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23rd RoEduNet Conference: Networking in Education and Research, RoEduNet 2024

作者： Vizitiu, Cristian Turcin, Ioan Dominey, Kevin Alexander Softic, Selver Dinculescu, Adrian-Catalin Bocanet, Vlad I. Dugan, Cosmin Moraru, Sorin-Aurel The Space Applications and Technologies Laboratory Institute of Space Science-Subsidiary of INFLPR Magurele Romania Transilvania University of Brasov Faculty of Electrical Engineering and Computer Science Department of Automatics and Information Technology Brasov Romania CAMPUS 02 University of Applied Sciences Department Automation Technology Graz Austria CAMPUS 02 University of Applied Sciences Department IT and Business Informatics Graz Austria Robotics and Production Management Technical University of Cluj-Napoca Cluj-Napoca Faculty of Industrial Engineering Department of Manufacturing Engineering Romania Bucharest University Emergency Hospital Bucharest Romania

ISBN: (纸本)9798331540388

Perspiration is a physiological response in high-stress situations, that also plays a key role in thermoregulation and stress management. Understanding perspiration patterns is used for assessing physiological responses and optimizing human performance especially in professions such as emergency medical services and military operations, as well as for individuals exposed to Isolated Confined Extreme (ICE) environments such as astronauts and space analog mission participants. This paper defines a Digital Twin (DT) model, that will be integrated with an existing physical sweat simulator module (SSM) prototype. Its goal is to simulate human perspiration under high-stress conditions by integrating environmental, physiological, and psychological data. The system architecture is developed via Model-Based Systems engineering (MBSE). The scope of the designed virtual model is to accurately reflect physical perspiration, detect abnormal perspiration patterns, and generate recommendations regarding stress countermeasures. The goal is to influence the behavioral and/or physiological state of the physical twin, thereby creating a dynamic, responsive system. Machine learning (ML) models will be used to identify patterns in the environmental, physiological, and psychological inputs and predict sweat volume, chemical composition, and regional sweat distributions across the body. ML will also be used to detect abnormal perspiration patterns based on input data. The application of generative AI for creating test data from simulation templates is anticipated to facilitate the comparative analysis of two to three distinct profiles, such as astronauts, soldiers, and paramedics or firefighters, using simulated values. The SSM, with its integrated sensors, will serve the dual purpose of validating the model and providing realistic physical simulation. © 2024 IEEE.

关键词： Physiological models

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A Generative Adversarial Network for infrared and visible image fusion using adaptive dense generator and Markovian discriminator

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Optik 2023年第1期288卷

作者： Liu, Gang Liu, Yang Tang, Lili Bavirisetti, Durga Prasad Wang, Xiao School of Automation Engineering Shanghai University of Electric Power Shanghai200090 China Department of Computer Science Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Trondheim7034 Norway

Existing infrared and visible image fusion algorithms have problems with reduced saliency for infrared targets and a lack of background texture information. To address this problem, a generative adversarial network for image fusion based on an attention mechanism is proposed in this study. This network consists of a generator, a discriminator, and an adaptive decision block. The generator employs a DenseNet-based cascade approach, which substantially lowers the information loss during the convolution process. A Markovian discriminator, unlike global discriminators, is embedded in the network. It divides images into patches, causing the network to focus on local regions and distinguishing the generated images as a whole. In addition, an adaptive decision block is included in the model to build an intensity dynamic mapping mechanism. To guide the computation for a certain content loss, a weighted map is created based on the intensity information of infrared images. Unlike the other loss functions, the weighted parameters of each component of the loss function are adaptively evaluated. To demonstrate the effectiveness of the proposed method, qualitative and quantitative experiments on TNO and RoadScene datasets are carried out. The experimental findings suggest that the proposed technique retains more significant characteristics in source images and produces fused images with rich texture details. Furthermore, the system noise resistance of each approach is examined. Under the condition of different noise, the fusion results of our approach still exhibit less distortion and a higher peak signal-to-noise ratio when compared to other state-of-the-art fusion methods. © 2023 Elsevier GmbH

