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

作者： Wang, Hongjun Li, Zhiqi Zhao, Hui Yue, Youjun Tianjin University of Technology Tianjin Key Laboratory for Control Theory and Applications in Complicated System Tianjin300384 China

ISBN: (纸本)9781728164151

The main purpose of abnormal power consumption detection for power users is to maintain the legitimate rights and interests of normal users, improve the economic benefits of power grid companies, and thereby reduce non-technical losses (NTL). In order to realize the rational use of user-side data and improve the efficiency of power audit, this article proposes an improved SVM power theft detection model based on decision tree. First of all, to address the problem of less electricity stealing data, this article combines convolutional neural networks (CNN) and generative adversarial networks (GAN) for data generation, and uses the powerful feature extraction function of CNN to extract the features of different stealing methods to guide GAN. Then the improved support vector machine (SVM) model based on decision tree (DT) detects user data. When classifying, it combines SVM and KNN for classification, which solves the point of SVM near the decision plane. For the problem of low classification accuracy, simulation experiments finally verified the effectiveness of the proposed model. © 2020 IEEE.

关键词： Decision trees

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Study on Freshwater Fish Image Recognition Integrating SPP and DenseNet Network 17

Study on Freshwater Fish Image Recognition Integrating SPP a...

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

作者： Wang, Hongjun Shi, Yangyang Yue, Youjun Zhao, Hui Tianjin University of Technology Tianjin Key Laboratory for Control Theory and Applications in Complicated System Tianjin300384 China

ISBN: (纸本)9781728164151

With the development of image processing and machine vision, there are emerging fish species classification algorithms that use machine vision technology to identify specific scenes. In order to solve the problem of poor working environment and low efficiency of traditional artificial identification of fish species, this paper proposes a deep learning method combining SPP (spatial pyramid pooling) and DenseNet (dense neural network). The model is connects the spatial pyramid pooling layer in front of the full connection layer of DenseNet network. On the basis of maintaining good recognition accuracy of DenseNet, the integrated new network can increase scale invariance and reduce overfitting. Through the identification test of 6 kinds of freshwater fish, the classification accuracy rate reached 97%. Experimental results show that the new network SPP-Densenet can effectively identify species of freshwater fish images. © 2020 IEEE.

关键词： Image recognition

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A Robust Data Reconciliation Method Based on Shape Parameters 17

A Robust Data Reconciliation Method Based on Shape Parameter...

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

作者： Ni, Jianyun Qi, Yong Wang, Hainan Zhao, Mingyang Tianjin University of Technology Tianjin Key Laboratory for Control Theory and Applications in Complicated System Tianjin300384 China

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164151

Data reconciliation plays an important role in coordinating and meeting the law of conservation in industrial processes. Generally, the actual measured industrial data is easily contaminated by different overall errors. Therefore, it is important to build a robust data reconciliation method that minimizes the impact of severe errors on the data and improves its usability. This paper presents a robust estimator based on a new robust estimation function to improve the robustness of the data reconciliation method. First, for the newly proposed robust estimator, its main robustness and its objective and impact functions are analyzed. Then, the validity of the new robust estimator in data reconciliation is demonstrated in an example. © 2020 IEEE.

关键词： Data reconciliation Industrial data Robust estimation

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UAV aerial image mosaic algorithm based on FAST-Tomasi feature and Delaunay triangulation 17

UAV aerial image mosaic algorithm based on FAST-Tomasi featu...

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

作者： Zhao, Hui Du, YaXin Wang, Hongjun Yue, Youjun Tianjin University of Technology Tianjin Key Laboratory for Control Theory and Applications in Complicated System 300384 Tianjin China

ISBN: (纸本)9781728164151

In view of the current inability to meet the requirements of fast speed and strong stability in feature extraction, and the slow speed in feature point elimination, this paper proposes an UAV remote sensing image mosaic algorithm based on FAST-Tomasi features and Delaunay triangulation. Firstly, FAST feature detection algorithm is used to accelerate Shi-Tomasi algorithm to obtain fast and accurate feature points, the feature points are described by BRIEF operator, and the phase is conducted by hamming distance. Then, Delaunay triangulation network is constructed according to the initial matching points, and the similarity between the triangles in the two images to be stitched is calculated. The triangle pair with large similarity is the matching triangle pair, so the corresponding accurate matching points are obtained. Then, the improved SPHP algorithm is used for registration. Finally, the weighted algorithm is used to fuse the image. Experimental results indicate that the algorithm retains more correct matching points, the accuracy of eliminating false matching points is higher, and the matching speed is significantly improved. © 2020 IEEE.

