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3rd Asia Conference on Algorithms, Computing and Machine Learning, CACML 2024

作者： Chen, Jianwen Tsai, Meng-Shiun Lin, Wan-Ju Hung, Che-Lun Department of Computer Science National Tsing Hua University Hsinchu300044 Taiwan Department of Mechanical Engineering National Taiwan University Taipei106319 Taiwan Department of Mold and Die Engineering National Kaohsiung University of Science and Technology Kaohsung807618 Taiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei11221 Taiwan

ISBN: (纸本)9798400716416

Automated tool wear condition monitoring is essential for ensuring product quality. However, achieving both high detection precision and utilizing edge computing are critical for advancing intelligent manufacturing. Consequently, this article develops a tool wear detection system utilizing edge computing techniques. Firstly, a novel end-to-end ensemble UNet-based model is proposed. Through end-to-end feature fusion training, it effectively addresses the robustness and accuracy issues inherent in single models. Secondly, multiple neural computing sticks are involved to reduce the need for cloud transmission and preserve data privacy, thereby enabling scalability and flexibility in deployment. Experiments demonstrate that mean intersection over union (mIoU) and mean dice coefficient (mDice) in tool wear detection can be improved by over 3% and 2%, respectively. Furthermore, the execution time can be accelerated by a factor of 4. © 2024 ACM.

关键词： Smart manufacturing

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An Efficient Lightweight Authentication and Key Agreement Protocol for Patient Privacy

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computers, Materials & Continua 2021年第12期69卷 3495-3512页

作者： Seyed Amin Hosseini Seno Mahdi Nikooghadam Rahmat Budiarto Department of Computer Engineering Ferdowsi University of MashhadMashhad***Iran Department of Informatics Faculty of Science and TechnologyUniversitas Alazhar IndonesiaJakarta12110Indonesia

Tele-medical information system provides an efficient and convenient way to connect patients at home with medical personnel in clinical *** this system,service providers consider user authentication as a critical *** address this crucial requirement,various types of validation and key agreement protocols have been *** main problem with the two-way authentication of patients and medical servers is not built with thorough and comprehensive analysis that makes the protocol design yet has *** paper analyzes carefully all aspects of security requirements including the perfect forward secrecy in order to develop an efficient and robust lightweight authentication and key agreement *** secureness of the proposed protocol undergoes an informal analysis,whose findings show that different security features are provided,including perfect forward secrecy and a resistance to DoS ***,it is simulated and formally analyzed using Scyther *** results indicate the protocol’s robustness,both in perfect forward security and against various *** addition,the proposed protocol was compared with those of other related protocols in term of time complexity and communication *** time complexity of the proposed protocol only involves time of performing a hash function Th,i.e.,:O(12Th).Average time required for executing the authentication is 0.006 seconds;with number of bit exchange is 704,both values are the lowest among the other *** results of the comparison point to a superior performance by the proposed protocol.

关键词： Authentication key agreement protocol tele-medical Scyther perfect forward secrecy

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Modeling of Digital Controllers in Electric Power System Dynamic Simulations

Modeling of Digital Controllers in Electric Power System Dyn...

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Power Tech Conference

作者： Mehran Jafari Gautier Bureau Marco Chiaramello Adrien Guironnet Patrick Panciatici Petros Aristidou Dept. of Electrical Eng. Computer Eng. & Informatics Cyprus University of Technology Limassol Cyprus Réseau de Transport d’ Électricité (RTE) France

In modern electric power systems there are thousands of control devices, spanning from low-voltage distribution networks to high-voltage transmission networks, that manage the energy flows, ensure the secure operation and stabilize the system. To analyze the performance and assess the impact of controllers on the electricity grid, time-domain simulations are most frequently used. In recent decades, all the new controllers introduced in electric power systems are digital controllers and their analog counterparts are gradually being replaced. Nevertheless, many of the most frequently employed controllers are still modeled in time-domain simulations as analog (continuous) systems, employing transfer functions or differential-algebraic equations. This approach introduces a discrepancy between the real response of digital controllers and the simulated one. In this paper, we investigate the impact that correctly modeling digital controllers has on simulation accuracy and performance.

