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检索条件"主题词=Partitioning Algorithms"

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MAE-CNN: A Multi-Scale Attention Enhanced Convolutional Neural Network for CU partition prediction

MAE-CNN: A Multi-Scale Attention Enhanced Convolutional Neur...

引用

Electronic Communication and Artificial Intelligence (IWECAI), International Workshop on

作者： Junwei Chen Chengji Zhao Rongbang An Energy and Electricity Research Centre Jinan University Zhuhai Guangdong China

ISBN: (数字)9798350386943

ISBN: (纸本)9798350386950

With the continual advancements in video encoding technology, the H.265 and H.266 standards have significantly reduced bit rates and enhanced data compression efficiency compared to their predecessors. H.265 employs a quadtree partitioning mechanism, while H.266 further develops this approach with the introduction of a Quad-tree with Nested Multi-type Tree (QTMT) partitioning system. These complex partitioning mechanisms require the exploration of all potential CU division options and the calculation of corresponding rate- distortion (RD) costs, substantially increasing encoding complexity. To address this challenge, this study introduces a Multi-scale Attention Enhanced Convolutional Neural Network (MAE-CNN), specifically designed to reduce the complexity of intra-frame video encoding. This deep convolutional neural network, trained using CU data collected through an official test model, incorporates the Inception module and the Convolutional Block Attention Module (CBAM). It significantly improves the detection of image details and broad area features, while also enhancing the network's ability to identify crucial feature information. Integrating this model into the encoder shows that under the full intra-frame encoding configuration, the algorithm saves an average of 64.07% of encoding time, with less average quality loss and lower bitrate growth, significantly better than traditional encoding methods. This research not only markedly improves encoding efficiency but also broadens the applications of deep learning in the video encoding field.

关键词： Deep learning Bit rate Prediction algorithms Feature extraction Encoding Complexity theory partitioning algorithms

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A Hardware-Friendly Fast Block Partition Decision Algorithm Based on Histogram of Oriented Gradient for AV1

A Hardware-Friendly Fast Block Partition Decision Algorithm ...

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International Conference on Solid-State and Integrated Circuit Technology

作者： Guohao Xu Shiyan Yi Zhijian Hao Leilei Huang Hao Zhang Xiaoyang Zeng Yibo Fan State Key Laboratory of Integrated Chips and Systems Fudan University Shanghai China Institute of Microelectronic Circuits and Systems East China Normal University Shanghai China

ISBN: (数字)9798350361834

ISBN: (纸本)9798350361841

AV1 is a recently proposed video coding standard that significantly improves the video compression ratio at the same quality as HEVC but at the cost of substantially increasing the computational complexity. To address this challenge, we propose a fast hardware-oriented algorithm based on the histogram of the oriented gradient (HOG) to make a fast block partition decision, which is the highest time complexity step in AV1. Firstly, we utilize HOG to prune the vertical or horizontal partition. Secondly, the sum of absolute differences (SAD) of the child and parent blocks HOG is used to further accelerate the partition decision. Finally, we also make hardware-oriented optimizations for HOG calculation. Experimental results show that the proposed algorithm can save up to 44.32% time with only 1.46% BD-Rate increase.

关键词： Integrated circuit technology Histograms Costs Video compression partitioning algorithms Indexes Time complexity Optimization Standards High efficiency video coding

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Energy-Efficient Resource Management for Multi-UAV NOMA Networks Based on Deep Reinforcement Learning

Energy-Efficient Resource Management for Multi-UAV NOMA Netw...

