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7th Asia Conference on Power and Electrical engineering, ACPEE 2022

作者： Wu, Yingying Yan, Hongxia Chen, Yiyao Gong, Tiangang Xue, Yingnan Du, Xinhui Taiyuan University of Technology School of Electrical and Power Engineering Taiyuan China Shanxi Fengxing Measurement and Control Co.Ltd General Manager's Office Taiyuan China Shanxi Electric Power Survey and Design Institute International Engineering Branch Taiyuan China

ISBN: (纸本)9781665418195

A wavelet neural network (WNN) prediction model based on improved wolf pack algorithm(WPA) is proposed to meet the demand of Shanxi's spot market, which requires power sales companies to "quote quantity but not offer price". Customers are classified by considering the differences in electricity consumption characteristics of different industries. The bidirectional comparison method is used to preprocess the original data, which can effectively reduce the errors in the process of data measurement and acquisition. Using WNN to forecast the customer's day-ahead electrical load. Meantime, the intelligent algorithm is used to optimize the network parameters and strengthen the prediction performance of the WNN. The wolf pack algorithm is used to optimize the system, and the walking mode of the wolf pack is improved, which further to improve the optimization ability of the system. An example shows that the model is more accurate in short-term load prediction. Compared with before the improvement, the prediction accuracy has been improved by nearly 2%, and the accuracy has reached 96.4%, which can provide a strong guarantee of the electricity sales company to master the customer load curve, participate in the spot market, and improve its competitiveness. © 2022 IEEE.

关键词： Sales

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Simple Full-Spectrum Correlated k-Distribution Model based on Multilayer Perceptron

arXiv

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arXiv 2024年

作者： Wang, Xin Kuang, Yucheng Wang, Chaojun Di, Hongyuan He, Boshu School of Information and Communication Engineering Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science and Technology University Beijing102206 China Beijing100176 China Institute of Combustion and Thermal Systems School of Mechanical Electronic and Control Engineering Beijing Jiaotong University Beijing100044 China

While neural networks have been successfully applied to the full-spectrum k-distribution (FSCK) method at a large range of thermodynamics with k-values predicted by a trained multilayer perceptron (MLP) model, the required a-values still need to be calculated on-the-fly, which theoretically degrades the FSCK method and may lead to errors. On the other hand, too complicated structure of the current MLP model inevitably slows down the calculation efficiency. Therefore, to compensate among accuracy, efficiency and storage, the simple MLP designed based on the nature of FSCK method are developed, i.e., the simple FSCK MLP (SFM) model, from which those correlated k-values and corresponding ka-values can be efficiently obtained. Several test cases have been carried out to compare the developed SFM model and other FSCK tools including look-up tables and traditional FSCK MLP (TFM) model. Results show that the SFM model can achieve excellent accuracy that is even better than look-up tables at a tiny computational cost that is far less than that of TFM model. Considering accuracy, efficiency and portability, the SFM model is not only an excellent tool for the prediction of spectral properties, but also provides a method to reduce the errors due to nonlinear effects. Copyright © 2024, The Authors. All rights reserved.

关键词： Multilayer neural networks

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General Simultaneous Localization and Mapping Scheme for mmWave Communication Systems

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IEEE Internet of Things Journal 2024年第12期11卷 22521-22536页

作者： Du, Tao Yang, Jie Wen, Chao-Kai Xia, Shuqiang Jin, Shi Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Southeast University Key Laboratory Of Measurement And Control Of Complex Systems Of Engineering Ministry Of Education The Frontiers Science Center For Mobile Information Communication And Security Nanjing210096 China Institute Of Communications Engineering National Sun Yat-sen University Kaohsiung80424 Taiwan Zte Corporation The State Key Laboratory Of Mobile Network And Mobile Multimedia Technology Shenzhen518055 China Southeast University National Mobile Communications Research Laboratory The Frontiers Science Center For Mobile Information Communication And Security Nanjing210096 China

