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Innovative Research in Applied Science, Engineering and Technology (IRASET),Conference on

作者： Zaid Nouna Hamid Bouyghf Mohammed Nahid Issa Sabiri Laboratory of Engineering Sciences and Biosciences Faculty of Sciences and Techniques Hassan II University Mohammedia Morocco Laboratory of Systems Signals and Artificial Intelligence ENSET Hassan II University Mohammedia Morocco

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

Sudden cardiac arrest, frequently triggered by cardiac fibrillation, remains a significant global health concern. Defibrillation is a critical medical procedure that relies on precise electrical pulses to restore normal heart rhythm. To optimize pulse parameters, such as peak voltage and duration, we employ the Differential Evolution algorithm on a monophasic waveform. By utilizing the error between circuit simulation and targeted defibrillation parameters as an objective function, we aim to determine the optimal inductance and capacitance values of the defibrillator circuit. This approach enables the delivery of precisely tailored electrical shocks, potentially improving defibrillation success rates, reducing patient risk, and ultimately, contributing to advancements in cardiac care.

关键词： Inductance Power demand Heart beat Fibrillation Electric shock Circuit simulation Metaheuristics linear programming Circuit synthesis Defibrillation

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Optimizing V2V Communication in 5G/6G Networks with Enhanced Deep Cooperative Q-Learning

Optimizing V2V Communication in 5G/6G Networks with Enhanced...

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Next Generation Communication & Information Processing (INCIP), International Conference on

作者： Priyameet Kaur Keer N. Lakshmi Deepthi Ginni Nijhawan Neeraj Chahuan Ali Albawi Madhu Palati Master of Business Administration New Horizon College of Engineering Bangalore India Computer Science and Engineering (Data Science) Institute of Aeronautical Engineering Hyderabad India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq Dept. of EEE BMS Institute of Technology and Management Bangalore

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

Understanding the physical channel characteristics of V2V communications is important, but it is neither cost-effective nor practical to have full information about them. Since there is always delay and data-loss during communications, which are further accelerated when the wireless channel status and device parameters remain uncalibrated frequently, full or even partial information of the environment is not feasible, even for a short period. Moreover, the vehicular mobility can also introduce abrupt changes to communication. These uncertainties dictate that a system should be well-and adaptively-designed to handle changing information. V2V communications that exploit this information naturally serve as candidates for artificial Intelligence (AI) algorithms. Specifically, there are applications of cooperative learning strategies that are suitable, including machine learning, deep reinforcement learning, and deep multi-agent reinforcement learning. This paper applies novel Enhanced Deep Cooperative Q-Learning (DCO-DQN) model for V2V communication, to achieve the best trade-offs (latency/ reliability/safety) under various states, including short and long memory, asymptotic and non-asymptotic, possible and unpredictable outcomes scenarios. The proposed framework incorporates spatial wisdom and historical information into Euclidean spaces, accounting for the above behavior and challenges. For this reason, future V2V communication in 5G/6G networks may benefit from adopting our approach, as previously described. A thorough comparative study has been conducted to illustrate our advantages and shortcomings. Our analysis also offers insights on hybrid techniques and decentralized approaches, as possible means for future development.

关键词： Wireless communication 6G mobile communication Q-learning Uncertainty 5G mobile communication Vehicular ad hoc networks Interference linear programming Safety Optimization

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Bector-Chandra type linear programming duality under fuzzy environment with parabolic concave membership functions

Bector-Chandra type linear programming duality under fuzzy e...

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International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)

作者： Pratiksha Saxena Ravi Jain Department of Applied Mathematics Gautam Buddha University Greater Noida India Department of Mathematics Maharaja Agrasen Institute of Management Studies Rohini Delhi India

This paper presents the study of a pair of primal-dual fuzzy linear programming and establishes duality results. It is based on parabolic concave membership functions. Choice of parabolic concave membership functions makes it unique and leads to the nonlinear programming. Duality results have been established using the aspiration level approach. A numerical example is also taken to demonstrate the approach and verification of results.

