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Artificial Intelligence of Things (AIoT), IEEE Annual Congress on

作者： Nico Surantha David Jayaatmaja Sani Muhamad Isa Faculty of Science and Engineering Department of Electrical Electronics and Communication Engineering Tokyo City University Tokyo Japan Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350392296

ISBN: (纸本)9798350392302

The rapid development of Internet of Things (IoT) technology has enabled the widespread deployment of health monitoring systems. Traditionally, the health monitoring system has been limited by centralized processing and storage in the cloud, leading to latency issues and potential data loss. This paper introduces a smart sleep monitoring system based on edge computing, utilizing microservices architecture and caching techniques. The proposed system employs edge computing to enable data processing closer to the source, reducing latency and improving real-time monitoring capabilities. Caching is employed to reduce database load and optimize random access memory (RAM) usage. This research addresses latency and response time challenges on IoT health monitoring platforms in environments with poor network quality while optimizing database load and resource usage on Jetson Nano as the edge computing device. Using Electrocardiogram (ECG) data as input, the proposed system yields impressive performance metrics. The research results indicate that the proposed system can increase throughput by 26.92 KB/s, reduce response time by 18.8 ms, and decrease latency by 20.86 ms compared to the previous work. Message Queuing Telemetry Transport (MQTT) integration reduces CPU usage by approximately 40% and RAM usage by about 81.24%.

关键词： Cloud computing Databases Random access memory Microservice architectures Real-time systems Nanoscale devices Internet of Things

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Building a Simple COVID-19 Knowledge Graph in Bahasa Indonesia: A Preliminary Study

Building a Simple COVID-19 Knowledge Graph in Bahasa Indones...

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Biomedical Instrumentation and Technology Conference (IBITeC), International

作者： Arief Purnama Muharram Farhan Hilmi Taufikulhakim Ayu Purwarianti Faculty of Medicine Universitas Indonesia Jakarta Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Master Program of Informatics Bandung Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

COVID-19 is an acute respiratory disease that has become a pandemic worldwide. Many studies have been conducted to enhance our understanding of COVID-19. However, the abundance of information obtained from these studies has resulted in information overload. In this study, we purposed a simple COVID-19 Knowledge Graph in Bahasa Indonesia as a way to reconstruct knowledge to combat this information overload. We used Bahasa Indonesia in our study to explore its potential for constructing a Knowledge Graph (KG). The construction of our KG involved manual curation of medical literatures and annotation of entities and relationships by the domain experts. The KG was implemented using Neo4J version 5. We successfully demonstrated our COVID-19 KG, which consists of 240 nodes and 276 relationships with 15 and 14 node and relationship labels, respectively. Accessing the information within the KG is made effortless through the use of Cypher queries in Neo4J. Further research is still needed to develop the KG into a larger and better one. However, our COVID-19 KG can serve as a basis for further development.

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An Improved Vision Transformer Architecture-Based Computer Recognition Platform for Lilium Classification

An Improved Vision Transformer Architecture-Based Computer R...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Xiang-Rui Huang Huai-Jhan Ke Szu-Yu Kuo Liang-Bi Chen Department of Electrical Engineering Master Program in Electrical Engineering and Computer Science National Penghu University of Science and Technology Penghu Taiwan Department of Computer Science and Information Engineering National Penghu University of Science and Technology Penghu Taiwan Department of Shipping and Transportation Management National Kaohsiung University of Science and Technology Kaohsiung Taiwan

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Lilies are popular in the global flower market, but consumers often lack information about specific varieties. To address this issue, this paper proposes a computer recognition platform based on the Vision Transformer (ViT) architecture. The proposed platform uses an improved vision transformer (ViT) architecture to classify different types of lilies, allowing consumers to access information and names of various Lilium species. The experimental results show that the proposed lily classification model achieved a 96.4% accuracy rate in classifying six lily species.

关键词： Computer vision Electric potential Accuracy Computational modeling Computer architecture Computer applications Flowering plants Network architecture Transformers Consumer electronics

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Enhanced-SETL: A multi-variable deep reinforcement learning approach for contention window optimization in dense Wi-Fi networks

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Computer Networks 2024年 253卷

作者： Tu, Yi-Hao Lin, En-Cheng Ke, Chih-Heng Ma, Yi-Wei Department of Electrical Engineering National Taiwan University of Science and Technology Taipei106335 Taiwan Master Program of Information Technology and Applications National Quemoy University Kinmen892009 Taiwan Department of Computer Science of Information Engineering National Quemoy University Kinmen892009 Taiwan

