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International Symposium on Advances in Computer and Sensor Networks and systems, 2008

作者： Fei, Yin Dongliang, Zhang Department of Computer Science and Technology Tongji University The Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai 201804 China

ISBN: (纸本)9781427629807

In this paper, we present the parallel implementation of the traffic microsimulation PMTS (Parallel Microscopic Traffic Simulation) focusing on the performance issues. The parallelization of PMTS is domain decomposition, which means that each processor of the PC cluster is responsible for a different geographical area of the simulation region. We describe the transportation network graph partition and information exchange between domains. We demonstrate the time cost mathematics models for PMTS: the vehicle generation, vehicle position calculation, and vehicle information exchange between domains. The workload balance is obtained by adjusting the boundary lines according to the relative load of adjacent subnetworks. All these works have been proved to be effective when PMTS put into use and the experiment results are also provided which match our analysis.

关键词： Vehicles

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Upper Bounds on Lifetime of Three Dimensional Extended UWB Sensor Networks

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电子学报(英文版) 2010年第2期19卷 307-312页

作者： XU Juan JIANG Changjun GUO Aihuang HONG Yongfa LI Shu WANG Cheng School of Electronics and Information Engineering Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing of the Ministry of Education Tongji University Shanghai China College of Information Science and Engineering Shandong University of Science and Technology Qingdao China

The upper bounds on lifetime of three dimensional extended Time hopping impulse radio Ultrawide band (TH-IR UWB) sensor networks are derived using percolation theory arguments. The TH-IR UWB sensor network consists of n sensor nodes distributed in a cube of edge length n^1/3 according to a Poisson point process of unit intensity. It is shown that for such a static three dimensional extended TH-IR UWB sensor network, the upper bound on the lifetime is of order O(n^-1), while in the ideal case, the upper bound on the lifetime is longer than that of a static network by a factor of n ^2/3. Therefore sensor nodes moving randomly in the deployment area can improve the upper bound on network lifetime. The results also reveal that the upper bounds on network lifetime decrease with the number of nodes n, thus extended THIR UWB sensor networks aren't prone to be employed in large-scale network.

关键词：无线传感器网络三维扩展寿命延长超宽带上界静态网络生存时间移动节点

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Spatial-Temporal Dynamic Graph Diffusion Convolutional Network for Traffic Flow Forecasting

Spatial-Temporal Dynamic Graph Diffusion Convolutional Netwo...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Li, Yang Zhang, Yaying Tongji University Key Laboratory of Embedded System and Service Computing Ministry of Education China Shanghai Artificial Intelligence Laboratory Shanghai China

ISBN: (纸本)9798350324457

Traffic forecasting plays a crucial role in intelligent transportation systems and finds application in various domains. Accurate traffic forecasting remains challenging due to the time-varying correlations within the data and the heterogeneous correlations between regions. Although various dynamic spatial-temporal graph models have been proposed to address these challenges in recent years, most of them are burdened by high computation costs and not intuitive to understand. In this paper, we propose a spatial-temporal graph model, Spatial-Temporal Dynamic Graph Diffusion Convolutional Network (SDGDN) that provides an effective and efficient approach to traffic forecasting. From the perspective of traffic flow transition probabilities, SDGDN learns dynamic graph structures to capture the time-varying traffic transition relationships. Besides dynamic graph structures, static node features are employed in diffusion convolution to better capture heterogeneous regional features. Furthermore, we utilize temporal encoding and also generate varying graphs in each stacked layer to enhance the forecasting performance. Experiments results on five real-world datasets demonstrate that SDGDN outperforms most baseline models in terms of both performance and computation efficiency. © 2023 IEEE.

