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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Yaqi Duan Martin J. Wainwright Department of Technology Operations and Statistics Stern School of Business New York University New York NY Laboratory for Information and Decision Systems Statistics and Data Science Center and Department of Electrical Engineering and Computer Science and Department of Mathematics Massachusetts Institute of Technology Cambridge MA

ISBN: (纸本)9798331314385

We introduce a novel framework for analyzing reinforcement learning (RL) in continuous state-action spaces, and use it to prove fast rates of convergence in both off-line and on-line settings. Our analysis highlights two key stability properties, relating to how changes in value functions and/or policies affect the Bellman operator and occupation measures. We argue that these properties are satisfied in many continuous state-action Markov decision processes. Our analysis also offers fresh perspectives on the roles of pessimism and optimism in off-line and on-line RL.

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IoT for Sustainable Hospitality: A Systematic Review of Opportunities and Challenges for the Hospitality Industry Revolution

IoT for Sustainable Hospitality: A Systematic Review of Oppo...

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International Conference on Distributed computing in Sensor systems (DCOSS)

作者： Marios S. Poullas Elena Kakoulli Department of Computer Science Intelligent Systems Laboratory Neapolis University Pafos Pafos Cyprus Cell & Developmental Biology University College London London UK Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus

Technology has influenced the hospitality industry, and the Internet of Things (IoT) has become a focus for sustainability, revenue growth, and problem-solving. This systematic review examined the opportunities and challenges of implementing IoT technology for a sustainable hospitality. Through a thorough analysis of 28 studies, we found that IoT technology has great potential to address sustainability issues in the hospitality industry. However, there are still significant challenges that need to be addressed for the successful implementation of IoT technology in the hospitality industry. Overall, this study highlights the potential of IoT technology to revolutionize the hospitality industry and make it more sustainable. Further research is needed to address the challenges of IoT implementation in the hospitality industry and to evaluate the effectiveness of IoT technology in achieving sustainability goals.

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INSWF DNA signal analysis tool: Intelligent noise suppression window filter

INSWF DNA signal analysis tool: Intelligent noise suppressio...

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作者： Ahmad, Muneer Ahmad, Iftikhar Bilal, Muhammad Jolfaei, Alireza Mehmood, Raja Majid Department of Information Systems Faculty of Computer Sciences and Information Technology Universiti Malaya Kuala Lumpur Malaysia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Computer and Electronic Systems Engineering Hankuk University of Foreign Studies Yongin Korea Republic of Department of Computing Macquarie University SydneyNSW Australia Information and Communication Technology Department School of Electrical and Computer Engineering Xiamen University Malaysia Sepang Malaysia

DNA signals mainly differ from standard digital signals due to their biological data contents. Owing to unique properties of DNA signals the conventional signal processing techniques, such as digital filters, suffers with spectral leakage and results in insignificant noise suppression in DNA sequence analysis. This article presents an intelligent noise suppression window filter (INSWF) for DNA signal analysis. The filter demises the signal by separating high-level frequency contents and by identifying nucleotides with high fuzzy membership contribution at particular locations. The nucleotide contents of signals are later filtered by application of median filtering employing a combination of s-shaped and z-shaped filters. The fundamental characteristic of codons usage that causes uneven nucleotides segmentation has been tackled by finding the best fit of the curve in biological contents of filter. One of the fuzzy correlations existing between codons and median that nucleotides incorporated to reduce the signal noise to a larger magnitude. The INSWF filter outperformed the existing fixed-length digital filters tested over 250 benchmarked and random datasets of various species. A notable enhancement of 45% to 130% was achieved by significantly suppressing signal noise as compared with conventional digital filters in DNA sequence analysis. © 2020 John Wiley & Sons, Ltd.

关键词： Adaptive filtering

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2021 Monthly Rice Production in Chinese Coastal Provinces 3

2021 Monthly Rice Production in Chinese Coastal Provinces

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3rd International Conference on Big Data Analytics and Practices, IBDAP 2022

作者： Parmar, Ajeet Munasinghe, Thilanka Tubbs, Heidi Anyamba, Assaf Rensselaer Polytechnic Institute Department of Electrical Computer and Systems Engineering Troy United States Rensselaer Polytechnic Institute Department of Information Technology and Web Science Troy United States Oak Ridge National Laboratory Geospatial Science and Human Security Division Oak Ridge United States

ISBN: (数字)9781665469388

ISBN: (纸本)9781665469388

This paper explores the dynamics of rice production in the Chinese provinces of Liaoning, Jilin, Heilongjiang, Shanghai, Jiangsu, and Zhejiang and seeks to predict monthly rice production in the months of April through October using precipitation and Normalized Difference Vegetation Index as the predictor variables available. We utilize ridge and lasso regression models to predict the rice yield. Results indicate that a lasso regression model with an R2 value of 0.9991501 with an adjusted R2 value of 0.9991502 and a ridge regression model with an R2 value of 0.9865443 with an adjusted R2 value of 0.9870637 are possible. The lasso regression model does not account for all predictor variables while the ridge regression model does. Both models could be expanded upon to include more observations. © 2022 IEEE.

