scholarly journals Automating the Classification of Field Leakage Current Waveforms

2011 ◽  
Vol 1 (1) ◽  
pp. 8-12 ◽  
Author(s):  
D. Pylarinos ◽  
K. Siderakis ◽  
E. Pyrgioti ◽  
E. Thalassinakis ◽  
I. Vitellas
Keyword(s):  
Leakage Current ◽  
High Voltage ◽  
Marine Pollution ◽  
Field Measurements ◽  
Identification System ◽  
Data Set ◽  
Local Conditions ◽  
Wavelet Multiresolution Analysis ◽  
Current Monitoring

Leakage current monitoring is widely employed to investigate the performance of high voltage insulators and the development of surface activity. Field measurements offer an exact view of experienced activity and insulators’ performance, which are strongly correlated to local conditions. The required long term monitoring however, results to the accumulation of vast amounts of data. Therefore, an identification system for the classification of field leakage current waveforms rises as a necessity. In this paper, a number of 500 leakage current waveforms recorded on a composite post insulator installed at a 150 kV High Voltage Substation suffering from intense marine pollution, are investigated. The insulator was monitored for a period of 13 months. An identification system is designed based on the considered data employing Fourier analysis, wavelet multiresolution analysis and a neural network. Results show the large impact of noise in field measurements and the effectiveness of the discussed system on the considered data set.

scholarly journals Investigation of Leakage Current Waveforms Recorded in a Coastal High Voltage Substation

2011 ◽  
Vol 1 (3) ◽  
pp. 63-69 ◽  
Author(s):  
D. Pylarinos ◽  
K. Siderakis ◽  
E. Pyrgioti ◽  
E. Thalassinakis ◽  
I. Vitellas
Keyword(s):  
Leakage Current ◽  
Surface Activity ◽  
High Voltage ◽  
Marine Pollution ◽  
Field Measurements ◽  
Significant Information ◽  
Local Conditions ◽  
Outdoor Insulation ◽  
Different Types ◽  
Current Monitoring

Leakage current monitoring is a widely employed technique to monitor the performance of outdoor insulation. The evaluation of leakage current waveforms recorded in the field, offers significant information since insulation’s performance is strongly linked with local conditions, and the waveforms’ shape correlate to different types of surface activity. In this paper, an investigation of leakage current waveforms recorded in a 150 kV coastal Substations suffering which suffers intense marine pollution is presented. Investigation of the recorded waveforms verified the basic waveform shapes described in the literature. Further, several variations of the basic types and complex waveforms, as well as field related waveforms, are presented. The need for added categorization criteria in the case of field measurements is discussed.

scholarly journals Research on emitter individual identification technology based on Automatic Dependent Surveillance–Broadcast signal

2021 ◽  
Vol 17 (2) ◽  
pp. 155014772199262
Author(s):  
Shiwen Chen ◽  
Junjian Yuan ◽  
Xiaopeng Xing ◽  
Xin Qin
Keyword(s):  
Individual Recognition ◽  
Evidence Theory ◽  
Field Measurements ◽  
Training Data ◽  
Individual Identification ◽  
Identification System ◽  
Individual Feature ◽  
Data Set ◽  
Theoretical Simulation ◽  
The Individual

Aiming at the shortcomings of the research on individual identification technology of emitters, which is primarily based on theoretical simulation and lack of verification equipment to conduct external field measurements, an emitter individual identification system based on Automatic Dependent Surveillance–Broadcast is designed. On one hand, the system completes the individual feature extraction of the signal preamble. On the other hand, it realizes decoding of the transmitter’s individual identity information and generates an individual recognition training data set, on which we can train the recognition network to achieve individual signal recognition. For the collected signals, six parameters were extracted as individual features. To reduce the feature dimensions, a Bessel curve fitting method is used for four of the features. The spatial distribution of the Bezier curve control points after fitting is taken as an individual feature. The processed features are classified with multiple classifiers, and the classification results are fused using the improved Dempster–Shafer evidence theory. Field measurements show that the average individual recognition accuracy of the system reaches 88.3%, which essentially meets the requirements.

