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CIRED 2022 Shanghai Workshop

作者： Cao, Yongji Zhang, Hengxu Zhang, Xiaoning Li, Changgang Qin, Hao Gao, Zhimin Centre for Electric Power and Energy Department of Electrical Engineering Technical University of Denmark Kgs. Lyngby Denmark Key Laboratory of Power System Intelligent Dispatch and Control of the Ministry of Education Shandong University Jinan China School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (纸本)9781839538391

With the rapid development of distributed renewable energies, the number and severity of the active power disturbances that affect frequency stability have increased. The distributed energy storage system (DESS) characterized by high flexibility and dispatchability can provide distribution network with frequency regulation services after disturbances. This paper proposes a robust optimal scheduling approach for DESSs considering uncertain frequency changes. The concept of the probabilistic transient frequency deviation security margin (PTFDSM) is presented to quantify the power deficit that the system can tolerate. And an extended system frequency response model is employed to obtain the PTFDSM. Then, a robust optimization model is established to minimize the network loss and maintain the operating point of DESSs within the PTFDSM for frequency stability. The AC power flow constraints are introduced into the optimization model. And the second-order cone relaxation method is used to deal with the nonlinear constraints through relaxation variables. Based on the column-and-constraint generation method, the relaxed second-order cone optimization model is solved, and scheduled DESS power is attained. A case study is carried out to verify the effectiveness of the proposed approach. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Energy storage

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SliceProp: A Slice-Wise Bidirectional Propagation Model for Interactive 3D Medical Image Segmentation 1

SliceProp: A Slice-Wise Bidirectional Propagation Model for ...

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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Xu, Xin Lu, Wenjing Lei, Jiahao Qiu, Peng Shen, Hong-Bin Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Ninth People's Hospital Shanghai Jiao Tong University School of Medicine Department of Vascular Surgery China Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

ISBN: (纸本)9798350358780

Interactive medical image segmentation methods have become increasingly popular in recent years. These methods combine manual labeling and automatic segmentation, reducing the workload of annotation while maintaining high accuracy. However, most current interactive segmentation frameworks are limited to 2D image data, and are not suitable for 3D image data due to the large size and high complexity of 3D data, as well as the challenges posed by information asymmetry and sparse annotation. In this paper, we propose SliceProp, an interactive segmentation framework that implements slice-wise Label Bidirectional Propagation (LBP) for 3D medical image segmentation. SliceProp extends the interactive 2D image segmentation algorithm to 3D image segmentation, and can handle 3D data with large size and high complexity. Moreover, equipped with a Backtracking Feedback Check (BFC) module, SliceProp effectively addresses the issues of information asymmetry and spatial sparse annotation in 3D medical image segmentation. Additionally, we adopt an uncertainty-based criterion to pri-oritize the slices to be refined interactively, which enhances the efficiency of the interaction process by enabling the model to focus on the regions with the most unreliable predictions. SliceProp is evaluated on two datasets and achieves promising results compared to state-of-the-art methods. © 2023 IEEE.

关键词： Medical imaging

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Distributed Collaborative Optimization of a Multi-Region Integrated Energy system Based on Edge Computing Unit

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FRONTIERS IN ENERGY RESEARCH 2022年 10卷

作者： Wang, Mengxue Zhao, Haoran Tian, Hang Wu, Qiuwei Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China Department of Electrical Engineering Technical University of Denmark Lyngby Denmark

The coordinated optimization scheduling of the integrated energy systems is vital in multi-energy complementarity and hierarchical utilization. However, the centralized optimization is inferior to the distributed optimization of the large-scale multiregion integrated energy system (MRIES) in data processing capacity and information security. This study proposes a distributed computing architecture based on the edge computing unit (ECU), which takes the energy hub as the main body and sets the partitioning principle and method of MRIES. The ECU can finally realize the whole-system collaborative optimization of MRIES, which contains electrical, natural gas, and district heating networks through internal autonomous optimization and boundary information interaction with the cloud computing center. At the same time, an improved nested algorithm based on the consensus-alternating direction method of multipliers is proposed, which ensures the convergence of the mixed-integer linear program and effectively improves the convergence speed. Combining the advantages of the model and algorithm provides a theoretical and algorithmic support for the optimization research of the MRIES.

关键词： edge computing unit multiregional integrated energy system distributed collaborative optimization energy hub energy conservation

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Application of PID parameters optimization based on GWO in mould level system

Application of PID parameters optimization based on GWO in m...

