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检索条件"主题词=Linear Programming"

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Unsupervised Multi-View Outlier Detection via Optimal Graph Filtering

Unsupervised Multi-View Outlier Detection via Optimal Graph ...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhiguo Hu Ning Wang Peng Zhou Liang Du Yuhua Qian Cheng Wang Yanming Zhang School of Computer and Information Technology Shanxi University Taiyuan China School of Computer Science and Technology Anhui University Hefei China Institute of Big Data Science and Industry Shanxi University Taiyuan China Department of Computer Science and Engineering Tongji University Shanghai China China Mobile Communications Group Shanxi Co. Ltd. Taiyuan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Unsupervised multi-view outlier detection has garnered increasing attention in recent years, yet existing methods face persistent challenges. Many approaches rely predominantly on first-order neighborhood information, overlooking the richer insights offered by higher-order structures, which can degrade detection accuracy. Additionally, some methods suffer from outlier domination in their objective functions, leading to suboptimal performance. Integrating information effectively across multiple views also remains a significant hurdle. To address these challenges, we propose a novel Multi-View Outlier Detection method based on Optimal Graph Filtering (MODGF). Our approach detects outliers using a high-order graph filtering mechanism, ensuring consistency between feature and neighborhood spaces by sharing filtering parameters. Furthermore, we incorporate the Corr-entropy Induced Metric (CIM) to refine the objective function and introduce an efficient scoring strategy for enhanced detection reliability. Extensive experimental results demonstrate that our method is both stable and efficient across scenarios. The code is available at https://***/criticcc/MODGF.

关键词： Measurement Accuracy Filters Filtering linear programming Feature extraction Reliability Speech processing Anomaly detection Faces

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Feasibility of Nano Electrical Resistive Tomography for Subsurface Atomic Force Microscopy

Feasibility of Nano Electrical Resistive Tomography for Subs...

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International Conference on Mechatronics (ICM)

作者： Benjamin J. Stanwell Yuen Kuan Yong Andrew J. Fleming Precision Mechatronics Lab University of Newcastle Newcastle Australia

ISBN: (数字)9798331533892

ISBN: (纸本)9798331533908

This article investigates the feasibility of using a dual-cantilever atomic force microscope for imaging the electrical properties of a sample below the surface. Principles from macro-scale electrical resistive tomography are adapted to utilize measurements from a dual-probe atomic force microscope. A deep-learning method is employed to perform the inversion process and construct the tomography. Simulation results demonstrate that electrical resistive tomography is possible at the nanometre scale but improvements to the inversion algorithms are needed before moving to experimental applications.

关键词： Atomic measurements Atomic force microscopy Force measurement Noise Force Tomography Conductivity linear programming Noise measurement Electrical resistance measurement

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Entanglement Distribution Over Quantum Networks with Fairness Guarantees

Entanglement Distribution Over Quantum Networks with Fairnes...

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IEEE Conference on Wireless Communications and Networking

作者： Wenkang Cen Jinbei Zhang Kechao Cai Shihai Sun School of Electronic and Communication Engineering Sun Yat-sen University Shenzhen China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The entanglement distribution problem over quantum networks has been widely studied, with the objective of maximizing network throughput, that is, the number of entanglements distributed for all user pairs. However, most of the existing works only focus on throughput maximization while neglecting fairness considerations. In this paper, we first characterize the fairness of an entanglement distribution scheme by introducing a fairness factor based on Element-Wise Inequalities, referred to as EWI-fairness. The EWI-fairness requires that the entanglement distribution rate of each user pair exceeds a certain threshold, ensuring the fair distribution. Second, we enforce fairness guarantees into two existing distribution methods, Temporal Multiplexing Distribution (TMD) and Flow Multiplexing Distribution (FMD). Our theoretical analysis reveals that FMD-based scheme outperforms TMD-based scheme. Therefore, we focus on optimizing FMD-based scheme. Third, we formulate the fair entanglement distribution problem as a linear programming problem, where fairness requirements serve as constraints, aiming to identify the optimal FMD-based scheme with the highest throughput. Simulation results demonstrate that the optimized FMD-based scheme achieves a higher throughput compared to existing schemes under identical fairness requirements.

