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arXiv 2023年

作者： Zhou, Wei Yue, Guanghui Zhang, Ruizeng Qin, Yipeng Liu, Hantao Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada School of Biomedical Engineering Shenzhen University Shenzhen518060 China Beijing Institute of Technology Beijing100081 China School of Computer Science and Informatics Cardiff University CardiffCF24 4AX United Kingdom

Many dense 3D point clouds have been exploited to represent visual objects instead of traditional images or videos. To evaluate the perceptual quality of various point clouds, in this letter, we propose a novel and efficient Reduced-Reference quality metric for point clouds, which is based on Content-oriented sAliency Projection (RR-CAP). Specifically, we make the first attempt to simplify reference and distorted point clouds into projected saliency maps with a downsampling operation. Through this process, we tackle the issue of transmitting large-volume original point clouds to user-ends for quality assessment. Then, motivated by the characteristics of the human visual system (HVS), the objective quality scores of distorted point clouds are produced by combining content-oriented similarity and statistical correlation measurements. Finally, extensive experiments are conducted on SJTU-PCQA and WPC databases. The experimental results demonstrate that our proposed algorithm outperforms existing reduced-reference and no-reference quality metrics, and significantly reduces the performance gap between state-of-the-art full-reference quality assessment methods. In addition, we show the performance variation of each proposed technical component by ablation tests. © 2023, CC BY.

关键词： Luminance

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LMI-Based Non-Iterative Design Strategies for Guaranteed Cost Static Output Feedback Control for Linear Systems

LMI-Based Non-Iterative Design Strategies for Guaranteed Cos...

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International Conference on Electrical, Control and Instrumentation engineering (ICECIE)

作者： Hidetoshi Oya Takuya Nakagawa Shunya Nagai Kazushi Nakano Department of Computer Science Tokyo City University Tokyo Japan Graduate School of Integrative Science and Engineering Tokyo City University Tokyo Japan Department of Electronics and Control Engineering National Institute of Technology (KOSEN) Niihama College Ehime Japan Graduate School of Informatics and Engineering The University of Electro-Communications Tokyo Japan

This paper proposes a non-iterative LMI-based design strategy for guaranteed cost static output feedback controllers for linear systems. The proposed design approach is based on the well-known necessary and sufficient condition for the existence of the static output feedback stabilizing control for linear systems. In this paper, for linear systems we show the non-iterative LMI-based design procedure for static output feedback controllers which minimize the upper bound of a given quadratic cost function, and the effectiveness of the proposed static output feedback controller with guaranteed cost is presented through a simple numerical example.

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Fast Mapping of Flexibility Regions at TSO-DSO Interfaces under Uncertainty

arXiv

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arXiv 2022年

作者： Patig, Alice Stanojev, Ognjen Aristidou, Petros Kiprakis, Aristides Hug, Gabriela Eeh - Power Systems Laboratory Eth Zurich Switzerland Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Cyprus Institute for Energy Systems School of Engineering University of Edinburgh United Kingdom

The gradual decommissioning of fossil fuel-driven power plants, that traditionally provide most operational flexibility in power systems, has led to more frequent grid stability issues. To compensate for the lack of flexible resources, Distributed Energy Resources (DERs) in distribution networks can be employed. To facilitate the use of DERs, the aggregated flexibility in a distribution grid is commonly represented on a PQ-plane displaying the feasible active and reactive power exchange with the upstream grid. This paper proposes a fast feasible operating region mapping mechanism that utilizes a linear power flow approximation in combination with linearized generator, current, and voltage constraints to construct a high-dimensional polyhedral feasible set of DER power injections. The obtained polytope is projected onto the PQ-plane using Fourier-Motzkin Elimination to represent the aggregate network flexibility. Additionally, uncertainty in DER generation is addressed using chance-constraints. Our analysis of a modified 33-bus IEEE test system demonstrates that the proposed method obtains more accurate approximations than former geometric methods and is ten times faster than the tested optimization-based method. Copyright © 2022, The Authors. All rights reserved.

关键词： Electric load flow

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A machine learning method for predicting disease-associated microRNA connections using network internal topology data

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Healthcare Analytics 2023年 4卷 100215-100215页

作者： Jindal, Latika Sharma, Aditi Prasad, K.D.V. Irshad, Azeem Rivera, Richard Karimovna, Abdurakhimova Dilora Department of Computer Science and Engineering Medi-Caps university Indore India Department of computer science institute of engineering & technology Lucknow India Symbiosis Institute of Business Management Hyderabad India Symbiosis International (Deemed University) Pune India Asghar Mall College Rawalpindi Higher Education Department (HED) Govt. of the Punjab Pakistan Department of Informatics and Computer Science Escuela Politécnica Nacional Quito Ecuador Department of Corporate Finance and Securities Tashkent Institute of Finance Tashkent Uzbekistan

