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6th International Conference on Advanced Intelligent systems and Informatics, AISI 2020

作者： Zhang, Cheng Liu, Jia-Jing Chang, Kuo-Chi Wang, Hsiao-Chuan Lin, Yuh-Chung Chu, Kai-Chun Hsu, Tsui-Lien School of Information Science and Engineering Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China Institute of Environmental Engineering National Taiwan University Taipei Taiwan Department of Business Management Fujian University of Technology Fuzhou China Institute of Construction Engineering and Management National Central University Taoyuan Taiwan College of Mechanical and Electrical Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (纸本)9783030586683

With the rapid increase in population and electricity consumption, power grid has long formed a large-scale interconnection of systems. The power system is a complex multi-dimensional dynamic system, the traditional system parameter processing method has gradually shown its limitations, which affects the stability and reliability analysis of the system. This study introduces a popular time-frequency intelligent application analysis methodology for nonlinear nonstationary signalsHilbert-huang transform (HHT) algorithm, then summaries the applications of HHT method for low frequency oscillation of advanced power system, power quality detection and harmonic analysis with combining the research achievements of domestic and foreign scholars in recent years. However, this study discusses the interpolation function and endpoint effect of HHT method in practice and further research on its application in advanced power system. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Hilbert-Huang transform

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Study of Advanced Power Load Management Based on the Low-Cost Internet of Things and Synchronous Photovoltaic systems 6th

Study of Advanced Power Load Management Based on the Low-Cos...

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6th International Conference on Advanced Intelligent systems and Informatics, AISI 2020

作者： Turatsinze, Elias Chang, Kuo-Chi Li, Pei-Qiang Chang, Cheng-Kuo Chu, Kai-Chun Zhou, Yu-Wen Omer, Abdalaziz Altayeb Ibrahim School of Information Science and Engineering Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China Institute of Environmental Engineering National Taiwan University Taipei Taiwan Department of Business Management Fujian University of Technology Fuzhou China Institute of Construction Engineering and Management National Central University Taoyuan Taiwan College of Mechanical and Electrical Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (纸本)9783030586683

The main problem with todays power consumption is to determine which load is consuming much power so that it can be disconnected to reduce high electrical bills. The main concern of todays researches is to find an alternative way of installing a grid-connected PV system or other renewable resources free of harmful gases so that they add up to the electrical grids to provide enough power. This study presents how to synchronize a photovoltaic system with a grid to save money and electrical load management based on IoT technology. However, current sensing is used to sense the current when loads are connected and send the instantaneous value of current to the Arduino Uno which is programmed to find the total power consumed and then ESP 8266 WiFi module uploads the data to the Thingspeak for analysis and monitoring where utility company or users can access the information about the usage of electricity since many PV systems are in remote areas. A simulation of an inverter with PLL circuit for generating the desired signal waveform and frequency is done in MATLAB/Simulink and the objective was successfully achieved which showed that the output voltage is a sinusoidal waveform of the same frequency and same phase angle and a current sensor can be installed at the output of the inverter to sense the power generated for analysis and monitoring of the proposed PV system. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Internet of things

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Optimal Power Generation Output Considering Cascading Failure 1

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16th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2020 in conjunction with the 13th International Conference on Frontiers of Information technology, Applications and Tools, FITAT 2020

作者： Deng, Hui-Qiong Luo, Jie Li, Qin-Bin Zheng, Rong-Jin Li, Pei-Qiang Chang, Kuo-Chi School of Information Science and Engineering Fujian University of Technology Fuzhou350108 China Fujian Provincial University Engineering Research Genter of Smart Grid Simulation Analysis and Integrated Control Fuzhou350108 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China College of Mechanical and Electrical Engineering National Taipei University of Technology Taipei Taiwan Department of Business Administration North Borneo University College Sabah Malaysia

ISBN: (数字)9789813364202

ISBN: (纸本)9789813364196

Aiming at the cascading fault phenomenon of power system, this paper analyzes the disturbed branches involved in the initial fault and their relationship with the power generating nodes. Firstly, according to the performance of the initial fault branch in the cascading fault phenomenon and the action of relay protection, an expression form of evaluating the severity of the initial fault branch is given. Secondly, the relationship between the current of the residual system branch and the injection power of the nodes after the initial fault removal is analyzed. Then, on the basis of synthesizing the severity of the disturbed branch and its correlation with the power generating nodes, the key power generating nodes on the seriously disturbed branch are selected by clustering analysis. Finally, based on the key power generation nodes, the optimal power generation output model considering the power generation operation cost was established, the model was solved by particle swarm optimization (PSO), the model was simulated by IEEE39 bus system in MATLAB environment, and the correctness of the established model and method was verified. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Particle swarm optimization (PSO)