Utilizing high-resolution antenna arrays and wide bandwidth of the millimeter-wave (mmWave) spectrum in 5G new radio (NR) mmWave communication systems holds the potential for high-throughput data transmission while enabling user localization and environmental mapping. However, the majority of existing simultaneous localization and mapping (SLAM) algorithms rely on methods akin to the extended Kalman filter for generating initial map features. These methods prove ineffective when the measurement dimension is insufficient. Furthermore, there is a notable absence of research exploring mmWave prototype systems to evaluate and compare the performance and viability of various SLAM algorithms. To address these challenges, we propose an innovative probability hypothesis density (PHD) generation scheme for birth events and have developed a prototype system. Our approach, referred to as PHD-SLAM, exhibits remarkable effectiveness even in scenarios where the measurement dimension falls short of map features. This means it can function seamlessly with only delay or angle information available. Additionally, we have designed a 28GHz mmWave beam scanning prototype system that leverages the 5G NR frame for accomplishing SLAM algorithms. Following this, we conducted extensive simulations and experimental evaluations to gauge the performance of several leading-edge SLAM algorithms under diverse mmWave circumstances, encompassing PHD-based and belief propagation (BP) SLAM algorithms. Our analysis reveals that both PHD and BP SLAM can achieve agent localization precision within a decimeter and mapping precision within a meter, capitalizing on the angle parameters of mmWave signals. While BP SLAM showcases reduced computational demand, its estimation precision is marginally inferior to that of PHD-SLAM. © 2014 IEEE.

关键词： Belief propagation

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Interpretable Bearing Stage Division based on Shapelet and SVM

Interpretable Bearing Stage Division based on Shapelet and S...

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Data Driven control and Learning Systems (DDCLS)

作者： Xinye Li Xiaoyu Yang Hong Zhang Yanwei Wang Ying Zheng The Belt and Road Joint Laboratory on Measurement and Control Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Mechanical & Electrical Engineering Wuhan Institute of Technology Wuhan

The stage division of bearings is of great significance to ensure the safe operation of heavy machinery. By dividing bearing stages, early degradation points can be identified for bearing remaining useful life (RUL) prediction, and the accuracy of RUL prediction can be improved. Although the existing stage division methods can effectively divide the stages of bearings, most of the methods lack interpretability. In this paper, an interpretable rolling bearing stage division method based on shapelet and Support vector machine (SVM) is proposed. Firstly, a Manhattan distance-based shapelet is used to extract the bearing degradation features, which better discriminates the difference of bearing signals in different health states. Then, SVM is utilized to stage the degradation features. Finally, shapelet evolution diagrams are constructed to characterize the rolling bearing vibration signals at different stages, visually interpreting the bearing stage classification results. Based on the experimental study on the PRONOSTIA platform, the effectiveness and interpretability of the proposed method are verified. The results show that the proposed method can effectively classify the bearing health stages and visualize the interpretation of the stage classification results.

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Satellite Power System Anomaly Detection based on Spatio-Temporal Uncertainty Fusion

Satellite Power System Anomaly Detection based on Spatio-Tem...

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Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Zhipeng Wang Yanjiao Gao Jingyue Pang Yuchen Song Yu Peng School of Electronics and Information Engineering Harbin Institute of Technology Harbin China School of Measurement and Control Technology and Communication Engineering Harbin University of Science and Technology Harbin China School of Artificial Intelligence Chongqing Technology and Business University Chongqing China

Satellites have been widely used for military and civil applications. The power system is critical for the satellite’s operating safety. However, satellite power system anomaly monitoring is faced with challenges like large amounts of data, insufficient anomaly samples, and complex operating conditions. To address these challenging issues, this paper proposes a data-driven anomaly detection method that fuses the time-related historical data and spatial-associated sub-system monitor data. Firstly, the probabilistic prediction model is used to establish the prediction model based on time and space The corresponding prediction uncertainties of the temporal and spatial models are quantitatively represented. Then, the stochastic distribution model is introduced to fuse the two uncertainties to obtain the predicted mean value and fusion uncertainty of the current power system state. The mean value is taken as the expected result of the state, and the fused uncertainty interval is taken as the threshold of abnormal judgment to realize the detection of the abnormal condition. The telemetry data of the actual satellite power system are used for experimental and comparative analysis. The results show that the false negative rate of anomaly detection results can be effectively reduced, and the detection accuracy can be improved through the fusion of spatio-temporal uncertainty.

