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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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Magnetic Levitation Remote control Laboratory Based on Matlab and Websockets ⁎

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IFAC-PapersOnLine 2024年第9期58卷 235-240页

作者： Pavol Bistak Mikulas Huba Peter Drahos Igor Belai Damir Vrancic Institute of Automotive Mechatronics Faculty of Electrical Engineering and Information Technology Slovak University of Technology in Bratislava Slovakia Department of Systems and Control J. Stefan Institute Ljubljana Slovenia Faculty of Industrial Engineering Novo mesto Slovenia

This work proposes a novel architecture for constructing remote laboratories, employing modular building blocks: Matlab/Simulink software for control system design and simulation, WebSocket communication technology for continuous data exchange, and a front-end application developed using the Angular framework. To facilitate WebSocket communication on the Matlab server side, the MatlabWebSocket library is implemented. Beyond the Angular framework, interactivity within the remote laboratory is further enhanced through 3D visualization of the controlled system using a *** based library. These combined technologies are applied in the development of a remote laboratory for a fast, unstable, and nonlinear magnetic levitation system. The laboratory allows users to remotely set desired values and control parameters for experiments, while also providing continuous data visualization in various forms, including numerical readouts, graphs, and 3D animations. This approach demonstrates the effectiveness of the proposed architecture for building remote laboratories for complex systems.

关键词： magnetic levitation system remote laboratory Matlab/Simulink WebSocket 3D visualization unstable nonlinear dynamic system

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Solar Radiation Forecasting Based on Random Forest and XGBoost 7

Solar Radiation Forecasting Based on Random Forest and XGBoo...

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7th International Conference on Green technology and Sustainable Development, GTSD 2024

作者： Nguyen, Nhat-Truong Tuong Dao, Thai Cat Tuyen Nguyen, Le Nhut Nguyen, Thanh-Dat Le, Minh-Vuong French, Isobel Timothea Doan, Van-Tuong Kim Vo, Van Hoang Tran, Nghi C. Pham, Duc-Hung Nguyen, Van-Trung Ho Chi Minh City University of Technology and Education Department of Renewable Energy Viet Nam Ho Chi Minh City Industry and Trade College Department of Control Engineering and Automation Viet Nam LILAMA2 International Technology College Dongnai Viet Nam Institute of Cognitive Neuroscience National Central University Taiwan College of Information and Communication Technology Can Tho University Viet Nam Hung Yen University of Technology and Education Faculty Electrical and Electronic Engineering Viet Nam

ISBN: (纸本)9798350386073

Renewable energy offers a viable alternative to depleting fossil fuels, contributing to global warming and greenhouse gas emissions. Thus, solar energy is widely favored as a renewable energy source. However, the output power of solar systems is unstable and is highly dependent on environmental circumstances such as radiation, humidity, temperature, and wind. Therefore, accurately estimating solar radiation is crucial for successful integration of the solar system into existing electrical grid systems. In this study, two different machine learning models, XGBoost and Random Forest, were used to predict solar radiation data in Ho Chi Minh City, Vietnam. The results show that RMSE and MAE of the Random Forest model (61.09 and 24.37) is smaller compared to XGBoost (61.31 and 25.08). Regression value of the two models is close to 0.955. In summary, this paper demonstrates that these two machine learning algorithms effectively forecast solar radiation, with the Random Forest model showing better performance. © 2024 IEEE.

关键词： Solar irradiance

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Contrast enhancement of optical videoscope images

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Iran Journal of Computer Science 2023年第3期6卷 261-275页

作者： Ammar, Reda Aboshosha, Sahar El-Hag, Noha A. Saifeldeen, Reda El-Shafai, Walid El-Banby, Ghada M. Ashiba, Huda I. Khalaf, Ashraf A. M. Elazm, Atef Abou El-Rabaie, El-Sayed M. Abd El-Samie, Fathi E. El-Safrawey, Amir Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Department of Electronics and Electrical Comm. Faculty of Engineering Minia University Minya Egypt Department of Management of Information Systems Higher Institute of Commercial Science El-Mahala El-Kobra Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Department of Electronics and Electrical Communications Engineering Bilbis Higher Institute of Engineering Sharqia Bilbis Egypt Security Engineering Laboratory Computer Science Department Prince Sultan University Riyadh 11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia

The videoscope (VS) images have poor quality and low contrast. Hence, in this paper, three proposed frameworks to improve the quality of VS images are presented. The first framework depends on contrast-limited adaptive histogram equalization (CLAHE) with adaptive gamma correction. The second framework depends on homomorphic processing. The third framework is based on the homomorphic enhancement with histogram equalization. The first one is superior from the entropy perspective, while the third one is superior from the contrast perspective. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： Fuzzy enhancement Histogram equalization Homomorphic enhancement Spectral entropy

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Kalman Filter and Moving Average Method based Human-Robot Interaction Torque Estimation for a Lower Limb Rehabilitation Robot 8

Kalman Filter and Moving Average Method based Human-Robot In...

