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Consumer Electronics and Computer Engineering (ICCECE), 2021 IEEE International Conference on

作者： Changyu Li Intelligent Equipment College Shandong University of Science and Technology Taian China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

With the increasing concern for sustainable agriculture and efficient resource utilization, the study of crop planting strategy optimization under resource constraints has become an important topic. In this paper, an integrated framework combining simulation and optimization algorithms is proposed for developing sustainable crop cultivation strategies. A 0–1 linear programming model is constructed to maximize economic returns under resource constraints, taking into account constraints such as land area, crop rotation, and resource allocation. A simulated annealing algorithm was introduced to optimize the minimum planting area allocation of a single plot to ensure the rationality of spatial and temporal distribution. Based on the 2023 data of a rural area in the mountainous region of North China, the optimal planting strategies from 2024 to 2030 were predicted. The results show that the proposed method significantly improves resource utilization efficiency, ensures the stability of economic returns, and flexibly adapts to different resource constraint scenarios. This study provides theoretical support for sustainable agricultural development and useful reference for land use optimization.

关键词： Economics Crops Simulated annealing Production linear programming Prediction algorithms Agriculture Stability analysis Resource management Optimization

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Supplier selection under disruption risks using Stochastic Mixed linear programming techniques

Supplier selection under disruption risks using Stochastic M...

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International Conference on Advanced Logistics and Transport (ICALT)

作者： Faiza Hamdi Ahmed Ghorbel Faouzi Masmoudi Unit of Logistic Higher Institute of Industrial Management Sfax Tunisia Department of quantitative methods Faculty of Economics and Management Sfax Sfax Tunisia Laboratory of Mechanic Engineering School of Sfax Sfax Tunisia

Supplier selection is an important key of supply chain management and mainly with the presence of disruption risks. Given a set of customer orders for products, the decision maker needs to decide from which supplier to purchase custom parts required for each customer order to minimize total cost and mitigate the impact of disruption risks. Many approaches have been developed in the literature based on various formal modeling techniques. In this paper, Stochastic Mixed linear Program (MILP) techniques are used for the selection of suppliers under risk disruption. Two set of disruption scenarios are considered: (1) scenario with independent local disruption of each supplier, and (2) scenario with local and global disruption that may result in all suppliers simultaneously. The two percentiles: Value at risk (VaR) and conditional value at risk (CVaR) are used to model the risk of supply chain disruption. It be concluded that these percentiles are capable to optimizing the supply portfolio by minimizing expects worst-case per part via calculating the value at risk of expected cost part. The extension of this study seems very interesting for the risk analysis in complex supply chains.

关键词： Supply chains Reactive power Uncertainty Stochastic processes Risk management linear programming Economics

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Genetic Algorithm Hyperparameters Optimization for Auto-Encoder to Enhance Credit Card Fraud Detection

Genetic Algorithm Hyperparameters Optimization for Auto-Enco...

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International Conference on Web Research (ICWR)

作者： Seyyed Sadegh Hosseini Gholam Ali Montazer IT Department Tarbiat Modares University Tehran Iran

ISBN: (数字)9798331508913

ISBN: (纸本)9798331508920

In recent years, the increasing use of credit cards has led to a dramatic rise in fraudulent transactions. Identifying credit card fraud is crucial for preventing financial institutions and their customers from incurring substantial financial losses and for maintaining customer trust. Fraud detection systems play a key role in preventing unauthorized transactions. Auto-encoder networks, known for their ability to learn compact representations of data, can leverage this capability to identify anomalous transactions, which are typically indicative of fraud. However, their performance is often sensitive to hyperparameter choices, especially in imbalanced datasets. To address this, a genetic algorithm is utilized to dynamically optimize the hyperparameters of these networks, significantly improving fraud detection. This paper proposes using an optimized auto-encoder network, augmented with the genetic algorithm, to detect credit card fraud. On the Credit Card Fraud (CCF) dataset, the model achieved an F-score of 91.04 % and a Matthews correlation coefficient (MCC) of 0.8282, surpassing some baseline methods such as OCSVM, OCNN and COPOD. These results demonstrate the effectiveness of the genetic algorithm in improving the performance and robustness of fraud detection systems.

关键词： Training Measurement Credit cards Hyperparameter optimization linear programming Robustness Fraud Unsupervised learning Tuning Genetic algorithms

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A Two-timescale Primal-dual Algorithm for Decentralized Optimization with Compression

A Two-timescale Primal-dual Algorithm for Decentralized Opti...

