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检索条件"主题词=Partitioning Algorithms"

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Task Offloading and Resource Allocation based on Enhanced Artificial Hummingbird Algorithm in NOMA-Assisted Mobile Edge Computing

Task Offloading and Resource Allocation based on Enhanced Ar...

引用

International Conference on Communication Technology (ICCT)

作者： Jie Yang Di Chen Xuehong Cao Jing Zeng Nanjing Institute of Technology Nanjing China Nanjing University of Posts and Telecommunications Nanjing China Nanjing Institute of Technology Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9798350363760

ISBN: (纸本)9798350363777

Mobile edge computing (MEC) is a key technology of the Internet of Vehicles (IoV). Applying non-orthogonal multiple access (NOMA) technology to MEC can improve spectrum and avoid serious computing delay. This paper studies the optimization problem of task offloading and resource allocation under the NOMA-MEC system. By jointly optimizing the task partition ratio, transmit power allocation and computing resource allocation on the MEC server, the goal is to minimize the weighted total delay of the system. An enhanced artificial hummingbird algorithm (EAHA) is proposed to solve the optimization problem. Considering the task priority weights of each vehicle user, the SIC decoding mechanism of the NOMA technology is improved, and the chaos map, Levy flight strategy and whale algorithm are added to improve the population initialization and migration strategy in the AHA algorithm. The problem of uneven population distribution and repeated individuals in the original algorithm is solved, and the limitation of the algorithm that it is easy to fall into the local optimal solution is improved. Finally, the optimization solution of task offloading and resource allocation problems is realized, so that vehicle users can obtain the optimal offloading strategy. Simulation results show that the algorithm proposed in this paper can effectively reduce the total delay of the system and has considerable convergence speed and accuracy.

关键词： NOMA Multi-access edge computing Simulation Delays partitioning algorithms Resource management Servers Optimization Internet of Vehicles Convergence

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Automatic Design Method of Cable Laying Path in Substation

Automatic Design Method of Cable Laying Path in Substation

引用

Smart Grid and Energy Engineering (SGEE), International Conference on

作者： Congying Wu Peili Yan Xiaofan Song Hongming Shen Xiaoxu Wang State Grid Economic and Technological Research Institute Co. Ltd Beijing China State Grid Henan Economic and Technical Research Institute Zhengzhou Henan Province China

ISBN: (数字)9798331530686

ISBN: (纸本)9798331530693

In order to improve the efficiency and design quality of substation cable laying, and support the development of digital business, this problem research realizes the automatic design of substation cable laying through the principle of Dijkstra algorithm. This paper adopts the idea of partition grouping, and divides the substation into the same room, the same floor, the same building, indoor and outdoor areas, so as to reduce the number of path screening and reduce the calculation amount. Through the Dijkstra algorithm, the optimal cable laying path of the equipment at any starting point and end point can be calculated, and the cable laying length can be obtained to effectively support the automatic design of the substation.

关键词： Wiring Substations Design methodology Buildings Turning Software partitioning algorithms Smart grids Planning Floors

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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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Ramer-Douglas-Peucker Dynamic Time Warping (RDP-DTW): A Novel Data Reduction Based Dynamic Time Warping method for Time Series Classification

Ramer-Douglas-Peucker Dynamic Time Warping (RDP-DTW): A Nove...

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Advances in Electrical and Communication Technologies (ICAECOT), International Conference on

作者： Manel Khadiche Bachir Boucheham Salah Bougueroua Department of Computer Science Laboratoire de Recherche en Electronique de Skikda (LRES) University of 20 Août 1955 Skikda Algeria

ISBN: (数字)9798350353754

ISBN: (纸本)9798350353761

In this paper, we propose a variant of DTW that is based on Ramer Douglas Peucker line simplification algorithm (RDPLSA). We call the proposed method Ramer Douglas Peucker Dynamic Time Warping (RDP-DTW). The RDP-DTW is used here for time series classification with the specific aim of accurately and rapidly evaluating the similarity between two given time series without losing much of the significant data information. For that purpose, the RDP-DTW is combined with the 1NN (1 Nearest Neighbor) classifier. The proposed RDP-DTW consists of two steps. The first is a line simplification approach (RDPLSA). The RDPLSA proceeds to selecting the most significant points on the line of the time series according to a simplification tolerated error (say, $\epsilon$-Simp). The RDPLSA recursively partitions the line data and controls the compression quality based on $\epsilon$-Simp. This step is performed on the two-time series to match. The second step is to adjust the dynamic time warping (DTW) alignment method on the two obtained time series generated after dimension reduction. Experimental results are carried out on the $\mathbf{8 5}$ benchmark datasets from the UCR (University of California at Riverside) time-series classification archive. Results show the outperformance of the proposed method over 1NN LE-DTW (Local Extrema DTW) [1] a recently published time series classifier and the 1NN ED (Euclidean distance) classifier, on the number of wins and the average error rates.

