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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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Genetic Fuzzy + PI Path Tracking Control of a Nonholonomic Mobile Robot

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电子学报(英文版) 2011年第1期20卷 31-34页

作者： CAO Zhengcai ZHAO Yingtao WU Qidi College of Information Science and Technology Beijing University of Chemical Technology Beijing China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

A mobile robot is one of the well-known nonholonomic systems. The genetic fuzzy + PI path track- ing control applied to a mobile robot is presented. The pro- posed controller composes of two fuzzy + PI controllers which are employed to control the angular velocity to- gether, and one fuzzy + PI controller that is utilized to adjust the speed of mobile robot solely. Moreover, the fuzzy controller is designed to track reference path in ap- proaching phase and PI controller is operated to diminish the tracking error in stable phase. In order to obtain bet- ter tracking performance, Genetic algorithm (GA) is used to optimize controller parameters. Finally, computer sim- ulation results are given to illustrate the effectiveness of the proposed control scheme.

关键词：模糊PI控制器移动机器人机器人控制非完整系统路径跟踪遗传算法模糊控制器控制器参数

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Resource Allocation for Multi-Modal Semantic Communication in UAV Collaborative Networks

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IEEE Transactions on Communications 2025年

作者： Hu, Han Zhu, Xingwu Zhou, Fuhui Wu, Wei Hu, Rose Qingyang Zhu, Hongbo Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Wireless Communications Nanjing210003 China Nanjing University of Posts and Telecommunications Engineering Research Center of Health Service System Based on Ubiquitous Wireless Networks Ministry of Education Nanjing210003 China Southeast University School of Cyber Science and Engineering Nanjing211189 China Nanjing University of Aeronautics and Astronautics College of Artificial Intelligence Nanjing210000 China Xidian University State Key Laboratory of Integrated Services Networks Xian710071 China Nanjing University of Posts and Telecommunications College of Communication and Information Engineering China Utah State University Department of Electrical and Computer Engineering LoganUT210003 United States

Semantic communication is envisioned as a potential communication paradigm enabled by artificial intelligence and is promising to break the Shannon limit for future 6G networks. This paradigm benefits unmanned aerial vehicles (UAVs) to conserve communication resources and minimize latency by only transmitting task-relevant semantic information. However, resource allocation in the multiple collaborative UAV scenarios remains unexplored, particularly regarding multi-modal semantic communication. To tackle this challenge, this paper investigates a semantic-aware intelligent resource allocation method for multi-UAV-assisted semantic communication networks in the UAV image-sensing task-oriented scenario. A multi-modal semantic communication framework with multi-UAV relay collaboration is developed. At the semantic level, a novel quality of experience (QoE) and the transmission cost model are introduced, based on which a semantic-aware resource allocation problem is formulated, aiming to maximize QoE while minimizing the transmission cost by jointly optimizing the UAV trajectory, the spectrum bandwidth, the transmit power and the number of the transmitted semantic symbols. To deal with optimization challenges involving hybrid variables and coordination among UAVs, a multi-UAV hybrid decision-controlled deep reinforcement learning (DRL) scheme is proposed. Simulation results demonstrate the effectiveness of the proposed scheme compared with the benchmark schemes in achieving a good balance between the QoE and the transmission cost. © 1972-2012 IEEE.

关键词： Deep reinforcement learning

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Large-scale video compression： recent advances and challenges

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Frontiers of computer Science 2018年第5期12卷 825-839页

作者： Tao TIAN Hanli WANG Department of Computer Science and Technology Tongji University Shanghai 201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 China Shanghai Engineering Research Center of Industrial Vision Perception & Intelligent Computing Shanghai 200092 China

The evolution of social network and multimedia technologies encourage more and more people to generate and upload visual information, which leads to the generation of large-scale video data. Therefore, preeminent compression technologies are highly desired to facilitate the storage and transmission of these tremendous video data for a wide variety of applications. In this paper, a systematic review of the recent advances for large-scale video compression （LSVC） is presented. Specifically, fast video coding algorithms and effective models to improve video compression efficiency are introduced in detail, since coding complexity and compression efficiency are two important factors to evaluate video coding approaches. Finally, the challenges and fu- ture research trends for LSVC are discussed.