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Loophole-free test of macroscopic realism via high-order correlations of measurement

arXiv

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arXiv 2024年

作者： Wang, Ping Chen, Chong Liao, Hao Vorobyov, Vadim V. Wrachtrup, Jörg Liu, Ren-Bao College of Education for the Future Beijing Normal University Zhuhai519087 China Department of Physics The Chinese University of Hong Kong New Territories Shatin Hong Kong Centre for Quantum Coherence The Chinese University of Hong Kong New Territories Shatin Hong Kong The Hong Kong Institute of Quantum Information Science and Technology The Chinese University of Hong Kong New Territories Shatin Hong Kong National Engineering Laboratory on Big Data System Computing Technology Guangdong Province Engineering Center of China-made High Performance Data Computing System College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China 3rd Institute of Physics Research Center SCoPE and IQST University of Stuttgart Stuttgart70569 Germany New Cornerstone Science Laboratory The Chinese University of Hong Kong New Territories Shatin Hong Kong

Test of macroscopic realism (MR) is key to understanding the foundation of quantum mechanics. Due to the existence of the non-invasive measurability loophole and other interpretation loopholes, however, such test remains an open question. Here we propose a general inequality based on high-order correlations of measurements for a loophole-free test of MR at the weak signal limit. Importantly, the inequality is established using the statistics of raw data recorded by classical devices, without requiring a specific model for the measurement process, so its violation would falsify MR without the interpretation loophole. The non-invasive measurability loophole is also closed, since the weak signal limit can be verified solely by measurement data (using the relative scaling behaviors of different orders of correlations). We demonstrate that the inequality can be broken by a quantum spin model. The inequality proposed here provides an unambiguous test of the MR principle and is also useful to characterizing quantum coherence. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum theory

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Novel Truncated-rank Graph-structured and Tree-guided Sparse Linear Mixed Models for Variable Selection on Genome-wide Association Studies

Novel Truncated-rank Graph-structured and Tree-guided Sparse...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Xiang Liu, Xueling Diao, Jing Zheng, Mengyao Li, Jihe Xie, Yongyi Lai, Kang Geng, Xiao Song, Yijun Jiang, Linshan National University of Singapore School of Computing Singapore National University Health System Group Technology Office Singapore Peking University School Hospital of Stomatology National Center for Stomatology National Clinical Research Center for Oral Diseases Department of Preventive Dentistry China Harvard University Harvard T.H. Chan School of Public Health United States Peking University School of Software and Microelectronics China Tongji University Shanghai Skin Disease Hospital Institute of Dermatology School of Medicine China Nanyang Technological University School of Mechanical & Aerospace Engineering Singapore Peking University Third Hospital Engineering Research Center of Bone and Joint Precision Medicine Ministry of Education Orthopaedic Department China Zhejiang University of Finance & Economics Dongfang College China National University of Singapore Institute of Data Science Singapore

ISBN: (纸本)9798350386226

Variable selection for genome-wide association studies is a key focus for bioinformatics researchers in high-performance computing. The rapid growth of biological and biomedical data demands has led to high-dimensional, heterogeneous datasets characterized by non-i.i.d. properties and numerous response variables, often resulting in false negatives or positives in recovered results. Traditional methods, when nalively applied, yield suboptimal performance due to confounding factors. To account for the complex interdependencies in heterogeneous data and enhance the practical outcomes of genome-wide association studies, we introduce two methods, TGsLMM and TTsLMM, which balance effects between response and explanatory variables for subpopulation inference. Our unified framework performs sparse variable selection using graph-structured or tree-guided structures in a low-rank linear mixed model. Additionally, we extend our approach to high-dimensional datasets and adaptively select the covariance structure for genomic data. Extensive experiments on synthetic and three real-world datasets emphasize the robustness and effectiveness of our proposed methods, achieving the highest ROC area compared to baselines and superior results for future potential. © 2024 IEEE.

