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Robust Online Multi-Robot Simultaneous Exploration and Coverage Path Planning

IEEE ACCESS 2024年 12卷 72990-73003页

作者： Nair, Vishnu G. Dileep, M. V. Guruprasad, K. R. Manipal Acad Higher Educ Manipal Inst Technol Dept Aeronaut & Automobile Engn Udupi 576104 Karnataka India Chungnam Natl Univ Dept Aerosp Engn Daejeon 34134 South Korea IIT Kanpur Dept Mech Engn Kanpur 208016 Uttar Pradesh India

In this paper, a robust online Multi-robot Simultaneous exploration and coverage path planning problem is presented. The entire workspace is initially partitioned using a variant of Voronoi partitioning, Manhattan Voronoi, and the robots execute simultaneous exploration and coverage using Spanning Tree Coverage algorithm and cover the workspace. Once the robot(s) failure is detected the uncovered portions of the Voronoi cell of the failed robot will be shared between other eligible robots or a replacement strategy,if available, is performed. Simulation experiments within the V-rep environment is used to demonstrate and validate the performance of the proposed algorithm. Though the authors used the Spanning Tree Coverage algorithm for path planning for the purpose of demonstration, any suitable coverage algorithm may be used.

关键词： Robots Robot kinematics partitioning algorithms Task analysis Path planning Resource management Heuristic algorithms Multi-robot systems Coverage path planning robot failure dynamic partition boundaries Manhattan Voronoi multi-robot systems

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Communication-Aware Task Scheduling for Energy-Harvesting Nonvolatile Processors

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IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS 2020年第8期28卷 1796-1806页

作者： Wang, Yan Liu, Jinhui Hu, Jingtong Guangzhou Univ Sch Comp Sci Guangzhou 510082 Peoples R China Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China Univ Pittsburgh Swanson Sch Engn Dept Elect & Comp Engn Pittsburgh PA 15217 USA

With the advent of the Internet of Things (IoT), energy-harvesting nonvolatile processors (NVPs) have become promising platforms due to their durability when running on an intermittent power supply and fast read/write operations. However, the penalties caused by an increasing amount of data to be processed and growing communication demands pose critical challenges in the scheduling of tasks on energy-harvesting NVP platforms with tight energy and latency budgets. Together with the high power switching overhead that is induced under unstable power conditions, an increase in the amount of data to be processed significantly degrades the system performance. To overcome the problems of high communication and switching overheads for energy-harvesting NVP platforms, this article proposes a novel communication-aware task-scheduling technique. The algorithm first selects one or more executable tasks to be performed based on the task benefits and then calls a task partitioning algorithm to dynamically divide the scheduled tasks. We evaluate the performance of our proposed algorithm in comparison with the performance-aware task scheduling (PATH) and greedy iterative (GI) algorithms. Experimental results show that the proposed algorithm can reduce the execution time by 17.08% and 13.72% on average compared with the PATH and GI algorithms, respectively.

关键词： Task analysis Switches Nonvolatile memory partitioning algorithms Scheduling Heuristic algorithms Sensors Backup communication overhead energy harvesting nonvolatile processor (NVP) task scheduling

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COSINE: Compressive Network Embedding on Large-Scale Information Networks

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2022年第8期34卷 3655-3668页

作者： Zhang, Zhengyan Yang, Cheng Liu, Zhiyuan Sun, Maosong Fang, Zhichong Zhang, Bo Lin, Leyu Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China Beijing Univ Posts & Telecommun Sch Comp Sci Beijing 100876 Peoples R China Cornell Univ Dept Comp Sci Ithaca NY 14850 USA Tencent Search Prod Ctr WeChat Search Applicat Dept Beijing 100080 Peoples R China

There is recently a surge in approaches that learn low-dimensional embeddings of nodes in networks. However, for large-scale real-world networks, it's inefficient for existing approaches to store amounts of parameters in memory and update them edge by edge. With the knowledge that nodes having similar neighborhoods will be close to each other in the embedding space, we propose COSINE (COmpresSIve Network Embedding) algorithm, which reduces the memory footprint and accelerates the training process by parameter sharing among similar nodes. COSINE applies graph partitioning algorithms to networks and builds parameter sharing dependency of nodes based on the results of partitioning. In this way, COSINE injects prior knowledge about high-order structural information into models, which makes network embedding more efficient and effective. COSINE can be applied to any embedding lookup method and learn high-quality embeddings with limited memory and less training time. We conduct experiments on multi-label classification and link prediction, where baselines and our model have the same memory usage. Experimental results show that COSINE improves baselines by up to 23 percent on classification and 25 percent on link prediction. Moreover, the training time of all representation learning methods using COSINE decreases by 30 to 70 percent.

