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The 2nd International Conference on Emerging Trends in Engineering and Technology (ICETET’2014, London)

作者： Ahsan Ashfaq Akif Zia Khan State Key Laboratory of Alternate Electric Power System North China Electric Power University

High costs for fossil fuels and escalating installations of alternate energy sources are daunting main challenges in power systems by making the economic operation and planning of power system as high tasks among the major assignments in the power generation. In this paper we solve one of the main featured economic problems of power system by optimizing the economic load dispatch problem by linear programming and compared it with firefly algorithm and lambda iteration method. We have applied the proposed algorithm on different systems of generation units and the results proved the attainment of optimal point when compared with different algorithms.

关键词： Economic dispatch transmission losses modified methodology 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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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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Optimization of Air Conditioner Parameters Based on Dynamic Simulation and Genetic Algorithms

Optimization of Air Conditioner Parameters Based on Dynamic ...

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

作者： Xianghan Luo Ninghang Zeng School of Education Zhejiang International Studies University Hangzhou China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

This paper presents an optimization method for air conditioner parameters based on dynamic simulation and genetic algorithms, aiming to reduce the time required to reach the target room temperature. A dynamic simulation model was developed to analyze room temperature changes and estimate the time to achieve the target temperature. The model integrates room thermal properties, air conditioning performance, and principles of heat transfer. We considered a specific room model, including its dimensions, air mass, external wall area, and thermal transmittance, as well as the operating parameters of the air conditioner, such as airflow and cooling differential. To optimize the geometry, inlet/outlet configuration, and airflow characteristics of the air conditioner, we constructed an objective function to minimize cooling time and set a series of geometric and airflow constraints. Using the Adaptive Inversion Genetic Algorithm (AIGA), we enhanced the global and local optimization capabilities of the standard Genetic Algorithm (GA) through adaptive crossover, mutation, and inversion operations, optimizing the design parameters of the air conditioner. Finally, we optimized the air conditioner design parameters using a genetic algorithm, successfully reducing the cooling time from 100 seconds to 51 seconds while meeting volume and airflow balance constraints. The optimized air conditioner design parameters, including dimensions and inlet/outlet configurations, significantly improved air circulation efficiency, proving the effectiveness and practical potential of genetic algorithms in solving complex multi-constraint design problems.

关键词： Air conditioning Adaptation models Power engineering Temperature Atmospheric modeling Heuristic algorithms Optimization methods linear programming Standards Genetic algorithms

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A scoring method improvement of analytic hierarchy process using linear programming technique for vertical handover decision

A scoring method improvement of analytic hierarchy process u...

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International Symposium on Wireless Personal Multimedia Communications (WPMC)

作者： Wittaya Panjanda Olarn Wongwirat Faculty of Information Technology King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand

Recently, heterogeneous wireless networks have been integrated to provide mobile users to access services without restriction of different access networks, e.g., cellular systems and WLANs. Thus, the mobile users can obtain the services with higher data rate and quality in anywhere and at any time seamlessly. To support such scenario, it requires a vertical handover (VHO) mechanism that can decide to select the best connection among different access networks from various factors. An analytic hierarchy process (AHP) is one of the methods used in VHO decision by finding the optimal solution based on multiple attribute approach. However, there is a validity acceptance limitation of AHP, due to the score and weight of attributes, or network factors, are assigned by decision makers. This article proposes the VHO decision mechanism that improves a scoring method of AHP by deploying a linear programming (LP) technique instead of human knowledge. The LP is used to find the optimized values among various network factors. Then, the optimized values are taken to replace the human score in the AHP for VHO decision. The received signal strength (RSS), data rate, and throughput are used as the attributes, or variables, related to network factors. The experiment is conducted by simulation to verify the result of proposed VHO decision mechanism between a cellular network and WLANs. The simulation result expresses that the LP can provide the optimized network factors used for scoring in the AHP. It could also be implied that the proposed VHO decision mechanism yields the best network connection based on the optimized values obtained from the LP used instead of human knowledge.

关键词： Analytic hierarchy process Handover Throughput linear programming Wireless networks Mobile communication

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Representative Community Detection Algorithms for Attribute-Free Networks

Representative Community Detection Algorithms for Attribute-...

