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Relevant bibliographies by topics / Auto-picking

Academic literature on the topic 'Auto-picking'

Author: Grafiati

Published: 1 April 2023

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Journal articles on the topic "Auto-picking"

1

Jin, Xin, Shu Jun Li, Xue Jun Yang, Jian Min Wu, Zhong Jun Liu, and Han Kun Liu. "Developments in Research on Seedling Auto-Picking Device of Vegetable Transplanter." Applied Mechanics and Materials 364 (August 2013): 375–79. http://dx.doi.org/10.4028/www.scientific.net/amm.364.375.

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Seedling auto-picking device is one of the most important working parts of a vegetable automatic transplanter. Its performance determines the transplanting quality, reliability and operation efficiency. The current developments of the seedling auto-picking device were discussed, and several typical devices were introduced in this paper. The existing problems of seedling auto-picking devices of different picking methods were pointed out. With the advantages of accurate location, high efficiency and easy picking, the mandril-type seedling picking was thought as the better transplanting mode of vegetable plug seedling. At the same time, the development direction of auto-picking devices of vegetable automatic transplanter was provided.

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Yuan, Cheng, Mingjun Su, Changkuan Ni, Xingye Liu, Yunze Xu, and Xiangli Cui. "Horizon auto-picking with quantitative uncertainty evaluation by using a modified VQ-VAE framework." Journal of Geophysics and Engineering 19, no. 4 (July 29, 2022): 788–806. http://dx.doi.org/10.1093/jge/gxac051.

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Abstract In this paper, we propose a novel approach of deep-learning-based seismic horizon auto-picking that introduces a modified vector quantized variational autoencoder (VQ-VAE) framework to improve the accuracy of seismic horizon interpretation and, for the first time, quantitatively evaluate the uncertainty of the auto-picked horizon by exploiting the concept of entropy. Compared with the conventional VQ-VAE approach, the proposed method not only modifies the VQ-VAE model with more deep-learning channels at each layer of the network to enhance the performance of horizon auto-picking within the VQ-VAE framework, but also extends the 1D seismic labels with more continuous samplings within a single trace to boost the stability of auto-picked horizon in geologically complex settings and also significantly suppress the resulting uncertainty. To further improve the resulting accuracy in geologically complex settings, we introduce the directional structure tensor to extract a more reliable initial horizon and, moreover, a dilated horizon searching strategy to extend the capacity of the proposed method in dealing with the large fault displacement and reducing the computational cost simultaneously. Additionally, the resulting uncertainty quantitatively measured by entropy can also serve as an effective indicator to enable a further refinement of the auto-picked result accordingly. Both 2D example and 3D field applications are carried out to validate the effectiveness of the proposed method.

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Tanaka, Masahiro, and Hideki Fujiwara. "Visualization of Categorical Data by Hybridization of Two Types of Neural Networks." Journal of Advanced Computational Intelligence and Intelligent Informatics 4, no. 1 (January 20, 2000): 3–11. http://dx.doi.org/10.20965/jaciii.2000.p0003.

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The sandglass neural network is often used for nonlinear auto-association, where the principal information can be extracted by picking up the values of the middle layer. However, the boundary of the classes on this 2-1) surface tends to be complicated because no class information is used. In this paper, the hybridization of auto-associative network and the multi-layer perceptron for classification is proposed. The usefulness of this method is demonstrated by using clinical data.

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Yu, Jun Hua, Li Jia Xu, Ke Fan Ren, Wei Peng Zhang, Zhi Gang Lu, and Pei Da Wang. "Design and Test of a Mechanical Device for the Manipulator of the Watermelon Picking Machine." Applied Mechanics and Materials 442 (October 2013): 291–97. http://dx.doi.org/10.4028/www.scientific.net/amm.442.291.

