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Typical current modelling and feature extraction of high voltage circuit breaker towards condition analysis and fault diagnosis

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IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第8期14卷 1521-1527页

作者： Ji, Tianyao Ye, Xiuzhen Shi, Mengjie Li, Mengshi Wu, Qinghua SCUT Sch Elect Power Engn Guangzhou 510641 Peoples R China

This study proposes a coil current model and an energy storage motor current (ESMC) model of circuit breakers (CBs) with spring operated mechanism. To make sure the signals generated by the models are identical to the actual ones, this study proposes a stochastic optimisation algorithm to optimise the model parameters. Based on the data produced by the optimised models, two fault diagnosis methods are proposed to assess operational condition and detect faults. The first method is based on fast template matching, which adopts k-means clustering algorithm to cluster the data and form a template library. The second one combines deep belief network and Softmax classifier, which can not only extract high level information of the characteristic signals, but also avoid the negative impact of the large dimension on classification results. In the simulation studies, the two methods are tested on various scenarios and their merits are demonstrated, respectively, where the latter one shows superior performance.

关键词： optimisation coils circuit breakers pattern clustering fault diagnosis feature extraction belief networks stochastic processes signal classification power engineering computing electric motors feature extraction high voltage circuit breaker condition analysis coil current model energy storage motor current model spring operated mechanism stochastic optimisation algorithm fault diagnosis methods fast template matching k-means clustering algorithm template library characteristic signals ESMC CBs deep belief network Softmax classifier

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Custom optical imaging system for ex-vivo breast cancer detection based on spectral signature

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SURGICAL ONCOLOGY-OXFORD 2020年 35卷 547-555页

作者： Aref, Mohamed Hisham Aboughaleb, Ibrahim H. El-Sharkawy, Yasser H. Mil Tech Coll Biomed Engn Dept El Fangary St Cairo Egypt

Background and purpose: Breast cancer is a popular well-known tumor in women globally and the subsequent driving reason for malignancy death. The purpose of the present study is to develop Low cost, commercial, and affordable system that discriminates malignant from normal breast tissues by exploiting the unique properties of Hyperspectral (HS) Imaging. Materials and methods: The difference in the optical properties of the investigated breast tissues gives various reactions to light transmission, absorption, and especially the reflection over the spectral range. A custom optical imaging system (COIS) was designed to assess variable responses to monochromatic LEDs (415, 565, 660 nm) to highlight the differences in the reflectance properties of malignant/normal tissue. Statistical analysis was computed for determining the ideal wavelength to differentiate between normal and malignant regions. The experiment was repeated using the same LEDs, and low-cost CCD camera to examine the capability of such a system to discriminate between normal and malignant tissue. Results: Spectral images obtained by Hyperspectral camera, have been analyzed to reveal the difference of reflectance malignant and normal breast tissue. Superficial spectral reflection image with blue LED (415 nm) showed high variance (10.11). However, a more-depth reflection image with red LED (660 nm) showed low variance (4.44). So the optimum contrast image was produced by combining the three spectral information images from blue, green, and red LED. The COIS using a commercial CCD camera was in agreement with the HS camera. Conclusions: The novel COIS of the commercial Low-cost CCD Camera is reliable and can be used with endoscopy technique as an assistant tool for surgical doctor to make decision and assess the resection edges in real time during surgery.

关键词： Breast cancer Hyperspectral imaging Low-cost CCD camera Monochromatic LED k-means clustering algorithm Spectral signature

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High-velocity impact behaviors and post-impact tension performance of 2D-C/SiC composite beams

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CERAMICS INTERNATIONAL 2020年第9期46卷 13282-13291页

作者： Hu, Wei Guan, Tianhao Gao, Mengting Ren, Tengfei Huang, Jinzi Suo, Tao Wang, Bo Zhang, Chao Northwestern Polytech Univ Sch Aeronaut Xian 710072 Shaanxi Peoples R China Northwestern Polytech Univ Joint Int Res Ctr Impact Dynam & Engn Applicat Xian 710072 Peoples R China Northwestern Polytech Univ Shaanxi Key Lab Impact Dynam & Engn Applicat IDEA Xian 710072 Peoples R China

2D-C/SiC composites have widely been used in aeronautical and aerospace engineering, but their mechanical behaviors under small-mass and high-speed impact have not been thoroughly studied yet. In this paper, 2D-C/SiC beam specimens were impacted by a single-stage light-gas gun and the fracture processes were captured by a high-speed camera. Post-impact internal and surface damage morphologies were scanned by a CT and a SEM, respectively. Similar damage modes were revealed by high-speed images. Subsequently, quasi-static post-impact tension tests were conducted to understand the residual mechanical properties. Acoustic emission (AE) signals of specimens were detected during the tests and then classified by the k-means algorithm. Therefore, evolutions of matrix damage, interfacial delamination and fiber fracture were recognized. At the same time, strain value was obtained by digital image correlation (DIC) method and main crack propagations were obvious in strain contours. A combination of the AE and DIC methods very well monitored the real-time damage during post-impact testing, which further revealed the damage during impact phase.