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State-based Sandbox Tool for Distributed Malware Detection with Avoid Techniques 13

State-based Sandbox Tool for Distributed Malware Detection w...

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13th International Conference on Dependable Systems, Services and Technologies, DESSERT 2023

作者： Rehida, Pavlo Markowsky, George Sachenko, Anatoliy Savenko, Oleg Khmelnytskyi National University Department of Computer Engineering and Information Systems Khmelnytskyi Ukraine Kennesaw State University Department of Software Engineering and Game Development Marietta United States Kazimierz Pulaski University of Technology and Humanities Department of Informatics Radom Poland Research Institute for Intelligent Computer Systems West Ukrainian National Unversity Ternopil Ukraine

ISBN: (纸本)9798350396119

This paper deals with the problem of detecting the malware by using emulation approach. Modern malware include various avoid techniques, to hide its anomaly actions. Advantages of using sandbox and emulation technologies are described. Various anti-emulation techniques that are used in modern malware considered. Obfuscation as one primary approach to hide malware malicious actions described and discussed. State of emulator is presented, and the advantages of its usage are covered. Distributed model for malware detection is considered. Basic emulator and its current capabilities presented. Prepared files that represent malware are described. Experimental results for developed files that differs with included avoid techniques are presented. Disadvantages of proposed approach is described. Future research and sandbox improvement are described. © 2023 IEEE.

关键词： anti-emulation techniques malware detection obfuscation techniques sandbox

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Automated Detection of Poultry Farms from Aerial Images for Actionable AI System toward Biosecurity Applications

Automated Detection of Poultry Farms from Aerial Images for ...

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2023 IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2023

作者： Li, Yu Das, Rana Duong, Cuong Lim, Teng-Teeh Haithcoat, Timothy Ersoy, Ilker Shyu, Chi-Ren University of Missouri Department of Electrical Engineering and Computer Science ColumbiaMO United States University of Missouri Biological Engineering ColumbiaMO United States University of Missouri Plant Science and Technology Extension ColumbiaMO United States University of Missouri Institute for Data Science and Informatics Columbia United States University of Missouri Institute for Data Science and Informatics Electrical Eng. and Computer Science Dept. ColumbiaMO United States

ISBN: (纸本)9798350359527

Poultry productions have shifted towards larger farms and often cluster in certain regions. However, many of the smaller farms with a considerable amount of production are not considered concentrated animal feeding operation under regulation in many regions, and therefore are not required to obtain a permit. As a result, information of these operations is not being recorded and monitored, contributing to an increased risk for biosecurity issues. The development in computer vision and availability of high-resolution remote sensing imagery allows the possibility for automatic localization and operation estimation of the unregistered farms, filling in the missing information needed for biosecurity risk management. The farm information extracted from images can be incorporated with other available geospatial data, such as road networks, watersheds, etc., to build a comprehensive knowledge base for actionable plans. In the current study, we present a framework for automated localization, analysis, and visualization of poultry farms from remote sensing images. A robust machine learning method was developed to computationally link the visual features from remote sensing imagery to Missouri Department of Natural Resources Concentrated Animal Feeding Operation data, to identify the animal farm type, location, and potential capacity in the region. The developed process can be performed automatically on images of different timestamps to analyze temporal and spatial development of poultry production facilities of a region. This research contributes to the effective management and regulation of poultry and potentially other animal farms, providing insights into farm characteristics and capacities while facilitating informed decision-making. © 2023 IEEE.

关键词： Object detection

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Design and Simulation of an Affordable Vehicle Speed Detection System

Design and Simulation of an Affordable Vehicle Speed Detecti...

