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Lightweight Neural Network-Based Infrared Image and Anomalous Heat Region Recognition for Electrical Equipment 18th

Lightweight Neural Network-Based Infrared Image and Anomalou...

引用

18th Annual Conference of China Electrotechnical Society, ACCES 2023

作者： Han, Zhikai Zhang, Jinglei Jia, Xin School of Electrical Engineering and Automation Tianjin University of Technology Tianjin300384 China Tianjin Key Laboratory of Complex System Control Theory and Application Tianjin300384 China

ISBN: (纸本)9789819710676

In order to accurately and quickly recognize electrical devices and abnormal heating parts under infrared conditions, we propose a lightweight neural network-based method for identifying electrical devices and their abnormal heating parts. First, we construct a fast target detection network for real-time detection. Second, we propose an efficient feature retention module to avoid the feature loss problem in multi-layer convolutional operations. In addition, we introduce an attention mechanism based on the coordinate information interaction strategy to localize the target more accurately. Finally, we use a lightweight weighted bidirectional feature pyramid network to improve the detection accuracy at different scales. Experimental results on a dataset of 12109 electrical infrared images containing six types of electrical devices and anomalously heated areas show that the proposed method achieves good results, with an average recognition accuracy of 93.9% and a processing speed of 222 frames/second. © Beijing Paike Culture Commu. Co., Ltd. 2024.

关键词： Infrared imaging

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Master-slave game-based optimal operation of microgrids containing customer-owned photovoltaics 8

Master-slave game-based optimal operation of microgrids cont...

引用

8th International Conference on energy system, Electricity, and Power, ESEP 2023

作者： Liu, Xinyue Ji, Yuehui Shi, Shaobo Liu, Junjie Gao, Qiang Xu, Zhao Tianjin Key Laboratory of Complex System Control Theory and Application TJ 300384 China School of Electrical Engineering and Automation Tianjin University of Technology TJ 300384 China

ISBN: (数字)9781510679801

ISBN: (纸本)9781510679795

Due to the dispersed and disorderly feathers of distributed photovoltaic (PV) power generation on the user side, the difficulties of the low-carbon transformation of the power grid and sustainable development of large-scale distributed PV power generation is increased. Based on a master-slave game, this article proposes an optimizing scheme for the operation of microgrids, where the users built PV and shared energy storages. Firstly, this paper introduces the operational framework of the system and the functions of micro-network operator (MNO) and load aggregator (LA). The optimization operation models for MNO and LA are modelled in the framework of Stackelberg master-slave game. The game relationship between MNO and LA is analyzed. Yalmip tool with the CPLEX solver are introduced to solve the differential evolution algorithm. Final, the effectiveness of the proposed method is verified in numerical examples. © 2024 SPIE.

关键词： energy storage

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Tissue Surface-enhanced Raman Spectroscopy Combined with Machine Learning Algorithms for Rapid Detection of Alveolar Echinococcosis 25

Tissue Surface-enhanced Raman Spectroscopy Combined with Mac...

引用

25th IEEE China Conference on system Simulation Technology and its application, CCSSTA 2024

作者： Huang, Jiahui Zheng, Xiangxiang Lv, Guodong Li, Xiaojing Wu, Guohua Xu, Liang Tianjin University of Technology Tianjin Key Laboratory of Complex System Control Theory and Application Tianjin China The First Affiliated Hospital of Xinjiang Medical University State Key Laboratory of Pathogenesis Prevention and Treatment of High Incidence Diseases in Central Asia Urumqi China Beijing University of Posts and Telecommunications School of Electronic Engineering Beijing China

