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International Symposium on Advanced Computing and Communication (ISACC)

作者： Logeswaran K Suresh P Savitha S Swati Sah Prasanna Kumar Kr Rajadevi R Vasugi M Sujit B Akilesh T Department of Artificial Intelligence Center of Excellence in Real-Time Analytic Kongu Engineering College Erode Tamilnaud India Department of Database Systems Vellore Institute of Technology Vellore Tamil Nadu India Department of Computer Science and Engineering K.S.R College of Engineering Tiruchengode Tamil Nadu India School of Engineering and Technology Sharda University Greater Noida Uttar Pradesh India Department of Information Technology Kongu Engineering College Erode Tamilnaud India Department of Artificial Intelligence Kongu Engineering College Erode Tamilnaud India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Ensuring the development of high-quality leather products in the industrial sector requires a strong focus on leather quality assurance. In this study, a unique method for enhancing leather quality assurance using Convolutional Neural Networks (CNNs) is presented. Our suggested solution is able to detect and classify leather defects with amazing accuracy and efficiency by utilizing deep learning. CNN integration for leather fault categorization and detection offers a more reliable and affordable solution, ensuring better product quality and satisfied customers. Existing methods for inspecting leather products often suffer from inconsistent quality control, subjective manual inspection processes, and the complexity of accurately identifying and classifying various types of leather defects. By addressing these challenges, the proposed system aims to empower leather manufacturers with a reliable and efficient solution for ensuring consistent product quality and reducing production costs. Convolutional Neural Networks (CNNs) are integrated to provide a highly automated and effective solution that overcomes these scaling challenges. The proposed CNN-based method uses deep learning algorithms to decode images, which allows the system to swiftly and precisely analyse enormous volumes of visual data. To make sure the CNN can differentiate between various defect classes and keep a low rate of false positives and false negatives, its accuracy, precision, recall, and F1 score should be carefully assessed.

关键词： Deep learning Visualization Quality assurance Accuracy Quality control Product design Quality assessment Convolutional neural networks Reliability Defect detection

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Anomaly Detection in Industrial Production Products Using OPC-UA and Deep Learning 13

Anomaly Detection in Industrial Production Products Using OP...

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13th International Conference on Data Science, Technology and Applications, DATA 2024

作者： Velesaca, Henry O. Carrasco, Doménica Carpio, Dario Holgado-Terriza, Juan A. Gutierrez-Guerrero, Jose M. Toscano, Tonny Sappa, Angel D. ESPOL Polytechnic University Escuela Superior Politécnica del Litoral ESPOL Campus Gustavo Galindo Km. 30.5 Vía Perimetral P.O. Box 09-01-5863 Guayaquil Ecuador Software Engineering Department University of Granada Granada18014 Spain Abbott Laboratories Granada18004 Spain Computer Vision Center Bellaterra Barcelona08193 Spain

ISBN: (纸本)9789897587078

In the realm of industrial manufacturing, detecting defects in products is critical for maintaining quality. Traditional methods relying on human inspection are often error-prone and time-consuming. However, advancements in automation and computer vision have led to smarter industrial control systems. This paper explores a novel approach to identifying defects in industrial processes by integrating OPC-UA and YOLO v8. OPC-UA provides a secure communication standard, enabling seamless data exchange between devices, while YOLO v8 provides accurate object detection. By combining these technologies, manufacturers can monitor production lines in near real-time, analyze defects promptly, and take corrective actions. As a result, product quality and operational efficiency are improved. A case study involving tinplate lid defect detection demonstrates the effectiveness of the proposed approach. The system architecture, including PLC integration, image acquisition, and YOLO v8 implementation, is detailed, followed by the performance evaluation of the OPC-UA server and YOLO v8 model integration. Results indicate efficient communication with low Round Trip Times and End-to-End delay, highlighting the potential of this approach for defect detection. The code is available at GitHub: https://***/hvelesaca/OPC-UA-YOLOv8-Lid-Anomaly-Detection, facilitating further research. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Tin plate

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CPF: A Cross-Layer Prefetching Framework for High-Density Flash-Based Storage

