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

作者： Vinay A. Vaishampayan Thilaharani Antony Amirthagunaraj Yogarathnam Faculty of the Engineering and Environmental Science College of Staten Island-City University of New York NY USA Interdiscivlinary Science Department Brookhaven National Laboratory Upton NY USA

ISBN: (数字)9798350352528

ISBN: (纸本)9798350352535

Wind energy's role in the global electric grid is set to expand significantly. New York State alone anticipates offshore wind farms (WFs) contributing 9GW by 2035. Integration of energy storage emerges as crucial for this advancement. In this study, we focus on a WF paired with a captive battery energy storage system (BESS). We aim to ascertain the power capacity credit for a BESS with specified nameplate energy (MWh) and power capacity (MW). Unlike prior methods rooted in reliability theory, we define a power alignment function, which leads to a straightforward definition of capacity and incremental capacity for the BESS. We develop a solution method based on a linear programming formulation. Our analysis utilizes wind data, collected by NYSERDA off Long Island's coast and load demand data from NYISO. Additionally, we present theoretical insights into BESS sizing and a key time-series property influencing BESS capacity, aiding in simulating wind and demand for estimating BESS energy requirements.

关键词： Battery energy storage system Wind energy Wind farms Reliability theory programming linear programming

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A Class of Algorithms for Solving LP Problems by Prioritizing the Constraints

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American Journal of Operations Research 2023年第6期13卷 177-205页

作者： Dimitris G. Tsarmpopoulos Christina D. Nikolakakou George S. Androulakis Department of Business Administration University of Patras Patras Greece Department of Digital Systems University of the Peloponnese Sparta Greece

linear programming is a method for solving linear optimization problems with constraints, widely met in real-world applications. In the vast majority of these applications, the number of constraints is significantly larger than the number of variables. Since the crucial subject of these problems is to detect the constraints that will be verified as equality in an optimal solution, there are methods for investigating such constraints to accelerate the whole process. In this paper, a technique named proximity technique is addressed, which under a proposed theoretical framework gives an ascending order to the constraints in such a way that those with low ranking are characterized of high priority to be binding. Under this framework, two new linear programming optimization algorithms are introduced, based on a proposed Utility matrix and a utility vector accordingly. For testing the addressed algorithms firstly a generator of 10,000 random linear programming problems of dimension n with m constraints, where , is introduced in order to simulate as many as possible real-world problems, and secondly, real-life linear programming examples from the NETLIB repository are tested. A discussion of the numerical results is given. Furthermore, already known methods for solving linear programming problems are suggested to be fitted under the proposed framework.

关键词： linear programming Binding Constraints Redundant Constraints Proximity Technique Constraint Ranking Constraint Detection

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Music Note Feature Recognition based on Dynamic Time Warping Algorithm

Music Note Feature Recognition based on Dynamic Time Warping...

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Image Processing, Electronics and Computers (IPEC), Asia-Pacific Conference on

作者： Weida Su School of Music and Dance Mudanjiang Normal University Mudanjiang China

ISBN: (数字)9798350374407

ISBN: (纸本)9798350374414

Traditional methods for classifying and recognizing musical note features suffer from low accuracy. In response, we propose a music note feature recognition method leveraging the Dynamic Time Warping (DTW) algorithm. The approach begins with an in-depth analysis of musical notes, utilizing the similarity matrix of note similarity under the standard distance of notes as the DTW distance matrix. From this matrix, we derive a criterion for selecting note features, which serves as the objective function for optimizing the subset of music note features. By operating with multiple populations, we obtain the expected value of higher musical note classification accuracy from various populations, serving as the quality evaluation for multi-population classification. Subsequently, a global objective evaluation is conducted on each musical note based on the evaluation values, facilitating music note classification and recognition. Experimental results validate the efficacy of the proposed method, demonstrating its ease of adoption in computer-assisted systems. Moreover, it achieves higher accuracy in music note feature classification and recognition, while reducing the overall recognition time compared to existing musical note feature extraction methods.

关键词： Computers Image recognition Accuracy Heuristic algorithms Music linear programming Feature extraction Classification algorithms Standards

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Research on Multi-Objective Recipe Optimization Design Based on Genetic Algorithm and NSGA-II

Research on Multi-Objective Recipe Optimization Design Based...

