models generated after training were accelerated by TensorRT and then deployed on Jetson Xavier NX.
4
Pratama, Yovi, and Errissya Rasywir. "Eksperimen Penerapan Sistem Traffic Counting dengan Algoritma YOLO (You Only Look Once) V.4." JURNAL MEDIA INFORMATIKA BUDIDARMA 5, no. 4 (October 26, 2021): 1438. http://dx.doi.org/10.30865/mib.v5i4.3309.
Full textAbstract:
Traffic counting is the activity of counting traffic (vehicles) that pass on the road in a certain period. The purpose of traffic counting is to collect traffic data, determine traffic characteristics, determine vehicle composition and measure traffic performance. With the YOLO V.4 algorithm, changes in the position, size and volume of the detected object can be carried out in several tests. Although not all the results of using this algorithm are perfect on all data, the results tend to be good. This is related to the services provided in the form of a convolutional layer on YOLO reducing downsample or reducing image dimensions by using anchor boxes, this algorithm can also increase accuracy. The YOLO V.4 algorithm utilizes an image feature scanning model using the concepts of angles and directions mathematically. From the results of experiments carried out in this study, obtained detection results that have a fairly good accuracy in the results of separating frames from video data. Irregular transformations of position, dimension, composition and direction can still be captured as the same feature. YOLO's ability in feature engineering is an acknowledgment that has been successfully proven in this research.
5
Hernandez, M., T. Cremades, J. M. Vazquez, E. A. Martinez, and J. Roca. "77 EFFECT OF SUGAR SUPPLEMENTATION ON THE CRYOPRESERVATION OF BOAR SPERMATOZOA." Reproduction, Fertility and Development 20, no. 1 (2008): 119. http://dx.doi.org/10.1071/rdv20n1ab77.
Full textAbstract:
The optimization of cryopreservation extenders is an essential issue for the successful establishment of boar sperm cryobanks. The aim of the present study was to investigate the importance of sugar supplementation to the freezing extender and/or to the thawing diluents, and the interactions between these treatments on post-thaw survival of boar spermatozoa. Pooled sperm-rich fractions from 5 mature hybrid boars (5 ejaculates per boar) were divided into 7 aliquots, centrifuged at 2300g 3 min, and diluted to a final sperm concentration of approximately 1000 � 106 spermatozoa mL–1 with the freezing extender prior to freezing in 0.5-mL plastic straws; a standard freeze–thaw procedure with computer-controlled freezing equipment was utilized. All of the freezing extenders were based on Tris-citric acid buffer supplemented with 20% egg yolk and 0 mm (TC, no glucose), 0.05 mm, 2 mm, 4 mm, 10 mm, 50 mm, or 185 mm glucose (all media adjusted to 310 mOsm kg–1; pH 6.8). After thawing at 37�C/20 s, semen was immediately diluted 1:2 (v/v) with either TC (no glucose) or BTS (205 mm glucose, 20.39 mm NaCl, 5.4 mm KCl, 15 mm NaHCO3, and 3.35 mm EDTA). Post-thaw sperm motility (assessed with a computer-assisted semen analysis system) and plasma membrane and acrosome integrity (viability, assessed simultaneously by flow cytometry using triple fluorescent staining) were evaluated at 0, 30, and 150 min post-thaw and used to estimate the success of cryopreservation. Data were analyzed as a split plot design using a mixed model ANOVA including cryopreservation extender, thawing diluent, incubation time, and interactions as fixed effects and replicates as a random effect. The freezing extender did not have any significant effect on the percentage of motile or viable spermatozoa at either thawing or after 150 min (P > 0.05). There was a significant effect of incubation time (P < 0.0001) and thawing diluent (P < 0.0001) on motility and viability, with a significant interaction between them on motility (P = 0.018). Motility was similar (P > 0.05) at time 0 in both thawing diluents (mean � SEM: 49.4 � 3.7 v. 46.2 � 3.9% for BTS and TC, respectively), but decreased in Tris-diluted samples in a time-dependent manner (54.6 � 1.9 v. 42.5 � 2.6% at 30 min, and 40 � 3.4 v. 27.1 � 3.7% at 150 min). In contrast, viability was significantly higher (P < 0.05) in BTS-diluted samples at 0 (53.9 � 3.7 v. 49.7 � 3.8%), 30 (55 � 3.1 v. 47.7 � 3.31), and 150 min (51 � 4.2 v. 43.7 � 4.4%). These results indicate that, despite common beliefs, sugars are not necessary for cryopreservation but are beneficial for maintaining boar sperm metabolism for a longer time after thawing.
6
Mr. Rahul Sharma. "Recognition of Anthracnose Injuries on Apple Surfaces using YOLOV 3-Dense." International Journal of New Practices in Management and Engineering 4, no. 02 (June 30, 2015): 08–14. http://dx.doi.org/10.17762/ijnpme.v4i02.36.
Full textAbstract:
Plant ailment is one of the essential drivers of harvest yield decrease. With the advancement of PC vision and profound learning innovation, independent discovery of plant surface sore pictures gathered by optical sensors has become a significant research bearing for convenient yield ailment analysis. Right now, anthracnose injury identification strategy dependent on profound learning is proposed. Right off the bat, for the issue of lacking picture information brought about by the irregular event of apple illnesses, notwithstanding conventional picture expansion strategies, Cycle-Consistent Adversarial Network (CycleGAN) profound learning model is utilized right now achieve information increase. These strategies adequately enhance the decent variety of preparing information and give a strong establishment to preparing the identification model. Right now, the premise of picture information increase, thickly associated neural system (DenseNet) is used to streamline highlight layers of the YOLO-V3 model which have lower goals. DenseNet extraordinarily improves the usage of highlights in the neural system and upgrades the identification consequence of the YOLO-V3 model. It is checked in tests that the improved model surpasses Faster R-CNN with VGG16 NET, the first YOLO-V3 model, and other three cutting edge arranges in discovery execution, and it can understand continuous recognition. The proposed technique can be all around applied to the recognition of anthracnose injuries on apple surfaces in-plantations.
7
Hernandez, M., J. M. Vazquez, E. A. Martinez, and J. Roca. "119 OXIDATIVE STRESS DURING THE CRYOPRESERVATION OF BOAR SPERMATOZOA." Reproduction, Fertility and Development 19, no. 1 (2007): 177. http://dx.doi.org/10.1071/rdv19n1ab119.
Full textAbstract:
The cryopreservation procedure causes dramatic changes in boar sperm survival but it is yet unclear where and how the process affects spermatozoa. Cryopreservation damage appears partly associated with oxidative stress and reactive oxygen species (ROS) generation. The present study evaluates the effect that various steps of a conventional cycle of cryopreservation have on the intracellular production of ROS by boar spermatozoa (spz). Sperm-rich fractions collected from 2 mature boars (3 ejaculates per boar), cooled to 17�C, and kept for 16 h were cryopreserved following a standard freeze–thaw process with 0.5-mL plastic straws. The production of ROS was recorded in 5 steps of the cryopreservation process. These steps were as follows: step (1) after collection, when the fresh semen was extended (1:1, v/v) in Beltsville Thawing Solution (BTS, 205 mM glucose, 20.39 mM NaCl, 5.4 mM KCl, 15.01 mM NaHCO3, and 3.35 mM EDTA); step (2) after cooling and storage for 16 h at 17�C; step (3) after centrifugation (2400g for 3 min) and re-extension of the pellet with lactose-egg yolk extender; step (4) at 5�C, after the addition of lactose-egg yolk-glycerol-Equex Stem Paste to 1 � 109 spz mL; and step (5) immediately after thawing at 37�C for 20 s. For the ROS measurement, all samples were re-extended in BTS (3 � 106 spz mL-1) and incubated without (basal ROS level) or with ROS inducers (1 mM tert-butyl hydroperoxide) for 120 min at 37�C and 5% CO2. Cells were simultaneously stained with 22,72-dichlorodihydrofluorescein diacetate (1 �M) to estimate the production of ROS, and propidium iodide (12 �M) to exclude dead sperm from the analysis. Samples were evaluated at 30 min and 120 min by flow cytometry (Coulter Epics XL; Coulter Corporation, Miami, FL, USA); further analyses of the parameters were done by FCSExpress software (DeNovo Software, Thornhill, Ontario, Canada). ROS production was expressed as the mean of the green intensity fluorescence units of the viable sperm population. Data from 3 replicates were analyzed as a split plot design using a mixed model ANOVA including cryopreservation step, boar, and incubation time as fixed effects and replicate as random effect. Results indicated that the basal ROS formation remained relatively low and constant (P = 0.95) through the cryopreservation process, without differences between boars (P = 0.559), although with a significant increase after 120 min of incubation (P < 0.001). However, the exposure to tert-butyl hydroperoxide significantly increased the intracellular ROS formation in all of the steps (P < 0.001), showing significant differences between them, and being especially raised at steps 3 and 4. In conclusion, the present study confirms that the basal intracellular ROS production during cryopreservation of boar sperm is low. Nevertheless, the susceptibility of those spermatozoa to external stresses vary through the cryopreservation process, especially after centrifugation and later extension at 17�C and after the slow cooling at 5�C. This work was supported by CICYT (AGF2005-00706), Madrid, Spain
8
Sultana, Syada Nizer, Halim Park, Sung Hoon Choi, Hyun Jo, Jong Tae Song, Jeong-Dong Lee, and Yang Jae Kang. "Optimizing the Experimental Method for Stomata-Profiling Automation of Soybean Leaves Based on Deep Learning." Plants 10, no. 12 (December 10, 2021): 2714. http://dx.doi.org/10.3390/plants10122714.
Full textAbstract:
Stomatal observation and automatic stomatal detection are useful analyses of stomata for taxonomic, biological, physiological, and eco-physiological studies. We present a new clearing method for improved microscopic imaging of stomata in soybean followed by automated stomatal detection by deep learning. We tested eight clearing agent formulations based upon different ethanol and sodium hypochlorite (NaOCl) concentrations in order to improve the transparency in leaves. An optimal formulation—a 1:1 (v/v) mixture of 95% ethanol and NaOCl (6–14%)—produced better quality images of soybean stomata. Additionally, we evaluated fixatives and dehydrating agents and selected absolute ethanol for both fixation and dehydration. This is a good substitute for formaldehyde, which is more toxic to handle. Using imaging data from this clearing method, we developed an automatic stomatal detector using deep learning and improved a deep-learning algorithm that automatically analyzes stomata through an object detection model using YOLO. The YOLO deep-learning model successfully recognized stomata with high mAP (~0.99). A web-based interface is provided to apply the model of stomatal detection for any soybean data that makes use of the new clearing protocol.
9
Zhou, Xiao, Lang Jiang, Caixia Hu, Shuai Lei, Tingting Zhang, and Xingang Mou. "YOLO-SASE: An Improved YOLO Algorithm for the Small Targets Detection in Complex Backgrounds." Sensors 22, no. 12 (June 18, 2022): 4600. http://dx.doi.org/10.3390/s22124600.
Full textAbstract:
To improve the detection ability of infrared small targets in complex backgrounds, an improved detection algorithm YOLO-SASE is proposed in this paper. The algorithm is based on the YOLO detection framework and SRGAN network, taking super-resolution reconstructed images as input, combined with the SASE module, SPP module, and multi-level receptive field structure while adjusting the number of detection output layers through exploring feature weight to improve feature utilization efficiency. Compared with the original model, the accuracy and recall rate of the algorithm proposed in this paper were improved by 2% and 3%, respectively, in the experiment, and the stability of the results was significantly improved in the training process.