关键词： Confounding factors genome-wide association study mixed model variable selection

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Reconstructing the evolution history of networked complex systems

arXiv

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arXiv 2024年

作者： Wang, Junya Zhang, Yi-Jiao Xu, Cong Li, Jiaze Sun, Jiachen Xie, Jiarong Feng, Ling Zhou, Tianshou Hu, Yanqing School of Systems Science and Engineering Sun Yat-sen University Guangzhou510006 China Department of Statistics and Data Science College of Science Southern University of Science and Technology Shenzhen518055 China Department of Data Analytics and Digitalisation School of Business and Economics Maastricht University Maastricht6200MD Netherlands Tencent Inc Shenzhen518000 China Center for Computational Communication Research Beijing Normal University Zhuhai519087 China School of Journalism and Communication Beijing Normal University Beijing100875 China 138632 Singapore Department of Physics National University of Singapore 117551 Singapore School of Mathematics Sun Yat-sen University Guangzhou510275 China Center for Complex Flows and Soft Matter Research Southern University of Science and Technology Shenzhen518055 China

The evolution processes of complex systems carry key information in the systems’ functional properties. Applying machine learning algorithms, we demonstrate that the historical formation process of various networked complex systems can be extracted, including protein-protein interaction, ecology, and social network systems. The recovered evolution process has demonstrations of immense scientific values, such as interpreting the evolution of protein-protein interaction network, facilitating structure prediction, and particularly revealing the key co-evolution features of network structures such as preferential attachment, community structure, local clustering, degree-degree correlation that could not be explained collectively by previous theories. Intriguingly, we discover that for large networks, if the performance of the machine learning model is slightly better than a random guess on the pairwise order of links, reliable restoration of the overall network formation process can be achieved. This suggests that evolution history restoration is generally highly feasible on empirical networks. © 2024, CC BY-NC-ND.

关键词： Restoration

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Design and Comparison of Collaborative Filtering technology for Product Suggestions in E-Commerce

Design and Comparison of Collaborative Filtering Technology ...

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Automation, Computing and Renewable systems (ICACRS), International Conference on

作者： P. Rajasekar B. Mohanraj S. N. Padhi N. Sivakumar Lavanya J Charles Prabu V Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Information Technology Karpagam College of Engineering Coimbatore Tamil Nadu India Department of Mechanical Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Computer Science and Engineering Panimalar Engineering College Chennai City Campus Chennai Tamil Nadu India Department of Fashion Designing SRM Institute of Science and Technology CSH Kattankulathur Campus Chennai Tamil Nadu India Department of Computer Science Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (纸本)9781665460859

E-commerce websites and internet purchasing have never been more necessary. Product recommendations are a key tactic employed by online buying platforms. This study compares two different filtering algorithms that are used in product recommendationson websites like Amazon and Flipkart. Reviews of electrical devices, user data, and timestamps were all gathered from Kaggle and put into a dataset. These specifics then undergo several preprocessing steps. The steps are data visualization, duplicate value analysis, missing value analysis, and datatype analysis. The exhibited data is then used to train and test the machine learning models. For this purpose, two machine learning models are developed using two different algorithms. The algorithms K-Nearest Neighbor(KNN) and Support Vector Decomposition (SVD) are employed. The models are then trained to acquire used in the prediction procedure after that. The best models for the prediction process are then determined by scrutinizing the results of both models. The Root Mean Square Error (RMSE), Mean Square Error (MSE), and Mean Absolute Error (MAE) values are located and compared because the findings of the analysis weren't *** erroneous data are plotted into a graph and evaluated to further demonstrate the point. As the SVD method is discovered to be the best of the group, the algorithm's greatest and worst predictions are discovered and examined. Even the poorest model constructed for the study's predictions turned out to be superior to several other prediction algorithms used for product suggestions. This algorithm can be deployed into the backend of any e-commerce website making it more effective.

关键词： Training Support vector machines Analytical models Machine learning algorithms data visualization Machine learning Predictive models

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The Frontier of Geospatial AI Deep Learning Applications in data Mining and Spatial Analysis

The Frontier of Geospatial AI Deep Learning Applications in ...