关键词： Regression analysis

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Joint Image and Feature Enhancement for Object Detection under Adverse Weather Conditions

Joint Image and Feature Enhancement for Object Detection und...

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International Joint Conference on Neural Networks (IJCNN)

作者： Mengyu Yin Mingyang Ling Kan Chang Zijian Yuan Qingpao Qin Boning Chen School of Computer and Electronic Information Guangxi University Nanning China School of Electrical Engineering Guangxi University Nanning China Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi University Nanning China School of Computing and Information Systems The University of Melbourne Melbourne Australia

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Object detection under adverse weather conditions remains a challenging problem to date. To address this problem, a joint image and feature enhancement method called JE-YOLO is proposed. Firstly, a lightweight image enhancement network is used to enhance the low-quality image captured under adverse weather conditions. Secondly, to provide rich information for detection, two detection backbones are applied in parallel to extract features from both the low-quality image and its enhanced result. Afterwards, the extracted features are further enhanced by a foreground-guided feature refinement module (FFRM), which introduces a task-driven attention mechanism and explores inter-layer correlation. Finally, the enhanced features from different branches are fused by the adaptive multi-branch weighting (AMW) strategy, and then fed to the neck and head of detector. Experiments are carried out on both the low-light and foggy conditions, and the results demonstrate that compared with state-of-the-art (SOTA) methods, the proposed JE-YOLO is able to achieve the highest accuracy of detection in all cases. Code will be available at https://***/Murray-Yin/JE-YOLO.

关键词： Head Neural networks Object detection Detectors Feature extraction Neck Reliability

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Design and Application of a One-Stop Mobile Application Sharing Platform for Steel Enterprises 42

Design and Application of a One-Stop Mobile Application Shar...

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42nd Chinese Control Conference, CCC 2023

作者： Chen, Xinzhe Chen, Luefeng Hu, Jie Yang, Qingfeng Wu, Min Pedrycz, Witold School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2G7 Canada

ISBN: (纸本)9789887581543

As China's steel production accounts for an increasing share of the world's output, the intelligent transformation of the steel industry is becoming increasingly urgent. To address issues such as low levels of mobile informationization in steel enterprises and the lack of an industry-specific mobile application platform, it is of great significance to establish a shared mobile application platform for the steel industry. In this paper, the requirements of the platform were analyzed, and the platform's functions were designed. The software design of the platform was then carried out, and the entire mobile application sharing platform was developed, effectively improving the production management efficiency of steel enterprises. The results indicate that the platform can effectively meet the needs of steel enterprises and has significant engineering significance. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Informatization

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A Multi-Player Potential Game Approach for Sensor Network Localization with Noisy Measurements

A Multi-Player Potential Game Approach for Sensor Network Lo...

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IEEE Conference on Decision and Control

作者： Gehui Xu Guanpu Chen Baris Fidan Yiguang Hong Hongsheng Qi Thomas Parisini Karl H. Johansson Department of Electrical and Electronic Engineering Imperial College London London UK Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Mechanical and Mechatronics Engineering University of Waterloo Waterloo ON Canada Department of Control Science and Engineering Tongji University shanghai China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai China Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing China Department of Electronic Systems Aalborg University Denmark Department of Engineering and Architecture University of Trieste Italy

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Sensor network localization (SNL) is a challenging problem due to its inherent non-convexity and the effects of noise in inter-node ranging measurements and anchor node position. We formulate a non-convex SNL problem as a multiplayer non-convex potential game and investigate the existence and uniqueness of a Nash equilibrium (NE) in both the ideal setting without measurement noise and the practical setting with measurement noise. We first show that the NE exists and is unique in the noiseless case, and corresponds to the precise network localization. Then, we study the SNL for the case with errors affecting the anchor node position and the inter-node distance measurements. Specifically, we establish that in case these errors are sufficiently small, the NE exists and is unique. It is shown that the NE is an approximate solution to the SNL problem, and that the position errors can be quantified accordingly. Based on these findings, we apply the results to case studies involving only inter-node distance measurement errors and only anchor position information inaccuracies.

