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Robotics, Engineering, science, and technology (RESTCON), International Conference on

作者： Soma Yamamoto Yuho Fukushige Hideaki Nishizawa Panart Khajornrungruang Keisuke Suzuki Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan

In semiconductor processing to form surface shapes, photolithography and dry etching are used. In this case, the vacuum process requires improvements cost and productivity. We propose a sonic-assisted processing method using the resonance phenomenon of stainless used steel plate diaphragms. This method is characterized by vibrating the diaphragm with sound pressure to vibrate the particles and microfabricate on Cu thin film [1][2]. To evaluate the sonic-assisted processing method, we attempted to process Cu wafers by transmitting vibrations perpendicular to the wafer to a SUS plate diaphragm as a micro-shape transfer process using a micro-pattern pad. As a result, 100 µm length and 20 µm width grooves were formed on the Cu wafer surface.

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Research on spam message recognition algorithm based on improved naive Bayes

Research on spam message recognition algorithm based on impr...

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2022 International Conference on Intelligent Transportation, Big Data and Smart City, ICITBS 2022

作者： Fan, Ji-Hui Li, Xu-Yao Teng, Shao-Hua St.Paul University Philippines Department of Graduate School Cagayan Tuguegarao Philippines Guangzhou Institute of Science and Technology School of Computer Science and Engineering Guangzhou China Olympic Park Group Guangzhou China Guangdong University of Technology School of Computer Science and Engineering Guangzhou China

ISBN: (纸本)9781665497213

This paper focuses on the recognition algorithm of spam messages, and collects and arranges the data through the research on the current situation of spam messages at home and abroad. At the same time, businesses seek benefits by sending spam short interest such as advertising and fraud, which seriously endangers the information security and normal life of SMS users. Based on the text content of SMS, this paper establishes a recognition model to accurately identify spam SMS, so as to solve the problem of spam SMS filtering. Using python programming, Gaussian naive Bayes is used to realize text classification, and a high classification accuracy is achieved. The experiment shows that the recognition rate of this algorithm is 93.72%. Experiments show that the improved Bayesian method can effectively identify spam messages. © 2022 IEEE.

关键词： Python

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Mechanism for Enhancement of Functionality in Deep Echo State Network by Optimizing Leaking Rate 3

Mechanism for Enhancement of Functionality in Deep Echo Stat...

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3rd International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2023

作者： Inoue, Shuichi Nobukawa, Sou Nishimura, Haruhiko Watanabe, Eiji Isokawa, Teijiro Graduate School of Information and Computer Science Chiba Institute of Technology Narashino Japan Yamato University Faculty of Informatics Osaka Japan National Institute for Basic Biology Okazaki Japan Graduate School of Engineering University of Hyogo Himeji Japan

ISBN: (纸本)9798350300604

Deep echo state network (Deep-ESN) model consists of multiple reservoir layers, that can respond to layer-specific different time-scales. This dynamical characteristic leads to enhance performance of ESN. However, neither the design guidelines for the hyperparameters of the network and individual neurons nor the mechanism to produce the diverse dynamical response have been clarified. In this study, we proposed an approach to generate the dynamical responses with different time-scales in each layer by adjusting the leaking rate of neurons. Through the evaluations time-series prediction task for different leaking rates, multiscale entropy analysis for each reservoir layer, and cross-correlation between adjacent layers, we found that when the leaking rate is set to low, the layer-specific dynamics with different time-scales are generated, as well as a mechanism whereby the signal propagates to subsequent layers with a delay, i.e., queue characteristic. These characteristics produce a high memory capacity. Consequently, diverse responses with delay leads to an enhancement of Deep-ESN functionality. © 2023 IEEE.

关键词： Deep learning

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Probabilistic Model Building Evolutionary Algorithm Using the Same Type and Size of Model for Any Binary Optimization Problems

Probabilistic Model Building Evolutionary Algorithm Using th...

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International Symposium on Soft Computing and Intelligent Systems (SCIS)

作者： Atushi Kishimoto Kei Ohnishi Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

We propose $a$ new probabilistic model building evolutionary algorithm that uses the same type and size of probabilistic model for any binary optimization problems. The proposed method represents a population as a binary matrix for any binary optimization problems and solves a given problem by transforming the population in the form of matrix to better one with 512 production rules of cellular automaton. Concretely, the proposed method builds a probabilistic model for generating new solution candidates while applying the 512 production rules to the matrix representing the population. The probabilistic model consists of the 512 inverse production rules and their application probabilities. The strong point of the proposed method is to be able to transform any binary optimization problems to the same size of 512-bit production rule optimization problem. Since the proposed method treats any binary optimization problems in a unified manner, we can easily explore a robust method for various binary optimization problems as the proposed method with particular configurations. As the first evaluation of the proposed method, we apply the proposed method to the same kind but different sizes of problems. The results show that depending strongly on the bit patterns of the global optima, the proposed method with the same configurations realizes a good scalability of the number of function evaluations required for obtaining near optimum to the problem size growth.

关键词： Scalability Buildings Automata Production Evolutionary computation Transforms Probabilistic logic Optimization

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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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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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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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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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