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6th IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2024

作者： Mou, Chenglin Wu, Kaiqi Du, Pengcheng Yang, Kaibo School of Civil Engineering and Architecture Henan University of Science and Technology Department of Engineering Mechanics Luoyang471099 China School of Information Engineering Henan University of Science and Technology Department of Computer Science and Technology Luoyang471099 China School of Information Engineering Henan University of Science and Technology Department of Automation Luoyang471099 China School of International Education Henan University of Science and Technology Department of Civil Engineering Luoyang471099 China

ISBN: (纸本)9798350374315

Tower solar thermal power generation is a new type of low-carbon and environmentally friendly clean energy technology. In this paper, a single-objective optimization model is established with the maximum annual average thermal output per unit mirror area of the heliostat field as the objective function. By optimizing the heliostat width, heliostat height and heliostat coordinate, the annual average output thermal power can be maximized while meeting the rated power. The heliostat field is distributed in a circular shape. As the seasons change, the light intensity received by the heliostat field is uneven, so the optical efficiency of the heliostats is also different. We simplify the heliostat field into S area (area with higher optical efficiency) and W area (area with lower optical efficiency) based on the optical efficiency distribution of heliostats. At different locations, According to the heliostat width, heliostat height and heliostat coordinate generated by random functions, substituting these parameters into the efficiency model in this paper, and the random search algorithm is used to traverse the maximum annual output thermal power and the corresponding heliostat parameters. In the shadow occlusion loss model, we have grid to set up the heliostat and use the coordinate conversion to solve the shadow occlusion loss efficiency. © 2024 IEEE.

关键词： Heliostats (instruments)

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High-Performance Computing for Accurate Weather Forecast: Leveraging Advanced Algorithms and HPC Systems for Improved Predictions 3

High-Performance Computing for Accurate Weather Forecast: Le...

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Mariappan, Umasree Balakrishnan, D. Karunya, T.Blessing Ponraj, Anitha Abhishekh, P. Devi, R. Karishma P.S.R. Engineering College Department of Information Technology Tamil Nadu Sivakasi626140 India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Krishnankoil626126 India Kalasalingam Academy of Research and Education Department of Information Technology Tamil Nadu Krishnankoil626126 India Academy of Research and Education Department of Electronics and Communication Engineering Kalasalingam Tamil Nadu Krishnankoil626126 India

ISBN: (纸本)9798350354171

High-performance computing (HPC) has revolutionized the field of weather forecasting by enabling the use of advanced algorithms and powerful computing systems to predict weather patterns with greater accuracy. Weather sensors and satellites collect vast amounts of data on temperature, pressure, humidity, wind speed, and precipitation, which are then processed and analyzed in near real-time to generate accurate forecasts. This article highlights the role of HPC in improving weather forecasting by leveraging Scalable Probabilistic Approximation (SPA) and HPC systems. Weather modelling is used to improve our understanding of how processes work in land, oceans, and the atmosphere. It also helps predict weather between days and seasons, as well as weather projection for the future. By leveraging these technologies and techniques, HPC is revolutionizing weather pattern prediction, enabling more accurate and timely forecasts that can help protect lives and livelihoods. High-performance computing and advanced algorithms are crucial for improving the accuracy of weather forecasting. By leveraging these technologies, researchers can better predict weather patterns, which has significant implications for various industries and applications. © 2024 IEEE.

关键词： Wind forecasting

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Segregation of Dehusked Arecanut using Artificial Intelligence Technique on Raspberry Pi 2

Segregation of Dehusked Arecanut using Artificial Intelligen...