关键词： Ceramic matrix composites Foreign object damage k-means clustering algorithm Digital image correlation

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An Efficient Content Based Image Retrieval using an Optimized Neural Network for Medical Application

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MULTIMEDIA TOOLS AND APPLICATIONS 2020年第31-32期79卷 22277-22292页

作者： kumar, R. Biji Marikkannu, P. Pentagram Res Ctr Pvt Ltd Hyderabad India Anna Univ Dept Informat Technol Chennai Tamil Nadu India

Content Based Image Retrieval (CBIR) is a popular method to search and retrieve the similar images. For medical applications, it plays an important role to find the diseasessuch as breast cancer in human body. Many existing methods were presented for improving the performance of CBIR method. Nevertheless, retrieval time and accuracy of CBIR are further to be improved. To solve this issue, an optimal classifier is to be used in CBIR. In this paper, Artificial Neural Network based on Particle Swarm Optimization based (PSO-ANN) is presented as an optimized classifier. Also, the features of images such as shape, texture, mean and standard deviation are extracted. To increase the speed of the classification, these extracted features are to be clustered using k-means clustering algorithm. From the clustered features, similar images of query image are retrieved using the proposed PSO-ANN classifier. Simulation results prove that performance of this proposed CBIR outperforms than that of existing methods in terms of accuracy and CBIR time.

关键词： CBIR Particle Swarm Optimization Artificial Neural Network PSO-ANN k-means clustering algorithm Gray Level Co-occurrence Matrix

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Characterization of multiple sclerosis lesions with distinct clinical correlates through quantitative diffusion MRI

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NEUROIMAGE-CLINICAL 2020年 28卷 102411-102411页

作者： Martinez-Heras, Eloy Solana, Elisabeth Prados, Ferran Andorra, Magi Solanes, Aleix Lopez-Soley, Elisabet Montejo, Carmen Pulido-Valdeolivas, Irene Alba-Arbalat, Salut Sola-Valls, Nuria Sepulveda, Maria Blanco, Yolanda Saiz, Albert Radua, Joaquim Llufriu, Sara Hosp Clin Barcelona Inst Invest Biomed August Pi & Sunyer IDIBAPS Ctr Neuroimmunol Lab Adv Imaging Neuroimmunol Dis Barcelona Spain Univ Barcelona Barcelona Spain Univ Oberta Catalunya E Hlth Ctr Barcelona Spain UCL Dept Med Phys & Bioengn Ctr Med Image Comp London England UCL Dept Neuroinflammat UCL Inst Neurol NMR Res UnitQueen Sq MS Ctr London England IDIBAPS Imaging Mood & Anxiety Related Disorders IMARD Gr Barcelona Spain CIBERSAM Barcelona Spain Karolinska Inst Dept Clin Neurosci Ctr Psychiat Res Stockholm Sweden Kings Coll London Inst Psychiat Psychol & Neurosci Dept Psychosis Studies Early Psychosis Intervent & Clin Detect EPIC Lab London England

Diffusion magnetic resonance imaging can reveal quantitative information about the tissue changes in multiple sclerosis. The recently developed multi-compartment spherical mean technique can map different microscopic properties based only on local diffusion signals, and it may provide specific information on the underlying microstructural modifications that arise in multiple sclerosis. Given that the lesions in multiple sclerosis may reflect different degrees of damage, we hypothesized that quantitative diffusion maps may help characterize the severity of lesions "in vivo" and correlate these to an individual's clinical profile. We evaluated this in a cohort of 59 multiple sclerosis patients (62% female, mean age 44.7 years), for whom demographic and disease information was obtained, and who underwent a comprehensive physical and cognitive evaluation. The magnetic resonance imaging protocol included conventional sequences to define focal lesions, and multi-shell diffusion imaging was used with b-values of 1000, 2000 and 3000 s/mm(2) in 180 encoding directions. Quantitative diffusion properties on a macro- and micro-scale were used to discriminate distinct types of lesions through a kmeans clustering algorithm, and the number and volume of those lesion types were correlated with parameters of the disease. The combination of diffusion tensor imaging metrics (fractional anisotropy and radial diffusivity) and multi-compartment spherical mean technique values (microscopic fractional anisotropy and intra-neurite volume fraction) differentiated two type of lesions, with a prediction strength of 0.931. The B-type lesions had larger diffusion changes compared to the A-type lesions, irrespective of their location (P < 0.001). The number of A and B type lesions was similar, although in juxtacortical areas B-type lesions predominated (60%, P < 0.001). Also, the percentage of B-type lesion volume was higher (64%, P < 0.001), indicating that these lesions were larger. The number