Machine Learning Verdict of EEG Signals in Brain Computer Interface

2018 ◽  
pp. 429-441
Author(s):  
M. Jeyanthi ◽  
C. Velayutham
Keyword(s):  
Nearest Neighbor ◽  
Technology Development ◽  
Vital Role ◽  
Svm Classifier ◽  
K Nearest Neighbor ◽  
Data Mining Technique ◽  
Data Set ◽  
Eeg Data ◽  
Irrelevant Attributes

In Science and Technology Development BCI plays a vital role in the field of Research. Classification is a data mining technique used to predict group membership for data instances. Analyses of BCI data are challenging because feature extraction and classification of these data are more difficult as compared with those applied to raw data. In this paper, We extracted features using statistical Haralick features from the raw EEG data . Then the features are Normalized, Binning is used to improve the accuracy of the predictive models by reducing noise and eliminate some irrelevant attributes and then the classification is performed using different classification techniques such as Naïve Bayes, k-nearest neighbor classifier, SVM classifier using BCI dataset. Finally we propose the SVM classification algorithm for the BCI data set.

scholarly journals Classification of jujube defects in small data sets based on transfer learning

2021 ◽  
Author(s):  
Jianping Ju ◽  
Hong Zheng ◽  
Xiaohang Xu ◽  
Zhongyuan Guo ◽  
Zhaohui Zheng ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Loss Function ◽  
Training Model ◽  
Parameter Distribution ◽  
Test Accuracy ◽  
Small Data ◽  
Data Sets ◽  
Data Set ◽  
Small Data Sets

AbstractAlthough convolutional neural networks have achieved success in the field of image classification, there are still challenges in the field of agricultural product quality sorting such as machine vision-based jujube defects detection. The performance of jujube defect detection mainly depends on the feature extraction and the classifier used. Due to the diversity of the jujube materials and the variability of the testing environment, the traditional method of manually extracting the features often fails to meet the requirements of practical application. In this paper, a jujube sorting model in small data sets based on convolutional neural network and transfer learning is proposed to meet the actual demand of jujube defects detection. Firstly, the original images collected from the actual jujube sorting production line were pre-processed, and the data were augmented to establish a data set of five categories of jujube defects. The original CNN model is then improved by embedding the SE module and using the triplet loss function and the center loss function to replace the softmax loss function. Finally, the depth pre-training model on the ImageNet image data set was used to conduct training on the jujube defects data set, so that the parameters of the pre-training model could fit the parameter distribution of the jujube defects image, and the parameter distribution was transferred to the jujube defects data set to complete the transfer of the model and realize the detection and classification of the jujube defects. The classification results are visualized by heatmap through the analysis of classification accuracy and confusion matrix compared with the comparison models. The experimental results show that the SE-ResNet50-CL model optimizes the fine-grained classification problem of jujube defect recognition, and the test accuracy reaches 94.15%. The model has good stability and high recognition accuracy in complex environments.

scholarly journals Underwater Holographic Sensor for Plankton Studies In Situ Including Accompanying Measurements

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4863
Author(s):  
Victor Dyomin ◽  
Alexandra Davydova ◽  
Igor Polovtsev ◽  
Alexey Olshukov ◽  
Nikolay Kirillov ◽  
...  
Keyword(s):  
World Ocean ◽  
Field Tests ◽  
Field Measurements ◽  
Measuring Systems ◽  
Water Turbidity ◽  
Background Data ◽  
Average Size ◽  
Size Dispersion