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2021 International Conference on Internet, education and Information Technology, IEIT 2021

作者： Sitong, Liu Xiangwei, Kong Peifeng, Hao Hezhan, Gao Software College Northeastern University Shenyang China School of Mechanical Engineering Automation Northeastern University Key Laboratory of Vibration and Control of Aero-Propulsion System Ministry of Education Liaoning Province Key Laboratory of Multidisciplinary Design Optimization of Complex Equipment Shenyang China

ISBN: (纸本)9781665425636

Mold level stability is a very important factor in the continuous casting process. To effectively control the liquid level at the set value, solve the problems of disturbance, nonlinearity, and time-varying in the system, as well as modeling difficulties caused by uncertain factors, and aiming at the shortcomings of conventional PID control, the gray wolf algorithm is introduced the gray wolf optimization algorithm is used to self-tune the PID parameters, and the objective function is constructed based on the adjustment time and steady-state error of the mold liquid level system. Finally, the simulation and experimental results show that the algorithm optimization proposed in this paper is compared with the particle swarm algorithm, and the step response of the system is smoother. Gray Wolf optimized PID control has a fast response speed, with an adjustment time of 3.87s, which is lower than 13.46s for conventional PID control and 5.58s for the PSO algorithm. It can significantly improve the control accuracy and other performance indicators of the system. It provides a reference for improving the level control of the continuous casting mold. © 2021 IEEE.

关键词： Three term control systems

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Few-shot Learning for Rolling Bearing Fault Diagnosis Based on Residual Convolutional Neural Network 4

Few-shot Learning for Rolling Bearing Fault Diagnosis Based ...

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4th International Conference on Artificial Intelligence and Big Data, ICAIBD 2021

作者： Cui, Zihao Kong, Xiangwei Hao, Peifeng Software College Northeastern University Shenyang China School of Mechanical Engineering Automation Liaoning Province Key Laboratory of Multidisciplinary Design Optimization of Complex Equipment Northeastern University Key Laboratory of Vibration and Control of Aero-Propulsion System Ministry of Education Shenyang China

ISBN: (纸本)9780738131702

This paper proposes a new Siamese network model for fault diagnosis with limited training samples. We introduce residual blocks to the Siamese network model for few-shot learning. By using the same or different types of sample pairs to learn, our model further improves the accuracy of the Siamese network in bearing fault diagnosis under different workloads and noisy environments. Experiments on the CWRU and MFPT data set showed that the proposed model outperforms the convolutional Siamese network in bearing fault diagnosis. The accuracy of our model has achieved more than 93% accuracy with 90 training samples on the CWRU and MFPT data set. All our models and experiments are available from https://***/WunaiDczh/Few_data_Learing. © 2021 IEEE.

关键词： Failure analysis

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PID Parameter Optimization of Secondary Cooling Water Distribution Model for Continuous Casting Based on Improved Artificial Bee Colony Algorithm 4

PID Parameter Optimization of Secondary Cooling Water Distri...

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4th International Conference on Artificial Intelligence and Big Data, ICAIBD 2021

作者： Wang, Yibing Kong, Xiangwei Hao, Peifeng Software College Northeastern University Shenyang China School of Mechanical Engineering Automation Liaoning Province Key Laboratory of Multidisciplinary Design Optimization of Complex Equipment Northeastern University Key Laboratory of Vibration and Control of Aero-Propulsion System Ministry of Education Shenyang China

ISBN: (纸本)9780738131702

Dynamic control of secondary cooling in continuous casting is the key technology to improve the quality and efficiency of the slab. In order to improve the quality and efficiency of the slab, a new control strategy of secondary cooling water distribution based on an improved artificial bee colony algorithm is proposed. Based on the traditional artificial bee colony algorithm, adding the learning factor to the optimal individual learning factor and random individual of the current population and integrating the output overshoot mechanism into the objective function is effective to avoid the great overshoot phenomenon caused by the abruptly change of casting speed and the large fluctuation of casting surface temperature. The transfer function was constructed based on the coupling relationship between the distribution of secondary cooling water and slab surface temperature. The Simulink simulation model was established, which was compared with the PSO-DSWPI Method and conventional PID controller. The results showed that the improved artificial bee colony algorithm has a more minor error, higher control accuracy and more smooth step response, which provided a new method for realizing more accurate control of secondary cooling dynamic water distribution and had practical significance for improving slab quality. © 2021 IEEE.

关键词： Cooling water

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Isoform Function Prediction Based on Heterogeneous Graph Attention Networks

Isoform Function Prediction Based on Heterogeneous Graph Att...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Guo, Kuo Li, Yifan Chen, Hao Shen, Hong-Bin Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Institute of Image Processing and Pattern Recognition Shanghai200240 China Carnegie Mellon University School of Computer Science Computational Biology Department PittsburghPA15213 United States

ISBN: (纸本)9798350337488

Isoforms refer to different mRNA molecules transcribed from the same gene, which can be translated into proteins with varying structures and functions. Predicting the functions of isoforms is an essential topic in bioinformatics as it can provide valuable insights into the intricate mechanisms of gene regulation and biological processes. Conventionally, gene function labels are standardized in Gene Ontology (GO) terms. However, traditional methods for predicting isoform function are largely limited by the absence of isoform-specific labels, sparse annotations, and the vast number of GO terms. To address these issues, we propose HANIso, a deep learning-based method for isoform function prediction. HANIso leverages a pretrained protein language model to extract features from protein sequences. It also integrates heterogeneous information, such as isoform sequence features, GO annotations, and isoform interaction data, using a Heterogeneous Graph Attention Network (HAN). This allows the model to learn the importance of different sources of information and their semantic relationships through the attention mechanism. Our method can predict function labels at both the gene level and isoform level. We conduct experiments on two species datasets, and the results demonstrate that our method outperforms existing methods on both AUROC and AUPRC. HANIso has the potential to overcome the limitations of traditional methods and provide a more accurate and comprehensive understanding of isoform function. © 2023 IEEE.

