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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8786卷 79-94页

作者： Zhong, Mingjie Ding, Zhijun Sun, Haichun Wang, Pengwei The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai200092 China Department of Computer Science University of Pisa Pisa56127 Italy

This paper presents a novel clustering algorithm based on clustering coefficient. It includes two steps: First, k-nearest-neighbor method and correlation convergence are employed for a preliminary clustering. Then, the results are further split and merged according to intra-class and inter-class concentration degree based on clustering coefficient. The proposed method takes correlation between each other in a cluster into account, thereby improving the weakness existed in previous methods that consider only the correlation with center or core data element. Experiments show that our algorithm performs better in clustering compact data elements as well as forming some irregular shape clusters. It is more suitable for applications with little prior knowledge, e.g. hotspots discovery. © Springer International Publishing Switzerland 2014.

关键词： Clustering algorithms

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Multicast capacity scaling for cognitive networks: General extended primary network

Multicast capacity scaling for cognitive networks: General e...

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2010 IEEE 7th International Conference on Mobile Adhoc and Sensor systems, MASS 2010

作者： Wang, Cheng Li, Xiang-Yang Tang, Shaojie Jiang, Changjun Department of Computer Science Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China Tsinghua National Laboratory for Information Science and Technology Tsinghua University Beijing China Department of Computer Science Illinois Institute of Technology Chicago IL 60616 United States

ISBN: (纸本)9781424474882

We study the capacity scaling laws for the cognitive network that consists of the primary hybrid network (PhN) and secondary ad hoc network (SaN). PhN is further comprised of an ad hoc network and a base station based (BS-based) network. SaN and PhN are overlapping in the same deployment region, operate on the same spectrum, but are independent with each other in terms of communication requirements. The primary users (PUs), i.e., the ad hoc nodes in PhN, have the priority to access the spectrum. The secondary users (SUs), i.e., the ad hoc nodes in SaN, are equipped with cognitive radios, and have the functionalities to sense the idle spectrum and obtain the necessary information of primary nodes in PhN. We assume that PhN adopts one out of three classical types of strategies, i.e., pure ad hoc strategy, BS-based strategy, and hybrid strategy. We aim to directly derive multicast capacity for SaN to unify the unicast and broadcast capacity under two basic principles: (1) The throughput for PhN cannot be undermined in order sense due to the presence of SaN. (2) The protocol adopted by PhN does not alter in the interest of SaN, anyway. Depending on which type of strategy is adopted in PhN, we design the optimal-throughput strategy for SaN. We show that there exists a threshold of the density of SUs according to the density of PUs beyond which it can be proven that: (1) when PhN adopts the pure ad hoc strategy or hybrid strategy, SaN can achieve the multicast capacity of the same order as it is stand-alone;(2) when PhN adopts the BS-based strategy, SaN can asymptotically achieve the multicast capacity of the same order as if PhN were absent, if some conditions of the relations among the number of SUs, PUs, the destinations of each multicast sessions in SaN, and the base stations in PhN hold. ©2010 IEEE.

关键词： Base stations

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Social incentive policies to engage commercial building occupants in demand response

Social incentive policies to engage commercial building occu...

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2014 IEEE International Conference on Automation Science and Engineering, CASE 2014

作者： Li, Sisi Deng, Kun Zhou, Meng Chu Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07012 United States Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China Coordinated Science Laboratory University of Illinois at Urbana-Champaign UrbanaIL61801 United States

Occupants' comfort is the primary target in a building operation. However their efforts are often neglected and ruled out from traditional control strategies of energy-efficient building management systems. Occupant-engaged control strategies have recently attracted many research attentions and demonstrated great potentials for energy saving. With them, occupants' behavior is incorporated into the closed-loop control methods in which their initiatives actively contribute to building services and energy utility by explicitly expressing their preferences. This work proposes an occupant-engaged demand response (DR) strategy for building automation in which occupants are actively engaged to adapt their energy consumption in response to incentive opportunities designed by facility managers. A model-based study and a Nash-Equilibrium-based solution are provided to assist facility managers with the design of social incentive policies to promote occupant participation, along with the guarantee of lucrativeness for a DR event. © 2014 IEEE.

关键词： Energy efficiency

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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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Decomposition based efficient algorithms for knowledge reduction

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ICIC Express Letters 2010年第3期4卷 1025-1032页

作者： Jiao, Na Miao, Duoqian Zhang, Hongyun Department of Computer Science and Technology Ministry of Education of China Tongji University Shanghai 201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education of China Tongji University Shanghai 201804 China

Knowledge reduction, one of essential issues for data mining, has alwaysbeen a, hot topic due to the explosive growth of inform,a,tion. However, whenhandling large-scale data, m,any current knowledge reduction m,ethod,s based, onrough set theory are incapable. In this paper, efficient algorithms forknowledge reduction are put forward, based, on decomposition principle. Theidea, of decomposition is to break a, complex problem, down into a,m,a,ster-problem, and, several sub-problem,s that are simpler, m,orem,a,na,gea,ble and, m,ore solvable by using existing induction methods, thenjoining them, together in order to solve the original problem,. Compared, withsom,e traditional methods, the efficiency of the proposed algorithms can beillustrated by experiments with standard datasets from, UCI database. ICIC International © 2010.

