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Joint 8th International Conference on Digital Arts, Media and Technology and 6th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2023

作者： Georgieva, Tsvetelina Bencheva, Nina Daskalov, Plamen Yahoui, Hamed Khemmarath, Sitha Norasane, Khonesavanh Chanthanouvong, Lamnguen Leaunglith, Youlananh Keovorabouth, Vinai University of Ruse Angel Kanchev Automation and Mechatronics Department Ruse Bulgaria University of Ruse Angel Kanchev Telecommunications Department Ruse Bulgaria Automation &processing Claude Bernard University Lyon 1 Department of Mechanical Engineering Lyon France Savannakhet University Faculty of Engineering Savannakhet Laos Savannakhet University Faculty of Information Technology Technology Computer Network Dept. Savannakhet Laos Savannakhet University Faculty of Information Technology Savannakhet Laos

ISBN: (纸本)9798350396546

The paper presents part of the work fulfilled under the Asean Factori 4.0 Erasmus+ project focused on the implementation of industrial automation in the education in 6 universities from 3 countries in South-East Asia: Thailand, Laos and Cambodia. The implementation of the project tasks, related to increasing the capacity and more precisely to the training of the professors from the Asian Universities is presented. According to the planned activities, professors from the University of Ruse conduct training for teachers from two universities: Chulalongkorn University, Thailand and Savannakhet University, Laos. The phases of training included in the project activities and how many of them have already been implemented are briefly presented. The program and the tasks of the teachers practical training for building the necessary teaching capacity in Savannakhet University and implementation of sustainable training in the field of industrial automation were presented and analyzed. Conclusions and future training of the staff of Asian universities planned under the project are presented in the paper. © 2023 IEEE.

关键词： Personnel training

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Artificial Neural Network Modeling for Predicting Thermal Conductivity of EG/Water-Based CNC Nanofluid for Engine Cooling Using Different Activation Functions

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Frontiers in Heat and Mass Transfer 2024年第2期22卷 537-556页

作者： Munirul Hasan Mustafizur Rahman Mohammad Saiful Islam Wong Hung Chan Yasser M.Alginahi Muhammad Nomani Kabir Suraya Abu Bakar Devarajan Ramasamy Faculty of Computing Universiti Malaysia Pahang Al-Sultan AbdullahPekanPahang26600Malaysia Faculty of Mechanical and Automotive Engineering Technology Universiti Malaysia Pahang Al-Sultan AbdullahPekanPahang26600Malaysia Department of Management and Information Technology St.Francis College179 Livingston StBrooklynNY11201USA Dell Technologies Data Analyticsand AutomationBukit TengahBukit Mertajam14000Malaysia Department of Computer Science Adrian CollegeAdrianMIUSA Department of Computer Science&Engineering United International University BangladeshUnited CityMadani AvenueBaddaDhaka1212Bangladesh

A vehicle engine cooling system is of utmost importance to ensure that the engine operates in a safe temperature *** most radiators that are used to cool an engine,water serves as a cooling *** performance of a radiator in terms of heat transmission is significantly influenced by the incorporation of nanoparticles into the cooling *** and uniformity of nanoparticle distribution are the two major factors for the practical use of *** shape and size of nanoparticles also have a great impact on the performance of heat *** researchers are investigating the impact of nanoparticles on heat *** study aims to develop an artificial neural network(ANN)model for predicting the thermal conductivity of an ethylene glycol(EG)/waterbased crystalline nanocellulose(CNC)nanofluid for cooling internal combustion *** implementation of an artificial neural network considering different activation functions in the hidden layer is made to find the bestmodel for the cooling of an engine using the *** of the model with different activation functions in artificial neural networks are analyzed for different nanofluid concentrations and *** artificial neural networks,Levenberg–Marquardt is an optimization approach used with activation functions,including Tansig and Logsig functions in the training *** findings of each training,testing,and validation phase are presented to demonstrate the network that provides the highest level of *** best result was obtained with Tansig,which has a correlation of 0.99903 and an error of 3.7959×10^(–8).It has also been noticed that the Logsig function can also be a good model due to its correlation of 0.99890 and an error of 4.9218×10^(–8).Thus ourANNwith Tansig and Logsig functions demonstrates a high correlation between the actual output and the predicted output.