关键词： Generative adversarial networks

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Research and Application of Machine Learning in Automatic Program Generation

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Chinese Journal of Electronics 2020年第6期29卷 1001-1015页

作者： ZHANG Xiaojiang JIANG Ying Yunnan Key Laboratory of Computer Technology Application Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming University of Science and Technology

With the development of artificial intelligence,machine learning has been applied in more and more *** order to improve the quality and efficiency of software,automatic program generation is becoming a research *** recent years,machine learning has also been gradually applied in automatic program *** trees,language models,and cyclic neural networks have been applied in code generation,code completion and code knowledge *** efficiency of software development has been improved to a certain extent using machine *** at the automatic program generation,this paper analyzes and summarizes the models of machine learning,the modifications involved in the models and the application *** research direction is discussed from the aspects of programmer behavior and automatic program generation of machine learning.

关键词： automatic programming data mining decision trees learning (artificial intelligence) neural nets software engineering programmer behavior cyclic neural networks language models decision trees code knowledge mining code completion code generation automatic program generation machine learning Machine learning Code generation Code completion Code knowledge mining Application effect

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Analysis of the EMF Parameters of the Stator Winding in the Run-Down Mode for Diagnosing Faults in an Induction Motor

Analysis of the EMF Parameters of the Stator Winding in the ...

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2023 IEEE International Conference on Smart Information Systems and Technologies, SIST 2023

作者： Yussupova, Assel Potapenko, Alexandra Neftissov, Alexandr Urazalimova, Dariya Serbin, Oleh Toraighyrov University Department of Electrical Engineering and Automation Pavlodar Kazakhstan Toraighyrov University Faculty of Computer Science Pavlodar Kazakhstan Astana It University Research and Innovation Center 'Industry 4.0' Astana Kazakhstan Chief Executive Officer Ceo & Executive Director Yaroslav Mudryi Ukraine

ISBN: (纸本)9798350335040

In industry, as well as in power plants and substations of developed countries, induction motor (IM) are the most common electrical machine. They consume a large amount of the generated electricity. At the same time, they account for a sufficient number of failures of all electrical equipment. In this case, the proportion of failures due to a break in a squirrel-cage rotor, according to various authors, can reach about 5-10%. However, experimental studies at a number of plants and power plants in the Pavlodar region have shown that the damage factor of the squirrel-cage IM rotor is much higher. As a rule, the breakage of the squirrel-cage rotor rods does not lead to an immediate failure of the IM, but is accompanied by a deterioration in its electromechanical characteristics. Basically, the harm from the operation of such IM is expressed in increased energy consumption. Moreover, a year of its operation leads to an excessive consumption of electricity, comparable to the cost of the IM itself. The article shows that today there are a large number of proposals for a method for diagnosing damage to the short-circuited rotor winding of an induction motor (IM) during operation. However, their sensitivity is limited by the need for detuning from fluctuations in the parameters of the electrical network, vibrations of the IM and the uneven moment of resistance of its load. Most of these effects can be eliminated if IM diagnostics are carried out based on the analysis of the EMF parameters in the stator winding in the rundown mode. The authors have analyzed the parameters of the EMF of the stator winding at all stages of the rundown mode and substantiated the use of the presented method for diagnosing faults in an induction motor. © 2023 IEEE.

