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SSRN 2024年

作者： Xiong, Jianbin Qian, Wenbo Zhou, Jiehan Wang, Qi Quan, Guoyuan Department of Automation Guangdong Polytechnic Normal University Guangzhou China Guangzhou Intelligent Building Equipment Information Integration and Control Key Laboratory Guangzhou China University of Oulu Oulu Finland

Fault diagnosis plays a significant role in Power system Transmission Lines (PSTL). Existing methods are inadequate for real-time and accurate diagnosis. To deal with redundant data and noise interference, this paper proposes a hybrid method for fault diagnosis for PSTL (HFDPSTL), based on Genetic Algorithm optimized Variational Mode Decomposition (GA-VMD) and Wavelet Decomposition (WD) combined withPrincipal Component Analysis (PCA) and K-Nearest Neighbor (KNN). First, HFDPSTL decomposes fault signals into a series of Intrinsic Modal Functions (IMF) by using GA-VMD. Second, HFDPSTL decomposes and reconstructs IMFs by using WD to obtain new voltage and current signals. Third, HFDPSTLapplies PCA to reduce fault feature dimensions, and applies KNN for pattern recognition. Experimental results demonstrate that HFDPSTL outperforms Empirical Mode Decomposition (EMD) with the accuracy of GA-VMD-WD by 29.822% , and Variational Mode Decomposition (VMD) with the accuracy of GA-VMD-WD by 2.559%. Overall, it achieves an average accuracy of 99.449%. © 2024, The Authors. All rights reserved.

关键词： Variational mode decomposition

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Optimization of Extreme Learning Machine Based on Improved Beetle Antennae Search for Slot Die Coating Prediction

Optimization of Extreme Learning Machine Based on Improved B...

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第35届中国控制与决策会议

作者： Haonan Yang Ding Liu Jun-Chao Ren Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology

In the actual production of slot die coating,the minimum coating thickness and the maximum substrate moving speed could only be judged by production experience,and there was no accurate prediction model due to the nonlinear characteristics of fluid ***,building a reasonable and efficient prediction model for slot die coating is now an urgent and challenging *** this paper,an optimized extreme learning machine(ELM) based on improved beetle antennae search(IBAS) algorithm is proposed for slot die coating *** optimized ELM model can well learn the nonlinear characteristics of the system and make accurate predictions,thus solving the traditional inaccurate empirical *** the prediction accuracy of ELM depends on the selection of weights and biases,the IBAS optimization algorithm is used to quickly search for the optimal value of weights and biases in the ELM *** algorithm improves the generation mechanism of antennae on the basis of the original algorithm,so that the algorithm can converge *** the same time,the search strategy of the algorithm is improved to avoid falling into the local optimal *** predicting the production data of slit coating,the feasibility and effectiveness of IBAS-ELM model are proved.

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Design and Wave Propagation Characterization of Starchiral Metamaterials

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Acta Mechanica Solida Sinica 2022年第2期35卷 215-227页

作者： Yajun Xin Han Wang Cong Wang Jinxin Yao Qian Ding Haoqiang Gao Shuliang Cheng Yongtao Sun Key Laboratory of Green Construction and Intelligent Maintenance for Civil Engineering of Hebei Province Yanshan UniversityQinhuangdao 060000China Hebei Province Low-Carbon and Clean Building Heating Technology Innovation Center Yanshan UniversityQinhuangdao 060000China Department of Mechanics and Tianjin Key Laboratory of Nonlinear Dynamics and Control Tianjin UniversityTianjin 300072China State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures Shijiazhuang Tiedao UniversityShijiazhuang 050043China

Metamaterials with simple artificial topological properties made from industrially produced single-phase materials have extraordinary innovation and *** this paper,we investigated wave propagation characteristics of a hexastarchiral lattice metamaterial with periodic assemblies and used the finite element(FE)method to study the band gap ***,in order to understand the mechanism of band gaps,we discussed the mode shapes of unit cells and found that the bending of the ligament and the overall rotation have a high correlation with the generation of omnidirectional band *** relationships bet ween geometric parameters and band gap properties have also been systematically studied,which demonstrated that the band gaps of the proposed metamaterial could be reasonably predicted through the evolution of geometric ***,the transmission characteristics of finite sandwich panel structures composed of periodic unit cells were calculated to verify the correctness of the band gap simulation results,which proved that the proposed artificial metastructure has potential application value in vibration and noise reduction projects.

关键词： Hexastarchiral lattice metamaterials Band gap properties Finite element method Rotational resonance

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Hierarchy-based extended CBBA for multi-robot task assignment

Hierarchy-based extended CBBA for multi-robot task assignmen...

