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International Conference on Magnetism and Its Application, ICMIA 2019

作者： Asih, Retno Dhari, Rosana Martina Baqiya, Malik Anjelh Astuti, Fahmi Harsono, Heru Saiyasombat, Chatree Kawamata, Takayuki Noji, Takashi Sato, Hidetaka Kato, Masatsune Watanabe, Isao Darminto Department of Physics Faculty of Science Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia Meson Science Laboratory RIKEN Nishina Center Wako351-0198 Japan Department of Physics Faculty of Mathematic and Natural Science University of Brawijaya Malang65145 Indonesia Synchroton Light Research Institute 111 University Avenue Muang District Nakhon Ratchasima3000 Thailand Department of Applied Physics Graduate School of Engineering Tohoku University Sendai980-8579 Japan Center of Excellence in Automotive Control and System Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia

ISBN: (纸本)9783035717044

Effects of Mn-substitution on magnetic properties of Zn1-xMnxO (MZO) nanoparticles with x= 0.00, 0.03, 0.05 and 0.07 have been investigated along with their local structure. The study on Mn K-edge XANES spectra of MZO reveals that the oxidation state increases by the Mn-substitution, which further implies that MZO exhibits a mixed valence state of Mn3+/Mn4+. The local structure analysis on Mn K-edge EXAFS spectra shows that the coordination number (CN) of Mn reduces by increasing Mn concentration, thus the amount of oxygen vacancy (VO) increases by the Mn-substitution. Interestingly, the magnetization of MZO also tend to increase as the Mn concentration increases. The M(H) curves exhibit linear (paramagnetic) behavior, showing no evidence of room-temperature ferromagnetism. Our results show that the magnetism of MZO is related to the correlation between the Mn magnetic moment and VO. © 2020 Trans Tech Publications Ltd, Switzerland.

关键词： Oxygen vacancies

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Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Learning

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Lea...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Weipeng Cao Xuyang Yao Zhiwu Xu Yinghui Pan Yixuan Sun Dachuan Li Bohua Qiu Muheng Wei Guangdong Laboratory of Artificial Intelligence and Digital Economy (Shenzhen) Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model's capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods.

关键词： Visualization Adaptation models Semantics Zero shot learning Neural networks Termination of employment Transformers Market research Sun Optimization

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Deep learning-based power control for uplink cell-free massive MIMO systems

arXiv

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arXiv 2021年

作者： Zhang, Yongshun Zhang, Jiayi Jin, Yu Buzzi, Stefano Ai, Bo School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University Beijing100044 China Department of Electrical and Information Engineering University of Cassino and Lazio Meridionale Cassino Italy State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China

In this paper, a general framework for deep learning-based power control methods for max-min, max-product and max-sum-rate optimization in uplink cell-free massive multiple-input multiple-output (CF mMIMO) systems is proposed. Instead of using supervised learning, the proposed method relies on unsupervised learning, in which optimal power allocations are not required to be known, and thus has low training complexity. More specifically, a deep neural network (DNN) is trained to learn the map between fading coefficients and power coefficients within short time and with low computational complexity. It is interesting to note that the spectral efficiency of CF mMIMO systems with the proposed method outperforms previous optimization methods for max-min optimization and fits well for both max-sum-rate and max-product optimizations. Copyright © 2021, The Authors. All rights reserved.

关键词： MIMO systems

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Analysis of Fault Characteristics and Distance Protection Adaptability for VSC-HVDC-Connected Offshore Wind Farms

Analysis of Fault Characteristics and Distance Protection Ad...

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IEEE Asia Power and Energy engineering Conference (APEEC)

作者： Fang Peng Houlei Gao Weiying Miao Xinchi Feng Bin Xu Yitong Wu Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China Substation Maintenance Center State Grid Hangzhou Power Supply Company Hangzhou China State Grid Nanjing Power Supply Company Nanjing China School of Electrical Engineering Shandong University Jinan China

With the characteristics of low loss, low harmonic content and large transmission capacity, voltage source converter based high voltage direct current transmission (VSC-HVDC) has become the focus of the development for large-capacity and long-distance power transmission system connected with offshore wind farms. However, since the power supplies on both sides are controlled, the fault characteristics are changed, and the performance of the traditional distance protection is influenced. Based on the control strategy, the amplitude and phase angle of the equivalent positive and negative sequence impedances at the two sides of the transmission line are analyzed, and the current phasor expressions of the wind farm side and the VSC-HVDC side of the faulted line are derived. Then the adaptability of distance protection using power frequency component (DPPFC) of transmission line for VSC-HVDC-connected offshore wind farm is analyzed under different fault conditions. The results show that the amplitude and phase angle of the equivalent positive and negative sequence impedances of the wind farm and the VSC-HVDC system are affected by the control strategy of the converter and the fault conditions. The DPPFC is not suitable for VSC-HVDC-connected offshore wind farm system. The ability to avoid the effect of fault resistance of the DPPFC at the wind farm side gets worse. The simulation conducted by EMTP-RV verifies the effectiveness of the analysis.