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2nd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2023

作者： Adesh, N.D. Harshith, M. Bhardwaj, Siddh Renuka, A. Manipal Academy of Higher Education Manipal Institute of Technology Department of Information and Communication Technology Karnataka Manipal India Manipal Academy of Higher Education Manipal Institute of Technology Department of Computer Science and Engineering Karnataka Manipal India

ISBN: (纸本)9798350314045

Real-time object identification is of significantly importance across many industries, including agriculture. Since it has the potential to optimize efficiency and accuracy in tasks such as grading and quality control. This research introduces a novel methodology for real-time detection of arecanut objects utilizing the YOLOv5 model on a Raspberry Pi platform. A thorough dataset of arecanut was collected and compiled, wherein the arecanut were categorized into several categories based on their husk characteristics. The YOLOv5 model underwent training using the provided dataset, and data preprocessing was conducted to optimize precision accuracy to its highest potential. Subsequently, the model was implemented on the Raspberry Pi, considering the limitations imposed by the hardware. The model was optimized for the processing resources of the Raspberry Pi by the implementation of quantization techniques, resulting in improved efficiency for real-time inference. By conducting thorough experimentation and evaluation, the study provides evidence of the efficacy of proposed methodology, which enables real-time object detection to grade arecanut on the Raspberry Pi platform. The proposed solution exhibits the potential to bring about a revolutionary transformation in arecanut processing and comprehensively enhance agricultural practices. This is achieved via the provision of a tool that is both cost-effective and efficient for the purpose of object-detecting duties. © 2023 IEEE.

关键词： Grading

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Network Traffic Anomaly Detection: A Powerful Tool for DDoS Attack Identification 3

Network Traffic Anomaly Detection: A Powerful Tool for DDoS ...

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Balakrishnan, D. Mariappanx, Umasree Karunya, T.Blessing Shri, M.Punitha Harini, S.Kesava Harishkrishna, R. Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Krishnankoil626126 India P.S.R. Engineering College Department of Information Technology Tamil Nadu Sivakasi626140 India Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Tamil Nadu Krishnankoil626126 India Kalasalingam Academy of Research and Education Department of Information Technology Tamil Nadu Krishnankoil626126 India

ISBN: (纸本)9798350354171

Distributed Denial-of-Service (DDoS) attacks pose a significant threat to network security and service availability. Traditional detection methods often struggle to differentiate between legitimate traffic and malicious attacks. This work explores the potential of network traffic anomaly detection for accurate DDoS attack identification. We propose a novel approach utilizing a hybrid ARIMA-SWGARCH model. The ARIMA component effectively captures the inherent seasonality and trends within network traffic data, while the SWGARCH component models the volatility clustering often observed during DDoS attacks. By analyzing the residuals of the ARIMA model with the SWGARCH framework, we aim to identify significant deviations indicative of DDoS activity. This hybrid model offers several advantages over existing methods, including improved accuracy, adaptability to varying traffic patterns, and the ability to capture both level and volatility changes in traffic flow. The effectiveness of the proposed approach is evaluated using real-world network traffic datasets, demonstrating its potential as a powerful tool for DDoS attack identification. © 2024 IEEE.

关键词： Network security

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Fast Mesh Reconstruction from Single View Based on GCN and Topology Modification

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computer Systems Science & engineering 2023年第5期45卷 1695-1709页

作者： Xiaorui Zhang Feng Xu Wei Sun Yan Jiang Yi Cao Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China Department of Electrical and Computer Engineering University of WindsorWindsorONN9B 3P4Canada

3D reconstruction based on single view aims to reconstruct the entire 3D shape of an object from one *** existing methods reconstruct the mesh surface of complex objects,the surface details are difficult to predict and the reconstruction visual effect is poor because the mesh representation is not easily integrated into the deep learning framework;the 3D topology is easily limited by predefined templates and inflexible,and unnecessary mesh self-intersections and connections will be generated when reconstructing complex topology,thus destroying the surface details;the training of the reconstruction network is limited by the large amount of information attached to the mesh vertices,and the training time of the reconstructed network is too *** this paper,we propose a method for fast mesh reconstruction from single view based on Graph Convolutional Network(GCN)and topology *** use GCN to ensure the generation of high-quality mesh surfaces and use topology modification to improve the flexibility of the ***,a feature fusion method is proposed to make full use of the features of each stage of the image *** use 3D open dataset ShapeNet to train our network and add a new weight parameter to speed up the training *** experiments demonstrate that our method can not only reconstruct object meshes on complex topological surfaces,but also has better qualitative and quantitative results.

