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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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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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Design and implementation of communication module in passive ultrasonic sensor networks

Design and implementation of communication module in passive...

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2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering, CMCE 2010

作者： Han, Lina Zhao, Rui Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Third Research Institute of The Ministry of Public Security Shanghai China

ISBN: (纸本)9781424479566

Passive sensor networks can achieve accurate detection of target under complex environment. In order to adapt to different communication demands of sensor networks in different environments, this paper designed and implemented a wireless and wired mixed communication module, that is, a communication system combined short-distance wireless communication based on radio frequency technology with broadband cable communication. Simulation results show that our system can achieve the desired requirements in different circumstances. © 2010 IEEE.

关键词： Ultrasonic applications

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Multi‐granularity re‐ranking for visible‐infrared person re‐identification

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CAAI Transactions on Intelligence Technology 2023年第3期8卷 770-779页

作者： Yadi Wang Hongyun Zhang Duoqian Miao Witold Pedrycz Department of Computer Science and Technology Tongji UniversityShanghaiChina Key Laboratory of Embedded System and Service Computing Ministry of EducationShanghaiChina Department of Electrical and Computer Engineering University of AlbertaEdmontonAlbertaCanada System Research Institute Polish Academy of SciencesWarsawPoland

Visible‐infrared person re‐identification(VI‐ReID)is a supplementary task of single‐modality re‐identification,which makes up for the defect of conventional re‐identification under insufficient *** is more challenging than single‐modality ReID because,in addition to difficulties in pedestrian posture,camera shoot-ing angle and background change,there are also difficulties in the cross‐modality *** works only involve coarse‐grained global features in the re‐ranking calculation,which cannot effectively use fine‐grained ***,fine‐grained features are particularly important due to the lack of information in cross‐modality re‐*** this end,the Q‐center Multi‐granularity K‐reciprocal Re‐ranking Algorithm(termed QCMR)is proposed,including a Q‐nearest neighbour centre encoder(termed QNC)and a Multi‐granularity K‐reciprocal Encoder(termed MGK)for a more comprehensive feature *** converts the probe‐corresponding modality features into gallery corresponding modality features through modality transfer to narrow the modality *** takes a coarse‐grained mutual nearest neighbour as the dominant and combines a fine‐grained nearest neighbour as a supplement for similarity *** experiments on two widely used VI‐ReID benchmarks,SYSU‐MM01 and RegDB have shown that our method achieves state‐of‐the‐art ***,the mAP of SYSU‐MM01 is increased by 5.9%in all‐search mode.

关键词： computer vision recognition

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A hierarchical clustering strategy of processing class imbalance and its application in fraud detection 21

A hierarchical clustering strategy of processing class imbal...

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21st IEEE International Conference on High Performance computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and systems, HPCC/SmartCity/DSS 2019

作者： Zhang, Youjun Liu, Guanjun Zheng, Lutao Yan, Chungang Key Laboratory of the Ministry of Education for Embedded System and Service Computing Shanghai Electronic Transactions and Information Service Collaborative Innovation Center Tongji University Shanghai China

ISBN: (纸本)9781728120584

With the Internet and mobile communications becoming an indispensable part of people's daily lives, online transactions have become one of the most common payment methods. However, transaction fraud incidents also occur frequently, causing a larger number of economic losses. Therefore, transaction fraud detection is significant. The methods of machine learning are often used to detect fraudulent transactions from a larger amount of transaction data. However, the class imbalance problem reduces the performance of these methods. There are mainly four factors causing this problem: imbalanced class distribution, sample size, class separability and within-class concept. The existing improvement strategies for class imbalance mainly focus on the first factor but omit other three ones. This paper considers the four factors to propose a comprehensive model called clustering tree. Constructing a clustering tree includes two steps: 1) we first select a clustering algorithm considering the class separability;and then 2) we use this clustering algorithm to construct a tree that can be used to determine if an incoming transaction is illegal. The root node of this tree contains all samples, and we consider both the imbalanced class distribution and the within-class concept when samples are hierarchically divided into sub-nodes during the constructing process. We compare the proposed method with five state-of-the-art ones on two real transaction datasets, and the experimental results show that our method works better. © 2019 IEEE.

关键词： Clustering algorithms

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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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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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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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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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