关键词： Multiplexing Simulation Throughput linear programming Quantum networks

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A DRL Framework to Optimize Energy Efficiency for Quadrature Rate-Splitting Multiple Access

A DRL Framework to Optimize Energy Efficiency for Quadrature...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Anh-Tien Tran Thanh Phung Truong Dang-Huy Mac Dongwook Won Jaemin Kim Nhu-Ngoc Dao Sungrae Cho School of Computer Science and Engineering Chung-Ang University Seoul Korea South Department of Computer Science and Engineering Sejong University Seoul Korea South

ISBN: (数字)9798331507022

ISBN: (纸本)9798331507039

Maximizing energy efficiency (EE) in Multiple-Input Single-Output (MISO) downlink networks employing Quadrature Rate-Splitting Multiple Access (Q-RSMA) is a challenging task due to the non-convex optimization problem involving power allocations, beamforming vectors, and rate allocations under multiple constraints. In this paper, we propose a Deep Reinforcement Learning (DRL) framework based on the Deep Deterministic Policy Gradient (DDPG) algorithm to maximize EE. We handle the minimum rate constraints by formulating the rate allocation as a linear programming (LP) problem, which allows for a computationally efficient solution. The beamforming vector normalization is clarified to ensure the unit norm constraints are satisfied. Simulation results demonstrate the effectiveness of the proposed approach in achieving high EE while satisfying all system constraints.

关键词： Array signal processing Simulation Downlink Deep reinforcement learning linear programming Energy efficiency Vectors Resource management MISO Optimization

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Study on the Optimized Allocation of Electricity-Hydrogen Hybrid Energy Storage Capacity

Study on the Optimized Allocation of Electricity-Hydrogen Hy...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Zhongyuan Xiao Xing Zhang Qingyi Wang Jinglong Cen Chang Xu Xiang Li School of Automation China University of Geosciences Wuhan China Wuhan Second Ship Design & Res Inst Wuhan China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

With the proposal of dual-carbon policy, it is especially important to improve the economy and stability of microgrid while how to reduce the carbon emission of the system. To this end, this paper firstly constructs an electric-hydrogen hybrid energy storage system and proposes an electric-hydrogen hybrid storage stepwise control strategy. Finally, the system economy, energy utilization, load loss rate and carbon emissions are normalized to obtain the weighted integrated objective function under the minimum of each energy storage capacity. The results show that compared with the traditional electrochemical energy storage method, the electric-hydrogen hybrid storage significantly improves the stability of the system and reduces the carbon emission under the premise of close economy.

关键词： Economics Carbon dioxide Microgrids linear programming Control systems Stability analysis Resource management Proposals Indexes Energy storage

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Optimal Coordination of Adaptive Overcurrent Relay for Microgrid System Using Hybrid PSO-LP

Optimal Coordination of Adaptive Overcurrent Relay for Micro...

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International Electrical Engineering Congress (iEECON)

作者： Wachirawich Tantichaiwanich Keerati Chayakulkheeree School of Electrical Engineering Institute of Engineering Suranaree University of Technology Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

This paper specializes on determining of optimal overcurrent relay coordination, which is imperative for primary and backup overcurrent relays using hybrid particle swam optimization (PSO) and linear programing (LP). Inspection is conducted on a 4-bus radial system connected to distributed energy resources (DER). The pick-up current is determined using PSO, meanwhile the time multiplier setting (TMS) is determined by LP, to solve the overcurrent relay (OCR) coordination problem. The results demonstrate a significant reduction in relay operating time, showing improved efficiency compared to conventional methods.

关键词： Electrical engineering Adaptive systems Optical character recognition Microgrids Inspection linear programming Distributed power generation Relays Particle swarm optimization Optimization

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Using the quasi-oppositional artificial hummingbird algorithm, a probabilistic optimal power flow for an integrated renewable power system comprising wind and solar

Using the quasi-oppositional artificial hummingbird algorith...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： Manas Mahato Alok Kumar Manish Kumar Singh Sneha Sultana Sourav Paul Susanta Dutta Provas Kumar Roy Electrical Engineering Dr. B. C. Roy Engineering College Durgapur India Electrical Engineering Kalyani Government Engineering College Kalyani India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Optimal Power Flow (OPF) is crucial for efficient and sustainable power system management, aiming to minimize operational costs and emissions while meeting system constraints. This paper introduces the artificial hummingbird algorithm (AHA) to solve the OPF problem, enhanced with Quasi-Oppositional Based Learning (QOBL) for improved convergence and solution accuracy. The proposed QOAHA is validated on the IEEE 57-bus system, demonstrating superior performance compared to existing optimization techniques in cost and emission reduction. By combining the exploration capability of AHA with QOBL’s accelerated search, the algorithm achieves robust and efficient results. This hybrid approach offers a promising direction for addressing complex power system challenges.