ISBN: (纸本)9798350366600

Alveolar echinococcosis (AE) remains a serious worldwide public health problem as a very harmful human tapeworm disease. Rapid and accurate diagnosis of AE is crucial for treatment and postoperative recovery. This study explored the applicability of tissue surface Raman spectroscopy (SERS) combined with machine learning for rapid AE detection. Specifically, liver tissue section samples were obtained by creating an AE mouse model and SERS spectra were acquired. Five kinds of machine learning methods were used to construct AE diagnosis model, among which the linear kernel support vector machine (SVM) model had the best classification result, and the accuracy rate reached 95.7%. Then, the linear SVM model was subjected to feature significance analysis, which demonstrated that the biochemicals corresponding to the spectra of 641,716 and 1372 cm-1 might be potential biomarkers. Gaussian noise is further added to test the noise interference resistance of different machine learning algorithms, and it is found that SVM with Gaussian radial basis function (RBF) kernel function has better noise interference resistance. The results show that tissue SERS spectroscopy combined with SVM algorithm for rapid detection of AE has a promising application. © 2024 IEEE.

关键词： Raman scattering

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Short Term Power Load Forecasting Method Based on VMD-ResNet 5

Short Term Power Load Forecasting Method Based on VMD-ResNet

引用

5th International Conference on Power and energy Technology, ICPET 2023

作者： Li, Dahua Chen, Wenkai Wang, Sun Tian, He Chen, Hao Tianjin University of Technology Tianjin Key Laboratory of Complex System Control Theory and Application Tianjin China Tianjin Tongshi Zhiyan Technology Co. Ltd Tianjin China State Grid Tianjin Jinghai Power Supply Co. Ltd Tianjin China

ISBN: (纸本)9798350339673

The load of a region is often influenced by multiple factors, resulting in strong nonlinearity and temporal characteristics of load data, which increases the difficulty of accurately predicting load. To improve the accuracy of power load forecasting, this paper proposes a short-term power load forecasting method based on VMD-ResNet. Firstly, at the data level, the Variational Modal Decomposition (VMD) method is used to decompose historical loads and temperatures to obtain a more stable Intrinsic Mode Function (IMF). Then, different IMFs are integrated and input into the first stage prediction model along with seasonal and holiday information to extract internal features of the data for preliminary prediction;The second stage is a Deep Residual Network (ResNet) that uses the forecast data from the first stage as input to predict the load over the next 24 hours. The data adopts real load data from Panama City from January 2015 to June 2020. The average absolute percentage error (MAPE) of the VMD-ResNet load forecasting model is 1.3%. Compared with load forecasting networks such as SVR, LSTM, CNN-LSTM, ResNet, etc., the MAPE of the test set has decreased by 2.36%, 1.8%, 1.2%, and 0.8%, respectively. Experiments have shown that VMD-ResNet can deeply mine data characteristics and has good predictive performance. © 2023 IEEE.

关键词： deep learning deep residual network load forecasting variational mode decomposition

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Tissue Surface-enhanced Raman Spectroscopy Combined with Machine Learning Algorithms for Rapid Detection of Alveolar Echinococcosis

Tissue Surface-enhanced Raman Spectroscopy Combined with Mac...

引用

system Simulation Technology and its application (CCSSTA), IEEE China Conference on

作者： Jiahui Huang Xiangxiang Zheng Guodong Lv Xiaojing Li Guohua Wu Liang Xu Tianjin Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology Tianjin China State Key Laboratory of Pathogenesis Prevention and Treatment of High Incidence Diseases in Central Asia The First Affiliated Hospital of Xinjiang Medical University Urumqi China School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350366600

ISBN: (纸本)9798350366617

Alveolar echinococcosis (AE) remains a serious worldwide public health problem as a very harmful human tapeworm disease. Rapid and accurate diagnosis of AE is crucial for treatment and postoperative recovery. This study explored the applicability of tissue surface Raman spectroscopy (SERS) combined with machine learning for rapid AE detection. Specifically, liver tissue section samples were obtained by creating an AE mouse model and SERS spectra were acquired. Five kinds of machine learning methods were used to construct AE diagnosis model, among which the linear kernel support vector machine (SVM) model had the best classification result, and the accuracy rate reached $95.7 \%$. Then, the linear SVM model was subjected to feature significance analysis, which demonstrated that the biochemicals corresponding to the spectra of 641,716 and $1372 \mathrm{~cm}^{-1}$ might be potential biomarkers. Gaussian noise is further added to test the noise interference resistance of different machine learning algorithms, and it is found that SVM with Gaussian radial basis function (RBF) kernel function has better noise interference resistance. The results show that tissue SERS spectroscopy combined with SVM algorithm for rapid detection of AE has a promising application.

