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10th International Conference on Intelligent Human-Machine systems and Cybernetics, IHMSC 2018

作者： Zhang, Yufeng Liu, Na Guo, Shan Tong, Jun Zhou, Qixun Xi'an University of Science and Technology Xi'an China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

ISBN: (纸本)9781538658369

In this paper, a single stator twin external rotors ironless AFPMSM for robot applications is studied. The design method of the motor is proposed. The magnetic circuit calculation and the main motor size equations are obtained. To solve the problem of low air-gap flux density in the ironless disk-type PMSM, the armature windings adopts the single-layer concentrated windings, which effectively reduces the air-gap length, increasing the motor power density, and they are initially verified with 3D-FEA. Finally, the mechanical structure of the motor is designed, a prototype is built and experiments are performed. The results show that the back-EMF error is within 2%, the air-gap flux density can reach 0.75T, and the motor has good acceleration performance, which verifies the rationality and feasibility of the design. © 2018 IEEE.

关键词： Permanent magnets

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Method on the fault detection and diagnosis for the railway turnout based on the current curve of switch machine

Method on the fault detection and diagnosis for the railway ...

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2nd International Conference on Mechanical Engineering, Industrial Electronics and Informatization, MEIEI 2013

作者： Mo, Xue Mei Fang, Yu Yang, Yun Guo The Key Laboratory of Embedded System and Service Computing Ministry of Education China Shanghai Railway Telecommunication Equipment Ltd Co. Shanghai China

ISBN: (纸本)9783037858905

This paper proposes a method of the fault detection and diagnosis for the railway turnout based on the current curve of switch machine. Exact curve matching fault detection method and SVM-based fault diagnosis method are adopted in the paper. Based on envelope and morpheme match algorithm, exact curve matching method is used to match the detected current curve with the reference curve so as to predict whether the curve would have fault or not. Moreover, the SVM-based fault diagnosis method is used to make sure that the fault conditions could be diagnosed intelligently. Finally, the experimental results show that the proposed method can accurately identify the turnout fault status in the conversion process, and the accuracy rate in the diagnosis of the fault location is above 98%, which verify the effectiveness of the method in the fault detection and diagnosis. © (2013) Trans Tech Publications, Switzerland.

关键词： Fault detection

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Utilizing adaptive techniques to supress noise in high frequency ground wave radar

Utilizing adaptive techniques to supress noise in high frequ...

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4th Microwave and Radar Week MRW-2010 - 11th International Radar Symposium, IRS 2010

作者： Gui, Renzhou Key Laboratory of Embedded System and Service Computing Ministry of Education College of Electronics and Information Engineering Tongji University Shanghai 200092 China State Key Laboratory of Millimeter Waves China

ISBN: (纸本)9789955690184

Reducing noise disturbances in the frequency segment of high frequency (HF) ground wave radar and restraining the sidelobes of strong targets that interfere with the detection of weak targets are the interesting Topic. A new method based on an adaptive techniques that solves these problems is proposed. By changing the working time of the frequency spectrum monitor (FSM), we have shown not only that radar can run in the frequency segments with lower noise disturbances, but also that the noise data produced by FSM can be exploited effectively. There is no correlation between the noise and the useful echo signal, though the correlation between noises over very short time periods is strong,. Exploiting the phenomena, we can adjust system parameters in real-time by adaptive methods to solve the two problems,namely sidelobe disturbance of strong targets and noise distrubance in the frequency segment.

关键词： Bandpass filters

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An Initialization Method based on The Core Clusters for Locality-weight Fuzzy C-Means Clustering 4th

An Initialization Method based on The Core Clusters for Loca...

