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International Conference on Inventive Research in Computing Applications (ICIRCA)

作者： Abin George Sham H. Mankar N V S Suryanarayana Manish Gupta R Sathish A Priya Knowledge and technology management Division Council of Scientific & Industrial Research National Aerospace Laboratories Bangaluru Karnataka India Department of Mechanical Engineering Pimpri Chinchwad College of Engineering and Research Pune Maharashtra India Administrative Officer Central Tribal University of Andhra Pradesh Vizianagaram Andhra Pradesh India Division of Research and Development Lovely Professional University Phagwara Punjab India Department of Computer Science Shri Nehru Maha Vidayala College of Arts and Science Coimbatore Tamil Nadu India School of Architecture Koneru Lakshmaiah Educational Foundation Gundur Andhra Pradesh India

The solid waste management is the process of proper decomposition of waste materials within a period of time. This includes the collection of garbage's and then proceeded through certain measures for decomposition. There are various methods adopted in the garbage separation process. This includes the artificial intelligence techniques for the estimation and determination of the solid waste through automatic detection and separation of the garbage waste using control and sensing units. They are integrated with the internet of things to enable the two way communication system. This helps to visualize the functioning of the system adopting the digital platform. The proposed system is implemented through the smart dust bin held in every household that automatically senses the non-biodegradable and biodegradable waste materials. The classification of the waste materials are identified through the image processing techniques.

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Real-Time Measurement-Driven Reinforcement Learning Control Approach for Uncertain Nonlinear Systems

arXiv

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arXiv 2023年

作者： Abouheaf, Mohamed Boase, Derek Gueaieb, Wail Spinello, Davide Al-Sharhan, Salah College of Technology Architecture and Applied Engineering Bowling Green State University Bowling GreenOH43403-0001 United States School of Electrical Engineering and Computer Science University of Ottawa 800 King Edward Avenue OttawaONK1N 6N5 Canada Department of Mechanical Engineering 161 Louis Pasteur OttawaONK1N 6N5 Canada Machine Intelligence Research Labs AuburnWA98071-2259 United States

The paper introduces an interactive machine learning mechanism to process the measurements of an uncertain, nonlinear dynamic process and hence advise an actuation strategy in real-time. For concept demonstration, a trajectory-following optimization problem of a Kinova robotic arm is solved using an integral reinforcement learning approach with guaranteed stability for slowly varying dynamics. The solution is implemented using a model-free value iteration process to solve the integral temporal difference equations of the problem. The performance of the proposed technique is benchmarked against that of another model-free high-order approach and is validated for dynamic payload and disturbances. Unlike its benchmark, the proposed adaptive strategy is capable of handling extreme process variations. This is experimentally demonstrated by introducing static and time-varying payloads close to the rated maximum payload capacity of the manipulator arm. The comparison algorithm exhibited up to a seven-fold percent overshoot compared to the proposed integral reinforcement learning solution. The robustness of the algorithm is further validated by disturbing the real-time adapted strategy gains with a white noise of a standard deviation as high as 5%. © 2023, CC BY-NC-ND.

关键词： Reinforcement learning

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Assessment of PM2.5-associated mortality burden among the elderly: Insights into demographic, socio-economic, and geographic factors

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Air Quality, Atmosphere & Health 2024年第7期17卷 1535-1545页

作者： Sharma, Ayushi Hsiao, Hsi-yu Liu, Jia-Yi Lung, Shih-Chun Candice Su, Huey-Jen Shen, Ching-Fen Chen, Nai-Tzu Wu, Pei-Chih Lin, Cheng-Yu Liang, Sheng-Fu Cheng, Tain-Junn Chan, Ta-Chien Tsay, Yaw-Shyan Chung, Hsin-Ying Wang, Yu-Chun Department of Environmental Engineering College of Engineering Chung Yuan Christian University Taoyuan Taiwan Department of Civil Engineering College of Engineering Chung Yuan Christian University Taoyuan Taiwan Research Center for Environmental Changes Academia Sinica Taipei City Taiwan Department of Environmental and Occupational Health National Cheng Kung University Tainan City Taiwan National Cheng Kung University Hospital Pediatrics Office Tainan City Taiwan Department of Green Energy and Environmental Resources Chang Jung Christian University Tainan City Taiwan Sleep Medicine Center College of Medicine National Cheng Kung University Hospital National Cheng Kung University Tainan Taiwan Department of Computer Science and Information Engineering & Institute of Medical Informatics National Cheng Kung University Tainan City Taiwan Department of Neurology and Occupational Medicine Chi Mei Medical Center Tainan City Taiwan School of Medicine College of Medicine National Sun Yat-Sen University Kaohsiung City Taiwan Research Center for Humanities and Social Sciences Academia Sinica Taipei City Taiwan Department of Architecture National Cheng Kung University Tainan City Taiwan Department of Plant Industry National Pingtung University of Science and Technology Tainan City Taiwan