10
Wu, Z., X. Chen, Y. Gao, and Y. Li. "RAPID TARGET DETECTION IN HIGH RESOLUTION REMOTE SENSING IMAGES USING YOLO MODEL." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 1915–20. http://dx.doi.org/10.5194/isprs-archives-xlii-3-1915-2018.
Full textAbstract:
Object detection in high resolution remote sensing images is a fundamental and challenging problem in the field of remote sensing imagery analysis for civil and military application due to the complex neighboring environments, which can cause the recognition algorithms to mistake irrelevant ground objects for target objects. Deep Convolution Neural Network(DCNN) is the hotspot in object detection for its powerful ability of feature extraction and has achieved state-of-the-art results in Computer Vision. Common pipeline of object detection based on DCNN consists of region proposal, CNN feature extraction, region classification and post processing. YOLO model frames object detection as a regression problem, using a single CNN predicts bounding boxes and class probabilities in an end-to-end way and make the predict faster. In this paper, a YOLO based model is used for object detection in high resolution sensing images. The experiments on NWPU VHR-10 dataset and our airport/airplane dataset gain from GoogleEarth show that, compare with the common pipeline, the proposed model speeds up the detection process and have good accuracy.
11
Zhao, Tingting, Xiaoli Yi, Zhiyong Zeng, and Tao Feng. "MobileNet-Yolo based wildlife detection model: A case study in Yunnan Tongbiguan Nature Reserve, China." Journal of Intelligent & Fuzzy Systems 41, no. 1 (August 11, 2021): 2171–81. http://dx.doi.org/10.3233/jifs-210859.
Full textAbstract:
YTNR (Yunnan Tongbiguan Nature Reserve) is located in the westernmost part of China’s tropical regions and is the only area in China with the tropical biota of the Irrawaddy River system. The reserve has abundant tropical flora and fauna resources. In order to realize the real-time detection of wild animals in this area, this paper proposes an improved YOLO (You only look once) network. The original YOLO model can achieve higher detection accuracy, but due to the complex model structure, it cannot achieve a faster detection speed on the CPU detection platform. Therefore, the lightweight network MobileNet is introduced to replace the backbone feature extraction network in YOLO, which realizes real-time detection on the CPU platform. In response to the difficulty in collecting wild animal image data, the research team deployed 50 high-definition cameras in the study area and conducted continuous observations for more than 1,000 hours. In the end, this research uses 1410 images of wildlife collected in the field and 1577 wildlife images from the internet to construct a research data set combined with the manual annotation of domain experts. At the same time, transfer learning is introduced to solve the problem of insufficient training data and the network is difficult to fit. The experimental results show that our model trained on a training set containing 2419 animal images has a mean average precision of 93.6% and an FPS (Frame Per Second) of 3.8 under the CPU. Compared with YOLO, the mean average precision is increased by 7.7%, and the FPS value is increased by 3.
12
Huang, Li, Lijia Xu, Yuchao Wang, Yingqi Peng, Zhiyong Zou, and Peng Huang. "Efficient Detection Method of Pig-Posture Behavior Based on Multiple Attention Mechanism." Computational Intelligence and Neuroscience 2022 (July 16, 2022): 1–12. http://dx.doi.org/10.1155/2022/1759542.
Full textAbstract:
Due to the low detection precision and poor robustness, the traditional pig-posture and behavior detection method is difficult to apply in the complex pig captivity environment. In this regard, we designed the HE-Yolo (High-effect Yolo) model, which improves the Darknet-53 feature extraction network and integrates DAM (Dual attention mechanism) of channel attention mechanism and space attention mechanism, to recognize the posture behaviors of the enclosure pigs in real-time. First, the pig data set is clustered and optimized by the K-means algorithm to obtain a new anchor frame size. Second, the DSC (Depthwise separable convolution) and h-switch activation function are innovatively introduced into the Darknet-53 feature extraction network, and the C-Res (Contrary residual structure) unit is designed to build Darknet-A feature extraction network, so as to avoid network gradient explosion and ensure the integrity of feature information. Subsequently, DAM integrating the spatial attention mechanism and the channel attention mechanism is established, and it is further combined with the Incep-abate module to form DAB (Dual attention block), and HE-Yolo is finally built by Darknet-A and DAB. A total of 2912 images of 46 enclosure pigs are divided into the training set, the verification set, and the test set according to the ratio of 14 : 3:3, and the recognition performance of HE-Yolo is verified according to the parameters of the precision P, the recall R, the AP (i.e., the area of P-R curve) and the MAP (i.e., the average value of AP). The experiment results show that the AP values of HE-Yolo reach 99.25%, 98.41%, 94.43%, and 97.63%, respectively, in the recognition of four pig-posture behaviors of standing, sitting, prone and sidling of the test set. Compared with other models such as Yolo v3, SSD, and faster R–CNN, the mAP value of HE-Yolo is increased by 5.61%, 4.65%, and 0.57%, respectively, and the single-frame recognition time of HE-Yolo is only 0.045 s. In the recognition of images with foreign body occlusion and pig adhesion, the mAP values of HE-Yolo are increased by 4.04%, 4.94%, and 1.76%, respectively, while compared with other models. Under different lighting conditions, the mAP value of HE-Yolo is also higher than that of other models. The experimental results show that HE-Yolo can recognize the pig-posture behaviors with high precision, and it shows good generalization ability and luminance robustness, which provides technical support for the recognition of pig-posture behaviors and real-time monitoring of physiological health of the enclosure pigs.
13
Mahasin, Marsa, and Irma Amelia Dewi. "Comparison of CSPDarkNet53, CSPResNeXt-50, and EfficientNet-B0 Backbones on YOLO V4 as Object Detector." International Journal of Engineering, Science and Information Technology 2, no. 3 (September 14, 2022): 64–72. http://dx.doi.org/10.52088/ijesty.v2i3.291.
Full textAbstract:
YOLO v4 has a structure consisting of 3 parts: backbone, neck, and head. The backbone is a part of the YOLO v4 structure that serves as a feature extractor from the image; the backbone is also a convolutional neural network that can be replaced with another convolutional neural network. Many backbones are recommended by previous research, such as CSPDarkNet53, CSPResNeXt-50, and EfficientNet-B0. Therefore, research needs to be done to determine the effect of different backbones on the YOLO v4 model. One of the research objects that can be used is a microfossil. Research on the detection of microfossils is fundamental to assist paleontologists in knowing the species of microfossils as a determinant of rock age and distinguishing between similar microfossils. In this research, three backbones consisting of CSPDarkNet53, CSPResNeXt-50, and EfficientNet-B0 were used to train and detect image sets of 5 species of foraminiferal microfossils. The results were evaluated to determine the advantages of each backbone. There are a few metrics are that being used for evaluation, namely precision, recall, f1-score, average precision (AP), mean average precision (mAP), frames per second (FPS), and model size. As a result, the mean average precision (mAP) of the CSPDarkNet53 model reached 83.41%, the highest compared to CSPResNeXt-50 and EfficientNet-B0, which get a value of 81,00% and 81,76%. CSPResNeXt-50 model has a precision of 75.60%, recall of 81.10%, and f1-score of 78%. CSPDarkNet53 model also got the highest FPS value of 33.4FPS. However, the YOLO v4 model with the EfficientNet-B0 backbone is the lightest model, with only 156.8 MB.
14
Lu, Xuefeng. "Research on Fingerprint Security Based on Improved Yolo Algorithm." Mobile Information Systems 2022 (September 7, 2022): 1–11. http://dx.doi.org/10.1155/2022/5133471.
Full textAbstract:
The quantitative identification technology based on the statistical law of fingerprint features has become a new research difficulty and focus, and the automatic detection and classification of fingerprint features are the basis for realizing automatic fingerprint feature statistics. In this paper, a YOLO-based fingerprint feature detection method was proposed. First, a fingerprint feature dataset was established, which contained a total of 4,000 annotated fingerprint images; then, according to the characteristics of small size and dense distribution of fingerprint feature points, the YOLO network structure was improved, the original large target feature detection layer by 32-fold downsampling was deleted, and a new small feature fusion layer was added; the FPN, PAN, and SPP structures were used to achieve local and global feature extraction through multiple-scale fusion methods; finally, the SE channel attention mechanism module was added to effectively enhance the model robustness and detection ability of dense small objects. The experimental results show that compared with the improved FP-YOLO model of the original model, when the detection speed is basically unchanged, the mAP0.5 value is increased from 93.0% to 97.4%, and the weight is reduced by 3/4.
15
Li, Jinrui, Libin Chen, Jian Shen, Xiongwu Xiao, Xiaosong Liu, Xin Sun, Xiao Wang, and Deren Li. "Improved Neural Network with Spatial Pyramid Pooling and Online Datasets Preprocessing for Underwater Target Detection Based on Side Scan Sonar Imagery." Remote Sensing 15, no. 2 (January 11, 2023): 440. http://dx.doi.org/10.3390/rs15020440.
Full textAbstract:
Fast and high-accuracy detection of underwater targets based on side scan sonar images has great potential for marine fisheries, underwater security, marine mapping, underwater engineering and other applications. The following problems, however, must be addressed when using low-resolution side scan sonar images for underwater target detection: (1) the detection performance is limited due to the restriction on the input of multi-scale images; (2) the widely used deep learning algorithms have a low detection effect due to their complex convolution layer structures; (3) the detection performance is limited due to insufficient model complexity; and (4) the number of samples is not enough because of the dataset preprocessing methods. To solve these problems, an improved neural network for underwater target detection—which is based on side scan sonar images and fully utilizes spatial pyramid pooling and online dataset preprocessing based on the You Look Only Once version three (YOLO V3) algorithm—is proposed. The methodology of the proposed approach is as follows: (1) the AlexNet, GoogleNet, VGGNet and the ResNet networks and an adopted YOLO V3 algorithm were the backbone networks. The structure of the YOLO V3 model is more mature and compact and has higher target detection accuracy and better detection efficiency than the other models; (2) spatial pyramid pooling was added at the end of the convolution layer to improve detection performance. Spatial pyramid pooling breaks the scale restrictions when inputting images to improve feature extraction because spatial pyramid pooling enables the backbone network to learn faster at high accuracy; and (3) online dataset preprocessing based on YOLO V3 with spatial pyramid pooling increases the number of samples and improves the complexity of the model to further improve detection process performance. Three-side scan imagery datasets were used for training and were tested in experiments. The quantitative evaluation using Accuracy, Recall, Precision, mAP and F1-Score metrics indicates that: for the AlexNet, GoogleNet, VGGNet and ResNet algorithms, when spatial pyramid pooling is added to their backbone networks, the average detection accuracy of the three sets of data was improved by 2%, 4%, 2% and 2%, respectively, as compared to their original formulations. Compared with the original YOLO V3 model, the proposed ODP+YOLO V3+SPP underwater target detection algorithm model has improved detection performance through the mAP qualitative evaluation index has increased by 6%, the Precision qualitative evaluation index has increased by 13%, and the detection efficiency has increased by 9.34%. These demonstrate that adding spatial pyramid pooling and online dataset preprocessing can improve the target detection accuracy of these commonly used algorithms. The proposed, improved neural network with spatial pyramid pooling and online dataset preprocessing based on the YOLO V3 method achieves the highest scores for underwater target detection results for sunken ships, fish flocks and seafloor topography, with mAP scores of 98%, 91% and 96% for the above three kinds of datasets, respectively.