CPF: A Cross-Layer Prefetching Framework for High-Density Fl...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Longfei Luo Han Wang Dingcui Yu Yina Lv Liang Shi Software/Hardware Co-design Engineering Research Center Ministry of Education School of Computer Science and Technology East China Normal University Shanghai China Department of Computer Science City University of Hong Kong Hong Kong China

ISBN: (数字)9783981926385

ISBN: (纸本)9798350348606

The pseudo-single-level-cell (pSLC) technique is widely adopted in high-density flash-based storage to mitigate the performance and endurance problem of high-density flash memory. Furthermore, prefetching schemes can compensate for performance differences among storage tiers. Existing prefetchers are implemented in the operating system (OS) or storage layers. However, OS layer prefetchers are conservative since it is a challenge to achieve both high accuracy and large coverage simultaneously. Storage layer prefetchers are sub-optimal due to the performance differences between pSLC and DRAM. In this paper, a cross-layer prefetching framework (CPF) is proposed to prefetch data selectively. The basic idea is that high-accuracy data will be prefetched to DRAM and large-coverage data will be prefetched to the pSLC flash in storage. To make it practical, an adaptive regulator is further designed to dynamically adjust the cross-layer prefetching to ensure accuracy and coverage. Evaluations show that CPF can improve read performance and reduce data transfer costs significantly.

关键词： Cross layer design Costs Regulators Adaptive systems Prefetching Operating systems Random access memory

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A Phase Estimation Algorithm for Quantum Speed-Up Multi-Party Computing

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computers, Materials & Continua 2021年第4期67卷 241-252页

作者： Wenbin Yu Hao Feng Yinsong Xu Na Yin Yadang Chen Zhi-Xin Yang Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Jiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Department of Computer Science and Engineering Michigan State UniversityEast Lansing48824MIUSA State Key Laboratory of Internet of Things for Smart City and Department of Electromechanical Engineering University of Macao999078Macao

Security and privacy issues have attracted the attention of researchers in the field of IoT as the information processing scale grows in sensor *** computing,theoretically known as an absolutely secure way to store and transmit information as well as a speed-up way to accelerate local or distributed classical algorithms that are hard to solve with polynomial complexity in computation or *** this paper,we focus on the phase estimation method that is crucial to the realization of a general multi-party computing model,which is able to be accelerated by quantum algorithms.A novel multi-party phase estimation algorithm and the related quantum circuit are proposed by using a distributed Oracle operator with *** proved theoretical communication complexity of this algorithm shows it can give the phase estimation before applying multi-party computing efficiently without increasing any additional ***,a practical problem of multi-party dating investigated shows it can make a successful estimation of the number of solution in advance with zero communication complexity by utilizing its special statistic *** simulations present the correctness,validity and efficiency of the proposed estimation method.

关键词： Edge computing security multi-party computing quantum algorithm phase estimation communication complexity

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Playing the video games during COVID-19 pandemic and its effects on player's well-being 2

Playing the video games during COVID-19 pandemic and its eff...

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Noaman, Samar Billi Ibrahim, Amer Ali, Liaqat Iqbal, M.W. Ashraf, Asma Haseeb, Usama Muneer, Salman Almajed, Rasah Hamid, K. Canadian University Dubai Faculty of Management Dubai United Arab Emirates American University in the Emirates College of Computer and Information Technology Dubai United Arab Emirates University of Science and Technology of Fujairah College of Engineering and Technology Fujairah United Arab Emirates Superior University Department of Software Engineering Lahore54000 Pakistan Superior University Department of Computer Science Lahore54000 Pakistan NCBA&E School of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan American University in the Dubai College of Computer and Information Technology United Arab Emirates

ISBN: (纸本)9798350335644

COVID-19 affected our lives intensly. That state of affairs made humans helpless. They had been depressed and experienced loneliness. At that time many human beings were determined to play video games just to loosen up their minds. Many games changed into an additional source of revenue wherein during COVID-19 they were playing while earning money. With these advantages, there were also some poor effects was accrued. A number of players remained playing video games post COVID-19. The carried out survey is based on the social video games results on players' well-being and additionally on the effects of gamers' health and their sensible lifestyles. We are going to investigate the behavior of gamers engaged with video games during the COVID-19 lockdown and the video games affects on their well-being, the time they served in playing video games, and the consequential effect on their behavior and social and mental well-being. The results provide a start line for empirically grounded discussions on video games at some stage in the pandemic, their use, and potential outcomes. Different agegroups of players have been investigated. Most players are between 18 and 30 years. A number of the gamers during lock down played a few hours but most of players were males who spent most of their day playing video games. However, now the ratio of playing video games is reduced as examined with past circumstances. Roughly we can say that the condition as a whole is better, the reason why players enforced video games in their post COVID-19 practical life1. © 2023 IEEE.