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This Paper designs a mechanical device for the manipulator of the watermelon picking machine against the low mechanical degree of watermelon picking machine. The mechanical device utilizes a mechanical arm to drive the end effector to run and the end effector is responsible for clamping and shearing watermelon vines, which avoids vine disturbance and sorts out vines to be easily cut down through the process design of clamping, promoting, and re-shearing. In addition, this Paper applies Pro/E modeling, finite element analysis, and simulation analysis to complete the 3D model design of the mechanical device and transforms the 3D model into 2D drawings in Auto CAD to complete the manufacturing and assembly of the manipulator, and the test result verifies the mechanical device may realize the reliable picking of watermelons.

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WU, L., Z. LUO, J. ZHOU, and H. WANG. "AN AUTO-PICKING STACKING VELOCITY ALGORITHM BASED ON AN AI AND PR APPROACH." International Journal of Pattern Recognition and Artificial Intelligence 03, no. 01 (March 1989): 103–11. http://dx.doi.org/10.1142/s0218001489000097.

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Stacking velocity is a very important parameter in seismic data processing. Until now the determination of stacking velocity has been done manually. This article proposes an automatic algorithm for picking stacking velocity. The algorithm uses artificial intelligence and pattern recognition techniques.

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6

Ren, Guo Qiang, Bo Zhang, Ya Fei Lian, and Zi Sheng Zhang. "The Auto-Control of pH Value Based on Labview." Advanced Materials Research 466-467 (February 2012): 79–83. http://dx.doi.org/10.4028/www.scientific.net/amr.466-467.79.

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Grounded on Labview platform, the grid of technology of solution pH value contributes to a new device----the auto-control technology of solution pH value. This device is easier to operate just by the parameter input in the interface. And it breaks the limit of single admeasuring apparatus, meanwhile adopts the method of picking the average value by multi-metering, which avoids the delay effect caused by solution mixing link. The Acid precipitation-Flocculation process in paper making sewage sets an example for the pH value auto-control and supports PAC control of pH value with software concept..

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Ren, Guo Qiang, Chang Xie, Wen Zhao Li, Zi Sheng Zhang, and Zhi Qiang Liu. "The Auto-Control of pH Value Based on Labview." Applied Mechanics and Materials 235 (November 2012): 181–85. http://dx.doi.org/10.4028/www.scientific.net/amm.235.181.

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Grounded on Labview platform, the grid of technology of solution pH value contributes to a new device----the auto-control technology of solution pH value. This device is easier to operate just by the parameter input in the interface. And it breaks the limit of single admeasuring apparatus, meanwhile adopts the method of picking the average value by multi-metering, which avoids the delay effect caused by solution mixing link. The Acid precipitation-Flocculation process in paper making sewage sets an example for the pH value auto-control and supports PAC control of pH value with software concept.

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Lu, Jianqi, Shanyou Li, Peiyang He, Zhinan Xie, Yan Zhao, Jindong Song, Qiang Ma, and Dongwang Tao. "Energy- and Predominant-Period-Dependent P-Wave Onset Picker (EDP-Picker)." Seismological Research Letters 91, no. 4 (June 10, 2020): 2355–67. http://dx.doi.org/10.1785/0220190260.

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Abstract An energy- and predominant-period-dependent (EDP) P-wave onset automatic picking (EDP-picker) algorithm is proposed to deal with the problem of inaccurate P-wave onset picking in cases in which the P-wave onset is hidden in high-amplitude ambient noise or the energy difference between the seismic P-wave and ambient noise is indistinguishable. The algorithm evaluates the energy change using a characteristic variable ΔE, which describes the energy increment of the P wave above ambient noise. The period change is evaluated using two variables with respect to the predominant period, namely Tpd as proposed by Hildyard et al. (2008) and ΔTpd as the gradient of Tpd. The EDP-picker algorithm has two steps: (1) threshold-based cursory P-wave onset picking and (2) precise P-wave onset picking using an Akaike information criterion function, in which both energy information and period information are considered. All three parameters are determined in a 1 s sliding window. The proposed algorithm is verified on a large dataset comprising 13,481 vertical strong ground motion records for 570 events selected from K-NET (Japan) and China Strong Motion Networks Center data. For all records with an epicentral distance of less than 150 km, 93.5% of residuals of manual picks and auto picks are within ±0.5 s. The results demonstrate that EDP-picker is robust and suitable for real-time systems.

