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Guo, Weihong, Shucai Liu, Yaoning Liu, and Shuangshuang Chen. "Application of electrical resistivity imaging to detection of hidden geological structures in a single roadway." Open Geosciences 12, no. 1 (October 19, 2020): 1083–93. http://dx.doi.org/10.1515/geo-2020-0175.
Повний текст джерелаАнотація:
AbstractLocating concealed geological structures in coal seams on both sides of a coal mine excavation roadway is of vital importance for safe production. Conventional electrical resistivity imaging methods mostly arrange observation systems on the roadway roof and floor, so they are inevitably deficient when it comes to detecting concealed geological structures in coal seams. According to the electric field distribution characteristics of artificial field sources for electrical resistivity imaging methods and utilizing the shielding of current by roadway cavities, this paper proposes the parallel coal seam detection method that arranges observation systems in coal seams on the roadway side to detect concealed geological structures in coal seams. On the basis of introducing the principles of consequent detection methods, this paper investigates the influence of roadway cavities on observation results and offers a method of correcting the influence of roadway cavities. In view of the geoelectric characteristics of typical concealed geological structures in working faces, this paper establishes numerical models to verify the feasibility of the parallel coal seam detection method. As indicated by the calculation results, the consequent pole–dipole (A-MN) observation system is the most ideal in terms of dividing the geoelectric interfaces of concealed geological structures in working faces, and its detection effect is influenced significantly by the coal seam thickness and the electric differences between surrounding rock and anomalous bodies. Coal seam resistivity slightly influences detection of the consequent pole–dipole system. According to practical application effects, the parallel coal seam detection method can solve the problem of detecting concealed geological structures in “single-roadway” working faces.
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Genesca, A., L. Tusell, R. Alvarez, L. Ponsà, R. Miró, and J. Egozcue. "O-094. Methods of detecting genotoxic influences." Human Reproduction 12, Suppl_2 (June 1997): 44–45. http://dx.doi.org/10.1093/humrep/12.suppl_2.44-a.
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Hofer-Schmitz, Katharina, Ulrike Kleb, and Branka Stojanović. "The Influences of Feature Sets on the Detection of Advanced Persistent Threats." Electronics 10, no. 6 (March 17, 2021): 704. http://dx.doi.org/10.3390/electronics10060704.
Повний текст джерелаАнотація:
This paper investigates the influences of different statistical network traffic feature sets on detecting advanced persistent threats. The selection of suitable features for detecting targeted cyber attacks is crucial to achieving high performance and to address limited computational and storage costs. The evaluation was performed on a semi-synthetic dataset, which combined the CICIDS2017 dataset and the Contagio malware dataset. The CICIDS2017 dataset is a benchmark dataset in the intrusion detection field and the Contagio malware dataset contains real advanced persistent threat (APT) attack traces. Several different combinations of datasets were used to increase variety in background data and contribute to the quality of results. For the feature extraction, the CICflowmeter tool was used. For the selection of suitable features, a correlation analysis including an in-depth feature investigation by boxplots is provided. Based on that, several suitable features were allocated into different feature sets. The influences of these feature sets on the detection capabilities were investigated in detail with the local outlier factor method. The focus was especially on attacks detected with different feature sets and the influences of the background on the detection capabilities with respect to the local outlier factor method. Based on the results, we could determine a superior feature set, which detected most of the malicious flows.
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Yang, Ya-xun, Wen-hao Chai, De-chuang Liu, Wei-de Zhang, Jia-cheng Lu, and Zhi-kui Yang. "An Impact-Echo Experimental Approach for Detecting Concrete Structural Faults." Advances in Civil Engineering 2021 (December 20, 2021): 1–8. http://dx.doi.org/10.1155/2021/8141015.
Повний текст джерелаАнотація:
For the current problem of detection of grouting defects in posttensioned prestressed concrete members, the paper takes a single-layer arrangement of prestressed pipes as the object of study. The influence law of the main factors such as pipe material, defect size, defect critical surface location, and prestressing reinforcement location on the results of the impact-echo method for detecting concrete grouting defects was studied. Firstly, the ABAQUS finite element software was used to simulate these factors to obtain the influence law on the detection results, and a modal test was conducted to verify them. The results show that the impact-echo method can effectively test the location of defects and the degree of burial depth, and the pipe material influences the test results, and the impact of corrugated metal pipe is smaller and more accurate than the PVC pipe. In addition, the greater the plate thickness frequency drift rate, the larger the transverse size of the defect, so the plate thickness frequency drift rate and the measured defect depth are combined to quantitatively determine the depth of the defect.
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Moro, Bruna L. P., Tatiane F. Novaes, Laura R. A. Pontes, Thais Gimenez, Juan S. Lara, Daniela P. Raggio, Mariana M. Braga, and Fausto M. Mendes. "The Influence of Cognitive Bias on Caries Lesion Detection in Preschool Children." Caries Research 52, no. 5 (2018): 420–28. http://dx.doi.org/10.1159/000485807.
Повний текст джерелаАнотація:
We aimed to evaluate whether children’s caries experience exerts an influence on the performance of visual and radiographic methods in detecting nonevident proximal caries lesions in primary molars. Eighty children (3–6 years old) were selected and classified as having a lower (≤3 decayed, missing, or filled surfaces; dmf-s) or higher (> 3 dmf-s) caries experience. Two calibrated examiners then assessed 526 proximal surfaces for caries lesions using visual and radiographic methods. As a reference standard, 2 other examiners checked the surfaces after temporary separation. Noncavitated and cavitated lesion thresholds were considered and Poisson multilevel regression analyses were conducted to evaluate the influence of caries experience on the performance of diagnostic strategies. Accuracy parameters stratified by caries experience were also derived. A statistically significant influence of caries experience was observed only for visual inspection, with more false-positive results in children with a higher caries experience at the noncavitated lesion threshold, and more false results at the cavitated threshold. The detection of noncavitated caries lesions in children with a higher caries experience was overestimated (specificity = 0.696), compared to children with a lower caries experience (specificity = 0.918), probably due to confirmation bias. However, the examiners underestimated the detection of cavitated lesions in children with a higher caries experience (sensitivity = 0.143) compared to lower-caries-experience children (sensitivity = 0.222), possibly because of representativeness bias. The radiographic method was not influenced by children’s caries experience. In conclusion, children’s caries experience influences the performance of visual inspection in detecting proximal caries lesions in primary teeth, evidencing the occurrence of cognitive biases.
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Yang, Zhihai, and Zhongmin Cai. "Detecting Anomalous Ratings in Collaborative Filtering Recommender Systems." International Journal of Digital Crime and Forensics 8, no. 2 (April 2016): 16–26. http://dx.doi.org/10.4018/ijdcf.2016040102.
Повний текст джерелаАнотація:
Online rating data is ubiquitous on existing popular E-commerce websites such as Amazon, Yelp etc., which influences deeply the following customer choices about products used by E-businessman. Collaborative filtering recommender systems (CFRSs) play crucial role in rating systems. Since CFRSs are highly vulnerable to “shilling” attacks, it is common occurrence that attackers contaminate the rating systems with malicious rates to achieve their attack intentions. Despite detection methods based on such attacks have received much attention, the problem of detection accuracy remains largely unsolved. Moreover, few can scale up to handle large networks. This paper proposes a fast and effective detection method which combines two stages to find out abnormal users. Firstly, the manuscript employs a graph mining method to spot automatically suspicious nodes in a constructed graph with millions of nodes. And then, this manuscript continue to determine abnormal users by exploiting suspected target items based on the result of first stage. Experiments evaluate the effectiveness of the method.
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Bartsch, Sarah M., Kim F. Wong, Owen J. Stokes-Cawley, James A. McKinnell, Chenghua Cao, Gabrielle M. Gussin, Leslie E. Mueller, et al. "Knowing More of the Iceberg: How Detecting a Greater Proportion of Carbapenem-Resistant Enterobacteriaceae Carriers Influences Transmission." Journal of Infectious Diseases 221, no. 11 (November 5, 2019): 1782–94. http://dx.doi.org/10.1093/infdis/jiz288.