16
Liu, Lisang, Chengyang Ke, He Lin, and Hui Xu. "Research on Pedestrian Detection Algorithm Based on MobileNet-YoLo." Computational Intelligence and Neuroscience 2022 (October 30, 2022): 1–12. http://dx.doi.org/10.1155/2022/8924027.
Full textAbstract:
To address the problem that large pedestrian detection networks cannot be directly applied to small device scenarios due to the heavyweight and slow detection speed, this paper proposes a pedestrian detection and recognition model MobileNet-YoLo based on the YoLov4-tiny target detection framework. To address the problem of low accuracy of YoLov4-tiny, MobileNetv3 is used to optimize its backbone feature extraction network, and the MFF model is proposed to fuse the output of the first two layers to solve the information loss problem, and the attention mechanism CBAM is introduced after strengthening the feature extraction network to further improve the detection efficiency; then the 3 × 3 convolution is replaced by the depth separable convolution, which greatly reduces the number of parameters and thus improves the detection rate, then propose Ordinary data augmentation to efficiently augment the dataset and dynamically adjust the target detection anchor frame using the k-means++ clustering algorithm. Finally, the model weights trained by the VOC2007 + 2012 dataset were applied to the pedestrian dataset for retraining by the transfer learning method, which effectively solved the problem of scarce samples and greatly shortened the training time. The experimental results on the VOC2007 + 2012 dataset show that the average means accuracy of the MobileNet-YoLo model compared to YoLov4-tiny, MobileNet-YoLov4, MobileNet-YoLov3, and YoLov5s by 5.00%, 1.30%, 3.23%, and 0.74%, respectively and have reached the level to realize the landed application.
17
Du, Linghao, Rui Wang, Lin Cui, Xiaolin Min, Qingyi Liu, Yande Ren, Kai Huang, and Peirui Bai. "Automatic body region localization in 3D-CT images based on the improved YOLO model." MATEC Web of Conferences 355 (2022): 03022. http://dx.doi.org/10.1051/matecconf/202235503022.
Full textAbstract:
Automatic body region localization in medical three-dimensional (3D)-CT images is a critical step of computerized body-wide Automatic Anatomy Recognition (AAR) system, which can be applied for radiotherapy planning and interest slices retrieving. Currently, the complex internal structure of human body and time consuming computation are the main challenges for the localization. Therefore, this paper introduces and improves the YOLO-v3 model into the body region localization for these problems. First, seven categories of body regions in a CT volume image I are defined based on the modification version of our previous work. Second, an improved YOLO-v3 model is trained to classify each axial slice into one of the seven categories. Then, the effectiveness of the proposed method is evaluated on 3D-CT images that collected from 220 subjects. The experimental results demonstrate that the slice localizing error is less than 3 NoS (Number of slices), which is competitive to the state-of-the-art methods. Beyond this, our method is simple and computationally efficient owing to its less training time, and the average computational time for localizing a volume CT images is about 3 second, which shows potential for a further application.
18
Wang, Hengtao, and Shang Zhang. "A Robust and Lightweight Detector for Ship Target with Complex Background in SAR Image." Journal of Sensors 2022 (August 13, 2022): 1–16. http://dx.doi.org/10.1155/2022/8199418.
Full textAbstract:
Accurate target detection technology on ships can improve the comprehensive perception ability of weapon equipment. For SAR ship target detection in complex environments, false and missing alarms are serious. We design a new real-time ship target detection algorithm 3S-YOLO in SAR images. Firstly, reconstruct the network structure, adjust the relationship between receptive field and multiscale fusion, and realize the lightweight processing of feature extraction network and feature fusion network. Then, the network is pruned and compressed by the FPGM pruning algorithm to accelerate the reasoning speed. Finally, the Varifocal-EIoU loss function is designed to balance the positive and negative samples and overlapping losses and highlight the contribution of positive samples. To verify the effectiveness of the 3S-YOLO algorithm, verification is carried out in public datasets SSDD and HRSID. The results show that the accuracy of the model can be improved to 99.2% and 95.6%, respectively, after optimization. After pruning, the model volume decreased significantly and could be compressed to 190 KB. Model reasoning time can be reduced to less than 3 ms. Compared with the current mainstream algorithms, 3S-YOLO has achieved good results in all aspects to meet the real-time ship target detection in SAR images.
19
Guo, Shih-Sian, Kuo-Hua Lee, Liyun Chang, Chin-Dar Tseng, Sin-Jhe Sie, Guang-Zhi Lin, Jih-Yi Chen, Yi-Hsin Yeh, Yu-Jie Huang, and Tsair-Fwu Lee. "Development of an Automated Body Temperature Detection Platform for Face Recognition in Cattle with YOLO V3-Tiny Deep Learning and Infrared Thermal Imaging." Applied Sciences 12, no. 8 (April 16, 2022): 4036. http://dx.doi.org/10.3390/app12084036.
Full textAbstract:
This study developed an automated temperature measurement and monitoring platform for dairy cattle. The platform used the YOLO V3-tiny (you only look once, YOLO) deep learning algorithm to identify and classify dairy cattle images. The system included a total of three layers of YOLO V3-tiny identification: (1) dairy cow body; (2) individual number (identity, ID); (3) thermal image of eye socket identification. We recorded each cow’s individual number and body temperature data after the three layers of identification, and carried out long-term body temperature tracking. The average prediction score of the recognition rate was 96%, and the accuracy was 90.0%. The thermal image of eye socket recognition rate was >99%. The area under the receiver operating characteristic curves (AUC) index of the prediction model was 0.813 (0.717–0.910). This showed that the model had excellent predictive ability. This system provides a rapid and convenient temperature measurement solution for ranchers. The improvement in dairy cattle image recognition can be optimized by collecting more image data. In the future, this platform is expected to replace the traditional solution of intrusive radio-frequency identification for individual recognition.
20
Kim, Joon-ho, Seung-hye Jung, Bong-ihn Seok, and Hyun-ju Choi. "The Relationship among Four Lifestyles of Workers amid the COVID-19 Pandemic (Work–Life Balance, YOLO, Minimal Life, and Staycation) and Organizational Effectiveness: With a Focus on Four Countries." Sustainability 14, no. 21 (October 28, 2022): 14059. http://dx.doi.org/10.3390/su142114059.
Full textAbstract:
This study empirically analyzes the effects of four lifestyles of office workers (work and life balance, you only live once (YOLO), minimal life, and staycation), which have been changed during the COVID-19 pandemic, on organizational effectiveness (measured by job satisfaction, organizational commitment, and organizational citizenship behavior). A questionnaire survey was conducted over four months through a global research firm. In total, 649 valid questionnaires were collected. A structural equation model analysis was performed on valid samples using SmartPLS statistics. The results were as follows: (1) Work and life balance, YOLO, and minimal life had a statistically significant positive effect on job satisfaction. (2) Minimal life had a statistically significant positive effect on organizational commitment. (3) Work and life balance, and staycation had statistically significant positive effects on organizational citizenship behavior. (4) Job satisfaction had a statistically significant positive effect on organizational commitment. (5) Job satisfaction and organizational commitment had a statistically significant positive effect on organizational citizenship behavior. This is the first empirical study to focus on four lifestyles (work–life balance, YOLO, minimal life, and staycation). The results show that job satisfaction was affected the most by YOLO,’ that organizational commitment was affected the most by minimal life, and that organizational citizenship behavior was affected the most by work–life balance.
21
Di, Jie, and Qing Li. "A method of detecting apple leaf diseases based on improved convolutional neural network." PLOS ONE 17, no. 2 (February 1, 2022): e0262629. http://dx.doi.org/10.1371/journal.pone.0262629.
Full textAbstract:
Apple tree diseases have perplexed orchard farmers for several years. At present, numerous studies have investigated deep learning for fruit and vegetable crop disease detection. Because of the complexity and variety of apple leaf veins and the difficulty in judging similar diseases, a new target detection model of apple leaf diseases DF-Tiny-YOLO, based on deep learning, is proposed to realize faster and more effective automatic detection of apple leaf diseases. Four common apple leaf diseases, including 1,404 images, were selected for data modeling and method evaluation, and made three main improvements. Feature reuse was combined with the DenseNet densely connected network and further realized to reduce the disappearance of the deep gradient, thus strengthening feature propagation and improving detection accuracy. We introduced Resize and Re-organization (Reorg) and conducted convolution kernel compression to reduce the calculation parameters of the model, improve the operating detection speed, and allow feature stacking to achieve feature fusion. The network terminal uses convolution kernels of 1 × 1, 1 × 1, and 3 × 3, in turn, to realize the dimensionality reduction of features and increase network depth without increasing computational complexity, thus further improving the detection accuracy. The results showed that the mean average precision (mAP) and average intersection over union (IoU) of the DF-Tiny-YOLO model were 99.99% and 90.88%, respectively, and the detection speed reached 280 FPS. Compared with the Tiny-YOLO and YOLOv2 network models, the new method proposed in this paper significantly improves the detection performance. It can also detect apple leaf diseases quickly and effectively.
22
Neupane, Chiranjivi, Anand Koirala, and Kerry B. Walsh. "In-Orchard Sizing of Mango Fruit: 1. Comparison of Machine Vision Based Methods for On-The-Go Estimation." Horticulturae 8, no. 12 (December 19, 2022): 1223. http://dx.doi.org/10.3390/horticulturae8121223.
Full textAbstract:
Estimation of fruit size on-tree is useful for yield estimation, harvest timing and market planning. Automation of measurement of fruit size on-tree is possible using RGB-depth (RGB-D) cameras, if partly occluded fruit can be removed from consideration. An RGB-D Time of Flight camera was used in an imaging system that can be driven through an orchard. Three approaches were compared, being: (i) refined bounding box dimensions of a YOLO object detector; (ii) bounding box dimensions of an instance segmentation model (Mask R-CNN) applied to canopy images, and (iii) instance segmentation applied to extracted bounding boxes from a YOLO detection model. YOLO versions 3, 4 and 7 and their tiny variants were compared to an in-house variant, MangoYOLO, for this application, with YOLO v4-tiny adopted. Criteria developed to exclude occluded fruit by filtering based on depth, mask size, ellipse to mask area ratio and difference between refined bounding box height and ellipse major axis. The lowest root mean square error (RMSE) of 4.7 mm and 5.1 mm on the lineal length dimensions of a population (n = 104) of Honey Gold and Keitt varieties of mango fruit, respectively, and the lowest fruit exclusion rate was achieved using method (ii), while the RMSE on estimated fruit weight was 113 g on a population weight range between 180 and 1130 g. An example use is provided, with the method applied to video of an orchard row to produce a weight frequency distribution related to packing tray size.
23
Mattson, K. M., A. T. DeVries, J. Krebs, and N. M. Loskutoff. "161 CRYOPRESERVATION OF CORN SNAKE, ELAPHE GUTATTA, SEMEN." Reproduction, Fertility and Development 21, no. 1 (2009): 179. http://dx.doi.org/10.1071/rdv21n1ab161.
