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A parallel ant colonies approach to de novo prediction of protein backbone in CASP8/9

Science China(information Sciences) 2013年第10期56卷 226-238页

作者： LV Qiang WU HongJie WU JinZhen HUANG Xu LUO XiaoHu QIAN PeiDe School of Computer Science and Technology Soochow University Jiangsu Provincial Key Lab for Information Processing Technologies School of Electronic and Information Engineering Suzhou University of Science and Technology

Predicting the three-dimensional structure of proteins from amino acid sequences with only a few remote homologs,or de novo prediction,remains a major challenge in computational *** modeling of the protein backbone represents the initial phase of a protein structure prediction *** a parallel ant colony optimization based on sharing one pheromone matrix,this report proposes a parallel approach to predict the structure of a protein *** parallel approach combines various sources of energy functions and generates protein backbones with the lowest energies jointly determined by the various energy *** free modeling targets in CASP8/9 are used to evaluate the performance of the *** 13 targets in CASP8,two out of the predicted model1s selected by our approach are the best of the published CASP8 results,and seven out of the model1s are ranked in the top *** 29 targets in CASP9,20 out of the best models from our predictions are ranked in the top 10,and 11 out of the model1s are ranked in the top 10.

关键词： protein backbone prediction parallel ant colony optimization

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An efficient strategy for enhancing robustness and immunization in wireless sensor networks

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Journal of Networks 2009年第3期4卷 170-177页

作者： Shu-Kui, Zhang Sheng-Rong, Gong Zhi-Ming, Cui Quan, Liu Institute of Computer Science and Technology Soochow University Suzhou 215006 China Jiangsu Provincial Key Laboratory of Computer Information Processing Technology Soochow University Suzhou 215006 China

To provide cost-effective protection for sensor networks, We introduce an immunization method where the percentage of required vaccinations for immunity are close to the optimal value of a targeted immunization scheme. Our strategy retains the advantage of being purely local, without the need of knowledge on the global network structure. The method consists of selecting the vertex with the highest degree within a distance d and asking for a neighbor that has more links than himself or more than a given threshold and immunizing him. Analysis and simulation results show that the above method is valid. It enhances network robustness against virus and provide useful hints in the search of optimal immunization strategies as the development and deploying of a sensor network system. © 2009 ACADEMY PUBLISHER.

关键词： Complex networks

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Blockchain-aided Mobile Edge Intelligence With Sparse Rewarded Computing Resource Allocation

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IEEE Transactions on Vehicular technology 2025年

作者： Niu, Wei Shi, Long Wang, Zhe Yang, Weiwei Guo, Yongan Nanjing University of Science and Technology School of Electronic and Optical Engineering Nanjing210094 China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China Dongguan University of Technology School of Electrical Engineering and Intelligentization Dongguan523000 China Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Intelligent Information Processing and Communication Technology Nanjing210003 China Nanjing University of Posts and Telecommunications School of Communications and Information Engineering Nanjing210003 China

In the blockchain-aided mobile edge intelligence (B-MEI) network, base stations (BSs) each with an edge server not only execute tasks offloaded from users but also serve as blockchain nodes to ensure trustworthy data management of task execution in a decentralized manner. A challenging issue of B-MEI is that, both task execution and blockchain consensus demand significant computing resources. Insufficient resource allocation inevitably leads to resource wastage and even degrades system performance, especially in scenarios where task arrivals and wireless channels are dynamically changing. Driven by this issue, we first formulate the dynamic allocation of computing resources as a Markov Decision Process (MDP) to maximize the average transaction throughput of the B-MEI system. In particular, we find that, due to the time-consuming nature of blockchain consensus, the MDP rewards stemmed from the block generation is typically delayed and sparse. To address this issue, we propose a novel reinforcement learning algorithm, named Stable Hindsight Credit Assignment (SHCA), to effectively learn the stable credit assignment for sparse rewards across intermediate actions to optimize policy decisions. Simulation results demonstrate that the proposed algorithm improves the average transaction throughput by 78% compared with the baseline algorithm. © 1967-2012 IEEE.

