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Short-term Load Forecasting of an Integrated Energy System Based on STL-CPLE with Multitask Learning

Protection and Control of Modern Power Systems 2024年第6期9卷 71-92页

作者： Suxun Zhu Hengrui Ma Laijun Chen Bo Wang Hongxia Wang Xiaozhu Li Wenzhong Gao the College of Energy and Electrical En-gineering Qinghai UniversityXining 810016China the School of Electrical and Automation Wuhan UniversityWuhan 430072China the Department of Electrical and Computer Engineering University of DenverDenver CO 80208USA Engineering Research Center for Renewa-ble Energy Generation and Grid Connection Technology Ministry of EducationXinjiang UniversityUrumchi 830046China

Multienergy loads in integrated energy sys-tems(IESs)exhibit strong volatility and randomness,and existing multitask sharing methods often encounter nega-tive migration and seesaw problems when addressing complexity and competition among *** line with these considerations,a short-term multienergy load joint prediction method based on seasonal-trend decomposition using LOESS(STL)and convolutional progressive lay-ered extraction(CPLE)is proposed,called ***,STL is applied to model regular and uncertain load information into interpretable trend,seasonal,and re-sidual ***,joint modeling is performed for the same type of components of multienergy loads.A one-dimensional convolutional neural network(1DCNN)is constructed to extract deeper feature *** approach works in concert with the progressive layered extraction sharing method,and convolutional shared and task-specific experts are developed to acquire common and distinctive representations of multienergy loads, re-spectively. Task-specific parameters are gradually sepa-rated through progressive routing. Finally, a subtask network is built to learn temporal dependencies using long short-term memory (LSTM). Simulation validation is performed on the IES dataset at the Tempe campus of Arizona State University, and the experiments show that the STL-CPLE method exhibits higher prediction accu-racy than do the other methods.

关键词： Integrated energy system multienergy load forecasting convolutional progressive layer extrac-tion network seasonal-trend decomposition.

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Robust Control of Feeding Speed for Coal Mine Tunnel Drilling Machines 25

Robust Control of Feeding Speed for Coal Mine Tunnel Drillin...

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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Liu, Xiao Chen, Luefeng Wu, Min Cao, Weihua Lu, Chengda Pedrycz, Witold School of Automation China University of Geosciences Wuhan China School of Automation The Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems The Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University Department of Computer Engineering Sariyer Istanbul34396 Turkey

Changes in coal seam hardness cause fluctuations in the feed resistance at the drill bit during the drilling process, leading to unstable feeding speed. This paper proposes a robust dynamic output feedback controller to suppress disturbances caused by the variations in coal seam hardness in the feed system. Firstly, an unknown parameter measuring coal seam hardness is introduced, and an uncertain model of the feeding system is established based on the finite element model of the drill string. By designing weighted functions based on industrial field requirements and constructing a generalized plant, the controller achieves loop shaping, reducing the low-frequency impact of coal seam hardness variations on the feed system and suppressing the systems resonance peak. Simulation results demonstrate that the controller effectively suppresses parameter variations and external disturbances caused by changes in coal seam hardness, achieving stable control of the drilling speed. © 2024 IEEE.

关键词： Robust control

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Failure Prediction Mechanism of Disk Devices Based on LSTM 2

Failure Prediction Mechanism of Disk Devices Based on LSTM

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2nd Asia-Pacific Conference on Communications Technology and computer Science, ACCTCS 2022

作者： Xu, Zhenpeng Ma, Jinwei Liu, Yu Jiangsu Automation Research Institute Department of Computer Lianyungang China School of Computer Engineering Weifang University Weifang China

ISBN: (纸本)9781665400343

In order to reduce the impact of the disk device failure on the information system performance fluctuation and business services, a disk device failure prediction mechanism based on LSTM is proposed based on the disk state feature extraction and neural network model. Firstly, based on the SMART information, the performance data and the function attenuation characteristics of the disk, the characteristic index of the disk device is selected and extracted. Then, a disk device failure prediction mechanism is established based on the improved LSTM by using the characteristic index as the training set. Finally, the active failure prediction of disk device was developed. Performance analysis shows the failure prediction mechanism of disk device proposed can obtain online prediction function through the data pre-processing methods such as SMART data item selecting, disk loss feature construction, the data item cleaning, and the sample standardization. The proposal is superior to the traditional neural network model in terms of the error square sum and correction decision coefficient. © 2022 IEEE.

