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International Automatic Control Conference

作者： Kai-Tai Song Ching-Hao Meng Institute of Electrical and Control Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Graduate Degree Program of Robotics College of Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan

This paper presents a design to improve the robustness of visual SLAM(vSLAM). A processing step of feature-removal is added to the tracking thread of the conventional ORB-SLAM2 algorithm to improve the localization accuracy of a mobile robot in an environment with moving persons. Instance segmentation and motion tracking are intergrated to identify motion state of people in an image. ORB feature points belonging to moving persons are removed for further processing of the vSLAM pipeline. The advantage of this method is that the vSLAM can remove feature points of moving people, while retain those belonging to static people in the environment, which improves the accuracy of robot pose estimation. The improved ORB-SLAM2 algorithm has been implemented in a NVIDIA Xavier embedded system, which is integrated to a mobile robot. In practical robot navigation experiments, the average positioning error of the proposed method is within 4cm for 22.4m travel distance. Compared with conventional ORB-SLAM2, the average accuracy of our vSLAM method improves 97% in a dynamic environment with moving people.

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Twenty years of e-learning in health science: A bibliometric 7

Twenty years of e-learning in health science: A bibliometric

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7th International Conference on computer Science and Computational Intelligence, CSCI 2022

作者： Maulana, Fairuz Iqbal Febriantono, M. Aldiki Raharja, Dwi Risza Budi Khaeruddin Herasmara, Ridho Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Diploma III Electronic Technology Vocational Study Program University of Muhammadiyah Malang Indonesia Electrical Engineering Faculty of Science and Technology Raden Rahmat Islamic University Malang Indonesia

Education about health sciences has historically been limited in the curriculum of health professionals and largely inaccessible to the public. In practice, most of the health science education is still running conventionally. Supposedly with the advancement of technology and the use of the internet everywhere, learning such as e-learning can be important, especially in the health sector. Until this research was conducted, only 514 academic documents about e-learning in health sciences were found for 20 years from 2001 to 2020, obtained in searching on the Scopus database. This study presents a comprehensive overview of studies related to E-learning in the Health Sciences sector. This study uses bibliometric analysis and indexed digital methods to map scientific publications throughout the world. This research employs the Scopus database to gather information, as well as the Scopus online analysis tool and Vosviewer to show the bibliometric network. The method consists with five stages: determining search keywords, initial search results, refinement of search results, initial compilation, and data analysis. Among the most published and indexed articles by Scopus, papers published by researchers in the United States have the highest number of publications (80), followed by United Kingdom (63) and Australia with 45 academic publications. The processed data shows the pattern and trend of increasing the number of international publications in E-learning in Health Sciences field, which Scopus index. © 2022 Elsevier B.V.. All rights reserved.

关键词： Publishing

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Contract-Inspired Contest Theory for Controllable Image Generation in Mobile Edge Metaverse

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IEEE Transactions on Mobile Computing 2025年

作者： Liu, Guangyuan Du, Hongyang Wang, Jiacheng Niyato, Dusit Kim, Dong In Nanyang Technological University College of Computing and Data Science Energy Research Institute @ NTU Interdisciplinary Graduate Program Singapore University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong Hong Kong Nanyang Technological University College of Computing and Data Science Singapore Sungkyunkwan University Department of Electrical and Computer Engineering Korea Republic of

The rapid advancement of immersive technologies has propelled the development of the Metaverse, where the convergence of virtual and physical realities necessitates the generation of high-quality, photorealistic images to enhance user experience. However, generating these images, especially through Generative Diffusion Models (GDMs), in mobile edge computing environments presents significant challenges due to the limited computing resources of edge devices and the dynamic nature of wireless networks. This paper proposes a novel framework that integrates contract-inspired contest theory, Deep Reinforcement Learning (DRL), and GDMs to optimize image generation in these resource-constrained environments. The framework addresses the critical challenges of resource allocation and semantic data transmission quality by incentivizing edge devices to efficiently transmit high-quality semantic data, which is essential for creating realistic and immersive images. The use of contest and contract theory ensures that edge devices are motivated to allocate resources effectively, while DRL dynamically adjusts to network conditions, optimizing the overall image generation process. Experimental results demonstrate that the proposed approach not only improves the quality of generated images but also achieves superior convergence speed and stability compared to traditional methods. This makes the framework particularly effective for optimizing complex resource allocation tasks in mobile edge Metaverse applications, offering enhanced performance and efficiency in creating immersive virtual environments. © 2002-2012 IEEE.