Recent studies have shown that aberrations or mutations of microRNAs (miRNA) can cause various diseases. Therefore, identifying disease-associated microRNAs can aid in diagnosing and treating related disorders. However, getting precise correlations through biological research is expensive and time-consuming. This research suggests a machine learning method (HNDLM) based on network internal topology data to anticipate disease-miRNA connections. HNDLM applies the recently described network embedding technique to biological networks rather than creating similar ones. According to the experimental findings, HNDLM performs better in terms of accuracy and Area Under the ROC Curve (AUC) value when compared to the traditional algorithms MIDPE, MIDP, WBSMDA, RLSMDA, Collective Prediction based on Transductive Learning (CPTL), and Human Disease-related MiRNA Prediction (HDMP). The top 30 potential miRNAs suggested by HNDLM can also be verified by earlier research in the case study. HNDLM can identify probable disease-miRNA correlations, which will aid in the investigation of illness path physiology and advance the field of bioinformatics. © 2023 The Authors

关键词： Bioinformatics Correlations Link prediction Machine learning MicroRNAs

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Correction to: Concept Design Evaluation of Sustainable Product–Service Systems: A QFD–TOPSIS Integrated Framework with Basic Uncertain Linguistic Information

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Group Decision and Negotiation 2024年第3期33卷 513-514页

作者： Yang, Qiang Chen, Zhen-Song Zhu, Jiang-Hong Martínez, Luis Pedrycz, Witold Skibniewski, Mirosław J. School of Economics and Management Chongqing Jiaotong University Chongqing China Research Center of Integrated Customs-Port Logistics & Shipping Development Chongqing China School of Civil Engineering Wuhan University Wuhan China College of Traffic and Transportation Chongqing Jiaotong University Chongqing China Department of Computer Science University of Jaén Jaén Spain Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Department of Civil and Environmental Engineering University of Maryland College Park USA Chaoyang University of Technology Taichung Taiwan Institute for Theoretical and Applied Informatics Polish Academy of Sciences Gliwice Poland

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Critical Factors of Immersive Learning in Higher Education: A Systematic Literature Review

Critical Factors of Immersive Learning in Higher Education: ...

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International Conference on ICT For Smart Society (ICISS)

作者： Sunardi Meyliana Arief ramadhan Yusep Rosmansyah Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Information System Departement School of information system Bina Nusantara University Jakarta Indonesia School of Computing Telkom University Bandung Indonesia School of electrical engineering and informatics Bandung Institute of Technology Bandung Indonesia

Immersive learning has gained significant attention with the rising trend of spatial computing, particularly in the after-pandemic era. Numerous research has explored the potential of immersive learning in higher education, primarily on the educational sector. However, prior research has frequently focused too narrowly on the effects of technology and neglected to address the crucial element influencing successful immersive learning in higher education. This study seeks to pinpoint the crucial element contributing to the development of immersive learning experiences. The methodology uses a systematic literature review (SLR) from 2018 up to 2023 to investigate the critical factors of immersive Learning in Higher Education. From the 728 papers initially retrieved, 274 were considered potential candidates, and ultimately, 86 articles were selected based on their relevance to the research question. The results reveal that the critical factors include learning design, technology, immersion, engagement, interactivity, and usability. Academic interests will benefit from this SLR's consequences as institutions create models for designing suitable immersive learning, especially within the context of higher education.

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Improving Pseudo Relevance Feedback with Term Relationship using Firefly Algorithm 2

Improving Pseudo Relevance Feedback with Term Relationship u...

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2nd International Conference on Sustainable engineering and Creative Computing, ICSECC 2020

作者： Hasani, Muhammad Fikri Mandala, Rila Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (纸本)9781728175881

When searching for information with an information retrieval (IR) system, sometimes the results of the search documents provided by the system do not match the information needs of the user. Pseudo Relevance Feedback (PRF) based Query expansion (QE) tries to overcome these problems by adding words that are expected to improve retrieval results from top N ranked documents retrieved. The use of firefly algorithm (FA) as one of the optimization methods has been proven by the previous study to improve the performance of the IR system. However, in that study the weighting of words was done using the rocchio function of the Pseudo Relevant Document (PRD), so it is feared that the performance of IR system will be reduced if the number of relevant documents in PRD is little or none at all. Therefore, scoring by term relationship between query and PRD is used in this study combined with rocchio algorithm. The results of the study showed that usage of term relationship word co-occurrence or word similarity can improve the performance of the IRS that was previously built. In addition, word co-occurrence with jaccard have the best performance compared to the previous study and other combinations. FA itself was able to choose the optimal terms, even though the number of top N ranked documents increased. Furthermore, the combination of term relationship and rocchio algorithm can increase the convergence rate than the ones without rocchio algorithm. © 2020 IEEE.

关键词： Information retrieval

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An Exploratory Investigation into Sustaining Cybersecurity Protection Through the Implementation of SETA

An Exploratory Investigation into Sustaining Cybersecurity P...