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Abstract Background Clinical testing detects a fraction of carbapenem-resistant Enterobacteriaceae (CRE) carriers. Detecting a greater proportion could lead to increased use of infection prevention and control measures but requires resources. Therefore, it is important to understand the impact of detecting increasing proportions of CRE carriers. Methods We used our Regional Healthcare Ecosystem Analyst–generated agent-based model of adult inpatient healthcare facilities in Orange County, California, to explore the impact that detecting greater proportions of carriers has on the spread of CRE. Results Detecting and placing 1 in 9 carriers on contact precautions increased the prevalence of CRE from 0% to 8.0% countywide over 10 years. Increasing the proportion of detected carriers from 1 in 9 up to 1 in 5 yielded linear reductions in transmission; at proportions >1 in 5, reductions were greater than linear. Transmission reductions did not occur for 1, 4, or 5 years, varying by facility type. With a contact precautions effectiveness of ≤70%, the detection level yielding nonlinear reductions remained unchanged; with an effectiveness of >80%, detecting only 1 in 5 carriers garnered large reductions in the number of new CRE carriers. Trends held when CRE was already present in the region. Conclusion Although detection of all carriers provided the most benefits for preventing new CRE carriers, if this is not feasible, it may be worthwhile to aim for detecting >1 in 5 carriers.
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Daxiang, X., T. Debao, W. Xiongfei, and W. Qiao. "A Dynamic Threshold Cloud Detecting Approach based on the Brightness Temperature from FY-2 VISSR Data." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W3 (April 29, 2015): 617–25. http://dx.doi.org/10.5194/isprsarchives-xl-7-w3-617-2015.
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The traditional statistical methods and radiation transfer theory methods for cloud detecting have a high adaptability just only in those areas with a uniform surface coverage and noncomplex terrain. Therefore, adapted to large spatial and temporal scales, in this work a cloud detection method is developed, seeking the main influencing factors of the change of Brightness Temperature(BT) of clear sky and their relationships, researching the change regularity and normal fluctuation range of BT on the basis of function fitting, setting the cloud detecting dynamic threshold depending on the cloud spectral characteristics, and making accuracy assessment in order to ensure higher adaptability and accuracy of this cloud detecting method. In this paper, a dynamic threshold algorithm is presented for cloud detection using daytime imagery from the VISSR sensor on board FY-2C/D/E, which is the first generation geostationary satellite. And the land surface/brightness temperature influence functions are analysis and established, including latitude, longitude, altitude, time, land cover. The theoretical temperature value of clear sky can be calculated through these influence functions. Then, the dynamic threshold cloud detection model is proposed based on the high temporal resolution of VISSR data. Meanwhile, the land surface emissivity is considered as the main factor to the change range of brightness temperature which determines the dynamic threshold for cloud detection. Finally, the dynamic threshold cloud detecting model is evaluated using FY-2C/D/E VISSR data covering China, and the Kappa of dynamic method is maximum, equalling 0.6195, which is much higher than the indexes for the reflectivity and BT fixed methods, equalling 0.4511 and 0.403, respectively. Consequently, the dynamic threshold cloud detecting method provides an important improvement because the spatial, temporal and geographic characteristics were considered into the model.
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Zhong, Yan Hui, Bei Zhang, Ming Ming Ren, Cheng Chao Guo, and Chu Chu Xia. "Method of Analyzing the Size of Voids Beneath Rigid Pavement Slabs." Advanced Materials Research 418-420 (December 2011): 1516–19. http://dx.doi.org/10.4028/www.scientific.net/amr.418-420.1516.
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The size of voids beneath rigid pavement slabs is an important factor which influences the bearing capacity of roads. GPR is an advanced method for detecting voids beneath rigid pavement slabs. Combined with drilling and sampling, equations including parameters of GPR wave amplitude, horizontal size and vertical size of voids are established for analyzing the size of voids. Then, based on the equations, the horizontal size and vertical size of voids can be calculated according to the actual GPR wave amplitude.
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He, Yong, and Zhongmin Cui. "Evaluating Robust Scale Transformation Methods With Multiple Outlying Common Items Under IRT True Score Equating." Applied Psychological Measurement 44, no. 4 (November 15, 2019): 296–310. http://dx.doi.org/10.1177/0146621619886050.
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Item parameter estimates of a common item on a new test form may change abnormally due to reasons such as item overexposure or change of curriculum. A common item, whose change does not fit the pattern implied by the normally behaved common items, is defined as an outlier. Although improving equating accuracy, detecting and eliminating of outliers may cause a content imbalance among common items. Robust scale transformation methods have recently been proposed to solve this problem when only one outlier is present in the data, although it is not uncommon to see multiple outliers in practice. In this simulation study, the authors examined the robust scale transformation methods under conditions where there were multiple outlying common items. Results indicated that the robust scale transformation methods could reduce the influences of multiple outliers on scale transformation and equating. The robust methods performed similarly to a traditional outlier detection and elimination method in terms of reducing the influence of outliers while keeping adequate content balance.
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Hongyao, Wang, Tian Jie, Meng Guoying, Zhou Junying, and Hua Gang. "Multi-loop magnetisation method for detecting coal mine wire rope defects." Insight - Non-Destructive Testing and Condition Monitoring 62, no. 9 (September 1, 2020): 540–49. http://dx.doi.org/10.1784/insi.2020.62.9.540.
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Existing methods for detecting magnetic leakage signals from damaged wire ropes require axial saturation magnetisation and the accuracy of the detection equipment depends on the degree of saturation of the magnetisation. Moreover, owing to the special characteristics of the structure, diameter and operating environment of the wire rope, it is difficult to achieve the expected magnetisation effect. Consequently, in addition to other issues, the detection accuracy and versatility of the equipment are low. This study proposes a method based on spatial multi-dimensional orthogonal array loop magnetisation to detect coal mine wire rope defects. First, a sensor excitation structure model is developed. Then, a method for detecting the radial magnetic flux using permanent magnet arrays is analysed and the influence of the number of permanent magnet radial magnetic blocks on the magnetisation effect is studied. In addition, a wire rope detection method based on the principle of clustering is investigated and used to discuss the influence of the shape, structure and size of the sensor on the magnetisation effect. Finally, through laboratory experiments, the applicability of the test model and the proposed method are verified. The results show that the magnetisation effect is better and more cost-effective when the number of permanent magnet radial magnetic blocks is eight and that the proposed detection method can effectively identify damaged wire rope joints. Furthermore, the proposed equipment achieves a signal-tonoise ratio of 28 dB, thereby improving the reliability of damage detection.
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Sari, Y., P. B. Prakoso, and A. R. Baskara. "Evaluation the influence of distance-based K-means method for detecting moving vehicles." IOP Conference Series: Materials Science and Engineering 1212, no. 1 (January 1, 2022): 012044. http://dx.doi.org/10.1088/1757-899x/1212/1/012044.
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Abstract Detecting moving vehicles is one of important elements in the applications of Intelligent Transport System (ITS). Detecting moving vehicles is also part of the detection of moving objects. K-Means method has been successfully applied to unsupervised cluster pixels for the detection of moving objects. In general, K-Means is a heuristic algorithm that partitioned the data set into K clusters by minimizing the number of squared distances in each cluster. In this paper, the K-Means algorithm applies Euclidean distance, Manhattan distance, Canberra distance, Chebyshev distance and Braycurtis distance. The aim of this study is to compare and evaluate the implementation of these distances in the K-Means clustering algorithm. The comparison is done with the basis of K-Means assessed with various evaluation paramaters, namely MSE, PSNR, SSIM and PCQI. The results exhibit that the Manhattan distance delivers the best MSE, PSNR, SSIM and PCQI values compared to other distances. Whereas for data processing time exposes that the Braycurtis distance has more advantages.
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Hao, Yaojun, Peng Zhang, and Fuzhi Zhang. "Multiview Ensemble Method for Detecting Shilling Attacks in Collaborative Recommender Systems." Security and Communication Networks 2018 (October 11, 2018): 1–33. http://dx.doi.org/10.1155/2018/8174603.