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2013 IEEE 4th International Conference on Software Engineering and service Science

作者： Lei Gu The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University School of Computer Science and Technology Nanjing University of Posts and Telecommunication

ISBN: (纸本)9781467349970

The Locality-weight fuzzy c-means clustering method has been presented *** this approach can improve the clustering accuracies,it often gains the unstable clustering results because some random samples are employed for the initial *** this paper,an initialization method based on the core clusters is used for the locality-weight fuzzy c-means *** core clusters can be formed by constructing the σ-neighborhood graph and their centers are regarded as the initial centers of the locality-weight fuzzy c-means *** investigate the effectiveness of our approach,several experiments are done on three *** results show that our proposed method can improve the clustering performance compared to the previous locality-weight fuzzy c-means clustering.

关键词： the initialization clustering methods core clusters locality-weight fuzzy c-means neighborhood graph

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Adaptive Granulation for Efficient Classification on Noise Labeled Images 9

Adaptive Granulation for Efficient Classification on Noise L...

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9th IEEE International Conference on Computational Intelligence and Applications, ICCIA 2024

作者： Cai, Kecan Zhang, Hongyun Miao, Duoqian Tongji University Department of Computer Science and Technology Shanghai China Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China

ISBN: (纸本)9798350352214

Current semi-supervised learning-based sample selection methods for noisy label image classification typically utilize all clean and noisy samples for model training. However, not all noisy samples contribute positively to model training. This paper introduces a novel semi-supervised image data granulation method that employs adaptively generated granular noisy sample subsets in place of the original noisy samples to enhance classification efficiency. The granular data generated retain the essential features of the original data, thereby improving efficiency without compromising classification accuracy. The quality of the granular data is assessed using coverage and specificity criteria, standard metrics for evaluating information granules. The proposed method consists of three main components: (i)selecting clean and noisy samples through network co-training, (ii)calculating granular noisy sample subsets by adaptive granulation, and (iii)optimizing the network model using a semi-supervised strategy. Experimental results on benchmark datasets with varying noise rates demonstrate that our method significantly improves the efficiency of noisy label image classification while maintaining accuracy. © 2024 IEEE.

关键词： Self-supervised learning

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General measure of knowledge granularity in incomplete information system

General measure of knowledge granularity in incomplete infor...

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GCIS, WRI Global Congress on Intelligent systems

作者： Zhao, Lei Zhao, Mingqing Shandong University of Science and Technology Qingdao China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

ISBN: (纸本)9780769543048

This essay introduces the concepts of knowledge granularity and information system. On this basis, we propose two new more general knowledge granularities: the combination granularity and polynomial granularity, which include the current common knowledge granularity. And discuss the use of their combination. The results for the establishment of granular computing in the incomplete information system have some theoretical significance and practical values. © 2010 IEEE.

关键词： Information systems

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Particle swarm optimization based on pairwise comparisons 9th

Particle swarm optimization based on pairwise comparisons

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9th International Conference on Swarm Intelligence, ICSI 2018

作者： Zhang, JunQi Chen, JianQing Zhu, XiXun Wang, ChunHui Department of Computer Science and Technology Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China

ISBN: (纸本)9783319938141

Particle swarm optimization (PSO) is a widely-adopted optimization algorithm which is based on particles’ fitness evaluations and their swarm intelligence. However, it is difficult to obtain the exact fitness evaluation value and is only able to compare particles in a pairwise manner in many real applications such as dose selection, tournament, crowdsourcing and recommendation. Such ordinal preferences from pairwise comparisons instead of exact fitness evaluations lead the traditional PSO to fail. This paper proposes a particle swarm optimization based on pairwise comparisons. Experiments show that the proposed method enables the traditional PSO to work well by using only ordinal preferences from pairwise comparisons. © Springer International Publishing AG, part of Springer Nature 2018.

关键词： Swarm intelligence

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Non-intrusive load disaggregation using semi-supervised learning method

Non-intrusive load disaggregation using semi-supervised lear...

