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An Intelligent Deterministic Scheduling Method for Ultralow Latency Communication in Edge Enabled Industrial Internet of Things

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IEEE Transactions on Industrial Informatics 2023年第2期19卷 1756-1767页

作者： Lu, Yinzhi Yang, Liu Yang, Simon X. Hua, Qiaozhi Sangaiah, Arun Kumar Guo, Tan Yu, Keping School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Advanced Robotics and Intelligent Systems Laboratory School of Engineering University of Guelph GuelphONN1G2W1 Canada Computer School Hubei University of Arts and Science Xiangyang441000 China Future Technology Research Center National Yunlin University of Science and Technology Douliu City64002 Taiwan Graduate School of Science and Engineering Hosei University Tokyo184-8584 Japan Riken Center for Advanced Intelligence Project Riken Tokyo103-0027 Japan

Edge enabled Industrial Internet of Things (IIoT) platform is of great significance to accelerate the development of smart industry. However, with the dramatic increase in real-time IIoT applications, it is a great challenge to support fast response time, low latency, and efficient bandwidth utilization. To address this issue, time sensitive network (TSN) is recently researched to realize low latency communication via deterministic scheduling. To the best of our knowledge, the combinability of multiple flows, which can significantly affect the scheduling performance, has never been systematically analyzed before. In this article, we first analyze the combinability problem. Then, a noncollision theory based deterministic scheduling (NDS) method is proposed to achieve ultralow latency communication for the time-sensitive flows. Moreover, to improve bandwidth utilization, a dynamic queue scheduling (DQS) method is presented for the best-effort flows. Experiment results demonstrate that NDS/DQS can well support deterministic ultralow latency services and guarantee efficient bandwidth utilization. © 2005-2012 IEEE.

关键词： Bandwidth

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Visualization and Simulation of Any MPU Architecture Design with Meta-Instruction

Visualization and Simulation of Any MPU Architecture Design ...

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International Symposium on Communications and Information Technologies (ISCIT)

作者： Tomoyuki Morimoto Ziming Wang Jiahui Luo Sota Ogawa Toshiyuki Tsutsumi Department of Computer Science School of Science and Technology Meiji University Kanagawa Japan VIAX Co. Ltd. Computer Science Program Graduate School of Science and Technology Meiji University Kanagawa Japan Sony Semiconductor Solutions Corporation NEC Corporation PLUSEM Co. Ltd.

ISBN: (数字)9798350353952

ISBN: (纸本)9798350353969

Our conventional architecture design tool converted instructions of microprocessor (MPU) into meta-instructions with both semantic and functional expressions, and it displayed a circuit diagram of the meta-instruction. The tool was able to perform dynamic simulation only on an educational processor called COMET. However, the meta-instruction must be able to express the arbitrary instruction of at least different significant existing MPUs in order to design arbitrary MPUs. Furthermore, it should be possible to visualize all instructions and then execute them. Therefore, we have improved the meta-instruction so that the architecture design tool applies to major MPUs: MIPS, ARM, and RISC-V in addition to COMET. The improved architecture design tool visualizes and simulates the circuit diagram of the improved meta-instruction. The result shows the possibility of the meta-instruction-based architecture design tool being able to design any MPU.

关键词： Comets Visualization Translation Microprocessors Semantics Hardware Design tools Information and communication technology Engines

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Bayesian Network Risk Method for Complex System Engineering Operations 15

Bayesian Network Risk Method for Complex System Engineering ...

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15th IEEE International Conference on Humanoid, Nanotechnology, Information technology, Communication and Control, Environment, and Management, HNICEM 2023

作者： Xiong, Na Dasig, Daniel D. Zheng, Zhu Nanchang Institute of Technology Nanchang City China Graduate Studies College of Science and Computer Studies De la Salle University Dasmarinas Philippines Graduate School Jose Rizal University Mandaluyong City Philippines

ISBN: (纸本)9798350381177

This study proposed a risk assessment method based on Bayesian networks for complex system engineering with multiple types of work, strong concealment, and multiple uncertain factors. Starting from the analysis of the engineering operation process, various influencing factors that may bring risks were identified, and a Bayesian network risk assessment model was established. To address the problem of lacking input data for multi-state root nodes, a triangular fuzzy function was applied to determine the probability value, thus to address the difficulty in obtaining conditional probability values for multi-state nodes, a knowledge matrix was constructed and DS evidence theory is applied to fuse and process expert information. Simulation analysis shows that Bayesian network causal reasoning can determine engineering operation risk indicators, diagnostic reasoning can determine key influencing factors and reasonable preventive improvement measures can be formulated based on this. © 2023 IEEE.

关键词： Risk assessment

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Find the Best Rule of Time Series Data Mining with Cluster Analysis 3

Find the Best Rule of Time Series Data Mining with Cluster A...

