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8th International Conference on Information technology, Information Systems and Electrical engineering, ICITISEE 2024

作者： Prayogo, Rizal Dwi Nambo, Hidetaka Graduate School of Natural Science and Technology Kanazawa University Division of Electrical Engineering and Computer Science Kanazawa Japan Institute of Philosophy in Interdisciplinary Sciences Kanazawa University Faculty of Transdisciplinary Sciences Kanazawa Japan

ISBN: (纸本)9798350368970

Dealing with class imbalance is a significant issue in classification tasks that often leads to lower prediction performance. Many data augmentation methods have been suggested to tackle this problem, but their effectiveness can vary depending on the dataset. Utilizing augmentation methods can improve the learning and generalization abilities of the proposed convolutional neural network (CNN) model. However, it is challenging to find a single data augmentation method that can effectively address class imbalance problems across all scenarios. In this paper, we present a technique known as smart data augmentation (SDA) that is aimed at effectively improving classification performance by enhancing imbalanced data. SDA involves an equation that can be used to create an augmentation technique, combining different sampling methods for addressing multiclass imbalance and offering easy customization options. We perform the data balancing strategy including undersampling, oversampling, and SDA-based sampling. We evaluate our proposed framework using a CNN model to improve the detection of brain tumors in imbalanced data. Our experiments demonstrate that SDA-based sampling improves brain tumor detection performance by 3.546% in accuracy, 3.310% in precision, 3.432% in recall, and 3.371 % in Fl-score compared to the imbalanced data scenario. The findings suggest that utilizing SDA together with CNN can improve the classification performance of brain tumor detection with imbalanced data. © 2024 IEEE.

关键词： Brain tumor detection class imbalance problem convolutional neural network smart data augmentation

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GACL-Net:Hybrid Deep Learning Framework for Accurate Motor Imagery Classification in Stroke Rehabilitation

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computers, Materials & Continua 2025年第4期83卷 517-536页

作者： Chayut Bunterngchit Laith H.Baniata Mohammad H.Baniata Ashraf ALDabbas Mohannad A.Khair Thanaphon Chearanai Sangwoo Kang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Computing Gachon UniversitySeongnam13120Republic of Korea Computer Science Department Faculty of Information TechnologyThe World Islamic Sciences and Education UniversityAmman11947Jordan Intelligent Systems Department Faculty of Artificial IntelligenceAl-Balqa Applied UniversityAl-Salt19117Jordan IT Infrastructure Department Qatrana Cement CompanyAmman11821Jordan Division of Industrial and Logistics Engineering Technology Faculty of Engineering and TechnologyKing Mongkut’s University of Technology North BangkokRayong CampusRayong21120Thailand

Stroke is a leading cause of death and disability worldwide,significantly impairing motor and cognitive *** rehabilitation is often hindered by the heterogeneity of stroke lesions,variability in recovery patterns,and the complexity of electroencephalography(EEG)signals,which are often contaminated by *** classification of motor imagery(MI)tasks,involving the mental simulation of movements,is crucial for assessing rehabilitation strategies but is challenged by overlapping neural signatures and patient-specific *** address these challenges,this study introduces a graph-attentive convolutional long short-term memory(LSTM)network(GACL-Net),a novel hybrid deep learning model designed to improve MI classification accuracy and ***-Net incorporates multi-scale convolutional blocks for spatial feature extraction,attention fusion layers for adaptive feature prioritization,graph convolutional layers to model inter-channel dependencies,and bidi-rectional LSTM layers with attention to capture temporal *** on an open-source EEG dataset of 50 acute stroke patients performing left and right MI tasks,GACL-Net achieved 99.52%classification accuracy and 97.43%generalization accuracy under leave-one-subject-out cross-validation,outperforming existing state-of-the-art ***,its real-time processing capability,with prediction times of 33–56 ms on a T4 GPU,underscores its clinical potential for real-time neurofeedback and adaptive *** findings highlight the model’s potential for clinical applications in assessing rehabilitation effectiveness and optimizing therapy plans through precise MI classification.

关键词： Motor imagery EEG stroke rehabilitation deep learning brain-computer interface

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Detection of Faults in Solar Grids Using Convolutional Neural Network

Detection of Faults in Solar Grids Using Convolutional Neura...

