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RAMC: A RSU-assisted multi-channel coordination MAC protocol for VANET

RAMC: A RSU-assisted multi-channel coordination MAC protocol...

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作者： Liu, Kai Guo, Jinhua Lu, Ning Liu, Fuqiang Wang, Xinhong Wang, Ping School of Electronics and Information Engineering Key Laboratory of Embedded System and Service Computing Tongji University 4800 Cao'an Road 201804 Shanghai China Department of Computer and Information Science University of Michigan-Dearborn 4901 Evergreen Rd Dearborn MI 48128 United States

Dedicated Short Range Communication (DSRC) employs one control channel for safety-oriented applications and six service channels for non-safety commercial applications. However, most existing multichannel schemes require all neighboring vehicles periodically (e.g. every 100 milliseconds) tune to the control channel for a full update of safety-oriented data before they can switch to the service channels for non-safety services. The safety exchange interval increases with the increase of traffic density. Consequently, under high traffic densities, the service channels are often completely idle while the control channel is congested. We propose a RSU Assisted Multi-channel Coordination MAC (RAMC) protocol that fully utilizes all channels to provide simultaneous safety and non-safety communications. Within the radio range of a roadside unit (RSU), vehicles are free to tune to any service channel. The RSU monitors all the safety messages being transmitted in both the control and service channels. Periodically, the RSU broadcasts a consolidated traffic view report to all neighboring vehicles in all channels. Therefore, a vehicle can operate in a service channel as long as it needs to achieve high throughput for non-safety applications, while maintaining adequate and timely safety awareness. Our simulation results show that the proposed RAMC protocol consistently achieves very high percentage of non-safety usage, while maintaining high safety message delivery ratios in various traffic density conditions. Copyright © 2011 The Institute of Electronics, Information and Communication Engineers.

关键词： Dedicated short range communications

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LBRM algorithm for rule extraction based on rough membership

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2013 3rd International Symposium on Chemical Engineering and Material Properties, ISCEMP 2013

作者： Zhang, Xian Yong Miao, Duo Qian College of Mathematics and Software Science Sichuan Normal University Chengdu Sichuan 610068 China Department of Computer Science and Technology Tongji University Shanghai 201804 China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 201804 China

ISBN: (纸本)9783037858363

Rule extraction is a main goal for rough set theory. This paper mainly constructs a new algorithm (LBRM Algorithm) for rule extraction based on rough membership. The confidence principle is established based on rough membership. Thus, LBRM Algorithm is proposed by utilizing discretization and clearness strategies under the fuzzy environment, and is applied to both interval rules and general rules in fuzzy classification. LBRM Algorithm effectiveness is illustrated by a medical example. In particular, LBRM Algorithm integrates the confidence on both previous LBR Algorithm and fundamental rough membership, and has some improvements on rule confidence. © (2013) Trans Tech Publications, Switzerland.

关键词： Rough set theory

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Research of Meridian Diagnosis system Based on SVM

Research of Meridian Diagnosis System Based on SVM

引用

The 3rd International Conference on Bioinformatics and Biomedical Engineering(iCBBE 2009)(第三届生物信息与生物医学工程国际会议)

作者： Guo-Xia Zhang Fu-Qiang Liu Chun-Lin Song Pei-Yuan Lai BW&M Institute The Key Laboratory of Embedded System and Service ComputingMinistry of Education Tongji University ShanghaiChina

Aiming at the deficiency of the current meridian diagnosis algorithms, SVM is applied to meridian diagnosis system. The system structure is described firstly, then the model selection of SVM is discussed in detail by taking chronic pharyngitis as an example: one-against-one method is used to realize multi-class;the problem of non-symmetrical samples of C-SVM is solved by giving positive and negative samples of different weights;a margin-based bound on generalization method is used to search parameters of the model. Finally, test results show that the classifier, which is realized and tested using vc++6.0, possess a very high recognition rate and can be applied to meridian diagnosis system.

关键词： Meridians Diagnosis system SVM Nonsymmetrical Samples Generalization Bound

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A Petri Nets Based Approach for Detecting the Data Races and Deadlocks in OpenMP Program

A Petri Nets Based Approach for Detecting the Data Races and...