关键词： Stators

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Self-aware Hierarchical Neuromorphic Architecture

Self-aware Hierarchical Neuromorphic Architecture

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International Joint Conference on Neural Networks (IJCNN)

作者： Zilu Wang Xin Yao School of Mechanical Engineering and Automation Harbin Institute of Technology (Shenzhen) Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Faculty of Business Lingnan University Hongkong China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

We introduce a neuromorphic architecture devised by mapping biologically inspired concepts onto a computational framework for self-awareness, offering a new way to embed inherent autonomy directly within hardware. Leveraging this architecture as a blueprint, we have designed a memristor-based circuit capable of autonomous decision-making, employing a bio-inspired hierarchical design approach. The core of this approach involves translating the functions of biological neurons and synapses into memristor-based circuits, which act as efficient computational units at the bottom layer of the circuit. It ensures that the developed circuit strikes a balance between functional flexibility and computational efficiency. Experimental results show that our implemented circuit provides efficient computational capabilities for effective decision-making, thanks to its memristor-based bio-inspired hierarchical implementation. Furthermore, its neuromorphic architecture inspired by self-awareness facilitates the easy simulation of reliable autonomous behaviors. Through the analysis of hardware overhead and power consumption, our circuit proves to be hardware-friendly. Our work represents progress towards developing memristor-based neuromorphic circuits characterized by high computational performance and autonomous functionality.

关键词： Power demand Neuromorphics Microprocessors Decision making Neurons computer architecture Hardware

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Essential proteins identification based on weak consensus model and neighborhood aggregation centrality

Essential proteins identification based on weak consensus mo...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Hu, Zhipeng Li, Gaoshi Wu, Jingli Luo, Xinlong Liu, Jiafei Peng, Wei Zhu, Xiaoshu Guangxi Normal University Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guilin541004 China Guangxi Normal University Guangxi Key Lab of Multi-Source Information Mining & Security Guilin541004 China Guangxi Normal University College of Computer Science and Engineering Guilin541004 China Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China Guilin University of Electronic Science and Technology School of Computer and Information Security & School of Software Engineering Guilin China

ISBN: (纸本)9798350337488

Essential proteins play an essential role in cell survival and replication. Currently, more and more computational methods are developed to identify essential proteins, which overcome the time-consuming, costly and inefficient shortcomings with biological experimental methods. In order to improve the recognition rate, some new methods by fusing multiple features are developed, but they seldom consider the connection among features. After analyzing a large number of methods based on multi-feature fusion, a weak consensus model to fuse features is proposed in this paper. Then, this paper uses the weak consensus model to fuse protein-protein interaction network, gene expression data, and orthologous data, thus proposing a new method, WOL. Then experiments are performed on one *** dataset. The results show that compared with WDC, PeC, ION, JDC, NCCO and E_POC, WOL has a higher recognition rate. © 2023 IEEE.

关键词： Essential proteins orthologous data protein-protein interaction weak consensus model

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Method to design a neural network with minimal number of neurons for approximation problems

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IFAC-PapersOnLine 2022年第7期55卷 568-573页

作者： H. Ruppert A. Krug Y.A.W. Shardt Faculty of Electrical Engineering Heilbronn University of Applied Sciences Heilbronn Germany Faculty of Computer Science and Automation TU Ilmenau Ilmenau Germany

The widespread use of neural networks to model complex processes requires that a parsimonious model of the process be obtained. One of the main variables in neural networks is the number of neurons in the hidden layer. Selecting an inappropriate number of neurons can lead to over- or underfitting. Therefore, a method is required which determines the appropriate number of neurons in order to approximate a defined system response or time function. This paper presents a proposition to determine the appropriate number of neurons in a feedforward neural network, based on the number of inflection points included in the system response or the time function. The results show that the proposed method has marginal approximation errors (no underfitting) and overfitting can never occur because the minimal number of neurons for the approximation problem is used. To verify the effectiveness of this method, simulations were carried out on a second-order system with and without noise, the Lotka-Volterra equations, and the Runge function.