关键词： Feature extraction

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Minimum Time Headway in Platooning Systems Under the MPF Topology for Different Wireless Communication Scenario

Minimum Time Headway in Platooning Systems Under the MPF Top...

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作者： Abolfazli, Elham Besselink, Bart Charalambous, Themistoklis Aalto University School of Electrical Engineering Department of Electrical Engineering and Automation Espoo02150 Finland University of Groningen Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence Groningen Netherlands University of Cyprus School of Engineering Department of Electrical and Computer Engineering Nicosia1678 Cyprus

The multiple-predecessor following (MPF) topology is used in vehicle platoons to make it robustly string stable and reduce the minimum employable time headway. It has been demonstrated that communication imperfections such as time delays coming from wireless communications can affect string stability as well as the minimum time headway required to guarantee string stability. Specifically, it was shown that the larger the time delay, the longer the minimum time headway will be. However, by utilizing on-board vehicle sensors, such as radar, lidar and cameras, the distance and speed of nearby vehicles can be measured almost instantaneously, i.e., with almost no delay. Another effective parameter on string stability and minimum time headway is the heterogeneity of the vehicles. Due to the immense complexity of the MPF topology, string stability analysis of this topology in literature has been confined to homogeneous platoons. In this paper, we consider the case of heterogeneous platoons under the MPF topology with the use of the combination of sensors and wireless communications for receiving information. Following that, we find conditions to guarantee the internal and string stability for the heterogeneous case and propose the minimum time headway required to guarantee string stability. Finally, we provide a table, in which we propose the minimum time headway for two other wireless communication scenarios as well: (i) having no communication delay and (ii) having fully-delayed information, i.e., all information, whether it comes from the ego vehicle or its predecessors, is delayed. In addition to exploring the analysis of string stability for the vehicles with more possible connections (vehicles after the $r^{th}$ vehicle, when information from $r$ immediate vehicles is used), we study the string stability conditions (with which we aim at avoiding collisions) and find the minimum time headway for the first few vehicles (vehicle $r$ and all its predecessors). Numerical

关键词： Stability criteria

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Variance-Constrained Filtering Fusion for Nonlinear Cyber-Physical Systems With the Denial-of-Service Attacks and Stochastic Communication Protocol

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IEEE/CAA Journal of Automatica Sinica 2022年第6期9卷 978-989页

作者： Hang Geng Zidong Wang Yun Chen Xiaojian Yi Yuhua Cheng IEEE School of Automation Engineering University of Electronic Science and Technology of ChinaChengdu 611731China College of Electrical Engineering and Automation Shandong University of Science and TechnologyQingdao 266590China Department of Computer Science Brunel University LondonUxbridgeMiddlesexUB83PHUnited Kingdom Key Laboratory for IoT and Information Fusion Technology of Zhejiang School of AutomationHangzhou Dianzi UniversityHangzhou 310018China School of Mechatronical Engineering Beijing Institute of TechnologyBeijing 100081China

In this paper,a new filtering fusion problem is studied for nonlinear cyber-physical systems under errorvariance constraints and denial-of-service *** prevent data collision and reduce communication cost,the stochastic communication protocol is adopted in the sensor-to-filter channels to regulate the transmission order of *** sensor is allowed to enter the network according to the transmission priority decided by a set of independent and identicallydistributed random *** the defenders’view,the occurrence of the denial-of-service attack is governed by the randomly Bernoulli-distributed *** the local filtering stage,a set of variance-constrained local filters are designed where the upper bounds(on the filtering error covariances)are first acquired and later minimized by appropriately designing filter *** the fusion stage,all local estimates and error covariances are combined to develop a variance-constrained fusion estimator under the federated fusion ***,the performance of the fusion estimator is examined by studying the boundedness of the fused error covariance.A simulation example is finally presented to demonstrate the effectiveness of the proposed fusion estimator.