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2012 Third International Conference on Theoretical and Mathematical Foundations of Computer Science(ICTMF 2012)

作者： Yulong Xian Zhijun Ding Department of Computer Science and Technology Tongji University The Key Laboratory of Embedded System and Service Computing Ministry of EducationTongji University

Because of OpenMP programs shielding the underlying parallel execution and scheduling details,data races and deadlocks are tend to occur during program ***,this paper puts forward the modeling method of OpenMP programs based on Petri *** flow of programs are modeled according to the semantics of program control statements and directives of OpenMP programs;Data flow of programs are modeled by abstracting read and write operations related to shared *** two detection algorithms of data race and deadlock for OpenMP program are given based on the coverability tree of Petri ***,corresponding software tool is designed and implemented,and an OpenMP program example of the dining philosophers problem is analyzed to indicate the effectiveness of this method and tools.

关键词： OpenMP, Data races, Deadlock, Static detection, Petri nets

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A comprehensive QoS determination model for Infrastructure-as-a-service clouds

A comprehensive QoS determination model for Infrastructure-a...

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2013 IEEE International Conference on Automation Science and Engineering, CASE 2013

作者： Xia, Yunni Zhou, Mengchu Luo, Xin Zhu, Qingsheng Software Theory and Technology Chongqing Key Lab Chongqing University Chongqing 400030 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 201804 China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ 07102 United States

ISBN: (纸本)9781479915156

Cloud computing is a recently developed new technology for complex systems with massive service sharing, which is different from the resource sharing of the grid computing systems. In a cloud environment, service requests from users go through numerous provider specific steps from the instant it is submitted to when the service is fully delivered. Quality modeling and analysis of clouds are not easy tasks because of the complexity of the provisioning mechanism and the dynamic cloud environment. This study proposes an analytical modelbased approach for quality evaluation of Infrastructure-as-a-service cloud and consider expected request completion time, rejection probability, and system overhead rate as key QoS metrics. It also features with the modeling of different warming and cooling strategies of machines and the ability to identify the optimal balance between system overhead and performance. © 2013 IEEE.

关键词： Quality control

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Multi-label Learning for Protein Subcellular Location Prediction

Multi-label Learning for Protein Subcellular Location Predic...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiao Wang Guo-Zheng Li Jia-Ming Liu Rui-Wei Zhao The Key Laboratory of Embedded System and Service Computing Ministry of Education Department of Control Science and Engineering Tongji University Shanghai China

Protein subcellular localization aims at predicting the location of a protein within a cell using computational methods. Knowledge of subcellular localization of proteins indicates protein functions and helps in identifying drug targets. Prediction of protein subcellular localization is an important but challenging problem, particularly when proteins may simultaneously exist at, or move between, two or more different subcellular location sites. Most of the existing protein subcellular localization methods are only used to deal with the single-location proteins. To better reflect the characteristics of multiplex proteins, we formulate prediction of subcellular localization of multiplex proteins as a multi-label learning problem. We present and compare two multi-label learning approaches, which exploit correlations between labels and leverage label-specific features, respectively, to induce a high quality prediction model. Experimental results on six protein data sets under various organisms show that our described methods achieve significantly higher performance than any of the existing methods. Among the different multi-label learning methods, we find that methods exploiting label correlations performs better than those leveraging label-specific features.

关键词： Proteins Correlation Protein engineering Multiplexing Machine learning Humans Learning systems

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A New MPI Parallel computing Environment Based on Fibre-channel Token-Routing Network for Materials Simulation Research

A New MPI Parallel Computing Environment Based on Fibre-chan...

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2011 International Conference on Advanced Materials in Microwaves and Optics（AMMO 2011）

作者： Xiaohui ZENG Dongsheng LIU Wenlang LUO Lixin FU school of Electronics and Information Engineering Jinggangshan University The Key Laboratory of Embedded System and Service Computing Ministry of EducationTongji University

In order to apply our high efficiency fibre-channel token-routing network(shortened as FC-TR network) to the field of materials simulation research, a new MPI parallel computing environment is proposed and designed, and independently developed a parallel programming environment FC-TR-MPI based on FC-TR network. In FC-TR-MPI, a new method was applied to point-to-point communication that the network communications between processes in the same computing node were changed into memory operations;moreover, according to the underlying software and hardware features of FC-TR network, new algorithms were proposed to optimize the communication performance of some collective communications. Experimental results show that, compared with Sca MPI parallel programming environment, FC-TR-MPI has a higher parallel efficiency and speedup.