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2022 Chinese Automation Congress, CAC 2022

作者： Liu, Jiajun Xia, Weiguo Li, Fuqiang Wang, Peng China National Aeronautical Radio Electronic Research Institute National Key Laboratory of Science and Technology on Avionics System Integration China Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education School of Control Science and Engineering China

ISBN: (纸本)9781665465335

This paper studies the task assignment problem of a multi-robot system with complex constraints including the robot speed limit, the task execution time window constraints, the arrival time, the trip cost, and different priorities of the tasks. A new bidding function is then designed for each robot. Based on the k-means algorithm, a hierarchy-based extended consensus-based bundle algorithm (CBBA) is proposed to find the optimal solution for the task allocation problem. Finally, simulation examples are presented to show the feasibility of the proposed algorithm and comparisons with existing algorithms show that the proposed algorithm can reduce the task assignment time with guaranteed reward values. © 2022 IEEE.

关键词： Multipurpose robots

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Optimization of Extreme Learning Machine Based on Improved Beetle Antennae Search for Slot Die Coating Prediction

Optimization of Extreme Learning Machine Based on Improved B...

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Chinese control and Decision Conference, CCDC

In the actual production of slot die coating, the minimum coating thickness and the maximum substrate moving speed could only be judged by production experience, and there was no accurate prediction model due to the nonlinear characteristics of fluid motion. Therefore, building a reasonable and efficient prediction model for slot die coating is now an urgent and challenging task. In this paper, an optimized extreme learning machine (ELM) based on improved beetle antennae search (IBAS) algorithm is proposed for slot die coating prediction. The optimized ELM model can well learn the nonlinear characteristics of the system and make accurate predictions, thus solving the traditional inaccurate empirical judgment. As the prediction accuracy of ELM depends on the selection of weights and biases, the IBAS optimization algorithm is used to quickly search for the optimal value of weights and biases in the ELM network. IBAS algorithm improves the generation mechanism of antennae on the basis of the original algorithm, so that the algorithm can converge quickly. At the same time, the search strategy of the algorithm is improved to avoid falling into the local optimal solution. By predicting the production data of slit coating, the feasibility and effectiveness of IBAS-ELM model are proved.

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Task Order Filing Generation Algorithm Based on Multidimensional Association Rules and Similarity Matching

Task Order Filing Generation Algorithm Based on Multidimensi...

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2024 International Conference Optoelectronic Information and Optical Engineering, OIOE 2024

作者： Zhang, Xinyue Lv, Weimin Wei, Dingjiang Yang, Dongsheng Yang, Lin Naval Aviation University Yantai China No. 91593 of PLA Sanya China The Laboratory for Military-Civilian Integration Emergency Command and Control Jinan University Guangzhou China School of Public Management/Emergency Management Jinan University Guangzhou China Key Laboratory of Information System Engineering National University of Defense Technology Changsha China School of Information and Intelligent Engineering University of Sanya Academician Guoliang Chen Team Innovation Center University of Sanya Sanya China

ISBN: (数字)9781510688209

ISBN: (纸本)9781510688193

This paper examines the potential for leveraging the order dispatch algorithm in the commercial domain to enhance the intelligent allocation of military combat tasks, thereby improving the speed and accuracy of command decision-making in the context of intelligent warfare. The study proposes an "order-based" command and control model, which aims to overcome the efficiency bottleneck and information lag problem in traditional combat task allocation. The algorithmic design incorporates multi-dimensional association rules and similarity matching algorithms to guarantee the optimal matching of combat tasks and resources, thereby achieving the objectives of rapid response, efficient resource utilization, and enhanced combat effectiveness. This paper focuses on the challenges and advantages of this model in the context of naval warfare. It highlights the strategic value of order-based command and control in non-direct confrontation missions, such as reconnaissance, patrol, and intelligence gathering. The utilization of the "combat cloud," artificial intelligence, and a highly integrated command network enables the precise allocation of resources and the rapid execution of tasks. This, in turn, enhances the ISR capability, network-centered warfare effectiveness, and cross-domain synergistic warfare capability in naval warfare. The study also delineates the specific implementation process of the algorithm, which includes multi-dimensional association rule mining and similarity matching based on historical cases, with the objective of generating optimal combat scenarios and ensuring the effective execution of task orders. © 2025 SPIE.

关键词： Naval warfare

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Parameter estimation of time fractional heat conduction equation for monocrystalline silicon

Parameter estimation of time fractional heat conduction equa...

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2021 China Automation Congress, CAC 2021

作者： Shi, Shuyan Liu, Ding Zhao, Zhongdan Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Shananxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an710048 China

ISBN: (纸本)9781665426473

In this paper, a time-fractional heat conduction model is established to describe the heat transfer process of monocrystalline silicon in the Czochralski method. The numerical solution of the fractional-order model is obtained by using implicit difference scheme. In addition, an improved grey wolf algorithm is proposed to estimate the parameters of the fractional heat conduction model using the experimental temperature data. The numerical experiment results show that the time-fractional heat conduction model has a higher explanatory ability, compared with the classical heat conduction model. The Improved Grey Wolf algorithm proposed in this paper is effective in solving the parameter estimation problem of the time-fractional heat conduction equation. © 2021 IEEE

关键词： Heat conduction

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A Forecast Error Correction Method Based on Seq2Seq and Auto Encoder for Short-term Wind Power Forecast Enhancement

A Forecast Error Correction Method Based on Seq2Seq and Auto...