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Data Augmentation Integrated with Feature-Enhanced Convolutional Neural Network for Imbalanced Fault Diagnosis in Rolling Bearings

Data Augmentation Integrated with Feature-Enhanced Convoluti...

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Data Driven control and Learning Systems (DDCLS)

作者： Li Wang Qilin Qu Yanwei Wang David Shan-Hill Wong Ying Zheng China -Belt and Road Joint Laboratory On Measurement and Control Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Mechanical & Electrical Engineering Wuhan Institute of Technology Wuhan Department of Chemical Engineering National Tsing-Hua University Hsinchu Taiwan

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Imbalance in fault samples is a key problem with most rolling bearing datasets in real industrial processes, limiting the performance of fault detection. In addition, existing fault diagnosis methods for rolling bearings do not adopt a modular division between the initial layer and the classification layer. This lack of modularization inhibits the full activation of the network's feature extraction capabilities and impedes subsequent model improvements. In this paper, a novel fault diagnosis method based on data augmentation integrated with a feature-enhanced convolutional neural network (DA-FECNN) is proposed. First, Variational Autoencoder-Generative Adversarial Network (VAE-GAN) is utilized to expand the number of fault samples and improve the diversity of few-shot. Second, a uniform feature-enhanced structure is designed with a feature extraction module, a multi-scale classification module, and a reconstruction module. The network's ability on feature extraction is improved by focusing on high quality features. Thereby, accurate fault classification is realized simultaneously. Experiments on real world data show the effectiveness of the proposed method on fault diagnosis task.

关键词： Fault diagnosis Learning systems Accuracy Limiting Rolling bearings Feature extraction Data augmentation

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Simulation of High Temperature Thin Film Thermocouple on Engine Blade Surface

Simulation of High Temperature Thin Film Thermocouple on Eng...

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2022 International Conference on Mechatronics and Automation technology, ICMAT 2022

作者： Zhang, Meiju Tian, Bian Ma, Rong Huang, Manguo Liang, Xiaobo Liu, Jiangjiang Zhang, Zhongkai Beijing Changcheng Aeronautic Measurement and Control Technology Research Institute Beijing101111 China Aviation Key Laboratory of Science and Technology on Special Condition Monitoring Sensor Technology Beijing101111 China State Key Laboratory for Mechanical Manufacturing Systems Engineering Xi'an Jiaotong University Xi'an710049 China

ISBN: (纸本)9781643683669

In order to avoid the instability flutter of aero-engine blade and prevent the failure of thin film thermocouple caused by the blade in complex environment, the structural strength of the thermocouple on the actual blade surface was studied in this paper. Firstly, based on the 3D scanning of the real blade, the blade model is established after the point cloud graphics. The surface thermocouple model of the blade is established in Solidworks for simulation, and the modal and harmonic response analysis of the blade is simulated. Secondly, the vibration of the curved thin film thermocouple and the stress distribution of the blade under the acceleration shock were studied in Comsol, and the failure weakness was analyzed to avoid the failure of the thin film thermocouple caused by the bending, fracture or shedding of the thin film thermocouple on the blade due to vibration and shock, providing the most suitable thermocouple selection for the engine blade. © 2022 The authors and IOS Press.

关键词： Thermocouples

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Research and Suggestions for Evaluation Metrics of On-board Video Desensitization

Research and Suggestions for Evaluation Metrics of On-board ...

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Information Science, Parallel and Distributed Systems (ISPDS), International Conference on

作者： Xiaolong Huang Te Hu Haojie Ji Yupeng Chen Wangdong Lu Peng He Guowei Sun Jingyan Wang State Key Laboratory of Intelligent Vehicle Safety Technology Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science & Technology University Beijing China Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science & Technology University Beijing China China Automotive Engineering Research Institute Co. Ltd Chongqing China Zhongtong Bus Holding Co. Ltd Liaocheng China

ISBN: (数字)9798350352719

ISBN: (纸本)9798350352726

Sensitive data leakage has become an urgent problem to be solved as more images based functionalities are being developed in vehicles. However, there is a scarcity of evaluation for on-board videos data desensitization. This research analyzes on-board video desensitization process including the image pre-processing stage, sensitive area localization stage and sensitive area desensitization stage. Considering that, this paper presents several evaluation methods and metrics of sensitive target detection performance, privacy-utility evaluation of video file metadata and image, so as to provide reference for the related research on the desensitization evaluation of on-board video.