关键词： MRI Diffusion magnetic resonance imaging k-means clustering algorithm MS lesion types Multiple Sclerosis

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Extraction of Multiple Diseases in Apple Leaf Using Machine Learning

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INTERNATIONAL JOURNAL OF IMAGE AND GRAPHICS 2022年第3期22卷

作者： Singh, Swati Gupta, Sheifali Tanta, Ankush Gupta, Rupesh Himachal Pradesh Univ Elect & Commun Engn Univ Inst Technol Shimla 171005 India Chitkara Univ Inst Engn & Technol Chandigarh 140401 Punjab India Chitkara Univ Sch Engn & Technol Chandigarh 174103 Himachal Prades India

This paper proposes a novel algorithm of segmentation of diseased part in apple leaf images. In agriculture-based image processing, leaf diseases segmentation is the main processing task for region of interest extraction. It is also extremely important to segment the plant leaf from the background in case on live images. Automated segmentation of plant leaves from the background is a common challenge in the processing of plant images. Although numerous methods have been proposed, still it is tough to segment the diseased part of the leaf from the live leaf images accurately by one particular method. In the proposed work, leaves of apple having different background have been segmented. Firstly, the leaves have been enhanced by using Brightness-Preserving Dynamic Fuzzy Histogram Equalization technique and then the extraction of diseased apple leaf part is done using a novel extraction algorithm. Real-time plant leaf database is used to validate the proposed approach. The results of the proposed novel methodology give better results when compared to existing segmentation algorithms. From the segmented apple leaves, color and texture features are extracted which are further classified as marsonina coronaria or apple scab using different machine learning classifiers. Best accuracy of 96.4% is achieved using k nearest neighbor classifier.

关键词： Image segmentation brightness preserving dynamic fuzzy histogram equalization k-means clustering algorithm support vector machine k nearest neighbor Naive Bayes

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Real-Time Fire Detection Method for Electric Vehicle Charging Stations Based on Machine Vision

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WORLD ELECTRIC VEHICLE JOURNAL 2022年第2期13卷 23页

作者： Zhang, Shiyu Yang, Qing Gao, Yuchen Gao, Dexin Qingdao Univ Sci Technol Coll Automat & Elect Engn Qingdao 266061 Peoples R China Qingdao Univ Sci Technol Coll Informat Sci & Technol Qingdao 266061 Peoples R China

During the charging process of electric vehicles (EV), the circuit inside the charger plug is connected in series, the charger input voltage does not match the rated input voltage, the temperature caused by the severe heating of the charging time is too high for too long, and other factors are very likely to trigger a fire in the vehicle charging pile. In this paper, an improved You Only Look Once v4 (YOLOv4) real-time target detection algorithm based on machine vision is proposed to monitor the site based on existing monitoring equipment, transmit live video information in real-time, expand the monitoring range, and significantly reduce the cost of use. During the experiment, the improved neural network model was trained by a homemade fire video image dataset, and a k-means clustering algorithm iwasintroduced to recalculate the anchor frame size for the specific object of flame;the existing dataset was used to perform multiple divisions by using a tenfold cross-validation algorithm, thus avoiding the selection of chance hyperparameters and models that do not have generalization ability because of special divisions. The experimental results show that the improved algorithm is fast and accurate in detecting large-size flames in real-time and small-size flames at the beginning of a fire, with a detection speed of 43 fps/s, mAP value of 91.53%, and F1 value of 0.91. Compared with YOLOv3 and YOLOv4 models, the improved model is sensitive to detecting different sizes of flames. It can suppress false alarms well in a variety of complex lighting environments. The prediction frame size fits the area where the target is located, the detection accuracy remains stable, and the comprehensive performance of the network model is significantly improved to meet the demand of real-time monitoring. It is significant for developing the EV industry and enhancing emergency response capability.

