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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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C2G2FSnake: automatic tongue image segmentation utilizing prior knowledge

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Science China(Information Sciences) 2013年第9期56卷 154-167页

作者： SHI MiaoJing LI GuoZheng LI FuFeng The Key Laboratory of Embedded System and Service Computing Ministry of Education Department of Control Science and Engineering Tongji University Laboratory of Information Access and Synthesis of TCM Four Diagnosis Shanghai University of Traditional Chinese Medicine

Extraction of the tongue body from digital images is essential for automated tongue diagnoses in traditional Chinese medicine. This paper presents a fully automated active contour initial method that utilizes prior knowledge of the tongue shape and its location in tongue images. Then colorspace information is introduced to control curve evolution. Combining the geometrical Snake model with the parameterized GVFSnake model, a novel approach for automatic tongue segmentation: C2G2FSnake (color control-geometric & gradient flow Snake) is proposed. This method increases the curve velocity but decreases the complexity. C2G2FSnake greatly extends practical usage to tongue segmentation, at the same time increasing the precision. Compared with other state-of-the-art works using different images of tongue color, C2G2FSnake realizes automatic tongue segmentation with greatly improved accuracy.

关键词： tongue diagnosis tongue segmentation color space-control Snake parameterized Snake geomet- ric Snake

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Cross-Domain Semantic Segmentation of Urban Scenes via Multi-Level Feature Alignment

Cross-Domain Semantic Segmentation of Urban Scenes via Multi...

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International Conference on Pattern Recognition

作者： Bin Zhang Shengjie Zhao Rongqing Zhang Key Laboratory of Embedded System and Service Computing Ministry of Education China School of Software Engineering Tongji University Shanghai China

Semantic segmentation is an essential task in plenty of real-life applications such as virtual reality, video analysis, autonomous driving, etc. Recent advancements in fundamental vision-based tasks ranging from image classification to semantic segmentation have demonstrated deep learning-based models' high capability in learning complicated representation on large datasets. Nevertheless, manually labeling semantic segmentation dataset with pixel-level annotation is extremely labor-intensive. To address this problem, we propose a novel multi-level feature alignment framework for cross-domain semantic segmentation of urban scenes by exploiting generative adversarial networks. In the proposed multi-level feature alignment method, we first translate images from one domain to another one. Then the discriminative feature representations extracted by the deep neural network are concatenated, followed by domain adversarial learning to make the intermediate feature distribution of the target domain images close to those in the source domain. With these domain adaptation techniques, models trained with images in the source domain where the labels are easy to acquire can be deployed to the target domain where the labels are scarce. Experimental evaluations on various mainstream benchmarks confirm the effectiveness as well as robustness of our approach.

关键词： Image segmentation Solid modeling Semantics Neural networks Virtual reality Feature extraction Robustness

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Designing and Analysis of a New Book-Lending system Based on Logic Petri Nets

Designing and Analysis of a New Book-Lending System Based on...

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International Conference on Electric Information and Control Engineering

作者： Xia Jiang WenJing Luan Department of Computer Laiwu Vocational and Technical College The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University

A new book-lending system is designed and analyzed based on logic Petri nets in this paper. The batch processing function and indeterminacy of readers are included in the system. Its logic Petri net model is establish... 详细信息

ISBN: (纸本)9781424480364

关键词： Book-lending system Batch processing Designing Verification Logic Petri net

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Block diagonal precoding based power allocation for coordinated multi-point transmission

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Journal of Communications 2011年第7期6卷 524-528页

作者： Han, Jing Wang, Ping Liu, Fuqiang Zhu, Yin Broadband Wireless Communications and Multimedia Laboratory Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Shanghai Key Laboratory of Digital Media Processing and Transmission Shanghai China

This paper studies power allocation in coordinated multi-point (CoMP) transmission of 3GPP LTE-Advanced system with remote radio units(RRUs) power constraints. We apply block diagonal (BD) precoding to downlink transmission, and assume perfect knowledge of downlink channels and transmit messages at each transmit point. We propose a modified water-filling power (MWF) allocation algorithm in order to maximize the downlink sum capacity, at the same time the low complexity is achieved. The interior-point method is also used to solve the optimization problem. Simulations show that interior-point method converges after only a few iterative steps and the system capacity is near-optimal. As for complexity and power efficiency, MWF achieves a good compromise. © 2011 ACADEMY PUBLISHER.

关键词： Iterative methods

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