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3rd International Conference on Computational science and Information Management, ICoCSIM 2019

作者： Buaton, Relita Zarlis, Muhammad Mawengkang, Herman Efendi, Syahril Graduate Program of Computer Science Department of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

Data mining is an analytical process of knowledge discovery in large and complex data sets. Many studies wish to explore data, to find information so that knowledge can be obtained through the grouping process, classification, rules discovery, associations and data mining visualization which shows similarity. Periodic data often occurs in business applications and sciences that has big size, high dimension and continuously updated. The similarity in periodic data is based on several approaches. One of common approaches is to transform periodic series into other domains so that dimensions are reduced, followed by index mechanism. Many studies of time series do not give optimal result because limited to extracting data not able to represent time series and its pattern which is then change into rules. Rules can be found in time series data, but they are still constrained by over fitting and difficult to present. It causes time series data and non linier function of data mining decision can't be optimal. The basic idea in the method proposed is to do periodic discretization for sub-sequential formation. These sub-sequences are grouped through a measure of similarity. The simple rule-finding technique is applied to obtain hidden rules in the temporal pattern. The optimal time series data expected to generate the uncertainty trend, previously unknown and can be used to make decisions or forecasting in the future. © Published under licence by IOP Publishing Ltd.

关键词： Data mining

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Towards classification and comprehensive analysis of AI-based COVID-19 diagnostic techniques: A survey

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Artificial Intelligence in Medicine 2024年 151卷 102858-102858页

作者： Kosar, Amna Asif, Muhammad Ahmad, Maaz Bin Akram, Waseem Mahmood, Khalid Kumari, Saru Department of Computer Science Lahore Garrison University Lahore Pakistan Karachi Pakistan Graduate School of Engineering Science and Technology National Yunlin University of Science and Technology Taiwan Douliu Taiwan Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Douliu Taiwan Departement of Mathematics Chaudhary Charan Singh University Meerut India

The unpredictable pandemic came to light at the end of December 2019, known as the novel coronavirus, also termed COVID-19, identified by the World Health Organization (WHO). The virus first originated in Wuhan (China) and rapidly affected most of the world's population. This outbreak's impact is experienced worldwide because it causes high mortality risk, many cases, and economic falls. Around the globe, the total number of cases and deaths reported till November 12, 2022, were >600 million and 6.6 million, respectively. During the period of COVID-19, several diverse diagnostic techniques have been proposed. This work presents a systematic review of COVID-19 diagnostic techniques in response to such acts. Initially, these techniques are classified into different categories based on their working principle and detection modalities, i.e. chest X-ray imaging, cough sound or respiratory patterns, RT-PCR, antigen testing, and antibody testing. After that, a comparative analysis is performed to evaluate these techniques' efficacy which may help to determine an optimum solution for a particular scenario. The findings of the proposed work show that Artificial Intelligence plays a vital role in developing COVID-19 diagnostic techniques which support the healthcare system. The related work can be a footprint for all the researchers, available under a single umbrella. Additionally, all the techniques are long-lasting and can be used for future pandemics. © 2024 Elsevier B.V.

关键词： Coronavirus

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Social Media Analysis using Sentiment Analysis on COVID-19 from Twitter 6

Social Media Analysis using Sentiment Analysis on COVID-19 f...

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6th International Conference on Information technology, Information Systems and Electrical Engineering, ICITISEE 2022

作者： Iqbal Maulana, Fairuz Heryadi, Yaya Lukas Suparta, Wayan Arifin, Yulyani Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta11480 Indonesia Faculty of Engineering Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta55281 Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350399615

The development of the internet is getting faster, participating in encouraging the emergence of new and innovative information. In filtering the various information that appears, we need a recommended system to perform well for users in today's internet era. A well-performing recommendation system in question is a reliable recommendation algorithm. This algorithm is fundamental to analyzing various information, such as responses on social media based on user behavior data related to the topic of COVID. This data is crawled from tweets on social media Twitter. The data analysis algorithm obtained uses Python, which is then visualized in the form of a diagram. The processed data is user comments on Twitter, and the text data is analyzed using Python, using more than 60000 data sets taken to form visualizations and conclusions. From sentiment analysis, polarity and subjectivity data are obtained to be analyzed, which are negative, neutral, or positive. The result is show positive tweets with 29.2%, negative tweets is 13%, and 57.8% neutral tweets. Lastly, sentiment analysis can help people effectively infer vast and complex data from social media like Twitter. © 2022 IEEE.

关键词： COVID-19 python sentiment analysis social media twitter

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Smart Home Security System Using Face Recognition Based on IoT- CNN 2

Smart Home Security System Using Face Recognition Based on I...

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2nd International Conference on Information technology Research and Innovation, ICITRI 2023

作者： Febriantono, M. Aldiki Zuhair, Alvin Khaeruddin School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia University of Islam Blitar Electrical Engineering Department Blitar Indonesia University of Muhammadiyah Malang Diploma III Electronic Technology Vocational Study Program Malang Indonesia

ISBN: (纸本)9798350324945

Home security is a crucial aspect that requires careful attention, particularly when it comes to addressing theft concerns. Hence, implementing smart door technology equipped with facial recognition holds promising potential for enhancing home security. This study aims to develop a more secure and regulated home entry system by leveraging Internet of Things (IoT) technology and Machine Learning computer Vision for facial recognition. The system integrates IoT devices, such as cameras and automatic doors, wherein facial image data is captured by the camera and processed using the Convolutional Neural Network (CNN) algorithm to identify individuals. Once an individual is recognized, the system grants access to the home through an automated door. By relying on facial features, the system effectively restricts unauthorized access and safeguards homes against theft risks. Therefore, the advancement of a safer and more controlled home entry system utilizing IoT technology and Machine Learning computer Vision holds tremendous benefits for homeowners. © 2023 IEEE.