关键词： Renewable energy sources Costs Metaheuristics Learning (artificial intelligence) Probabilistic logic Minimization linear programming Hybrid power systems Topology Load flow

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GeoDiffuser: Geometry-Based Image Editing with Diffusion Models

GeoDiffuser: Geometry-Based Image Editing with Diffusion Mod...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Rahul Sajnani Jeroen Vanbaar Jie Min Kapil Katyal Srinath Sridhar Brown University Amazon Robotics

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

The success of image generative models has enabled us to build methods that can edit images based on text or other user input. However, these methods are imprecise, require additional information, or are limited to only 2D image edits. We present GeoDiffuser, a zero-shot optimization-based method that unifies common 2D and 3D image-based object editing capabilities into a single method. Our key insight is to view image editing operations as geometric transformations. We show that these transformations can be directly incorporated into the attention layers in diffusion models to implicitly perform editing operations. Our training-free optimization method uses an objective function that seeks to preserve object style but generate plausible images, for instance with accurate lighting and shadows. It also inpaints disoccluded parts of the image where the object was originally located. Given a natural image and user input, we segment the foreground object [27] and estimate a corresponding transform which is used by our optimization approach for editing. Figure 1 shows that GeoDiffuser can perform common 2D and 3D edits like object translation, 3D rotation, and removal. We present quantitative results, including a perceptual study, that shows how our approach is better than existing methods.

关键词： Image segmentation Computer vision Three-dimensional displays Accuracy Translation Optimization methods Lighting Transforms Diffusion models linear programming

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Optimal Scheduling Scheme for Ore Transshipment Yard Based on Probabilistic Calculation Model 24

Optimal Scheduling Scheme for Ore Transshipment Yard Based o...

引用

Proceedings of the 2024 4

作者： Ding Tairan Wu Yuhao Zhang Yang Hailing Ma Southwestern University of Finance and Economics Chengdu Sichuan China Monash University Melbourne Melbourne Australia

ISBN: (纸本)9798400718298

Steel is one of the most important basic materials in modern industry, and how to improve its transportation efficiency is an important factor affecting the growth of economic benefits. This paper studies the optimal loading and unloading scheduling problem in ore transshipment yards involving both road and rail transport. A mathematical model was established, and dynamic programming and probabilistic models were used to optimize the loading and unloading process. When facing complex problems, the advantages of both can be leveraged simultaneously. Establishing a dynamic programming framework that incorporates probabilistic factors can more accurately reflect the actual situation and lead to more effective solutions. The research aims to reduce transportation costs and improve logistics efficiency by optimizing scheduling. The paper primarily analyzes issues such as the uncertainty of train arrival times, the need for a second truck to accelerate the ore loading process, and demurrage fees incurred. It presents the optimal loading and unloading scheduling *** the first problem, a dynamic programming model was used to analyze the optimal loading and unloading scheme for iron ore over three days. It is assumed that trains arrive during a fixed time from 8:00 to 18:00, and detailed models are established for train loading time, truck transport time, and other factors. Depending on the train arrival time and ore stockpile at the loading platform, the need for a second truck is determined, and the minimum cost is calculated. Results show the minimum cost on the first day was 24,000 yuan, 26,000 yuan on the second day, and 69,333.33 yuan on the third day. The paper discusses the train arrival times, ore stockpiles, and work arrangements at the loading platform for each day in detail. In the second problem, the train arrival times are assumed to be random between 8:00 and 18:00. A probabilistic model was introduced to deal with the uncertainty in train arrival times.

关键词： linear programming

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Design of Crop Planting Strategy Based on Genetic Algorithm

Design of Crop Planting Strategy Based on Genetic Algorithm

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Electronics, Energy Systems and Power Engineering (EESPE), IEEE International Conference on

作者： Yingjia Hu Haoran Wang Jiayi Shi Shandong University of Science and Technology Jinan Shandong China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

With the intensification and intelligent development of agricultural production, optimizing planting strategies to obtain maximum profits has become increasingly important. This study uses Matlab to process data, constructs a linear programming model, and applies genetic algorithms and Monte Carlo simulations to explore strategies for optimizing crop and land allocation to maximize planting income over the next seven years under crop rotation and cost constraints. The study also considered crop market volatility, introduced alternative and complementary factors, and evaluated the impact of these factors on planting strategies and total returns through multiple regression analysis and stochastic dynamic programming models, aiming to provide comprehensive planting strategies for adapting to market and climate change.

关键词： Costs Monte Carlo methods Crops Stochastic processes Production linear programming Mathematical models Regression analysis Optimization Genetic algorithms Climate change

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