关键词： MPI,parallel computing environment,fibre-channel,materials simulation

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SGED-Net: A Self-organizing Graph Embedding Deep Network for Travel Time Estimation

SGED-Net: A Self-organizing Graph Embedding Deep Network for...

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International Joint Conference on Neural Networks (IJCNN)

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

In recent years, the travel time prediction has been receiving sustained attention because of the prevalence of location based applications, smart city engineering and online car-hailing. In this paper, a Self-organizing Graph Embedding Deep Network (SGED-Net) model is proposed to address the challenging Origin-Destination(OD) based travel time estimation (TTE). Specifically, SGED-Net comprises four modules, involving travel feature extraction, spatial association graph generation, graph embedding, and travel time deep learning prediction modules. First, we comprehensively extract three travel characteristics and feed them into a modified LightGBM component for learning the importance sort of features for different trips. Second, considering the lack of intermediate trajectories in the OD-based TTE, we employ a self-organizing feature mapping (SOM) approach to obtain the prominent nodes among pick-up and drop-off locations. Meanwhile clustering algorithm is used to divide a city into n-clusters districts of variant sizes. The topology learned by SOM is combined with the districts divided by clustering algorithm to obtain a district-based spatial association graph. Moreover, an improved SDNE algorithm is leveraged to gain a low-dimension spatial association representation while preserving the global and local structure of the graph. Then, we design a deep neural network for learning the captured spatial association representation. Finally, a series of experiments in two real-world large-scale datasets demonstrate the SGED-Net's excellent performance.

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Cross-Modal Representation Reconstruction for Zero-Shot Classification

Cross-Modal Representation Reconstruction for Zero-Shot Clas...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Yu Wang Shenjie Zhao Key Laboratory of Embedded System and Service Computing Ministry of Education P. R. China Mojie Technology (Ningbo)

ISBN: (纸本)9781728176055;9781728176062

Zero-shot learning (ZSL) aims to recognize novel classes without training samples through transferring knowledge from seen classes, based on the assumption that both the seen and unseen classes share a latent semantic space. Previous works either focus on directly learning various mapping functions between visual space and semantic space, or searching a latent common subspace to alleviate semantic gap between different modalities. However, few methods directly learn modality-invariant representations for ZSL. In this paper, we propose a Cross-Modal Representation Reconstruction (CM-RR) framework to bridge the semantic gap between visual features and semantic attributes, as well as introducing a novel regularizer for automatically feature selection. Moreover, an iterative optimization process based on the ALM (Augmented Lagrangian Method) algorithm with the alternating direction strategy is developed to solve the proposed formulation. Extensive experiments on four benchmark datasets show the effectiveness of the proposed approach, and even the performance surpasses some deep learning based methods.

关键词： Training Visualization Semantics Signal processing algorithms Signal processing Feature extraction Iterative algorithms

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A fast restarting particle swarm optimizer

A fast restarting particle swarm optimizer

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Congress on Evolutionary Computation

作者： Junqi Zhang Xiong Zhu Wei Wang Jing Yao Department of Computer Science and Technology Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

Particle swarm optimization (PSO) is a swarm intelligence technique that optimizes a problem by iterative exploration and exploitation in the search space. However, PSO cannot achieve the preservation of population diversity on solving multimodal optimization problems, and once the swarm falls into local convergence, it cannot jump out of the local trap. In order to solve this problem, this paper presents a fast restarting particle swarm optimization (FRPSO), which uses a novel restarting strategy based on a discrete finite-time particle swarm optimization (DFPSO). Taking advantage of frequently speeding up the swarm to converge along with a greater exploitation capability and then jumping out of the trap, this algorithm can preserve population diversity and provide a superior solution. The experiment performs on twenty-five benchmark functions which consists of single-model, multimodal and hybrid composition problems, the experimental result demonstrates that the performance of the proposed FRPSO algorithm is better than the other three representatives of the advanced PSO algorithm on most of these functions.

关键词： Particle swarm optimization Sociology Statistics Convergence Oscillators Algorithm design and analysis Benchmark testing

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