关键词： Support vector machines Analytical models Machine learning algorithms Accuracy Biological system modeling Surface resistance Raman scattering Interference Machine learning Kernel

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Short Term Power Load Forecasting Method Based on VMD-ResNet

Short Term Power Load Forecasting Method Based on VMD-ResNet

引用

International Conference on Power and energy Technology (ICPET)

作者： Dahua Li Wenkai Chen Sun Wang He Tian Hao Chen Tianjin Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology Tianjin China Tianjin Tongshi Zhiyan Technology Co. Ltd Tianjin China State Grid Tianjin Jinghai Power Supply Co. Ltd Tianjin China

The load of a region is often influenced by multiple factors, resulting in strong nonlinearity and temporal characteristics of load data, which increases the difficulty of accurately predicting load. To improve the accuracy of power load forecasting, this paper proposes a short-term power load forecasting method based on VMD-ResNet. Firstly, at the data level, the Variational Modal Decomposition (VMD) method is used to decompose historical loads and temperatures to obtain a more stable Intrinsic Mode Function (IMF). Then, different IMFs are integrated and input into the first stage prediction model along with seasonal and holiday information to extract internal features of the data for preliminary prediction; The second stage is a Deep Residual Network (ResNet) that uses the forecast data from the first stage as input to predict the load over the next 24 hours. The data adopts real load data from Panama City from January 2015 to June 2020. The average absolute percentage error (MAPE) of the VMD-ResNet load forecasting model is 1.3%. Compared with load forecasting networks such as SVR, LSTM, CNN-LSTM, ResNet, etc., the MAPE of the test set has decreased by 2.36%, 1.8%, 1.2%, and 0.8%, respectively. Experiments have shown that VMD-ResNet can deeply mine data characteristics and has good predictive performance.

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Design of a Capsule Robot system for Gastric Hemorrhage Detection using Luminol 20

Design of a Capsule Robot System for Gastric Hemorrhage Dete...

引用

20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： Mao, Yuchen Guo, Jian Guo, Shuxiang Fu, Qiang Tianjin University of Technology Tianjin Key Laboratory for Control Theory & Application Complicated Systems Intelligent Robot Laboratory School of Electrical Engineering and Automation Binshui Xidao Extension 391 Tianjin300384 China Shenzhen Institute of Advanced Biomedical Robot Co. Ltd No.12 Ganli Sixth Road Jihua Street Longgang District Shenzhen518100 China Key Laboratory of Convergence Medical Engineering System and Healthcare Technology The Ministry of Industry and Information Technology School of Life Science Beijing Institute of Technology No.5 Zhongguancun South Street Beijing100081 China

ISBN: (纸本)9798350320831

Gastric hemorrhage in upper gastrointestinal bleeding is an obvious indicator of deteriorating gastric disease. This paper proposes a capsule robot system for detecting bleeding in the stomach. The patient ingests a capsule robot containing luminol reagent, which detects bleeding in the stomach via chemiluminescence. The MCU receives the optical sensor signal, and the data is transmitted via radio frequency. The external lower computer receives and forwards the data to the upper computer for processing and display. The data is used to determine the presence and amount of stomach bleeding. Chemiluminescence is an excellent sensor, requiring no external energy supply, and demonstrating excellent sensitivity and selectivity. Combining chemical and electronic sensors presents opportunities for further development. Capsule robot detection reduces the discomfort and risk of secondary injury associated with traditional endoscopy. This paper establishes an experimental platform using porcine blood to simulate human blood, demonstrating the feasibility of the system. © 2023 IEEE.