In this paper, we introduce the Enhanced Smart Exponential-Threshold-Linear (Enhanced-SETL) algorithm, a new approach that uses the multi-variable Deep Reinforcement Learning (DRL) framework to simultaneously optimize multiple settings of the Contention Window (CW) in IEEE 802.11 wireless networks. Unlike traditional DRL methods that adjust only a single CW parameter, our innovative approach simultaneously optimizes both the CW minimum (CWmin) and CW Threshold (CWThreshold), significantly improving network traffic control. We utilize a Double Deep Q-learning Network (DDQN) for dynamic updates of these CW settings, broadcasted across the dense Wi-Fi networks. This dual adjustment method, coupled with dynamic, data-driven updates, not only enhances throughput, but also reduces collision rates, and ensures fairness access across both static and dynamic wireless environments. Enhanced-SETL achieves a throughput improvement ranging from 3.55% up to 43.73% and from 3.98% up to 30.15% in static and dynamic scenarios over standard protocols and state-of-the-art DRL models, while maintaining a fairness index near 99% across diverse stations, showcasing its effectiveness and adaptability in various network conditions. © 2024

关键词： Wireless local area networks (WLAN)

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Study for the Location of Equipment Fault Detectors in the electrical Distribution Systems

SSRN

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SSRN 2022年

作者： Robles Lovato, Pablo Daniel Gomez, Roberto Cabrera, Diego Program in Electrical Engineering Salesian Polytechnic University Cuenca010205 Ecuador Master’s Program in Electrical Engineering Smart Grid Research Group Salesian Polytechnic University Cuenca010205 Ecuador

In this article work on the location of the fault in the electrical distribution system, based on the processing of short-circuit signals analysis, we propose the simulation of cases with CYME, the signal obtained and decomposed employing the Wavelet transform. The description to use the minimum expanding tree method makes that the location of the faults is optimal to reduce the reconnection time, taking into account the placement of reclosers that will serve as storers of information. To process, it is necessary to develop a location algorithm that analyses transient phenomena with good precision in time frequency and decomposes the signal at different levels obtaining the necessary parameters for determining the fault distance. © 2022, The Authors. All rights reserved.

关键词： Wavelet transforms

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Review ofPractical Psychometrics: A Guide for Test Users

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Structural Equation Modeling: A Multidisciplinary Journal 2024年第5期31卷 932-934页

作者： Musyaffa’ Ahmad Muhammad Faisal a Graduate Program Department of Electrical Engineering and Information Technology Faculty of Engineering Universitas Gadjah Mada Jl. Grafika No. 2 UGM Campus Yogyakarta55281 Indonesia b Master of Mathematics Education Program Faculty of Mathematics and Natural Sciences Yogyakarta State University Jl. Colombo 1 Yogyakarta55281 Indonesia

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The Evolution of Non-invasive Hemoglobin Measurement with IoT and AI-Based Optical Sensors

The Evolution of Non-invasive Hemoglobin Measurement with Io...

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International Conference on electrical engineering, Computer Science and Informatics (EECSI)

作者： Tole Sutikno Lina Handayani Rochana Ruliyandari Oktomi Wijaya Hendril Satrian P Watra Arsadiando Master Program of Electrical Engineering Universitas Ahmad Dahlan Yogyakarta Indonesia Faculty of Public Health Universitas Ahmad Dahlan Yogyakarta Indonesia Embedded System and Power Electronics Research Group (ESPERG) Yogyakarta Indonesia

ISBN: (数字)9798350355314

ISBN: (纸本)9798350355321

Hemoglobin measurement has significantly evolved with the integration of IoT and artificial intelligence (AI) technologies. This paper presents the evolution of non-invasive hemoglobin measurement using IoT and AI-based optical sensors. IoT and AI-based optical sensors have revolutionized the field of hemoglobin quantification, allowing us to measure this important biomarker in a way that isn't invasive or difficult to handle. The use of optical sensing technology, along with IoT frameworks and AI methods, has improved the accuracy and usefulness of hemoglobin tests, showing a promising way to improve healthcare outcomes. However, challenges remain in the field of non-invasive hemoglobin measurement, such as precision constraints, data security, and privacy safeguards. Despite these challenges, the future landscape of non-invasive hemoglobin measurement leveraging IoT and AI-powered optical sensors appears poised to transform healthcare delivery significantly. Together, IoT and AI-based optical sensors have the potential to revolutionize healthcare monitoring and diagnosis.

