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检索条件"主题词=partitioning algorithms"

共 15745 条记录，以下是161-170 订阅

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Sum-Rate Maximization for Partitioned-IRS-Assisted Hybrid NOMA Networks

Sum-Rate Maximization for Partitioned-IRS-Assisted Hybrid NO...

引用

IEEE Conference on Wireless Communications and Networking

作者： Songchao Chen Fang Liu Yuanan Liu School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Intelligent Reflecting Surfaces (IRS) have emerged as a revolutionary solution in next-generation (6G) wireless communication systems. Comprising numerous programmable reflecting units, IRS technology improves data transmission rates by dynamically adjusting the phase shifts of reflected signals. Non-orthogonal multiple access (NOMA) is a technique that can greatly enhance spectral efficiency and system sum-rate. In this paper, we consider a partitioned-IRS-assisted NOMA system in which a base station (BS) transmits superposed downlink signals to multiple users. Firstly, we establish the system sum-rate maximization problem of optimal joint IRS phase shifts and the partition sub-surfaces matching scheme by adapting combined channel strength(CSS)-based hierarchical pairing algorithm. However, since the formulated problem is non-convex, we use an alternating optimization (AO) technique to decompose the non-convex optimization problem into two subproblems for iterative solving. To further optimize the sum-rate, we also propose a NOMA user selection criteria based on the minimum signal-to-noise ratio (SNR) difference. The simulation results show a significant improvement in the performance of the IRS-assisted NOMA system compared to various traditional benchmarking schemes.

关键词： Wireless communication NOMA Spectral efficiency System performance Simulation Downlink partitioning algorithms Iterative methods Optimization Signal to noise ratio

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An Efficient Dense Pedestrian Detection Model Based on RT-DETR

An Efficient Dense Pedestrian Detection Model Based on RT-DE...

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Power Electronics, Computer Applications (ICPECA), IEEE International Conference on

作者： Chaofan Wu Tao He Kun Yang School of Computer Science University of South China Hengyang China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

Pedestrian detection is a vital study field within object detection. With the development of deep learning, models perform exceptionally well in pedestrian detection for sparse scenarios. However, their performance often fails in complex and crowded dense pedestrian scenarios, particularly when there is a concentration of small objects. Challenges such as low detection accuracy, high miss rates, and high false-positive rates persist in these scenarios. To enhance dense pedestrian detection performance, we propose a model named RT-DETR-LKG, based on RT-DETR. The improved model incorporates LSKA (Large Separable Kernel Attention) and the GELAN (Generalized Efficient Layer Aggregation Network) modules (LKG). The backbone network integrates the LSKA, which generates spatial attention weights for feature maps via multiple convolutional layers. This process enables feature extraction across various receptive fields and applies weighting to input channels, resulting in more refined feature extraction and enhancement. The hybrid encoder network applies the GENLAN module. It achieves rich feature extraction with minimal computational cost by combining channel partitioning and repeated convolutions. The network's feature extraction ability is also kept. In bounding box regression, the WIoU loss function is used instead of the GIoU loss function to improve anchor selection for occluded and small object prediction boxes. Experimental results indicate that the new RT-DETR algorithm performs better in crowded pedestrian scenarios. It has a mean average precision of 86.1%, which is 1.2% higher than the original model.

关键词： Deep learning Pedestrians Attention mechanisms Accuracy Object detection Feature extraction Prediction algorithms Power electronics partitioning algorithms Kernel

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Optimal Topology Design in Radially Distributed Electrification Scenarios with Distributed Generation

Optimal Topology Design in Radially Distributed Electrificat...

引用

Grid Edge Technologies Conference & Exposition (Grid Edge), IEEE PES

作者： Weixi Wang Robert Flores Jack Brouwer Advanced Power and Energy Program University of California Irvine Irvine California

ISBN: (数字)9798350352528

ISBN: (纸本)9798350352535

It is known that climate change related power outages could be addressed with microgrids. Forming reliable microgrids requires connecting distributed energy sources so that electricity balance can be achieved. In this paper, a straightforward topology design methodology that focuses upon improving local power balance in island operation mode while observing power quality requirements and keeping most loads in service is proposed and then applied to various real-life electrification and islanding scenarios. A revised Multilevel Graph partitioning algorithm is implemented and compared with several other methods. The quantified results present a better balance of power and are considered adding to reliability within the community, with the proposed algorithm achieving the overall best degree of local power balance.