关键词： Chemiluminescence

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Image Segmentation Method of Crop Diseases Based on Improved Segnet Neural Network 17

Image Segmentation Method of Crop Diseases Based on Improved...

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17th IEEE International Conference on Mechatronics and Automation, ICMA 2020

作者： Yue, Youjun Li, Xuesong Zhao, Hui Wang, Hongjun Tianjin University of Technology Tianjin Key Laboratory of Complex System Control Theory and Application Tianjin300384 China

ISBN: (纸本)9781728164151

In crop disease image segmentation, traditional convolutional neural networks have the problem of low image accuracy. For this reason, this paper proposes a segmentation method that combines conditional random fields with segnet networks. First, input the training set in the data set into the segnet network for training, and obtain the initial segmentation result and the network with updated parameters;secondly, the image segmented by the segnet network is sent to the conditional random field in the form of pixels and classification vectors corresponding to the pixels At the same time, construct an energy function to represent the relative relationship between pixels, and then keep training;in order to get the conditional random field in the training set, you need to adjust and optimize the image segmented by the segnet network to get it;then in the verification set, the segnet network and the weight parameters of the conditional random field model are further adjusted and optimized to obtain the final conditional random field and segnet network;finally, the final conditional random field and segnet network are applied to the crop disease image segmentation. The test results show that compared with the Otsu threshold and Grabcut segmentation methods, the segmentation accuracy P, recall rate R, and comprehensive evaluation index F of this method are all the maximum, and the average processing time of a single image is 0.86 s;compared with pspnet and deeplab methods, Under the premise that the segmentation accuracy P, the recall rate R, and the comprehensive evaluation index F are basically unchanged, the average processing time of a single image is significantly reduced. The results show that the best comprehensive effect can be achieved by segmenting crop disease images in complex environments using the method in this paper. © 2020 IEEE.

关键词： Image segmentation

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Fault Type identification in Transmission Lines based on VMD-FE and IGWO-KELM 17

Fault Type identification in Transmission Lines based on VMD...

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17th IEEE International Conference on Mechatronics and Automation, ICMA 2020

作者： Yue, Youjun Li, Lixin Wang, Hongjun Zhao, Hui Tianjin University of Technology Tianjin Key Laboratory of Complex System Control Theory and Application Tianjin300384 China

ISBN: (纸本)9781728164151

In view of the damage of transmission line short-circuit faults and low fault identification rate, A novel fault identification method based on variational mode decomposition-fuzzy entropy and improved grey wolf algorithm - kernel extreme learning machine is proposed. First, the VMD is used to decompose the three-phase current after the fault to obtain a series of three-phase stable modal components;secondly, the fuzzy entropy value of each group of components is calculated separately as the fault characteristics of the transmission line;Finally, The fault characteristics of the transmission line are input to the kernel extreme learning machine for training to obtain the diagnosis model. The experimental results show that the method is not affected by the distance of the fault, the initial phase angle of the fault and the transition resistance, and can accurately identify the fault type. © 2020 IEEE.

关键词： Variational mode decomposition

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application of Hybrid energy Storage Device in High Power Direct-current Power Supply 17

Application of Hybrid Energy Storage Device in High Power Di...

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17th IEEE International Conference on Mechatronics and Automation, ICMA 2020

作者： Liu, Yue Wang, Chunjie Chen, Peng Yin, Jinliang School of Electrical and Electronic Engineering Tianjin University of Technology Tianjin Complex System Control Theory and Application Key Laboratory Tianjin China

ISBN: (纸本)9781728164151

The drop-away current of 1500V low-voltage direct-current circuit breaker used in metro DC traction power supply system needs to be checked regularly. The instantaneous power is very high when the detection device is working, and the field power supply is often difficult to meet the requirements. Therefore, an energy storage unit is needed to supply power to the detector. In this paper, a design scheme of high-power DC power supply with supercapacitor and battery as energy storage is proposed, and the energy management and discharge control strategies are studied in theory and simulation, which verify the effectiveness and feasibility of the scheme. © 2020 IEEE.

关键词： Drops

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