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Electronics and Devices, Computational Science (ICEDCS), International Conference on

作者： Caoyan Wang Haorui Tan Yusheng Lai Yixuan Wang Maynooth International Engineering College Fuzhou University Fuzhou China

ISBN: (数字)9798331527624

ISBN: (纸本)9798331527631

This paper presents a study on the optimization of recipes based on genetic algorithms and NSGA-II for multi-objective design. To address recipe optimization under different objectives, the study divides the optimization into single-objective and multi-objective categories, establishing a single-objective optimization model based on GA and a multi-objective optimization model based on NSGA-II. For the single-objective model, the objective functions include maximizing the protein amino acid score and minimizing meal costs, while the multi-objective optimization aims to simultaneously satisfy both objectives. For the maximization of amino acid scores, men improved from 93.5 to 98.1077, and women from 89 to 96.1483. In the case of minimizing meal costs, men improved from 18.5 to 6.5, and women from 16 to 6. The multi-objective optimization model achieved notable results, increasing the amino acid scores for men from 80 to 98.5064 and reducing meal costs from 28 to 6, while for women, the scores improved from 81 to 97.0679 and the costs decreased from 45 to 3. The comprehensive superior evaluation values were calculated as [99.2373, 90.9347], [97.2297, 88.9271], and [90.2272, 81.9246], with the multi-objective optimization model demonstrating the most effective performance.

关键词： Proteins Costs Scientific computing Optimization models Amino acids linear programming Planning MATLAB Optimization Genetic algorithms

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Extending adversarial attacks to produce adversarial class probability distributions

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 491-532页

作者： Jon Vadillo Roberto Santana Jose A. Lozano Department of Computer Science and Artificial Intelligence University of the Basque Country UPV/EHU Donostia Spain Department of Computer Science and Artificial Intelligence University of the Basque Country UPV/EHU Donostia Spain and Basque Center for Applied Mathematics Bilbao Spain

Despite the remarkable performance and generalization levels of deep learning models in a wide range of artificial intelligence tasks, it has been demonstrated that these models can be easily fooled by the addition of imperceptible yet malicious perturbations to natural inputs. These altered inputs are known in the literature as adversarial examples. In this paper, we propose a novel probabilistic framework to generalize and extend adversarial attacks in order to produce a desired probability distribution for the classes when we apply the attack method to a large number of inputs. This novel attack paradigm provides the adversary with greater control over the target model, thereby exposing, in a wide range of scenarios, threats against deep learning models that cannot be conducted by the conventional paradigms. We introduce four different strategies to efficiently generate such attacks, and illustrate our approach by extending multiple adversarial attack algorithms. We also experimentally validate our approach for the spoken command classification task and the Tweet emotion classification task, two exemplary machine learning problems in the audio and text domain, respectively. Our results demonstrate that we can closely approximate any probability distribution for the classes while maintaining a high fooling rate and even prevent the attacks from being detected by label-shift detection methods.

关键词： adversarial examples deep neural networks robust classification class probability distributions linear programming

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Efficient Hyperparameter Optimization Using Deep Q-Network and BRKGA

Efficient Hyperparameter Optimization Using Deep Q-Network a...

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Congress on Evolutionary Computation

作者： Kosei Kobayashi Masayoshi Aritsugi Pedro Henrique González Israel Mendonça Graduate School of Science and Technology Kumamoto University Kumamoto Japan Faculty of Advanced Science and Technology Kumamoto University Kumamoto Japan System Engineering and Computer Science Program Federal University of Rio de Janeiro Rio de Janeiro Brazil

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Hyperparameter optimization (HPO) is paragon to maximize performance when designing machine learning models. Among different HPO methods, Genetic Algorithm (GA) based optimization is considered effective because it allows a wide and diverse range of solutions to be explored. However, GA's exploratory nature makes this type of algorithm to evaluate many solutions that do not improve the overall performance. This is specially costly when the objective function to be evaluated is time-consuming, like in the HPO field. In this paper, we propose an efficient hybrid algorithm that is able to reduce computational cost by combining deep reinforcement learning with the Biased Random Key Genetic Algorithm (BRKGA), a variant of genetic algorithms. Our reinforcement learning agent has a decision-making role during the population's fitness calculation, in which it filters out chromosomes that would not improve the overall fitness of the population. The agent uses small amounts of pre-trained data to identify trends in potentially good solutions, and carry out its decision process. We conduct experiments on eight different datasets to assess the effectiveness of the proposed method, and the results show that the proposed method can significantly reduce the computation time of hyperparameter search using BRKGA (up to 44% reduction in computational time) without compromising the quality of the solution (no statistically difference in results).

关键词： Machine learning algorithms Filters Sociology Evolutionary computation Hyperparameter optimization Market research linear programming

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Autonomous Localization Method for Railway Trains Based on Multi-Source Information Fusion

Autonomous Localization Method for Railway Trains Based on M...

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Electronic Engineering and Information Systems (EEISS), International Conference on

作者： Hanxuan Zhang Ju Huo Muyao Xue Jianbao Zhou School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China Shanghai Space Propulsion Technology Research Institute Shanghai China Harbin Nuoxin University of Technology Measurement and Control Technology Co. Ltd. Harbin China

ISBN: (数字)9798350351033

ISBN: (纸本)9798350351040

Aiming at the problems of high cost and difficult maintenance in the practical application of the traditional "vehicle-ground cooperation" train positioning technology in special section railway projects. In this paper, a method for autonomous positioning of railway trains based on multisource information fusion is proposed. First, develop a kilometer mark detection thread based on VINS Fusion, and build a lightweight optical symbol recognition system to identify the character area of the kilometer mark to obtain the position information of the electronic map. Secondly, based on the LSD line detection algorithm combined with the way of dynamically expanding the line, the coordinates of the vertex coordinates of the kilometer mark are extracted. Then, a global optimization objective function containing the position information constraints of the kilometer mark is established, and the global positioning accuracy is improved through the graph optimization method. This solves the problem that existing intelligent positioning of railway trains cannot eliminate accumulated errors in the absence of loop closure detection. Finally, the semi-physical simulation results show that the positioning error of the original positioning system can be reduced by more than 57%.

