Automation and Remote Control最新文献_第2页

An Extension of the Feedback Linearization Method in the Control Problem of an Inverted Pendulum on a Wheel 轮上倒摆控制问题中反馈线性化方法的扩展

IF 0.7 4区计算机科学

Automation and Remote Control Pub Date : 2024-08-23 DOI: 10.1134/s0005117924040052

L. B. Rapoport, A. A. Generalov, B. A. Barulin, M. D. Gorbachev

{"title":"An Extension of the Feedback Linearization Method in the Control Problem of an Inverted Pendulum on a Wheel","authors":"L. B. Rapoport, A. A. Generalov, B. A. Barulin, M. D. Gorbachev","doi":"10.1134/s0005117924040052","DOIUrl":"https://doi.org/10.1134/s0005117924040052","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>This paper continues previous studies on designing stabilizing control laws for a mechanical system consisting of a wheel and a pendulum suspended on its axis. The control objective is to simultaneously stabilize the vertical position of the pendulum and a given position of the wheel. The difficulty of this problem is that the same control is used to achieve two targets, i.e., stabilize the pendulum angle and the wheel rotation angle. Previously, the output feedback linearization method was applied to this problem. The sum of the pendulum angle and the wheel rotation angle was taken as the output. For the closed loop system to be not only asymptotically stable in the output but also to have asymptotically stable zero dynamics, a dissipative term was added to the output-stabilizing control law. Below, a two-parameter modification of this law is described. Along with the dissipative term, we introduce a positive factor. The more general parameterization allows stabilizing this system in the cases where the control law proposed previously appeared ineffective. The properties of the new control law are investigated, and the attraction domain is estimated. The estimation procedure is reduced to checking the feasibility of linear matrix inequalities.</p>","PeriodicalId":55411,"journal":{"name":"Automation and Remote Control","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142198065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On Forced Oscillations in a Relay System with Hysteresis 论带有滞后的继电器系统中的强迫振荡

IF 0.7 4区计算机科学

Automation and Remote Control Pub Date : 2024-08-23 DOI: 10.1134/s0005117924040088

Zh. T. Zhusubaliyev, U. A. Sopuev, D. A. Bushuev

引用次数: 0

Self-Adjusted Consensus Clustering with Agglomerate Algorithms 利用聚类算法进行自调整共识聚类

IF 0.7 4区计算机科学

Automation and Remote Control Pub Date : 2024-08-06 DOI: 10.1134/s0005117924030044

B. G. Mirkin, A. A. Parinov

引用次数: 0

Plausible Reasoning in an Algorithm for Generation of Good Classification Tests 生成良好分类测试算法中的合理推理

IF 0.7 4区计算机科学

Automation and Remote Control Pub Date : 2024-08-06 DOI: 10.1134/s0005117924030056

X. A. Naidenova, V. A. Parkhomenko, T. A. Martirova, A. V. Schukin

引用次数: 0

Interestingness Indices for Building Neural Networks Based on Concept Lattices 基于概念网格构建神经网络的趣味性指数

IF 0.7 4区计算机科学

Automation and Remote Control Pub Date : 2024-08-06 DOI: 10.1134/s000511792403010x

M. M. Zueva, S. O. Kuznetsov

引用次数: 0

On the Use of Digital Twin Data in Models Related to Considering the Environment Impact on Enterprises 论数字孪生数据在企业环境影响考量相关模型中的应用

IF 0.7 4区计算机科学

Automation and Remote Control Pub Date : 2024-08-06 DOI: 10.1134/s0005117924030093

E. D. Viazilov, D. A. Melnikov, O. A. Minkov

引用次数: 0

Smart Routes: A System for Development and Comparison of Algorithms for Solving Vehicle Routing Problems with Realistic Constraints 智能路线：开发和比较解决具有现实约束条件的车辆路线问题的算法系统

IF 0.7 4区计算机科学

Automation and Remote Control Pub Date : 2024-08-06 DOI: 10.1134/s0005117924030081