关键词： measurement Location awareness Information science Target recognition Current measurement Distributed databases Object detection

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Classification of the Heartbeats in Electrocardiograms with K-Nearest Neighbors Algorithm, Random Forests, and Support Vector Machines - A Pilot Study 21st

Classification of the Heartbeats in Electrocardiograms with ...

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21st Scientific Conference on Medical and Sport Technologies, 2024

作者： Vural, Muzaffer Samed Heryan, Katarzyna Sieciński, Szymon Bilko, Pawel Grzegorzek, Marcin Faculty of Automatic Control Electrical Engineering and Biomedical Engineering Department of Measurement and Electronics AGH University of Science and Technology Aleja Adama Mickiewicza 30 Kraków30-059 Poland Institute of Medical Informatics University of Lübeck Ratzeburger Allee 160 Lübeck23562 Germany Faculty of Biomedical Engineering Department of Medical Informatics and Artificial Intelligence Silesian University of Technology Franklina Delano Roosevelta 40 Zabrze41-800 Poland Department of Clinical Engineering Academy of Silesia Rolna 43 Katowice40-555 Poland German Research Center for Artificial Intelligence Ratzeburger Allee 160 Lübeck23562 Germany

ISBN: (纸本)9783031821424

Electrocardiography (ECG) is vital for diagnosing and monitoring cardiovascular diseases. Automatic interpretation of ECG signals, particularly classifying heartbeats, is essential in diagnosing these conditions. We compared the performance of three classifiers (K-Nearest Neighbors (KNN), Random Forests (RF), and Support Vector Machine (SVM)) using heartbeat and ECG signal features. We trained the classifiers on 173 heartbeats from 11 recordings in the MIT-BIH Arrhythmia (MITDB) Database. To address imbalance, we used SMOTE (Synthetic Minority Over-sampling Technique) to increase the number of abnormal heartbeats. We divided the data into 70% for training set and 30% for testing set, evaluating performance through 10-fold cross-validation. Results demonstrated state-of-the-art performance for KNN, RF, and SVM. KNN achieved an overall accuracy, positive predictive value, and sensitivity of 0.977, 0.978, and 0.978, respectively. RF achieved an overall accuracy, precision, and sensitivity of 0.971, 0.972, and 0.971, respectively. SVM achieved a recall of 0.988, 0.989, and 0.988. SVM exhibited the highest overall performance in heartbeat classification (0.99), followed by KNN (0.98) and RF (0.97). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Support vector machines

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Aero-Engine Remaining Useful Life Prediction Using A Multi-Dimensional Convolutional Feature Fusion Framework

Aero-Engine Remaining Useful Life Prediction Using A Multi-D...

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Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Liangcheng Wang Yang Wang Hong Zhang Yanwei Wang Ying Zheng The Belt and Road Joint Laboratory on Measurement and Control Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Mechanical & Electrical Engineering Wuhan Institute of Technology Wuhan Hubei

Accurate prediction of aero-engine remaining useful life (RUL) is essential for providing reliable maintenance or alarm decisions. The extraction of degraded features has a great impact on the accuracy of RUL prediction. This paper proposes a feature fusion framework that relies on a multi-dimensional convolutional neural network (MD-CNN). First, the data of each working condition is normalized separately to extract degradation features more effectively. Subsequently, the temporal features are extracted using the one-dimensional convolutional neural network (1D-CNN), while the spatial local features are captured through the utilization of the two-dimensional convolutional neural network (2D-CNN). Finally, the long short-term memory network (LSTM) is utilized as the prediction model to establish the mapping relationship from fusion features to RUL. The result shows that the performance in predicting using the proposed method surpasses that of the existing methods.

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