关键词： Face recognition

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Defect Detection and Classification in Printed Circuit Boards using Convolutional Neural Networks 2

Defect Detection and Classification in Printed Circuit Board...

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Austria, Yolanda D. Fajardo, Arnel C. Graduate School Program Technological Institute of the Philippines Quezon City Philippines College of Computing Science Information and Communication Technology Isabela State University Isabela Cauayan City Philippines

ISBN: (纸本)9798350347579

PCB defect detection and classification is vital in electronics manufacturing to ensure high-quality and safe products. Manual inspection is time-consuming, costly, and prone to errors. To address this, automated methods using deep learning models have gained preference. These models learn from diverse datasets of defect images or videos. In this study, a deep learning model was developed and trained using a dataset containing various defect classes, including breakout, copper, crack, cut, excessive conductor, missing conductor, mouse bite, pinhole, spur, and scratch. The model showed promising results in detecting defects across all classes. © 2023 IEEE.

关键词： Image segmentation

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IoT for Water Quality Categorization 5

IoT for Water Quality Categorization

引用

5th International Conference on Cybernetics and Intelligent Systems, ICORIS 2023

作者： Sastrohartono, Hermantoro Krisdiarto, Andreas Wahyu Uktoro, Arief Ika Rahutomo, Reza Suparyanto, Teddy Pardamean, Bens Institute of Agriculture Stiper Faculty of Agricultural Technology Agricultural Engineering Department Yogyakarta Indonesia Bina Nusantara University School of Information Systems Information System Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics & Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350369489

In agricultural water research, the adoption of Internet of Things (IoT) technology has emerged as a pivotal approach for large-scale data collection. Water availability in the context of water quality is very important, both for domestic and industrial purposes. For domestic purposes, drinking water and bathing water are separated. Meanwhile, for the palm oil industry, boiler filler is differentiated from additional process water (dilution water). Water quality parameters can be assessed from turbidity and Total Dissolve Solid (TDS). Measurements using measuring instruments separately and repeatedly require significant energy, time, and costs. This research was conducted with the primary objective of presenting a novel method for categorizing water quality with the approach of IoT sensor technology. The research methodology entailed the utilization of an integrated IoT water sensors system in conjunction with manual water categorization. The methods consist of (1) system design, (2) design and installation of sensor and IoT-based microcontrollers, and (3) accuracy and precision testing compared with laboratory measurements. The precision of the integrated IoT water sensors was assessed through a dedicated sensor precision test, resulting in an accuracy rate of 94.4% for the turbidity sensor and 97.5% for the TDS sensor. Notably, this approach successfully discriminated drinking water with valid categorization, while other water types, including groundwater, water with tea, and water with coffee, yielded null categorization results. © 2023 IEEE.

关键词： Internet of things

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The effects of growth environments on WS2 in electrocatalytic hydrogen evolution reaction

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Journal of Physics and Chemistry of Solids 2024年 185卷

作者： Chang, Yung-Huang Huang, Chien-Sheng Chen, Yuan-Tsung Chiang, Chia-Yu Bachelor Program in Industrial Technology National Yunlin University of Science and Technology Yunlin Taiwan Department of Electronic Engineering National Yunlin University of Science and Technology Yunlin Taiwan Graduate School of Materials Science National Yunlin University of Science and Technology Yunlin Taiwan

Transition metal dichalcogenides (TMDs) recently have attracted much attention because of their outstanding catalytic activity, low cost, and earth abundance in the hydrogen evolution reaction (HER). Among these TMDs, MoS2 and WS2 are recognized as promising HER electrocatalysts and have been explored to be one of the candidates for replacing precious metals. In this work, the electrocatalytic activity of WS2 grown in Ar-only, Ar + H2, and ambient environments were studied, where the WS2 was synthesized by thermolysis from (NH4)2WS4 precursor. Different surface morphologies are shown under the different growth atmospheres. The structures of pure WS2, WS2 and WO3 mixed, and almost WO3 were prepared in different growth atmospheres of Ar-only, Ar + H2, and ambient environment, respectively. All the materials prepared in these different environments exhibit high crystalline characteristic. Especially, the pure WS2 grown in an Ar-only environment presents the preferred (103) lattice plane. The pure WS2 performs the best H2 evolution efficiency, indicating that high crystallinity and preferred (103) lattice plane could be relative to the electrocatalytic activity. The studies provide fundamental insight for further design and preparation of electrocatalysts, useful in the development of clean energy. © 2023 Elsevier Ltd

关键词： Electrocatalysts

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