Повний текст джерелаАнотація:
Faced with the evolving attacks in collaborative recommender systems, the conventional shilling detection methods rely mainly on one kind of user-generated information (i.e., single view) such as rating values, rating time, and item popularity. However, these methods often suffer from poor precision when detecting different attacks due to ignoring other potentially relevant information. To address this limitation, in this paper we propose a multiview ensemble method to detect shilling attacks in collaborative recommender systems. Firstly, we extract 17 user features by considering the temporal effects of item popularity and rating values in different popular item sets. Secondly, we devise a multiview ensemble detection framework by integrating base classifiers from different classification views. Particularly, we use a feature set partition algorithm to divide the features into several subsets to construct multiple optimal classification views. We introduce a repartition strategy to increase the diversity of views and reduce the influence of feature order. Finally, the experimental results on the Netflix and Amazon review datasets indicate that the proposed method has better performance than benchmark methods when detecting various synthetic attacks and real-world attacks.
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Hui, Jing, Yan Hua Jin, and Wei Xie. "Research on Anti-Islanding Technology for Photovoltaic Grid-Connected Based on the Positive Power Feedback." Advanced Materials Research 805-806 (September 2013): 82–87. http://dx.doi.org/10.4028/www.scientific.net/amr.805-806.82.
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The standards of grid-connected photovoltaic (PV) system and the cause of islanding in formation are introduced at first, and then common passive and active detecting islanding methods are discussed in this paper. Considering the shortcomings of traditional methods, such as failing to detect islanding, larger non-detection zone and poor quality of output power of the inverter, etc., a novel detecting method which is called as positive power feedback disturbance is proposed, so as to overcome these problems. The proposed method has the characteristics of no non-detection zone, little influence on power quality and rapid respond. Finally, according to professional standards of IEEE Std. 2000-929 and UL1471, the simulation as well as the experiment of the proposed method was carried out. The results of simulation and experiment showed the good expressions from the novel method.
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Planque, Mélanie, Thierry Arnould, Patricia Renard, Philippe Delahaut, Marc Dieu, and Nathlie Gillard. "Highlight on Bottlenecks in Food Allergen Analysis: Detection and Quantification by Mass Spectrometry." Journal of AOAC INTERNATIONAL 100, no. 4 (July 1, 2017): 1126–30. http://dx.doi.org/10.5740/jaoacint.17-0005.
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Abstract Food laboratories have developed methods for testing allergens in foods. The efficiency of qualitative and quantitative methods is of prime importance in protecting allergic populations. Unfortunately, food laboratories encounter barriers to developing efficient methods. Bottlenecks include the lack of regulatory thresholds, delays in the emergence of reference materials and guidelines, and the need to detect processed allergens. In this study, ultra-HPLC coupled to tandem MS was used to illustrate difficulties encountered in determining method performances. We measured the major influences of both processing and matrix effects on the detection of egg, milk, soy, and peanut allergens in foodstuffs. The main goals of this work were to identify difficulties that food laboratories still encounter in detecting and quantifying allergens and to sensitize researchers to them.
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Chen, Jinpeng, Wen Zhang, Pei Zhang, Pinguang Ying, Kun Niu, and Ming Zou. "Exploiting Spatial and Temporal for Point of Interest Recommendation." Complexity 2018 (August 29, 2018): 1–16. http://dx.doi.org/10.1155/2018/6928605.
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An increasing number of users have been attracted by location-based social networks (LBSNs) in recent years. Meanwhile, user-generated content in online LBSNs like spatial, temporal, and social information provides an ever-increasing chance to study the human behavior movement from their spatiotemporal mobility patterns and spawns a large number of location-based applications. For instance, one of such applications is to produce personalized point of interest (POI) recommendations that users are interested in. Different from traditional recommendation methods, the recommendations in LBSNs come with two vital dimensions, namely, geographical and temporal. However, previously proposed methods do not adequately explore geographical influence and temporal influence. Therefore, fusing geographical and temporal influences for better recommendation accuracy in LBSNs remains potential. In this work, our aim is to generate a top recommendation list of POIs for a target user. Specially, we explore how to produce the POI recommendation by leveraging spatiotemporal information. In order to exploit both geographical and temporal influences, we first design a probabilistic method to initially detect users’ spatial orientation by analyzing visibility weights of POIs which are visited by them. Second, we perform collaborative filtering by detecting users’ temporal preferences. At last, for making the POI recommendation, we combine the aforementioned two approaches, that is, integrating the spatial and temporal influences, to construct a unified framework. Our experimental results on two real-world datasets indicate that our proposed method outperforms the current state-of-the-art POI recommendation approaches.
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Schubert, Konstantin Jonas, and Axel Siegfried Herrmann. "A Compensation Method for Environmental Influences on Passive Lamb Wave Based Impact Evaluation for CFRP." Key Engineering Materials 569-570 (July 2013): 1265–72. http://dx.doi.org/10.4028/www.scientific.net/kem.569-570.1265.
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One of the largest issues remaining on the way to in situ Structural Health Monitoring of composite structures using Lamb waves is the impact that non-damaging factors like temperature changes and humidity absorption have on most measurement strategies. While some of these tasks have been successfully conquered, others, especially related to slowly developing influences like humidity absorption or mechanical ageing, remain challenging. In this paper, a method to approach this problem for a Lamb-wave based passive impact detection system is presented. Passive approaches use the waves generated by the impact event itself to both localize said event and evaluate whether it was large enough to damage the structure. For this, the impacts energy has to be estimated from sensors detecting the Lamb waves. The problem provided by changing conditions within the material is that the locally measurable wave amplitude due to an impact event of a certain energy is altered if the material properties change. This might happen due to temperature changes, mechanical loads, humidity absorption, fluid loads and other factors. The main idea of the presented approach is to mix a passive and an active system. Piezoelectric elements are used to generate Lamb waves to obtain the attenuation coefficients of the material before and after hot/wet-conditioning. These coefficients are then used to estimate the impact energy from passive sensor responses. Both the approach and experimental validation performed with low velocity impacts from an impact hammer are presented to show the ability to correctly calculate impact forces after conditioning.
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Yuan, Zhou Zhi Yuan, Bo Hai Ji, Zhong Qiu Fu, Rong Liu, and Miao Cheng. "Electrical Resistance Method for Fatigue Crack Detection of Steel Deck." Applied Mechanics and Materials 361-363 (August 2013): 1397–401. http://dx.doi.org/10.4028/www.scientific.net/amm.361-363.1397.
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The present study employs an electrical resistance method for fatigue crack detection in steel deck. The detection influential factors are analyzed via the finite element analysis under different electrode space and deck width. As a result, the electrode space influenced on detecting precision, and the smaller the better. The resistance measurement method is presented, and the formula of fracture damage ratio and the equivalent crack depth are established. It is proved by fatigue crack detection experiment of U-rib specimen, which shows that using electrical resistance method to detect fatigue crack is feasible.
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Araujo, Juliana de, Cláudia Andréia Gräff, and Wendel Paulo Silvestre. "Qualitative tests for the determination of fraud in raw milk: evaluation of the influence of analytical parameters of the tests and the stability of the samples as a function of time and preservation form." Research, Society and Development 10, no. 11 (September 6, 2021): e450101119860. http://dx.doi.org/10.33448/rsd-v10i11.19860.
Повний текст джерелаАнотація:
The present work aimed to evaluate the robustness of qualitative tests of frauds of addition of chlorine/hypochlorite, hydrogen peroxide and starch in raw milk, which parameters have the greatest influence on these analytical methods, and to evaluate the influence of time and mode of preservation of intentionally adulterated samples. The effects of the variation of some parameters of the analytical procedures were evaluated, making combinations between the different factors, determining the degree of influence of these variations on the tests. The stability time of the frauds in the samples was evaluated, keeping them under refrigeration (3±2 °C) and carrying out the tests on the same day, and under freezing (≤ -18 °C) for 5, 9 and 15 days, later analyzed, verifying if the frauds were detected. According to the results, it could be observed that the chlorine/hypochlorite detection method was adequate, detecting the addition of the adulterant in all combinations of variables and in all forms of conservation of the samples. The method for detecting the addition of hydrogen peroxide proved to be adequate for the samples analyzed immediately (< 24 h), detecting the presence of the substance in all combinations. However, the method did not detect the presence of peroxide in the combinations for the frozen samples, indicating that the method can generate false negatives in samples that are not fresh. The starch detection method did not detect the presence of this contaminant in all combinations of variables. The combinations that used only a drop of the Lugol’s iodine solution showed false negatives, so the amount of added solution is a critical variable for the method. However, for the other combinations, the method proved to be adequate for both fresh and frozen samples, detecting the addition of starch, regardless of storage time.