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8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Liang, Xu Yan, Yuchen Su, Tingting Guo, Zhao Liu, Shengda Zhang, Haojian He, Guangping North China University of Technology Department of Mechanical and Electrical Engineering Beijing100144 China Beijing University of Technology Faculty of Information Technology Beijing100124 China School of Power and Mechanical Engineering Wuhan University Wuhan430072 China Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9798350300178

Human-robot interaction torque (HT) plays an important role in the application of lower limb rehabilitation robots. To accurately and quickly obtain the HT applied by the subject during the rehabilitation training, an estimation approach of HT is proposed on the basis of the strong tracking Kalman filter (STKF) and modified moving average method in this paper. First, the dynamics and its corresponding state space model of the human-robot hybrid system are established. Second, the STKF is designed to track the sudden change of the HT rapidly. Based on the hull moving average (HMA) and the weighted moving average (WMA), the hull-weighted moving average with a variable time window (H-WMA) is introduced to improve the estimation accuracy of the HT. Finally, experiments were conducted using a lower limb rehabilitation robot. The experimental results demonstrate its accurate estimation capabilities for assessing the HT. In addition, the proposed method has an effective tracking ability even when the HT changes suddenly. © 2023 IEEE.

关键词： Kalman filters

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A simplified pursuit-evasion game with reinforcement learning

Periodica polytechnica Electrical engineering and computer s...

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Periodica polytechnica electrical engineering and computer science 2021年第2期65卷 160-166页

作者： Paczolay, Gabor Harmati, Istvan Department of Control Engineering and Information Technology Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics 2 Magyar Tudósok krt BudapestH-1117 Hungary

In this paper we visit the problem of pursuit and evasion and specifically, the collision avoidance during the problem. Two distinct tasks are visited: The first is a scenario when the agents can communicate with each other online, meanwhile in the second scenario they have to only rely on the state information and the knowledge about other agents' actions. We propose a method combining the already existing Minimax-Q and Nash-Q algorithms to provide a solution that can better take the enemy as well as friendly agents' actions into consideration. This combination is a simple weighting of the two algorithms with the Minimax-Q algorithm being based on a linear programming problem. © 2021 Budapest University of technology and Economics. All rights reserved.

关键词： Reinforcement learning

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Successive statistical iterative reconstruction, 3D-filtering and region growing methods for high-quality 3D visualization of cone-beam CT image 2022

Successive statistical iterative reconstruction, 3D-filterin...

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7th International Conference on Multimedia and Image Processing, ICMIP 2022

作者： Dong, Jian Zhang, Siyuan Yang, Xiaoxia Zhang, Jingyu Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin University of Technology Tianjin300222 China Department of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin300222 China

ISBN: (纸本)9781450387408

Our study is to improve the accuracy of bone morphology depicted in dental cone-beam computed tomography (CBCT) images. First, successive statistical iterative reconstruction was used to reduce unavoidable streak artefacts. Then 3-dimentional filtering (Gaussian-Laplacian filter) and region growing methods were examined to present clear bone morphology. CBCT examinations were performed with the following principal exposure parameters: I-mode, FOV 10cm in diameter, 120 kV, 15 mA, 0.2 mm slice thickness, and exposure time of 10s. Ordered subset-expectation maximization (OS-EM) algorithm was applied for unavoidable streak artefact reduction. 3D Laplacian sharpening to images preprocessed by Gaussian smoothing was sequentially tested. Region growing method with dilation and erosion was used to segment maxillofacial tissue. Streak artefact induced by metallic prosthetic appliances was reduced by applying successive iterative OS-EM algorithm. Multi-planar reconstruction (MPR) images at left side molar plane and mid-sagittal plane were presented to validate effect of OS-EM algorithm and 3D Gaussian-Laplacian filter. Maxillofacial tissue was segmented and presented to show the effect of region growing method. Streak artefact reducing method, 3D filtering method for image smoothing and sharpening, and region growing method with dilation and erosion were effective to improve accuracy of bone morphology in dental CBCT images. © 2022 ACM.