关键词： electric vehicle charging stations machine vision fire detection YOLOv4 k-means clustering algorithm

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PRkAR2A-derived circular RNAs promote the malignant transformation of colitis and distinguish patients with colitis-associated colorectal cancer

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CLINICAL AND TRANSLATIONAL MEDICINE 2022年第2期12卷 e683页

作者： Wan, Daiwei Wang, Sentai Xu, Zhihua Zan, Xinquan Liu, Fei Han, Ye Jiang, Min Wu, Airong Zhi, Qiaoming Soochow Univ Dept Gen Surg Affiliated Hosp 1 Suzhou 215006 Peoples R China Soochow Univ Dept Gastroenterol Affiliated Hosp 1 Suzhou 215006 Peoples R China Soochow Univ Dept Oncol Affiliated Hosp 1 Suzhou 215006 Peoples R China

Background Emerging studies have proved that colonic inflammation caused by refractory inflammatory bowel disease (IBD) can initiate the colitis-associated cancer (CAC), but the transition from inflammation to carcinoma is still largely unknown. Methods In this study, mouse colitis and CAC models were established, and the RNA-seq by circRNA microarray was employed to identify the differentially expressed circRNAs and mRNAs in different comparisons (DSS vs. NC and AOM/DSS vs. DSS). The bioinformatics analyses were used to search the common characteristics in mouse colitis and CAC. Results The k-means clustering algorithm packaged these differential expressed circRNAs into subgroup analysis, and the data strongly implied that mmu_circ_0001109 closely correlated to the pro-inflammatory signals, while mmu_circ_0001845 was significantly associated with the Wnt signalling pathway. Our subsequent data in vivo and in vitro confirmed that mmu_circ_0001109 could exacerbate the colitis by up-regulating the Jak-STAT3 and NF-kappa B signalling pathways, and mmu_circ_0001845 promoted the CAC transformation through the Wnt signalling pathway. By RNA blasting between mice and humans, the human RTEL1- and PRkAR2A-derived circRNAs, which might be considered as homeotic circRNAs of mmu_circ_0001109 and mmu_circ_0001845, respectively, were identified. The clinical data revealed that RTEL1-derived circRNAs had no clinical significance in human IBD and CAC. However, three PRkAR2A-derived circRNAs, which had the high RNA similarities to mmu_circ_0001845, were remarkably up-regulated in CAC tissue samples and promoted the transition from colitis to CAC. Conclusions Our results suggested that these human PRkAR2A-derived circRNAs could be novel candidates for distinguishing CAC patients and predicted the prognosis of CAC.

关键词： circular RNA colitis-associated colorectal cancer PRkAR2A RNA sequencing k-means clustering algorithm

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Robust Static Transmission Expansion Planning Considering Contingency and Wind Power Generation

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JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS 2020年第2期31卷 461-470页

作者： de Paula, Arthur N. de Oliveira, Edimar J. de Oliveira, Leonardo W. Honorio, Leonardo M. Univ Fed Juiz de Fora Rua Jose Lourenco Kelmer S-N BR-36036900 Juiz De Fora MG Brazil

This paper proposes a framework based on the Benders decomposition to obtain a scenario-based robust static transmission expansion planning by considering N-1 security criterion, transmission losses and uncertainties in wind power generation. The model is solved by a bi-level approach that seeks to minimize investment cost as well as penalty costs of wind spill and load curtailment. The wind uncertainty is modeled by grouped historical wind series through k-means clustering technique maintaining the wind correlation between different geographic regions. Case studies are performed in the well-known power systems: IEEE-RTS 24-bus test system and an equivalent Brazilian Southern 46-bus system. In addition, a detailed tutorial case is also presented with a modified version of Garver 6-bus test system.

关键词： Transmission expansion planning Transmission active losses Benders decomposition N-1 security criterion Wind power uncertainty k-means clustering algorithm

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Automatic data clustering using nature-inspired symbiotic organism search algorithm

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kNOWLEDGE-BASED SYSTEMS 2019年第Jan.1期163卷 546-557页

作者： Zhou, Yongquan Wu, Haizhou Luo, Qifang Abdel-Baset, Mohamed Guangxi Univ Nationalities Coll Informat Sci & Engn Nanning 530006 Peoples R China Key Lab Guangxi High Sch Complex Syst & Computat Nanning 530006 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Jiangsu Peoples R China Zagazig Univ Fac Comp & Informat El Zera Sq Zagazig 44519 Sharqiyah Egypt

The symbiotic organism search (SOS) is a recently proposed metaheuristic optimization algorithm that simulates the symbiotic interaction strategies adopted by organisms to survive and propagate in an ecosystem. clustering is a popular data analysis and data mining technique, and k-means clustering is one of the most commonly used methods. However, its effectiveness is highly dependent on the initial solution, and the algorithm may become trapped around local optima. In view of these drawbacks of the k-means method, this paper describes the use of the SOS algorithm to solve clustering problems. Ten standard datasets from the UCI Machine Learning Repository are used to evaluate the effectiveness of SOS against that of optimization algorithms including differential evolution, cuckoo search, flower pollination, particle swarm optimization, artificial bee colony, multi-verse optimizer, and k-means. Experimental results show that the SOS algorithm not only achieves superior accuracy, but also exhibits a higher level of stability. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Symbiotic organism search k-means clustering algorithm clustering analysis Metaheuristic optimization

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