关键词： Resource allocation

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A Cascaded Co-evolutionary Model for Attribute Reduction and Classification Based on Coordinating Architecture with Bidirectional Elitist Optimization

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Chinese Journal of Electronics 2017年第1期26卷 13-21页

作者： DING Weiping WANG Jiandong LI Yuehua CHENG Xueyun School of Computer Science and Technology Nantong University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Provincial Key Laboratory for Computer Information Processing Technology Soochow University College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics

A cascaded co-evolutionary model for Attribute reduction and classification based on Coordinating architecture with bidirectional elitist optimization(ARC-CABEO) is proposed for the more practical applications. The regrouping and merging coordinating strategy of ordinary-elitist-role-based population is introduced to represent a more holistic cooperative co-evolutionary framework of different populations for attribute reduction. The master-slave-elitist-based subpopulations are constructed to coordinate the behaviors of different elitists, and meanwhile the elitist optimization vector with the strongest balancing between exploration and exploitation is selected out to expedite the bidirectional attribute co-evolutionary reduction process. In addition, two coupled coordinating architectures and the elitist optimization vector are tightly cascaded to perform the co-evolutionary classification of reduction subsets. Hence the preferring classification optimization goal can be achieved better. Some experimental results verify that the proposed ARC-CABEO model has the better feasibility and more superior classification accuracy on different UCI datasets, compared with representative algorithms.

关键词： Attribute reduction for classification Bidirectional elitist optimization Coordinating architecture Cascaded co-evolutionary model

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A Fast Calculation of Metric Scores for Learning Bayesian Network

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International Journal of Automation and computing 2012年第1期9卷 37-44页

作者： Qiang Lv Xiao-Yan Xia Pei-De Qian School of Computer Science and Technology Soochow University Suzhou 215006 PRC Jiangsu Provincial Key Lab for Computer Information Processing Technology Suzhou 215006 PRC

Frequent counting is a very so often required operation in machine learning algorithms. A typical machine learning task, learning the structure of Bayesian network （BN） based on metric scoring, is introduced as an example that heavily relies on frequent counting. A fast calculation method for frequent counting enhanced with two cache layers is then presented for learning BN. The main contribution of our approach is to eliminate comparison operations for frequent counting by introducing a multi-radix number system calculation. Both mathematical analysis and empirical comparison between our method and state-of-the-art solution are conducted. The results show that our method is dominantly superior to state-of-the-art solution in solving the problem of learning BN.

关键词： Frequent counting radix-based calculation ADtree learning Bayesian network metric score

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Multi-level threshold image segmentation based on PSNR using artificial bee colony algorithm

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Research Journal of Applied Sciences, Engineering and technology 2012年第2期4卷 104-107页

作者： Yun-Fei, Cao Yong-Hao, Xiao Wei-Yu, Yu Yong-Chang, Chen School of Electronic and Information Engineering South China University of Technology Guangzhou China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou Jiangsu 215006 China

Image segmentation is still a crucial problem in image processing. It hasn yet been solved very well. In this study, we propose a novel multi-level thresholding image segmentation method based on PSNR using artificial bee colony algorithm (ABCA). PSNR is considered as an objective function of ABCA. The multi-level thresholds (t*1, t*2 ,...., t*n-1, t*n) are those maximizing the PSNR. We compare entropy and PSNR in segmenting gray-level images. The experiments results demonstrate proposed method is effective and efficient. © Maxwell Scientific Organization, 2012.

关键词： Image segmentation

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Prof. Rudolf Emil Kalman OBITUARY

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IEEE CONTROL SYSTEMS MAGAZINE 2017年第1期37卷 151-152页

作者： Antoulas, Athanasios Georgiou, Tryphon T. Khargonekar, Pramod P. Ozguler, A. Bulent Sontag, Eduardo D. Yamamoto, Yutaka JiangSu Provincial Key Lab for Computer Information Processing Technology School of Computer Science and Technology Soochow University Suzhou China

Recounts the career and contributions of Professor Rudolf Emil Kalman.