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IEEE Conference on Vehicular Technology (VTC)

作者： Xiangda Lin Helin Yang Kailong Lin Liang Xiao Zhaoyuan Shi Wanting Yang Zehui Xiong Department of Information & Communication Engineering Xiamen University Xiamen China School of Computer and Information Anqing Normal University Anhui China Pillar of Information System Technology & Design Singapore University of Technology and Design Singapore

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

Cellular-connected unmanned aerial vehicles (UAVs) play an essential role in cellular networks. Combined with non-orthogonal multiple access (NOMA) technique, UAVs can provide better performance in various communication scenarios. In this paper, we investigate a NOMA-enhanced UAV-assisted cellular network where multiple UAVs are deployed as aerial base stations to provide communication services for mobile ground users in the presence of a malicious jammer. We propose a two-step learning-based resource scheduling approach. First, an algorithm based on K-means clustering is proposed to partition ground users (GUs) to reduce mutual interference. Moreover, a cooperative multi-agent twin delayed deep deterministic algorithm is proposed to jointly optimize UAVs' trajectories, power allocation and GU association to maximize the system energy efficiency (EE) while guaranteeing minimum quality-of-service (QoS) requirements. Extensive results demonstrate that the proposed solution can efficiently improve EE and QoS performances under jamming attacks compared with existing popular approaches.

关键词： Cellular networks NOMA Vehicular and wireless technologies Clustering algorithms Quality of service Energy efficiency partitioning algorithms

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Research on Seabed Slope Classification and Survey Path Optimization Based on Genetic algorithms and Plane Fitting Method

Research on Seabed Slope Classification and Survey Path Opti...

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Image Processing and Computer Applications (ICIPCA), IEEE International Conference on

作者： Yonghang Hong Yexin Cheng Ziyu Peng Business School Hohai University Nanjing China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

Responding to the surge in maritime exploration and the increasing need for precise seabed mapping, this paper introduces a route planning method enhanced by an optimization algorithm to improve accuracy and efficiency. We develop three route planning models: north-south, east-west, and diagonal east-west directions. Utilizing a genetic algorithm for regional optimization and a planar fitting model to determine the shortest path in each region, our analysis reveals that the diagonal east-west direction yields the shortest route. The findings indicate improvements in route length, reduced missed areas, and lower overlap rates. This study offers a potent optimization algorithm for route planning, setting the stage for future advancements in the field.

关键词： Surveys Image processing Fitting Solids Planning Classification algorithms partitioning algorithms Surges Optimization Genetic algorithms

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Grasp Trajectory Planning for Vehicle-Mounted Robotic Arm based on Improved DDPG Algorithm

Grasp Trajectory Planning for Vehicle-Mounted Robotic Arm ba...

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Chinese Automation Congress (CAC)

作者： Yong Li Linbing Ke Chaoxing Zhang Yanjun Chen Yang Yang Yunjie Qin Ministry of Education Chongqing University of Posts and Telecommunications Key Laboratory of Industrial Internet of Things and Network Control Chongqing China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

In response to the problems of slow convergence and poor control effect when the traditional Deep Deterministic Policy Gradient (DDPG) algorithm is used to train the vehicle-mounted robotic arm, an improved DDPG algorithm is proposed in this paper. According to the control characteristics of the vehicle-mounted robotic arm, the state space, action space, and reward and punishment function are designed. Then the operation area is partitioned, and the baseline pose of each area is taught. A guidance function is designed and introduced to guide the algorithm training with the baseline pose to accelerate the algorithm's convergence. Finally, the traditional reward and punishment function is improved according to the guidance training characteristics. The simulation experiments show that the improved DDPG algorithm has significantly improved the convergence efficiency, planning success rate, and control efficiency compared with the traditional DDPG algorithm, especially since the planning success rate can reach about 93.8%.

关键词： Training Space vehicles Automation Trajectory planning Aerospace electronics Manipulators partitioning algorithms Planning Faces Convergence

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Semi-Supervised Multi-View Ensemble Clustering with Subspaces Extraction and Constraints Propagation

Semi-Supervised Multi-View Ensemble Clustering with Subspace...