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Intelligent systems and Advanced Applications (ICISAA), International Conference on

作者： Ajit Kumar Mishra Vikash Verma R. Manivannan N. Krishna Kumar Mudunuru Srividya Sanskriti Sharma Department of Electrical & Electronics Engineering IES College of Technology Bhopal M.P India Dept of Artificial Intelligence & Machine Learning GGITS Jabalpur M.P India School of Computing Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai TN India Dept.of CSE Guru Nanak Institutions Technical Campus Ibrahimpatam Hyderabad Telangana India Dept. of CSE MLR Institute of Technology Hyderabad Telangana India Dept of Computer Science & Business System GGITS Jabalpur M.P India

ISBN: (数字)9798331539948

ISBN: (纸本)9798331539955

Modern artificial intelligence uses geographical data mining and regional analysis to analyze large datasets. Deep learning is a new geographic AI method. Example: Geographic Deep Mining Network (GDMN). The Spatial Feature Extractor (SFE), Temporal Geospatial Attention Mechanism (TGAM), and Geospatial data Fusion Module (GDFM) comprise this technique. We examined six popular approaches and found GDMN performed best. It worked successfully for several reasons. GDMN always outperforms its competitors in memory, F1 score, IoU, training time, and inference time. This is always true. These findings suggest that GDMN might boost geospatial AI’s data mining and location analysis abilities. After all the effort, we have developed a solid basis for location intelligence, making it useful in various subject areas. Because it’s versatile, mapping specialists may use GDMN for better environmental monitoring or city development. Deep learning helps GDMN make better space choices and uncover insights in large, challenging data sets that no one knew about. GDMN’s cutting-edge approaches make space and time research and prediction simpler. This wide perspective offers a more full and all-encompassing picture of today’s challenges than previous techniques. This makes it impossible to overstate how significant this work is to location intelligence. Its unmatched research and insights usher in a new era.

关键词： Deep learning Training Urban areas Transforms Feature extraction Solids Spatial databases Geospatial analysis data mining Artificial intelligence

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Current Problems and Proposed Solutions for the Electronic Commerce Sector

Current Problems and Proposed Solutions for the Electronic C...

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Computer science and Emerging Technologies (CSET), International Conference on

作者： Hassan Al Wahshat Riyad Al-Rousan Wafa Abdulkarem Al-Haithami Syarilla Iryani Ahmad Saany Fatma Susilawati Mohamad Rohit Kumar Department of Management Information System Faculty of Business Ajloun National University Ajloun Jordan Department of Virtual and Augmented Reality College of Information Technology University of Petra (UOP) Jordan Faculty of Finance and Administrative Sciences MEDIU Malaysia Faculty of Informatics and Computing UniSZA Malaysia School of Computer Science and Engineering Jain Deemed To Be University Bangalore India

The rapid growth of electronic commerce (e-commerce) has revolutionized the global business landscape, transforming the way consumers and businesses interact, purchase, and sell goods and services. However, this sector faces a myriad of challenges that impede its sustainable growth and development. This research paper critically examines the current problems encountered by the electronic commerce sector and proposes viable solutions to address these issues. The study identifies three major problem areas affecting the sector: (1) Security and Privacy Concerns, (2) Logistics and Supply Chain Bottlenecks, and (3) Customer Experience and Trust. Regarding security and privacy, the proliferation of cyber-attacks, data breaches, and identity theft has eroded consumer confidence, hindering e-commerce adoption. The paper aims to investigate the latest technologies and best practices, such as blockchain, advanced encryption techniques, and robust authentication methods, to fortify e-commerce platforms against cyber threats and safeguard user data. The second problem area, logistics and supply chain bottleneck, poses challenges related to order fulfilment, inventory management, and last-mile delivery. Through an in-depth analysis, the research proposes innovative approaches like utilizing machine learning algorithms for predictive inventory management, integrating Internet of Things (IoT) devices for real-time tracking, and exploring drone and autonomous vehicle deliveries to enhance overall logistics efficiency. The third issue addressed in this paper pertains to customer experience and trust. The inability to tangibly experience products before purchase, coupled with ambiguous return policies, often deters potential customers from engaging in e-commerce transactions. The study delves into augmented reality (AR) and virtual reality (VR) applications to simulate product experiences and advocates for transparent and customer centric return policies. This research paper sheds li

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Retraction Note: Radar optical communication for analysing aerial targets with frequency bandwidth and clutter suppression by boundary element mmwave signal model

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Optical and Quantum Electronics 2024年第12期56卷 1-1页

作者： Kavitha, V. P. Prabakar, D. Ranjith subramanian, S Balambigai, S. Department of ECE SRM Institute of Science and Technology Chennai India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Insitute of Science and Technology Kattankulathur India Department of Electronic and Communication Engineering Jeppiaar Engineering College Chennai India Department of ECE Kongu Engineering College Perundurai India

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