关键词： Artificial neural network activation function thermal conductivity nanocellulose

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Model Reference-Based Control with Guaranteed Predefined Performance for Uncertain Strict-Feedback Systems

arXiv

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arXiv 2025年

作者： Shahna, Mehdi Heydari Humaloja, Jukka-Pekka Mattila, Jouni Automation and Mechanical Engineering Faculty of Engineering and Natural Sciences Tampere University Tampere33101 Finland Electrical & Computer Engineering Technical University of Crete Chania73100 Greece

To address the complexities posed by time- and state-varying uncertainties and the computation of analytic derivatives in strict-feedback form (SFF) systems, this study introduces a novel model reference-based control (MRBC) framework which applies locally to each subsystem (SS), to ensure output tracking performance within the specified transient and steady-state response criteria. This framework includes 1) novel homogeneous adaptive estimators (HAEs) designed to match the uncertain nonlinear SFF system to a reference model, enabling easier analysis and control design at the SS level, and 2) model-based homogeneous adaptive controllers enhanced by logarithmic barrier Lyapunov functions (HAC-BLFs), intended to control the reference model provided by HAEs in each SS, while ensuring the prescribed tracking responses under control amplitude saturation. The inherently robust MRBC achieves uniformly exponential stability using a generic stability connector term, which addresses dynamic interactions between the adjacent SSs. The parameter sensitivities of HAEs and HAC-BLFs in the MRBC framework are analyzed, focusing on the system’s robustness and responsiveness. The proposed MRBC framework is experimentally validated through several scenarios involving an electromechanical linear actuator system with an uncertain SFF, subjected loading disturbance forces challenging 0 − 95% of its capacity. © 2025, CC BY.

关键词： Lyapunov functions

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Experimental implementation of chaos-based video encryption using visible light communication technology

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Applied Optics 2025年第9期64卷 C101-C118页

作者： Fotso, Sorel Bagio Nono Mbé, Jimmi Hervé Talla Takotue, Jean Ernest Takougang, Armand Cyrille Nzeukou Ndjiongue, Alain Richard Research Unit of Condensed Matter Electronics and Signal Processing Department of Physics University of Dschang P.O. Box 67 Dschang Cameroon Research Unit of Automation and Applied Computer Department of Electrical Engineering IUT-FV Bandjoun University of Dschang P.O. Box: 134 Bandjoun Cameroon Laboratory of Industrial Systems and Environmental Engineering Fotso Victor University Institute of Technology University of Dschang P.O. Box 134 Bandjoun Cameroon School of Electrical and Information Engineering Faculty of Engineering and the Built Environment University of the Witwatersrand Johannesburg South Africa

Simulations are commonly used to develop and evaluate video encryption algorithms. Although these approaches are useful for demonstrating theoretical feasibility and algorithm performance, they neglect practical challenges and real-world communication conditions. This oversight creates a critical gap in evaluating their effectiveness and security in practical applications. In this paper, we describe a novel, to the best of our knowledge, video encryption scheme that employs a modified chaotic Colpitts oscillator, a customized Josephus problem, and ribonucleic acid (RNA) operations, and is implemented in visible light communication (VLC) environment. The modified Colpitts oscillator reveals significant phenomena, the most prominent for this study being chaos. The encryption scheme involves block scrambling and pixel-level permutation employing a customized Josephus problem and a column and row shifting technique. Diffusion is then achieved through a custom Josephus problem-oriented RNA operation combined with basic RNA operations and cipher block chaining (CBC). Based on the results, we found that the customized Josephus problem-based permutation performed more efficiently than traditional methods, whereas the column and row shifting permutation was more robust. By eliminating key sequence coding and item-to-item operations, the proposed RNA operation reduces computational overhead. This video encryption scheme is proven to be effective and resilient at securing video data transmitted over the VLC channel, and it constitutes a significant advancement in areas such as surveillance, medical imaging, and military communication requiring robust secured video data. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Medical imaging