关键词： Resource allocation

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Disentangled Motion Modeling for Video Frame Interpolation

arXiv

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arXiv 2024年

作者： Lew, Jaihyun Choi, Jooyoung Shin, Chaehun Jung, Dahuin Yoon, Sungroh Interdisciplinary Program in AI Seoul National University Korea Republic of Department of Electrical and Computer Engineering Seoul National University Korea Republic of School of Computer Science and Engineering Soongsil University Korea Republic of AIIS ASRI and INMC Seoul National University Korea Republic of

Video Frame Interpolation (VFI) aims to synthesize intermediate frames between existing frames to enhance visual smoothness and quality. Beyond the conventional methods based on the reconstruction loss, recent works have employed generative models for improved perceptual quality. However, they require complex training and large computational costs for pixel space modeling. In this paper, we introduce disentangled Motion Modeling (MoMo), a diffusion-based approach for VFI that enhances visual quality by focusing on intermediate motion modeling. We propose a disentangled two-stage training process. In the initial stage, frame synthesis and flow models are trained to generate accurate frames and flows optimal for synthesis. In the subsequent stage, we introduce a motion diffusion model, which incorporates our novel U-Net architecture specifically designed for optical flow, to generate bi-directional flows between frames. By learning the simpler low-frequency representation of motions, MoMo achieves superior perceptual quality with reduced computational demands compared to the generative modeling methods on the pixel space. MoMo surpasses state-of-the-art methods in perceptual metrics across various benchmarks, demonstrating its efficacy and efficiency in VFI. © 2024, CC BY-NC-SA.

关键词： Pixels

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Implementation of LEACH Protocol for Cluster Head Selection in D2D Cooperative Communication Systems

Implementation of LEACH Protocol for Cluster Head Selection ...

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2021 International Conference on computer System, Information Technology, and electrical engineering, COSITE 2021

作者： Raziah, Isyatur Yunida, Yunida Muharar, Rusdha Away, Yuwaldi Nasaruddin, Nasaruddin Universitas Syiah Kuala Doctoral Program School of Engineering Banda Aceh Indonesia Universitas Syiah Kuala Department of Electrical and Computer Engineering Banda Aceh Indonesia

ISBN: (纸本)9781665425094

Device-to-device communication (D2D) is a promising technology for future mobile networks that allow users to communicate directly without using the cellular system's infrastructure. However, since the battery power of each mobile device is limited, it is necessary to extend the network life by reducing its energy consumption. Clustering-based protocols could reduce the energy consumption by balancing all nodes into cluster heads (CH). This paper implements the recent hierarchical routing based on the Low Energy Adaptive Hierarchical Clustering (LEACH) protocol for a CH selection to increase device lifetime in cooperative D2D communication systems. First, the CH is selected based on each device's distance and the residual energy. Then, the LEACH protocol is implemented to choose CH in the D2D cooperative communication system to reduce energy consumption and increase the network life and aims to increase the reliability of the cooperative D2D communication system. Moreover, the proposed system model is provided to analyze cluster formation and energy consumption using computer simulation. The simulation results show that implementing the LEACH protocol can increase network life by knowing the number of dead devices and reducing energy consumption in cooperative D2D communication systems. © 2021 IEEE.

关键词： Energy utilization

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Bayesian Games for Optimal Cybersecurity Investment with Incomplete Information on the Attacker 16

Bayesian Games for Optimal Cybersecurity Investment with Inc...

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16th International Conference on Probabilistic Safety Assessment and Management, PSAM 2022

作者： Zhao, Yunfei Huang, Linan Zhu, Quanyan Smidts, Carol Nuclear Engineering Program Department of Mechanical and Aerospace Engineering The Ohio State University 201 W 19th Ave ColumbusOH43210 United States Department of Electrical and Computer Engineering New York University 370 Jay Street BrooklynNY11201 United States

The trend of digitization in various industrial systems has exposed them to an increasing number of cyberattacks. Therefore, it is of vital importance to reduce the cybersecurity risk of industrial systems through cost-effective decisions on cybersecurity investment. In making such decisions, the defender is usually faced with challenges that arise from incomplete information about the attacker. In this paper, we propose a Bayesian game approach to model the optimal cybersecurity investment strategy under such situations. In this approach, the defender categorizes the attacker into a finite number of types, e.g., various levels of capability, and assigns a probability distribution over the different types of attackers. Then, the defender optimizes his/her cybersecurity investment based on risk assessment considering the possible attack efforts of these various types of attackers, with the objective of minimizing the expected cyberattack loss and the cybersecurity investment cost. The proposed method is demonstrated using a numerical example. We perform a sensitivity analysis for model parameters that can be difficult to obtain in practical applications, e.g., the defender's loss caused by a successful attack. Key observations of the example include the threshold principle (i.e., the defender should not make any investment if the loss of a successful attack is below a certain threshold) and the conservation of loss (i.e., losses for one type of attacker may correspond to gains for another type of attacker). The proposed method can be used to support cybersecurity investment decisions by industrial system owners. © 2022 Probabilistic Safety Assessment and Management, PSAM 2022. All rights reserved.

关键词： Investments

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Measurement of Muscle Stimulation and Hot Compression for Rehabilitation 3

Measurement of Muscle Stimulation and Hot Compression for Re...