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： Madhuri Mahima Chinthaguntla C Priyadharsini Bachelor of Technology in Computer Engineering Division of Data Science and Cybersecurity Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

The imperative need to guarantee the reliability and efficiency of solar energy systems inspired the development of a novel method for solar panel defect identification. As solar panels increasingly become a significant contributor to the global energy grid, it becomes crucial to address the challenges associated with their maintenance and performance optimization. Issues such as reduced efficiency due to external factors like weather, shading, and debris, as well as internal faults within the panels themselves, underscored the necessity for a more effective and reliable defect identification system. Additionally, the growing demand for clean and sustainable energy sources further emphasized the importance of maximizing the potential of solar power generation through improved maintenance practices. Therefore, the need to overcome the limitations of conventional detection techniques and enhance the reliability of solar energy production served as the primary inspiration for developing the novel method for solar panel defect identification. This research introduces an innovative approach utilizing the Visual Geometry Group (VGG16) Convolutional Neural Network (CNN) model, renowned for its efficacy in computer vision tasks. By critically reviewing existing literature and methodologies, and leveraging machine learning and deep learning techniques, the study demonstrates the potential of deep learning models like VGG16 to significantly improve solar panel fault detection accuracy to 99% during training and 90% during validation indicating its effectiveness in addressing critical challenges in solar panel maintenance and performance optimization.

关键词： Geometry Deep learning Training Visualization Computational modeling Solar energy Maintenance Solar panels Convolutional neural networks Optimization

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Multi-Layer Network Formation Through HAPS Base Station and Transmissive RIS-Equipped UAV

Multi-Layer Network Formation Through HAPS Base Station and ...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Khennoufa, Faical Abdellatif, Khelil Yanikomeroglu, Halim Ozturk, Metin Elganimi, Taissir Kara, Ferdi Rabie, Khaled El-Oued University Lgeere Lab Department of Electrical Engineering El-Oued Algeria Carleton University Ntn Lab Department of Systems and Computer Engineering Ottawa Canada Ankara Yildirim Beyazit University Electrical and Electronics Engineering Ankara Turkey University of Tripoli Department of Electrical and Electronic Engineering Libya Kth Royal Institute of Technology Division of Communications Systems Sweden Zonguldak Bulent Ecevit University Department of Computer Engineering Zonguldak Turkey Department of Computer Engineering Center for Communication Systems and Sensing Dhahran Saudi Arabia

ISBN: (纸本)9798350368369

In order to bolster future wireless networks, there has been a great deal of interest in non-terrestrial networks, especially aerial platforms including high-altitude platform stations (HAPS) and uncrewed aerial vehicles (UAVs). These platforms can integrate advanced technologies such as reconfigurable intelligent surfaces (RIS) and non-orthogonal multiple access (NOMA). In this regard, this paper proposes a multi-layer network architecture consisting of HAPS and UAV, where the former acts as a HAPS super macro base station (HAPS-SMBS), while the latter serves as a relay node for the ground Internet of Things (IoT) devices. The UAV is equipped with active transmissive RIS, which is a novel technology with promising benefits. We also utilize multiple-input single-output (MISO) technology, i.e., multiple antennas at the HAPS-SMBS and a single antenna at the IoT devices. Additionally, we consider NOMA as the multiple access technology as well as the existence of hardware impairments as a practical limitation. We compare the proposed system model with various scenarios, all involving the HAPS-SMBS and RIS-equipped UAV relay combination, but with different types of RIS, antenna configurations, and access technologies. Sum rate and energy efficiency are used as performance metrics, and the findings demonstrate that, in comparison to all benchmarks, the proposed system yields significant performance gains. Moreover, hardware impairment limits the system performance at high transmit power levels. © 2025 IEEE.

关键词： Enterprise resource planning

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Board 320: Integrating Playful Learning: A Mixed-Reality Approach to Enhance Computational Thinking in Young Learners

Board 320: Integrating Playful Learning: A Mixed-Reality App...