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The effect of graph complexity in an energy-based FDI approach

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IFAC-PapersOnLine 2024年第4期58卷 622-627页

作者： Jan H. Smith George van Schoor Kenneth R. Uren Thomas Schäfer School of Electrical Electronic and Computer Engineering Faculty of Engineering North-West University South Africa Unit for Energy and Technology Systems Faculty of Engineering North-West University Institute for Process Technology Process Automation and Measurement Technology Zittau/Görlitz Germanuy

This paper introduces a structured approach for creating node signature graphs in energy graph-based visualization for industrial systems, focusing on anomaly detection. It examines the relationship between graph cardinality and the singular value decomposition analysis applied in the energy graph-based visualization method. The results reveal a trade-of between graph complexity and singular value resolution. The study highlights how graph structure affects energy flow monitoring and anomaly detection. It notably enables fault detection in components not represented as nodes, enhancing the methodology in energy-based system monitoring.

关键词： Fault detection isolation Graph reduction Graph matching Hybrid FDI Industrial process Thermal flow process Graph-based anomaly detection

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Stereo Matching for Face Images Based on Feature Points

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Chinese Journal of Electronics 2023年第2期19卷 241-244页

作者： Yinghui Wang Bing Xiao Qiongfang Wang Faculty of Computer Science and Engineering Xi'an University of Technology Xi'an China Department of Computer Science Shaanxi Normal University Xi'an China Faculty of of Automation and Information Engineering Xi'an University of Technology Xi'an China

Stereo matching is the key of reconstructing 3D face model from 2D face images and Barnard's algorithm is one of the classical stereo matching algorithms based on feature points. In order to enhance the stability of extracting feature points, reduce computational complexity and minimize the effect of subjective factors, in this paper, Barnard's algorithm of edge point matching was improved from two aspects: edge point sets of left and right face images were extracted with dyadic wavelet transform, rather than comparing gray values of adjacent pixels; on the other hand, edge points in binocular stereo face images were matched under epipolar constraint in parallel binocular stereo matching. Experimental results demonstrate that stability and efficiency of stereo matching algorithm are both improved.

关键词： Wavelet transforms Technological innovation Solid modeling Three-dimensional displays Image edge detection Education Medical services

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A low-cost buoyant force measurement device for monitoring sugar concentration in water solution

A low-cost buoyant force measurement device for monitoring s...

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Electrical, computer, Communications and Mechatronics engineering (ICECCME), International Conference on

作者： Luka Kaučić Ivana Kovačević Ivan Aleksi Tomislav Keser Josip Zidar Snježana Rimac-Drlje Department of Computer Engineering and Automation Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia Department of Communications Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

Monitoring sugar concentration during fermentation is crucial for producing high-quality alcoholic beverages. Traditional methods for measuring sugar concentration can be costly and time-consuming, especially for small-scale producers. In this study, we developed a low-cost buoyant force measurement device for monitoring sugar concentration in water solutions. The device consists of a buoyant object fully submerged in the solution and connected to a load-cell sensor. As the sugar concentration in the liquid increases, the buoyant force on the object increases, and the load-cell measures this force. Proposed device is calibrated using solutions of known sugar concentrations and its high accuracy and precision is presented. The device is tested in controlled environment to ensure accurate tracking changes in sugar concentration over time. Proposed device can be a valuable tool for small-scale producers looking to optimize their fermentation processes while minimizing costs.

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Hybrid Machine Learning and Deep Learning CNN Models for Apple Trees Leaves Pathologies Classification 12

Hybrid Machine Learning and Deep Learning CNN Models for App...