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3rd International Conference on electrical, Communication and computer engineering, ICECCE 2021

作者： Chirakanphaisarn, Neramitr Thongkanluang, Thadsanee Thammasat University Faculty of Engineering Department of Electrical and Computer Engineering Thailand Suratthani Rajabhat University Program in Materials Technology Manufacturing Faculty of Science and Technology Thailand

ISBN: (纸本)9781665438971

This research is to study the electric muscle stimulation system and hot compress. As well as focusing on building tools for applications in rehabilitation medicine and physical therapy. The neuromuscular system is an important mechanism in human life. In the event that the muscles are stressed or contracted while the subject is recovering, the need for recovery and stimulation tools is an important part of the physical therapy. We designed electrical circuits for automatic control and used an ARDUINO microcontroller to connect analog and digital ports to transmit and receive clinical parameters. The number of voluntary subjects was 20. Each subject remembered to perform 5 stimulation sessions for 10 minutes. Total period that the researcher collects data is approximately 3 months. The trials were based on ethical studies in humans, where trials and researches showed that subjects were 980% satisfied with very few side effects, and subjects had improved muscle development. © 2021 IEEE.

关键词： Muscle

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A WEARABLE GRAPHENE TRANSISTOR-BASED BIOSENSOR FOR MONITORING IL-6 BIOMARKER 25

A WEARABLE GRAPHENE TRANSISTOR-BASED BIOSENSOR FOR MONITORIN...

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25th International Conference on Miniaturized Systems for Chemistry and Life Sciences, MicroTAS 2021

作者： Laliberte, Kaitlyn E. Scott, Patrick Khan, Niazul I. Mahmud, Md Shaad Song, Edward Department of Electrical and Computer Engineering University of New Hampshire United States Materials Science Program University of New Hampshire United States

ISBN: (纸本)9781733419031

Graphene field-effect transistor (GFET) is becoming an increasingly popular biosensing platform for monitoring health conditions through biomarker detection. Moreover, the graphene's 2-dimensional geometry makes it ideal for implementing flexible wearable devices. In this work, we present a printable graphene transistor-based biosensor that can be used as a wearable sensor to detect a protein biomarker Interleukin-6 (IL-6). Our device contains a voltage control, a current meter, and a power source all integrated into a single miniaturized platform making it a truly skin-mounted wearable device. © 2021 MicroTAS 2021 - 25th International Conference on Miniaturized Systems for Chemistry and Life Sciences. All rights reserved.

关键词： Graphene transistors

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Hybrid Deep Learning Model for Accurate Short-Term Electricity Price Forecasting

Hybrid Deep Learning Model for Accurate Short-Term Electrici...

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IEEE Texas Power and Energy Conference (TPEC)

作者： Arash Moradzadeh Mostafa Mouhammadpourfard Yang Weng Suhas Pol S. M. Muyeen Department of Electrical Engineering Qatar University Doha Qatar National Wind Institute Texas Tech University Lubbock TX USA School of Electrical Computer and Energy Engineering Arizona State University Tempe AZ USA Renewable Energy Program Texas Tech University Lubbock TX USA

ISBN: (数字)9798331541125

ISBN: (纸本)9798331541132

Accurate short-term electricity price forecasting (STEPF) is critical for efficient energy market operations, guiding investment strategies, resource allocation, and consumer behavior. This study introduces a hybrid deep learning approach specifically designed to improve STEPF accuracy by leveraging historical Hourly Ontario Energy Price (HOEP) data from 2017 to 2019. The model integrates advanced techniques, including data preprocessing and denoising through a Stacked Denoising Autoencoder (SDAE), along with enhanced temporal modeling via Bidirectional Long Short-Term Memory (BiLSTM) and Gated Recurrent Unit (GRU) networks. By capturing the complex dynamics inherent in electricity pricing data, the proposed hybrid model significantly enhances forecasting accuracy. Trained on data from 2017 and 2018, with 2019 used for testing, the model achieves a strong correlation coefficient (R = 99.86%) and substantially lowers forecasting errors. Comparative evaluations against established forecasting methods highlight the model's superior performance. This work demonstrates the practical value of deep learning techniques in the energy sector, particularly in responding to the volatility of demand and supply in real-time electricity markets.

关键词： Deep learning Accuracy Electricity Noise reduction Bidirectional long short term memory Predictive models Data models Stakeholders Forecasting Testing

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Deep Learning Networks for Handwritten Bangla Character Recognition

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IAENG International Journal of Applied Mathematics 2023年第4期53卷 1页

作者： Begum, H. Islam, M.M. Eva, H.S. Emon, N.H. Siddique, F.A. Department of Electrical and Electronic Engineering East West University A/2 Aftabnagar Dhaka Bangladesh Department of Electrical Engineering and Computer Science South Dakota State University United States Engineering and Design Department Reverie Power and Automation Engineering Limited Bangladesh Program in Smart System Integrated Solutions Aalto University Finland

In recent years, deep convolutional networks (DCNN) have gained popularity for different classification (or recognition) tasks. In this paper, three well known DCNN structures were used, i.e., AlexNet, SqueezeNet and GoogLeNet, and their classification performances in recognizing handwritten Bangla isolated characters were compared. These networks have simpler structures compared to the recent DCNNs. Experiments on a standard Bangla database revealed that the overall performance of GoogLeNet is slightly better than the other two networks. Further analysis using saliency maps of the test samples revealed the important features that are learned by the networks for classifying characters. This information led us to understand why classification of some samples fail and how to rectify these. © (2023), (International Association of Engineers). All Rights Reserved.

关键词： Classification (of information)

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