关键词： 3D surface reconstruction deep learning GCN topology modification end-to-end framework

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A Recurrent Neural Network for Image Deblocking Detection and Quality Enhancement 5

A Recurrent Neural Network for Image Deblocking Detection an...

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5th International Conference on Smart Systems and Inventive technology, ICSSIT 2023

作者： Venkatesan, R. Pandiaraj, A. Selvakumar, M. Bannari Amman Institute of Technology-Sathyamangalam Department of Information Technology India School of Computing Kalasalingam Academic of Research and Education-Srivilliputhur Department of Computer Science and Engineering India

ISBN: (纸本)9781665474672

With the advancement of intelligent media, it is becoming much easier to control and adjust progressed video without leaving any visual snippets of data. Since video pressure is so prevalent in modern accounts, the use of deblocking systems to conceal the video adjusting may be a viable alternative. Modified JPEG (MJPEG) is a well-known video format in which each video outline or wound around the field of an electronicvideo gatheringis squeezed independently as a JPEG image. The change may use strong sight and sound deblocking techniques to cover the video changing follows by isolating the MJPEG video into JPEG picture follows. This research study proposes a novel approach to perceive deblocking, which can consequently learn portrayals that are dependent upon a critical learning structure. Initially Recurrent Neural Network (RNN) is trained based on the available datasets to become familiar with the features of deblocking techniques.. Some parts of the images are eliminated with the RNN by applying a proper assessed sliding-window to review the entire image. In order to secure the final discriminative component, a close-by pooling technique is used to set the created image representation. © 2023 IEEE.

关键词： Image segmentation

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Revolutionizing Healthcare: A Smart Pill Detection System Empowered by Deep Learning for Intelligent Drug Recognition 2

Revolutionizing Healthcare: A Smart Pill Detection System Em...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Dhayanithi, A. Dalali, Shivakumar Madhavi, B. Surla, Govindu Marapelli, Bhaskar Arigela, Arun Kumar Sri Sairam Engineering College Department of Information Technology India Pes University Department of Computer Science and Engineering R R Campus India Srkr Engineering College Department of Information Technology Bhimavaram India Department of Cse Andhra Pradesh Mylavaram India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh India Department of Csit Telangana Hyderabad500043 India

ISBN: (纸本)9798350384277

In the dynamic landscape of healthcare, the accurate identification of medications stands as a critical pillar for patient safety and effective medical management. Traditional methodologies for identifying pills often rely on manual processes, placing a considerable burden on healthcare professionals and leaving room for errors, particularly when patients are unable to furnish precise prescription details. Compounding this challenge is the common practice of patients discarding medication containers, further complicating the identification process. At its core, the system harnesses the cutting-edge capabilities of artificial intelligence, specifically delving into advanced deep learning techniques. The overarching goal is to automate the intricate task of pill detection from images while simultaneously providing intelligent recognition of various medicinal *** by the robust Python programming language and underpinned by the sophisticated MobileNet architecture, the system aspires to achieve unprecedented accuracy and efficiency in the identification of pills and recognition of medicinal drugs. The training and testing processes unfold across a diverse dataset, encompassing a myriad of pill *** underscore the system's adaptability and real-world applicability within healthcare scenarios. The underlying ambition of this project is to furnish healthcare professionals with a state-of-the-art tool that not only mitigates the risk of medication errors but also optimizes time and resource utilization. Beyond its clinical applications, the system extends its impact to patients, affording them access to comprehensive information about their prescribed medications, thereby enhancing overall treatment comprehension and adherence. Over the ensuing sections, this project unfurls its narrative, delving into the intricate details of its methodology, implementation strategies, and the meticulous evaluation that underscores its potential to revolutionize healthcare t

关键词： Deep learning

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Effective Code Testing Strategies in DevOps: A Comprehensive Study of Techniques and Tools for Ensuring Code Quality and Reliability 4

Effective Code Testing Strategies in DevOps: A Comprehensive...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Charan, Kolli Karthikeya, Nadipineni Narasimharao, Pujala Bhogeswara Devi, S Anjali Abhilash, Vaka Srinivas, P.V.V.S. Koneru Lakshmaiah Education Foundation Department of Computer Science and Information Technology Andhra Pradesh Vaddeswaram522502 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522502 India