关键词： Islanding Simulation Power quality Microgrids Voltage Electrification partitioning algorithms Topology Power system reliability Reliability

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Chronic Kidney Disease Prediction Using Machine Learning and Deep Learning Models

Chronic Kidney Disease Prediction Using Machine Learning and...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Shaik Rafi Nuti Revanth K Veera Raghava Reddy K Mahesh Babu Y Likhith Prasanna Kumar N Vijay Kumar Dept of CSE Narasaraopeta Engineering College Narasaraopet Palnadu Andhra Pradesh India Dept of CSE Narasaraopeta Engineering College Narasaraopet Andhra Pradesh India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Chronic kidney disease is a noticeable health condition that can persist throughout an individual’s life, resulting from either kidney malignancy or diminished kidney function. In this work, we investigate how several machine learning techniques might provide an early CKD diagnosis. While previous research has extensively explored this area, our aim is to refine our approach by employing predictive modeling techniques. Initially, we considered 25 variables alongside the class property. The data set used in this study underwent extensive processing, including changing the names of colours for clarity, converting identified colours to numbers, treating unique values with letters handling of partitioned values, fixing incorrect values, filling null values with mean, and encoding categorical values into mathematical notation. In addition, Principal component analysis (PCA) was also employed to lower dimensionality. Our findings demonstrated that the XG Boost classifier surpassed e very other algorithm, with an accuracy of 0.991.

关键词： Technological innovation Image color analysis Null value Predictive models Prediction algorithms Chronic kidney disease Mathematical models partitioning algorithms Random forests Principal component analysis

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FedLCA: Synchronous Layer-Wise Compensated Aggregation for Straggler Mitigation

FedLCA: Synchronous Layer-Wise Compensated Aggregation for S...

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IEEE Conference on Wireless Communications and Networking

作者： Jiale Wang Nan Li Xinghan Wang Tingting Yang Dalian Maritime University Dalian China Peng Cheng Laboratory Shenzhen China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

In this paper, we introduce a layer-wise compensated aggregation algorithm designed for federated learning in dynamic environments. To reduce data transmission and maintain model continuity, our method implements a hierarchical update strategy that avoids artificially modular partition of neural networks and thus more flexible in practice. Specifically, weight layers closer to the output, which significantly impact model performance, receive direct updates. Conversely, layers further from the output with minimal influence, are updated using gradient compensation from the previous iteration. To ensure consistency across the model, we compensate these less influential weight layers with residual values from earlier rounds, thus enhancing integration and continuity. Experimental results from two datasets confirm that our algorithm not only secures stable convergence but also matches the generalization performance of traditional federated averaging, even under conditions with dropout rates as high as 90%.

关键词： Training Performance evaluation Machine learning algorithms Federated learning Heuristic algorithms Prevention and mitigation Artificial neural networks partitioning algorithms Time-varying systems Convergence

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Efficient Policy Iteration and Value Iteration via State Space Clustering

Efficient Policy Iteration and Value Iteration via State Spa...

引用

IEEE Southeastcon

作者： Avinash Paidi Pabitra Mitra Department of Artificial Intelligence Indian Institute of Technology Kharagpur Kharagpur India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur Kharagpur India

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

This paper presents a novel state-space clustering technique specifically designed for Reinforcement Learning (RL) problems with discrete state and action spaces. Building on this clustering methodology, we propose a Hierarchical Reinforcement Learning (HRL) framework to improve the efficiency of policy iteration (PI) and value iteration (VI) algorithms. Our approach partitions the state space into well-defined, homogeneous clusters and identifies representative prototype states for each cluster. Transitioning between clusters is facilitated through macro-actions, which are derived using the Depth First Search (DFS) algorithm. By focusing value function updates exclusively on these prototype states during PI and VI operations, the proposed method substantially reduces computational overhead without compromising performance. Experimental evaluations demonstrate that the proposed technique generates highly cohesive clusters, enhances policy learning quality, and accelerates the convergence of the PI and VI algorithms.

关键词： Buildings Clustering algorithms Prototypes Focusing Reinforcement learning partitioning algorithms Convergence

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Online Music Recommendation Algorithm Based on Heterogeneous Neural Network

Online Music Recommendation Algorithm Based on Heterogeneous...

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Cybersecurity, Internet of Things and Soft Computing (CITSC), Asia-Europe Conference on

作者： Bo Li Chao Dai Qinghai Normal University Xining City China

ISBN: (数字)9798331504205

ISBN: (纸本)9798331504212

Traditional recommendation algorithms mainly rely on historical interaction information between users and music projects, but ignore the collaborative signals in user interactions, and are limited by the sparsity of interaction data. To solve these problems, this paper combines heterogeneous graph neural networks and designs the HNMRA algorithm by splitting the graph structure, decomposing the complex network into multiple focused on specific relationships subgraphs. By using the user's explicit feedback, such as user comments, we need to calculate preference weights. On each subgraph, the GNN learns the user and music attributes and behavior preference representations by using the attention mechanism to fuse information from different subgraphs, generating more precise user and music embedding representations. Based on these embedding representations, the top-K recommendation is performed, and the K most likely interesting music is recommended to the user. The advantage of the algorithm is that it can handle multidimensional relationships simultaneously, improve the recommendation accuracy by using explicit feedback, and achieve personalized preference modeling. To solve the challenges of computational complexity and data sparsity, this paper proposes optimizing the GNN structure design, adopting efficient graph partitioning and parallel processing technologies, and combining implicit feedback, such as playback times, to enhance model robustness. Experimental results show that the proposed research not only improves the comprehensiveness and accuracy of recommendations, but also provides personalized recommendation results, which has high research value and application potential.