关键词： Cyber-physical system(CPS) denial-of-service attack stochastic communication protocol(SCP) variance-constrained filtering fusion

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Learning to Branch in Combinatorial Optimization With Graph Pointer Networks

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 157-169页

作者： Rui Wang Zhiming Zhou Kaiwen Li Tao Zhang Ling Wang Xin Xu Xiangke Liao IEEE the Xiangjiang Laboratory and the College of Systems Engineering National University of Defense TechnologyChangsha 410073China the Institute of Automation Chinese Academy of SciencesBeijing 100190China the College of Systems Engineering National University of Defense TechnologyChangsha 410073 the Hunan Key Laboratory of Multi-Energy System Intelligent Interconnection Technology Changsha 410073China the Department of Automation Tsinghua UniversityBeijing 100084China the College of Intelligence Science and Technology National University of Defense TechnologyChangsha 410073China the College of Computer Science and Technology National University of Defense TechnologyChangsha 410073China

Traditional expert-designed branching rules in branch-and-bound(B&B) are static, often failing to adapt to diverse and evolving problem instances. Crafting these rules is labor-intensive, and may not scale well with complex *** the frequent need to solve varied combinatorial optimization problems, leveraging statistical learning to auto-tune B&B algorithms for specific problem classes becomes attractive. This paper proposes a graph pointer network model to learn the branch rules. Graph features, global features and historical features are designated to represent the solver state. The graph neural network processes graph features, while the pointer mechanism assimilates the global and historical features to finally determine the variable on which to branch. The model is trained to imitate the expert strong branching rule by a tailored top-k Kullback-Leibler divergence loss function. Experiments on a series of benchmark problems demonstrate that the proposed approach significantly outperforms the widely used expert-designed branching rules. It also outperforms state-of-the-art machine-learning-based branch-and-bound methods in terms of solving speed and search tree size on all the test instances. In addition, the model can generalize to unseen instances and scale to larger instances.

关键词： Branch-and-bound(B&B) combinatorial optimization deep learning graph neural network imitation learning

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Optimization and FPGA Implementation of RANSAC Algorithm Based on HLS 5

Optimization and FPGA Implementation of RANSAC Algorithm Bas...

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5th Asia-Pacific Conference on Image Processing, Electronics and computers, IPEC 2024

作者： Lian, Chenglong Xue, Yanbing Li, Xiaojie Hu, Panshi Feng, Chong School of computer and communication engineering Dalian Jiaotong University Dalian116028 China College of automation and electrical engineering Dalian Jiaotong University Dalian116028 China School of information and communication engineering Dalian Minzu University Dalian116600 China

ISBN: (纸本)9781643685243

With the rapid development of artificial intelligence technology, research on autonomous driving technology is becoming increasingly popular. In the autonomous driving panoramic imaging system, it is necessary to use image stitching technology to perform real-time stitching of image information collected by multiple cameras around the vehicle body. Therefore, requirements for image data processing include large data volume, high speed, and low power consumption. The parallel computing characteristics of Field Programmable Gate Array (FPGA) can accelerate key image stitching algorithms. This article focuses on hardware acceleration of the RANSAC (Random Sample Consensus) algorithm in image stitching. The algorithm is designed in parallel using high-level synthesis (HLS) technology and optimized accordingly to find a suitable method for algorithm optimization. Comparing the FPGA algorithm circuit with the Open CV algorithm program, the hardware running time of the algorithm is only 2.816ms, and the processing speed is 38 times faster than the latter. The RANSAC algorithm implemented by FPGA meets the requirements of real-time image processing and can be applied in real-time image stitching systems. © 2024 The Authors.