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Syzdykbayev, Meirman, Bobak Karimi, and Hassan A. Karimi. "A Method for Extracting Some Key Terrain Features from Shaded Relief of Digital Terrain Models." Remote Sensing 12, no. 17 (August 30, 2020): 2809. http://dx.doi.org/10.3390/rs12172809.
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Detection of terrain features (ridges, spurs, cliffs, and peaks) is a basic research topic in digital elevation model (DEM) analysis and is essential for learning about factors that influence terrain surfaces, such as geologic structures and geomorphologic processes. Detection of terrain features based on general geomorphometry is challenging and has a high degree of uncertainty, mostly due to a variety of controlling factors on surface evolution in different regions. Currently, there are different computational techniques for obtaining detailed information about terrain features using DEM analysis. One of the most common techniques is numerically identifying or classifying terrain elements where regional topologies of the land surface are constructed by using DEMs or by combining derivatives of DEM. The main drawbacks of these techniques are that they cannot differentiate between ridges, spurs, and cliffs, or result in a high degree of false positives when detecting spur lines. In this paper, we propose a new method for automatically detecting terrain features such as ridges, spurs, cliffs, and peaks, using shaded relief by controlling altitude and azimuth of illumination sources on both smooth and rough surfaces. In our proposed method, we use edge detection filters based on azimuth angle on shaded relief to identify specific terrain features. Results show that the proposed method performs similar to or in some cases better (when detecting spurs than current terrain features detection methods, such as geomorphon, curvature, and probabilistic methods.
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Huang, Zheng, and Zhong Cheng Bai. "Study on Fluorimetric Method Determination of Glucose Molecule in Microchannels." Advanced Materials Research 443-444 (January 2012): 366–70. http://dx.doi.org/10.4028/www.scientific.net/amr.443-444.366.
Повний текст джерелаАнотація:
A new method of the fluorescent detection is introduced to use for detecting glucose molecule marked by fluorescence in microchannels. The fluorescent detection for glucose molecule is successfully realized in microchannels. The experiment results show that it is feasible to use sodium fluorescein marking the glucose molecule. At the same time, the experiment analyzes that experimental results have been influenced by wavelength of exciting light and acidity and basicity of solution. It is concluded that the glucose molecule marked with sodium fluorescein can be identified effectively in microchannels in this experiment. The new technology based on the study will lead to new application in bioanalysis and biological detection.
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Peng, Jin Jie, and Xiao Ping Fan. "Simulating Research on a New Current Controlling Method." Advanced Materials Research 354-355 (October 2011): 1016–21. http://dx.doi.org/10.4028/www.scientific.net/amr.354-355.1016.
Повний текст джерелаАнотація:
A new current detecting algorithm is proposed in the paper, which is applied in an improved hybrid active power filter. The filter equipment can be applied in high voltage system to suppress harmonics and compensate reactive power. The topology and filtering principle of the filter equipment is given. The harmonic suppressing function is adopted to analyze the influence by the grid impedance, quality factor and losing resonance of the resonance circuit. The feasibility of the proposed system is validated in theory. A single-phase current detecting method is also proposed. The corresponding current control is formed combined the DC-link current detecting method. The simulating results prove that the system is feasibility and the current control is validity. Harmonic and reactive power are suppressed well.
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Garden, Jenni G., Clive A. McAlpine, Hugh P. Possingham, and Darryl N. Jones. "Using multiple survey methods to detect terrestrial reptiles and mammals: what are the most successful and cost-efficient combinations?" Wildlife Research 34, no. 3 (2007): 218. http://dx.doi.org/10.1071/wr06111.
Повний текст джерелаАнотація:
The selection of methods for wildlife surveys is a decision that will influence the accuracy and comprehensiveness of survey outcomes. The choice of methods is commonly based on the species of interest, yet is often limited by the project budget. Although several studies have investigated the effectiveness of various survey techniques for detecting terrestrial mammal and reptile species, none have provided a quantitative analysis of the costs associated with different methods. We compare the detection success and cost efficiency of cage traps, Elliott traps, pit-fall traps, hair funnels, direct observation, and scat detection/analysis for detecting the occurrence of terrestrial reptile and small mammal species in urban bushland remnants of Brisbane City, Queensland, Australia. Cage traps and Elliott traps coupled with hair funnels were the most cost-effective methods for detecting the highest number of ground-dwelling mammal species. Pit-fall traps and direct observations were the most cost-effective methods for maximising the number of reptile species identified. All methods made a contribution to overall detection success by detecting at least one species not detected by any other method. This suggests that a combination of at least two complementary methods will provide the most successful and cost-efficient detection of reptile and mammal species in urban forest remnants. Future studies should explicitly test these findings and examine efficient trapping combinations across different habitat types and for other fauna groups.
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Zhu, Jianbin, Xiaojun Shi, and Shuanghua Zhang. "Machine Learning-Based Grammar Error Detection Method in English Composition." Scientific Programming 2021 (December 18, 2021): 1–10. http://dx.doi.org/10.1155/2021/4213791.
Повний текст джерелаАнотація:
The detection of grammatical errors in English composition is an important task in the field of NLP. The main purpose of this task is to check out grammatical errors in English sentences and correct them. Grammatical error detection and correction are important applications in the automatic proofreading of English texts and in the field of English learning aids. With the increasing influence of English on a global scale, a huge breakthrough has been made in the task of detecting English grammatical errors. Based on machine learning, this paper designs a new method for detecting grammatical errors in English composition. First, this paper implements a grammatical error detection model based on Seq2Seq. Second, this paper implements a grammatical error detection and correction scheme based on the Transformer model. The Transformer model performs better than most grammar models. Third, this paper realizes the application of the BERT model in grammar error detection and error correction tasks, and the generalization ability of the model has been significantly enhanced. This solves the problem that the forward and backward cannot be merged when the Transformer trains the language model. Fourth, this paper proposes a method of grammatical error detection and correction in English composition based on a hybrid model. According to specific application scenarios, the corresponding neural network model is used for grammatical error correction. Combine the Seq2Seq structure to encode the input sequence and automate feature engineering. Through the combination of traditional model and deep model, the advantages are complemented to realize grammatical error detection and automatic correction.
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Wu, Dan Hui, and Xin Wu. "Research on the Influence of Noise to Weak Signal Detection Based on Duffing Equation." Applied Mechanics and Materials 65 (June 2011): 5–8. http://dx.doi.org/10.4028/www.scientific.net/amm.65.5.
Повний текст джерелаАнотація:
In view of existing questions of misjudgment, low efficiency, and error in detecting weak signal using chaos critical state. The basic form of Duffing equation is firstly established and analyzed in this paper; and then, it describes the principle of weak signal detection based on the change of phase trace; The influence of Noise to weak signal detection performance is researched using chaos critical state detection method; Stochastic resonance phenomenon of the small-signal two-dimensional nonlinear system is found, and that, the research in the paper provides some foundation for enhancing detection signal SNR under the strong noise background and a new way for detecting weal signal using stochastic resonance
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Zubi, Zakaria Suliman, and Abdul Wahab Mohamed Ibrahim. "Use of Naive Bayesian Filtering in the Intrusion Detection System (IDS)." International Journal of Circuits, Systems and Signal Processing 16 (March 11, 2022): 831–42. http://dx.doi.org/10.46300/9106.2022.16.102.