关键词： Dimensionality reduction Error analysis Heuristic algorithms Time series analysis Euclidean distance Benchmark testing Time measurement Communications technology Classification algorithms partitioning algorithms

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A Parallel Volume Visualization Algorithm for Unstructured Grid

A Parallel Volume Visualization Algorithm for Unstructured G...

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Electrical, Automation and Computer Engineering (ICEACE), International Conference on

作者： Cheng Chen Yang Chao China Aerodynamics Research and Development Center Computational Aerodynamics Institute Mianyang China

ISBN: (数字)9798350368208

ISBN: (纸本)9798350368215

Volume rendering is one of the most important visualization methods for unstructured grid data. However, the existing serial volume rendering algorithms for unstructured meshes are not efficient enough to meet the needs of large-scale data volume visualization. In order to solve the performance bottleneck, this paper proposes a parallel volume rendering algorithm. Firstly, we design a parallel KD-tree algorithm to split volume data and track the transparent relationship. Secondly, each process uses an independent visual pipeline to calculate the volume rendering image. Finally, a tree synthesis strategy is used to synthesize the final image. Experimental results show that the algorithm can be efficiently applied to the visualization of large-scale unstructured grid data.

关键词： Image segmentation Visualization Image synthesis Instruction sets Pipelines Data visualization Computer architecture Rendering (computer graphics) partitioning algorithms Servers

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Leveraging Graph Clustering for Differentially Private Graph Neural Networks

Leveraging Graph Clustering for Differentially Private Graph...

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IEEE International Conference on Big Data

作者： Sager Kudrick Renata Dividino Computer Science Department Brock University Canada

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Differential Privacy is a standard approach for ensuring privacy in deep learning models, but its effectiveness is less certain when applied to message-passing Graph Neural Networks (GNNs). GNNs, which process graph-structured data, generate node representations by aggregating information from neighboring nodes. At the k-th layer, GNNs propagate information across a node’s k-hop neighborhood, causing interconnected nodes to influence each other’s representations. Consequently, protecting the privacy of a single node, edge, or feature often requires safeguarding information about related graph elements as well. Previous methods have addressed this issue by injecting noise into the data, though finding the right balance is challenging; while more noise increases privacy, excessive noise degrades model output. Other approaches use custom architectures to decouple neighborhood aggregation from node representation learning, but these solutions often struggle to scale to large graphs. We propose a strategy for training subgraph-level DP-GNNs by extracting disjoint subgraphs from the training dataset and applying the DP-SGD algorithm, treating each subgraph as an independent sample. This method protects the privacy of target nodes and their neighbors. Our graph partitioning approach is inspired by community detection techniques, which help preserve relevant connections within partitions. By restructuring the training data, our solution enhances privacy protection while maintaining model utility, ultimately outperforming existing techniques.

关键词： Training Representation learning Privacy Noise Training data Graph neural networks Data models partitioning algorithms Protection Standards

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Quantum Fidelity Based Fuzzy C-Means Clustering Algorithm

Quantum Fidelity Based Fuzzy C-Means Clustering Algorithm

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Quantum Computing and Engineering (QCE), IEEE International Conference on

作者： Oumayma Ouedrhiri Usef Faghihi Fadel Toure Oumayma Banouar Mathematics and Computer science department University of Quebec in Trois-Rivières Trois-Rivières Canada Computer science department Faculty of Science and techniques Marrakesh Morocco

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

Among clustering algorithms, fuzzy clustering stands out for its ability to offer a nuanced representation of the data by assigning degrees of membership to clusters, providing a more flexible and adaptive approach than the rigid partitioning of hard clustering algorithms. This has proved highly advantageous, particularly for image segmentation problems. Numerous approaches have been proposed to improve the Fuzzy C-means (FCM) algorithm using quantum computing, some are quantum-inspired and others can be run on quantum simulators. In this paper, a study was conducted on Quantum Fuzzy Means (QFCM) approaches. Then, a novel QFCM algorithm is introduced to address the challenges associated with these current algorithms, particularly in handling large datasets and incorporating genuine fuzzy system principles. Using concepts from quantum computing, our approach aims to improve distance calculations between data points by using a quantum distance measure. This method enables significant acceleration of the clustering process especially when dealing with extensive datasets. Moreover, our proposed algorithm integrates a structured fuzzy system framework into the membership matrix calculation, enhancing the precision and interpretability of the clustering results. Furthermore, unlike other FCM algorithms, which often lack explicit representation of fuzzy logic principles, our approach incorporates a well-defined fuzzy system to capture the inherent uncertainty and ambiguity in real-world data.