Since long-term exposure to PM2.5 is a growing concern in Taiwan, therefore, our study aimed to investigate the association between different PM2.5 concentrations and cause-specific mortalities, as well as explore the influence of demographic, socio-economic, and geographic determinants on mortality. Utilizing a Distributed Lag Non-Linear Model (DLNM), we estimated the PM2.5-attributable mortality burden among the elderly population (above 65 years) in Taiwan from 2005 to 2018. Our analysis considered various thresholds defined by the World Health Organization (WHO) to assess the impact of PM2.5 concentrations, ranging from standard Air Quality Guidelines (AQG) levels (0–15 μg/m3) to higher interim targets (up to 80 μg/m3). Our study reveals that achieving AQG levels (< 15 μg/m3) in Taiwan could potentially prevent 5571 elderly deaths, including 889 deaths from CVDs, 934 deaths from RTIs, and 680 deaths from chronic diseases. We observed a rapid increase in attributable deaths at lower PM2.5 concentrations, with a subsequent marginal increase at higher levels of exposure. We also identified several factors that modify the effects of PM2.5 exposures among elderly. For example, we found that higher elderly population ratios and high-income households were associated with an elevated risk of PM2.5-related deaths. In contrast, higher social welfare expenditure, better availability of medical practitioners per square kilometer, and higher forest-to-land ratios were associated with positive effects, indicating their potential in reducing mortality risk. This study fills a knowledge gap by examining the health consequences of varying PM2.5 levels and explored the complex interplay between demographic, socio-economic, and geographic factors in relation to PM2.5-related mortality.

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DoS-Aware Quantized Control of Nonlinear Systems via Linearization ⁎

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IFAC-PapersOnLine 2020年第2期53卷 3054-3059页

作者： Rui Kato Ahmet Cetinkaya Hideaki Ishii Department of Computer Science Tokyo Institute of Technology Yokohama 226-8502 Japan Information Systems Architecture Science Research Division National Institute of Informatics Tokyo 101-8430 Japan

This paper deals with a quantized feedback stabilization problem of nonlinear networked control systems via linearization. In particular, we study circumstances where the communication channel is interrupted by Denial-of-Service (DoS) attacks and its data rate is limited. We employ a deterministic DoS attack model which constraints the amount of attacks only by their frequency and duration, allowing us to capture a large class of potential attacks. To achieve asymptotic stabilization, we propose a resilient dynamic quantizer in the sense that it does not saturate in the presence of packet losses caused by DoS attacks. A sufficient condition for stability is derived by restricting the average frequency and duration of attacks. Since our result only guarantees local stability, we explicitly investigate an estimate of the region of attraction, which may be reduced by attacks. A simulation example is presented for demonstration of our results.

关键词： DoS attacks quantized control nonlinear systems stability analysis linearization

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MV-Sketch: A Fast and Compact Invertible Sketch for Heavy Flow Detection in Network Data Streams

MV-Sketch: A Fast and Compact Invertible Sketch for Heavy Fl...

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2019 IEEE Conference on computer Communications, INFOCOM 2019

作者： Tang, Lu Huang, Qun Lee, Patrick P. C. Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong State Key Lab of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9781728105154

Fast detection of heavy flows (e.g., heavy hitters and heavy changers) in massive network traffic is challenging due to the stringent requirements of fast packet processing and limited resource availability. Invertible sketches are summary data structures that can recover heavy flows with small memory footprints and bounded errors, yet existing invertible sketches incur high memory access overhead that leads to performance degradation. We present MV-Sketch, a fast and compact invertible sketch that supports heavy flow detection with small and static memory allocation. MV-Sketch tracks candidate heavy flows inside the sketch data structure via the idea of majority voting, such that it incurs small memory access overhead in both update and query operations, while achieving high detection accuracy. We present theoretical analysis on the memory usage, performance, and accuracy of MV-Sketch. Trace-driven evaluation shows that MV-Sketch achieves higher accuracy than existing invertible sketches, with up to 3.38 × throughput gain. We also show how to boost the performance of MV-Sketch with SIMD instructions. © 2019 IEEE.

关键词： Data structures

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Influence of surface albedo and vegetation fractional coverage on thermal environment of mining area

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Harbin Gongye Daxue Xuebao/Journal of Harbin Institute of technology 2022年第12期54卷 117-126页

作者： Hou, Chunhua Li, Fuping He, Baojie Ma, Pengkun Song, Wen Zhao, Jingjing College of Mining Engineering North China University of Science and Technology Hebei Tangshan063210 China Department of Computer Science Tangshan Normal University Hebei Tangshan063000 China Faculty of Architecture and Urban Planning Chongqing University Chongqing400405 China Faculty of Built Environment University of New South Wales Sydney2052 Australia Institute of Urban Boundary Layer and Atmospheric Environment Institute of Urban Meteorology CMA Beijing100089 China Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China