Повний текст джерелаАнотація:
Recently, the main critical part of all organizational data systems is the security since it is threatened by several network attacks which in turn influences on the world financial system. Thus, the most used system in dealing with networks problems is the Intrusion Detection System (IDS). It is used to monitor the system performance and send alerts when there is anomalous activity existence in which the administrator of the system should respond to these alerts rapidly. In this paper, we proposed a statistical Naïve Bayesian method which will be used in the Intrusion Detection Systems ( IDS) systems in different scenarios such as analyzing the HTTP service based traffic and identify the HTTP normal connections and attacks. On the other hand, a comparative study between them based on the performance parameters will be analyzed in order to determine the most effective and efficient statistical method in detecting various types of attacks.
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Grbalová, S., V. Večerek, B. Tremlová, P. Chloupek, and V. Pištěková. "Comparison of hygiene and sanitation levels by bioluminescence method at different units producing components for instant soups." Czech Journal of Food Sciences 21, No. 4 (November 18, 2011): 129–36. http://dx.doi.org/10.17221/3489-cjfs.
Повний текст джерелаАнотація:
Different production units within an instant soups manufacturer facility were compared by bioluminescence method detecting surface hygiene and sanitation quality. The evaluation was based on the limits determined by test examinations in the facility. It was confirmed that the results were influenced by the surface material and its condition (damage, roughening) as well as by complexity of the surface tested. The most important factor, however, was the attitude of different persons in charge of sanitation and preventive procedures. No influence was found of the type of the raw material processed (of animal or plant origin).
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Li, Yan, Jiahong Guo, Xiaomin Guo, Kaizhou Liu, Wentao Zhao, Yeteng Luo, and Zhenyu Wang. "A Novel Target Detection Method of the Unmanned Surface Vehicle under All-Weather Conditions with an Improved YOLOV3." Sensors 20, no. 17 (August 28, 2020): 4885. http://dx.doi.org/10.3390/s20174885.
Повний текст джерелаАнотація:
The USV (unmanned surface vehicle) is playing an important role in many tasks such as marine environmental observation and maritime security, for the advantages of high autonomy and mobility. Detecting the targets on the surface of the water with high precision ensures the subsequent task implementation. However, the changes from the lights and the surface environment influence the performance of the target detecting method in a long-term task with USV. Therefore, this paper proposed a novel target detection method by fusing DenseNet in YOLOV3 to improve the stability of detection to decrease the feature loss, while the target feature is transmitted in the layers of a deep neural network. All the image data used to train and test the proposed method were obtained in the real ocean environment with a USV in the South China Sea during a one month sea trial in November 2019. The experiment results demonstrate the performance of the proposed method is more suitable for the changed weather conditions though comparing with the existing methods, and the real-time performance is available in practical ocean tasks for USV.
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WANG, SHUENN-JYI, CHUNG-KAI HSIEH, and TSORNG-LIN CHIA. "VIDEO-BASED APPROACH FOR DETECTING PROHIBITED ACTIVITIES ON SPORTING COURTS." International Journal of Image and Graphics 13, no. 04 (October 2013): 1350019. http://dx.doi.org/10.1142/s0219467813500198.
Повний текст джерелаАнотація:
Video surveillance cameras are ubiquitous this decade. With the popularization of sports, costly courts are built widespread. To protect the sport ground against from damage, some activities, such as biking and in-line skating, are prohibited in the court. Traditional video surveillance systems can not prevent these activities in time. In this paper, we propose a video-based approach for detecting prohibited activities that can damage a court. The approach involves prohibited activity analysis and prohibited activity detection. The first stage generates the leg-angle curves of a prohibited activity. A background subtraction procedure is applied to extract moving objects; moving objects are then normalized to minimize influences of various scaling conditions. Leg-angle curves of prohibited activities are generated by computing leg angles. The second stage detects specific prohibited activities by analyzing leg-angle curves obtained from the input video sequences. Our proposed method can detect the prohibited activities in a court, thus preventing such activities in time.
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Liu, Yun-Lin, Jing-Jing Shi, Jun-Qi Huang, Guang-Shuo Wei, and Zhi-Xin Wu. "Grouting Defect Detection of Lapped Bar Connections Based on Impact-Echo Method." Shock and Vibration 2019 (October 14, 2019): 1–14. http://dx.doi.org/10.1155/2019/1934240.
Повний текст джерелаАнотація:
Grouted lap-splice connections are widely used for connecting precast concrete components. Grouting defects in the connections significantly influence the structural performance of the whole connection, which leads to the need for grouting defect detection. In this study, the impact-echo (IE) method was used for detecting defects in grouted lap-splice connections. Grouted connections with different levels of artificial grout defects were prepared in a shear wall, and the IE method was used to measure the frequency response. In addition, finite element (FE) analysis based on ABAQUS was conducted to simulate the tests. Based on the validated FE model, a parametric study was conducted to investigate the effect of the depth of the grout hole on the amplitude spectrum. The results indicated that (1) the IE method offered a good potential for grouting defect detection in grouted lap-splice connections; (2) the proposed FE model could well predict the frequency response of the grouting hole; and (3) the measured frequency and amplitude of the grouting hole in an impact-echo test would be considerably influenced by the hole depth.
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Cai, Ming Shan. "Weak Signal Detection Principle Based on Chaotic Duffing Oscillator and its Simulation Method." Advanced Materials Research 108-111 (May 2010): 834–37. http://dx.doi.org/10.4028/www.scientific.net/amr.108-111.834.
Повний текст джерелаАнотація:
Based on Matlab as the software platform, chaos detection principle and methods used for weak signals detection are studied. The model used for simulation is established, then the simulation results of weak periodic signal detection in strong noise atmosphere are given and the steps for detecting weak signals with chaos method are listed. Furthermore, the influence of sampling period on system’s performance is studied. Simulation results show that the chao detection approach proposed in this paper can detect the signal even if it is small to 10-10v, and even when it is in the environment with strong noise, small signal with magnitude of only 5×10-9 v can be found. Chaos method has strong capability for weak signal detection which lay important foundation for exploiting virtual instrument.
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He, Xing Zheng. "Detecting Technology for External Anticorrosive Coating Defects of Buried Pipelines." Applied Mechanics and Materials 615 (August 2014): 165–68. http://dx.doi.org/10.4028/www.scientific.net/amm.615.165.
Повний текст джерелаАнотація:
The external anticorrosive coating is the shelter for preventing buried steel pipelines from corrosive damage. Many pipelines face severe corrosive problems because of the performance decrease of the coating, especially during long-term services. This situation usually causes safety accidents. Periodic inspection to the coating is an important way to ensure running safety. Aimed at the detecting technology, the methods for locating the pipelines and the means of on-line defects detecting with no excavation for the coating were studied and introduced. The ways for direction detecting, depth measuring and location coordinates detecting were pointed out. The basic principles of data analysis were also outlined. The various influences coming from external environment factors to the detecting results were analyzed.
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Guo, Li, Yu Liao, Hongying Yao, Jinhao Chen, and Manran Wang. "An Electrical Insulator Defects Detection Method Combined Human Receptive Field Model." Journal of Control Science and Engineering 2018 (July 3, 2018): 1–9. http://dx.doi.org/10.1155/2018/2371825.
Повний текст джерелаАнотація:
Nondestructive inspection of electrical insulators subjected to the high electrical stress and environmental damage is fundamental for reliable operation of a transmission lines. The breakage and defect of the insulator have great influence on the safe of transmission lines, and insulator defect detection with difference types is a complex work. This paper proposed an insulator defect detection method inspired by human receptive field model, which meets the requirements for detecting defect insulator in a simple background. In this method, the defect detection combined human receptive field model of human visual system is constructed and applied on the different insulators, so as to achieve accurate detection of the insulator defected parts. Experimental results show that the method can accurately and robustly detect the defect (such as cracks and damage) of electrical insulator in case of noise affect.
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Guan, Shao Peng, Yu Ling Wang, Chang Xin Nai, and Jing Cai Liu. "Influence of Power Supply Current in Electrical Landfill Leakage Detection Method." Applied Mechanics and Materials 29-32 (August 2010): 2686–91. http://dx.doi.org/10.4028/www.scientific.net/amm.29-32.2686.