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Pattern Recognition and Artificial Intelligence (PRAI), International Conference on

作者： Chenye Yang Yifan Shi Rongxin Guo School of Engineering Huaqiao University Quanzhou China

ISBN: (数字)9798350350890

ISBN: (纸本)9798350350906

Semi-supervised multi-view clustering extends the principles of multi-modal analysis by focusing on the partitioning of data into distinct groups based on limited number of pairwise constraints. Guided by pairwise constraints, the existing methods suffer from a lack of integration at the level of view-specific subspaces and partitions, which may lead to information loss of each view. In order to sovle this problem, this paper proposes a semi-supervised multi-view ensemble clustering framework with subspaces extraction and constraints propagation (SSMVEC). For each view, a constrained multi-view broad self-encoding network (CMVBSN) is firstly designed to integrate cross-view affinity, within-view projection and pairwise constraints to extract a compressed feature subspace and derive a base partition. By absorbing the information from both pairwise constraints and multiple partitions, the bidirectional constraint propagation is adopted to refines the unified similarity matrix. Finally, the graph-cut algorithm is applied to obtain the consensus partition. Demonstrated by the analysis of parameters, comparison methods and incremental constraints on multiple multi-view datasets, SSMVEC can effectively leverage pairwise constraints and achieve overall better performance than several multi-view clustering methods.

关键词： Measurement Clustering methods Noise Clustering algorithms Focusing Semisupervised learning Feature extraction Stability analysis partitioning algorithms Data mining

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Optimization Strategy for DFIG Rotor Kinetic Energy and Energy Storage Output Using Partitioned Variable Step Size Algorithm

Optimization Strategy for DFIG Rotor Kinetic Energy and Ener...

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Energy, Power and Grid (ICEPG), International Conference on

作者： Xu Lin Weihua Wang Mingshuai Pan Da Yu Zhenfan Yu Yu Cai Junhe Chen Yujun Sun Power Dispatching and Controlling Center of Guangdong Power Grid Company Limited Guangdong Power Grid Company Limited GuangZhou Guangdong Province China National Instituten of Guangdong Advanced Energy Storage Company Limited GuangZhou Guangdong Province China

ISBN: (数字)9798350377798

ISBN: (纸本)9798350377804

Wind-storage systems are widely used in wind power frequency support, but there is still a lack of optimized coordinated control strategies. This paper proposes a wind-storage coordinated control based on a partitioned variable step-size optimization algorithm. It integrates inertia power support commands into traditional turbine-side MPPT control, with energy storage employing droop control. Throughout different stages of wind-storage frequency regulation, adjustments are made to the turbine-side converter and energy storage-side variable step-size adjustment factors. This determines the output of both the wind turbine inertia and the energy storage battery, leveraging the rotor kinetic energy and coordinated control of energy storage to maximize inertia support while meeting economic requirements. The theoretical analysis is supported by a 2MW doubly-fed induction generator simulation platform, confirming the effectiveness of the proposed control strategy.

关键词： Frequency modulation Rotors Wind power generation Wind turbines partitioning algorithms Batteries Kinetic energy Transient analysis Frequency control Optimization

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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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Method for Anomaly Detection in Distributed Photovoltaic Power Generation Based on Kernel Principal Component-OPTICS Clustering

Method for Anomaly Detection in Distributed Photovoltaic Pow...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Lei Fu Meifang Wei Zhuo Peng Sheng Su Technical Skills Training Center State Grid Hunan Electric Power Co. Ltd. Changsha China Changsha University of Science and Technology Changsha China

ISBN: (数字)9798331523527

ISBN: (纸本)9798331523534

Addressing the lack of professional monitoring in Distributed Photovoltaic Power Generation Systems (DPV), this study proposes a novel method for detecting anomalies in distributed photovoltaic power output. This method leverages the output correlation of adjacent photovoltaic stations and combines Kernel Principal Component Analysis (KPCA) with the OPTICS clustering algorithm to perform cluster partitioning and dimensionality reduction on the power output data of photovoltaic power stations. The specific steps are as follows: First, the OPTICS algorithm is used to partition the output data of multiple photovoltaic stations into clusters; then, Kernel Principal Component Analysis is applied to reduce the dimensionality of the clustered data. Based on this, an equal-weighted weighted average method is employed to plot the output curve that reflects the overall output condition of the cluster under different weather conditions. Finally, the output range is fitted using quantile regression, which serves as the basis for anomaly detection in DPV clusters.

关键词： Photovoltaic systems Technological innovation Clustering algorithms Optics partitioning algorithms Kernel Forecasting Anomaly detection Principal component analysis Meteorology

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