ISBN: (纸本)9798350300093

This research study focuses on exploring effective code testing strategies in DevOps for ensuring code quality and reliability. DevOps is an approach that emphasizes the collaboration and integration between software development and IT operations teams to automate the software delivery process and ensure fast and continuous delivery of high-quality software. Code testing is an essential part of the DevOps process that ensures that the software code is working as expected and meets the user's requirements. This research aims to provide a comprehensive overview of the different techniques and tools used for code testing in DevOps. The study explores various testing techniques, such as unit testing, integration testing, acceptance testing, and automated testing, along with tools such as Pytest, Robot Framework, and Selenium. Additionally, the research examines how code coverage analysis, mutation testing, and load testing can help improve code quality and reliability. The study presents a systematic analysis of the strengths and weaknesses of each testing technique and tool, along with their applications in different stages of the DevOps process. Furthermore, the research provides insights into the challenges and best practices for implementing code testing in a DevOps environment. The results of this study contribute to the existing knowledge of code testing in DevOps and provide guidance for developers, testers, and IT professionals to improve the quality and reliability of their software code. © 2023 IEEE.

关键词： Software design

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Investigating the Impact of Containerization on the Deployment Process in DevOps 2

Investigating the Impact of Containerization on the Deployme...

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

作者： Narasimhulu, Modugula Mounika, Darapureddy Veera Varshini, Puttagunta Amarendra, K. Rao, T.K. Rama Krishna Koneru Lakshmaiah Education Foundation Department of Computer Science and Information Technology Andhra Pradesh Vaddeswaram522502 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522502 India

ISBN: (纸本)9798350347579

This research investigates the impact of containerization on the deployment process in DevOps. Containerization is a technology that enables developers to package their applications and dependencies into lightweight, portable containers that can run anywhere. This study aims to explore how containerization affects various aspects of the deployment process, such as speed, consistency, portability, scalability, and security. Through a comprehensive review of existing literature and case studies, the study identifies the benefits and challenges of using containers in DevOps and highlights best practices and security considerations for organizations adopting containerization. The research findings provide insights into how containerization can improve the deployment process in DevOps, enabling organizations to make informed decisions about incorporating containerization into their software development and deployment workflows. © 2023 IEEE.

关键词： Scalability

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The Challenges and Opportunities of Cloud Security as a Service for Enhancing IoT Security

The Challenges and Opportunities of Cloud Security as a Serv...

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2023 IEEE International Conference on Paradigm Shift in information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Mandal, Sanjeev Kumar Hemelatha, S. Sasirekha, R. Chavan, Gurunath T. Kaneria, Ojasvee Jaffar, A Mohamed Department of Computer Science and Information Technology Karnataka Bangalore India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Atlas SkillTech University Department of Isme Maharastra Mumbai India

ISBN: (纸本)9798350329773

The internet of things (Iota) has had a remarkable impact on modern lifestyles, revolutionizing the way we display, communicate, and have interaction with the sector round us. In conjunction with this new generation, but, come new challenges for organizations in terms of safety. Iota devices frequently lack the security features wished for defensive towards cyber attacks and statistics robbery, leaving them prone to malicious actors. Cloud safety as a carrier (Cases) might also provide the solution to those challenges, via providing a more suitable layer of protection for Iota networks. This new sort of protection answer utilizes cloud-based technologies to provide comfortable get admission to, multi-thing authentication, safety analytics, and strong management abilities for Iota networks. With the aid of imparting this comfy environment, Cases can offer organizations with the warranty that their Iota information can remain comfortable, even within the face of ever-evolving cyber threats. At the identical time, however, heavily investing in Cases increases a number of questions. Factoring in implementation fees, compliance requirements, and commercial enterprise goals may be a daunting task for corporations, and Cases might not always offer a greatest solution. It's vital for businesses to correctly determine their safety necessities when thinking about Cases, and to understand the benefits. © 2023 IEEE.

关键词： Cybersecurity

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