Full textAbstract:
The purpose of this investigation was to develop a protocol for cryopreserving snake semen using the corn snake, Elaphe gutatta, as the model species. This experiment is part of a five year investigation where the influences of diluents, cryoprotectants, cooling and thawing rates on sperm survival were studied. This report presents one protocol found to be effective for cryopreserving corn snake semen as determined by post-thaw motility parameters in vitro. Semen was collected by applying pressure to the lower abdomen and continuing distally towards the cloaca to remove any feces or urates. The cloaca was washed using PBS, then a more local pressure was applied to each side of the vent to cause the hemipenes to evert and subsequently ejaculate. The semen (approximately 5 μL) was then collected using a sterile transfer pipette, placed in 120 μL Biladyl A containing 20% egg yolk (Minitube, 13502/0501), and analyzed for motility, rate of forward progression (RFP; 0–5), and concentration. The semen was further diluted at room temperature at 1:1 v/v with Biladyl A containing 20% egg yolk and 34% Glycerol (Sigma, G2025), yielding a final concentration of 17% Glycerol. The diluted semen was then loaded into 250-μL straws and slowly cooled for 1 hour. The straws were then placed 1 inch above a liquid nitrogen bath for ten minutes and finally plunged into the nitrogen where it remained frozen. The cryopreserved semen was thawed by placing the straws into a 50°C water bath for 8 s, then emptied into microcentrifuge tubes and the sperm were evaluated for motility and RFP. The mean motility of the fresh samples was 72.5% (66.4–77.7%). The mean post-thaw motility of sperm over six trials was 27.1% (17.8–50.2%). The mean RFP was 0.75 (0.5–1.0). The differences between fresh and post-thawed mean motilities were shown to be significant using a chi-square analysis (P < 0.0001). Density gradient centrifugation (DGC) was applied in one trial where the semen had an initial post-thaw motility of 50.2% with an RFP of 0.5. After the centrifugation treatment, the motility increased to 64.8% with an RFP of 3. The DGC media was composed of 400 μL 45% Percoll (Sigma, P4937) layered over 400 μL 90% Percoll. The density gradients were centrifuged at 700g for 30 min after which time the pellets were washed in 500 μL pre-warmed TL Hepes Solution (Lonza, 04-616F) and centrifuged at 300g for 10 min to remove the Percoll. The resulting sperm pellets were then resuspended in a small volume of the pre-warmed Hepes. Thus far, the protocol using 17% Glycerol in Biladyl A with 20% egg yolk has proven to be the most successful for cryopreserving corn snake semen. The use of DGC enhanced the number of usable sperm leaving sperm of higher motility and RFP possibly due to the absence of seminal plasma or cryoprotective agents that may detrimentally affect sperm quality. There are no known reports of the use of DCG with snake semen. Further studies are underway to improve these results and successfully use cryopreserved snake semen for artificial insemination and cryobanking for the long-term genetic management of endangered snake species.
24
Alqaysi, Hiba, Igor Fedorov, Faisal Z. Qureshi, and Mattias O’Nils. "A Temporal Boosted YOLO-Based Model for Birds Detection around Wind Farms." Journal of Imaging 7, no. 11 (October 27, 2021): 227. http://dx.doi.org/10.3390/jimaging7110227.
Full textAbstract:
Object detection for sky surveillance is a challenging problem due to having small objects in a large volume and a constantly changing background which requires high resolution frames. For example, detecting flying birds in wind farms to prevent their collision with the wind turbines. This paper proposes a YOLOv4-based ensemble model for bird detection in grayscale videos captured around wind turbines in wind farms. In order to tackle this problem, we introduce two datasets—(1) Klim and (2) Skagen—collected at two locations in Denmark. We use Klim training set to train three increasingly capable YOLOv4 based models. Model 1 uses YOLOv4 trained on the Klim dataset, Model 2 introduces tiling to improve small bird detection, and the last model uses tiling and temporal stacking and achieves the best mAP values on both Klim and Skagen datasets. We used this model to set up an ensemble detector, which further improves mAP values on both datasets. The three models achieve testing mAP values of 82%, 88%, and 90% on the Klim dataset. mAP values for Model 1 and Model 3 on the Skagen dataset are 60% and 92%. Improving object detection accuracy could mitigate birds’ mortality rate by choosing the locations for such establishment and the turbines location. It can also be used to improve the collision avoidance systems used in wind energy facilities.
25
Surbakti, Agung Wibowo Ardiyanta, and Rahmi Eka Putri. "Penghitung Pengunjung dan Deteksi Masker Menggunakan OpenCV dan YOLO." CHIPSET 3, no. 02 (October 30, 2022): 83–93. http://dx.doi.org/10.25077/chipset.3.02.83-93.2022.
Full textAbstract:
The spread of COVID-19 that occurs through droplets can be avoided by reducing contact between individuals, so it is necessary to limit visitors, especially in crowded places such as shopping centers to avoid transmission between visitors. This study utilizes YOLOv3 object detection to recognize objects from camera image input, which is implemented on the Raspberry Pi 4, to identify visitors and use masks. The results of the identification of human objects will be calculated to determine the number of visitors at the shopping center. Then a buzzer sound warning is given when visitors are not wearing masks, if visitors exceed the capacity limit, they are also given a warning via an android application to the building manager. The results of the model detection show the mAP value of 77.92% for 3 classes of mask objects, without masks and humans.
26
Bučko, Boris, Eva Lieskovská, Katarína Zábovská, and Michal Zábovský. "Computer Vision Based Pothole Detection under Challenging Conditions." Sensors 22, no. 22 (November 17, 2022): 8878. http://dx.doi.org/10.3390/s22228878.
Full textAbstract:
Road discrepancies such as potholes and road cracks are often present in our day-to-day commuting and travel. The cost of damage repairs caused by potholes has always been a concern for owners of any type of vehicle. Thus, an early detection processes can contribute to the swift response of road maintenance services and the prevention of pothole related accidents. In this paper, automatic detection of potholes is performed using the computer vision model library, You Look Only Once version 3, also known as Yolo v3. Light and weather during driving naturally affect our ability to observe road damage. Such adverse conditions also negatively influence the performance of visual object detectors. The aim of this work was to examine the effect adverse conditions have on pothole detection. The basic design of this study is therefore composed of two main parts: (1) dataset creation and data processing, and (2) dataset experiments using Yolo v3. Additionally, Sparse R-CNN was incorporated into our experiments. For this purpose, a dataset consisting of subsets of images recorded under different light and weather was developed. To the best of our knowledge, there exists no detailed analysis of pothole detection performance under adverse conditions. Despite the existence of newer libraries, Yolo v3 is still a competitive architecture that provides good results with lower hardware requirements.
27
Zou, Lichuan, Hong Zhang, Chao Wang, Fan Wu, and Feng Gu. "MW-ACGAN: Generating Multiscale High-Resolution SAR Images for Ship Detection." Sensors 20, no. 22 (November 21, 2020): 6673. http://dx.doi.org/10.3390/s20226673.
Full textAbstract:
In high-resolution Synthetic Aperture Radar (SAR) ship detection, the number of SAR samples seriously affects the performance of the algorithms based on deep learning. In this paper, aiming at the application requirements of high-resolution ship detection in small samples, a high-resolution SAR ship detection method combining an improved sample generation network, Multiscale Wasserstein Auxiliary Classifier Generative Adversarial Networks (MW-ACGAN) and the Yolo v3 network is proposed. Firstly, the multi-scale Wasserstein distance and gradient penalty loss are used to improve the original Auxiliary Classifier Generative Adversarial Networks (ACGAN), so that the improved network can stably generate high-resolution SAR ship images. Secondly, the multi-scale loss term is added to the network, so the multi-scale image output layers are added, and multi-scale SAR ship images can be generated. Then, the original ship data set and the generated data are combined into a composite data set to train the Yolo v3 target detection network, so as to solve the problem of low detection accuracy under small sample data set. The experimental results of Gaofen-3 (GF-3) 3 m SAR data show that the MW-ACGAN network can generate multi-scale and multi-class ship slices, and the confidence level of ResNet18 is higher than that of ACGAN network, with an average score of 0.91. The detection results of Yolo v3 network model show that the detection accuracy trained by the composite data set is as high as 94%, which is far better than that trained only by the original SAR data set. These results show that our method can make the best use of the original data set, improve the accuracy of ship detection.
28
Wang, Chen, Yulu Dai, Wei Zhou, and Yifei Geng. "A Vision-Based Video Crash Detection Framework for Mixed Traffic Flow Environment Considering Low-Visibility Condition." Journal of Advanced Transportation 2020 (January 17, 2020): 1–11. http://dx.doi.org/10.1155/2020/9194028.
Full textAbstract:
In this paper, a vision-based crash detection framework was proposed to quickly detect various crash types in mixed traffic flow environment, considering low-visibility conditions. First, Retinex image enhancement algorithm was introduced to improve the quality of images, collected under low-visibility conditions (e.g., heavy rainy days, foggy days and dark night with poor lights). Then, a Yolo v3 model was trained to detect multiple objects from images, including fallen pedestrians/cyclists, vehicle rollover, moving/stopped vehicles, moving/stopped cyclists/pedestrians, and so on. Then, a set of features were developed from the Yolo outputs, based on which a decision model was trained for crash detection. An experiment was conducted to validate the model framework. The results showed that the proposed framework achieved a high detection rate of 92.5%, with relatively low false alarm rate of 7.5%. There are some useful findings: (1) the proposed model outperformed empirical rule-based detection models; (2) image enhancement method can largely improve crash detection performance under low-visibility conditions; (3) the accuracy of object detection (e.g., bounding box prediction) can impact crash detection performance, especially for minor motor-vehicle crashes. Overall, the proposed framework can be considered as a promising tool for quick crash detection in mixed traffic flow environment under various visibility conditions. Some limitations are also discussed in the paper.
29
Fu, Xiaoming, Aokang Li, Zhijun Meng, Xiaohui Yin, Chi Zhang, Wei Zhang, and Liqiang Qi. "A Dynamic Detection Method for Phenotyping Pods in a Soybean Population Based on an Improved YOLO-v5 Network." Agronomy 12, no. 12 (December 17, 2022): 3209. http://dx.doi.org/10.3390/agronomy12123209.
Full textAbstract:
Pod phenotypic traits are closely related to grain yield and quality. Pod phenotype detection in soybean populations in natural environments is important to soybean breeding, cultivation, and field management. For an accurate pod phenotype description, a dynamic detection method is proposed based on an improved YOLO-v5 network. First, two varieties were taken as research objects. A self-developed field soybean three-dimensional color image acquisition vehicle was used to obtain RGB and depth images of soybean pods in the field. Second, the red–green–blue (RGB) and depth images were registered using an edge feature point alignment metric to accurately distinguish complex environmental backgrounds and establish a red–green–blue-depth (RGB-D) dataset for model training. Third, an improved feature pyramid network and path aggregation network (FPN+PAN) structure and a channel attention atrous spatial pyramid pooling (CA-ASPP) module were introduced to improve the dim and small pod target detection. Finally, a soybean pod quantity compensation model was established by analyzing the influence of the number of individual plants in the soybean population on the detection precision to statistically correct the predicted pod quantity. In the experimental phase, we analyzed the impact of different datasets on the model and the performance of different models on the same dataset under the same test conditions. The test results showed that compared with network models trained on the RGB dataset, the recall and precision of models trained on the RGB-D dataset increased by approximately 32% and 25%, respectively. Compared with YOLO-v5s, the precision of the improved YOLO-v5 increased by approximately 6%, reaching 88.14% precision for pod quantity detection with 200 plants in the soybean population. After model compensation, the mean relative errors between the predicted and actual pod quantities were 2% to 3% for the two soybean varieties. Thus, the proposed method can provide rapid and massive detection for pod phenotyping in soybean populations and a theoretical basis and technical knowledge for soybean breeding, scientific cultivation, and field management.