关键词： alternative splicing gene ontology heterogeneous graph attention network isoform function prediction protein language model

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Investigation of a tunnel magnetoresistance based vector magnetometer for magnetic anomaly detection 2

Investigation of a tunnel magnetoresistance based vector mag...

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2022 2nd International Conference on Advanced Materials and Mechatronics, ICAMM 2022

作者： Wang, Hongpeng Yang, Ruiping Luo, Wang Ge, Jian Liu, Huan Dong, Haobin School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

The development of high-performance and miniaturized magnetometers is important for small-scale target magnetic anomaly detection. In this work, a miniaturized three-axis vector magnetometer based on the tunnel magnetoresistance (TMR) technique is investigated for magnetic anomaly detection. First, the miniaturized three-axis TMR vector magnetometer is constructed, and then, the limit of detection tests and the magnetic anomaly detection tests are performed. The indoor experimental results show that the proposed magnetometer can capture the magnetic anomalies higher than 3.9 nT, with the low-frequency equivalent magnetic noise of 46.89 pT/√Hz@1Hz. The outdoor experimental results show that the proposed magnetometer can effectively detect small-scale magnetic targets such as hammers at a distance of about 1.5 m. With the advantages of small size, low power consumption, and high magnetic measurement sensitivity, the TMR magnetometer has great application prospects for long-term magnetic anomaly monitoring, high-density magnetic imaging, and other fields. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Magnetometers

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Deep Learning-Based LOS and NLOS Parameters Estimation, Tracking, and Identification

Deep Learning-Based LOS and NLOS Parameters Estimation, Trac...

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IEEE International Conference on Communications in China Workshops (ICCC)

作者： Kai Chen Jun Wang Jie Yang Le Liang Chao-Kai Wen Shi Jin National Mobile Communications Research Laboratory Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing China Institute of Communications Engineering National Sun Yat-sen University Kaohsiung Taiwan

ISBN: (数字)9798350377675

ISBN: (纸本)9798350377682

Millimeter-wave (mmWave) communication systems have large bandwidths and, combined with large-scale antenna arrays and beamforming technology, facilitate higher resolution of angle and delay measurements. This, in turn, provides opportunities for integrated sensing and communication (ISAC). Accurate and reliable estimation of mmWave channel state information is crucial for ISAC. In this paper, we propose a deep learning-based method for estimating channel multipath parameters, tracking, and identifying line-of-sight (LOS) and non-line-of-sight (NLOS) paths. Specifically, we leverage a network named “you only look once (YOLO)”, a highly efficient object detection network in computer vision, to detect the angles of arrival (AOAs) and angles of departure (AODs) of multipaths from received radio frequency signals. Moreover, we use a tracking-by-detection algorithm, ByteTrack, to track the variation of each path's AOA and AOD under a mobile scenario. Thanks to these vision-based channel estimation approaches, we reveal that LOS and NLOS paths exhibit different trajectory properties, allowing us to precisely identify LOS and NLOS paths, a challenge originally in the context of ISAC. We validate the proposed estimation, tracking, and identification methods on real measurement data and demonstrate that the proposed estimation method shows better performance compared to existing methods.

关键词： Antenna measurements Computer vision Simultaneous localization and mapping Millimeter wave measurements Estimation Channel estimation Transforms Integrated sensing and communication Millimeter wave communication Antenna arrays

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A nonlinear vibration model of fiber metal laminated thin plate treated with constrained layer damping patches

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European Journal of Mechanics, A/Solids 2024年 106卷

作者： Gu, Dawei Chu, Chen Wu, Quanhui Chen, Guinan Qi, Zichen Xie, Shaojun Xu, Zhuo Li, Hui Tan, Dapeng Wen, Bangchun College of Mechanical Engineering Zhejiang University of Technology Hangzhou310023 China Key Laboratory of Vibration and Control of Aero-Propulsion System Ministry of Education Northeastern University Shenyang110819 China School of Mechanical Engineering Northeast Electric Power University Jilin 132011 China

This paper explores the nonlinear vibration characteristics of fiber metal laminated thin plate(FMLTP) treated with constrained layer damping(CLD) patches. Additionally, based on the Jones-Nelson nonlinear material theory and the von Kármán geometric nonlinear theory, a theoretical model for FMLTP with CLD patches is established, considering material nonlinearity. Using the energy method, an energy equation is formulated and the nonlinear natural frequencies and vibration responses of the studied structure are iteratively determined through the Newton-Raphson method under various excitation amplitudes. Additionally, the modal damping ratios are calculated utilizing the complex modulus method. To validate the effectiveness of the model, a corresponding testing system is constructed, and tests are conducted on a 5-layer TA2/TC500 FMLTP treated with two pieces of CLD patches. The test results show excellent agreement with the theoretical results. Finally, the influences of different CLD patches treatment methods on the vibration characteristics of the FMLTP are discussed. © 2024 Elsevier Masson SAS

关键词： Newton-Raphson method

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