Повний текст джерелаАнотація:
The electrical method is effective on detecting and locating leaks in geomembrane liners for landfills. If a leak exists in the liner, an anomaly in the measured potential is generated in the immediate vicinity of the leak through which electrical current is flowing. But the power supply current above the liner can affect the potential distribution. A current point source model for power supply current was set up in this paper. The potential equation induced by the point source was derived. The analysis of potential expression shows that: 1) the potential generated by the power supply current is proportional to the current size and the medium resistivity; 2) as the medium thickness increases, the potential reduces; 3) to reduce the influence of power supply current, the power supply electrode should be away from the detection area as far as possible.
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Zhang, Qihan, Ya Shen, Liuyu Gong, Lin Shen, Haolin Xiao, Liangli Cao, Shanshan Wei, Fangrong Hu, Feijun Zhao, and Zhencheng Chen. "A New Thyroxine-Labeled Alkaline Phosphatase Chemiluminescence Immunoassay for Detection of Thyroxine Hormone." Science of Advanced Materials 14, no. 2 (February 1, 2022): 257–62. http://dx.doi.org/10.1166/sam.2022.4199.
Повний текст джерелаАнотація:
As the thyroxine (T4) precisely reflects the thyroid dysfunction for ourself, it becomes one of the most important indicators for patients to analyze the thyroid status. However, common methods for detecting T4 still contain an essential issue that natural T4 converts to biologically active triiodothyronine and influences the result unpredictable. In this study, a new T4 protein was synthesized and applied on a thyroxine-labeled alkaline phosphatase chemiluminescence immunoassay for detection of thyroxine hormone. Under the optimal conditions, the applied immunoassay shows a great linear range from 5 ng/mL to 300 ng/mL and exhibits comparable stability (12 months), sensitivity (1.01 ng/mL), and specificity (>0.5 ng/mL) compared with natural T4. Thus, the proposed new thyroxine-labeled alkaline phosphatase chemiluminescence immunoassay offers a better method to synthesis thyroxine and shows a potential in promoting the stability and sensitivity for thyroxine In-Vitro diagnosis.
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Zolotova, T. V., and D. A. Volkova. "Intelligent Data Processing Methods for the Atypical Values Correction of Stock Quotes." Statistics and Economics 19, no. 2 (April 6, 2022): 4–13. http://dx.doi.org/10.21686/2500-3925-2022-2-.
Повний текст джерелаАнотація:
Purpose of the study. The purpose of the study is to carry out a comparative analysis of various methods for correcting atypical values of statistical data on the stock market and to develop recommendations for their use.Materials and methods. The article analyzes Russian and foreign bibliography on the research problem. Consideration of machine learning methods for detecting and correcting outliers in time series is proposed. The mathematical basis of machine learning methods is the Z-score method, the isolation forest method, support vector method for outlier detection, and winsorization and multiple imputation methods for outlier correction. To create the models, the Jupyter Notebook software tool, which supports the Python programming language, was used. To implement machine-learning methods, data from stock quotes of the Moscow Exchange are used.Results. The results of machine learning algorithms are demonstrated for sets of real statistical data representing the closing prices of shares of three Russian companies “Sberbank”, “Aeroflot”, “Gazprom” in the period from 01.12.2019 to 30.11.2020, obtained from the website of the Investment Company “FINAM”. A comparative analysis of methods for detecting and correcting outliers by standard deviation has been carried out. The Z-score statistical method allows you to accurately determine the distance from the suspicious observation to the distribution center, which is an advantage. The disadvantage of this method is the influence of outliers on the mean and standard deviation, which can contribute to the masking of outliers or their incorrect detection. The isolation forest method recognizes outliers of various types, and when implementing the method, there are no parameters that require selection; but the disadvantage is the slower detection rate of outliers compared to other methods. The support vector machine is a very fast method and is reduced to solving a quadratic programming problem, which always has a unique solution. The winsorization method for correcting outliers reduces the effect of outliers on the mean and variance, which is an advantage, but may introduce bias due to the selection of thresholds to separate observations in the sample. The multiple imputation method creates for each missing value not one, but many imputations, which avoids a systematic error, but at the expense of high computational costs. For the initial data used in the work, the best result was shown by the implementation of the multiple imputation algorithm based on the detected outliers by the support vector method.Conclusion. There is no universal method for detecting and/or eliminating outliers in data analysis theory. In general, the determination of outliers is subjective, and the decision is made individually for each specific dataset, considering its characteristics or existing experience in this area. The practical implementation of the methods for detecting and eliminating outliers used in this work can be a tool for calculating more accurate indicators in any area, for example, to improve forecasting the stock price. As part of further work, it is possible to consider the optimization of the parameters used in the methods of detecting and correcting outliers to study their effect on the results of the models.
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Zolotova, T. V., and D. A. Volkova. "Intelligent Data Processing Methods for the Atypical Values Correction of Stock Quotes." Statistics and Economics 19, no. 2 (April 6, 2022): 4–13. http://dx.doi.org/10.21686/2500-3925-2022-2-4-13.
Повний текст джерелаАнотація:
Purpose of the study. The purpose of the study is to carry out a comparative analysis of various methods for correcting atypical values of statistical data on the stock market and to develop recommendations for their use.Materials and methods. The article analyzes Russian and foreign bibliography on the research problem. Consideration of machine learning methods for detecting and correcting outliers in time series is proposed. The mathematical basis of machine learning methods is the Z-score method, the isolation forest method, support vector method for outlier detection, and winsorization and multiple imputation methods for outlier correction. To create the models, the Jupyter Notebook software tool, which supports the Python programming language, was used. To implement machine-learning methods, data from stock quotes of the Moscow Exchange are used.Results. The results of machine learning algorithms are demonstrated for sets of real statistical data representing the closing prices of shares of three Russian companies “Sberbank”, “Aeroflot”, “Gazprom” in the period from 01.12.2019 to 30.11.2020, obtained from the website of the Investment Company “FINAM”. A comparative analysis of methods for detecting and correcting outliers by standard deviation has been carried out. The Z-score statistical method allows you to accurately determine the distance from the suspicious observation to the distribution center, which is an advantage. The disadvantage of this method is the influence of outliers on the mean and standard deviation, which can contribute to the masking of outliers or their incorrect detection. The isolation forest method recognizes outliers of various types, and when implementing the method, there are no parameters that require selection; but the disadvantage is the slower detection rate of outliers compared to other methods. The support vector machine is a very fast method and is reduced to solving a quadratic programming problem, which always has a unique solution. The winsorization method for correcting outliers reduces the effect of outliers on the mean and variance, which is an advantage, but may introduce bias due to the selection of thresholds to separate observations in the sample. The multiple imputation method creates for each missing value not one, but many imputations, which avoids a systematic error, but at the expense of high computational costs. For the initial data used in the work, the best result was shown by the implementation of the multiple imputation algorithm based on the detected outliers by the support vector method.Conclusion. There is no universal method for detecting and/or eliminating outliers in data analysis theory. In general, the determination of outliers is subjective, and the decision is made individually for each specific dataset, considering its characteristics or existing experience in this area. The practical implementation of the methods for detecting and eliminating outliers used in this work can be a tool for calculating more accurate indicators in any area, for example, to improve forecasting the stock price. As part of further work, it is possible to consider the optimization of the parameters used in the methods of detecting and correcting outliers to study their effect on the results of the models.
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Stokeld, Danielle, Anke S. K. Frank, Brydie Hill, Jenni Low Choy, Terry Mahney, Alys Stevens, Stuart Young, Djelk Rangers, Warddeken Rangers, and Graeme R. Gillespie. "Multiple cameras required to reliably detect feral cats in northern Australian tropical savanna: an evaluation of sampling design when using camera traps." Wildlife Research 42, no. 8 (2015): 642. http://dx.doi.org/10.1071/wr15083.
