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Research on representative day selection method based on grey relational and K-means clustering

Research on representative day selection method based on gre...

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Smart Grids and Energy systems.(SGES), international conference on

作者： Zihui Zhao Danhong Zhang Hai Ceng Xun Li Xinghua Zhou Shican Hao Huimin Zhang Yadi Xue Zhuhai Power Supply Bureau Guangdong Power Grid Co. Ltd Zhuhai China Guangdong Power Grid Co. Ltd Guangzhou China Beijing Join Bright Digital Power Technology Co. Ltd Beijing China North China Electric Power University Beijing China

ISBN: (数字)9798331539634

ISBN: (纸本)9798331539641

With the rapid development of renewable energy technology, distributed Generation (DG) has become an important component of the power system. The integration of distributed power sources not only improves the flexibility and reliability of power systems. but also brings new challenges to load forecasting, grid scheduling, and line loss calculation in power systems. In response to the problem of selecting representative days for theoretical line loss calculation, this paper considers the impact of distributed power generation on the power system and proposes a representative day selection method based on grey correlation and k-means clustering. Firstly, the grey correlation method is used to select the top ten indicators that are related to the daily line loss rate. Then, the K-means clustering analysis method is used to cluster the top ten indicators, divide the substation data into three categories, select the most effective clustering center, and calculate the distance between the substation data and the clustering center on a monthly basis. The closest day to the clustering center is used as the representative day of this month. Finally, experimental data from Zhuhai was selected to verify the effectiveness of the proposed method.

关键词： Renewable energy sources Correlation Substations Load forecasting Data preprocessing Euclidean distance Reliability theory distributed power generation Smart grids Power system reliability

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Impact of Warehouse Management Factors on Performance Improvement of 3rdParty Logistics Industry

Impact of Warehouse Management Factors on Performance Improv...

引用

2022 international Research conference on Smart computing and systems.Engineering, SCSE 2022

作者： Kodithuwakku, P.I.E. Wijayanayake, A.N. Kavirathna, C.A. University of Kelaniya Dept. of Industrial Management Sri Lanka

ISBN: (数字)9781665473750

ISBN: (纸本)9781665473750

The alarming attention towards the warehousing performance improvement based on different aspects of warehouse management factors has proven to be more critical, especially for the growth of the third-party logistics (3PL) industry. Warehouse performance alleviates discrepancies between warehousing management criteria and performance indicators to assure that the smooth flow of the supply chain aggregates its demanding areas to a satisfactory level. For the last few years, urgent requirements for enhancing improvement have been prominent, especially in the service sector attached with the practical performance in the 3PL industry. Despite analysing the criterion and its effectiveness, identification of the direct relationship between warehouse management factors and how it has impacted the warehousing performance based on the different aspects of indicators in the Sri Lankan 3PL industry, is not yet taken into consideration. This study uses a systematic literature review and expert opinions to identify the key warehousing factors of 3PL industry in Sri Lanka. In total, 12 key success factors were obtained, and those factors were grouped into three categories: operational, economical and environmental factors. A conceptual model is developed to identify the relationship between the warehousing critical success factors and warehousing performance. The study reveals the lack of attention paid to warehouse performance criteria along with warehouse management factors. The findings of this study could inspire the decision-makers who wish to improve the 3PL industry performance while improving warehouse performance in the 3PL industry in Sri Lanka. © 2022 IEEE.

关键词： Supply chains

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Intelligent Data Pipelines for Predictive Maintenance in Saas products

Intelligent Data Pipelines for Predictive Maintenance in Saa...

引用

Automation, computing and Renewable systems.(ICACRS), international conference on

作者： Shubham Dubey Sweta Priyadarshni Covetrus Inc New Jersey USA Deloitte Uttar Pradesh India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

In the rapid pace of today’s digital world, one thing that is most important is to make sure SaaS works without any setbacks. Downtime or performance may result in less customer satisfaction and huge revenue losses. Predictive maintenance allows one to undertake an immediate, proactive strategy to anticipate events and resolve problems well before the users are affected. However, its implementation in SaaS environments has complexities of its own because these are built up with a very distributed nature, and the volume of data is huge in these *** paper will review how intelligent data pipelines can help in predictive maintenance for SaaS products. By using smart data pipelines to intelligently gather, process, and analyze real-time data from all constituent parts of the SaaS infrastructure, patterns and anomalies may be spotted that predict an impending issue. In this paper, we present a design and implementation of scalable data pipelines with machine learning algorithms on logs, performance metrics, and user behavior data. We demonstrate, through case studies and experimental results, how such intelligent data pipelines reduce downtime, improve system reliability, and enhance customer satisfaction. Application of intelligent data pipes in SaaS operations marks one of the most important steps toward more resilient and effective software services.

关键词： Machine learning algorithms Accuracy Uncertainty Software as a service Pipelines Customer satisfaction distributed databases Predictive models Complexity theory Predictive maintenance

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A Hybrid Approach to Feature Selection for Cyber Threat Detection in IoT Networks 3

A Hybrid Approach to Feature Selection for Cyber Threat Dete...