30
Wu, Wenjuan, Dongchu Su, Bo Yuan, and Yong Li. "Intelligent Security Monitoring System Based on RISC-V SoC." Electronics 10, no. 11 (June 7, 2021): 1366. http://dx.doi.org/10.3390/electronics10111366.
Full textAbstract:
With the development of the economy and society, the demand for social security and stability increases. However, traditional security systems rely too much on human resources and are affected by uncontrollable community security factors. An intelligent security monitoring system can overcome the limitations of traditional systems and save human resources, contributing to public security. To build this system, a RISC-V SoC is first designed in this paper and implemented on the Nexys-Video Artix-7 FPGA. Then, the Linux operating system is transplanted and successfully run. Meanwhile, the driver of related hardware devices is designed independently. After that, three OpenCV-based object detection models including YOLO (You Only Look Once), Haar (Haar-like features), and LBP (Local Binary Pattern) are compared, and the LBP model is chosen to design applications. Finally, the processing speed of 1.25 s per frame is realized to detect and track moving objects. To sum up, we build an intelligent security monitoring system with real-time detection, tracking, and identification functions through hardware and software collaborative design. This paper also proposes a video downsampling technique. Based on this technique, the BRAM resource usage on the hardware side is reduced by 50% and the amount of pixel data that needs to be processed on the software side is reduced by 75%. A video downsampling technology is also proposed in this paper to achieve better video display effects under limited hardware resources. It provides conditions for future function expansion and improves the models’ processing speed. Additionally, it reduces the run time of the application and improves the system performance.
31
Zhang, Chengliang, Tianhui Li, and Wenbin Zhang. "The Detection of Impurity Content in Machine-Picked Seed Cotton Based on Image Processing and Improved YOLO V4." Agronomy 12, no. 1 (December 28, 2021): 66. http://dx.doi.org/10.3390/agronomy12010066.
Full textAbstract:
The detection of cotton impurity rates can reflect the cleaning effect of cotton impurity removal equipment, which plays a vital role in improving cotton quality and economic benefits. Therefore, several studies are being carried out to improve detection accuracy. Image processing technology is increasingly used in cotton impurity detection, in which deep learning technology based on convolution neural networks has shown excellent results in image classification, segmentation, target detection, etc. However, most of these applications focus on detecting foreign fibers in lint, which is of little significance to the parameter adjustment of cotton impurity removal equipment. For this reason, our goal was to develop an impurity detection system for seed cotton. In image segmentation, we propose a multi-channel fusion segmentation algorithm to segment the machine-picked seed cotton image. We collected 1017 images of machine-picked seed cotton as a dataset to train the detection model and tested and recognized 100 groups of samples, with an average recognition rate of 94.1%. Finally, the image segmented by the multi-channel fusion algorithm is input into the improved YOLOv4 network model for classification and recognition, and the established V–W model calculates the content of all kinds of impurities. The experimental results show that the impurity content in machine-picked cotton can be obtained effectively, and the detection accuracy of the impurity rate can increase by 5.6%.
32
Sumit, Shahriar Shakir, Dayang Rohaya Awang Rambli, Seyedali Mirjalili, Muhammad Mudassir Ejaz, and M. Saef Ullah Miah. "ReSTiNet: On Improving the Performance of Tiny-YOLO-Based CNN Architecture for Applications in Human Detection." Applied Sciences 12, no. 18 (September 17, 2022): 9331. http://dx.doi.org/10.3390/app12189331.
Full textAbstract:
Human detection is a special application of object recognition and is considered one of the greatest challenges in computer vision. It is the starting point of a number of applications, including public safety and security surveillance around the world. Human detection technologies have advanced significantly in recent years due to the rapid development of deep learning techniques. Despite recent advances, we still need to adopt the best network-design practices that enable compact sizes, deep designs, and fast training times while maintaining high accuracies. In this article, we propose ReSTiNet, a novel compressed convolutional neural network that addresses the issues of size, detection speed, and accuracy. Following SqueezeNet, ReSTiNet adopts the fire modules by examining the number of fire modules and their placement within the model to reduce the number of parameters and thus the model size. The residual connections within the fire modules in ReSTiNet are interpolated and finely constructed to improve feature propagation and ensure the largest possible information flow in the model, with the goal of further improving the proposed ReSTiNet in terms of detection speed and accuracy. The proposed algorithm downsizes the previously popular Tiny-YOLO model and improves the following features: (1) faster detection speed; (2) compact model size; (3) solving the overfitting problems; and (4) superior performance than other lightweight models such as MobileNet and SqueezeNet in terms of mAP. The proposed model was trained and tested using MS COCO and Pascal VOC datasets. The resulting ReSTiNet model is 10.7 MB in size (almost five times smaller than Tiny-YOLO), but it achieves an mAP of 63.74% on PASCAL VOC and 27.3% on MS COCO datasets using Tesla k80 GPU.
33
Ahmad, M., N. Ahmad, A. Riaz, and M. Anzar. "71 BOVINE SPERM DEATH KINETICS: CHANGES IN MOTILITY, ACROSOMES, AND PLASMA MEMBRANE." Reproduction, Fertility and Development 25, no. 1 (2013): 183. http://dx.doi.org/10.1071/rdv25n1ab71.
Full textAbstract:
Extent and timing of alterations in structures and functions of sperm after its placement in the female reproductive tract are important for successful fertilization. To our knowledge, the few reports are available on the kinetics of alterations in bovine sperm structures and functions during pathway to their death. Therefore, the present study was conducted to determine the changes in motility, acrosome and plasma membrane asymmetry in fresh and frozen–thawed semen during incubation at 37°C over the period of 24 h. Semen was collected from 3 breeding beef bulls, pooled, and considered as one replicate (total replicates = 5). Each pooled semen sample was diluted in Tris-citric acid egg yolk glycerol extender (pH 6.8), cooled to +4°C over 90 min, and then cryopreserved by a programmable cell freezer. Fresh (pooled semen) and frozen–thawed semen were incubated at 37°C for 24 h. Each semen sample was evaluated for sperm motility with computer-assisted semen analysis and acrosomal integrity and plasma membrane asymmetry using fluorescein isothiocyanate-peanut agglutinin/propidium iodide and Annexin V/propidium iodide assays, respectively, at 0, 2, 4, 6, 12, and 24 h of incubation at 37°C, with a flow cytometer. Statistical analysis was conducted using PROC MIXED model in statistical analysis system as 2 (semen types) × 6 (times) factorial model, using time as repeated measure. Progressive motility was higher (P < 0.05) in fresh than in frozen–thawed semen until 6 h. Progressive motility declined (P < 0.05) below the threshold level (i.e. 30%) much later (12 h) in fresh as compared with frozen–thawed semen (2 h). However, acrosomal integrity and plasma membrane asymmetry deteriorated (P < 0.05) below threshold at the same time interval (2 h) in both fresh and frozen–thawed semen. Viable sperm (AN–/PI–) remained higher (P < 0.05) during the first 6 h in fresh than in frozen–thawed semen and declined (P < 0.05) below the threshold at 12 h in fresh and at 6 h in frozen–thawed semen. In fresh semen, the necrotic sperm (AN–/PI+) population increased (P < 0.05) over time and reached maximum (97%) at 24 h. In frozen–thawed semen, a mixed population of late apoptotic (53%) and necrotic (34%) sperm was found at 24 h. In conclusion, the alterations in sperm motility, acrosomes, plasma membrane integrity, and asymmetry were slower in fresh than in frozen–thawed semen. Fresh sperm followed necrosis and frozen–thawed sperm underwent necrosis and apoptosis-like pathways, respectively. This study was supported by the Canadian Commonwealth Scholarship Program by the Canadian Bureau for International Education (CBIE), and Agriculture and Agri-Food Canada.
34
KATERMINA, TATYANA, and EVGENIJ LAZORENKO . "ARTIFICIAL INTELLIGENCE ELEMENTS FOR THE TASK OF DETERMINING THE POSITION OF THE VEHICLE IN THE IMAGE." Computational Nanotechnology 9, no. 3 (September 28, 2022): 9–18. http://dx.doi.org/10.33693/2313-223x-2022-9-3-9-18.
Full textAbstract:
The article is devoted to solving the problem of determining the boundaries of the vehicle on the image. This task is an intermediate step to solve other, more local tasks related to the identification of vehicles in the image or video stream. The article in detail considers existing methods and approaches to solving problems of computer vision, including modern architectures of neural networks. Tiny-YOLO-InceptionResNet was chosen as the primary neural network and was modified during the research process. The architecture of the resulting neural network is given in this paper. The training of the neural network was preceded by the preparation of a data set that allowed for a more rational use of computing resources during training. As a result of the research the model of finding the boundaries of the vehicle on the image was developed. The accuracy of this model is 88%.
35
Ramos, Kelvin J., Carlos A. Morgan, Jorge E. Cieza Montalvo, Antonio I. Rivasplata, Guillermo H. Ramírez, and Carlos E. Rodríguez Benites. "Decay Width of 3-3-1 Model Charged Higgs and Gauge Bosons." MOMENTO, no. 64 (January 5, 2022): 16–27. http://dx.doi.org/10.15446/mo.n64.97711.
Full textAbstract:
The total decay widths of the charged Higgs (H±2) and gauge bosons (V±) have been calculated in the version of the 3-3-1 Model with heavy leptons. In each case, we analyze the decay rates and determine the most likely channels to occur in order to identify the most relevant final events.
36
Du, Fu-Jun, and Shuang-Jian Jiao. "Improvement of Lightweight Convolutional Neural Network Model Based on YOLO Algorithm and Its Research in Pavement Defect Detection." Sensors 22, no. 9 (May 6, 2022): 3537. http://dx.doi.org/10.3390/s22093537.
Full textAbstract:
To ensure the safe operation of highway traffic lines, given the imperfect feature extraction of existing road pit defect detection models and the practicability of detection equipment, this paper proposes a lightweight target detection algorithm with enhanced feature extraction based on the YOLO (You Only Look Once) algorithm. The BIFPN (Bidirectional Feature Pyramid Network) network structure is used for multi-scale feature fusion to enhance the feature extraction ability, and Varifocal Loss is used to optimize the sample imbalance problem, which improves the accuracy of road defect target detection. In the evaluation test of the model in the constructed PCD1 (Pavement Check Dataset) dataset, the mAP@.5 (mean Average Precision when IoU = 0.5) of the BV-YOLOv5S (BiFPN Varifocal Loss-YOLOv5S) model increased by 4.1%, 3%, and 0.9%, respectively, compared with the YOLOv3-tiny, YOLOv5S, and B-YOLOv5S (BiFPN-YOLOv5S; BV-YOLOv5S does not use the Improved Focal Loss function) models. Through the analysis and comparison of experimental results, it is proved that the proposed BV-YOLOv5S network model performs better and is more reliable in the detection of pavement defects and can meet the needs of road safety detection projects with high real-time and flexibility requirements.
37
Ünver, Halil Murat, and Enes Ayan. "Skin Lesion Segmentation in Dermoscopic Images with Combination of YOLO and GrabCut Algorithm." Diagnostics 9, no. 3 (July 10, 2019): 72. http://dx.doi.org/10.3390/diagnostics9030072.
