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A Novel Hybrid Transformer-Based Framework for Solar Irradiance Forecasting under Incomplete Data Scenarios

IEEE Transactions on Industrial Informatics 2024年第6期20卷 8605-8615页

作者： Zhang, Hanjin Li, Bin Su, Shun-Feng Yang, Wankou Xie, Liping Southeast University School of Economics and Management Nanjing210096 China National Taiwan University of Science and Technology Department of Electrical Engineering Taipei106 Taiwan Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Nanjing210096 China Southeast University Shenzhen Research Institute Shenzhen518063 China

Accurate prediction of solar irradiance is crucial for the effective utilization of solar energy. However, in real-world scenarios, complex irradiance patterns and prevalent incomplete data pose challenges to precise forecasting, resulting in additional uncertainties and instability. To address these issues, this study proposes a novel irradiance forecasting model that integrates a Mask-Transformer data imputation module and a prediction module centered around the typical patterns representation mechanism. The Mask-Transformer leverages a mask modeling mechanism to model the context of missing data, facilitating accurate estimation of missing values and reducing noise and uncertainty in the input data. The typical patterns representation mechanism comprises a series decomposition module and a feature fusion module, providing the module with the capability to mitigate nonlinearity and nonstationarity in solar irradiance data. This enhancement leads to improved short-term forecasting performance while maintaining long-term forecasting capabilities. Experimental results on two datasets demonstrate that the proposed model exhibits sufficient robustness and accuracy, making it effective in scenarios with incomplete data. © 2005-2012 IEEE.

关键词： Forecasting

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ITD Sample Entropy and Probabilistic Neural Network Bearing Fault Diagnosis Model

ITD Sample Entropy and Probabilistic Neural Network Bearing ...

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UNIfied Conference of International Workshop on Defence Applications of Multi-Agent Systems, DAMAS 2023, International Conference on Maintenance engineering, IncoME-V 2023, International conference on the Efficiency and Performance engineering Network, TEPEN 2023

作者： Wang, Nan Wang, Hongjun Wang, Ze Wang, Liang Zhang, Zhuangzhuang School of Mechanical and Electrical Engineering Beijing Information Science and Technology University Beijing100192 China Key Laboratory of Modern Measurement and Control Technology BISTU Beijing100192 China High-End Equipment Intelligent Perception and Control Beijing International Science and Technology Cooperation Base BISTU Beijing100192 China

ISBN: (纸本)9783031494123

A new fault diagnosis method based on ITD sample entropy and probabilistic neural network is proposed. Firstly, the vibration signal of the equipment is decomposed using intrinsic time-scale decomposition (ITD) to obtain several proper rotation components (PRC) and a trend item that spreads from high frequency to low frequency. Secondly, the components that have a high degree of correlation with the original signal are selected as the research object, and their sample entropy is used as the feature vector. Finally, the feature vector is used to train the probabilistic neural network, and fault identification is performed using the probabilistic neural network. The proposed method is verified using experimental data to accurately identify the type and extent of equipment failure. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Failure analysis

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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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2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2023

作者： Wang, Liangcheng Wang, Yang Zhang, Hong Wang, Yanwei Zheng, Ying Huazhong University of Science and Technology The Belt and Road Joint Laboratory on Measurement and Control Technology School of Artificial Intelligence and Automation Wuhan430074 China Wuhan Institute of Technology School of Mechanical & Electrical Engineering Hubei Wuhan430073 China

ISBN: (纸本)9798350337754

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. © 2023 IEEE.

关键词： Convolution

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Deep Reinforcement Learning Vehicle Visual Navigation Based on Attention Mechanism and LSTM 16th

Deep Reinforcement Learning Vehicle Visual Navigation Based ...

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16th International Conference on Modelling, Identification and control, ICMIC 2024

作者： Yu, Zhentao Zhang, Guojun Li, Ruoting Liu, Peng School of Instrument and Electronics North University of China Taiyuan030051 China The State Key Laboratory of Dynamic Measurement Technology North University of China Taiyuan030051 China School of Electrical and Control Engineering North University of China Taiyuan030051 China

ISBN: (纸本)9789819617760

This paper studies the visual navigation of vehicles in unknown environments. Due to the lack of global information, the accuracy of graphical navigation method will be decline. We propose a double LSTM attention navigation model to improve vehicle navigation performance. The model utilizes a dual-channel LSTM network for processing time series data and introduces an attention mechanism into the asynchronous dominant actor critic algorithm to mitigate the reward sparsity problem. Through comparative with various models on the AI2-THOR platform, the enhanced model improves the cumulative rewards and success rates of visual navigation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep reinforcement learning

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Optimal Scheduling Model of Community Integrated Energy System Based on Cooperative Game Theory

Optimal Scheduling Model of Community Integrated Energy Syst...

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2023 Panda Forum on Power and Energy, PandaFPE 2023

作者： Du, Jialin Zhang, Sen Luo, Shihua Yuan, Yiping Hu, Weihao School of Mechanical and Electrical Engineering Sichuan Provincial Key Lab of Power System Wide Area Measurement and Control University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350321173

Currently, a large number of community integrated energy systems operate independently and lack coordination and cooperation with each other. On the whole, the complementary coupling potential between energy production and consumption has not been fully explored, which is not conducive to the efficient utilization of clean energy. To solve the above problem, this paper proposes an optimal scheduling model of community integrated energy system considering integrated demand response and carbon tax. Moreover, based on cooperative game theory, this paper adopts the Sharpley value to achieve fair and reasonable distribution of cooperation profits. The case study results indicate that the energy sharing model proposed in this article can achieve better economic benefits. © 2023 IEEE.