引用

3rd IEEE international conference on Device Intelligence, computing and Communication Technologies, DICCT 2025

作者： Bokhari, Mohammad Ubaidullah Khan, Mohd Zain Masoodi, Faheem Syeed Aligarh Muslim University Department of Computer Science Aligarh India School of Ict Bahrain Polytechnic Bahrain Inti International University Malaysia

ISBN: (纸本)9798331543358

The quick expansion of Internet of Things (IoT) devices have greatly impact multiple sectors, including healthcare, smart agriculture, and environmental monitoring, thereby impacting human lives. The heterogeneous nature and limited resources of these devices make them highly vulnerable to cyber threats. To address these security challenges, machine learning plays a significant role in the development of adaptive intrusion detection systems. Machine learning helps in identifying patterns from the vast amounts of data produced through different IoT devices, particularly for enhancing security. It facilitates in identifying assaults by analyzing trends and abnormalities in data that indicate threats, offering a proactive strategy for cyber security. In this study, we have explored the impact of different Methods of feature selection on the effectiveness of machine learning models in detection of IoT attacks. Feature Selection Methods such as Principal Component Analysis (PCA), Autoencoders (AE), Linear Discriminant Analysis (LDA) and a hybrid selection method combining SelectKBest and Recursive Feature Elimination (RFE) are employed to asses different machine learning models. The UNSW-NB15 dataset is used to evaluate the effectiveness of Random Forest (RF), XGBoost and Support Vector Machine (SVM) classifiers. The experimental results indicate that the hybrid feature selection approach considerably improves classification efficiency, particularly for the RF model which outperform the other having accuracy 99.91%, surpassing traditional dimensionality reduction techniques. The suggested approach effectively eliminates redundant attributes while retaining the most informative elements, leading to improved detection accuracy and other performance metrics. These findings underscore the practical applicability of hybrid feature selection techniques in cyber security, offering a scalable and efficient solution for IoT attack detection. © 2025 IEEE.

关键词： Network intrusion

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Comparative Study of Spatial Diversity Techniques in Log-Normal Shadowing Channels

Comparative Study of Spatial Diversity Techniques in Log-Nor...

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Device Intelligence, computing and Communication Technologies, (DICCT), international conference on

作者： Raj Ranjan Deepak Upadhyay Mukesh Soni Rashmi Sharma Mridul Gupta Nookala Venu Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of CSE University Centre for Research & Development Chandigarh University Mohali Punjab Department of CSE Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India Centre for Internet of Things Madhav Institute of Technology & Science Deemed University Gwalior India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

This study explores the effectiveness of spatial diversity techniques in log-normal shadowing channels, which are crucial for enhancing the reliability and efficiency of wireless communication systems. Extensive simulation and analysis are done to investigate different spatial diversity techniques i.e. maximal ratio combining (MRC), space-time block codes (STBC) and selection combining (SC) in combination with different signal-to-noise ratio (SNR) and shadowing variance conditions. We evaluate the effectiveness of the techniques through performance metrics including Bit Error Rate (BER), spectral efficiency, and signal-to-noise ratio. The results show that MRC and STBC perform better in most scenarios, which highlight the necessity of antenna diversity for system reliability and spectral efficiency. These findings while signaling the possible future enhancements of the wireless communication systems.working under shadowed propagation environments are of paramount value in optimizing the system designs.

关键词： Fading channels Spectral efficiency Spatial diversity System performance Diversity reception Bit error rate Reliability Optimization Shadow mapping Signal to noise ratio

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Assessing Participants Engagement in Virtual Meetings by Using Facial Landmarks and Deep Learning 24

Assessing Participants Engagement in Virtual Meetings by Usi...

引用

proceedings of the 3rd international conference on computing Advancements

作者： Muntasir Rahman Md. Shariar Mahmud Sachcha Nazmus Sakib Faysal Mahmud Md Abdullah Al Jubair Arfina Jannat Prity American International University Bangladesh Dhaka Bangladesh American International University-Bangladesh Dhaka Bangladesh American International University - Bangladesh (AIUB) Dhaka Bangladesh

ISBN: (纸本)9798400713828

This article explores the integration of facial landmarks and deep learning techniques for emotion recognition to enhance user engagement and communication quality in virtual meetings. With the increasing prevalence of virtual communication assessing participants engagement remains a critical challenge. Through the development of Deep Neural Network, Convolutional Neural Network, and hybrid CNN combined with Recurrent Neural Network models, by adapting these architectures to suit the specific requirements of engagement assessment, this study investigates the effectiveness of these algorithms in assessing engagement levels during virtual meetings. this study also contributes to the ongoing development of robust and reliable automated systems.capable of accurately assessing engagement in virtual meeting environments, with implications for remote education and work settings.

关键词： Deep Learning

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Method of determining the required number of database nodes in a distributed data processing system 3

Method of determining the required number of database nodes ...