Full textAbstract:
Skin lesion segmentation has a critical role in the early and accurate diagnosis of skin cancer by computerized systems. However, automatic segmentation of skin lesions in dermoscopic images is a challenging task owing to difficulties including artifacts (hairs, gel bubbles, ruler markers), indistinct boundaries, low contrast and varying sizes and shapes of the lesion images. This paper proposes a novel and effective pipeline for skin lesion segmentation in dermoscopic images combining a deep convolutional neural network named as You Only Look Once (YOLO) and the GrabCut algorithm. This method performs lesion segmentation using a dermoscopic image in four steps: 1. Removal of hairs on the lesion, 2. Detection of the lesion location, 3. Segmentation of the lesion area from the background, 4. Post-processing with morphological operators. The method was evaluated on two publicly well-known datasets, that is the PH2 and the ISBI 2017 (Skin Lesion Analysis Towards Melanoma Detection Challenge Dataset). The proposed pipeline model has achieved a 90% sensitivity rate on the ISBI 2017 dataset, outperforming other deep learning-based methods. The method also obtained close results according to the results obtained from other methods in the literature in terms of metrics of accuracy, specificity, Dice coefficient, and Jaccard index.
38
Guttmann, Anthony J., and Ian G. Enting. "The phase transition of the 3-dimensional 3-state potts model." Nuclear Physics B - Proceedings Supplements 17 (September 1990): 328–30. http://dx.doi.org/10.1016/0920-5632(90)90265-v.
Full text
39
Zhong, J., M. Li, J. Qin, Y. Cui, K. Yang, and H. Zhang. "REAL-TIME MARINE ANIMAL DETECTION USING YOLO-BASED DEEP LEARNING NETWORKS IN THE CORAL REEF ECOSYSTEM." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVI-3/W1-2022 (April 22, 2022): 301–6. http://dx.doi.org/10.5194/isprs-archives-xlvi-3-w1-2022-301-2022.
Full textAbstract:
Abstract. In recent years, with the advancement of marine resources and environment research, the ecological functions of reef-building coral reef ecosystems distributed in warm shallow waters of the ocean are being continuously discovered and valued by people. It is important for ecosystem protection to monitor the population of marine animals. Besides, many projects of Autonomous Underwater Vehicle (AUV) also need technology to perceive and understand environment information in real-time for better decision-making. Therefore, marine animal detection has become a challenge for researchers to study nowadays. Deep neural network models have been used to solve fish-related tasks and gained encouraging achievements, but there are still many problems in this field. In this paper, several YOLO-based methods are chosen for comparison. Experiment results indicate that these methods can recognize the marine animals in coral reef quickly and accurately. Finally, several recommendations for model improvement according to assessment results are presented.
40
Щуков, Денис Александрович. "THE IDYLLIC MODEL OF V. A. ZHUKOVSKY IN N. V. GOGOL’S POEM “GANZ KUCHELGARTEN”." Tomsk state pedagogical university bulletin, no. 3(221) (May 16, 2022): 115–25. http://dx.doi.org/10.23951/1609-624x-2022-3-115-125.
Full textAbstract:
Введение. Жанр идиллии, развивающийся в мировой литературе еще с античности, является определяющим для поэтики русской литературы первой трети XIX в. Эстетика романтизма помогла ему развиться, и таким образом возникла романтическая идиллия.Большой вклад в развитие идиллии в России внес В. А. Жуковский, основоположник романтизма в русской литературе. Масштаб творчества поэта был достаточно велик и оказал влияние на произведения современников, среди которых был и Н. В. Гоголь, проявлявший в юношеские годы немалый интерес к идиллической тематике.В связи с этим целью статьи является определение особенностей реализации идиллической модели В. А. Жуковского в ранней поэме Гоголя «Ганц Кюхельгартен».Материал и методы. Материалом исследования является идиллическая поэма Н. В. Гоголя «Ганц Кюхельгартен», а также оказавшие на нее влияние произведения В. А. Жуковского, среди которых идиллия «Деревенский сторож в полночь»; «павловские стихотворения»; стихотворения «Теон и Эсхин», «Тоска», «Лалла Рук», «Там небеса и воды ясны!»; элегии «Славянка» и «Вечер»; романс «Желание»; песня «Путешественник»; статья «О слоге простом и украшенном».В работе используются биографический и сравнительный методы исследования.Результаты и обсуждение. Жанр идиллии очень близок В. А. Жуковскому. Идиллическое мировоззрение органично существует в его поэтическом сознании. Период сильного интереса поэта к идиллии синхронизируется с его юностью. В молодости он черпает новые поэтические формы преимущественно из европейских идиллий. В них Жуковского привлекают образы простой, обыденной жизни, сфокусированность на сельской природе, патриархальных нравах. Поэт особенно вдохновляется натурфилософскими аспектами.Отличительными чертами произведений Жуковского, созданных в данном жанре, являются описательность, обилие статических природных картин, вольный стих. Кроме того, отдельно выделим и авторскую установку на воспроизведение естественной динамики жизни. Поэт выступает живописцем, который стремится в своих картинах передать мельчайшие детали внутренней жизни природы.Однако это лишь эксплицитная часть идиллий Жуковского. Намного важнее их имплицитная часть. Под ней мы подразумеваем сакральное, религиозное и мистическое. Мы фиксируем определенную эволюцию Жуковского-идиллика. От воспевания пейзажей и изображения жизни человека на лоне природы поэт осознанно переходит к более сложному творческому этапу, фокусируясь на мистическом и прислушиваясь к своему религиозному чувству.Что касается других особенностей жанра идиллии в творчестве Жуковского, подобно контрастности, двоемирию романтизма, произведения этого жанра, переводимые им, есть сплав, синтез прекрасного и таинственного в мире. Неслучайно многие из них открываются ночными пейзажами.Таковыми были особенности реализации жанра идиллии в творческой системе Жуковского. Именно под их влиянием пишет свое первое произведение, поэму «Ганц Кюхельгартен», Н. В. Гоголь.Идиллическая модель Жуковского в поэме Гоголя «Ганц Кюхельгартен» идентифицируется практически сразу. Это подтверждает, во-первых, топос поэмы (сельская глубинка). Во-вторых, читателю явлены картины пасторальной жизни. Немаловажно, что в начале поэмы возникает образ семьи (патриархальность – это важный аспект идиллии). Кроме того, в поэме Гоголя обнаруживаются и иные традиционные для идиллии топосы (дом, сад).По примеру Жуковского Гоголь использует в произведении мотивы еды, сна и музыки. Они широко представлены в тексте и подчеркивают идиллические моменты поэмы.Геройный ряд можно считать также традиционным для идиллии (каноны которой в русской литературе первым заложил именно Жуковский), поскольку подробное представление автором семьи Луизы – это следование идиллической направленности к полноте жизни, которая здесь есть целостность семьи.В поэме наблюдается стремление героев к достижению счастья, персонажи мечтают об идиллической жизни. Однако в главном герое поэмы больше просматриваются романтические черты. И тем не менее в нем нет страсти героев Байрона. В итоге Ганц, скорее, следует линии поведения Эсхина, воплощенного Жуковским. Заключение. Таким образом, Гоголь при работе над своей юношеской поэмой «Ганц Кюхельгартен» ориентировался на идиллическую модель Жуковского. На это указывают традиционные элементы идиллий поэта, которые явно присутствуют в тексте Гоголя (образ семьи, пасторальные пейзажи, природные циклы, мотивы еды, сна, детства и т. п.). Однако Гоголь игнорирует некоторые важные элементы, свойственные идиллиям Жуковского. Так, гоголевский главный герой демонстрирует скорее черты романтического героя, нежели идиллического. Кроме того, в гоголевской поэме нет ярко выраженной мистической завесы. И, наконец, в ней, безусловно, ощутим диссонанс (субстрат романтического и идиллического). Причина его заключается в недостаточно продуманной компиляции художественных приемов ведущих европейских и русских литераторов. Introduction. The genre of idyll, which has been developing in world literature since Antiquity, is crucial for the poetics of Russian literature in the first third of the XIX century. The aesthetics of Romanticism helped it develop, and thus a romantic idyll arose. A great contribution to the development of idyll in Russia was made by V. A. Zhukovsky, the founder of Romanticism in Russian literature. The scale of the poet’s work was quite large and influenced the works of his contemporaries, among whom was N. V. Gogol, who showed considerable interest in idyllic themes in his youth. Aim and objectives. In this regard, the purpose of the article is to determine the features of V. A. Zhukovsky’s idyllic model implementation in N. V. Gogol’s early poem “Ganz Kuchelgarten”. Material and methods. The material of the study is the idyllic poem by N. V. Gogol “Ganz Kuchelgarten”, as well as the works of V. A. Zhukovsky that influenced it, including an idyll “The Village Watchman at Midnight”, “pavlovian poems”, the poems “Theon and Aeschines”, “Anguish” (“toska”), “Lalla-Rookh”, “There the heavens and waters are clear!”, the elegies “Slavyanka” and “Evening”, the romance “Desire”, the song “Traveller”, the article “About a simple and decorated style”. Biographical and comparative research methods are used in the work. Results and discussion. The genre of idyll is very close to V. A. Zhukovsky. An idyllic worldview exists organically in his poetic consciousness. The period of the poet’s strong interest in idyll is synchronized with his youth. In his youth, he draws new poetic forms mainly from European idylls. In them Zhukovsky is attracted by images of simple, everyday life, focus on rural nature, patriarchal mores. The poet is especially inspired by the natural philosophical aspects. The distinctive features of Zhukovsky’s works created in this genre are descriptiveness, an abundance of static natural paintings, free verse. In addition, we will separately highlight the author’s aim at reproducing the natural dynamics of life. The poet acts as a painter who strives to convey in his paintings the smallest details of the inner nature life. However, this is only an explicit part of Zhukovsky’s idylls. Their implicit part is much more important. By it we mean the sacred, the religious and the mystical. We see a certain evolution of Zhukovsky’s idyll. From the glorification of landscapes and the depiction of human life in the bosom of nature, the poet consciously moves to a more complex creative stage, focusing on the mystical and listening to his religious feeling. As for other features of the idyll genre in Zhukovsky’s work, like contrast or two-worldness of romanticism, the works of this genre translated by him are an alloy, a synthesis of the beautiful and the enigmatic in the world. It is no coincidence that many of them open with night landscapes. These were the features of the idyll genre implementation in Zhukovsky’s creative system. It was under their influence that N. V. Gogol wrote his first work, the poem “Ganz Kuchelgarten”. Zhukovsky’s idyllic model in Gogol’s poem “Ganz Kuchelgarten” is identified almost immediately. This is confirmed, firstly, by the topos of the poem (rural hinterland). Secondly, by the fact that the reader is shown pictures of pastoral life. It is also important that at the beginning of the poem there is an image of the family (patriarchy is an important aspect of the idyll). In addition, Gogol’s poem reveals other traditional topos for idyll (house, garden). Following the example of Zhukovsky, Gogol uses the motifs of food, sleep and music in the work. They are widely represented in the text and emphasize the idyllic moments of the poem. The heroic series can also be considered traditional for the idyll (the canons of which in Russian literature were first laid by Zhukovsky), since the author’s detailed representation of Louise’s family is following an idyllic orientation to the fullness of life, which here is the integrity of the family. The poem shows the desire of the characters to achieve happiness, they dream of an idyllic life. However, romantic features are more visible in the main character of the poem. And yet it lacks the passion of Byron’s heroes. At the end, Ganz rather follows the line of behavior of Aeschines, embodied by Zhukovsky. Conclusion. Thus, Gogol, when working on his youthful poem “Ganz Kuchelgarten”, was guided by the idyllic model of Zhukovsky. This is indicated by the traditional elements of the poet’s idylls, which are clearly present in Gogol’s text (the image of a family, pastoral landscapes, natural cycles, the motif of food, sleep and childhood). However, Gogol ignores some important elements inherent in Zhukovsky’s idylls. So, Gogol’s main character demonstrates the features of a romantic hero rather than an idyllic one. In addition, there is no pronounced mystical veil in Gogol’s poem. And finally, dissonance (the substratum of the romantic and idyllic) is certainly felt in it. The reason for it lies in the insufficiently thought-out compilation of leading European and Russian writers’ artistic techniques.