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IEEE/IAS Industrial and Commercial Power system Asia (I&CPS Asia)

作者： Haoyu Ma Ming Yang Zifen Han Bo Wang Jianfeng Che Menglin Li Key Laboratory of Power System Intelligent Dispatch Control Shandong University Jinan China State Grid Gansu Power Grid Corporation Lanzhou China National Key Laboratory of Renewable Energy Grid-Integration China Electric Power Research Institute Beijing China

ISBN: (数字)9798350352290

ISBN: (纸本)9798350352306

Accurate wind power forecast (WPF) is highly significant for the safe and economic operation of power systems, and implementing error correction is a common and effective manner to further enhance the forecasting performance. Nonetheless, existing error correction methods fail to consider temporal relationship and may inadvertently increase the error under certain circumstances, thereby compromising the local precision of forecast. In order to address these issues, this paper proposes an error correction model based on Seq2Seq and auto encoder (AE) to improve forecasting accuracy. Firstly, the seq2seq with attention mechanism is introduced to infer the probable error sequence through learning the temporal relationship between the numerical weather prediction sequence and the actual error sequence. Secondly, the samples that the seq2seq providing a positive error inference are collected and the AE model is employed to extract the meteorological features at these moments. Finally, on the basis of the above two models, the error correction method is constructed consisting of preliminary error inference and posterior correction determination. Using operational data from Ningxia province, the proposed method is validated for its remarkable superiority based on the evaluation metrics.

关键词： Accuracy Wind energy Heuristic algorithms Wind power generation Predictive models Feature extraction Error correction Numerical models Wind forecasting Forecasting

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RHNet: A Real-Time High-Resolution Network for Surface Defect Detection

RHNet: A Real-Time High-Resolution Network for Surface Defec...

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第43届中国控制会议

作者： Yijie Wang Zhigang Ren Jianpu Cai Zongze Wu Shengli Xie School of Automation Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of EducationGuangdong University of Technology Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangdong University of Technology College of Mechatronics and Control Engineering Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)Shenzhen University Key Laboratory of Intelligent Detection and The Internet of Things in Manufacturing (GDUT) Ministry of Education

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

In the industrial domain, surface defect detection after multiple processing steps is crucial for improving the outgoing quality of products. However, due to the characteristics of surface defects, such as low contrast, irregular shapes, small and elongated features, and significant noise. To address these issues, this paper proposes a novel real-time high-resolution network(RHNet). Unlike previous models, RHNet emphasizes the importance of modeling spatial information to capture small ***, the output feature maps of each module are maintained at 1/4 resolution of the original image. We introduce a short-term dual-branch module(SDBM) to handle real-time processing of high-resolution feature maps. Additionally, FPAPPM is proposed for swift extraction of deep information and multi-scale context. Through extensive experiments, we demonstrate that our proposed model exhibits robust defect modeling capabilities and detection performance while still meeting real-time requirements.

关键词： Surface defect inspection Anomaly detection Semantic segmentation Deep learning Pattern recognition

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A Low Delay AES Round Transform Architecture Using Constant Matrix Multiplications Merging Technologies 17

A Low Delay AES Round Transform Architecture Using Constant ...

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17th IEEE Conference on Industrial Electronics and Applications, ICIEA 2022

作者： Zheng, Xinxing Zhang, Xiaoqiang Yang, Fan Xu, Mingyu Yan, Han Wuhu Institute of Technology College of Information Engineering Wuhu China Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment Ministry of Education Wuhu China Engineering Research Center of Vehicle-Mounted Display Integration System Wuhu China Anhui Polytechnic University College of Electrical Engineering Wuhu China

ISBN: (纸本)9781665409841

This paper presents a low delay architecture design method for the hardware implementation of the Advanced Encryption Standard (AES) algorithm. The proposed architecture is designed by using constant matrix multiplications (CMM) merging technologies. To reduce the area cost in hardware implementations, S-box/InvS-box is usually implemented with composite field arithmetic (CFA) technologies. In this paper, CMM in CFA-based S-box/InvS-box are further merged with constant coefficient multiplications in MixColumns/ InvMixColumns, which can also be expressed as CMM forms. By the merging, the delay of the hardware implementation of encryption round transform is reduced at the cost of slight area cost increasing, and both delay and area cost are reduced in hardware implementations of decryption round transform. Hardware complexities analysis indicates that our designs have less delay compared with previous works. © 2022 IEEE.

关键词： Cryptography

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