关键词： linear programming programming Industries Standards Games Educational institutions

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Scalable computation of causal bounds

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 11224-11259页

作者： Madhumitha Shridharan Garud Iyengar Department of Industrial Engineering and Operations Research Columbia University New York City NY

We consider the problem of computing bounds for causal queries on causal graphs with unobserved confounders and discrete valued observed variables, where identifiability does not hold. Existing non-parametric approaches for computing such bounds use linear programming (LP) formulations that quickly become intractable for existing solvers because the size of the LP grows exponentially in the number of edges in the causal graph. We show that this LP can be significantly pruned, allowing us to compute bounds for significantly larger causal inference problems compared to existing techniques. This pruning procedure allows us to compute bounds in closed form for a special class of problems, including a well-studied family of problems where multiple confounded treatments influence an outcome. We extend our pruning methodology to fractional LPs which compute bounds for causal queries which incorporate additional observations about the unit. We show that our methods provide significant runtime improvement compared to benchmarks in experiments and extend our results to the finite data setting. For causal inference without additional observations, we propose an efficient greedy heuristic that produces high quality bounds, and scales to problems that are several orders of magnitude larger than those for which the pruned LP can be solved.

关键词： causal bounds partial identification of causal effects causal bounds with observations multi-cause setting with unobserved confounders linear programming

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Locally linear Embedding Based on Scrambled Halton Sequence Initialized Frilled Lizard Optimization

Locally Linear Embedding Based on Scrambled Halton Sequence ...

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IEEE Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Qihua Guo Jiashu Wang Longhui Hou Nengwei Lei Dan Li Wei Nai Zan Yang School of Electronic Information Huzhou College Huzhou China Huzhou Key Laboratory for Urban Multidimensional Perception and Intelligent Computing Huzhou College Huzhou China Public Teaching and Research Department Huzhou College Huzhou China

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

In the era of big data, the scale and complexity of data are ever increasing, traditional data dimensionality reduction methods can no longer meet the requirements of data users. In this context, manifold learning has now become an important research direction, which mainly achieves dimensionality reduction and mapping of nonlinear structural data in high-dimensional space to low-dimensional space, reduces data noise, and improves low-dimensional data representation ability. Locally linear embedding (LLE) is an important algorithm in manifold learning that projects data from high-dimensional space onto low-dimensional space while maintaining the linear relationship between data. However, the optimization objective function of LLE uses L2 norm measurement to approximate the error, which can easily amplify or reduce the error and cause oscillation in the error measurement. Therefore, in this paper, L1 norm instead of L2 norm is used to overcome this drawback, but introduces a new problem of the objective function becoming a non-smooth function. To solve this problem, in this paper, a derivative-free optimization method, which is also a swarm intelligence method called scrambled Halton sequence initialized frilled lizard optimization has been proposed, and via numerical experiment, its reliability and effectiveness has been verified.

关键词： Mechatronics Measurement uncertainty Noise Optimization methods linear programming Manifold learning Reliability Particle swarm optimization Information technology Oscillators

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Multi-Child DE - a Massively Parallel Differential Evolution Algorithm

Multi-Child DE - a Massively Parallel Differential Evolution...

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Computational Intelligence on Engineering/Cyber Physical Systems (CIES), IEEE Symposium on

作者： Rainer Storn Kenneth V. Price Radiocommunications Systems Division Rohde & Schwarz GmbH & Co. KG Muenchen Germany Unaffiliated Vacaville C.A. U.S.A.

ISBN: (数字)9798331508272

ISBN: (纸本)9798331508289

To optimize objectives that require extensive simulations, engineers need a powerful algorithm that can run in parallel to minimize wall-clock time. Evolutionary algorithms excel in both regards: they are powerful optimizers that can be implemented in parallel by assigning one processor to each population member. This paper, however, addresses the question: How can we minimize a simulation's wall-clock time if we have many more processors than there are population members? Our solution is the multi-child differential evolution (MCDE) algorithm in which each parent competes against more than one child. We benchmarked the scaling performance of the MCDE version of classic DE with a test bed of ten functions. We also compared MCDE to a parallel CMA-ES variant with three of our test bed's functions. Results from both experiments show that MCDE achieves a significant speedup that scales well with the number of processors.

关键词： Multicore processing Evolutionary computation Benchmark testing linear programming Optimization

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Solving the Balance Optimization Problem of Makespan and Total Computation Time for Cloud Computing Scheduling Based on NSGA-II

Solving the Balance Optimization Problem of Makespan and Tot...

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Digital Analysis and Processing, Intelligent Computation (DAPIC), International Conference on

作者： Can Wu Dan Liu College of Computer Science and Technology Changchun University of Science and Technology Changchun China

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

In the context of industrial upgrading, the exponential growth of computational demands for big data processing has emerged as a critical challenge. Cloud computing, with its evolving technological capabilities, presents a viable solution to address these escalating computational requirements. This study focuses on optimizing resource allocation efficiency through the establishment of a multiobjective optimization framework for cloud computing systems. Specifically, we formulate mathematical representations of objective functions and constraints while detailing corresponding encoding/decoding methodologies. The research employs the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to resolve task scheduling dilemmas in cloud environments, simultaneously optimizing makespan and total computational time through innovative applications of nondominated sorting and congestion distance comparison. A series of multi-scale comparative experiments demonstrate the algorithm's superior performance over conventional scheduling approaches.