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

作者： Haoming Liu Chung-Yiu Yau Hoi-To Wai Dept. of Systems Engineering & Engineering Management The Chinese University of Hong Kong

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

This paper proposes a two-timescale compressed primal-dual (TiCoPD) algorithm for decentralized optimization with improved communication efficiency over prior works on primal-dual decentralized optimization. The algorithm is built upon the primal-dual optimization framework and utilizes a majorization-minimization procedure. The latter naturally suggests the agents to share a compressed difference term during the iteration. Furthermore, the TiCoPD algorithm incorporates a fast timescale mirror sequence for agent consensus on nonlinearly compressed terms, together with a slow timescale primal-dual recursion for optimizing the objective function. We show that the TiCoPD algorithm converges with a constant step size. It also finds an $\mathcal{O}(1/T)$ stationary solution after T iterations. Numerical experiments on decentralized training of a neural network validate the efficacy of TiCoPD algorithm.

关键词： Training Machine learning algorithms Neural networks Signal processing algorithms Machine learning Signal processing linear programming Mirrors Speech processing Optimization

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Performance Comparison of Optimization Algorithms for Shortest Path Determination on Facial Edge Coordinates

Performance Comparison of Optimization Algorithms for Shorte...

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Computer Sciences, Engineering, and Technology Innovation (ICoCSETI), International Conference on

作者： Zen Nurkholik Ronny Mardiyanto Djoko Purwanto Departement of Electrical Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (数字)9798331508616

ISBN: (纸本)9798331508623

Optimization is a computational process to find the best solution by minimizing or maximizing an objective function. This research compares the performance of three optimization algorithms Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA) in determining the shortest path optimization between coordinate points extracted from detected facial edge images. Using the Canny edge detection method, 10 single facial images with a resolution of 120 x 120 pixels were processed to produce (X, Y) coordinates as input for the optimization process. The results show that GA consistently outperforms the other methods, achieving the shortest total distance in 9 out of 10 datasets. The results of the first image data comparison, the GA method produced the shortest total distance with a value of 18,199.59 pixels, while (SA) 21,072.28 pixels had a performance 15.78% worse than GA, and (PSO) 28,381.58 (pixels) showed a performance 55.94% worse than GA. SA showed competitive and optimal results in one dataset, while PSO consistently provided the longest distance, indicating its lower efficiency in this research.

关键词： Hands Technological innovation Image resolution Image edge detection Simulated annealing linear programming Space exploration Particle swarm optimization Genetic algorithms

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Using rapidly random trees and linear programming for stable operation of a multi-robot system on domes

Using rapidly random trees and linear programming for stable...

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RSI/ISM International Conference on Robotics and Mechatronics (ICROM)

作者： Mehrab Norouzitallab Kourosh Naderi Hadi Moradi School of Electrical and Computer Engineering University of Tehran Tehran Iran

In this paper, we propose a fast algorithm for a multi-robot system which enables the robots to stably maneuver on domes. The multi-robot system includes robots, a leader robot and one or more follower robots, which are connected to each other by strings and make a ring around the dome. The proposed algorithm employs multi-rapidly random trees (multi-RRTs) and linear programming (LP) to plan a stable path for the whole system which enables the leader robot to cover the whole dome. The main contribution of the paper is in reducing the high dimensionality of the multi-robot configuration space using linear programming. This would allow the multi-RRT grow faster to cover the whole configuration space. To show the effectiveness of the approach we simulated the behavior of a 4-robot system on a spherical dome which denotes the efficiency of our algorithm.

关键词： Multi-robot systems linear programming Robot kinematics Maintenance engineering Equations Stability analysis

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Multipitch tracking based on linear programming relaxation and sparsity-based pitch candidate estimation

Multipitch tracking based on linear programming relaxation a...

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International Symposium on Chinese Spoken Language Processing

作者： Feng Huang Tan Lee Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong SAR Speech Rehabilitation Technology Laboratory The Chinese University of Hong Kong Shenzhen

This paper presents a linear programming approach for tracking fundamental frequencies in acoustic signal that contains multiple speech sources and noise interference. A sparsity-based pitch estimation method is used to obtain pitch candidates for each signal frame. With conventional methods like exhaustive searching, the computational complexity of multipitch tracking grows exponentially with the number of pitch tracks. We propose to use a linear programming relaxation approach to solve the multiple pitch track searching problem. This approach has low computational complexity while it was found to attain global optimal solution with high probability. Experimental results show that the proposed algorithm is more efficient and more accurate than the conventional tracking method, extended dynamic programming.

