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Optimal Operation of Multi-Unit Diesel Power Plant

Power Technology and engineering 2024年第4期58卷 592-597页

作者： Vasileva, A.A. Vaskov, A.G. Sigel, A.S. Shestopalova, T.A. Shurkalov, P.S. Laboratory of Control Systems for Solar-Diesel Plants National Research University "Moscow Power Engineering Institute" Moscow Russia Department of Hydraulic Power and Renewable Energy Sources National Research University "Moscow Power Engineering Institute" Moscow Russia

Methods of mathematical modeling of the dependence of fuel consumption on the load of diesel gensets are considered. Accounting for such dependence in simulating the operation of solar-diesel hybrid power systems can reduce the consumption of diesel fuel and the operating hours of diesel generators. © Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Diesel power plants

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Modeling and analysis of an underwater biomimetic vehicle-manipulator system

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Science China(Information Sciences) 2022年第3期65卷 255-257页

作者： Xuejian BAI Yu WANG Shuo WANG Rui WANG Min TAN Wei WANG School of Artificial Intelligence University of Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology School of Control Science and Engineering Shandong University

In recent years, underwater vehicle-manipulator systems(UVMSs) have captured researchers' attention. Inspired by fish, bionic mechanisms were introduced into a *** underwater biomimetic vehicle-manipulator system(UBVMS) is actuated by two symmetrically arranged biomimetic undulatory fin propulsors [1]. Based on the UBVMS, some topics have been researched, such as freefloating autonomous operation [2, 3], three-dimensional helical path following [4], and underwater video processing [5].

关键词： underwater biomimetic vehicle underwater robots underwater vehicle-manipulator system biomimetic robots hydrodynamic modeling

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Improved Architecture and the Synthesis Algorithm for Bithreshold Neural Network Classifier 18

Improved Architecture and the Synthesis Algorithm for Bithre...

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18th IEEE International Conference on Computer Science and Information Technologies, CSIT 2023

作者： Kotsovsky, Vladyslav Batyuk, Anatoliy Uzhhorod National University Department of Information Management Systems and Technologies Uzhhorod Ukraine Lviv Polytechnic National University Department of Automated Control Systems Lviv Ukraine

ISBN: (纸本)9798350360462

The model of the 3-layer feed-forward neural network is introduced whose first hidden layer consists of bithreshold neurons and the other layers - of single-threshold ones. The proposed network is capable to recognize compact finite set of patterns using a union of hyperrectangular decision regions in n-dimensional space. We design a multiclass classifier on the base of such network, propose the synthesis algorithm for it and estimate the networks size as well as the time of computations. The simulation results demonstrate that the application of the additional hidden layer improves the accuracy of classification. © 2023 IEEE.

关键词： Multilayer neural networks

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Statistical Learning Theory for control: A Finite-Sample Perspective

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IEEE control systems 2023年第6期43卷 67-97页

作者： Tsiamis, Anastasios Ziemann, Ingvar Matni, Nikolai Pappas, George J. ETH Zürich Department of Information Technology and Electrical Engineering Zürich8092 Switzerland KTH Royal Institute of Technology Division of Decision and Control Systems Stockholm Sweden University of Pennsylvania Department of Electrical and Systems Engineering PhiladelphiaPA19104 United States

