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3rd International Conference on Smart Technologies and systems for Next Generation Computing, ICSTSN 2024

作者： Poonguzhali, N. Amarabalan, N. Jawaharlal Nehru University School of Computer and Systems Sciences New Delhi India Manakula Vinayagar Institute of Technology Dept. of Electrical and Electronics Engineering Puducherry India

ISBN: (纸本)9798350391565

Diabetes is an illness which is triggered due to high blood sugar levels. Ignoring diabetes is undesirable since the condition can have adverse impacts on the heart, kidneys, cholesterol levels, eyes, and almost all other organs. Diabetes will be easier to manage the earlier it is detected. To do this, researchers use a variety of machine learning techniques to more accurately forecast the onset of early diabetes in human bodies or in its patients. By constructing models using datasets that are available, machine learning techniques produce more accurate predictions. In the proposed work, Random Forest Algorithm is used to predict diabetes, as this technique outperforms in terms of accuracy. Individuals with diabetes, maintaining an appropriate diet is crucial. The majority of them aim to assist users in managing their blood sugar levels. A person's weekly meal preparation habits can be attributed to a myriad of intersecting elements, such as taste, healthfulness, financial constraints, personal preferences, and lifestyle. An individual might experience it difficult to choose what to eat. To assist with the management of diabetes, the proposed system advises using a customized meal planning strategy. The diet plan suggested in the system is based on decision tree technique which is diabetic-friendly that is also socially, economically, and culturally acceptable and that can satisfy all nutritional needs while being mindful of calories. Following the prediction of diabetes and the suggestion of a diet plan, the patient's medical information is stored in the EHR for convenient access. © 2024 IEEE.

关键词： Electronic health record

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Slotted ALOHA with Code Combining for IoT Networks 3

Slotted ALOHA with Code Combining for IoT Networks

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3rd IEEE International Mediterranean Conference on Communications and Networking, MeditCom 2023

作者： Tyrovolas, Dimitrios Chrysologou, Athanasios Chondrogiannis, Georgios Tegos, Sotiris Mekikis, Prodromos-Vasileios Diamantoulakis, Panagiotis Ioannidis, Sotiris Liaskos, Christos Chatzidiamantis, Nestor Karagiannidis, George Aristotle University of Thessaloniki Dept. of Electrical and Computer Engineering Thessaloniki Greece Technical University of Chania Dept. of Electrical and Computer Engineering Chania Greece University of Ioannina Computer Science Engineering Department Ioannina Greece Cyber Security Systems and Applied Ai Research Center Lebanon

ISBN: (纸本)9798350333732

Internet of Things (IoT) networks are expected to be a key enabler technology for smart cities, due to their ability to offer real-time monitoring. In this work, we propose a novel medium access control (MAC) protocol, inspired by the synergy of slotted ALOHA and hybrid automatic repeat request with code combining (HARQ-CC), termed as code combining slotted ALOHA (CCSA), to improve the performance of IoT networks with multiple randomly deployed sensors. Specifically, we provide analytical closed-form expressions for the network's outage probability as well as for its average throughput when CCSA is applied. Finally, we provide simulation results that reveal the authenticity of the derived expressions and reveal the capabilities of HARQ-CC to significantly improve the network's performance when the sensors' access is based on a random access scheme. © 2023 IEEE.

关键词： Wireless sensor networks

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Optimizing 3D Flight Paths for Multiple UAVs with Connectivity Management in Urban Delivery systems 19

Optimizing 3D Flight Paths for Multiple UAVs with Connectivi...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Lee, Yeongmin Syahran, Raihan Muhammad Spangenberger, Mael Yang, Huigyu Oh, Jiyong Choo, Hyunseung Dept. of Artificial Intelligence Sungkyunkwan University Suwon Korea Republic of Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of Convergence Research Institute Sungkyunkwan University Suwon Korea Republic of Dept. of AI Systems Engineering Sungkyunkwan University Suwon Korea Republic of Dept. of Informatics Télécom SudParis Évry France

ISBN: (纸本)9798331507817

The dynamic deployment of aerial vehicles in urban delivery scenarios demands precise route planning, reliable data links, and efficient use of network infrastructure. Although prior efforts have explored various aspects of unmanned navigation or communication optimization, existing approaches often overlook the combined impact of three-dimensional path selection and robust connectivity maintenance. To address this gap, this paper proposes a deep reinforcement learning framework employing Independent Proximal Policy Optimization (IPPO) to steer the flight paths of unmanned vehicles while curtailing needless interactions with base stations (BSs). By incorporating communication-specific metrics, Reference Signal Received Power (RSRP), the proposed system adapts to urban conditions and upholds resilient network links. Comprehensive performance evaluations reveal the potential of the framework to reduce excessive BS connections by up to 82.24%, preserve RSRP levels above -80 dBm, and balance handover frequencies across flight trajectories. These findings affirm the scalability and effectiveness of the method for achieving efficient aerial navigation and consistent communication in urban delivery services. © 2025 IEEE.