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International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Abdullah Mahfuzul Huda Mohammed Brayyich Noor Hanoon Haroon Abdul Azeem Khan rajan Verma University Institute of Computing Chandigarh University Punjab India College of Computing and Informatics Saudi Electronic University Riyadh KSA College of Technical Engineering National University of Science & Technology Dhi Qar Iraq Department of Computer Technical Engineering Technical Engineering College Al-Ayen University Thi-Qar Iraq Faculty of Islamic Technology Negara Brunei Darussalam Division of Research and Development Lovely Professional University Phagwara KKR & KSR Institute of Technology & Sciences Guntur Andhra Pradesh

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

Most modern businesses run their competitive operations online. Cybersecurity is crucial to commercial operations. No cybersecurity defensive solution is universal. Standard security strategy involves using hardware and software to counter known security threats that may become stronger over time. Attackers and security professionals fight constantly. A security approach based primarily on tools is untenable. However, some firms skip employee awareness training, resulting in biased cybersecurity breach decisions. This study objectively analyzes key factors that help enterprises and organizations deploy Security Education, Training, and Awareness (SETA) to find ways to maintain such protection. A dataset of UK corporate cybersecurity breach surveys is used to perform performance testing on eight supervised learning models. The entire research uses the model's built characteristics that are excellent at recognizing SETA-adopting enterprises and organizations. The trial shows that awareness is vital when organizations use SETA. Many firms don't know how to safeguard employees from cybersecurity risks when they use online products and services, such as external web services.

关键词： Training Surveys radio frequency Analytical models Web services Standards organizations Information security computer security Protection Testing

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Deep Reinforcement Learning-based Obstacle Avoidance for Robot Movement in Warehouse Environments 6

Deep Reinforcement Learning-based Obstacle Avoidance for Rob...

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6th IEEE International Conference on Civil Aviation Safety and Information technology, ICCASIT 2024

作者： Li, Keqin Chen, Jiajing Yu, Dezhi Dajun, Tao Qiu, Xinyu Lian, Jieting Ji, Ryan Zhang, Shengyuan Wan, Zhenyu Sun, Baiwei Hong, Bo Ni, Fanghao Han, Jiaqi Ama University Department of Computer Science Quezon Philippines Courant Institute New York University 251 Mercer St New YorkNY10012 United States University of California Berkeley University Avenue and Oxford St School of Information BerkeleyCA94720 United States Carnegie Mellon University Master of Science in Mechanical Engineering School of Engineering 5000 Forbes Avenue PittsburghPA United States Northeastern University College of Engineering Seattle United States School of Professional Studies New York University Master of Science in Database Technology 50 West 4th Street New YorkNY United States Cornell University College of Engineering 616 Thurston Ave IthacaNY14853 United States Cornell University Master of Professional Science in Applied Statistics-Data Science College of Computing and Information Science 616 Thurston Ave IthacaNY United States Independent Researcher Beijing China University of California Irvine Master of Computer Science Donald Bren School of Information and Computer Sciences 510 Aldrich Hall IrvineCA United States Northern Arizona University School of Informatics Computing and Cyber Systems FlagstaffAZ United States Shanghai Institute of Technology Shanghai China

ISBN: (纸本)9798350389418

This paper proposes a deep reinforcement learning based on the warehouse environment, the mobile robot obstacle avoidance Algorithm. Firstly, the value function network is improved based on the pedestrian interaction. We can learn to obtain the relative importance of the current state and the historical trajectory state as well as the joint impact on the robot's obstacle avoidance strategy. Secondly, the reward function of reinforcement learning is designed based on the spatial behaviour of pedestrians, and the robot is punished for the state where the angle changes too much, so as to achieve the requirement of comfortable obstacle avoidance. Finally, the feasibility and effectiveness of the deep reinforcement learning-based mobile robot obstacle avoidance algorithm in the warehouse environment in the complex environment of the warehouse are verified through simulation experiments. © 2024 IEEE.

关键词： Deep reinforcement learning

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A Simulation-Optimization Model for Automated Parcel Lockers Network Design in Urban Scenarios in Pamplona (Spain), Zakopane, and Krakow (Poland)

A Simulation-Optimization Model for Automated Parcel Lockers...

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Simulation Winter Conference

作者： Bartosz Sawik Javier Faulin Adrian Serrano-Hernandez Aitor Ballano Department of Business Informatics and Engineering Management AGH University of Science and Technology Krakow POLAND Department of Statistics Institute of Smart Cities Computer Science and Mathematics Public University of Navarre Pamplona SPAIN

ISBN: (纸本)9781665476621

The constant rise of e-commerce coupled with extremely fast deliveries is a significant contributor to saturate city centers' mobility. To address this issue, the development of a convenient Automated Parcel Lockers (APLs) network improves last-mile distribution by reducing the number of transportation vehicles, the distances driven, and the delivery stops. This article aims to define and compare APL networks in the cities of Pamplona (Spain), Zakopane and Krakow (Poland). Thereby, a bi-criteria weighted-sum simulation optimization model is developed for a representative year for the aforementioned cities. The simulation forecasts the e-commerce demands whereas the optimal APL network is obtained with a bi-criteria maximum APL revenues and minimum network costs. Meaningful results are obtained from the multi-criteria hybrid model outcomes as well as from the cities comparison. These outcomes suggest efficient APLs networks considering cultural and demographic factors for a massive use of APLs in high-demand periods.

关键词： Costs Urban areas Transportation Predictive models Electronic commerce Cultural differences Optimization

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