引用

3rd IEEE international conference on Advanced Trends in Information Theory, ATIT 2021

作者： Mukhin, Vadym Zavgorodnii, Valerii Nikitin, Valerii Kornaga, Yaroslav Fartushnyi, ivan Stepanov, Andrew Department of System Design National Technical University of Ukraine "igor Sikorsky Kyiv Polytechnic institute" Kiev Ukraine Department of information Systems and Technologies National Technical University of Ukraine "igor Sikorsky Kyiv Polytechnic institute" Kiev Ukraine Department of Economic Cybernetics National Technical University of Ukraine "igor Sikorsky Kyiv Polytechnic institute" Kiev Ukraine

ISBN: (纸本)9781665438452

We developed a method of determining the required number of database nodes, which is based on the mechanism of determining the weight coefficient of query types. it allows to obtain the required number of database nodes in a distributed system in order to meet the requirements for query processing parameters and increase their processing speed. This method allows to qualitatively and quantitatively determine the number of database nodes for a given set of a distributed data processing system parameters, that will provide the fastest query processing. © 2021 iEEE.

关键词： Query processing

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Islanded Hybrid DC Micro Grid Power Scheme with Super Capacitor 3

Islanded Hybrid DC Micro Grid Power Scheme with Super Capaci...

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3rd IEEE international Virtual conference on Innovations in Power and Advanced computing Technologies, i-PACT 2021

作者： Kandaswamy, K.V. Sahoo, Sarat Kumar Yanine, Fernando Velammal Engineering College Electronics and Instrumentation Chennai India Parala Maharaja Engineering College Dept. of Electrical Engineering Odisha Berhampur India Universidad Finis Terrae School of Engineering Av Pedro de Valdivia 1509 Santiago Chile

ISBN: (纸本)9781665426916

The study looks at a hybrid control approach for an islanded solar low voltage photovoltaic-based dc microgrid, with the goal of overcoming the limitations of both centralized and distributed control schemes. A Photo Volatile system with battery storage is used on bus 1, battery storage with supercapacitor is used on bus 2, and variable loads like automobile applications are used on bus 3. The suggested hybrid control approach is capable of effortlessly transitioning between high and low bandwidth communication to construct a distributed control system in the event of a central failure. The central supervisory control system is in responsible of adjusting grid settings as well as sending and receiving data to and from local node controllers, that govern bus voltage and energy management. The study demonstrates how system transients are absorbed using battery and supercapacitor devices during load changes. The simulation depicts the continuous flow of information and decision-making processes via each level of control while taking subsystem restrictions into consideration. © 2021 IEEE.

关键词： distributed parameter control systems

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Speed Violation Detection and Enforcement with CNN and IoT Integration

Speed Violation Detection and Enforcement with CNN and IoT I...

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Smart Generation computing, Communication and Networking (SMART GENCON), international conference on

作者： Shreyasi Bhattacharya Ramakrishnan Raman Symbiosis Law School (SLS) Pune & Symbiosis International (Deemed University) Pune Maharashtra India Symbiosis Institute of Business Management Pune & Symbiosis International (Deemed University) Pune Maharashtra India

Worldwide traffic accidents and deaths are largely attributable to drivers going too fast for conditions. This research introduces a unique method for detecting and enforcing speeding violations using Convolutional Neural Networks (CNNs) and the Internet of Things (IoT). The system employs deep learning and collects data in real-time from IoT devices. In this case, it describes developing a CNN–based method for analyzing traffic camera footage in real-time. The CNN model reliably detects moving objects and estimates their velocity. Accurate vehicle tracking is achieved by integrating data from IoT sensors, such as global positioning and vehicle identification systems. Cloud computing allows for the transfer and processing of data in near real-time. It shows that this combined method may successfully identify speeding violations. The IoT data verified the results, indicating that the CNN model had an accuracy of over 90% in speed estimation, which allowed for automated enforcement measures. Enforcement response times can also be drastically reduced by including real-time monitoring and alerting. The system provides a viable approach for speed violation identification and enforcement by merging CNN image processing with IoT data, leading to safer road environments and fewer accidents. It predicts this technique will significantly improve road safety on a global scale, which will, in turn, revolutionize traffic management.

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RTTAC-An Access Control Model Based on Blockchain and Role-Trusted Transfer

RTTAC-An Access Control Model Based on Blockchain and Role-T...

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Computer Science and Blockchain (CCSB), international conference on

作者： Feng Wang Yang Lu Yang Liu Zhiruo Zhang Henan university of technology Distributed Computing & BlockChain Lab Zhengzhou China

Aiming to address the data security issues in data sharing, current approaches such as RBAC, ABAC, and blockchain-based data sharing platforms suffer from problems like role redundancy, role abuse, complex authorization, and data user privacy leakage. In response, a role-based trusted transfer access control model based on trust and attributes is proposed. This model is built upon Attribute-Based Access Control (ABAC) while integrating Role-Based Access Control (RBAC). Users obtain roles based on their attributes and trust levels, and roles determine access permissions based on trust level and defined policies, and then determine the transferability of roles, at the same time, the transfer of roles is limited by trust levels. Encryption techniques and off-chain storage systems.are utilized to protect data privacy and integrity, thereby alleviating the storage pressure on the blockchain. Finally, the performance and effectiveness of the proposed model were evaluated through experimental analysis, demonstrating its capability to achieve trustworthy role transfer and enhance the security and privacy of on-chain sharing schemes.

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