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检索条件"主题词=Partitioning Algorithms"

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Research on Hierarchical Fault Location of Active Distribution Network

Research on Hierarchical Fault Location of Active Distributi...

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Artificial Intelligence and Power Systems (AIPS), International Conference on

作者： Rui Li Jianrhuan Xian Yu Business School Shanghai Dianji University Shanghai China

ISBN: (数字)9798350392210

ISBN: (纸本)9798350392227

With the transformation of economic development mode, the acceleration of urbanization, the optimization and upgrading of value energy structure, the structure of distribution network is becoming more and more complex, which brings great challenges to the fault location of distribution network. In this paper, the characteristics of existing fault location methods are analyzed, and a dual-population hybrid intelligent algorithm BPSOGA (binary particle swarm optimization genetic binary), which combines improved particle swarm optimization with genetic algorithm, is proposed. At the same time, the idea of partition location is integrated to reduce the dimension of distribution network fault location model and improve the location speed. The experimental results show that the proposed algorithm has improved the positioning speed, positioning accuracy and fault tolerance rate.

关键词： Accuracy Simulation Urban areas Distribution networks Fault location Genetics partitioning algorithms Power systems Particle swarm optimization Optimization

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Multi-Criteria Dynamic Partition Modeling Approach for State Estimation in Complex Active Distribution Networks

Multi-Criteria Dynamic Partition Modeling Approach for State...

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Power System and Green Energy Conference (PSGEC)

作者： Yuchun Luo Rong Cai Jiang Tian State Grid Electric Power Research Institute Co. Nanjing China State Grid Suzhou Power Supply Company Suzhou China

ISBN: (数字)9798350365573

ISBN: (纸本)9798350365580

Partition modeling serves as the basis for distributed state estimation in complex active distribution networks. Considering the dynamic and complex nature of distribution network operational states, this paper proposes a multi-criteria dynamic partition modeling approach for state estimation in complex active distribution networks, providing a foundational partition model for state estimation. Firstly, based on the configuration analysis of real-time measurements, virtual measurements, and pseudo measurements in active distribution networks, a multi-criteria comprehensive indicator system for state estimation is established. Secondly, a genetic algorithm is employed to determine the optimal comprehensive indicators for each region. The dynamic partition model is constructed by integrating optimal indicators, operational states, and control requirements, and the triggering mechanism for partition structure updates is designed. Finally, the proposed dynamic partition modeling method is validated through improved IEEE 118-node simulation examples, demonstrating its ability to achieve rational dynamic partitioning in complex active distribution networks.

关键词： Accuracy Computational modeling Heuristic algorithms Power system dynamics Distribution networks Real-time systems Computational efficiency partitioning algorithms Active distribution networks State estimation

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Evidential c-Means Clustering with City-Block and Mahalanobis Distances Learning

Evidential c-Means Clustering with City-Block and Mahalanobi...

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International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC

作者： Haoye Qiu Jiaming Lyu Donglai Wang Kaili Zhang Ziying Cai School of Computer Science and Technology Hainan University Haikou China

ISBN: (数字)9798331540241

ISBN: (纸本)9798331540258

As a representative partitioning clustering algorithm, fuzzy c-means (FCM) has attained great success in data clustering. However, FCM and its variants face challenges in complex scenarios, especially with noise, outliers, and clusters overlap. Fortunately, evidential c-means (ECM) via the credal partition can effectively represent the uncertainty and imprecision. Nevertheless, one of its limitations remains the usage of Euclidean distance, which renders it susceptible to noise and outliers, and ineffective in capturing the correlation among diverse features. In this paper, we propose an enhanced ECM with City-Block and Mahalanobis distances learning. The numerous experiments are designed to demonstrate the effectiveness of the proposed algorithms conducted on synthetic and authentic datasets.

关键词： Uncertainty Correlation Human-machine systems Noise Clustering algorithms Euclidean distance partitioning algorithms Faces Cybernetics

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Power Iteration Graph Clustering With functools Higher Order Methods

Power Iteration Graph Clustering With functools Higher Order...

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International Workshop on Semantic Media Adaptation and Personalization (SMAP)

作者： Georgios Drakopoulos Georgios Bardis Phivos Mylonas Department of Informatics Ionian University ICE Department University of West Attica

ISBN: (数字)9798331504502

ISBN: (纸本)9798331504519

Graph mining operations take place on an unprecedented scale, dictating the need for scalability in both algorithms and implementation. In the context of graph partitioning, which tantamounts to community structure discovery, power iteration clustering (PIC) is an important and efficient method since it computes the primary eigenvector of a graph or of the Laplacian thereof. PIC converges for broad categories of matrices and can be applied to non-normal ones. Functional paradigm aspects like list transformations as well as capabilities like partially implemented objects and cached results are offered by the Python module functools. These can be combined with other recent major Python concepts such as decorators and lambda expressions to yield elegant and efficient code. PIC being a matrix free method can benefit from functools. As a concrete application a functional implementation of PIC has been applied to two Twitter graphs with different characteristics with encouraging results as evaluated by the Shilouette score and the Gini index.