Learning algorithms have become an integral component to modern engineering solutions. Examples range from self-driving cars and recommender systems to finance and even critical infrastructure, many of which are typically under the purview of control theory. While these algorithms have already shown tremendous promise in certain applications [1], there are considerable challenges, in particular, with respect to guaranteeing safety and gauging fundamental limits of operation. Thus, as we integrate tools from machine learning into our systems, we also require an integrated theoretical understanding of how they operate in the presence of dynamic and system-theoretic phenomena. Over the past few years, intense efforts toward this goal - an integrated theoretical understanding of learning, dynamics, and control - have been made. While much work remains to be done, a relatively clear and complete picture has begun to emerge for (fully observed) linear dynamical systems. These systems already allow for reasoning about concrete failure modes, thus helping to indicate a path forward. Moreover, while simple at a glance, these systems can be challenging to analyze. Recently, a host of methods from learning theory and high-dimensional statistics, not typically in the control-theoretic toolbox, have been introduced to our community. This tutorial survey serves as an introduction to these results for learning in the context of unknown linear dynamical systems (see 'Summary'). We review the current state of the art and emphasize which tools are needed to arrive at these results. Our focus is on characterizing the sample efficiency and fundamental limits of learning algorithms. Along the way, we also delineate a number of open problems. More concretely, this article is structured as follows. We begin by revisiting recent advances in the finite-sample analysis of system identification. Next, we discuss how these finite-sample bounds can be used downstream to give guaranteed performa

关键词： Learning algorithms

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Investigating the Effect of Ground Distance on the Characteristics of a Coupled Meander Microstrip Line 3

Investigating the Effect of Ground Distance on the Character...

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3rd IEEE International Conference on Problems of Informatics, Electronics and Radio engineering, PIERE 2024

作者： Murmansky, Mikhail S. Kuzmin, Nikita O. Mikhailenko, Viacheslav E. Tomsk State University of Control Systems and Radioelectronics Department of Ultra High Frequency and Quantum Radio Engineering Tomsk Russia Tomsk State University of Control Systems and Radioelectronics Engineer of research Laboratory of 'Safety and Electromagnetic Compatibility of Radioelectronic Facilities' Tomsk Russia

ISBN: (纸本)9798331516321

This paper investigates the influence of the distance between the ground plane and a coupled meander microstrip line on its characteristics in the frequency and time domains. It is shown that at a distance of 2.5 mm the ground plane has no impact on the characteristics in both domains. The structure with a ground plane has the greatest influence at a distance of 0.5 mm from the line, resulting in a decrease in the average values of | S11| by 1.5 dB and an increase in the average values of | S21| and | S41| by 1 and 3 dB, respectively. Electromagnetic modeling of the investigated structure was carried out in the frequency range from 0 to 20 GHz using COMSOL Multiphysics. N-norms were employed for a more detailed assessment of the time-domain response. The analysis of the N-norms showed that increases in the distance to the ground structure do not affect the N1 values. The structure with a distance to the ground plane of 0.5 mm possesses the maximum N2-5 values. © 2024 IEEE.

关键词： Ultra-wideband (UWB)

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Hydrogen Pinch Analysis with Recycled Hydrogen Approach by Energy management of Refinery Processes in Optimal State 9

Hydrogen Pinch Analysis with Recycled Hydrogen Approach by E...

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9th International Conference on Technology and Energy management, ICTEM 2024

作者： Rizi, Daryoush Tavangar Nazari, Mohammad Hassan Gharehpetian, G.B. Hosseinian, Seyed Hossein Fani, Maryam Khorsandi, Amir Amirkabir University of Technology Department of Physics and Energy Engineering Tehran Iran Smart Control Systems Department Tehran Iran Amirkabir University of Technology Department of Electrical Engineering Tehran Iran

ISBN: (纸本)9798350329797

Special goals in the oil industry lead experts to the optimal use of hydrogen in refinery and petrochemical units. In this research, the necessity of hydrogen management and refinery hydrogen distribution network in refineries has been investigated. In this method, based on the pressure, the hydrogen distribution network in the refinery is checked in two modes. In the first case, the optimization is done only with the equipment that is currently available in the refinery. Here in, the output of the hydrogen production unit and the operating costs will not decrease. Even so, in the second case, assuming the economic possibility of purchasing a new device called pressure oscillation absorption hydrogen recovery units (PSA), optimization has been done and the results have been analyzed and reviewed. In the simulation of the problem in question, GAMS software has been used in the form of nonlinear programming in the network to reduce hydrogen production in the hydrogen production and recycling unit, which has led to a 54.13% reduction in operating costs. A successful implementation of this initiative has resulted in considerable financial savings of 1.21 million dollars for the refinery on an annual basis. Cost of existing network is 235 million dollars, while PSA costs 1.025 million dollars. The results show that the objectives of this research have been well achieved. © 2024 IEEE.