关键词： Game theory

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Strip Weld Inspection Based on Improved VGG16 8

Strip Weld Inspection Based on Improved VGG16

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8th International Conference on Signal and Image Processing, ICSIP 2023

作者： Fu, Sisi Chen, Yuan Xian, Dexin Kong, Shiyuan Wang, Hao Wang, Jinsong Changchun University of Science and Technology School of Optoelectronic Engineering Guangdong Zhongshan China Harbin University of Science and Technology School of Measurement and Control Technology and Communication Engineering Heilongjiang Harbin China Zhongshan Institute School of Optoelectronic Engineering Changchun University of Science and Technology Guangdong Zhongshan China Changchun University of Science and Technology Zhongshan Institute Changchun University of Science and Technology School of Optoelectronic Engineering Guangdong Zhongshan China

ISBN: (纸本)9798350397932

As one of the indispensable materials in modern industry, high precision and high efficiency defect detection of strip weld has become an important part of production and quality control. In response to the shortcomings of the existing strip weld detection technology with low detection accuracy, this paper proposes a deep learning based strip weld defect detection and classification technique. First, the CBAM attention mechanism is introduced into the network structure to improve the attention to important features;second, the traditional convolution layer is replaced by using the dilated convolution to reduce the computation while increasing the perceptual field;finally, the original ReLU activation function is replaced by the PReLU activation function to effectively overcome the problem of neuron death in the Relu activation function. The improved model finally achieves a recognition rate of 98.1% in the test set, which is 22.1% higher than that of the unimproved model, greatly improving the accuracy and robustness of the algorithm. © 2023 IEEE.

关键词： Convolution

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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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Asynchronous State Estimator Design for Markov Switched Systems with Incomplete Probabilities and Redundant Channels 42

Asynchronous State Estimator Design for Markov Switched Syst...

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42nd Chinese control Conference, CCC 2023

作者： Chen, Haiyang Gao, Fangzheng Mao, Xueyi School of Control Science and Engineering Tiangong University Tianjin300387 China School of Automation Nanjing Institute of Technology Nanjing211167 China Nanjing Communications Institute of Technology Publicity Department Nanjing211188 China

ISBN: (纸本)9789887581543

This paper is concerned about the H∞ asynchronous state estimator design for Markov switched systems (MSSs) with incomplete transition probabilities and redundant channels. A hidden Markov chain (HMC) is introduced to characterize the asynchronous degree between the state estimator mode and the system mode. Firstly an asynchronous full-order state estimator is devised for the MSSs. Then, by Lyapunov functional and stochastic analysis, new criteria are derived such that the augmented system consisting of the target system and state estimator is H∞ stochastically finite-time boundedness. After that, with matrix decoupling technique, a design algorithm is obtained in form of linear matrix inequalities (LMIs). Finally, the practical one-link robotic manipulator is utilized to validate the developed results. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Linear matrix inequalities

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Robust Target Detection and Localization for UAV Autonomous Landing with Partially Occluded Targets 15th

Robust Target Detection and Localization for UAV Autonomous ...

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15th International Conference on Genetic and Evolutionary Computing, ICGEC 2023

作者： Shao, Mingmei Liu, Yanyan Tang, Jiaze Liu, Huanyu Li, Junbao Faculty of Computing Harbin Institute of Technology Harbin150080 China National Key Laboratory of Electromagnetic Space Security Tianjin300308 China Electrical Measurement Technology and Intelligent Control Institute School of Instrumentation Science and Engineering Harbin Institute of Technology Harbin150080 China

ISBN: (纸本)9789819700677

This paper presents an approach for robust target detection and localization in the context of UAV autonomous landing with partially occluded targets. Since the performance of the autonomous landing system largely depends on the accurate detection and positioning of the landing target, it is very important to deal with the local occlusion caused by obstacles or environmental factors. In order to improve the overall performance of the system, we propose a fault-tolerant target detection method and target completion strategy. The proposed target detection method combines image processing technology with feature extraction algorithm, which can recognize and locate the landing target even in the case of occlusion. The target completion strategy aims to recover the missing part of the occluded target and make the detection and location algorithm execute more reliably. We perform experiments on image data sets with different horizontal object occlusion to evaluate the performance of the proposed method. The results show that our method successfully detects and locates the landing target under different occlusion levels, and significantly improves the performance of UAV autonomous landing system in the real scene. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Unmanned aerial vehicles (UAV)

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Data Augmentation Integrated with Feature-Enhanced Convolutional Neural Network for Imbalanced Fault Diagnosis in Rolling Bearings 13

Data Augmentation Integrated with Feature-Enhanced Convoluti...

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13th IEEE Data Driven control and Learning Systems Conference, DDCLS 2024

作者： Wang, Li Qu, Qilin Wang, Yanwei Wong, David Shan-Hill Zheng, Ying School of Artificial Intelligence and Automation Huazhong University of Science and Technology China -Belt and Road Joint Laboratory on Measurement and Control Technology Wuhan430074 China School of Mechanical & Electrical Engineering Wuhan Institute of Technology 693 Chuxiong Road Wuhan430073 China National Tsing-Hua University Department of Chemical Engineering Hsinchu30071 Taiwan

ISBN: (纸本)9798350361674

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. © 2024 IEEE.

关键词： Convolutional neural networks

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