关键词： Training partitioning algorithms Predictive models Sparse matrices Knowledge engineering Computational modeling Memory management Node classification link prediction large-scale real-world network network embedding model compression

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A Quadratic Convergent Iteration-Variable-Correction-Based Method for Optimal Power Flow of Transmission-Distribution-Coupled Systems

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IEEE SYSTEMS JOURNAL 2022年第2期16卷 3360-3371页

作者： Tang, Kunjie Dong, Shufeng Song, Yonghua Zhejiang Univ Coll Elect Engn Hangzhou 310027 Peoples R China Univ Macau State Key Lab Internet Things Smart City Macau 999078 Peoples R China Univ Macau Dept Elect & Comp Engn Macau 999078 Peoples R China

An iteration-variable-correction (IVC) based method is proposed for solving optimal power flow (OPF) of transmission-distribution-coupled (TDC) systems. First, a TDC system can be divided into a single coordinator and multiple agents. Then, the basic formulation, subproblem construction, and selection of iteration variables are given based on the conventional heterogeneous decomposition algorithm (HGD). Considering the limitations of the conventional HGD, the IVC is proposed. According to the general theory of the IVC, when it is applied to solve TDC OPF, the voltage magnitude of boundary buses and the multipliers corresponding to boundary power injections will be corrected every four iterations with first-order and second-order differential operations on historical results in the previous four iterations. The quadratic convergence of the IVC is proved under several weak assumptions. The numerical experiments demonstrate that the proposed IVC has the same accuracy as the method based on the centralized OPF model. Also, better convergence and efficiency can be achieved under the IVC compared with some conventional decomposition methods, including alternating direction method of multipliers, auxiliary problem principle method, analytical target cascading method, and the HGD.

关键词： Convergence partitioning algorithms Optimization Numerical models Mathematical model Master-slave Linear programming Iteration-variable-correction (IVC) based method optimal power flow (OPF) quadratic convergence transmission-distribution-coupled (TDC) systems

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Scalable Offline Optimization of Industrial Wireless Sensor Networks

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2011年第2期7卷 328-339页

作者： Palopoli, Luigi Passerone, Roberto Rizano, Tizar Univ Trento Dipartimento Ingn & Sci Informaz I-38128 Povo TN Italy

Sensor networks are increasingly used to control and monitor industrial and manufacturing processes. In this paper, we consider the problem of optimizing a cost function for wireless sensor networks of this kind under energy consumption constraints. We focus, in particular, on the problem of coverage optimization through scheduling. Following existing approaches, we use a mixed integer linear program formulation. We show how to use partitioning techniques to decompose the problem into separate subproblems, solved individually, overcoming the exponential complexity typical of integer linear programming, while minimizing the loss in optimality. In addition, we evaluate the achieved degree of optimality by computing relatively tight bounds with respect to the optimal solution. Finally, we employ simple but effective heuristics to further improve our solution. The results show that our procedure is very efficient and scalable, and is able to find solutions that are very close to optimal. These characteristics make our approach a perfect fit for large and fixed deployments of wireless sensors, typical in factory automation and industrial applications. To show the generality of the approach, we apply our methodology to three different models of varying complexity.

关键词： Design methodology modeling optimization methods partitioning algorithms wireless sensor networks (WSNs)

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ACCELERATION OF THE LBG ALGORITHM

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IEEE TRANSACTIONS ON COMMUNICATIONS 1994年第2-4期42卷 1518-1523页

作者： WU, XL GUAN, L UNIV WATERLOO DEPT SYST DESIGN ENGNWATERLOO N2L 3G1ONTARIOCANADA

A concentric spherical search technique is proposed to speed up the clustering process in VQ design. A linear data structure is incorporated into the LBG algorithm to keep and update the information about the proximity among the codewords. This proximity information can significantly reduce the number of candidate codewords to be the closest to a given training vector. An improved k-means type VQ design algorithm is proposed based on the new search technique and the supporting data structure. The new algorithm is simple to implement, valid for general error metric, and demonstrated by our experiments to be considerably faster than previous algorithms.

关键词： Acceleration Algorithm design and analysis Clustering algorithms Data structures Vectors Process design partitioning algorithms Data compression Nearest neighbor searches Tree data structures

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Towards Enhancing the Performance of Parallel FP-Growth on Spark

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IEEE ACCESS 2022年 10卷 286-296页

作者： Essam, Amr Abdel-Fattah, Manal A. Abdelhamid, Laila Helwan Univ Fac Comp & Artificial Intelligence Dept Informat Syst Cairo 12317 Egypt

Frequent itemset mining (FIM) is a crucial tool for identifying hidden patterns in information. FP-Growth is an FIM algorithm used to find associations. When the data size increases, the execution of FIM algorithms on a single machine suffers from computational problems, such as memory and time consumption. For these reasons, parallel and distributed processing on platforms such as Spark is essential. The parallel frequent pattern (PFP) is the implementation of FP-Growth in Spark. The main problem with PFP is that it does not consider the load balancing between cluster units. This research proposes an enhanced balanced parallel frequent pattern "EBPFP" algorithm to enhance and balance the PFP. The proposed algorithm (EBPFP) proposes two ideas. First, a strategy for load balancing between groups is proposed to ensure that the items are evenly divided between the nodes, and the cluster resources are used more effectively. Second, the improved conditional pattern base (ICPB) method aims to remove infrequent items from the conditional pattern base before constructing local FP-Trees. The experimental results show that the proposed EBPFP algorithm outperforms PFP, and the difference in running time between EBPFP and PFP was 21.56% and 39.72%, respectively.