关键词： inflection point number of neurons feed forward neural network approximation hyperbolic tangents

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A Data Transmission Approach Based on Ant Colony Optimization and Threshold Proxy Re-encryption in WSNs

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Journal of Artificial Intelligence and Technology 2022年第1期2卷 23-31页

作者： Jing Liu Zenghui Liu Chenyu Sun Junxi Zhuang College of Computer Science Faculty of Information TechnologyBeijing University of TechnologyBeijing 100124China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of EducationBeijing 100124China College of Automation Engineering Beijing PolytechnicBeijing 100176China

Wireless sensor networks(WSNs)have become increasingly popular due to the rapid growth of the Internet of *** open wireless transmission media are easy to attack,security is one of the primary design concerns for *** solutions consider routing and data encryption as two isolated issues,providing incomplete ***,in this paper,we divide the WSN communication process into a data path selection phase and a data encryption *** propose an improved transmission method based on ant colony optimization(ACO)and threshold proxy re-encryption for WSNs,and we named it as *** method resists internal and external attacks and ensures safe and efficient data *** the data path selection stage,the ACO algorithm is used for network *** improvement of the pheromone concentration is *** order to resist attacks from external attackers,proxy re-encryption is extended to WSN in the data encryption *** threshold secret sharing algorithm is introduced to generate a set of re-encryption key fragments composed of random numbers at the source *** confirm the performance of our model via simulation studies.

关键词： wireless sensors network ant colony optimization pheromone proxy re-encryption threshold

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Temperature Characteristics of Oil-Immersed Transformer Based on Fiber Bragg Grating Temperature Sensing Technology

SSRN

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SSRN 2024年

作者： Luo, Chuan Li, Zizhou Yang, Bo Zhao, Zhengang Li, Chuan Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Yunnan Key Laboratory of Computer Technology Applications Kunming650500 China

Oil-immersed transformer hot-spot temperature and location directly affects the load capacity and service life of the transformer. The traditional hot-spot temperature measurement is indirect and large deviation. Through the fiber Bragg grating technology, the temperature of the internal components of the transformer under variable operating conditions can be measured directly, reflecting the temperature characteristics of the transformer hot-spot location. Based on this, two full dielectric stress-insensitive FBG temperature sensors with different package forms are designed, with sensitivities of 10.22∼10.65 pm/◦C, linearity of 0.99%∼1.65%, and repeatability of 1.07%∼1.45%. Multiple FBG temperature sensors are installed on the iron-core, windings, and oil channel to build a monitoring system. In the temperature rise test, the temperature inside the transformer variation with space and time under variable operating conditions are obtained. The hot-spot winding temperature rise is 62.9◦C, and the top-oil temperature rise is 39.6◦C. The hot-spot location changes with the operating conditions. © 2024, The Authors. All rights reserved.

关键词： Fiber Bragg gratings

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Process of Magnetization in Strong Magnetic Fields of Amorphous Fe-Based Alloys

Process of Magnetization in Strong Magnetic Fields of Amorph...

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作者： Stambula, S. Nabialeka, M. Paszta, P. Nabialek, M.M. Pietrusiewicz, P. Somidin, F. Jez, B. Department of Physics Faculty of Production Engineering and Materials Technology Czestochowa University of Technology al. Armii Krajowej 19 Częstochowa42-200 Poland Department of Technology and Automation Faculty of Mechanical Engineering and Computer Science Czestochowa University of Technology al. Armii Krajowej 19c Częstochowa42-200 Poland Center of Excellence Geopolymer and Green Technology Universiti Malaysia Perlis Taman Muhibbah Perlis Arau02600 Malaysia

Amorphous alloys exhibit a similar course of the magnetization process as crystalline materials. This means that there is also a domain structure in their volume, the changes of which describe the magnetization process well. These defects are a kind of inhomogeneity of the material structure and become an area where there are disturbances in the alignment of spins in relation to the intensity of the external magnetic field. These disturbances are the reason for the change in the course of the M-H curve. Using the theory developed by H. Kronmüller, it is possible to study the process of magnetization of amorphous ferromagnetic alloys in strong magnetic fields. The work analyzes the change in the process of magnetization of amorphous iron alloys along with the change in their chemical composition. © 2025 Polska Akademia Nauk. All rights reserved.

关键词： Amorphous alloys

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