The paper presents an underwater holographic sensor to study marine particles—a miniDHC digital holographic camera, which may be used as part of a hydrobiological probe for accompanying (background) measurements. The results of field measurements of plankton are given and interpreted, their verification is performed. Errors of measurements and classification of plankton particles are estimated. MiniDHC allows measurement of the following set of background data, which is confirmed by field tests: plankton concentration, average size and size dispersion of individuals, particle size distribution, including on major taxa, as well as water turbidity and suspension statistics. Version of constructing measuring systems based on modern carriers of operational oceanography for the purpose of ecological diagnostics of the world ocean using autochthonous plankton are discussed. The results of field measurements of plankton using miniDHC as part of a hydrobiological probe are presented and interpreted, and their verification is carried out. The results of comparing the data on the concentration of individual taxa obtained using miniDHC with the data obtained by the traditional method using plankton catching with a net showed a difference of no more than 23%. The article also contains recommendations for expanding the potential of miniDHC, its purpose indicators, and improving metrological characteristics.

scholarly journals ResCaps: an improved capsule network and its application in ultrasonic image classification of thyroid papillary carcinoma

2021 ◽  
Author(s):  
Xiongzhi Ai ◽  
Jiawei Zhuang ◽  
Yonghua Wang ◽  
Pin Wan ◽  
Yu Fu
Keyword(s):  
Papillary Carcinoma ◽  
Network Model ◽  
Reliability And Validity ◽  
Thyroid Papillary Carcinoma ◽  
Data Set ◽  
Ultrasonic Image ◽  
First Choice ◽  
Thyroid Papillary ◽  
Ultrasonic Images

AbstractUltrasonic image examination is the first choice for the diagnosis of thyroid papillary carcinoma. However, there are some problems in the ultrasonic image of thyroid papillary carcinoma, such as poor definition, tissue overlap and low resolution, which make the ultrasonic image difficult to be diagnosed. Capsule network (CapsNet) can effectively address tissue overlap and other problems. This paper investigates a new network model based on capsule network, which is named as ResCaps network. ResCaps network uses residual modules and enhances the abstract expression of the model. The experimental results reveal that the characteristic classification accuracy of ResCaps3 network model for self-made data set of thyroid papillary carcinoma was $$81.06\%$$ 81.06 % . Furthermore, Fashion-MNIST data set is also tested to show the reliability and validity of ResCaps network model. Notably, the ResCaps network model not only improves the accuracy of CapsNet significantly, but also provides an effective method for the classification of lesion characteristics of thyroid papillary carcinoma ultrasonic images.

A comparison of methodological approaches to the subfamilial classification of the Naididae (Oligochaeta)

1987 ◽  
Vol 65 (3) ◽  
pp. 691-707 ◽  
Author(s):  
A. F. L. Nemec ◽  
R. O. Brinkhurst
Keyword(s):  
Data Matrix ◽  
Jaccard Coefficient ◽  
Data Set ◽  
Parsimony Method ◽  
Fit Statistics ◽  
Reconstruction Methods ◽  
Linkage Methods ◽  
The Family ◽  
First Time

A data matrix of 23 generic or subgeneric taxa versus 24 characters and a shorter matrix of 15 characters were analyzed by means of ordination, cluster analyses, parsimony, and compatibility methods (the last two of which are phylogenetic tree reconstruction methods) and the results were compared inter alia and with traditional methods. Various measures of fit for evaluating the parsimony methods were employed. There were few compatible characters in the data set, and much homoplasy, but most analyses separated a group based on Stylaria from the rest of the family, which could then be separated into four groups, recognized here for the first time as tribes (Naidini, Derini, Pristinini, and Chaetogastrini). There was less consistency of results within these groups. Modern methods produced results that do not conflict with traditional groupings. The Jaccard coefficient minimizes the significance of symplesiomorphy and complete linkage avoids chaining effects and corresponds to actual similarities, unlike single or average linkage methods, respectively. Ordination complements cluster analysis. The Wagner parsimony method was superior to the less flexible Camin–Sokal approach and produced better measure of fit statistics. All of the aforementioned methods contain areas susceptible to subjective decisions but, nevertheless, they lead to a complete disclosure of both the methods used and the assumptions made, and facilitate objective hypothesis testing rather than the presentation of conflicting phylogenies based on the different, undisclosed premises of manual approaches.

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