关键词： Laplace equations Codes Social networking (online) Scalability Blogs Clustering algorithms partitioning algorithms Indexes

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Cost-Effective Reliability-Constrained Multi-Domain Service Function Chain Orchestration: A Metaheuristic Approach

Cost-Effective Reliability-Constrained Multi-Domain Service ...

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Qian Zhang Jin Sun Yi Zhang School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

The orchestration of a service function chain (SFC) is responsible for the coordination of multiple virtual network functions (VNFs) in a sequence and the deployment of VNF nodes and links onto physical resources, for the purpose of delivering high-quality network services. This paper studies a multi-domain SFC orchestration problem that aims at minimizing the cost of deploying SFCs across multiple domains while ensuring the overall reliability of SFC requests. We establish a centralized multi-domain framework to formulate the SFC orchestration problem into a combinatorial optimization problem and propose a firefly algorithm (FA)-based metaheuristic approach, named as FA_SFCO, to solve the formulated problem. The proposed FA_SFCO uses a sequence of VNFs in the SFC to represent the orchestration solution, and converts all firefly individuals into valid VNF sequences to facilitate the evolutionary process of FA_SFCO. For each converted VNF sequence, FA_SFCO employs a series of operations, including backup instance allocation, SFC partitioning, and intra-domain mapping, to determine the deployment of all VNFs in the sequence, such that the fitness value of the orchestration in terms of the total deployment cost can be evaluated. Evaluation results on a multi-domain substrate network justify that FA_SFCO outperforms competing algorithms with a significant reduction in deployment cost while meeting the reliability requirement.

关键词： Distributed processing Costs Service function chaining Metaheuristics partitioning algorithms Reliability Resource management Substrates

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Odor Source Search with a Wolf Pack Algorithm via Multi-UAV in Outdoor Environment

Odor Source Search with a Wolf Pack Algorithm via Multi-UAV ...

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International Conference on Industrial Artificial Intelligence (IAI)

作者： Wen-Qiang Wu Ji-Gong Li Jing Yang Tianjin Key Laboratory of Information Sensing and Intelligent Control School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin China

ISBN: (数字)9798350356618

ISBN: (纸本)9798350356625

Concerning the problem of odor source search with robots in a large outdoor environment, an odor source search method based on the improved wolf pack algorithm (IWPA) is proposed, in which each wolf corresponds to an unmanned aerial vehicle (UAV). The environment is firstly traversed by the UAVs in partitions by using rectangular wave- shaped paths. Once odor plume is detected by any UAV, the WPA is applied to guide the UAVs towards the odor source. Considering the limited number of UAVs, in order to improve the utilization rate of UAVs, the wolf pack is simplified into two roles: the alpha wolf and other wolves. Meanwhile, to avoid collisions between UAVs and enable UAVs to fully sample around the suspected target position, a besieging algorithm based on a virtual force field is designed to calculate next flight direction for the UAVs. During this period, the odor source position is determined by the alpha wolf which is constantly elected by comparing the fitness values of all wolves. Simulation results show that the IWPA method can find the odor source in short time without falling into local extremum, demonstrating good convergence and being suitable for odor source search tasks in outdoor environments.

关键词： Simulation Wind speed Force Search problems Autonomous aerial vehicles Trajectory partitioning algorithms Artificial intelligence Robots Convergence

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Minimization of Completion Time for Dynamic and Reliable Data Collection with Multiple UAVs

Minimization of Completion Time for Dynamic and Reliable Dat...

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Ubiquitous Communication (Ucom), International Conference on

作者： Xueli Guo Xinyi Liu Jiaqi Zhou Mengmeng Shi Xuerui Zhu Ziyao Zhu School of Information Engineering Chang'an University Xi'an China

ISBN: (数字)9798350374216

ISBN: (纸本)9798350374223

Unmanned Aerial Vehicles (UAVs) have emerged as a promising approach for Internet of Things (IoT) data collection, owing to their deployment flexibility, superior line-of-sight (LoS) communication links, and cost-effectiveness. However, their limited battery capacity restricts their application in many scenarios. This paper aims to minimize the maximum task completion time of all UAVs by jointly optimizing the scheduling of ground devices in a given large-scale geographic area and the UAV trajectories, while ensuring fairness in service provision by each UAV and the reliability of the data collection tasks. The proposed problem is a challenging non-convex optimization problem. To address this, we introduce a hover-fly-hover data collection scheme that guarantees a certain coverage rate in large-scale geographic areas. This scheme decomposes the overall problem into two sub-problems for solution. First, based on the concept of outage probability, we ensure the required Quality of Service (QoS) for devices within the target area. We calculate the optimal hovering altitude for the UAVs using a novel method to achieve the maximum coverage radius with sufficiently low outage probability. Consequently, we propose an Affinity Propagation (AP) clustering-based algorithm to partition the geographic area into an optimal number of clusters. Subsequently, we introduce a hierarchical dynamic path planning algorithm based on virtual UAV nodes, further reducing the UAV task completion time. Simulation results demonstrate that the proposed cluster-based dynamic path planning multi-UAV data collection scheme significantly outperforms benchmark schemes in terms of performance.