关键词： Coronavirus

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Optimizing CDN Architectures: Multi-Metric Algorithmic Breakthroughs for Edge and Distributed Performance

Optimizing CDN Architectures: Multi-Metric Algorithmic Break...

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International Conference on Computing, Networking and Communications (ICNC)

作者： Md Nurul Absur Sourya Saha Sifat Nawrin Nova Kazi Fahim Ahmad Nasif Md Rahat Ul Nasib Department of Computer Science City University of New York New York USA Department of Computer Science Chalmers University of Technology Gothenburg Sweden College of Computing and Software Engineering Kennesaw State University Georgia USA Samsung Austin Research Center Austin Texas USA

ISBN: (数字)9798331520960

ISBN: (纸本)9798331520977

A Content Delivery Network (CDN) is a powerful system of distributed caching servers that aims to accelerate content delivery, like high-definition video, IoT applications, and ultra-low-latency services, efficiently and with fast velocity. This has become of paramount importance in the post-pandemic era. Challenges arise when exponential content volume growth and scalability across different geographic locations are required. This paper investigates data-driven evaluations of CDN algorithms in dynamic server selection for latency reduction, bandwidth throttling for efficient resource management, real-time Round Trip Time analysis for adaptive routing, and programmatic network delay simulation to emulate various conditions. Key performance metrics, such as round-trip time (RTT) and CPU usage, are carefully analyzed to evaluate scalability and algorithmic efficiency through two experimental setups: a constrained edge-like local system and a scalable FABRIC testbed. The statistical validation of RTT trends, alongside CPU utilization, is presented in the results. The optimization process reveals significant trade-offs between scalability and resource consumption, providing actionable insights for effectively deploying and enhancing CDN algorithms in edge and distributed computing environments.

关键词： Content distribution networks Heuristic algorithms Scalability computer architecture Dynamic scheduling Fabrics Performance analysis Servers Resource management Optimization

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On the Digital Event-Based Implementation of a Glucose Regulator via Subcutaneous Insulin Infusion

On the Digital Event-Based Implementation of a Glucose Regul...

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IEEE Conference on Decision and Control

作者： M. Di Ferdinando S. Di Gennaro A. Borri G. Pola P. Pepe Department of Information Engineering Computer Science and Mathematics University of L'Aquila Coppito L'Aquila Italy Center of Excellence DEWS CoppitoL'AquilaItaly

In this paper, an event-based quantized sampled-data control strategy is proposed for the plasma glucose regulation problem in Type 2 diabetic patients. In particular, the proposed event-triggered digital glucose regulator is designed by exploiting a nonlinear time-delay model of the glucose-insulin regulatory system which takes into account the subcutaneous infusion of insulin. It is proved that the provided quantized sampled-data glucose controller, updated via a proposed event-based mechanism, guarantees the semi-global practical stability property of the related closed-loop tracking error system, with arbitrarily small steady-state tracking error. The stabilization in the sample-and-hold sense theory is used as a tool to prove the results. An approximation scheme based on first-order splines is used in order to cope with the problem of the possible non-availability in the buffer of the value of the system variables at some past times which are needed for the implementation of the proposed digital controller. The possible non-uniform quantization of the input/output channels as well as the case of time-varying sampling periods are included in the theory here developed. The validation of the proposed glucose control strategy is carried out via simulations.