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2024 ASEE Annual Conference and Exposition

作者： Hwang, Jaejin Kim, Yanghee Lee, Sungchul Mosher, Joshua Koradala, Nikitha Sohail, Mohammad Faizan Department of Industrial and Systems Engineering College of Engineering and Engineering Technology Northern Illinois University DeKalbIL United States Department of Educational Technology Northern Illinois University DeKalbIL United States Division of Computer Science and Engineering Sunmoon University Korea Republic of Department of Computer Science University of Wisconsin-Whitewater WhitewaterWI United States Department of Operations Management and Information Systems Northern Illinois University DeKalbIL United States

This study aimed to develop a pioneering augmented reality (AR) environment focused on nurturing early computational thinking skills in kindergarten through second-grade students. Rooted in embodied cognition and social robotics, the project seamlessly integrated AR technology with a physically embodied robot named Linibot. Children engaged in a chessboard-like grid navigation, guided by Linibot through a tablet displaying AR obstacles. The educational objectives encompassed building STEM problem-solving skills, understanding symbols and sequences, and fostering confidence in technology utilization. Pre- and post-tests were employed to assess the impact of the AR environment on computational thinking skills within an intervention group. Developed and refined iteratively over two and a half years, the environment underwent testing with thirty-five children in diverse settings, including a showcase event involving twenty children. The ongoing analysis of interaction logs aims to evaluate children's performance, refine AR obstacles and robot cues, and present findings in this poster session for discussions on implications for developmentally appropriate learning and ecologically valid assessment. Preliminary results from five students in the intervention group revealed improvements in task performance related to path-finding tasks compared to the pre-test. The average duration of task completion decreased by 42%, the average number of instances of avoiding difficult problems substantially decreased by 80%, and the number of missions achieved increased by 80%. This impact was more pronounced for students who faced greater challenges during the pre-tests. The preliminary findings suggest that the proposed AR educational tool not only enhanced students' computational thinking but also influenced their learning behavior, encouraging them to tackle difficult problems rather than avoid them. Future studies will conduct a comparative analysis between the control and intervent

关键词： Contrastive Learning

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Investigation on Structures of Axial Gap Type Magnetic Multiple Spur Gear for In-Wheel Motor System of Electric Vehicle

Investigation on Structures of Axial Gap Type Magnetic Multi...

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2023 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2023

作者： Kamijo, Taiga Aiso, Kohei Akatsu, Kan Aoyama, Yasuaki Shibaura Institute of Technology Electrical Engineering and Computer Science Tokyo Japan Shibaura Institute of Technology Department of Electrical Engineering Tokyo Japan Yokohama National University Division of Intelligent Systems Engineering Department of Faculty of Engineering Yokohama Japan Center for Technology Innovation - Materials Hitachi Ltd. Research & Development Group Ibaraki Japan

ISBN: (纸本)9798350314274

A novel drive system using Magnetic Multiple Spur Gear (MMSG) and multiple high-speed motors is characterized by small size, lightweight, and high efficiency even at high-speed region, it is expected to apply to in-wheel motor systems. However, there is a risk of contact between the rotors when applied to in-wheel motor system. In this study, a novel axial-gap MMSG is proposed to solve this problem, stabilize the air gap between the high-speed and low-speed rotors, and effectively utilize the magnets, and its performance is evaluated by finite element analysis. © 2023 IEEE.

关键词： Electric vehicles

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Exploration of Mandibular Inputs for Human-Machine Interfaces

Exploration of Mandibular Inputs for Human-Machine Interface...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Yaslam, Abdulaziz Feron, Eric King Abdullah University of Science and Technology Computer Electrical and Mathematical Sciences and Engineering Division Thuwal Saudi Arabia

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

The direct connection of the jaw to the brain allows it to retain its motor and sensory capabilities even after severe spinal cord injuries. As such, it can be an accessible means of providing inputs for people with paralysis to manipulate their environment. This paper explores the potential for using the jaw, specifically the mandible, as an alternative input to human-machine interface systems. Two tests were developed to test the mandible's ability to respond to visual stimuli. First, a visual response time test to measure the precision and accuracy of user input through a mandible-actuated button. Second, a choice response test to observe coordination between the mandible and a *** results show that the mean response time of mandible inputs is 8.3% slower than the corresponding mean response time of performing the same task with a thumb. The delay in response after making a decision is statistically insignificant between the mandible-and finger-actuated inputs with the mandible being 2.67% *** on these results, the increase in response time while using the mandibular input is minimal for new users. Coordination is feasible in tasks involving both the mandible and thumb. Extensive training with a made-to-fit device has the potential to enable a visual response time equivalent to the fingers in more complex tasks. The mandible is a viable option for accessible HMI for discreet inputs. Further testing into continuous input is needed to explore the mandible's potential as an input for body augments. © 2022 IEEE.