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Chen, Jiqing, Hongdu Zhang, Zhikui Wang, Jiahua Wu, Tian Luo, Huabin Wang, and Teng Long. "An image restoration and detection method for picking robot based on convolutional auto-encoder." Computers and Electronics in Agriculture 196 (May 2022): 106896. http://dx.doi.org/10.1016/j.compag.2022.106896.

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Buyurgan, Nebil, and Paiman Farrokhvar. "Supply Chain-Related Adverse Events and Patient Safety in Healthcare." International Journal of Healthcare Information Systems and Informatics 10, no. 2 (April 2015): 14–33. http://dx.doi.org/10.4018/ijhisi.2015040102.

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This research investigates adverse events and patient safety in healthcare due to poor supply chain management practices, and inadequate and disorganized product validation procedures. Focusing on commodity medical and surgical products, this research investigates correct product validation points for maximum patient safety. This study also explores benefits of standard product identifying technologies such as HIBC or GS1 data standards as well as automated validation systems such as barcode or Auto ID to minimize workflow interruptions. Site visits and phone interviews are conducted with six healthcare providers to document common product validation practices and procedures. Based on observations and collected data, a simulation model is developed. Different scenarios are compared for patient safety, care delay, and system efficiency. The results show that validation points during PAR picking or bedside product administration, and warehouse picking operations provide optimal overall system performance. The results also indicate that standard product identifying technologies and automated validation systems significantly impact the efficiency of supply chain.

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Books on the topic "Auto-picking"

1

Auto ID in manufacturing: Case histories of inventory control, work-in-process, picking & sorting, shipping & billing. Peterborough, NH: Helmers Pub., 1991.

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Auto Id in Manufacturing: Part 1 : Case Histories of Inventory Control, Work-In-Process, Picking & Sorting, Shipping & Billing (Id Systems Applicati). Helmers Pub, 1992.

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Book chapters on the topic "Auto-picking"

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Olmos, Jared, Rogelio Florencia, Francisco López-Ramos, and Karla Olmos-Sánchez. "Improvement of the Optimization of an Order Picking Model Associated With the Components of a Classic Volkswagen Beetle Using an Ant Colony Approach." In Advances in Human Resources Management and Organizational Development, 189–210. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8131-4.ch010.

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Warehouse operations, specifically order picking process, are receiving close attention of researches due to the need of companies in minimizing operational costs. This chapter explains an ant colony optimization (ACO) approach to improve the order picking process in an auto parts store associated with the components of a classic Volkswagen Beetle car. Order picking represents the most time-consuming task in the warehouse operational expenses and, according to the scientific literature, is becoming a subject matter in operational research. It implements a low-level, picker-to-part order picking using persons as pickers with multiple picks per route. The context of the case study is a discrete picking where users' orders are independent. The authors use mathematical modeling to improve de ACO metaheuristic approach to minimize the order-picking cost.

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Conference papers on the topic "Auto-picking"

1

Zhao, Don. "A Hybrid Method for First Break Auto Picking." In Symposium on the Application of Geophysics to Engineering and Environmental Problems 2012. Environment and Engineering Geophysical Society, 2012. http://dx.doi.org/10.4133/1.4721847.

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Tsai, Cheng-Han (Lance), and Jen-Yuan (James) Chang. "A New Approach to Enhance Artificial Intelligence for Robot Picking System Using Auto Picking Point Annotation." In ASME 2021 30th Conference on Information Storage and Processing Systems. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/isps2021-65218.