关键词： Location awareness Costs Simulation Symbols Optimization methods linear programming Rail transportation

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Multi-Objective Energy Optimization in Smart Distribution Network Considering Renewable Energy and Battery Storage System

Multi-Objective Energy Optimization in Smart Distribution Ne...

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Electrical Power Engineering, Communication and Computing Technology(ODICON), Odisha International Conference on

作者： Sigma Ray Kumari Kasturi Manas Ranjan Nayak Subhadra Sahoo Manoj Kumar Debnath Narendra Kumar Jena Department of Electrical Engg. Siksha ‘O’ Anusandhan University Bhubaneswar India Department of Electrical Engg. Biju Patnaik University of Technology Rourkela India

ISBN: (数字)9798350354379

ISBN: (纸本)9798350354386

In modern power systems, photovoltaic (PV) and battery energy storage systems (BESS) must be adopted appropriately due to their complicated inherent characteristics. This paper has constructed a multi-objective optimization problem to balance technical, economic, and environmental objectives related to PV-BESS unit allocation and power factor (pf) in the radial distribution system (RDS). A complete review of all three planning factors aids in effective and dependable PV modules, BESS units, and RDS energy management strategy (EMS) planning. A novel multi-objective Greylag Goose Optimization (MOGGO) technique is used to minimize total active power loss, net annual cost, and environmental emissions in order to obtain optimal decision variables within operational constraints. The proposed technique is validated on an IEEE 33 bus RDS, and extensive study shows its benefits. The findings demonstrate that MOGGO's effective and competent solutions improve RDS's technical, economic, and environmental benefits when combined with PV and BESS.

关键词： Photovoltaic systems Costs System performance Simulation linear programming Planning Resource management Energy management Optimization Standards

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Genetic Column Generation for Computing Lower Bounds for Adversarial Classification

arXiv

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arXiv 2024年

作者： Penka, Maximilian Department of Mathematics Technische Universität München Germany

Recent theoretical results on adversarial multi-class classification showed a similarity to the multi-marginal formulation of Wasserstein-barycenter in optimal transport. Unfortunately, both problems suffer from the curse of dimension, making it hard to exploit the nice linear program structure of the problems for numerical calculations. We investigate how ideas from Genetic Column Generation for multi-marginal optimal transport can be used to overcome the curse of dimension in computing the minimal adversarial risk in multi-class classification. Copyright © 2024, The Authors. All rights reserved.

关键词： linear programming

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HALO: Robust Out-of-Distribution Detection via Joint Optimisation

HALO: Robust Out-of-Distribution Detection via Joint Optimis...

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Secure and Trustworthy Machine Learning (SaTML), IEEE Conference on

作者： Hugo Lyons Keenan Sarah Erfani Christopher Leckie School of Computing and Information Systems The University of Melbourne Victoria Australia

ISBN: (数字)9798331517113

ISBN: (纸本)9798331517120

Effective out-of-distribution (ODD) detection is crucial for the safe deployment of machine learning models in real-world scenarios. However, recent work has shown that ODD detection methods are vulnerable to adversarial attacks, potentially leading to critical failures in high-stakes applications. This discovery has motivated work on robust ODD detection methods that are capable of maintaining performance under various attack settings. Prior approaches have made progress on this problem but face a number of limitations: often only exhibiting robustness to attacks on ODD data or failing to maintain strong clean performance. In this work, we adapt an existing robust classification framework, TRADES, extending it to the problem of robust ODD detection and discovering a novel objective function. Recognising the critical importance of a strong clean/robust trade-off for ODD detection, we introduce an additional loss term which boosts classification and detection performance. Our approach, called HALO (Helper-based Adver-sariaL ODD detection), surpasses existing methods and achieves state-of-the-art performance across a number of datasets and attack settings. Extensive experiments demonstrate an average AUROC improvement of 3.15 in clean settings and 7.07 under adversarial attacks when compared to the next best method. Furthermore, HALO exhibits resistance to transferred attacks, offers tuneable performance through hyperparameter selection, and is compatible with existing ODD detection frameworks out-of-the-box, leaving open the possibility of future performance gains. Code is available at: https://***/hugo0076/HALO.

关键词： Resistance Sensitivity Face recognition Machine learning Reliability theory Performance gain linear programming Robustness Optimization Image classification

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