关键词： Hamming distance

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Intelligent Management and control System of Polluted Site Remediation Based on Internet of Things

Intelligent Management and Control System of Polluted Site R...

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2020 IEEE International Conference on Advances in Electrical Engineering and Computer applications, AEECA 2020

作者： Sun, Peiye Zhang, Baofeng Tian, Rui Zhu, Junchao Tianjin University of Technology Tianjin Key Laboratory for Control Theory Applications in Complicated System Tianjin300384 China

ISBN: (纸本)9781728165202

For the lack of effective security guarantee and risk assessment and forewarning in the process of remediation of contaminated sites, as well as the potential safety problem of secondary pollution, an intelligent management and control system of polluted site remediation based on internet of things is proposed. The safety state of the working environment can be evaluated and forewarned by real-time monitoring the toxic gas state and the physiological state of the workers. Intelligent video monitoring, GPS and GIS technology are used to realize whole-process and all-round monitoring, so as to prevent secondary pollution and ensure the safe and orderly construction of pollution remediation. The Emergency command plan can be activated quickly with one key when dangerous abnormal state occurs. The application experiment of the system shows that the system is stable and robust, and can meet the technical requirements of the remediation of contaminated sites. © 2020 IEEE.

关键词： Big data

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Short-term Photovoltaic Power Prediction based on DE-GWO-LSTM 17

Short-term Photovoltaic Power Prediction based on DE-GWO-LST...

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

作者： Zhao, Hui Zhao, Zhili Wang, Hongjun Yue, Youjun Tianjin University of Technology Tianjin Key Laboratory for Control Theory and Applications in Complicated System Tianjin300384 China

ISBN: (纸本)9781728164151

In order to solve the uncertainty and randomness of photovoltaic output prediction caused by weather factors, and improve the accuracy of short-term photovoltaic power prediction, this study uses long short-term memory (LSTM) to establish a prediction model, and trains historical time series data to predict short-term photovoltaic power. First, a layer-by-layer training algorithm is used to construct a multi-hidden-layer network structure with excellent performance in regression prediction analysis. By analyzing the characteristics of the LSTM network function, a photovoltaic output prediction model is established. Then, the grey wolf optimization (GWO) algorithm of differential evolution algorithm (DE) is proposed to optimize the connection weight of LSTM to prevent the local minimum caused by random initialization, overcome the shortcomings of back-propagation algorithm used in traditional LSTM network, and improve the prediction accuracy of LSTM network. Finally, the simulation experiment of the improved LSTM model is carried out to test the prediction effect of the model under three kinds of weather conditions to prove the effectiveness of the model. © 2020 IEEE.

关键词： Long short-term memory

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Attention-Based Grayscale Image Colorization

Attention-Based Grayscale Image Colorization

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IEEE International Conference on Mechatronics and Automation

作者： Lei Sun Dongjie Ju Shi Ke School of Electrical Engineering and Automation Binshui Xidao Extension 391 Tianjin China Tianjin Key Laboratory for Control Theory and Complicated Industry Systems Tianjin University of Technology Binshui Xidao Extension 391 Tianjin China

Image colorization has always been a hot topic in computer vision. Since the emergence of deep learning and its excellent performance in many image-processing tasks, image colorization methods based on convolutional neural networks (CNN) emerge in an endless stream, including automatic colorization methods and A coloring method based on a reference image. In the coloring method based on the reference image, an existing problem is that an algorithm for calculating the similarity between the grayscale image and the reference image needs to be designed to map the color blocks in the reference image to the grayscale image. And we use the attention mechanism to spread color implicitly, and propose an attention-guided image colorization network, which extracts features and corresponding color information from the reference image through a reference encoding sub-network, and simultaneously passes a target encoding sub-network The network extracts grayscale features from grayscale images, uses attention to calculate the correlation between the two types of features, and then restores the features and reconstructs colors through the decoding network. Our method can quickly capture the color of the reference feature, accurately transfer it to the target feature, and effectively solve the problem of small target loss during downsampling. By validating on multiple datasets, the results show that our method is effective, outperforming multiple state-of-the-art image colorization algorithms.

