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Distributed Hybrid Model Predictive Secondary Control of DC Microgrids with Random Communication Disorders

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Journal of Modern Power Systems and Clean Energy 2024年第3期12卷 925-935页

作者： Meysam Yaribeygi Zeinab Karami Qobad Shafiee Hassan Bevrani the department of Electrical Engineering K.N.Toosi University of TechnologyTehranIranand he is now with the Sportradar CompanyTrondheimNorway the department of Engineering cybernetics(ITK) Norwegian University of Science and Technology(NTNU)TrondheimNorway the Smart/Micro Grids Research Center Department of Electrical EngineeringUniversity of KurdistanSanandajIran

A reliable and robust communication network is essential to exchange information between distributed generators(DGs)and accurately calculate their control actions in microgrids(MGs).However,the integration of the communication network and MGs poses challenges related to the flexibility,availability,and reliability of the ***,random communication disorders such as time delays and packet loss can negatively impact the system ***,it is essential to design a suitable secondary controller(SC)with a fast dynamic response to restore voltage and appropriate powersharing,while ensuring that the effects of random communication disorders are *** this regard,an optimal distributed hybrid model predictive secondary control method is presented in this *** simulations are carried out in a mixed simulation environment based on MATLAB and OMNET++,by considering IEEE 802.11(Wi Fi)using the recently developed Internet networking(INET)*** the implemented application layer,the recovery Unit is responsible for reducing the impact of random communication *** effectiveness and performance of the proposed method in comparison with a conventional model predictive control are verified by simulation results.

关键词： Microgrid model predictive control random communication disorder secondary control

Energy management of buildings with energy storage and solar photovoltaic:A diversity in experience approach for deep reinforcement learning agents

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Energy and AI 2024年第1期15卷 1-14页

作者： Akhtar Hussain Petr Musilek department of Electrical and Computer Engineering University of AlbertaEdmontonT6G 2G2ABCanada department of Applied cybernetics University of Hradec Králové50003Hradec KrálovéCzech Republic

Deep reinforcement learning(DRL)is a suitable approach to handle uncertainty in managing the energy consumption of buildings with energy storage ***,DRL agents are trained by randomly selecting samples from a data set,which can result in overexposure to some data categories and under/no exposure to other data ***,the trained model may be biased towards some data groups and underperform(provide suboptimal results)for data groups to which it was less *** address this issue,diversity in experience-based DRL agent training framework is proposed in this *** approach ensures the exposure of agents to all types of *** proposed framework is implemented in two *** the first step,raw data are grouped into different clusters using the K-means clustering *** clustered data is then arranged by stacking the data of one cluster on top of *** the second step,a selection algorithm is proposed to select data from each cluster to train the DRL *** frequency of selection from each cluster is in proportion to the number of data points in that cluster and therefore named the proportional selection *** analyze the performance of the proposed approach and compare the results with the conventional random selection method,two indices are proposed in this study:the flatness index and the divergence *** model is trained using different data sets(1-year,3-year,and 5-year)and also with the inclusion of solar *** simulation results confirmed the superior performance of the proposed approach to flatten the building’s load curve by optimally operating the energy storage system.

关键词： Battery energy storage Building demand management Deep reinforcement learning Diversity in experience Energy management

Reconstructing the Absorption Function in a Quasi-Linear Sorption Dynamic Model via an Iterative Regularizing Algorithm

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Advances in Applied Mathematics and Mechanics 2024年第1期16卷 237-252页

作者： Alexey Shcheglov Jingzhi Li Chao Wang Alexander Ilin Ye Zhang Faculty of Computational Mathematics and cybernetics Shenzhen MSU-BIT UniversityShenzhenGuangdong 518172China Faculty of Computational Mathematics and cybernetics Lomonosov Moscow State UniversityMoscowRussia department of Mathematics&National Center for Applied Mathematics Shenzhen&SUSTech International Center for Mathematics Southern University of Science and TechnologyShenzhenGuangdong 518055China School of Mathematics and Statistics Beijing Institute of TechnologyBeijing 100081China

This study addresses the parameter identification problem in a system of time-dependent quasi-linear partial differential equations(PDEs).Using the integral equation method,we prove the uniqueness of the inverse problem in nonlinear ***,using the method of successive approximations,we develop a novel iterative algorithm to estimate sorption *** stability results of the algorithm are proven under both a priori and a posteriori stopping rules.A numerical example is given to show the efficiency and robustness of the proposed new approach.