关键词： large-scale video compression fast video coding compression efficiency

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Research on road topology based mobility prediction schemes

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High Technology Letters 2007年第2期13卷 140-144页

作者：陈林 Chen Hongzhong Jiang Changjun Key Lab of Embedded System and Service Computing of the Ministry of Education Department of Computer Science and Technology Tongji University Shanghai 201804 P.R.China

Geographic routing has been introduced in mobile ad hoc networks and sensor networks. But its per-formance suffers greatly from mobility-induced location errors that can cause Lost Link （LLNK） and LOOP problems. Thus various mobility prediction algorithms have been proposed to mitigate the errors, but sometimes their prediction errors are substantial. A novel mobility prediction technique that incorpo-rates both mobile positioning information and road topology knowledge was presented. Furthermore, the performance of the scheme was evaluated via simulations, along with two other schemes, namely, Linear Velocity Prediction （LVP） and Weighted Velocity Prediction （WVP） for comparison purpose. The results of simulation under Manhattan mobility model show that the proposed scheme could track the movement of a node well and hence provide noticeable improvement over LVP and MVP.

关键词： geographic muting mobility prediction road topology shortest path Manhattan mo-bility model

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Traffic flow prediction model based on deep belief network and genetic algorithm

Traffic flow prediction model based on deep belief network a...

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作者： Zhang, Yaying Huang, Guan Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai200092 China

Traffic flow prediction plays an indispensable role in the intelligent transportation system. The effectiveness of traffic control and management relies heavily on the prediction accuracy. The authors propose a model based on deep belief networks (DBNs) to predict the traffic flow. Moreover, they use Fletcher-Reeves conjugate gradient algorithm to optimise the fine-tuning of model's parameters. Since the traffic flow has various features at different times such as weekday, weekend, daytime and nighttime, the hyper-parameters of the model should adapt to the time. Therefore, they employ the genetic algorithm to find the optimal hyper-parameters of DBN models for different times. The dataset from Caltrans Performance Measurement system was used to evaluate the performance of their models. The experimental results demonstrate that the proposed model achieved better performance in different times. © The Institution of Engineering and Technology 2018.

关键词： Genetic algorithms

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Multi-View Metrics Enhanced Heterogeneous Graph Neural Network for Extractive Summarization

Multi-View Metrics Enhanced Heterogeneous Graph Neural Netwo...

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2021 China Automation Congress, CAC 2021

作者： Zhang, Chaobo Wang, Junli Qi, Hongda Yan, Chungang Jiang, Changjun Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China

ISBN: (纸本)9781665426473

Currently, many works regarded extractive summarization as a binary classification problem. However, the metric of the summary classification is always too singular and cannot fully utilize sentence features. To address this issue, we propose a multi-view metrics enhanced heterogeneous graph neural network for extractive summarization (MHGS). Firstly, the text is modeled as a heterogeneous graph, which contains two types of nodes, namely, words and sentences. Secondly, in the graph update layer, cross-sentence relationships and long-distance associations are obtained through the indirect connection of words and the direct connection between sentences. Then, we design multi-view metrics including relevance, redundancy, new information, and Rouge to help the model improve the quality of the summary. Finally, to evaluate the effectiveness of the method, some experiments are conducted on the CNN & DailyMail dataset. The experimental results show a generally higher performance of MHGS compared with related methods. © 2021 IEEE

关键词： Graph neural networks

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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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Local-directed network coding in vehicular ad-hoc networks

Local-directed network coding in vehicular ad-hoc networks

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18th International Symposium on Intelligent Signal Processing and Communication systems, ISPACS 2010

作者： Haojie, Luo Zhang, Dongdong Ye, Chen Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 China

ISBN: (纸本)9781424473717

In this paper, we proposed a novel network coding method called local-directed network coding to improve the throughput of the vehicular ad-hoc network. Different from other vehicular network coding methods, we consider the direction of packet transmission. In detail, the intermediate node selects one main packet and some sub-packets from forwards and backwards sending queues, and then encodes these packets into one packet to broadcast. Simulation results show that our approach can efficiently improve the throughput of application layer in vehicular networks. © 2010 IEEE.

关键词： Network coding

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