关键词： Heuristic algorithms Quality of service Data collection Probability Autonomous aerial vehicles Trajectory Power system reliability partitioning algorithms Internet of Things Vehicle dynamics

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Distributed Brute-Force Password Recovery with a Partitioned Quantum Grover's Algorithm

Distributed Brute-Force Password Recovery with a Partitioned...

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Computational Intelligence and Network Systems (CINS), International Conference on

作者： Ahcene Bounceur University of Sharjah College of Computing and Informatics Sharjah UAE

ISBN: (数字)9798331504106

ISBN: (纸本)9798331504113

In the booming field of quantum computing, Grover's Algorithm emerges as a pivotal quantum search algorithm, theoretically capable of outperforming classical brute-force search methods by exploiting the principles of superposition and quantum parallelism. However, the practical application of Grover's Algorithm to crack complex passwords is limited by the nascent stage of quantum technology, which is currently challenged by limitations in qubit count and coherence times. This paper proposes a novel distributed quantum computing framework that aims to circumvent these limitations by partitioning the search space for a complex password among an array of quantum processors. Each processor executes Grover's Algorithm on a fraction of the total search space, thereby reducing the complexity and quantum resource requirements for each individual computation. The partial results are then transmitted to a central classical computer, which synthesizes them into the final password. This distributed approach not only makes the task feasible for (with) present-day quantum systems but also presents a scalable model that could adapt to the rapid advancements in quantum technologies. By dissecting the complexity of the quantum brute-force problem into smaller, more manageable units, this method holds the potential to significantly reduce the time complexity of password recovery processes and thus presents a profound progress (implications) for the fields of cryptography and cybersecurity.

关键词： Quantum computing Program processors Search methods Force Qubit Passwords Parallel processing partitioning algorithms Complexity theory Time complexity

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A Noval Unsupervised Learning Big Data Mining Algorithm Based on Spark Framework

A Noval Unsupervised Learning Big Data Mining Algorithm Base...

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Artificial Intelligence Technology (ACAIT), Asian Conference on

作者： Bei Cheng Research Center for Educational Evaluation and Inspection China National Academy of Educational Sciences Beijing China

ISBN: (数字)9798331517090

ISBN: (纸本)9798331517106

The Spark framework has been widely used for the unsupervised learning of big data mining algorithms; however, it has problems. That is, the multiple data exchange iterations between master and worker nodes result in low operation efficiency. This study proposes a k-means clustering algorithm based on flora colony (FC) optimization. FC optimization was introduced to optimize the k-means clustering processes through the repeated dispersal, replication, and chemotaxis operations of the flora. Hence, the fitness of data clustering is continuously improved until the maximum number of operations or the minimum clustering accuracy threshold is reached. Then, a stable big data clustering-mining algorithm is obtained. Based on the Spark’s data partitioning framework, the proposed algorithm executes the FC-k-means algorithm on each partition to obtain local cluster centers, summarizes the results, recycles them to the master node, and runs the algorithm on the master node to aggregate all results to form the final cluster center. Simultaneously, parallel FC-k-means operations are performed on the Spark framework to obtain a better initial cluster center, which improves accuracy and convergence speeds. Experimental results show that this parallel implementation improves model accuracy and reduces runtime.

关键词： Accuracy Runtime Flora Clustering algorithms Big Data partitioning algorithms Sparks Data mining Unsupervised learning Optimization

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Research on High Frequency Electromagnetic Scattering Characteristics of Dielectric-PEC Composite Targets

Research on High Frequency Electromagnetic Scattering Charac...

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International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Yan Zhu Si-Yuan He Zhi-Dan Bian Xiao-Yi Wang Lu-Min Luo School of Electronic Information Wuhan University Wuhan China

ISBN: (数字)9798350355581

ISBN: (纸本)9798350355598

The prediction of radar characteristics of complex medium targets has become an important research topic in the field of radar target characteristics. In this paper, within the framework of high-frequency method, a set of more perfect research method of electromagnetic scattering characteristics of medium-containing complex targets is established. Firstly, based on the geometrical model and material electromagnetic parameters of the complex electric target with dielectric material, the geometrical and material model partition and description method of the complex electric target with dielectric material are studied. Secondly, a multiple-action illumination surface judgement algorithm for dielectric targets is proposed to obtain the set of single- and multiple-coupled reflected ray paths on the surface, and the set of multiple-coupled transmitted ray paths in the interior formed by transmission on the dielectric surface. Finally, the scattering field of each ray collection is calculated using the physical optics algorithm, and the scattering characteristics of the whole target are obtained by coherent superposition. The effectiveness and accuracy of the PO method and the GO-PO algorithm are verified using several typical models.

关键词： Computational modeling Electromagnetic scattering Dielectric materials Physical optics Lighting Radar Dielectrics partitioning algorithms High frequency Electromagnetics

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