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Independent global constraints-aware web service composition optimization

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Information Technology Journal 2009年第2期8卷 181-187页

作者： Fang, Xianwen Jiang, Changjun Fan, Xiaoqin Department of Computer Science and Technology Tongji University Shanghai 201804 China Key Lab of Embedded System and Service Computing Ministry of Education Shanghai 201804 China Department of Information and Computer Science Anhui University of Science and Technology Huainan Anhui 232001 China

In semi-automatic service composition, developers should construct the process model according to concrete application requirement and then the instance services are bound automatically for every abstract task and make the composite service with optimal performance. This study presents independent global constrains-aware Web service composition method based on associate Petri net (APN) and genetic algorithm (GA). Firstly, an APN modeling methods which can describe multi-attribute multi-constraint relations and associate relation between component services is proposed. Secondly, combining with the properties of APN, GA is used to search a legal firing sequence with the biggest trust value in the APN model and the composite service corresponding to the legal firing sequence is the optimal solution. Finally, the experimental simulation is given out. Theoretical analysis and experimental results indicate that this method owns both lower computation cost and higher success ratio of service composition. © 2009 Asian Network for Scientific Information.

关键词： Web services

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Analysis and Design of Ironless Axial Flux Permanent Magnet Synchronous Motor 10

Analysis and Design of Ironless Axial Flux Permanent Magnet ...

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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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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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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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Robust analog precoding designs for millimeter wave MIMO transceivers 20

Robust analog precoding designs for millimeter wave MIMO tra...

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20th International ITG Workshop on Smart Antennas, WSA 2016

作者： Xia, Pengfei Heath, Robert W. Prelcic, Nuria Gonzalez Key Lab of Embedded System and Service Computing Tongji University Shanghai China University of Texas at Austin AustinTX United States University of Vigo Vigo Pontevedra Spain

ISBN: (纸本)9783800741779

Millimeter wave communication provides high data rates thanks to large arrays at the transmitter and receiver, coupled with large bandwidth channels. Exploiting the arrays is challenging due to the need to configure precoding at the transmitter based on the large frequency selective channel. In this paper we exploit the power iteration principle and propose a robust analog precoding training algorithm that can be applied in both frequency division duplex transmission (FDD) systems and time division duplex transmission (TDD) systems with or without RF calibration. We further analyze the convergence of the proposed algorithm and show how it converges to the singular value decomposition optimality exponentially. We propose null space projection on top of the power iteration to form multiple orthogonal beams at the transmitter and receiver. Strongest tap selection with proper energy pruning is used to collect as much precoding gain as possible from a frequency selective fading channel. The exponential effective SINR mapping performance is evaluated and demonstrates that the overall approach works effectively. Numerical simulation results demonstrate algorithm robustness and the algorithm works effectively not only for the simplified millimeter wave directional channels, but also for more general rich scattering channels. © VDE VERLAG GMBH · Berlin · Offenbach, Germany.

关键词： Millimeter waves

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Spatial-Temporal Flow Holistic Interaction Graph Convolution Network for Bidirectional Traffic Flow Forecasting

Spatial-Temporal Flow Holistic Interaction Graph Convolution...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Zhang, Canyang Zheng, Qi Zhang, Yaying Tongji University Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China Shanghai Artificial Intelligence Laboratory Shanghai China

ISBN: (纸本)9798350324457

Traffic flow forecasting is indispensable in modern urban life. Considering the complexity, variability and strong timeliness of traffic flow, traffic flow forecasting is a worth exploring but challenging research field. To achieve better traffic flow forecasting effect, we focus on two critical aspects that assume noteworthy importance: i) the features inside the traffic outflows and inflows. ii) the supplementary information regarding exterior region which is the area outside the grid division regions. To address these challenges, we propose a novel deep learning model Spatial-Temporal Flow Holistic Interaction Graph Convolution Network (STHGCN). In STHGCN, graph convolution based modules are applied through multi-step simulation. An exterior region feature estimation module is designed to estimate the influence of the special exterior region through the characteristics of complete trajectories, which enables a more comprehensive reasoning for traffic flow forecasting in grid division regions. Furthermore, a flow feature fusion integrator and stackable convolution modules are proposed to aggregate the intermediate features extracted from various perspectives, which simulate the constantly-updating and interlinked states of traffic flows through the process of multi-layer feature separation and fusion. We conduct extensive experiments on real-world traffic datasets and our proposed model outperforms all baselines. © 2023 IEEE.

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