关键词： Operating costs

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Intelligent obstacle avoidance algorithm for safe urban monitoring with autonomous mobile drones

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Journal of Electronic Science and Technology 2024年第4期22卷 19-36页

作者： Didar Yedilkhan Abzal E.Kyzyrkanov Zarina A.Kutpanova Shadi Aljawarneh Sabyrzhan K.Atanov Department of Computer Engineering Astana IT UniversityAstana010000Kazakhstan Department of Computer and Software Engineering L.N.Gumilyov Eurasian National UniversityAstana010000Kazakhstan Department of Automation and Control Systems L.N.Gumilyov Eurasian National UniversityAstana010000Kazakhstan Department of Software Engineering Jordan University of Science and TechnologyIrbid22110Jordan

The growing field of urban monitoring has increasingly recognized the potential of utilizing autonomous technologies,particularly in drone *** deployment of intelligent drone swarms offers promising solutions for enhancing the efficiency and scope of urban condition *** this context,this paper introduces an innovative algorithm designed to navigate a swarm of drones through urban landscapes for monitoring *** primary challenge addressed by the algorithm is coordinating drone movements from one location to another while circumventing obstacles,such as *** algorithm incorporates three key components to optimize the obstacle detection,navigation,and energy efficiency within a drone ***,the algorithm utilizes a method to calculate the position of a virtual leader,acting as a navigational beacon to influence the overall direction of the ***,the algorithm identifies observers within the swarm based on the current *** further refine obstacle avoidance,the third component involves the calculation of angular velocity using fuzzy *** approach considers the proximity of detected obstacles through operational rangefinders and the target’s location,allowing for a nuanced and adaptable computation of angular *** integration of fuzzy logic enables the drone swarm to adapt to diverse urban conditions dynamically,ensuring practical obstacle *** proposed algorithm demonstrates enhanced performance in the obstacle detection and navigation accuracy through comprehensive *** results suggest that the intelligent obstacle avoidance algorithm holds promise for the safe and efficient deployment of autonomous mobile drones in urban monitoring applications.

关键词： Drone swarms Fuzzy logic Intelligent solution Smart city Urban monitoring

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A VNS-NPGA approach to multi-objective optimization of hub-and-spoke logistics network

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Journal of Traffic and Transportation engineering(English Edition) 2025年第2期12卷 390-409页

作者： Changxi Ma Chuwei Shi Yun Yang Yongpeng Zhao Zhuye Xu Bo Du School of Traffic and Transportation Lanzhou Jiaotong University Key Laboratory of Railway Industry on Plateau Railway Transportation Intelligent Management and Control Lanzhou Jiaotong University School of Tourism and Management Yangling Vocational and Technical College School of New Energy and Power Engineering Lanzhou Jiaotong University National Engineering Research Center of Highway Maintenance Technology Changsha University of Science & Technology Department of Management Griffith University

To realize low-cost freight transport in the logistics network and improve the network operation efficiency, a multi-objective optimization model and the corresponding algorithm for a hub-and-spoke logistics network are proposed based on the multi-level location of hub points and channels layout. By considering the structure of the multi-level hub-andspoke logistics network and the features of the connectivity between the hub and spoke points, the multi-objective optimization model is constructed with two objectives of minimizing the total network operation costs and the total network service time. By considering the characteristics of decision variables and models, a variable neighborhood search(VNS)-niched Pareto genetic algorithm(NPGA) approach with a three-stage encoding structure chromosome is proposed, where the VNS algorithm nested in NPGA is used for individual variable neighborhood search to optimize individual channel level genes,and NPGA is adopted to solve the multi-objective optimization model. To evaluate the performance of the proposed VNS-NPGA approach, a real-life case study based on a smallscale Australia Post data set was conducted, and 25 nodes of the Australia Post and 14nodes of the Gansu Province 3-level hub-and-spoke logistics networks were established,respectively. The analysis results indicated that the network structure of multi-level hub points could avoid the detour problem existing in the traditional hub-and-spoke network,and showed better applicability in the narrow geographical structure. Compared to the traditional multi-objective evolutionary algorithms, VNS-NPGA can obtain better solutions through the distributed optimization of channel levels, avoiding the problem that a single algorithm cannot effectively deal with coupling relationships in genes.