关键词： Clustering algorithms Sparks Itemsets Task analysis Data mining Load management partitioning algorithms Big data data mining association rule analysis frequent pattern growth algorithm spark load balancing

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Survivable Routing, Spectrum, Core and Band Assignment in Multi-Band Space Division Multiplexing Elastic Optical Networks

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2022年第11期40卷 3442-3455页

作者： Luo, Zhihuan Yin, Shan Zhao, Ligang Wang, Zhenhao Zhang, Wenchao Jiang, Liyou Huang, Shanguo Beijing Univ Posts & Telecommun State Key Lab Informat Photon & Opt Commun Beijing 100876 Peoples R China

The rapid growth in Internet traffic has contributed to the need to expand network transmission capacity. Multi-band (MB) using existing standard single-mode fibers (SMFs) in the free band is an ideal way to increase the capacity of the fiber. However, the introduction of MB has transferred space division multiplexing - elastic optical networks (SDM-EONs) to multi band-SDM-EONs (MB-SDM-EONs), and changed routing, spectrum and core assignment (RSCA) problem to the routing, spectrum, core and band assignment (RSCBA) problem. At the same time, it introduces a new issue named stimulated Raman scattering (SRS), which affects transmission quality. Although an expanded fiber can carry more services, a large number of services will be interrupted if a link fails, which will bring huge losses. Therefore, it is necessary to propose a reasonable protection strategy to ensure reliable transmission quality. In this paper, the survivable RSCBA problem in MB-SDM-EONs is studied, which copes with link failures and considers SRS in signal-to-noise ratio (SNR) analysis. In order to solve the problem, we propose a band partition protection scheme: working and protection resources are allocated in different frequency bands for increasing SNR in a fault-free network. We formulate this problem and band partition protection scheme as an integer linear programming (ILP) model, which takes into account inter-core crosstalk and SNR analysis. Furthermore, a heuristic algorithm based on genetic algorithm is proposed for large-scale networks. We evaluate the performance of the proposed approaches through experimental simulation. The results demonstrate that both proposed approaches are effective in finding the optimal solutions.

关键词： Optical fiber networks Signal to noise ratio Resource management C-band Routing partitioning algorithms Optical scattering Genetic algorithm (GA) multi band (MB) routing- spectrum- core-band assignment (RSCBA) signal-to-noise ratio (SNR) space division multiplexing (SDM) survivability

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Cluster-Volume-Based Merging Approach for Incrementally Evolving Fuzzy Gaussian Clustering-eGAUSS

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2020年第9期28卷 2222-2231页

作者： Skrjanc, Igor Univ Ljubljana Fac Elect Engn Ljubljana 1000 Slovenia

In this article, a new dynamic merging approach for incrementally evolving clustering is presented. This means that the cluster partitions are incrementally learned on-line from streams of data. The criterion of merging is based on the comparison between the sum of volumes of two clusters that fulfill the criteria of a minimal number of samples in the cluster and the expected volume of the newly generated merged cluster. The newly generated merged cluster is conducted by using the weighted averaging of cluster centers and the calculation of the joint covariance matrix from the covariance matrices of the clusters. It has been shown that the proposed new evolving algorithm eGAUSS+ together with the new merging concept is very easy to implement, can work on higher-dimensional data sets, can perform all necessary computation on-line, and can produce reliable clusters.

关键词： Merging Covariance matrices Clustering algorithms Data models partitioning algorithms Eigenvalues and eigenfunctions Computational modeling Data stream dynamic merging evolving clustering evolving cluster models incremental learning volume of hyperellipsoids

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Toward Occlusion Handling in Visual Tracking via Probabilistic Finite State Machines

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IEEE TRANSACTIONS ON CYBERNETICS 2020年第4期50卷 1726-1738页

作者： Liu, Chenghuan Huynh, Du Q. Reynolds, Mark Univ Western Australia Sch Comp Sci & Software Engn Perth WA 6009 Australia

Visual tracking has been an active research area in computer vision for decades. However, the performance of existing techniques is still challenged by various factors, such as occlusion and change in appearance of the target. In this paper, we propose a novel framework based on correlation filtering and probabilistic finite state machines (FSMs) to handle occlusion. In our tracking framework, the target is partitioned into several parts whose occlusion states are automatically detected. A set of states for the target is defined in terms of the combination of the parts' occlusion states. The probabilistic FSMs are then used to model the target's state transitions so as to reduce the effect of noise in the output response maps of correlation filters. Our target model's update strategy is adaptable online depending on the estimated state of the target. Extensive experiments have been performed on several public benchmarks and the proposed algorithm achieves competitive results against state-of-the-art techniques.

关键词： Target tracking Correlation Visualization partitioning algorithms Probabilistic logic Adaptation models Support vector machines Correlation filters occlusion handling probabilistic finite state machines visual tracking

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