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Robust Non-parametric Knowledge-based Diffusion Least Mean Squares over Adaptive Networks

arXiv

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arXiv 2023年

作者： Ashkezari-Toussi, Soheila Sadoghi-Yazdi, Hadi Department of Computer Engineering Ferdowsi University of Mashhad Mashhad Iran Center of Excellence on Soft Computing and Intelligent Information Processing Ferdowsi University of Mashhad Mashhad Iran

The present study proposes incorporating of non-parametric knowledge to the diffusion least-mean-squares algorithm in the framework of a maximum a posteriori (MAP) estimation. The proposed algorithm leads to a robust estimation of an unknown parameter vector in a group of cooperative estimators. Utilizing kernel density estimation and buffering some intermediate estimations, the prior distribution and conditional likelihood of the parameters vector in each node are calculated. Pseudo Huber loss function is used for designing the likelihood function. Also, an error thresholding function is defined to reduce the computational overhead as well as more relaxation against noise, which stops the update every time an error is less than a predefined threshold. The performance of the proposed algorithm is examined in the stationary and non-stationary scenarios in the presence of Gaussian and non-Gaussian noise. Results show the robustness of the proposed algorithm in the presence of different noise types. Copyright © 2023, The Authors. All rights reserved.

关键词： Gaussian noise (electronic)

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A Method for Extracting Small Water Areas Based on an Improved FPN Network

A Method for Extracting Small Water Areas Based on an Improv...

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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Yufeng Yu Meixiang Wang Qun Zhao Yong Wang Qingqing Chen Department of Computer and Software Hohai University Nanjing Jiangsu China School of Computer Engineering Nanjing Institute of Technology Nanjing Jiangsu China Bank of Nanjing Co. Jiangsu Nanjing China Aerosun Corporation Information Center Nanjing Jiangsu China

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

Small water areas such as river branches, lakes, and ponds are vital components of aquatic ecosystems closely linked to human activities. Extracting small water areas from remote sensing images holds significant importance in water resource planning, investigation, sustainable water resource management, and environmental protection. However, the current small water area extraction methods based on remote sensing often encounter the problems of small target, few features, difficult localization and easy loss of edge features. Therefore, this paper proposes a small water area extraction model based on IFPN_BEM network. The model leverages a Transformer Encoder module with deformable attention mechanisms to globally model deep features of the FPN network and optimizes small water area edges using the BEM module, which further improves the extraction accuracy of small water areas. Experimental results on the GID dataset demonstrate that this model provides more complete and accurate extraction of small water areas, achieving precision, F1 score, and IoU accuracy metrics of 81.3%, 84.4%, and 73.4% respectively. These metrics show improvements of 9.5%, 8.2%, and 8.5% over the original FPN network, outperforming UNet, DeepLabV3+, and the improved FPN + PointRend networks across all metrics.

关键词： Measurement Deformable models Accuracy Attention mechanisms Image edge detection Feature extraction Transformers Data mining Water resources Remote sensing

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Markov Chains to Explore the Nanosystems for the Biophysical Studies of Cancers

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Biophysical Reviews and Letters 2024年第4期19卷 335-346页

作者： Khaled A. Al-Utaibi Alessandro Nutini Sadiq M. Sait S. Iqbal Computer Science and Software Engineering Department University of Ha’il Ha’il Saudi Arabia Centro Studi Attivitá Motore – Biology and Biomechanics Department Via di tiglio 94 Lucca Italy Department of Computer Engineering King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Interdisciplinary Research Center for Smart Mobility and Logistics King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Department of General Medicine Shahdara Hospital Lahore Pakistan

The immune response is essential for the human body to function well and to survive against the sudden and chronic diseases such as viral & bacterial infections and cancers. In the immunosurveillance process, Natural Killer (NK) cells are one of the main elements in controlling the development of such infections and, for this reason, they have become the subject of “in-depth” studies especially for the application of new forms of immunotherapy. NK cells can rapidly destroy both autologous and tumor cells in vitro and for this reason the interest in their function is increasingly growing. Their presence in the tumor micro-environment (TME) also assumes prognostic value since the repertoire of NK cell receptors has been linked to anti-tumor function. In this work, a Markov chain modeling approach is proposed to analyze the network of interactions that NK cells carry out with other immune elements in the defense against cancer such as CD4+ cells and CD8+ cells and dendritic cells (DCs) that activate and enhance immune responses. The probabilistic approach used is promising since it helps to understand the balance and the communication in the micro-environment, in a realistic manner. The advantage of discrete time Markov chain approach is that, it can be further extended to complex networks using the state-of-the-art algorithms and can also be translated for the novel AI tools for the cytokines and protein databases.

关键词： Network modeling Gauss–Markov process COVID-19 cancer metastasis

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