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2023 International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications, ICMERALDA 2023

作者： Thabet, Hassaan Th. H. Al-Hilali, Aqeel A. Al-Safi, Mohammed G. S. Thabit, Thabit H. Mahmood, Firas M. Al-Hadba University College Dept. of Computer Technology Engineering Mosul Iraq Al-Farahidi University Dept. of Medical Instruments Engineering Techniques Baghdad Iraq Al-Esraa University College Dept. of Accounting Baghdad Iraq Ninevah University Dept. of Computer Engineering and Information Mosul Iraq Cihan University-Erbil Dept. of Informatics and Software Engineering Kurdistan Region Iraq

ISBN: (纸本)9798350369359

The objective of this paper is to detect the over speed of vehicles on the roads. Although maximum speed limit signs exist on the roads, many accidents keep on because of high-speed driving. It is necessary to solve these problems through automation techniques. This paper describes a speed detection system for vehicles, the traffic police can easily check the speed of vehicles by using this system. This system consists of a PLC (Programmable Logic Controller) LOGO! V8.3, two Laser sensors, an HMI (Human Machine Interface), a speed camera, and a LOGO! CMR (Communication Module Radio). The detected speed is displayed on the HMI display, moreover, if a vehicle crosses the limited speed, this system displays the over-speed on HMI, and the speed camera is activated to capture an image of that vehicle. Also, an SMS (Short Messages Service) will be sent by the CMR to the traffic authority informing them about the violation to take the necessary legal actions. The system can also store the collected data for documentation purposes. The authors describe a simple, affordable technique for monitoring vehicles' speeds in this paper. © 2023 IEEE.

关键词： Vehicles

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Human-Spa: An Online Platform Based on Spatial Transcriptome Data for Diseases of Human Systems

Human-Spa: An Online Platform Based on Spatial Transcriptome...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Su, Yunyun Cui, Feifei Yan, Shiyu Zou, Quan Cao, Chen Zhang, Zilong Hainan University School of Computer Science and Technology Haikou China Hainan University School of Life Sciences Haikou China University of Electronic Science and Technology of China Institute of Fundamental and Frontier Sciences Chengdu China Nanjing Medical University School of Biomedical Engineering and Informatics Nanjing China

ISBN: (纸本)9798350337488

Spatial transcriptomics has become a major method for high-throughput analysis of gene expression at the current level of cells, which can directly study gene expression changes in disease cells, reveal the occurrence and development mechanism of diseases, identify potential therapeutic targets related to diseases, and provide new clues for disease diagnosis and treatment. Although there have been major breakthroughs in the analysis and acquisition of transcriptome data, there are still many challenges in how to effectively manage, share and utilize these valuable data resources in the study of human systemic diseases. To solve this problem, we are committed to building a spatial transcriptome database website related to human systemic diseases, aiming to provide reliable datasets related to multiple diseases, and provide certain data information and analysis results to accelerate the progress of human systemic diseases research. This paper will introduce the construction process of the database website in detail, and discuss its application prospects in disease research, with the aim of promoting further development and innovation in the field of human health. Here, Human-spa mainly includes 12 Human systems, 38 disease types, 55 datasets, and Human-Spa provides a very friendly web interface for visualization and dataset parsing. In conclusion, the construction of Human-Spa will provide a powerful tool and resource platform for Human disease research. Human-Spa is available for free at http://***/ © 2023 IEEE.

关键词： data analysis database website diseases of human system spatial transcriptome

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An AI-Based Multi-task Framework for Cardiac Function Assessment through Echocardiograms 15

An AI-Based Multi-task Framework for Cardiac Function Assess...

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15th International Conference on Graphics and Image Processing, ICGIP 2023

作者： Guo, Aoyang Mo, Haimiao Hu, Zhijian Wu, Hongjia Liang, Juan School of Computer Science and Informatics Cardiff University CardiffCF10 3AT United Kingdom School of Management Hefei University of Technology Anhui Hefei230009 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore School of Computer Science National University of Defense Technology Hunan Changsha410003 China Department of Cardiology Sun Yat-sen Memorial Hospital of Sun Yat-sen University Guangzhou510120 China