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12th International Scientific Conference on computer Science, COMSCI 2024

作者： Matov, Petar Iavorov Lazarova, Milena Kirilova Filipova-Petrakieva, Simona Kirilova Taralova, Ina Technical University of Sofia Faculty of Computer Systems and Technologies Department of Computer Systems 8 Kliment Ohridski blvd. Sofia1000 Bulgaria Technical University of Sofia Faculty of Automation Department of Theoretical Electrical Engineering 8 Kliment Ohridski blvd. Sofia1000 Bulgaria Ecole Centrale de Nantes LS2N UMR CNRS 6004 Nantes France

ISBN: (纸本)9798350392166

Agricultural development in recent years has been based entirely on smart management. Leaves diseases on apple trees is a very common problem. The present work is a continuation of previous research of the author's team to solve it. The recently proposed apple leaves pathology prediction model based on deep learning convolutional neural network (CNN) is extended to hybrid models. The latter are a combination of a deep learning CNN and classical machine learning techniques. To increase the classification accuracy two hybrid models are proposed: CNN+SVM (Support Vector Machines) and CNN+RF (Random Forest). The CNN is based on modified DenseNet architecture and SVM and RF models are integrated as CNN output is fed to SVM and RF models respectively to ensure higher classification accuracy. The suggested hybrid models are evaluated on a data set that comprises 1800 images of pathologies in apple trees' leaves. The experimental results prove the higher accuracies of the hybrid deep + machine learning models compared to the deep learning CNN model. © 2024 IEEE.

关键词： Adversarial machine learning

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Research on network performance of D2D communication based on interference management 6

Research on network performance of D2D communication based o...

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6th International Conference on Mechatronics and Intelligent Robotics, ICMIR 2022

作者： Song, Hao Peng, Yi Wang, Rui Zhao, Xiong Faculty of Computer Information Kunming Metallurgy College Kunming650033 China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Faculty of Information and Intelligent Engineering Yunnan University of Business Management Kunming650106 China

ISBN: (纸本)9781510656635

D2D(Device-to-device) improves communication quality by reusing cellular users' spectrum resources, becoming one of the key technologies for super-large data processing and massive devices accessing in the 5G. Due to load imbalance and co-frequency interference caused by distance change or increase in the number of users, this paper introduces interference management strategy in the 5G scenario where D2D communication and cellular communication coexist. By establishing up and down link resource model of D2D user reuse cellular communication to measure user throughput, and then control transmitting power and the distance between user and base station to reduce the influence of interference on the system. Simulation results show that compared with other traditional algorithms, the network performance of D2D communication based on interference management is significantly improved. © 2022 SPIE.

关键词： Data handling

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Automatic Detection of Tuberculosis with the Help of Image Processing and Machine Learning Classifier 7

Automatic Detection of Tuberculosis with the Help of Image P...

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7th International Conference on Computing in engineering and Technology, ICCET 2022

作者： Sharma, Anshu Malhotra, Rahul Sharma, Anurag Department of Computer Science & Engineering CT University Ludhiana India Department of Electronics & Communication Engineering CT Group of Institutions Jalandhar India Faculty of Engineering Design and Automation GNA University Phagwara India

ISBN: (纸本)9781839537042

Tuberculosis is the substantial irresistible disease present in human that principally influences lungs, this tuberculosis is one the main ten noticeable reasons for death for human. It has very typical symptoms so without testing it isn't sure that patient has infected with TB. In this paper, an automated framework dependent on image handling is proposed to help specialists and radiologists in deciphering Chest X-beams (CXR) for early identification of lung Tuberculosis (TB). CXR can show the abnormalities in lungs as well as TB which helps to precisely and rapidly distinguish TB on the grounds that early treatment will provide to patients and disease will stop spreading in body. The proposed system comprised of preprocessing, segmentation, features extraction, and classification. In this paper proposed a strategy of image segmentation and AI to conclusion and characterize the tuberculosis (TB). These tool and procedures are utilized to earlier uncover of tuberculosis (TB) illness. Additionally arrange the sort of TB. The framework recognizes the tuberculosis with more prominent precision. © 2022 The Institution of engineering and Technology.

关键词： Diseases

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