关键词： Image reconstruction

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Robotic fish:Opening a new era of underwater detection

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The Innovation 2025年第3期6卷 13-14页

作者： Dongfang Li Suet To Zhicheng Xu Guanghong Liu Rob Law Edmond Q.Wu Limin Zhu State Key Laboratory of Ultraprecision Machining Technology Department of Industrial and Systems EngineeringThe Hong Kong Polytechnic UniversityHong Kong 999077China School of Electrical Engineering and Automation Fuzhou UniversityFujian 350108China China Electronics Technology Group Corporation Information Science Research InstituteBeijing 100086China School of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China Asia-Pacific Academy of Economics and Management Faculty of Business AdministrationUniversity of MacaoMacao 999078China Department of Computer Science and Engineering Shanghai JiaoTong UniversityShanghai 200240China

Escalating competition within aquatic environments,particularly in oceans,underscores the imperative for innovative solutions in underwater *** solutions necessitate the development of intelligent underwater equipment,with robotic fish emerging as a promising contender.1 Leveraging advancements in robotics and artificial intelligence,robotic fish offer a suite of advantages that position them as transformative assets in underwater exploration and *** fish are autonomous robots designed based on biomimetics principles that mimic the appearance of fish and can autonomously swim and perform specific tasks in water.

关键词： robotic fish intelligent underwater equipmentwith autonomous robots underwater detection oceans artificial intelligencerobotic fish aquatic environments innovative solutions

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A Novel Transfer Learning Approach for Detecting Partial Shading in Photovoltaic Systems 11

A Novel Transfer Learning Approach for Detecting Partial Sha...

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11th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2024

作者： Teta, Ali Medkour, Maissa Chennana, Ahmed Chouchane, Ammar Himeur, Yassine Atalla, Shadi Mansoor, Wathiq Belabbaci, El Ouanas University of Djelfa Applied Automation and Industrial Diagnostics Laboratory Department of Electrical Engineering Djelfa Algeria LI3C Laboratory University of Biskra Biskra Algeria University Center of Barika Batna05001 Algeria University of Dubai College of Engineering and Information Technology Dubai United Arab Emirates Faculty of Technology Bejaia06000 Algeria

ISBN: (纸本)9798350367300

In Photovoltaic Systems, detecting partial shading is critical for optimizing energy output and ensuring system reliability. This paper presents a novel method for partial shading detection in photovoltaic systems, leveraging transfer learning to improve accuracy and efficiency. By utilizing a pre-trained InceptionV3 model, discriminative features are extracted from time series signals. To align with the architectural requirements of InceptionV3, these time series signals are transformed into 2D pixel-mapped images. The proposed model is rigorously validated using both balanced and unbalanced scenarios on the Grid-connected PV System Faults (GPVS-Faults) dataset, achieving remarkable accuracies of 96.73% and 94.59% in the respective scenarios. This approach represents a significant advancement in accurately identifying partial shading, thus enhancing the performance and reliability of solar energy systems. © 2024 IEEE.

关键词： Solar power generation

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Efficient Deep Learning Modalities for Object Detection from Infrared Images

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Computers, Materials & Continua 2022年第8期72卷 2545-2563页

作者： Naglaa F.Soliman E.A.Alabdulkreem Abeer D.Algarni Ghada M.El Banby Fathi E.Abd El-Samie Ahmed Sedik Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Electronics and Communications Faculty of EngineeringZagazig UniversityZagazig44519Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of the Robotics and Intelligent Machines Faculty of Artificial IntelligenceKafrelSheikh UniversityKafr el-sheikhEgypt

For military warfare purposes,it is necessary to identify the type of a certain weapon through video stream tracking based on infrared(IR)video *** vision is a visual search trend that is used to identify objects in images or video *** military applications,drones take a main role in surveillance tasks,but they cannot be confident for longtime ***,there is a need for such a system,which provides a continuous surveillance task to support the drone *** a system can be called a Hybrid Surveillance System(HSS).This system is based on a distributed network of wireless sensors for continuous *** addition,it includes one or more drones to make short-time missions,if the sensors detect a suspicious *** paper presents a digital solution to identify certain types of concealed weapons in surveillance applications based on Convolutional Neural Networks(CNNs)and Convolutional Long Short-Term Memory(ConvLSTM).Based on initial results,the importance of video frame enhancement is obvious to improve the visibility of objects in video *** accuracy of the proposed methods reach 99%,which reflects the effectiveness of the presented *** addition,the experimental results prove that the proposed methods provide superior performance compared to traditional ones.

关键词： Deep learning object detection military applications OFDM SPIHT IoT

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