Повний текст джерелаАнотація:
Context Feral cats are a major cause of mammal declines and extinctions in Australia. However, cats are elusive and obtaining reliable ecological data is challenging. Although camera traps are increasingly being used to study feral cats, their successful use in northern Australia has been limited. Aims We evaluated the efficacy of camera-trap sampling designs for detecting cats in the tropical savanna of northern Australia. We aimed to develop a camera-trapping method that would yield detection probabilities adequate for precise occupancy estimates. Methods First, we assessed the influence of two micro-habitat placements and three lure types on camera-trap detection rates of feral cats. Second, using multiple camera traps at each site, we examined the relationship between sampling effort and detection probability by using a multi-method occupancy model. Key results We found no significant difference in detection rates of feral cats using a variety of lures and micro-habitat placement. The mean probability of detecting a cat on one camera during one week of sampling was very low (p = 0.15) and had high uncertainty. However, the probability of detecting a cat on at least one of five cameras deployed concurrently on a site was 48% higher (p = 0.22) and had a greater precision. Conclusions The sampling effort required to achieve detection rates adequate to infer occupancy of feral cats by camera trap is considerably higher in northern Australia than has been observed elsewhere in Australia. Adequate detection of feral cats in the tropical savanna of northern Australia will necessitate inclusion of more camera traps and a longer survey duration. Implications Sampling designs using camera traps need to be rigorously trialled and assessed to optimise detection of the target species for different Australian biomes. A standard approach is suggested for detecting feral cats in northern Australian savannas.
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Wei, Wenfu, Chongliang Liang, Zefeng Yang, Pan Xu, Xin Yan, Guoqiang Gao, and Guangning Wu. "A novel method for detecting the pantograph–catenary arc based on the arc sound characteristics." Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 233, no. 5 (October 28, 2018): 506–15. http://dx.doi.org/10.1177/0954409718799792.
Повний текст джерелаАнотація:
In recent years, the speed and the current transmission density of high-speed railways are increased. As a result, the rate of the occurrence of pantograph–catenary arc also increases, which directly influences the service performance of the pantograph–catenary system. The detection and identification of the pantograph–catenary arc is of great significance for evaluating the off-line level to ensure the safe operation of the pantograph–catenary system. The currently used conventional optical arc detection method would be distorted by strong sunlight or other environmental lights in the actual operation, which would affect the accuracy of detection. On the contrary, the arc sound signal is not affected by the diurnal variation and can be easily obtained; it can also be distinguished well from other interfering sounds. In this paper, a new method that uses the arc sound is proposed to realize the effective detection of the pantograph–catenary arc. The frequency spectrum and the intensity characteristics of the pantograph–catenary arc sound are analyzed. The results show that the frequency band of the arc sound signal is wide and mainly gets distributed at 5–17 kHz. The curve of the sound waveform is drawn by calculating the short-time average energy, which is used to obtain the arc’s starting time, duration, and intensity, for calculating the off-line rate.
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Li, Yan, and Gang Li. "Research on a Current Direct Control Method." Advanced Materials Research 354-355 (October 2011): 881–85. http://dx.doi.org/10.4028/www.scientific.net/amr.354-355.881.
Повний текст джерелаАнотація:
A current direct control is proposed in the paper. The topology structure of a hybrid filtering equipment is presented. The traditional current detecting and controlling method for the structure is discussed. The relationship between the grid current and harmonic suppressing current is educed, which proves that the step of harmonic detection be left out in theory. A current direct control based on the grid current error is proposed, which is simple. It is not necessary to detect harmonic and reactive current. So influence factors on the real time and precision of the filter are reduced. The simulating model of the hybrid filtering equipment is constructed and the new current direct control is applied in the model. The simulating results show that the good performance of harmonic suppression and reactive compensation is obtained with the current direct control.
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Liu, Hao, Langzhou He, Fan Zhang, Zhen Wang, and Chao Gao. "Dynamic community detection over evolving networks based on the optimized deep graph infomax." Chaos: An Interdisciplinary Journal of Nonlinear Science 32, no. 5 (May 2022): 053119. http://dx.doi.org/10.1063/5.0086795.
Повний текст джерелаАнотація:
As complex systems, dynamic networks have obvious nonlinear features. Detecting communities in dynamic networks is of great importance for understanding the functions of networks and mining evolving relationships. Recently, some network embedding-based methods stand out by embedding the global network structure and properties into a low-dimensional representation for community detection. However, such kinds of methods can only be utilized at each single time step independently. As a consequence, the information of all time steps requires to be stored, which increases the computational cost. Besides this, the neighbors of target nodes are considered equally when aggregating nodes in networks, which omits the local structural feature of networks and influences the accuracy of node representation. To overcome such shortcomings, this paper proposes a novel optimized dynamic deep graph infomax (ODDGI) method for dynamic community detection. Since the recurrent neural network (RNN) can capture the dynamism of networks while avoiding storing all information of dynamic networks, our ODDGI utilizes RNN to update deep graph infomax parameters, and thus, there is no need to store the knowledge of nodes in full time span anymore. Moreover, the importance of nodes is considered using similarity aggregation strategy to improve the accuracy of node representation. The experimental results on both the real-world and synthetic networks prove that our method surpasses other state-of-the-art dynamic community detection algorithms in clustering accuracy and stability.
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Peng, Ming, Dengyi Wang, Liu Liu, Zhenming Shi, Jian Shen, and Fuan Ma. "Recent Advances in the GPR Detection of Grouting Defects behind Shield Tunnel Segments." Remote Sensing 13, no. 22 (November 16, 2021): 4596. http://dx.doi.org/10.3390/rs13224596.
Повний текст джерелаАнотація:
Injecting grout into the gaps between tunnel shield segments and surrounding rocks can reduce ground subsidence and prevent ground water penetration. However, insufficient grouting and grouting defects may cause serious geological disasters. Ground penetrating radar (GPR) is widely used as a nondestructive testing (NDT) method to evaluate grouting quality and determine the existence of defects. This paper provides an overview of GPR applications for grouting defect detection behind tunnel shield segments. State-of-the-art methodologies, field cases, experimental tests and signal processing methods are discussed. The reported field cases and model test results show that GPR can detect grouting defects behind shield tunnel segments by identifying reflected waves. However, some subsequent problems still exist, including the interference of steel bars and small differences in the dielectric constants among media. Recent studies have focused on enhancing the signal-to-noise ratio and imaging methods. Advanced GPR signal processing methods, including full waveform inversion and machine learning methods, are promising for detecting imaging defects. Additionally, we conduct a preliminary experiment to investigate environmental noise, antenna configuration and coupling condition influences. Some promising topics, including multichannel configuration, rapid evaluation methods, elastic wave method scanning equipment for evaluating grout quality and comprehensive NDT methods, are recommended for future studies.
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Xu, Hao, Zhongqing Su, Li Cheng, and Jean-Louis Guyader. "On a hybrid use of structural vibration signatures for damage identification: a virtual vibration deflection (VVD) method." Journal of Vibration and Control 23, no. 4 (August 8, 2016): 615–31. http://dx.doi.org/10.1177/1077546315581944.
Повний текст джерелаАнотація:
A damage identification method named virtual vibration deflection (VVD) was developed, the principle of which was formulated based on the “weak” modality of the pseudo-excitation (PE) approach previously established. In essence, VVD is based on locating structural damage within a series of “sub-regions” divided from the entire structure under inspection, and each sub-region was considered as a “virtual” structure undergoing independent vibration. The corresponding vibration deflection of the “virtual” structure was then used to derive the damage index of VVD. Besides various advantages inheriting from the PE approach, for example, capability of detecting damage without baseline signals and pre-developed benchmark structures, VVD exhibits improved detection accuracy and particularly enhanced noise immunity compared with the PE approach, attributed to a hybrid use of multi-types of vibration signatures (MTVS). As a proof-of-concept investigation, a beam model was used in a numerical study to examine the philosophy of VVD. And the influences from different factors (i.e., level of measurement noise and measurement density) on the detection accuracy of VVD were discussed based on the numerical model. An experiment was carried out subsequently to identify the locations of multiple defects contained in an aluminum beam-like structure. Identification results constructed by the PE approach, VVD using single-type of vibration signatures, and VVD using MTVS, were presented, respectively, for the purpose of comparison.
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Sun, Jian, and Wei Guo Lin. "Features Extraction of CO2 Signal with Operational Conditions Adaptability." Applied Mechanics and Materials 109 (October 2011): 131–35. http://dx.doi.org/10.4028/www.scientific.net/amm.109.131.