关键词： Inverse problem quasi-linear dynamic model uniqueness method of successive approximations stability

Robot-Oriented 6G Satellite-UAV Networks: Requirements, Paradigm Shifts, and Case Studies

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China Communications 2024年第2期21卷 74-84页

作者： Peng Wei Wei Feng Yunfei Chen Ning Ge Wei Xiang department of Electronic Engineering Tsinghua UniversityBeijing 100084China department of Engineering University of DurhamDH13LEDurhamUK School of Computing Engineering and Mathematical SciencesLa Trobe UniversityMelbourneVIC 3086Australia

Networked robots can perceive their surroundings, interact with each other or humans,and make decisions to accomplish specified tasks in remote/hazardous/complex environments. Satelliteunmanned aerial vehicle(UAV) networks can support such robots by providing on-demand communication services. However, under traditional open-loop communication paradigm, the network resources are usually divided into user-wise mostly-independent links,via ignoring the task-level dependency of robot collaboration. Thus, it is imperative to develop a new communication paradigm, taking into account the highlevel content and values behind, to facilitate multirobot operation. Inspired by Wiener’s cybernetics theory, this article explores a closed-loop communication paradigm for the robot-oriented satellite-UAV network. This paradigm turns to handle group-wise structured links, so as to allocate resources in a taskoriented manner. It could also exploit the mobility of robots to liberate the network from full coverage,enabling new orchestration between network serving and positive mobility control of robots. Moreover,the integration of sensing, communications, computing and control would enlarge the benefit of this new paradigm. We present a case study for joint mobile edge computing(MEC) offloading and mobility control of robots, and finally outline potential challenges and open issues.

关键词： closed-loop communication mobility control satellite-UAV network structured resource allocation

Pricing Binary Variables of System-wide Constraints for Power System Optimization

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CSEE Journal of Power and Energy Systems 2024年第3期10卷 1131-1144页

作者： Jiaxun Li Wei Lin Zhifang Yang Mikhail Davidson State Key Laboratory of Power Transmission Equipment and System Security and New Technology College of Electrical EngineeringChongqing UniversityChongqing 400030China department of Electrical Engineering The Hong Kong Polytechnic UniversityHong Kong SAR 999077China Faculty of Computational Mathematics and cybernetics Moscow State UniversityMoscow 119991Russia IEEE

With the trend of multiple energies or flexible demand in power systems,binary variables appear in systemwide constraints,which are the foundation of marginal pricing currently in *** appropriate pricing method incentivizes compliance of market participants;otherwise,compliance can be incentivized by paying discriminatory uplift payments which jeopardize transparency of *** paper proposes two theorems to examine whether the binary variables brought by multiple energies and flexible demand will impact compliance under marginal *** first theorem shows sufficient conditions with which marginal pricing with fixed binary variables incentivizes compliance,while the second theorem shows sufficient conditions to require uplift *** improve transparency by reducing uplift payments under cases which fall into the second theorem,this paper further proposes a pricing method by combining 1)designed constraints to price binary variables in system-wide constraints,and 2)convex hull pricing to price binary variables in private *** of the proposed theorems and pricing method is verified in an electricity-gas case(consisting of the IEEE 30-bus system and the NGS 10-node system)and the IEEE 118-bus test system.

关键词： Binary variables impact analysis pricing under non-convexities

On Some Properties of Graph of Prefix Code

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Journal of Applied Mathematics and Physics 2024年第4期12卷 1571-1581页

作者： Nikolai I. Krainiukov Mikhail E. Abramyan Boris F. Melnikov Faculty of Computational Mathematics and cybernetics Shenzhen MSU-BIT University Shenzhen China department of Algebra and Discrete Mathematics Southern Federal University Rostov-on-Don Russian Federation

We investigate decomposition of codes and finite languages. A prime decomposition is a decomposition of a code or languages into a concatenation of nontrivial prime codes or languages. A code is prime if it cannot be decomposed into at least two nontrivial codes as the same for the languages. In the paper, a linear time algorithm is designed, which finds the prime decomposition. If codes or finite languages are presented as given by its minimal deterministic automaton, then from the point of view of abstract algebra and graph theory, this automaton has special properties. The study was conducted using system for computational Discrete Algebra GAP. .