关键词： Accuracy Computational modeling Scalability Parallel processing Graph neural networks User experience Robustness partitioning algorithms Computational complexity Recommender systems

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A Parameter Selection Strategy for the Generalized Fuzzy-Possibilistic C-Means Algorithm

A Parameter Selection Strategy for the Generalized Fuzzy-Pos...

引用

International Symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Mirtill-Boglárka Naghi Levente Kovács László Szilágyi Doctoral School of Applied Mathematics and Applied Informatics Óbuda University Budapest Hungary Comput. Intell. Research Group Sapientia Univ. Tg. Mureş Romania Physiological Controls Research Center Óbuda University Budapest Hungary Computational Intelligence Research Group Sapientia University Tg. Mureş Romania

ISBN: (数字)9798350379365

ISBN: (纸本)9798350379372

The generalized fuzzy-possibilistic $c$ -means algorithm (GFPCM) was proposed as a correction of the so-called fuzzy-possibilistic $c$ -means, (FPCM) that failed to adapt its possibilistic term to the number of clustered data. GPFCM added an extra parameter to the objective function of FPCM that set up a tradeoff between the strength of the probabilistic and possibilistic term. However, a formula for the appropriate value of this extra parameter $\beta$ was not given at its introduction. This paper aims to establish a parameter selection technique that would ensure fine partitioning by the use of the GFPCM algorithm.

关键词： Systematics Noise Clustering algorithms Approximation algorithms Probabilistic logic Linear programming partitioning algorithms Noise measurement Informatics Machine intelligence

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Enumerating the K-Fold Configurations in Multi-Class Classification Problems

Enumerating the K-Fold Configurations in Multi-Class Classif...

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Conference on Data Science and Machine Learning Applications (CDMA)

作者： Attila Fazekas György Kovács University of Debrecen Debrecen Hungary Analitycal Minds Ltd. Beregsurány Hungary

ISBN: (数字)9798331539696

ISBN: (纸本)9798331539702

K-fold cross-validation is a widely used tool for assessing classifier performance. The reproducibility crisis [6] faced by artificial intelligence partly results from the irreproducibility of reported k-fold cross-validation-based performance scores. Recently, we introduced numerical techniques [10] to test the consistency of claimed performance scores and experimental setups. In a crucial use case, the method relies on the combinatorial enumeration of all k-fold configurations, for which we proposed an algorithm in the binary classification case. In this paper, the algorithm is generalized to multi-class classification.

关键词： Machine learning algorithms Machine learning Data science Reproducibility of results partitioning algorithms Classification algorithms Multiaccess communication

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Design and Research of AI Accelerators for Onboard Computing Platforms Targeting YOLOs Network

Design and Research of AI Accelerators for Onboard Computing...

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International Conference on Computer Research and Development (ICCRD)

作者： Yunlong Wang Hui Zhou Xia Liu Bing Li Yifan Ge Beijing Aerospace Automatic Control Institute Beijing Aerospace Automatic Control Institute Beijing China

ISBN: (数字)9798331531881

ISBN: (纸本)9798331531898

Convolutional Neural Networks (CNNs) play a crucial role in computer vision, with wide applications in image recognition, object tracking, and trajectory optimization. Due to constraints in computation, power consumption, and size in aerospace vehicles, deploying CNN algorithms on target platforms presents a significant challenge in aerospace applications. This paper proposes an accelerator design for the YOLOv3-tiny network tailored to aerospace applications. By employing techniques such as loop partitioning, loop unrolling, and weight sharing, the proposed accelerator enhances computational performance and data reuse efficiency. Additionally, by integrating convolution, activation, and pooling operations into a unified module, this approach reduces the need for intermediate buffers, saving memory and improving acceleration performance. Compared to accelerators that require buffering of convolution results, the proposed design demonstrates superior performance. The accelerator is implemented on the ZYNQ7020 FPGA platform, achieving a peak computational performance of 114.53 GOPS.

关键词： Convolution Computational efficiency partitioning algorithms Timing Convolutional neural networks Field programmable gate arrays Trajectory optimization Research and development Pipeline processing Radio spectrum management

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