Повний текст джерелаАнотація:
A method for indirect online over-temperature detection of isolators in high voltage switchgear is proposed，which is based on Na Superionic Conductor CO2 sensor. The over-temperature detecting principle is put forward and an over-temperature detecting system based on the method is introduced. The reliability of over-temperature detection depends on signal noise ratio (SNR), but the sensor and signal processing determine the SNR of CO2 signal. Based on quantitative analysis, it is concluded that Na Superionic Conductor CO2 sensor has higher SNR. By signal processing, the necessity of moving average filter for SNR is demonstrated. A temperature compensation circuit is adopted to eliminate the influence of temperature. In view of the characteristics of rude signal, an appropriate method of real-time digital filtering is used to improve the Signal-to-Noise Ratio. The different features of the signal under over-temperature condition have been analyzed and compared to those under normal conditions. The paper introduces a reliable algorithm for identifying over-temperature detection based on sequential section differences. Experiments prove that the method is feasible.
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Wang, Ping Jiang, Chang Jie Xu, Ji Hong Chen, and Xiao Qi Tang. "Injecting Mold Protection Method Based on Machine Vision." Advanced Materials Research 590 (November 2012): 475–82. http://dx.doi.org/10.4028/www.scientific.net/amr.590.475.
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Aimed at solving the problem of mold damage caused by a foreign body in the mold before mold clamping, this paper proposes a solution, which applies image processing technology such as background updating and the difference image algorithm to solve it. Not only can it judge whether there is a foreign body in the mold but it can also detect whether the product is perfect by comparing the foreground image with the background image at the appropriate time (before mold clamping or after mold opening) and by calculating the qualified rate of pixel in all ROIs (Region of Interest). To eliminate the influence of vibration and of changes in brightness in the surrounding environment on the detecting results, this paper utilizes near infrared illumination technology and the background updating algorithm. In addition, the ROI is set to improve the detecting speed and accuracy.
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Pugen, Yan, Qiu Haiou, Lu Cuili, and Tang Zhiyong. "Determination of Nitrogen Oxides with Rhodamine B by Fluorescence Quenching Method." International Journal of Spectroscopy 2011 (April 3, 2011): 1–4. http://dx.doi.org/10.1155/2011/281931.
Повний текст джерелаАнотація:
NO2 can be transformed into nitrite by the absorption of Rhodamine B solution, which will make fluorescence quenching of Rhodamine B in acidic medium. According to this mechanism, a new method for detecting nitrogen oxides in the air is developed by a fluorescence spectrophotometry. The influence of environmental media and interfering substances in the fluorescence intensity of system was studied. Under the optimal experimental conditions, the decrease of fluorescence intensity varies linearly with the concentration of NO2 − over the range of 0.009~0.500 μg/ml with a correlation coefficient of 0.9992. The method is simple and has a lower limit of detection than the common methods. It can be a referee for the environmental evaluation.
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Zou, Zhong Quan, Xu Wang, and Zhi Mei Wang. "Application of Ultrasonic Testing in Concrete Filled Steel Tubular Arch Bridge." Advanced Materials Research 639-640 (January 2013): 1025–28. http://dx.doi.org/10.4028/www.scientific.net/amr.639-640.1025.
Повний текст джерелаАнотація:
Concrete Filled Steel Tube(CFST) is widely used in civil engineering structures because of its superior mechanical performance. Yet the mechanical behavior of CFST is highly depended on the construction quality of the filled concrete. Hence it is very important for the inspection of the construction quality of the filled concrete in CFST structures. In this paper, the ultrasonic testing technique was used to detect the defect of the filled concrete of a CFST arch bridge. During the inspection, the ultrasonic transducer was moved along the circumference of the cross-section of the arch, and the defect of the concrete was comprehensively judged by detecting the change of sonic time, sonic amplitude and sonic frequency. Based on the analysis of the ultrasonic transmission path, the influences of different defects on the sonic time, sonic amplitude and sonic frequency were discussed. The detecting results were verified by core-drilling method. The verification showed that different kinds of defects defected by ultrasonic testing was in good accordance with the drilling samples, which demonstrates the adaptability of the ultrasonic detection technique in the construction quality inspection of CFST structures.
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Tohara, Makoto, and Yuji Gotoh. "Electromagnetic inspection for defect of ferromagnetic tube using rectangular wave with DC bias." International Journal of Applied Electromagnetics and Mechanics 64, no. 1-4 (December 10, 2020): 281–88. http://dx.doi.org/10.3233/jae-209332.
Повний текст джерелаАнотація:
The ferromagnetic steel tubes are used as heat exchangers in power plants or oil plants. When the usual eddy current testing (ECT) of inner type is used for the defect inspection of these steel tubes, it is difficult to detect defects on the opposite side due to the influence of skin effects. In this research, the non-destructive inspection method using rectangular wave excitation current with DC bias is proposed for detecting of the opposite side defect. It is shown that the detecting of the opposite side defect in the steel tube is possible, and the detection sensitivity using the alternating magnetic field of rectangular wave is higher than that with sine wave. The detailed phenomenon of the inspection method is examined using the 3-D nonlinear FEM (finite element method) taking account of minor loop magnetizing properties using the play model method.
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Prager, Jens, Christian Höhne, and Mehbub Ur Rahman. "Synthetic Aperture Focusing Technique for Detecting Transverse Cracks in Austenitic and Dissimilar Welds." Key Engineering Materials 569-570 (July 2013): 1036–43. http://dx.doi.org/10.4028/www.scientific.net/kem.569-570.1036.
Повний текст джерелаАнотація:
The inspection of austenitic and dissimilar welds using ultrasound demands for sophisticated testing techniques. The application of reconstruction methods like the Synthetic Aperture Focusing Technique (SAFT) on the measurement results provides an appropriate approach for defect characterization and sizing. Nevertheless, the reconstruction algorithm has to consider the aniso-tropic wave propagation inside the inhomogeneous weld material. In recent years the detection of transverse cracks has become increasingly important for ensuring the structural integrity of pipes in the primary circuit of nuclear power plants or longitudinally welded, cladded pipes. However, relia-ble inspection techniques are hardly available. In this particular case, it is expected that the compar-atively long propagation path of the ultrasonic wave field inside the inhomogeneous weld material enhances the effect of anisotropy and influences the accuracy and the signal-to-noise-ratio of the reconstruction result. In this contribution we suggest an advanced ultrasonic testing technique for detecting and sizing of transversal cracks in austenitic and dissimilar welds. The method applies a SAFT reconstruction algorithm considering the anisotropy and the inhomogeneity. A V-arrangement of the transducers in pitch-catch technique is chosen to avoid a direct coupling on the weld face. The reconstruction algo-rithm is based on an extended 3-dimensional weld model and uses a ray-tracing approach for de-termining the wave propagation paths. Along with the reconstruction algorithm the transducer set-up and experimental results of different specimens with artificial transverse flaws are presented. The availability of the proposed method for crack sizing is assessed in comparison to conventional testing techniques.
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Gutiérrez, Sebastián, and Hiram Ponce. "An Intelligent Failure Detection on a Wireless Sensor Network for Indoor Climate Conditions." Sensors 19, no. 4 (February 19, 2019): 854. http://dx.doi.org/10.3390/s19040854.
Повний текст джерелаАнотація:
Wireless sensor networks (WSN) involve large number of sensor nodes distributed at diverse locations. The collected data are prone to be inaccurate and faulty due to internal or external influences, such as, environmental interference or sensor aging. Intelligent failure detection is necessary for the effective functioning of the sensor network. In this paper, we propose a supervised learning method that is named artificial hydrocarbon networks (AHN), to predict temperature in a remote location and detect failures in sensors. It allows predicting the temperature and detecting failure in sensor node of remote locations using information from a web service comparing it with field temperature sensors. For experimentation, we implemented a small WSN to test our sensor in order to measure failure detection, identification and accommodation proposal. In our experiments, 94.18% of the testing data were recovered and accommodated allowing of validation our proposed approach that is based on AHN, which detects, identify and accommodate sensor failures accurately.