关键词： Finite Languages Minimal Deterministic Automata Concatenation Codes Graph of Automaton Free Algebra

On the “Onion Husk” Algorithm for Approximate Solution of the Traveling Salesman Problem

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Journal of Applied Mathematics and Physics 2024年第4期12卷 1557-1570页

作者： Mikhail E. Abramyan Nikolai I. Krainiukov Boris F. Melnikov Faculty of Computational Mathematics and cybernetics Shenzhen MSU-BIT University Shenzhen China department of Algebra and Discrete Mathematics Southern Federal University Rostov-on-Don Russian Federation

The paper describes some implementation aspects of an algorithm for approximate solution of the traveling salesman problem based on the construction of convex closed contours on the initial set of points (“cities”) and their subsequent combination into a closed path (the so-called contour algorithm or “onion husk” algorithm). A number of heuristics related to the different stages of the algorithm are considered, and various variants of the algorithm based on these heuristics are analyzed. Sets of randomly generated points of different sizes (from 4 to 90 and from 500 to 10,000) were used to test the algorithms. The numerical results obtained are compared with the results of two well-known combinatorial optimization algorithms, namely the algorithm based on the branch and bound method and the simulated annealing algorithm. .

关键词： Branch and Bound Method Contour Algorithm “Onion Husk” Algorithm Simulated Annealing Method Traveling Salesman Problem

A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 790-804页

作者： Nechirvan Asaad Zebari Chira Nadheef Mohammed Dilovan Asaad Zebari Mazin Abed Mohammed Diyar Qader Zeebaree Haydar Abdulameer Marhoon Karrar Hameed Abdulkareem Seifedine Kadry Wattana Viriyasitavat Jan Nedoma Radek Martinek department of Information Technology Lebanese French UniversityErbilIraq department of Computer Science University of ZakhoZakhoKurdistan RegionIraq department of Computer Science College of ScienceNawroz UniversityDuhokKurdistan RegionIraq department of Artificial Intelligence College of Computer Science and Information TechnologyUniversity of AnbarRamadiIraq department of Telecommunications VSB‐Technical University of OstravaOstravaCzech Republic department of cybernetics and Biomedical Engineering VSB‐Technical University of OstravaOstravaCzech Republic department of Computer Network and Information Security Technical College of Informatics-AkreDuhok Polytechnic UniversityDuhokIraq Information and Communication Technology Research Group Scientific Research CenterAl‐Ayen UniversityThi‐QarIraq College of Computer Sciences and Information Technology University of KerbalaKarbalaIraq College of Agriculture Al‐Muthanna UniversitySamawahIraq department of Applied Data Science Noroff University CollegeKristiansandNorway Faculty of Commerce and Accountancy Chulalongkorn Business SchoolChulalongkorn UniversityBangkokThailand

Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.

关键词： brain tumour deep learning feature fusion model MRI images multi‐classification

Preference levels for clusters of alternatives

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International Journal of Modeling, Simulation, and Scientific Computing 2019年第4期10卷 1-19页

作者： Svetlana O.Smerchinskaya Nina P.Yashina mathematical cybernetics department Moscow Aviation Institute 4 Volokolamskoe ShosseMoscow 125993Russia

In the decision-making tasks for ranking the alternatives and choosing the best ones,procedures on the digraphs are often used.A digraph of the aggregated relation on a set of alternatives for information of experts or criteria is *** the digraph does not contain any cycles,then the Demukron algorithm for partitioning the digraph into levels can be used to order the alternatives by *** algorithm cannot be applied if there are clusters consisting of equivalent *** the paper the algorithm for partitioning an arbitrary digraph of into preference levels is *** contrast to the standard procedure,the digraph of the aggregated relation admits the presence of cycles,and,consequently,of equivalent *** vertexes in any cycle of digraph belong to one level of preference.

关键词： Decision-making clusters of alternatives majority graph preference levels cycle of digraph preference relation ranking of alternatives