ISBN: (纸本)9781510675018

The accurate assessment of cardiac function is crucial for preventing and controlling cardiovascular diseases and reducing global mortality rates. In recent years, the rapid advancement of machine learning and deep learning, particularly the utilization of artificial intelligence technologies such as convolutional neural networks and multi-task learning, has significantly improved the objectivity and precision of assessing echocardiogram images. However, existing methods lack a thorough exploration of the intrinsic relationship between ejection fraction (EF), end-diastolic volume (EDV), and end-systolic volume (ESV) calculations, which ultimately influence the accuracy of cardiac function assessment. Therefore, we propose an AI-Based Multi-task Framework for Cardiac Function Assessment through echocardiograms. The framework utilizes a 3DCNN network to concurrently extract spatial and temporal features from echocardiographic videos. It employs multi-task learning by assigning varying weights to sub-tasks, enhancing the prediction accuracy of ejection fraction through joint training. Experimental results on the publicly available Echonet-Dynamic dataset demonstrate that the proposed framework achieves promising performance in ejection fraction prediction, with mean absolute error, root mean square error, and R2 scores of 3.89%, 5.13%, and 0.82, respectively, surpassing other comparative methods. This framework will further aid clinicians in more accurate cardiac function assessment, offering promising prospects for its practical application. © 2024 SPIE. All rights reserved.

关键词： Deep neural networks

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Analyzing Machine Learning Techniques for Air Passenger Numbers Forecasting

Analyzing Machine Learning Techniques for Air Passenger Numb...

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International Joint Conference on computer Science and Software engineering (JCSSE)

作者： Pornsiri Khumla Kamthorn Sarawan Jantima Polpinij Sarayut Gonwirat Phoori Chantima Worapot Sommool Department of Logistics Engineering and Transportation Technology Faculty of Engineering and Industrial Technology Kalasin University Kalasin Thailand Department of Computer Engineering Faculty of Engineering and Industrial Technology Kalasin University Kalasin Thailand Department of Computer Science Faculty of Informatics Mahasarakham University Mahasarakham Thailand

ISBN: (数字)9798350381764

ISBN: (纸本)9798350381771

The imperative of forecasting air passenger numbers is underscored by its utility in strategic planning and operational optimization within the aviation sector. As a pivotal technique for predicting continuous data, machine learning methodologies offer a sophisticated avenue for enhancing forecast accuracy. This study embarks on a detailed examination of machine learning approaches to predict air passenger volumes in Thailand, employing a suite of models: Random Forest, XGBoost, Gradient Boosting, LightGBM and CatBoost. Comparative analysis reveals the Gradient Boosting model as the preeminent performer, demonstrating superior forecasting capabilities with an RMSE of 8,081 and a MAPE of 2.57%. These findings underscore the potential of machine learning techniques in refining the precision of air passenger number forecasts, offering significant implications for the planning and management of aviation operations.

关键词： Systematics Reviews Atmospheric modeling Refining Strategic planning Predictive models Boosting

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Neighbor Load Rank Based Load Balancing Routing in LEO Satellite Networks

Neighbor Load Rank Based Load Balancing Routing in LEO Satel...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Lu, Xueyu Wei, Wenting Wang, Kun Fu, Liying Li, Chenxi Wu, Celimuge Xidian University State Key Laboratory of Integrated Service Networks Xi'an China National Key Laboratory of Advanced Communication Networks Shijiazhuang China Xidian University School of Computer Science and Technology Xi'an China University of Electro-Communications Graduate School of Informatics and Engineering Tokyo Japan

ISBN: (纸本)9798350351255

The emerging space-terrestrial integrated networks (STIN) are envisioned to provide seamless connectivity for global coverage. As a promising key component for STIN, the Low Earth Orbiting (LEO) satellite network still faces significant technological challenges in routing for ensuring both reachability and efficiency, due to frequent topology changes and uneven load distribution. Load-balancing routing is a feasible solution to improve efficiency by alleviating regional overload, however, it may encounter either slow convergences or local perceptions. In this paper, we propose a Neighbor Load Rank (NLR) based load-balancing routing for LEO satellite networks, where potential congestion at the queue buffer of a satellite node is characterized by load scores of its neighboring satellites, so as to reduce the perspective limitation of local load-balancing routing. To accelerate routing convergence and reduce computational complexity, we design region delineation, boundary penalty and directional incentive strategies to obtain the approximate minimum hop count path. Meanwhile, we employ the topology-stabilizing model (TSM) to convert the frequent satellite-ground interconnection changes into traffic fluctuations. Simulations demonstrate that NLR can maintain low-latency capacity with lower transmission overhead and effectively balance the overloaded traffic. © 2024 IEEE.

关键词： Satellite communication systems

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