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International Conference on Computer and Automation Engineering, ICCAE

作者： Dongming Chen Xin Zou Yuxing He Mingzhao Xie Dongqi Wang Software College Northeastern University Shenyang China

ISBN: (数字)9798331533816

ISBN: (纸本)9798331533823

In the field of network analysis, methods for identifying community structure often involve optimizing a specific objective function to achieve a single optimal allocation from network nodes to communities. However, in practice, we often encounter multiple division schemes with high quality scores that are close to the overall optimum. In fact, an accurate depiction of the community structure is more appropriately achieved by a series of high-quality division schemes rather than relying on a single optimal solution alone. However, such a collection of network divisions may be difficult to interpret, as its size may rapidly expand to hundreds or even thousands. To this end, this paper introduces an innovative strategy to reveal the diversity of network community structures more comprehensively and to provide a set of more globally-visioned delineation results by clustering similar network divisions and then selecting representative divisions from each cluster.

关键词： Clustering methods Network analyzers Complex networks Network coding linear programming Partitioning algorithms Resource management Object recognition Detection algorithms Periodic structures

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Duality Theorems and Vector Measures in Optimal Transportation Theory

arXiv

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arXiv 2025年

作者： Gover, Shlomi

The optimal transportation problem, first suggested by Gaspard Monge in the 18th century and later revived in the 1940s by Leonid Kantorovich, deals with the question of transporting a certain measure to another, using transport maps or transport plans that minimize the total cost of transportation. This problem is very popular since it has a variety of applications in economics, physics, computer science and more. One of the main tools in this theory is the duality theorem, which states that the optimal total cost equals the value of a different optimization problem called the dual problem. In this work, I show how the problem and duality theorem can be generalized to an abstract formulation, in which I omit the use of measures. I show how this generalization implies a wide range of different optimal transport problems, and even other problems from game theory, linear programming and functional analysis. In particular, I show how the optimal transport problem can be generalized to deal with vector measures as a result of the abstract theorem and discuss the properties of this problem: I present its corresponding duality theorem and formulate conditions for the existence of transport plans, transport maps and solutions to the dual problem. Copyright © 2025, The Authors. All rights reserved.

关键词： linear programming

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Voxel-sensitive Wavelet-based Approach for Structural Distortion in Low-Dose CT Images

Voxel-sensitive Wavelet-based Approach for Structural Distor...

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

作者： Naragoni Saidulu Vinit Kumar Priya Ranjan Muduli Department of Electronics Engineering Indian Institute of Technology (BHU) Varanasi India

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The decimation of high-frequency (HF) features during exhaustive low-dose computed tomography(LDCT) denoising introduces structural deformation. This paper addresses the aforementioned issue by introducing a GAN framework that provides novel adversarial training via discriminators in the wavelet and spatial domains. The wavelet-domain discriminator forces the generator to gain knowledge of the HF features via HF wavelet details (LH, HL, HH) and minimizes structural distortion. The generator network uses a spatial-domain discriminator to preserve local and global pixel correlations without altering the low-frequency (LF) features. Furthermore, we develop a generator network using a novel stationary wavelet-based residual block (SWTRB), which adaptively integrates spatial and frequency-domain information. In addition, we propose a wavelet-domain objective function on HF components, further improving the diagnostic quality of CT images. The experimental results demonstrate that the proposed method outperforms several state-of-the-art techniques on publicly available datasets, including "2016 NIH-AAPM-Mayo Clinic LDCT " and "Low-dose CT image and projection."

关键词： Training Acoustic distortion Wavelet domain Computed tomography Noise reduction Hafnium Generative adversarial networks linear programming Generators Speech processing

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Multi-Objective Optimization for Disaster Evacuation using NSGA-II: A Case Study in Parangtritis

Multi-Objective Optimization for Disaster Evacuation using N...

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

作者： Cindy Inka Sari Lukito Edi Nugroho Rudy Hartanto Department of Electrical and Information Engineering Universitas Gadjah Mada Yogyakarta Indonesia

ISBN: (数字)9798331508616

ISBN: (纸本)9798331508623

Parangtritis, a popular tourist destination on the southern coast of Java, faces a high tsunami risk due to tectonic activity along nearby subduction zones. The main challenges in this area include a large number of tourists, vulnerable populations such as the elderly and disabled, and limited shelter capacity, which can lead to congestion during evacuation. To address these issues, this study proposes a multi-objective approach using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to support efficient and adaptive evacuation planning. The model optimizes five objective functions: evacuation distance, evacuation time, shelter capacity, congestion costs, and shelter distribution variance, while conducting sensitivity analysis to ensure solution stability. The implementation results show that NSGA-II effectively generates high-quality Pareto fronts, validated using hypervolume and Generational Distance (GD) metrics, and highlights trade-offs between evacuation objectives, offering data-driven insights for designing more efficient evacuation strategies in Parangtritis.

关键词： Measurement Technological innovation Costs Sensitivity analysis Disasters linear programming Tsunami Planning Optimization Subduction

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