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Optimal Dispatch of Integrated Energy System Considering Flexible Dual Response of Source-Load and Novel Carbon Reduction Mechanism

SSRN

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SSRN 2023年

作者： Wu, Yanjuan Wang, Caiwei Wang, Yunliang School of Electrical Engineering and A utomation Tianjin University of Technology Tianjin Key Laboratory for Con trol Theory And Applications in Complicated Industry Systems Tianjin Key L aboratory of New Energy Power Conversion Transmission and Intelligent Co ntrol Tianjin300382 China

A flexible source-load response scheduling approach for the integrated energy system (IES) is provided in this paper, along with a new carbon reduction mechanism, against the background of the new power system in order to further optimize operating costs and lower the carbon emissions of IES. A type of flexible dual response of source-load mechanism is proposed under the premise of ladder carbon trading, based on waste heat boiler on the supply side and demand response characteristics. The source side realizes thermoelectric coupling heat and electricity output response through gas turbine and waste heat boiler, time-shiftable and interruptible integrated demand response of electric, heat, and hydrogen are introduced in the load side. Finally, the cases are provided to demonstrate how well the scheduling method works. © 2023, The Authors. All rights reserved.

关键词： Waste heat

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Rotation forest based on multimodal genetic algorithm

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Journal of Central South University 2021年第6期28卷 1747-1764页

作者： XU Zhe NI Wei-chen JI Yue-hui School of Electrical and Electronic Engineering Tianjin University of TechnologyTianjin 300384China Academic Affairs Office Tianjin University of TechnologyTianjin 300384China Tianjin Key Laboratory for Control Theory&Applications in Complicated Industry Systems Tianjin 300384China

In machine learning,randomness is a crucial factor in the success of ensemble learning,and it can be injected into tree-based ensembles by rotating the feature ***,it is a common practice to rotate the feature space ***,a large number of trees are required to ensure the performance of the ensemble *** random rotation method is theoretically feasible,but it requires massive computing resources,potentially restricting its applications.A multimodal genetic algorithm based rotation forest(MGARF)algorithm is proposed in this paper to solve this *** is a tree-based ensemble learning algorithm for classification,taking advantage of the characteristic of trees to inject randomness by feature ***,this algorithm attempts to select a subset of more diverse and accurate base learners using the multimodal optimization *** classification accuracy of the proposed MGARF algorithm was evaluated by comparing it with the original random forest and random rotation ensemble methods on 23 UCI classification *** results show that the MGARF method outperforms the other methods,and the number of base learners in MGARF models is much fewer.

关键词： ensemble learning decision tree multimodal optimization genetic algorithm

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In-situ Monitoring of Maximum and Average Chip Junction Temperature within Multichip IGBT Power Modules Using Kelvin Emitter Voltage

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IEEE Journal of Emerging and Selected Topics in Power Electronics 2025年

作者： Yang, Jianxiong Ran, Li Che, Yanbo Du, Mingxing Tianjin University of Technology Tianjin Key Laboratory of Control Theory and Applications in Complicated System Tianjin300384 China Chongqing University State Key Laboratory of Power Transmission Equipment and System Security and New Technology School of Electrical Engineering Chongqing400044 China University of Warwick School of Engineering CoventryCV4 7AL United Kingdom Tianjin University Key Laboratory of Smart Grid of Ministry of Education School of Electrical and Information Engineering Tianjin300072 China

Parallel configurations of power chips within a multichip IGBT power module (M-IGBT-PM) are employed to meet the required current and power ratings. When the internal chips of an M-IGBT-PM experience uneven temperature distributions and package aging, it becomes challenging to determine the maximum and average temperatures of the chips within the module. This paper investigates the relationship between four transient thermal-sensitive electrical parameters (T-TSEPs) derived from the Kelvin emitter voltage and the chip junction temperature, while establishing relevant mathematical models. It compares the sensitivity and linearity of these four T-TSEPs, and also assesses the influence of external gate resistance, bus voltage, and load current on each of them. A key innovation in this paper is the proposal of a method for monitoring the maximum and average temperatures across the chips within the M-IGBT-PM, which is not influenced by the load current. The experimental results suggest that the temperature derived from the quasi-turn-on delay time is a good approximation of the maximum chip temperature, whereas the temperatures predicted by the other T-TSEPs are nearer to the average temperature. © 2025 IEEE.

关键词： Insulated gate bipolar transistors (IGBT)

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