关键词： Fuzzy logic Image segmentation Quantum computing Uncertainty Clustering algorithms Classification algorithms partitioning algorithms Time complexity Recommender systems Fuzzy systems

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Learning-Enabled CPS for Edge-Cloud Computing

Learning-Enabled CPS for Edge-Cloud Computing

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International Symposium on Industrial Embedded Systems

作者： Luigi Capogrosso Shengjie Xu Enrico Fraccaroli Marco Cristani Franco Fummi Samarjit Chakraborty University of Verona Verona Italy University of North Carolina at Chapel Hill USA

ISBN: (数字)9798350388633

ISBN: (纸本)9798350388640

Many Cyber-Physical System (CPS), such as autonomous vehicles and robots, rely on compute intensive Machine Learning (ML) algorithms, especially for perception processing. A growing trend is to implement such ML algorithms in the cloud. However, the data transfer overhead and the delay introduced in the process necessitate some form of edge-cloud solution. Here, a part of the processing is done locally and the rest on the cloud, and how to do this partitioning is being explored in the body of work referred to as Split Computing (SC). In this position paper, we explore different SC architectures and discuss their implications on controller design for CPS. In particular, we discuss the delay and state estimation accuracy of these different SC architectures and how they would impact the design of the feedback controllers using them.

关键词： Machine learning algorithms Embedded systems Computer architecture Machine learning Market research Data transfer Delays partitioning algorithms State estimation Robots

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Optimizing the Deployment of Multi-UAVs in Emergency Response Networks

Optimizing the Deployment of Multi-UAVs in Emergency Respons...

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International Baltic Electronics Conference, BEC

作者： Ali Hassan Sadaf Javed Rizwan Ahmad Waqas Ahmed Adnan Kiani Muhammad Mahtab Alam National University of Sciences and Technology (NUST) Islamabad Pakistan Pakistan Institute of Engineering and Applied Sciences (PIEAS) Islamabad Pakistan Nottingham Trent University England Thomas Johann Seebeck Department of Electronics Tallinn University of Technology Tallinn Estonia

ISBN: (数字)9798350365115

ISBN: (纸本)9798350365122

A Multi-Unmanned Aerial Vehicle (UAV) network is a promising solution for providing wide-area search capabilities, wireless coverage, high-resolution imaging, and real-time data in surveillance, communication, and disaster management. Our problem is NP-hard and cannot be directly solved due to coverage, distance, capacity, deployment, and usage constraints. In this paper, we propose two schemes for efficient UAV deployment: Modified Density-Based Spatial Clustering of Applications with Noise (MDBSCAN) and a 2-layer proposed algorithm. Using the air-to-ground model, we analyze the relationship between UAV altitude and coverage radius. We consider four different environments: suburban, urban, dense urban, and high-rise dense urban. The proposed algorithms are compared with partitioning and greedy algorithms in terms of the number of UAVs required. Our 2-layer algorithm demonstrates superior performance across different environments, consistently requiring fewer UAVs than partitioning, MDBSCAN, and greedy algorithms.

关键词： Greedy algorithms Wireless communication Analytical models Atmospheric modeling Surveillance Clustering algorithms Autonomous aerial vehicles Real-time systems partitioning algorithms Air to ground communication

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Structural Breakpoint Detection in Noisy Time Series Using an Augmented Matrix Profile Index

Structural Breakpoint Detection in Noisy Time Series Using a...

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IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Marco Fuchs Mathematical Institute for Machine Learning and Data Science Catholic University of Eichstätt-Ingolstadt Ingolstadt Germany

ISBN: (数字)9798331530631

ISBN: (纸本)9798331530648

We introduce a method for partitioning a time series into segments. The method extends the recently introduced Fast Low-Cost Semantic Segmentation (FLUSS) algorithm to increase its robustness against noise and to automatically learn two required hyperparameters. FLUSS is part of the Matrix Profile framework and conceptualises the relationship between every subsequence and its nearest-neighbor as an arc spanning over the time series. It then determines segment boundaries by selecting time indexes which are spanned over by significantly fewer arcs than an average index, such that most subsequences on either side of the time index find their closest neighbor at the same side of the index. We extend FLUSS in two directions. First, we consider the Z nearest neighbors of each subsequence to compute its neighbors’ concentration along the time series as a measure for how characteristic a sequence itself is for a particular segment. This measure is then used to filter out non-characteristic sequences, aimed at increasing the robustness of the segmentation against noise. Second, FLUSS requires the user to set two important yet hard to determine hyperparameters, namely the number of segments and the length of exclusion zones around the index of an already found segment boundary. We introduce a procedure to learn both hyperparameters implicitly by setting the tolerance parameter of an iterative end-point fit algorithm, which extracts (local) minima from a meta time series denoting the number of arcs that cross a time series index. We test the method on a set of 32 diverse and publicly available univariate time series, where it demonstrates promising results.

关键词： Sensitivity Semantic segmentation Time series analysis Noise Robustness Time measurement partitioning algorithms Indexes Noise measurement Unsupervised learning

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