关键词： Deep reinforcement learning

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Benchmarking Convolutional Neural Network Inference on Low-Power Edge Devices 48

Benchmarking Convolutional Neural Network Inference on Low-P...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Ferraz, Oscar Araujo, Helder Silva, Vitor Falcao, Gabriel Instituto de Telecomunicações Portugal University of Coimbra Dept. of Electrical and Computer Engineering Portugal Institute of Systems and Robotics Portugal

ISBN: (纸本)9781728163277

The massive adoption of IoT devices, the recent developments in the efficiency of AI systems, and the increase of edge computational power, accelerated the deployment of edge AI systems. The implementation of these systems through the use of low-power embedded devices scattered across the edges of a network allows for reduced latency and cost, compared to traditional cloud-based AI computing systems. As a result of the low-complexity AI models and the available low-power embedded systems on the market, this paper provides a comparative study on the inference performance of convolutional neural networks for different edge devices, by exploiting low-power GPUs and dedicated AI hardware. The benchmark results were able to achieve 864 inferences/s for the Jetson AGX Xavier board on a pre-trained SqueezeNet, while reaching a high power efficiency of 52.6 inferences/s per Watt. For the dedicated Movidius neural stick, the system requires only 1.5 W for processing 24.2 inferences/s. © 2023 IEEE.

关键词： Artificial Intelligence Benchmarking Edge devices GPUs Inference Engine IoT Low-power

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An Adaptive Multi-Stage Evolution Algorithm for High-Dimensional Expensive Problems

An Adaptive Multi-Stage Evolution Algorithm for High-Dimensi...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Zhang, Boyuan Lai, Rui Gong, Guanghong Yuan, Haitao Yang, Jinhong Zhang, Jia School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Cssc Systems Engineering Research Institute Beijing China Southern Methodist University Dept. of Computer Science DallasTX75275 United States

ISBN: (纸本)9781665410205

Recently, many studies have used evolutionary algorithms (EAs) to optimize complex problems across various fields, including mechanical structure design, robotics, and cloud computing. EAs simulate the process of evolution to improve solutions to a given problem iteratively. However, EAs encounter significant challenges when dealing with high-dimensional expensive problems (HEPs). The large solution space and high computing cost of fitness evaluations (FEs) make optimization with limited FEs particularly difficult. To tackle this problem, an Adaptive Multi-stage Evolution Algorithm named AMEA is proposed. In AMEA, an adaptively enhanced teaching-learning-based optimization algorithm is adopted to explore the search space and find potential areas quickly. Then, in the next stage, the Gaussian process surrogate model and a genetic learning particle swarm optimization algorithm are adopted for further exploitation. Besides, this work proposes an adaptive stage switching criterion and an individual screening mechanism to enhance the optimization ability. AMEA demonstrates strong optimization performance when applied to HEPs. We compare AMEA with several state-of-the-art HEP optimization algorithms through seven benchmark functions, and the results show that it performs competitively with other algorithms. Finally, we also validate AMEA's effectiveness with a real-world computation offloading problem. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Robust Monocular Camera-Based Localization in Industrial Environments

Robust Monocular Camera-Based Localization in Industrial Env...

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2024 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2024

作者： Won, Yungu Choi, Yein Ryu, Hwangryol Moon, Youngmin Kwon, Kihoon Hwang, Sung Soo Handong Global University Dept. of Computer Science and Electrical Engineering Pohang Korea Republic of Research Institute of Industrial Science & Technology Smart Engineering Research Laboratory Pohang Korea Republic of School of Computer Science and Electrical Engineering Handong Global University Pohang Korea Republic of

ISBN: (纸本)9798331530839

This paper proposes a camera-based system for robust localization of workers or robots in large-scale factories and industrial sites. The system is designed with the convenience and efficiency of workers in mind, providing stable tracking capabilities. To achieve this, a monocular camera is attached to a safety helmet, enabling localization without restricting the worker's range of motion. Additionally, by combining Visual Simultaneous Localization and Mapping and Visual Place Recognition, the system tracks the location of workers or robots while minimizing tracking loss, ensuring consistent localization. Our methodology has enhanced the accuracy and reliability of position estimation by approximately 46.0%. By accurately identifying the location of workers or robots, the system aims to prevent accidents and ensure swift responses in emergency situations. © 2024 IEEE.

关键词： Industrial robots

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Efficient Shortest-Path Tree Construction Based on Graph Convolutional Networks 18

Efficient Shortest-Path Tree Construction Based on Graph Con...