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2019 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2019

作者： Miao, Nan Zhao, Shengjie Shi, Qingjiang Zhang, Rongqing Tongji University Key Laboratory of Embedded System and Service Computing of Ministry of Education Shanghai China Tongji University Sch. of Software Eng. the Key Lab. of Embedded System and Service Computing of Ministry of Education Shanghai China Tongji University School of Software Engineering Shanghai China

ISBN: (纸本)9781728159287

With the emerging of smart metering around the world, there is a growing demand to analyse the residential energy usage. In this paper, we propose a Deep Neural Network (DNN)-based approach for non-intrusive load monitoring (NILM), which can achieve effective and efficient estimation of individual appliance usage according to a single main meter reading in a non-intrusive manner. Considering practical situations, two training methods are provided. The first training approach is fully supervised learning, which requires a ground truth of label, indicating the state of the appliance (ON/OFF), to build a prediction model. The second training approach is semi-supervised learning, leading to better performance by F-Measure metric while only requiring some more unlabelled training data. Experimental results on the low-sample rate REDD dataset demonstrate the superior performance of our proposed DNN-based method compared with Hidden Markov Model (HMM)based and Graph Signal Processing (GSP)-based approaches. © 2019 IEEE.

关键词： Deep neural networks

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Game-theoretic private blockchain design in edge computing networks

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Digital Communications and Networks 2024年第6期10卷 1622-1634页

作者： Daoqi Han Yang Liu Fangwei Zhang Yueming Lu Key Laboratory of Trustworthy Distributed Computing and Service Ministry of EducationBeijing University of Posts and Telecommunications(BUPT)Beijing 100876China Beijing Institute of Astronautical Systems Engineering BeijingChina Beijing Institute of Technology BeijingChina

Considering the privacy challenges of secure storage and controlled flow,there is an urgent need to realize a decentralized ecosystem of private blockchain for cyberspace.A collaboration dilemma arises when the participants are self-interested and lack feedback of complete *** blockchains have similar faults,such as trustlessness,single-factor consensus,and heavily distributed ledger,preventing them from adapting to the heterogeneous and resource-constrained Internet of *** this paper,we develop the game-theoretic design of a two-sided rating with complete information feedback to stimulate collaborations for private *** design consists of an evolution strategy of the decision-making network and a computing power network for continuously verifiable *** formulate the optimum rating and resource scheduling problems as two-stage iterative games between participants and *** theoretically prove that the Stackelberg equilibrium exists and the group evolution is ***,we propose a multi-stage evolution consensus with feedback on a block-accounting workload for metadata *** continuously validate a block,the metadata of the optimum rating,privacy,and proofs are extracted to store on a lightweight ***,to increase resource utilization,surplus computing power is scheduled flexibly to enhance security by ***,the evaluation results show the validity and efficiency of our model,thereby solving the collaboration dilemma in the private blockchain.

关键词： Ecosystem Edge computing Feedback Stackelberg game Private blockchain Scalable computing power

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A hybrid differential evolution algorithm for meta-task scheduling in grids

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High Technology Letters 2009年第3期15卷 261-266页

作者：康钦马 Jiang Changiun He Hong Huang Qiangsheng The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 201804 P.R. China School of Infomlation Engineering Shandong University at Weihai Weihai 264209 P.R. China

Task scheduling is one of the core steps to effectively exploit the capabilities of heterogeneous re-sources in the *** paper presents a new hybrid differential evolution(HDE)algorithm for findingan optimal or near-optimal schedule within reasonable *** encoding scheme and the adaptation ofclassical differential evolution algorithm for dealing with discrete variables are discussed.A simple but ef-fective local search is incorporated into differential evolution to stress *** performance of theproposed HDE algorithm is showed by being compared with a genetic algorithm(GA)on a known staticbenchmark for the *** results indicate that the proposed algorithm has better perfor-mance than GA in terms of both solution quality and computational time,and thus it can be used to de-sign efficient dynamic schedulers in batch mode for real grid systems.

关键词： Hybrid differential evolution grid computing task scheduling genetic algorithm

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