关键词： Multi-level hub-and-spoke logistics network Multi-objective optimization model VNS-NPGA Three-stage encoding Individual variable neighborhood search

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Applying Data Science to Assess Cyber Security of Renewable Energy and Reserve Markets to Ensure System Reliability 9

Applying Data Science to Assess Cyber Security of Renewable ...

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9th International Conference on Technology and Energy management, ICTEM 2024

作者： Rizi, Daryoush Tavangar Nazari, Mohammad Hassan Hosseinian, Seyed Hossein Gharehpetian, G.B. Fani, Maryam Khorsandi, Amir Amirkabir University of Technology Department of Physics and Energy Engineering Tehran Iran Smart Control Systems Department Tehran Iran Amirkabir University of Technology Department of Electrical Engineering Tehran Iran

ISBN: (纸本)9798350329797

Cybersecurity plays a vital role in tackling threats and minimizing potential harm. The primary objective of the research is to investigate the impact of intervention on the energy market, specifically the unavailability of resources. This network serves as a small-scale representation of energy and reserve markets, encompassing both of the renewable recourse and lines. This is done by executing multiple situations in the network. After an attack in the system, minimizing costs is achieved through a problem solution approach. The CPLEX solver has used for solving MILP model. Furthermore, the gained optimal information is used for prediction and analysis, enabling the utilization of data science techniques, such as charts and machine learning methods, to accurately predict and identify various scenarios. The attack causes a shift in the peak and minimum of the load. Additionally, the examination of three machine learning algorithms has revealed the usage of support vector machine (SVM) in conjunction with neural networks such as RBF, a second-degree polynomial, and third-degree polynomial. According to the results, the highest performance accuracy of 86% is achieved by integrating SVM with a neural network structure. © 2024 IEEE.

关键词： Clean energy

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Fault Detection in Electric Drives Based on LSTM Autoencoder Model Machine Learning Approach 25

Fault Detection in Electric Drives Based on LSTM Autoencoder...

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25th IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2024

作者： Demidova, Galina Privalov, Daniil Semenov, Denis Lukichev, Dmitry Liu, Zhitao Anuchin, Alecksey Faculty of Control System and Robotics ITMO University Saint-Petersburg Russia Institute of Cyber-Systems and Control Zhejiang University Hangzhou China Department of Electric Drives Moscow Power Engineering Institute Moscow Russia

ISBN: (纸本)9798350389234

Fault detection in electric drives is crucial for ensuring operational reliability and minimizing downtime. This paper provides a brief overview of the methods based on machine learning used for fault detection in electric drives. Additional this research presents a machine learning approach that utilizes the Long Short-Term Memory Autoencoder model for fault detection deal with bearing faults. A flowchart illustrating the implementation steps of the Long Short-Term Memory Autoencoder algorithm is included. Additionally, a case study on bearing faults in an electric drive system is presented to demonstrate the advantage of the proposed approach. By training the Long Short-Term Memory Autoencoder on operational data, accurate prediction of bearing faults is achieved, enabling timely maintenance interventions. It is shown that in the 7th epoch of training, the model achieved a notably high accuracy more than 99%. The results highlight the potential of Long Short-Term Memory Autoencoder models as a valuable tool for fault detection in electric drives, thereby improving system reliability and performance. © 2024 IEEE.

关键词： Machine learning

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