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Abstract Artificial Intelligence (AI) has been widely used in different domains such as self-driving, automated optical inspection, and detection of object locations for the robotic pick and place operations. Although the current results of using AI in the mentioned fields are good, the biggest bottleneck for AI is the need for a vast amount of data and labeling of the corresponding answers for a sufficient training. Evidentially, these efforts still require significant manpower. If the quality of the labelling is unstable, the trained AI model becomes unstable and as consequence, so do the results. To resolve this issue, the auto annotation system is proposed in this paper with methods including (1) highly realistic model generation with real texture, (2) domain randomization algorithm in the simulator to automatically generate abundant and diverse images, and (3) visibility tracking algorithm to calculate the occlusion effect objects cause on each other for different picking strategy labels. From our experiments, we will show 10,000 images can be generated per hour, each having multiple objects and each object being labelled in different classes based on their visibility. Instance segmentation AI models can also be trained with these methods to verify the gaps between performance synthetic data for training and real data for testing, indicating that even at mAP 70 the mean average precision can reach 70%!

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Wei, S., O. Yonglin, Z. Qingcai, H. Jiaqiang, and S. Yaying. "Unsupervised Machine Learning: K-means Clustering Velocity Semblance Auto-Picking." In 80th EAGE Conference and Exhibition 2018. Netherlands: EAGE Publications BV, 2018. http://dx.doi.org/10.3997/2214-4609.201800919.

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Qiu, Chenyu, Bangyu Wu, Delin Meng, Xu Zhu, Meng Li, and Nan Qin. "Attention Neural Network Semblance Velocity Auto Picking with Reference Velocity Curve Data Augmentation." In IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021. http://dx.doi.org/10.1109/igarss47720.2021.9553655.

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Yang, Biqi, Xiaojie Gao, Kai Chen, Rui Cao, Yidan Feng, Xianzhi Li, Qi Dou, Chi-Wing Fu, Yun-Hui Liu, and Pheng-Ann Heng. "SESR: Self-Ensembling Sim-to-Real Instance Segmentation for Auto-Store Bin Picking." In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2022. http://dx.doi.org/10.1109/iros47612.2022.9981845.

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Yuan, C., M. Su, C. Ni, Y. Xu, and X. Cui. "Horizon Auto-Picking with Quantitative Uncertainty Evaluation by Using a Modified Vq-Vae Network." In 83rd EAGE Annual Conference & Exhibition. European Association of Geoscientists & Engineers, 2022. http://dx.doi.org/10.3997/2214-4609.202210311.

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7

Dossi, M., E. Forte, and M. Pipan. "Auto-picking and phase assessment by means of attribute analysis applied to GPR pavement inspection." In 2015 8th International Workshop on Advanced Ground Penetrating Radar (IWAGPR). IEEE, 2015. http://dx.doi.org/10.1109/iwagpr.2015.7292637.

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8

Wang, Wei, Jiangtao Liu, Xiaolin Lyu, Xin Hu, Yifan Li, Lamia Rouis, Mourad Khdhaouria, and Aldrin Rondon. "Application of Deep Learning in First-Break Picking of Shallow OBN Data." In Gas & Oil Technology Showcase and Conference. SPE, 2023. http://dx.doi.org/10.2118/213983-ms.

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Abstract Summary As well known, the modeling of the near-surface from first-break plays a significant role on the sub-surface imaging, reservoir characterization, and monitoring. Small errors in first-break picking can greatly impact the seismic velocity model building, so it is necessary to pick high-quality travel times. Geoscientists from around the world continues trying their best to address the near-surface challenges. Due to the rapid development of high-efficiency acquisition technique, such as WBH (wide-azimuth, broadband and high-density) acquisition technique and blended source acquisition technique, the quantity of seismic data, especially 3D seismic exploration, has leapt from GB to TB(some to PB), which sets a big challenge for first-break picking. Traditional first-break picking methods can't meet the production. In recent years, with the development of computer capacity and algorithm, artificial intelligence has changed our lives in many ways. In seismic exploration, artificial intelligence, like deep learning, has played a more and more important role now, from fault prediction, attribute identification to velocity and first break picking. Generally, deep learning is a new neural network which has multiple hidden layers, mostly over 3 layers, compared with traditional neural network. Deep learning includes Deep Belief Network (DBN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and so on. In this paper, Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) have been combined for first-break picking in a large 3D OBN project of Caspian Sea. A high precision near seabed velocity model is built based on the auto-picked first break with tomography inversion, which provides a good solution for static problem of the survey.