A. G. Soroka, G. V. Mikhelson, A. V. Mescheryakov, S. V. Gerasimov

{"title":"Smart Routes: A System for Development and Comparison of Algorithms for Solving Vehicle Routing Problems with Realistic Constraints","authors":"A. G. Soroka, G. V. Mikhelson, A. V. Mescheryakov, S. V. Gerasimov","doi":"10.1134/s0005117924030081","DOIUrl":"https://doi.org/10.1134/s0005117924030081","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>The problem of route optimization with realistic constraints is becoming extremely relevant in the face of global urban population growth. While we are aware of approaches that theoretically provide an exact optimal solution, their application becomes challenging as the problem size increases because of exponential complexity. We investigate the Capacitated Vehicle Routing Problem with Time Windows (CVRPTW) and compare solutions obtaining by exact solver SCIP [1] with heuristic algorithms such as LKH, 2-OPT, 3-OPT [2], the OR-Tools framework [3], and the deep learning model JAMPR [4]. We demonstrate that for problem of size 50 deep learning and classical heuristic solutions became close to SCIP exact solution but requires less time. Additionally for problems with size 100, SCIP exact methods ~13 times slower that neural and classical heuristics with the same route cost and on ~50% worse for the first feasible solution on the same time. To conduct experiments, we developed the Smart Routes platform for solving route optimization problems, which includes exact, heuristic, and deep learning models, and facilitates convenient integration of custom algorithms and datasets.</p>","PeriodicalId":55411,"journal":{"name":"Automation and Remote Control","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141929885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Transformer-Based Classification of User Queries for Medical Consultancy 基于变压器的医疗咨询用户查询分类

IF 0.7 4区计算机科学

Automation and Remote Control Pub Date : 2024-08-06 DOI: 10.1134/s0005117924030032

D. A. Lyutkin, D. V. Pozdnyakov, A. A. Soloviev, D. V. Zhukov, M. S. I. Malik, D. I. Ignatov

{"title":"Transformer-Based Classification of User Queries for Medical Consultancy","authors":"D. A. Lyutkin, D. V. Pozdnyakov, A. A. Soloviev, D. V. Zhukov, M. S. I. Malik, D. I. Ignatov","doi":"10.1134/s0005117924030032","DOIUrl":"https://doi.org/10.1134/s0005117924030032","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>The need for skilled medical support is growing in the era of digital healthcare. This research presents an innovative strategy, utilizing the RuBERT model, for categorizing user inquiries in the field of medical consultation with a focus on expert specialization. By harnessing the capabilities of transformers, we fine-tuned the pretrained RuBERT model on a varied dataset, which facilitates precise correspondence between queries and particular medical specialisms. Using a comprehensive dataset, we have demonstrated our approach’s superior performance with an Fl-score of over 91.8%, calculated through both cross-validation and the traditional split of test and train datasets. Our approach has shown excellent generalization across medical domains such as cardiology, neurology and dermatology. This methodology provides practical benefits by directing users to appropriate specialists for prompt and targeted medical advice. It also enhances healthcare system efficiency, reduces practitioner burden, and improves patient care quality. In summary, our suggested strategy facilitates the attainment of specific medical knowledge, offering prompt and precise advice within the digital healthcare field.</p>","PeriodicalId":55411,"journal":{"name":"Automation and Remote Control","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141929965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Genetic Engineering Algorithm (GEA): An Efficient Metaheuristic Algorithm for Solving Combinatorial Optimization Problems 遗传工程算法（GEA）：解决组合优化问题的高效元启发式算法

IF 0.7 4区计算机科学

Automation and Remote Control Pub Date : 2024-08-06 DOI: 10.1134/s000511792403007x

Majid Sohrabi, Amir M. Fathollahi-Fard, V. A. Gromov

{"title":"Genetic Engineering Algorithm (GEA): An Efficient Metaheuristic Algorithm for Solving Combinatorial Optimization Problems","authors":"Majid Sohrabi, Amir M. Fathollahi-Fard, V. A. Gromov","doi":"10.1134/s000511792403007x","DOIUrl":"https://doi.org/10.1134/s000511792403007x","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>Genetic Algorithms (GAs) are known for their efficiency in solving combinatorial optimization problems, thanks to their ability to explore diverse solution spaces, handle various representations, exploit parallelism, preserve good solutions, adapt to changing dynamics, handle combinatorial diversity, and provide heuristic search. However, limitations such as premature convergence, lack of problem-specific knowledge, and randomness of crossover and mutation operators make GAs generally inefficient in finding an optimal solution. To address these limitations, this paper proposes a new metaheuristic algorithm called the Genetic Engineering Algorithm (GEA) that draws inspiration from genetic engineering concepts. GEA redesigns the traditional GA while incorporating new search methods to isolate, purify, insert, and express new genes based on existing ones, leading to the emergence of desired traits and the production of specific chromosomes based on the selected genes. Comparative evaluations against state-of-the-art algorithms on benchmark instances demonstrate the superior performance of GEA, showcasing its potential as an innovative and efficient solution for combinatorial optimization problems.</p>","PeriodicalId":55411,"journal":{"name":"Automation and Remote Control","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141929962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Attacks on Machine Learning Models Based on the PyTorch Framework 对基于 PyTorch 框架的机器学习模型的攻击

IF 0.7 4区计算机科学

Automation and Remote Control Pub Date : 2024-08-06 DOI: 10.1134/s0005117924030068

D. E. Namiot, T. M. Bidzhiev

引用次数: 0