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18th International Conference on Ubiquitous Information Management and Communication, IMCOM 2024

作者： Park, Jisang Kang, Sukmin Vo, Van-Vi Choo, Hyunseung Sungkyunkwan University Dept. of Semiconductor and Display Engineering Suwon Korea Republic of Sungkyunkwan University Dept. of Electrical and Computer Engineering Suwon Korea Republic of Convergence Research Institute Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9798350331011

In the rapidly evolving landscapes of Wireless Sensor Networks (WSNs) and the Internet of Things (IoT), the integration of deep learning techniques into routing structures is emerging as a significant advancement. WSNs and IoT systems are pivotal in real-time monitoring and control across various sectors, including environmental monitoring, industrial automation, healthcare, smart cities, and more. These networks rely heavily on efficient data collection and seamless data exchange, facilitated by interconnected nodes and diverse devices. Traditional heuristic-based routing protocols in these networks have focused on optimizing energy consumption, reducing latency, ensuring reliable communication, and maintaining scalability. However, their limited adaptability to dynamic network conditions presents a challenge. The incorporation of deep learning into routing structures offers a solution. By learning and adjusting autonomously in response to changing conditions, deep learning-based routing can dynamically optimize decision-making. This leads to enhanced adaptability, efficiency, and performance, particularly in complex network environments. We propose a paradigm shift from heuristic to deep learning-driven routing structures, emphasizing the potential for improved network management and operational longevity in WSNs and IoT ecosystems. © 2024 IEEE.

关键词： Wireless sensor networks

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Equivalent Circuit Model for the Estimation of Secondary Cable Voltage Fluctuation Considering Transient Potential in Grounding Grid

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Transactions of the Korean institute of electrical Engineers 2022年第10期71卷 1383-1392页

作者： Choi, Gyeong-Ryun Kim, Kwangho Hae, Park Min Shim, Seonju Choi, Jong-Kee Kim, Yong-Sang Nah, Wansoo Dept. of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of Dept. Advanced Design Team Samsung Electronics Hwaseong Korea Republic of Dept. of KEPCO Research Institute Korea Republic of

In this paper, an equivalent circuit model of the surge transmission path was proposed to analyze the effect of potential fluctuations in the substation grounding grid on secondary cables. For the surge hitting the grounding grid, the surge waveform defined in the international standard surge immunity test standard IEC 61000-4-5 was referred to. Based on the FFT result of the surge waveform, the effective frequency band was set up to 20 MHz. In addition, an equivalent circuit model for each model was produced by dividing the structure of the substation into four parts, and the individual models were verified through comparison of the 3D EM simulation. Finally, the equivalent model of the complete system were verified by comparing the results from 3D simulation in the time domain, and the voltage induced at both ends of the secondary cable when the grounding network was struck was compared. © The Korean institute of electrical Engineers.

关键词： Timing circuits

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A Study on Optimal Scheduling Model of Digital Twin-based Energy Storage System using Artificial Intelligence

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Transactions of the Korean institute of electrical Engineers 2022年第6期71卷 819-825页

作者： Park, Hyang-A Byeon, Gil-Sung Kim, Jong-Yul Kim, Sung-Shin Dept. of Electrical and Computer Engineering Pusan National University Korea Republic of Energy Platform Research Center Korea Electrotechnology Research Institute Korea Republic of

Recently, due to the digitalization of power systems, the convergence technology of power systems and ICT has become important, and with the development of operational technologies, the importance of intelligent energy management is increasing. The importance of energy management through intelligent operating technology is emerging. In this paper, a digital twin model simulated by empirical simulation was constructed using the empirical ESS measurement data information collected from the power system. An optimal operation plan model for ESS was established by simulating the state and environment of the system in the virtual space of the digital twin similar to the actual system. It aims to improve the stable operation and efficiency of ESS through rapid situational awareness and correct decision-making by applying a data-based machine learning model rather than an existing optimization-based operation model. Copyright © The Korean institute of electrical Engineers

关键词： Energy storage

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Enhanced Driver Stress Prediction from Multiple Biosignals via CNN Encoder-Decoder Model 46

Enhanced Driver Stress Prediction from Multiple Biosignals v...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Singh Roha, Vishal Gaber Gendy, Maggie Ezzat Pavan, Kaveti Deserno, Thomas M. Ganapathy, Nagarajan Yuce, Mehmet R. Monash University Dept. of Electrical & Computer Systems Melbourne Australia Indian Institute of Technology Hyderabad Dept. of Biomedical Engineering Hyderabad India Technical University of Braunschweig Plri Lab for Medical Informatics Brunswick Germany

ISBN: (纸本)9798350371499

In this work, we present PhysioFuseNet, a novel framework designed to enhance driver stress state classification. PhysioFuseNet integrates a CNN-based encoder-decoder model with multimodal biosignal fusion. Using a driving simulator, different multimodal signals were acquired, namely electrocardiography, electrodermal activity, photoplethysmography, and respiration rate from (N = 25) healthy subjects. The experiment is of 35 minutes duration and contains different stress states (baseline (5 minutes), while normal, cognitive, and emotional sessions for 10 minutes). Multimodal features are extracted and employed in an encoder-decoder network. Extracted encoder features are combined through intermediate fusion and fed to support vector machine (SVM) and random forest (RF) classifiers. Experimental results demonstrate the efficacy of our approach, outperforming previous methods by achieving accuracies of 0.95 and 0.94 for SVM and RF, respectively. Notably, the framework excels in classifying emotional and cognitive stress states. In summary, the proposed framework could be useful in stress assessment in real-time and clinical conditions. © 2024 IEEE.

关键词： Photoplethysmography

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