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Sun*, Sam Zandong, Chenjian Li, Xukai Sun, Qiang Zhang, Yubao Liu, Yonggang Wang, Rong Rong, and Wenkui Yang. "Residual travel time auto picking methods and their influence on fracture detection based on full-azimuth anisotropic migration." In SEG Technical Program Expanded Abstracts 2014. Society of Exploration Geophysicists, 2014. http://dx.doi.org/10.1190/segam2014-1483.1.

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10

Lu, Kai, and Shihang Feng. "Auto-windowed Super-virtual Interferometry via Machine Learning: A Strategy of First-arrival Traveltime Automatic Picking for Noisy Seismic Data." In SEG 2018 Workshop: SEG Maximizing Asset Value Through Artificial Intelligence and Machine Learning, Beijing, China, 17-19 September 2018. Society of Exploration Geophysicists and the Chinese Geophysical Society, 2018. http://dx.doi.org/10.1190/aiml2018-03.1.

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Reports on the topic "Auto-picking"

1

Sadot, Einat, Christopher Staiger, and Zvi Kam Weizmann. functional genomic screen for new plant cytoskeletal proteins and the determination of their role in actin mediated functions and guard cells regulation. United States Department of Agriculture, January 2003. http://dx.doi.org/10.32747/2003.7587725.bard.

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The original objectives of the approved proposal were: 1. To construct a YFP fused Arabidopsis cDNA library in a mammalian expression vector. 2. To infect the library into a host fibroblast cell line and to screen for new cytoskeletal associated proteins using an automated microscope. 3. Isolate the new genes. 4. Characterize their role in plants. The project was approved as a feasibility study to allow proof of concept that would entail building the YFP library and picking up a couple of positive clones using the fluorescent screen. We report here on the construction of the YFP library, the development of the automatic microscope, the establishment of the screen and the isolation of positive clones that are plant cDNAs encoding cytoskeleton associated proteins. The rational underling a screen of plant library in fibroblasts is based on the high conservation of the cytoskeleton building blocks, actin and tubulin, between the two kingdoms (80-90% homology at the level of amino acids sequence). In addition, several publications demonstrated the recognition of mammalian cytoskeleton by plant cytoskeletal binding proteins and vice versa. The major achievements described here are: 1. The development of an automated microscope equipped with fast laser auto-focusing for high magnification and a software controlling 6 dimensions; X, Y position, auto focus, time, color, and the distribution and density of the fields acquired. This system is essential for the high throughput screen. 2. The construction of an extremely competent YFP library efficiently cloned (tens of thousands of clones collected, no empty vectors detected) with all inserts oriented 5't03'. These parameters render it well representative of the whole transcriptome and efficient in "in-frame" fusion to YFP. 3. The strategy developed for the screen allowing the isolation of individual positive cDNA clones following three rounds of microscopic scans. The major conclusion accomplished from the work described here is that the concept of using mammalian host cells for fishing new plant cytoskeletal proteins is feasible and that screening system developed is complete for addressing one of the major bottlenecks of the plant cytoskeleton field: the need for high throughput identification of functionally active cytoskeletal proteins. The new identified plant cytoskeletal proteins isolated in the pilot screen and additional new proteins which will be isolated in a comprehensive screen will shed light on cytoskeletal mediated processes playing a major role in cellular activities such as cell division, morphogenesis, and functioning such as chloroplast positioning, pollen tube and root hair elongation and the movement of guard cells. Therefore, in the long run the screen described here has clear agricultural implications.

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