关键词： Accuracy Electric variables measurement Medical services Optical variables measurement Real-time systems Optical sensors Reliability Artificial intelligence Medical diagnostic imaging Monitoring

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Implementation of Human Electrocution Prevention Protection in Low-Voltage Distribution Systems with Declared Load of 12 Kva

SSRN

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SSRN 2022年

作者： Robles Lovato, Pablo Daniel Pesantez Duran, Daniel Javier Correa Gavilanez, David Eduardo Program in Electrical Engineering Universidad Politécnica Salesiana Cuenca010205 Ecuador Master’s Program in Electrical Engineering Smart Grid Research Group Salesian Polytechnic University Cuenca010205 Ecuador

In low voltage systems, the flexibility of their handling by unqualified personnel causes countless electrocutions, sometimes resulting in human death. The current generated in low voltage with demands up to 12 kVA in the main distribution board and its easy access to its manipulation has led to the study that proposes to implement coordination of protections to protect people’s lives. For this purpose, case studies implement where the different configurations of the protection devices test, the case relay-relay-fuse configuration with variation in the length from the distribution transformer to the load. The relay-relay-relay behavior locates at an average distance from the distribution transformer to the load. Different randomized scenarios recreate to find the most optimal solution, optimally coordinate the protection devices, and thus protect human life. © 2022, The Authors. All rights reserved.

关键词： Transformer protection

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Verification of the Inactivation Effect of Near-UV LEDs using a Pulsed Lighting System on Staphylococcus aureus

Verification of the Inactivation Effect of Near-UV LEDs usin...

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Sustainable Smart Lighting World Conference & Expo (LS18), IEEE

作者： Hiroki Nomura Seiji Kihara Akinori Zukeran Jun Sawai Takayuki Misu Graduate School of Engineering Master's Degree Program in Electrical and Electronic Engineering Kanagawa Institute of Technology Atsugi Kanagawa Japan Department of Electrical and Electronic Engineering Faculty of Engineering Kanagawa Institute of Technology Atsugi Kanagawa Japan Department of Nutrition and Life Science Faculty of Health and Medical Sciences Kanagawa Institute of Technology Atsugi Kanagawa Japan

ISBN: (数字)9798331540197

ISBN: (纸本)9798331540203

The purpose of this study is to investigate the disinfection effect of Near-UV LED light, which is pulsed using PWM control, on Staphylococcus aureus. To evaluate the disinfection effect of Near-UV LED light, three types of Near-UV LEDs with wavelengths of 395 nm, 385 nm, and 365 nm were installed within a sealed space inside an incubator and energized with a forward current of 1000 mA and a forward voltage of 3.6 V, and the disinfection rate under each type of light was assessed. Additionally, the disinfection effect was evaluated when the same three types of Near-UV LEDs were pulsed with a 50% duty cycle. As a result, 100% disinfection was achieved with 365 nm Near-UV LED direct-current irradiation for 120 seconds, and a 93% disinfection rate was obtained with pulsed irradiation for 150 seconds.

关键词： Radiation effects Power demand Lighting Pulse width modulation Aerospace electronics Light emitting diodes

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AI Prescription Recognition System 9

AI Prescription Recognition System

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9th International Conference on Orange Technology, ICOT 2021

作者： Ou, Yang-Yen Wang, Hung-Min Lee, Tsung-Yen Tsai, An-Chao Wang, Jhing-Fa National Cheng Kung University Department of Electrical Engineering Tainan Taiwan National Pingtung University International Master Program in Information Technology and Applications Pingtung Taiwan

ISBN: (纸本)9781665478427

In order to improve the work efficiency of pharmacists, this work proposes a prescription recognition system. The prescription recognition system mainly consists of three parts, namely pre-processing, information extraction and post-processing. The pre-processing includes contrast enhancement of the input image, optical character recognition, and the merging and sorting of the characters after optical character recognition. In the information extraction part, we hybrid two different strategies, the rule-based method, and the deep learning-based method. Finally, post-processing corrects some common optical character recognition errors and performs format conversion on specific fields. On the user interface, we build a web application server with Django, which provides pharmacists to upload, edit, and manage prescriptions. In the deep-learning-based information extraction, the average F1-score reached 93.8%. In the experiment of the overall recognition system, we test 202 different prescriptions, and compare the three different information extraction strategies, which are rules-based method, deep-learning-based method, and hybrid method respectively. The hybrid method is improved in most fields, and the accuracy of all the fields in our system is greater than 93%. Finally, the system gets 4.38 point on the average opinion score. © 2021 IEEE.

关键词： Optical character recognition

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