关键词： High level synthesis

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Three-dimensional reconstruction of industrial parts from a single image

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Visual Computing for Industry,Biomedicine,and Art 2024年第1期7卷 340-351页

作者： Zhenxing Xu Aizeng Wang Fei Hou Gang Zhao School of Mechanical Engineering and Automation Beihang UniversityBeijing 100191China Key Laboratory of Aeronautics Smart Manufacturing Beihang UniversityBeijing 100191China State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China

This study proposes an image-based three-dimensional(3D)vector reconstruction of industrial parts that can gener-ate non-uniform rational B-splines(NURBS)surfaces with high fidelity and *** contributions of this study include three parts:first,a dataset of two-dimensional images is constructed for typical industrial parts,including hex-agonal head bolts,cylindrical gears,shoulder rings,hexagonal nuts,and cylindrical roller bearings;second,a deep learning algorithm is developed for parameter extraction of 3D industrial parts,which can determine the final 3D parameters and pose information of the reconstructed model using two new nets,CAD-ClassNet and CAD-ReconNet;and finally,a 3D vector shape reconstruction of mechanical parts is presented to generate NURBS from the obtained shape *** final reconstructed models show that the proposed approach is highly accurate,efficient,and practical.

关键词： Three-dimensional reconstruction Non-uniform rational B-splines Industrial parts Deep learning

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Formation control of unmanned rotorcraft systems with state constraints and inter-agent collision avoidance

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Autonomous Intelligent Systems 2023年第1期3卷 84-95页

作者： Panpan Zhou Shupeng Lai Jinqiang Cui Ben M.Chen Department of Mechanical and Automation Engineering The Chinese University of Hong KongShatinN.T.Hong Kong. Department of Electrical and Computer Engineering National University of Singapore4 Engineering Drive 3Singapore117583Singapore. Peng Cheng Laboratory Shenzhen518055China.

We present in this paper a novel framework and distributed control laws for the formation of multiple unmanned rotorcraft systems,be it single-rotor helicopters or multi-copters,with physical constraints and with inter-agent collision avoidance,in cluttered *** proposed technique is composed of an analytical distributed consensus control solution in the free space and an optimization based motion planning algorithm for inter-agent and obstacle collision *** specifically,we design a distributed consensus control law to tackle a series of state constraints that include but not limited to the physical limitations of velocity,acceleration and jerk,and an optimization-based motion planning technique is utilized to generate numerical solutions when the consensus control fails to provide a collision-free ***,a sufficiency condition is given to guarantee the stability of the switching process between the consensus control and motion ***,both simulation and real flight experiments successfully demonstrate the effectiveness of the proposed technique.

关键词： Multi-agent system Unmanned systems Rotorcraft systems Formation control Consensus control

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Weighted Feature Ranking Merging for Supervised Machine Learning 19th

Weighted Feature Ranking Merging for Supervised Machine Lea...

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19th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2024

作者： Coto-Palacio, Jessica Ortiz-Tandazo, Daniel Alejandro Bautista-Juárez, Alejandro Grangetto, Agustina Cabello-Solorzano, Kelsy León-Castro, Diana Santana-Morales, Paola Tallón-Ballesteros, Antonio J. International University of Andalusia Palos de la Frontera Huelva Spain University of Huelva Huelva Spain Department of Electronic Computer Systems and Automation Engineering University of Huelva Huelva Spain

ISBN: (纸本)9783031750090

This paper introduces a novel approach to ranking-based feature selection. Numerous metrics exist to assess univariate filters. This contribution identifies the top best features by employing two distinct methods, computes the global weights of each feature, and sorts these weights in decreasing order for those which are common to both procedures. According to the first part, this work is a feature selection proposal and concerning the last, it is besides a feature sorting approach. The benchmark data sets originate from NIPS 2003. This approach has been tested on problems with up to 20000 features and up to 7000 samples. The test results are very promising since, in some cases the outcomes significantly outperform the raw scenario, and in others, they surpass those of an approach which may be considered to be related to the current contribution. Finally, this study opens new avenues for research, particularly in examining the importance of specific feature sorting methods for certain supervised machine learning algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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