41
Frittelli, Marco, and Marco Maggis. "Disentangling price, risk and model risk: V&R measures." Mathematics and Financial Economics 12, no. 2 (October 14, 2017): 219–47. http://dx.doi.org/10.1007/s11579-017-0202-3.
Full text
42
Lutfita Sari, Tria, and Henny Dewi Koeswanti. "Penerapan Model Pembelajaran Berbasis Masalah untuk Meningkatkan Hasil Belajar." Journal of Education Action Research 3, no. 2 (April 10, 2019): 153. http://dx.doi.org/10.23887/jear.v3i2.17272.
Full textAbstract:
Penelitian tindakan kelas ini dilakukan untuk meningkatkan hasil belajar Tema 6 Subtema 3 Pembelajaran 1 dan 2 dengan menerapkan model Pembelajaran Berbasis Masalah pada siswa kelas V yang terdiri dari 16 siswa. Meningkatkan hasil belajar Tema 6 Subtema 3 Pembelajaran 1 dan 2 dengan menerapkan model Pembelajaran Berbasis Masalah. Faktor-faktor yang menyebabkan peningkatan hasil belajar Tema 6 Subtema 3 Pembelajaran 1 dan 2 dengan penerapan model Pembelajaran Berbasis Masalah Siswa Kelas V SD N Kutowinangun 10 Salatiga. Subjek penelitian adalah siswa kelas V dengan jumlah 16 siswa. Hasil penelitian adalah peningkatan hasil belajar sebelum tindakan dilakukan untuk siklus I dan siklus II. Hasil belajar pada pra siklus dengan persentase 31,25%, kemudian ada peningkatan di siklus I dengan persentase 43,75%. Selanjutnya hasil belajar siklus II meningkat dengan persentase 81,25%, dibuktikan dengan adanya penerapan model Pembelajaran Berbasis Masalah dapat meningkatkan hasil belajar Tematik Siswa Kelas V SD N Kutowinangun 10 Salatiga.
43
Horikoshi, Masaatsu, Yugo Abe, and Takeo Inami. "Gravity Loop Corrections to the Standard Model Higgs in Einstein Gravity." Communications in Physics 26, no. 3 (January 10, 2017): 229. http://dx.doi.org/10.15625/0868-3166/26/3/9033.
Full textAbstract:
We study one-loop quantum gravity corrections to the standard model Higgs potential \(V(\phi) \grave{\rm a}\) la Coleman-Weinberg and examine the stability question of \(V(\phi) \) in the energy region of Planck mass scale, \(\mu\simeq M_{\rm Pl}\) \((M_{\rm Pl}=1.22\times10^{19}{\rm GeV})\). We calculate the gravity one-loop corrections to \(V(\phi)\) in Einstein gravity by using the momentum cut-off \(\Lambda\). We have found that even small gravity corrections compete with the standard model term of \(V(\phi)\) and affect the stability argument of the latter part alone. This is because the latter part is nearly zero in the energy region of \(M_{\rm Pl}\).
44
Kontis, Panagiotis, Jan-Frederik Güth, and Christine Keul. "Accuracy of full-arch digitalization for partially edentulous jaws — a laboratory study on basis of coordinate-based data analysis." Clinical Oral Investigations 26, no. 4 (January 4, 2022): 3651–62. http://dx.doi.org/10.1007/s00784-021-04335-3.
Full textAbstract:
Abstract Objectives To compare the accuracy (trueness and precision) of direct digitization of four different dental gap situation with two IOS (intraoral scanner). Materials and methods Four partially edentulous polyurethane mandible models were used: (1) A (46, 45, 44 missing), (2) B (45, 44, 34, 35 missing), (3) C (42, 41, 31, 32 missing), and (4) D (full dentition). On each model, the same reference object was fixed between the second molars of both quadrants. A dataset (REF) of the reference object was generated by a coordinate measuring machine. Each model situation was scanned by (1) OMN (Cerec AC Omnicam) and (2) PRI (Cerec Primescan AC) (n = 30). Datasets of all 8 test groups (N = 240) were analyzed using inspection software to determine the linear aberrations in the X-, Y-, Z-axes and angular deviations. Mann–Whitney U and two-sample Kolmogorov–Smirnov tests were used to detect differences for trueness and precision. Results PRI revealed higher trueness and precision in most of the measured parameters ($${\overrightarrow{V}}_{E}$$ V → E 120.95 to 175.01 μm, $$\overrightarrow{V}_{E}$$ V → E (x) − 58.50 to − 9.40 μm, $$\overrightarrow{V}_{E}$$ V → E (z) − 70.35 to 63.50 μm), while OMN showed higher trueness for $$\overrightarrow{V}_{E}$$ V → E (y) regardless of model situation (− 104.90 to 34.55 μm). Model D revealed the highest trueness and precision in most of the measured parameters regardless of IOS ($$\overrightarrow{V}_{E}$$ V → E 120.95 to 195.74 μm, $$\overrightarrow{V}_{E}$$ V → E (x) − 9.40 to 66.75 μm,$$\overrightarrow{V}_{E}$$ V → E (y) − 14.55 to 51.50 μm, $$\overrightarrow{V}_{E}$$ V → E (z) 63.50 to 120.75 μm). Conclusions PRI demonstrated higher accuracy in the X- and Z-axes, while OMN depicted higher trueness in the Y-axis. For PRI, Model A revealed the highest distortion, while for OMN, Model B produced the largest aberrations in most parameters. Clinical relevance Current results suggest that both investigated IOS are sufficiently accurate for the manufacturing of tooth-borne restorations and orthodontic appliances. However, both hardware specifications of IOS and the presence of edentulous gaps in the dental model have an influence on the accuracy of the virtual model dataset.
45
Rotter, M. L., W. Koller, G. Wewalka, H. P. Werner, G. A. J. Ayliffe, and J. R. Babb. "Evaluation of procedures for hygienic hand-disinfection: controlled parallel experiments on the Vienna test model." Journal of Hygiene 96, no. 1 (February 1986): 27–37. http://dx.doi.org/10.1017/s0022172400062501.
Full textAbstract:
SUMMARYControlled parallel experiments were performed on the Vienna test model for the evaluation of procedures for hygienic hand-disinfection in three laboratories (Vienna, Mainz, Birmingham). The degerming activity of four procedures, each taking 1 min, was assessed repeatedly and compared with that of a standard disinfection procedure (ST) using isopropanol 60 % (v/v). The mean log reductions (mean log RF) for each procedure were as follows: n−propanol 50% (v/v) 4·85 and 5·14 in Vienna (V) and Mainz (M) respectively, ethanol 70 % (v/v) + chlorhexidinegluconate 0·5% (w/v), 4·01 (V), 3·76 (M) and 4·00 in Birmingham (B). Washing procedures were less effective, mean log RF 's of 3·19 (V), 3·49 (M) and 3·04 (B) were obtained with povidone-iodine soap, and 2·91 (V), 3·37 (M) and 3·27 (B) with a liquid phenolic soap. Analysis of variance on the data from Vienna and Mainz revealed significant differences of means not only between procedures (‘preparations’) but also on repeat testing. To compensate for the influence of variables such as test subjects, laboratory and day, the Vienna test model provides a method of standardization by testing a ST in parallel with the test procedure (P).Standardization of the results was obtained by pair-wise substraction, log . Analysis of variance on the resulting values demonstrated that comparability of the results between laboratories and on repeat testing was achieved. The relative variation of the measurements within the laboratories ranged from 0·9 to 4·2%. As assessed by power-analysis, a disinfection procedure will be detected as significantly (P= 0·1) inferior to the standard processes in 95 of 100 experiments if it produces a mean log RF that is at least 0·55–0·65 log units smaller than that of the standard.
46
Sugimura, Hotaruko, Yasuyuki Kaneno, and Takayuki Takasugi. "Alloying Behavior of Ni3Nb, Ni3V and Ni3Ti Compounds." Materials Science Forum 654-656 (June 2010): 440–43. http://dx.doi.org/10.4028/www.scientific.net/msf.654-656.440.
Full textAbstract:
The site preference of ternary additions in Ni3X-type GCP compounds was determined from the direction of solubility lobe of the GCP phase on the experimentally reported ternary phase diagrams. In Ni3Nb (D0a), Co and Cu preferred the substitution for Ni-site, Ti, V and W the substitution for Nb-site, and Fe the substitution for both sites. In Ni3V (D022), Co preferred the substitution for Ni-site, Cr the substitution for both sites, and Ti the substitution for V-site. In Ni3Ti (D024), Fe, Co, Cu, and Si preferred the substitution for Ni-site, Nb, Mo and V the substitution for Ti-site. The thermodynamic model, which was based on the change in total bonding energy of the host compound by a small addition of ternary solute, was applied to predict the site preference of ternary additions. The bond energy of each nearest neighbor pair used in the thermodynamic calculation was derived from the heat of compound formation by Miedema’s formula. The agreement between the thermodynamic model and the result of the literature search was excellent. Both transition and B-subgroup elements have two possibilities, i.e., the case of substitution for Ni-site or the case for X-site, depending on the relative value of two interaction energies.
47
Pankratov, Igor M., and Volodymyr Y. Bochko. "Nonlinear Cone Model for Investigation of Runaway Electron Synchrotron Radiation Spot Shape." 3, no. 3 (September 28, 2021): 18–24. http://dx.doi.org/10.26565/2312-4334-2021-3-02.
Full textAbstract:
The runaway electron event is the fundamental physical phenomenon and tokamak is the most advanced conception of the plasma magnetic confinement. The energy of disruption generated runaway electrons can reach as high as tens of mega-electron-volt and they can cause a catastrophic damage of plasma-facing-component surfaces in large tokamaks and International Thermonuclear Experimental Reactor (ITER). Due to its importance, this phenomenon is being actively studied both theoretically and experimentally in leading thermonuclear fusion centers. Thus, effective monitoring of the runaway electrons is an important task. The synchrotron radiation diagnostic allows direct observation of such runaway electrons and an analysis of their parameters and promotes the safety operation of present-day large tokamaks and future ITER. In 1990 such diagnostic had demonstrated its effectiveness on the TEXTOR (Tokamak Experiment for Technology Oriented Research, Germany) tokamak for investigation of runaway electrons beam size, position, number, and maximum energy. Now this diagnostic is installed practically on all the present-day’s tokamaks. The parameter v┴/|v||| strongly influences on the runaway electron synchrotron radiation behavior (v|| is the longitudinal velocity, v┴ is the transverse velocity with respect to the magnetic field B). The paper is devoted to the theoretical investigation of runaway electron synchrotron radiation spot shape when this parameter is not small that corresponds to present-day tokamak experiments. The features of the relativistic electron motion in a tokamak are taken into account. The influence of the detector position on runaway electron synchrotron radiation data is discussed. Analysis carried out in the frame of the nonlinear cone model. In this model, the ultrarelativistic electrons emit radiation in the direction of their velocity v→ and the velocity vector runs along the surface of a cone whose axis is parallel to the magnetic field B. The case of the small parameter v┴/|v||| (v┴/|v|||<<1, linear cone model) was considered in the paper: Plasma Phys. Rep. 22, 535 (1996) and these theoretical results are used for experimental data analysis.