关键词： Obituaries Kalman, Rudolf Emil

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Lightweight Vision Model-based Multi-user Semantic Communication Systems

arXiv

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arXiv 2025年

作者： Jiang, Feibo Tu, Siwei Dong, Li Wang, Kezhi Yang, Kun Liu, Ruiqi Pan, Cunhua Wang, Jiangzhou Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha410081 China School of Information Science and Engineering Hunan Normal University Changsha410081 China School of Computer Science Hunan University of Technology and Business Changsha410205 China Xiangjiang Laboratory Changsha410205 China Department of Computer Science Brunel University London United Kingdom School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom Changchun Institute of Technology China Wireless and Computing Research Institute ZTE Corporation Beijing100029 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China National Mobile Communications Research Laboratory Southeast University Nanjing China Purple Mountain Laboratories Nanjing China

Semantic Communication (SemCom) is a promising new paradigm for next-generation communication systems, emphasizing the transmission of core information, particularly in environments characterized by uncertainty, noise, and bandwidth constraints. However, existing image SemCom systems face several challenges, such as inefficient knowledge base construction, insufficient semantic encoding, and lack of multi-user semantic sharing. To address these issues, we propose a Lightweight Vision Model-based Multi-user Semantic Communication System (LVM-MSC). First, we construct a Lightweight Knowledge Base (LKB) based on the fast Segment Anything Model (SAM). LKB incorporates the extensive image knowledge of the SAM model while significantly reducing the number of parameters through its convolutional architecture. Next, we design an Efficient Semantic Codec (ESC) based on the Masked AutoEncoder (MAE) architecture. ESC enhances semantic compression at both the pixel and semantic levels and implements lightweight semantic decoding tailored for user devices. Furthermore, we propose a Multi-user Semantic Sharing (MSS) transmission for the multi-user SemCom. By calculating the similarity of semantic information among different users in the sharing semantic space, we unify the transmissions of similar semantic information through broadcasting, further improving the transmission efficiency. Finally, simulation results demonstrate the feasibility and effectiveness of the proposed LVM-MSC system. Copyright © 2025, The Authors. All rights reserved.

关键词： Latent semantic analysis

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A reliable SVD-DWT based watermarking scheme with Artificial Bee Colony Algorithm

International Journal of Digital Content Technology and its ...

引用

International Journal of Digital Content technology and its Applications 2012年第22期6卷 430-439页

作者： Chen, Yongchang Yu, Weiyu Feng, Jiuchao Electronic and Information Engineering South China University of Technology Guangzhou China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China

A robust image watermarking scheme based on singular value decomposition (SVD) and discrete wavelet transform (DWT) with Artificial Bee Colony Algorithm is proposed in this paper. Previous SVD based watermarking algorithms have a major drawback of false positive detection. For solving this problem, the similarity measure of U matrix for ownership is checked. To achieve the highest possible robustness without losing the transparency, an adaptive scale factor is obtained by the artificial bee colony (ABC) algorithm. Experimental results demonstrate that the performance of the proposed approach outperforms the existing methods.

关键词： Discrete wavelet transforms

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Adaptive detection of moving vehicle based on on-line clustering

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Journal of computers 2011年第10期6卷 2045-2052页

作者： Wu, Jian Xia, Jie Chen, Jian-Ming Cui, Zhi-Ming The Institute of Intelligent Information Processing and Application Soochow University Suzhou 215006 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou 215006 China

Detection of moving vehicles plays a very important role in Intelligent Transport. Aiming at the deficiency of moving vehicle detection, we proposed the adaptive detection method of moving vehicles based on the online clustering. First extracts background adaptively using a new online clustering algorithm which does not need to set any parameters when extract the background image. Then adaptively select the background updating rate according to the road disturbance of background and illumination changes after background building is completed. Finally, realize correct and complete moving object segmentation through foreground detection using background difference. Experimental results show that the proposed method is able to detect moving target accurately in the transport video, it not only has good self-adaptability and real-time but also insensitive to the light changes and background interference. © 2011 ACADEMY PUBLISHER.

关键词： Clustering algorithms

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