关键词： Assistive technology

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Optimization of Microwave Resonant Cavity Flowmeter Design for High Temperature Fluid Sensing Applications

Optimization of Microwave Resonant Cavity Flowmeter Design f...

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2023 IEEE International Conference on Electro Information technology, eIT 2023

作者： Fang, Tianyang Saniie, Jafar Bakhtiari, Sasan Heifetz, Alexander Illinois Institute of Technology Ecasp Research Laboratory Department of Electrical and Computer Engineering ChicagoIL United States Nuclear Science and Engineering Division Argonne National Laboratory LemontIL United States

ISBN: (纸本)9781665493765

This paper investigates optimization of parameters to enhance performance of a microwave resonant cavity transducer for high temperature fluid flow sensing in advanced reactors. The cylindrical microwave cavity flowmeter is a novel sensor that measures fluid flow velocity through deflection of the cavity wall due to dynamic fluid pressure. This sensor is designed to operate in harsh environments of a nuclear reactor because sensor material is resilient to high temperatures, ionizing radiation, and corrosion. Parameters that determine performance characteristics of the transducer include dimensions of the microwave cavity, cavity electromagnetic excitation method, and cavity material. We investigate transducer design through computer simulations, with the objective of maximizing cavity Q-factor. © 2023 IEEE.

关键词： Microwave resonators

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INNS: A Superficial Learning Scheme to Predict Complex Human Activity Behaviour using Improved Neural Network Schema 9

INNS: A Superficial Learning Scheme to Predict Complex Human...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Shifani, S. Agnes Paramaiyappan, M. Thamil, Selvi M.S Franklin Suresh, M. Deepa Giri, Jayant Ai-Safarini, Maram Y. Saveetha University Department of Ece Chennai India J.P. College of Engineering Department of Ece Tenkasi India Sathyabama Institute of Science and Technology Department of Mathematics Chennai India School of Sciences Srm Institute of Science and Technology Division of Mathematics Trichy India Yeshwantrao Chavan College of Engineering Division of Mechanical Engineering Nagpur India Lovely professional University Division of Research and Development Phagwara India Zarqa University Computer Science Department Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798350377972

Human Activity Recognition (HAR) is a crucial task in various domains such as healthcare, smart homes, and fitness tracking, where real-time, accurate detection of human activities is required. In this work, we propose a hybrid model combining a Cascaded AutoEncoder (CAE) for feature extraction with a Random Forest (RF) classifier for human activity recognition. The model was evaluated against 9 existing models, including CNN, LSTM, GRU, ResNet, and MobileNet, using various metrics such as accuracy, precision, recall, F1-score, and inference time. The proposed model achieved a superior accuracy of 93.87%, outperforming models like ResNet (90.11%) and MobileNet (91.23%). Additionally, the model showed competitive precision (92.45%), recall (92.89%), and Fl-score (92.50%), along with an efficient inference time of 21.3 ms, making it suitable for real-time applications. The low memory usage (185 MB) further supports its deployability in resource-constrained environments. These results demonstrate that the hybrid CAE + RF model offers an optimal balance between high classification performance and computational efficiency, making it a powerful solution for HAR tasks. © 2024 IEEE.

关键词： mHealth

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Software-Defined Ultrasonic Communication System using PPM-EMAT with High Transmission Rate

Software-Defined Ultrasonic Communication System using PPM-E...

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2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Huang, Xin Saniie, Jafar Bakhtiari, Sasan Heifetz, Alexander Illinois Institute of Technology Ecasp Research Laboratory Department of Electrical and Computer Engineering ChicagoIL United States Argonne National Laboratory Nuclear Science and Engineering Division LemontIL United States

ISBN: (纸本)9798350346459

A novel approach to ultrasonic communication systems introduces a software-defined system that utilizes Periodic-permanent-magnet electromagnetic acoustic transducers (PPM-EMATs) as transmitters and receivers. Traditional systems had limitations in adaptability and flexibility. The software-defined system provides enhanced versatility, adaptability, and portability, allowing customization for diverse applications. The use of PPM-EMATs is explored for optimizing energy transfer through metal plate channels, generating shear horizontal waves. Experimental tests validate the efficiency of PPM-EMATs, showing successful communication at a 10-kbps bitrate using ASK modulation with a high signal-to-noise ratio (SNR). The research highlights the potential of PPM-EMAT technology in high-speed software-defined ultrasonic communication systems. © 2023 IEEE.

关键词： Signal to noise ratio

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