关键词： Resistance Analytical models Power transmission lines Power transmission Wind farms Propagation losses Power conversion

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Cluster-based Relevance Analysis Between Construction Mode and BCR for Optimizing the DAS

Cluster-based Relevance Analysis Between Construction Mode a...

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Data Driven control and Learning systems (DDCLS)

作者： Jiran Zhu Hua Leng Haiguo Tang Min Fan Yaling Liu Qing Yang College of Electronic and Information Engineering Tongji University Jiading Shanghai China State Grid Corporation of Hunan Electric Power corporation research institute Changsha Hunan China College of Electrical and Information Engineering Hunan University Changsha Hunan China School of Automation Chongqing University Chongqing Key Laboratory of Complex System Safety and Control (Ministry of Education) Chongqing University Chongqing China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

Distribution automation system (DAS) is the key of intelligent power distribution. With the construction and operation of DAS, studying on the optimization of DAS has received much attention from both academia and industry. This study proposed cluster-based relevance analysis between construction mode and benefit-cost ratio (BCR) for optimizing the DAS. This method clusters the DASs of different cities, and analyzes the clustering results. Then, the method analyzes BCR of DAS in different categories and uses comparative analysis, grey relational analysis (GRA) and sensitivity analysis to analyze the relevance between the construction mode and BCR of typical city's DAS and determine the major factors affecting the BCR comprehensively. Finally, it integrates all analysis results to make optimization suggestions for DAS. The case study shows that the method is effective and feasible, which can provide direction and reference basis for the optimization of DAS in cities of different scale.

关键词： Urban areas Principal component analysis Investment Optimization Sensitivity analysis Power distribution Automation

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The Comparison of Different Visual Features for Visual Odometry

The Comparison of Different Visual Features for Visual Odome...

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IEEE Chinese Guidance, Navigation and control Conference (CGNCC)

作者： Yuehan Jiang Qing Wang Chaoyang Dong Min Zhou Kewei Zhong School of Automation Science and Electrical Engineering Beihang University Beijing People’s Republic of China School of Aeronautic Science and Engineering Beihang University Beijing People’s Republic of China Science and Technology on Special System Simulation Laboratory Beijing Simulation Center Beijing People’s Republic of China Shanghai Aerospace Control Technology Institute Shanghai People’s Republic of China

ISBN: (数字)9781538611715

ISBN: (纸本)9781538611722

For robots to work effectively, the availability of a map with detailed information surrounding the workspace is an important requirement for indoor and outdoor tasks. This is usually achieved with using visual odometry techniques with feature-based methods. In this paper, we compare the performance of three different feature extraction methods: Scale Invariant Feature Transform (SIFT), Speed Up Robust Features (SURF) and Oriented FAST Rotated BRIEF (ORB). This paper presents experimental results on standard evaluation datasets and all experiments use measurement of the number of image correspondences as well as the ratio of good matched for the evaluation purpose. The results of experiments demonstrate that the performances of three methods in processing time, matching capability and accuracy. SIFT presents its stability in most scenarios although it is very slow. SURF is faster than SIFT and outperform SIFT on some scenarios. ORB is the most efficient feature and shows strong performance.

关键词： Feature extraction Detectors Robustness Visualization Transform coding Real-time systems Lighting

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Global Finite-Time Stabilization for a Chain of Integrators in the Presence of Actuator Saturation by Homogeneous Feedback

Global Finite-Time Stabilization for a Chain of Integrators ...

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IEEE Conference on Decision and control

作者： Tan Hou Yuanlong Li Zongli Lin Department of Automation Shanghai Jiao Tong University Shanghai P. R. China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia Charlottesville VA USA Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai P. R. China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai P. R. China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia Charlottesville VA USA

This paper considers the problem of globally stabilizing a chain of integrators with actuator saturation in finite time. The proposed method is adapted from an existing scheduled low gain approach by relaxing the range of the value of the low gain parameter. Due to the special structure of the considered system, the proposed feedback law not only results in finite-time stability but also is homogeneous in the whole state space. The homogeneity of the proposed feedback law allows its implementation through the discretization of a compact set of the state space and thus obviation from numerical difficulties in the existing scheduled low gain design. A numerical example is presented to validate our theoretical results and demonstrate that our approach achieves faster convergence than the existing methods.