关键词： Speech Estimation linear programming Speech processing Dynamic programming Robustness Noise measurement

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Differentially Private and Communication-efficient Decentralized Learning Using Deep Quantizers

Differentially Private and Communication-efficient Decentral...

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

作者： Robin Francis Sundeep Prabhakar Chepuri Department of Electrical Communication Engineering Indian Institute of Science Bengaluru India

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Decentralized learning has emerged as a popular method due to its excellent scalability and parallel implementation of stochastic gradient methods. However, the main challenges in decentralized learning include the communication overhead and privacy concerns associated with sharing gradients with neighbors. In this work, we design a novel deep quantizer to address these simultaneously, ensuring differential privacy and communication efficiency. Specifically, we learn a deep quantizer such that the induced quantization noise can be modeled as additive Gaussian noise. We also propose an encoding-decoding scheme that ensures unbiasedness and bounded variance for the quantized output. For smooth objective functions, we bound the function values in terms of the norm of the gradient, the second-order gradient moment, and the induced quantization noise. We demonstrate superior performance of decentralized stochastic gradient with the proposed deep quantizer for a least square regression problem. Additionally, we analyze the performance of the proposed approach for various quantization levels, privacy budgets, batch sizes, and numbers of nodes in the network.

关键词： Privacy Gradient methods Quantization (signal) Scalability Gaussian noise Stochastic processes Gaussian distribution linear programming Encoding Speech processing

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A bi-level collaborative operation strategy for multiple virtual power plants based on interval robust linear optimization 3

A bi-level collaborative operation strategy for multiple vir...

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3rd International Conference on Smart Energy and Electrical Engineering, SEEE 2024

作者： Qi, Lei Ai, Qian Wang, Di Zhao, Jianli Liu, Tengzi Xiang, Jiani School of Electric Information and Electrical Engineering Shanghai Jiaotong University Shanghai200030 China State Grid Shanghai Municipal Electric Power Company Shanghai200122 China

With the widespread integration of distributed energy resources, virtual power plants (VPPs) have emerged as an advanced management model for distributed energy. As technology continues to advance, achieving efficient collaborative operation among multiple VPPs has become a pressing issue. In response, this paper proposes a bi-level collaborative operation strategy for multiple VPPs. At the upper level, a leader-follower game model is constructed between the market operator and the VPPs, enabling effective interaction through dynamic electricity price adjustments. At the lower level, a cooperative benefit distribution model based on the Shapley value method is developed to ensure fair and reasonable allocation of benefits among the VPPs. Finally, the effectiveness and rationality of the proposed strategy are verified through case studies. The results demonstrate that this strategy can significantly enhance the economic operating efficiency of the VPPs. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： linear programming

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Dissecting Submission Limit in Desk-Rejections: A Mathematical Analysis of Fairness in AI Conference Policies

arXiv

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arXiv 2025年

作者： Cao, Yuefan Li, Xiaoyu Liang, Yingyu Sha, Zhizhou Shi, Zhenmei Song, Zhao Zhang, Jiahao Zhejiang University China University of New South Wales Australia The University of Hong Kong Hong Kong University of Wisconsin-Madison United States Tsinghua University China The Simons Institute for the Theory of Computing at UC Berkeley United States

As AI research surges in both impact and volume, conferences have imposed submission limits to maintain paper quality and alleviate organizational pressure. In this work, we examine the fairness of desk-rejection systems under submission limits and reveal that existing practices can result in substantial inequities. Specifically, we formally define the paper submission limit problem and identify a critical dilemma: when the number of authors exceeds three, it becomes impossible to reject papers solely based on excessive submissions without negatively impacting innocent authors. Thus, this issue may unfairly affect early-career researchers, as their submissions may be penalized due to co-authors with significantly higher submission counts, while senior researchers with numerous papers face minimal consequences. To address this, we propose an optimization-based fairness-aware desk-rejection mechanism and formally define two fairness metrics: individual fairness and group fairness. We prove that optimizing individual fairness is NP-hard, whereas group fairness can be efficiently optimized via linear programming. Through case studies, we demonstrate that our proposed system ensures greater equity than existing methods, including those used in CVPR 2025, offering a more socially just approach to managing excessive submissions in AI conferences. © 2025, CC BY.

关键词： linear programming

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