48
Sakurada, Yuya, Yasuyuki Ota, Hiroki Watanabe, Hideyuki Murata, and Kensuke Nishioka. "Evaluation of Organic Thin Film Solar Cells Using 3-Diode Equivalent Circuit Model with Inverted Diode." Materials Science Forum 725 (July 2012): 179–82. http://dx.doi.org/10.4028/www.scientific.net/msf.725.179.
Full textAbstract:
We proposed a modified 3-diode equivalent circuit model with an inverted diode for the analysis of the characteristics of bulk hetero junction organic solar cells before and after heat treatment with the reverse bias of -8 V. The dark current density-voltage (J-V) characteristics of the bulk hetero junction organic solar cells before and after heat treatment were measured. From dark J-V characteristics of each organic solar cell, fitting of J-V curve between measured and calculated date was carried out using a modified 3-diode equivalent circuit model with an inverted diode. Using extracted diode parameters, the performances of the bulk hetero junction organic solar cells were compared and evaluated. The measured and calculated J-V characteristics of the bulk hetero junction organic solar cell agreed well, and the organic solar cells were able to be evaluated using the proposed method.
49
Tjiptady, Bella Cornelia, Rifki Zainur Rahman, Ratna Fajarwati Meditama, and Gede Widayana. "Jig and Fixture Redesign for Making Reamer on Head Cylinder." Jurnal Pendidikan Teknik Mesin Undiksha 9, no. 1 (March 25, 2021): 32–41. http://dx.doi.org/10.23887/jptm.v9i1.32597.
Full textAbstract:
Proses produksi telah banyak berevolusi dengan diperkenalkannya konsep manufaktur inovatif yang handal. Salah satu komponen mesin yang banyak diproduksi adalah cylinder head. Cylinder head harus tahan terhadap temperatur dan tekanan yang tinggi selama engine bekerja. Oleh sebab itu umumnya cylinder head dibuat dari besi tuang. Kendala yang ada saat ini yaitu proses pembuatan cylinder head kurang efektif dan efisien karena ketika menetapkan sudut untuk membuat reamer berbasis manual, selain itu setting benda kerja tidak otomatis sehingga membutuhkan waktu yang terlalu lama. Solusi dari permasalahan tersebut yaitu dengan adanya jig dan fixture. Metode penelitian yang digunakan adalah metodologi perancangan fixture (Society of Manufacturing Engineers). Berdasarkan hasil analisis, terdapat kelebihan dari jig dan fixture yang telah dirancang ulang yaitu: 1) memiliki stoper yang berfungsi untuk memberhentikan benda kerja, dengan sudut yang sudah ditentukan sehingga tidak perlu mensetting sudut kembali; 2) tidak mudah bergeser apabila fixture dipasang dan sejajar di meja frais; 3) terdapat dua engsel sehingga lebih balance; 4) pemasangan tidak rumit sehingga tidak memakan banyak waktu dalam pembuatan produk.Kata kunci: Jig and fixture; redesign; head cylinder The production process has evolved a lot with the introduction of innovative reliable manufacturing concepts. One of the engine components that are widely produced is the cylinder head. The cylinder head must withstand high temperatures and pressures while the engine is running. Therefore, generally the cylinder head is made of cast iron. The current constraint is that the cylinder head manufacturing process is less effective and efficient because when setting the angle to make the reamer a manual basis, besides that the workpiece setting is not automatic so it takes too long. The solution to this problem is the presence of jigs and fixtures. The research method used is the fixture design methodology (Society of Manufacturing Engineers). Based on the results of the analysis, there are advantages to the redesigned jig and fixture, namely: 1) it has a stoper which functions to stop the workpiece, at a predetermined angle so that there is no need to set the angle again; 2) it does not move easily when the fixture is installed and parallel to the milling table; 3) there are two hinges so that it is more balanced; 4) installation is not complicated so it does not take much time to manufacture the product.Keywords : Jig and fixture; redesign; head cylinder. DAFTAR RUJUKAN Basuki, B., Yoto., Suyetno A., & Tjiptady, B. C. (2020). Management Model of Manufacturing Workshop/Laboratory of Vocational Education in the Industry 4.0. 4th International Conference on Vocational Education and Training (ICOVET), Malang, Indonesia, 2020, pp. 127-130, doi: 10.1109/ICOVET50258.2020.9230188. Choong, G. Y. H., Canciani, A., & Defocatiis, D. S. A. (2020). An Adaptable Flexural Test Fixture for Miniaturised Polymer Specimens. Polymer Testing, 85, 106430. doi:10.1016/j.polymertesting.2020.106430 Craig, O., Picavea, J., Gameros, A., Axinte, D., & Lowth, S. (2020). Conformable Fixture Systems With Flexure Pins For Improved Workpiece Damping. Journal of Manufacturing Processes, 50, 638–652. doi:10.1016/j.jmapro.2019.12.045 Fonte, M., Reis, L., Infante, V., & Freitas, M. (2019). Failure Analysis of Cylinder Head Studs of a Four Stroke Marine Diesel Engine. Engineering Failure Analysis. doi:10.1016/j.engfailanal.2019.03.026 Gameros, A., Lowth, S., Axinte, D., Nagy-Sochacki, A., Craig, O., & Siller, H. R. (2017). State-Of-The-Art In Fixture Systems For The Manufacture And Assembly Of Rigid Components: A Review. International Journal of Machine Tools and Manufacture, 123, 1–21. doi:10.1016/j.ijmachtools.2017.07.004 Jing, G. X., Zhang, M. X., Qu, S., Pang, J. C., Fu, C. M., Dong, C., Zhang, Z. F. (2018). Investigation into diesel engine cylinder head failure. Engineering Failure Analysis, 90, 36–46. doi:10.1016/j.engfailanal.2018.03.008 Kamble, V. D., & Mathew, A. T. (2020). Brief Review of Methodologies for Creation of Cohesive Fixture Design. Materials Today: Proceedings, 22, 3353–3363. doi:10.1016/j.matpr.2020.04.285 Kampker, A., Bergweiler, G., Hollah, A., Lichtenthäler, K., & Leimbrink, S. (2019). Design and Testing of The Different Interfaces In A 3D Printed Welding Jig. Procedia CIRP, 81, 45–50. doi:10.1016/j.procir.2019.03.009 Krznar, N., Pilipović, A., & Šercer, M. (2016). Additive Manufacturing of Fixture for Automated 3D Scanning–Case Study. Procedia Engineering, 149, 197–202. doi:10.1016/j.proeng.2016.06.656 Kumar, S., Campilho, R. D. S. G., & Silva, F. J. G. (2019). Rethinking Modular Jigs’ Design Regarding the Optimization of Machining Times. Procedia Manufacturing, 38, 876–883. doi:10.1016/j.promfg.2020.01.169 Lu, R., Li, Y.-C., Li, Y., Jiang, J., & Ding, Y. (2020). Multi-agent Deep Reinforcement Learning Based Demand Response for Discrete Manufacturing Systems Energy Management. Applied Energy, 276, 115473. doi:10.1016/j.apenergy.2020.115473 Ma, S., Zhang, Y., Yang, H., Lv, J., Ren, S., & Liu, Y. (2020). Data-driven Sustainable Intelligent Manufacturing Based on Demand Response for Energy-Intensive Industries. Journal of Cleaner Production, 123155. doi:10.1016/j.jclepro.2020. 123155 Marsono, Yoto, Sutadji E., & Tjiptady, B. C. (2020). Career Development and Self-Efficacy Through Industrial Working Practice in Vocational Education," 4th International Conference on Vocational Education and Training (ICOVET), Malang, Indonesia, 2020, pp. 1-4, doi: 10.1109/ICOVET50258.2020.9230111 Nee, A. Y. C., Bhattacharyya, N., & Poo, A. N. (1987). Applying AI in Jigs and Fixtures Design. Robotics and Computer-Integrated Manufacturing, 3(2), 195–200. doi:10.1016/0736-5845(87)90102-5 Qolik, A., Nurmalasari, R., Yoto., & Tjiptady, B. C. (2020). The Role of Special Job Fair in Distributing Competitive Graduates in the 21st Century. 4th International Conference on Vocational Education and Training (ICOVET), Malang, Indonesia, 2020, pp. 115-118, doi: 10.1109/ICOVET50258.2020.9230064 Schuh, G., Bergweiler, G., Lichtenthäler, K., Fiedler, F., & Puente, R. S. (2020). Topology Optimisation and Metal Based Additive Manufacturing of Welding Jig Elements. Procedia CIRP, 93, 62–67. doi:10.1016/j.procir.2020.04.066 Seloane, W. T., Mpofu, K., Ramatsetse, B. I., & Modungwa, D. (2020). Conceptual Design of Intelligent Reconfigurable Welding Fixture for Rail Car Manufacturing Industry. Procedia CIRP, 91, 583–593. doi:10.1016/j.procir.2020.02.217 Siva, R., Siddardha, B., Yuvaraja, S., & Karthikeyan, P. (2020). Improving the productivity and tool life by fixture modification and renishaw probe technique. Materials Today: Proceedings, 24, 782–787. doi:10.1016/j.matpr.2020.04.386 Tjiptady, B. C., Rohman, M., Sudjimat, D. A., Ratnawati, D. (2020). Analisis Tegangan, Deformasi, dan Retak Pada Gas Turbine Blade dengan Metode Elemen Hingga. Jurnal Taman Vokasi. Vol 8, (2). doi : 10.30738/jtv.v8i2.8425 Tjiptady, B. C., Yoto., & Marsono. (2020). Entrepreneurship Development Design based on Teaching Factory to Improve the Vocational Education Quality in Singapore and Indonesia, 4th International Conference on Vocational Education and Training (ICOVET), Malang, Indonesia, pp. 130-134, doi: 10.1109/ICOVET50258.2020.9230222 Tohidi, H., & Algeddawy, T. (2016). Planning of Modular Fixtures in a Robotic Assembly System. Procedia CIRP, 41, 252–257. doi:10.1016/j.procir.2015.12.090 Vijaya, R. B., Elanchezhian, C., Rajesh, S., Jaya, P. S., Kumaar, B. M., & Rajeshkannan, K. (2018). Design and Development of Milling Fixture for Friction Stir Welding. Materials Today: Proceedings, 5(1), 1832–1838. doi:10.1016/j.matpr.2017.11.282
50
Chaturvedi, S., and Virendra Gupta. "Model of the Quark Mixing Matrix Involving its Eigenvalues." Modern Physics Letters A 18, no. 23 (July 30, 2003): 1635–42. http://dx.doi.org/10.1142/s0217732303011526.
Full textAbstract:
A model of the 3 × 3 Cabibbo–Kobayashi–Maskawa matrix, V, is presented in which the parameters are the eigenvalues and the components of its eigenvectors. In this model, the small departure of the experimentally determined V from being moduli symmetric (i.e. |Vij|=|Vji|) is controlled by the small difference between two of the eigenvalues. In case any two eigenvalues are equal, one obtains a moduli symmetric V depending on only three parameters. Our model gives very good fits to the available data including CP-violation. Our value of sin 2β≈0.7 and other parameters associated with the ''unitarity triangle'' [Formula: see text] are in good agreement with data and other analyses.