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Breast Cancer Diagnosis Using an Unsupervised Feature Extraction Algorithm Based on Deep Learning

Breast Cancer Diagnosis Using an Unsupervised Feature Extrac...

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Chinese control Conference

作者： Yawen Xiao Jun Wu Zongli Lin Xiaodong Zhao Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai China The Center for Bioinformatics and Computational Biology East China Normal University Shanghai China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia Charlottesville VA U.S.A School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China

Breast cancer is the most often detected cancer in women. At the same time, it is one of the most curable types of cancer if diagnosed early. With the development of the detection technology, a growing amount of clinical data and high-dimensional features can be used for breast cancer diagnosis. The high-dimensional data contributes to advances in the diagnostic technology, but also incurs a large amount of computational redundancy. Thus, extracting important information and reducing the feature dimension is critical to effective prediction and an accurate treatment decision. However, the previous works for breast cancer diagnosis are mainly based on labeled data that is difficult to obtain. To address this issue, in this paper, we demonstrate a new scheme, which integrates a deep learning based unsupervised feature extraction algorithm, the stacked auto-encoders, with a support vector machine model (SAE-SVM), for breast cancer diagnosis. The stacked auto-encoders with the greedy layer-wise pre-training and an improved momentum update algorithm is applied to capture essential information and extract necessary features of the original data. Then, a support vector machine model is employed to classify the samples with new features into malignant or benign tumors. The proposed method was tested on the Wisconsin Diagnostic Breast Cancer data set. The performance is evaluated using various measures and compared with the previously published results. The comparison results show that the proposed SAE-SVM method improves the accuracy to 98.25% and outperforms the other methods. The deep learning based unsupervised feature extraction significantly improves the performance of classification and provides a promising approach to breast cancer diagnosis.

关键词： Breast cancer Feature extraction Machine learning Support vector machines Electronic mail Principal component analysis

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The Trajectory Re-Planning Method of the Hyper-Sonic Vehicle Facing Sudden Threats

The Trajectory Re-Planning Method of the Hyper-Sonic Vehicle...

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International Conference on control and Automation (ICCA)

作者： Jie Ren Jianglong Yu Xiwang Dong Zhang Ren School of Automation Science and Electrical Engineering Beihang University Beijing China Institute of Unmanned System Beihang University Beijing P.R. China Institute of Artificial Intelligence Beihang University Beijing P.R. China Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical Engineering Beihang University Beijing P.R. China

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

Aiming at the trajectory re-planning problem of multiple hyper-sonic vehicles with the time coordination under sudden threats, a time-coordinated fast trajectory re-planning method was proposed to avoid the no-fly zone. Firstly, the three-dimensional trajectory planning problem of the vehicle is decomposed into the longitudinal trajectory planning problem and the transverse trajectory planning problem. Secondly, a single sudden threat is regarded as a circular no-fly zone, and the circumvention of no-fly zone can only be considered in a transverse trajectory planning. Based on the in-depth study of the longitudinal trajectory planning with flight time constraints and range constraints, a reinforcement learning method is introduced into the transverse trajectory re-planning, and a fast tilting angle flipping self decision method based on the reinforcement learning is proposed. Finally, by training DQN to generate a tilting angle reversal decision maker, a fast three-dimensional trajectory re-planning method that can avoid the no-fly zone is realized. The proposed trajectory re-planning method not only has a fast trajectory re-planning speed, but also greatly improves the penetration probability against sudden threats. The simulation results show the feasibility and effectiveness of the proposed method.

关键词： Training Automation Trajectory planning Simulation Reinforcement learning Trajectory Planning

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A New Island Genetic Algorithm for Path Planning Problems *

A New Island Genetic Algorithm for Path Planning Problems *

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IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Jinghao Zhang Zhe Chen Ning Li Department of Automation Shanghai Jiao Tong University Shanghai P. R. China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai P. R. China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai P. R. China Department of Automation Tsinghua University Beijing P. R. China

Genetic algorithm (GA) is an effective method for path planning problems. As a powerful variant of GA, island genetic algorithm (IGA) has considerable improvement in performance. In this paper, a new island model of GA is proposed to avoid the premature phenomenon and achieve better efficiency. First, a new method of creating subpopulations is presented based on K-means to expand the searching area for the optima. Meanwhile, recombining subpopulations is proposed as a new strategy to improve the diversity of populations and save computational time. Moreover, a method is designed based on Monte Carlo sampling to handle the uncertainty of maps. Comparative experiments are presented to verify the efficiency of the proposed algorithm. Then, a proper number of samples is found by simulation to balance the accuracy and the time cost of Monte Carlo sampling.

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