关键词： Cloud computing Job shop scheduling Processor scheduling Optimization models linear programming Computational efficiency Resource management Optimization Sorting Convergence

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Bayesian Persuasion with Externalities: Exploiting Agent Types 39

Bayesian Persuasion with Externalities: Exploiting Agent Typ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Shaki, Jonathan Gan, Jiarui Kraus, Sarit Bar-Ilan University Israel University of Oxford United Kingdom

ISBN: (纸本)157735897X

We study a Bayesian persuasion problem with externalities. In this model, a principal sends signals to inform multiple agents about the state of the world. Simultaneously, due to the existence of externalities in the agents’ utilities, the principal also acts as a correlation device to correlate the agents’ actions. We consider the setting where the agents are categorized into a small number of types. Agents of the same type share identical utility functions and are treated equitably in the utility functions of both other agents and the principal, while taking actions independently. We study the problem of computing optimal signaling strategies for the principal, under three different types of signaling channels: public, private, and semi-private. Our results include revelation-principle-style characterizations of optimal signaling strategies, linear programming formulations, and analysis of in/tractability of the optimization problems. It is demonstrated that when the maximum number of deviating agents is bounded by a constant, our LP-based formulations compute optimal signaling strategies in polynomial time. Otherwise, the problems are NP-hard. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： linear programming

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Feedback Indicators for Providing Carbon Impact of Vehicle Charging to Electric Vehicle Users

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Transportation Research Procedia 2023年 70卷 347-355页

作者： Nana Kofi Twum-Duah Lucas Hajiro Neves Mosquini Muhammad Salman Shahid Frédéric Wurtz Benoît Delinchant Univ. Grenoble Alpes CNRS Grenoble INP G2Elab F-38000 Grenoble France Univ. Grenoble Alpes CNRS Grenoble INP G-SCOP F-38000 Grenoble France University of Applied Sciences of Western Switzerland Energy Institute HEIA-FR 1700 Fribourg Switzerland

The purpose of this article is to introduce two indicators designed to evaluate and consequently influence the indirect emissions related to the operation of an EV. The first indicator, denoted as “Electric Vehicle CarbonFlex Potential” indicator, evaluates an EV's maximum and minimum achievable carbon emissions using an optimization approach and compares the user's resultant indirect carbon emissions to these boundaries, therefore, this indicator compares the users behavior to the optimal best and worst cases. The second indicator is “EcoCharge Time” indicator, which provides feedback to an EV user based on their charging behavior on the best and worst times of charging the vehicle in a day. Since human behavior cannot be controlled, such indicators are essential tools for influencing the behavior of EV users toward a desired optimal, in this case, a charging schedule with the lowest possible overall indirect emissions. The proposed indicators were tested on an EV dataset using the carbon intensity data from a number of countries and the results show that there exists considerable flexibility potential. Additionally, the results also showed the best charging times, which were typically clustered around, allowing for ease of use and understanding.

关键词： Indirect Carbon Emissions Energy Transition Carbon-free Transportation Feedback Indicators linear programming

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Two-Stage Robust Optimization for Transmission Network Expansion Planning Considering N-K Security Criterion

Two-Stage Robust Optimization for Transmission Network Expan...

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IEEE Asia Power and Energy Engineering Conference (APEEC)

作者： Yuhan Yin Jianyong Zheng Kunxiao Wu Fei Mei Shuai Wang Yuting Ding School of Electrical Engineering Southeast University Nanjing China College of Energy and Electrical Engineering Hohai University Nanjing China

ISBN: (数字)9798331517540

ISBN: (纸本)9798331517557

Considering the impact of extreme weather on the reliability of the power system is of great significance and value. First, the N-k uncertainty set is defined to describe the fault scenarios of generators and transmission lines. Next, the objective function and constraints of the transmission network expansion model are provided. The objective of the two-stage robust optimization planning model is a min-max-min problem. Therefore, the Column and Constraint Generation (C&CG) algorithm is used to solve the problem, iteratively obtaining the global optimal solution of the model. Finally, case study verifies the effectiveness of the transmission network source-network coordinated two-stage robust optimization planning model, demonstrating that it can achieve economic objectives while incorporating the reliability of transmission network planning and operation.

关键词： Uncertainty Power transmission lines Reliability engineering linear programming Generators Planning Power system reliability Security Optimization Meteorology

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