关键词： Location awareness Noise Games Position measurement Nash equilibrium Distance measurement Noise measurement

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AXI-REALM: A Lightweight and Modular Interconnect Extension for Traffic Regulation and Monitoring of Heterogeneous Real-Time SoCs

AXI-REALM: A Lightweight and Modular Interconnect Extension ...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Thomas Benz Alessandro Ottaviano Robert Balas Angelo Garofalo Francesco Restuccia Alessandro Biondi Luca Benini Integrated Systems Laboratory ETH Zurich Switzerland Department of Electrical Electronic and Information Engineering University of Bologna Italy Department of Computer Science and Engineering UC San Diego San Diego CA USA Department of Excellence in Robotics & AI Scuola Superiore Sant'Anna Pisa Italy

ISBN: (数字)9783981926385

ISBN: (纸本)9798350348606

The increasing demand for heterogeneous functionality in the automotive industry and the evolution of chip manufac-turing processes have led to the transition from federated to integrated critical real-time embedded systems (CRTESs). This leads to higher integration challenges of conventional timing predictability techniques due to access contention on shared resources, which can be resolved by providing system-level observability and controllability in hardware. We focus on the interconnect as a shared resource and propose AXI-REALM, a lightweight, modular, and technology - independent real-time extension to industry-standard AXI4 interconnects, available open-source. AXI-REALM uses a credit-based mechanism to distribute and control the bandwidth in a multi-subordinate system on periodic time windows, proactively prevents denial of service from malicious actors in the system, and tracks each manager's access and interference statistics for optimal budget and period selection. We provide detailed performance and implementation cost assessment in a 12nm node and an end-to-end functional case study implementing AXI-REALM into an open-source Linux-capable RISC-V SoC. In a system with a general-purpose core and a hardware accelerator's DMA engine causing interference on the interconnect, AXI-REALM achieves fair bandwidth distribution among managers, allowing the core to recover 68.2 % of its performance compared to the case without contention. Moreover, near-ideal performance (above 95 %) can be achieved by distributing the available bandwidth in favor of the core, improving the worst-case memory access latency from 264 to below eight cycles. Our approach minimizes buffering compared to other solutions and introduces only 2.45 % area overhead compared to the original SoC.

关键词： Bandwidth Interference Real-time systems Hardware Regulation Timing System-on-chip

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Open-Ended Coaxial Probe Based Chloride Concentration Prediction in Water Using Machine Learning Algorithms

Open-Ended Coaxial Probe Based Chloride Concentration Predic...

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International Youth Conference on Radio Electronics, electrical and Power engineering (REEPE)

作者： Hanima Kannan C H Veena Venugopal Deepa Gupta Parul Mathur Dhanesh G Kurup Priyanka Mathur Department of Electronics and Communication Engineering RF and Wireless Systems Laboratory Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Physical Science Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India School of Engineering and Applied Science Benette University Greater Noida UP India

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

Monitoring chloride levels in water is essential for environmental health, industrial processes, and safe drinking water, as both high and low concentrations can cause harm for living being. This paper presents a novel approach to detecting chloride concentrations using Open-Ended Coaxial Probe (OECP) sensing combined with machine learning algorithms. OECP sensing offers real-time, non-destructive, and precise monitoring, enabling early detection of contaminants. Scattering parameter ( $S$ 11) data across a broad frequency range was collected with varying chloride concentrations in controlled laboratory experiments. Thirteen machine learning algorithms were trained to predict chloride concentrations, with different datasets for training and testing to ensure robustness. Among these, CatBoost demonstrated the best performance. After fine tuning and cross-validation, the CatBoost model achieved a test Mean Square Error (MSE) of 0.00627 and Coefficient of Determination (R2) of 0.9936, showcasing its accuracy for applications in water monitoring, corrosion prevention, and safeguarding public health.

关键词： Machine learning algorithms Water quality Predictive models Real-time systems Sensors Scattering parameters Probes Public healthcare Tuning Testing

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MaDEVIoT: Cyberattacks on EV Charging Can Disrupt Power Grid Operation

arXiv

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arXiv 2023年

作者： Acharya, Samrat Khan, Hafiz Anwar Ullah Karri, Ramesh Dvorkin, Yury Pacific Northwest National Laboratory RichlandWA99354 United States Department of Electrical and Computer Engineering New York University BrooklynNY11201 United States Ralph O’Connor Sustainable Energy Institute Department of Electrical and Computer Engineering Department of Civil and Systems Engineering Johns Hopkins University BaltimoreMD21218 United States

This paper examines the feasibility of demand-side cyberattacks on power grids launched via internet-connected high-power EV Charging Stations (EVCSs). By distorting power grid frequency and voltage, these attacks can trigger system-wide outages. Our case study focuses on Manhattan, New York, and reveals that such attacks will become feasible by 2030 with increased EV adoption. With a single EVCS company dominating Manhattan, compromising a single EVCS server raises serious power grid security concerns. These attacks can overload power lines and trip over-frequency (OF) protection relays, resulting in a power grid blackout. This study serves as a crucial resource for planning authorities and power grid operators involved in the EV charging infrastructure roll-out, highlighting potential cyberthreats to power grids stemming from high-power EVCSs. © 2023, CC BY.

关键词： Relay protection

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