关键词： Rough set theory

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An optimized Apriori algorithm based on sparse matrix for intrusion detection

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Open Cybernetics and systemics Journal 2014年第1期8卷 8-11页

作者： Zeng, Xiaohui Lv, Jingxiang Li, Jingzhong Luo, Wenlang Dept. of Computer Science and Technology Jinggangshan University Ji’an China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

The original Apriori algorithm is widely used in the intrusion detection field, but it may consume incredible computing resources in the process of handling network packets. We propose our optimized-Apriori algorithm which can greatly improves the algorithm efficiency by means of reducing the data storage space and the number of frequent item sets. We take full advantage of the characteristics of sparse matrix to save storage space and shorten the running time. The experimental results show that our optimized-Apriori algorithm can make a larger progress in saving the storage space of network data to be processed, even shortening nearly half of the data mining time in the process of intrusion detection. © Zeng et al.

关键词： Data mining

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Bridge the Gap Between Network-Based Inference Method and Global Ranking Method in Personal Recommendation

Bridge the Gap Between Network-Based Inference Method and Gl...

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IEEE Conference on Industrial Electronics and Applications

作者： Xiwei Liu Department of Computer Science and Technology Tongji University China The Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai 200092 China

In this paper, we study the relationship between the network-based inference method and global ranking method in personal recommendation. By some theoretical analysis, we prove that the recommendation result under the... 详细信息

ISBN: (纸本)9781467386456

关键词： Eigenvalues and eigenfunctions Heating Bridges Inference algorithms Prediction algorithms Conferences Industrial electronics

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Adaptive Granulation for Efficient Classification on Noise Labeled Images

Adaptive Granulation for Efficient Classification on Noise L...

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International Conference on Computational Intelligence and Applications (ICCIA)

作者： Kecan Cai Hongyun Zhang Duoqian Miao Department of Computer Science and Technology Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China

ISBN: (数字)9798350352214

ISBN: (纸本)9798350352221

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.

关键词： Training Adaptation models Accuracy Computational modeling Noise Benchmark testing Noise measurement Standards Image classification Noise level

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Synchronization and control for multi-weighted and directed complex networks

arXiv

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arXiv 2021年

作者： Liu, Xiwei Department of Computer Science and Technology Tongji University China The Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai201804 China

The study of complex networks with multi-weights has been a hot topic recently. For a network with a single weight, previous studies have shown that they can promote synchronization. But for complex networks with multi-weights, there are no rigorous analysis to show that synchronization can be reached faster. In this paper, the complex network is allowed to be directed, which will make the synchronization analysis difficult for multiple couplings. In virtue of the normalized left eigenvectors (NLEVec) corresponding to the zero eigenvalue of coupling matrices, we prove that if the Chebyshev distance between NLEVec is less than some value, which is defined as the allowable deviation bound, then the synchronization and control will be realized with sufficiently large coupling strengths, i.e., all coupling matrices do accelerate synchronization. Moreover, adaptive rules are also designed for the coupling strength. © 2021, CC BY.

关键词： Eigenvalues and eigenfunctions

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Hierarchical attention generative adversarial networks for cross-domain sentiment classification

arXiv

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arXiv 2019年

作者： Zhang, Yuebing Miao, Duoqian Wang, Jiaqi Department of Computer Science and Technology Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China

Cross-domain sentiment classification (CDSC) is an importance task in domain adaptation and sentiment classification. Due to the domain discrepancy, a sentiment classifier trained on source domain data may not works well on target domain data. In recent years, many researchers have used deep neural network models for cross-domain sentiment classification task, many of which use Gradient Reversal Layer (GRL) to design an adversarial network structure to train a domain-shared sentiment classifier. Different from those methods, we proposed Hierarchical Attention Generative Adversarial Networks (HAGAN) which alternately trains a generator and a discriminator in order to produce a document representation which is sentimentdistinguishable but domain-indistinguishable. Besides, the HAGAN model applies Bidirectional Gated Recurrent Unit (Bi-GRU) to encode the contextual information of a word and a sentence into the document representation. In addition, the HAGAN model use hierarchical attention mechanism to optimize the document representation and automatically capture the pivots and non-pivots. The experiments on Amazon review dataset show the effectiveness of HAGAN. Copyright © 2019, The Authors. All rights reserved.

关键词： Generative adversarial networks

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