The influence of the changes in surface albedo on the thermal environment of the mining area due to the destruction of the original vegetation caused by the high-intensity mining of mineral resources was analyzed. Taking Malanzhuang Iron Mine in Qian'an city of Hebei province as an example, the statistical model method was adopted to retrieve the surface albedo based on the visible light band of Landsat remote sensing image, and the pixel trisection model was used to retrieve the vegetation fractional coverage based on NDVI-DFI. By means of radiative transfer equation method, the land surface temperature (LST) was retrieved based on the thermal infrared band of Landsat remote sensing image. With the aid of superposition analysis method, correlation analysis method, and regression analysis method, the response of spatiotemporal heterogeneity of surface thermal environment to the changes in photosynthetic vegetation coverage (fPV) and surface albedo was quantitatively and visually explored. Results showed that the spatial distribution of surface thermal environment in the study area showed obvious heterogeneity and regularity. The high temperature zone was mainly distributed in the northern Shaheshan stope, central Liuheyu dump, and the unreclaimed Baimashan dump in the south. The moderate temperature zone was mainly located at the edge of the high temperature zone and other bare surfaces. The low temperature zone was mainly located in the northern reclaimed tailings pond and reclaimed waste dump, as well as the green land and water bodies in the area. The spatiotemporal variation of photosynthetic vegetation coverage and surface albedo resulted in a large heterogeneity of the surface thermal environment during the summer midday in the study area. The mean surface temperature of underlying surface in the third phase of the image was ranked as follows: mining area bare rock > dump slag > reclamation vegetation > water body. The regression analysis results showed that f

关键词： Vegetation

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Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectives

arXiv

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arXiv 2023年

作者： Bousbiat, Hafsa Bousselidj, Roumaysa Himeur, Yassine Amira, Abbes Bensaali, Faycal Fadli, Fodil Mansoor, Wathiq Elmenreich, Wilfried University of Applied Sciences Institut für Informatic Wiener Neustadt2700 Austria University of Skikda Department of Computer Science Mezghich Skikda21000 Algeria University of Dubai College of Engineering and Information Technology Dubai United Arab Emirates University of Sharjah College of Computing and Informatics Sharjah United Arab Emirates De Montfort University Leicester Institute of Artificial Intelligence United Kingdom Qatar University Department of Electrical Engineering Doha2713 Qatar Qatar University College of Engineering Department of Architecture and Urban Planning Doha2713 Qatar University of Klagenfurt Smart Grids Research Group Klagenfurt9020 Austria

Consumer’s privacy is a main concern in Smart Grids (SGs) due to the sensitivity of energy data, particularly when used to train machine learning models for different services. These data-driven models often require huge amounts of data to achieve acceptable performance leading in most cases to risks of privacy leakage. By pushing the training to the edge, Federated Learning (FL) offers a good compromise between privacy preservation and the predictive performance of these models. The current paper presents an overview of FL applications in SGs while discussing their advantages and drawbacks, mainly in load forecasting, electric vehicles, fault diagnoses, load disaggregation and renewable energies. In addition, an analysis of main design trends and possible taxonomies is provided considering data partitioning, the communication topology, and security mechanisms. Towards the end, an overview of main challenges facing this technology and potential future directions is presented. © 2023, CC BY-NC-SA.

关键词： Internet of Energy

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An experimental investigation of the integration of smart building components with building information model (BIM) 36

An experimental investigation of the integration of smart bu...

引用

36th International Symposium on Automation and Robotics in Construction, ISARC 2019

作者： Afsari, K. Florez, L. Maneke, E. Afkhamiaghda, M. School of Construction Management Technology Purdue University United States Department of Architecture and Built Environment Northumbria University United Kingdom Department of Computer Graphics Technology Purdue University United States

Building Information Modeling (BIM) is a methodology to digitally represent all the physical and functional characteristics of a building. Importantly, in smart buildings smart components that are enabled with sensing and actuation need to be modeled accurately within the BIM model. This data representation needs to include multiple status of the smart component based on their performance to guide the design and construction process. However, currently there is not a clear methodology or guideline on how to embed smart components in the BIM model. Visualization techniques have been developed based on CAD technology to integrate smart components in the building model but these techniques have not been applied to BIM environment. To accurately model smart components, the component must be more than a single status representation and must contain complete and accurate dynamic data of the smart component. In this research, data properties, visualization techniques, and categorization of smart components is investigated. Then, through an experimental investigation, nine smart components across five building disciplines are modeled and embedded in a BIM model of a smart space. The model includes parameters that facilitate the data representation of the smart components. Data properties, data organization, and simulation of the smart component within the building model is explained. Challenges and future research is discussed. © 2019 International Association for Automation and Robotics in Construction I.A.A.R.C. All rights reserved.

关键词： Visualization

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Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

IEEE Transactions on Technology and Society

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IEEE Transactions on technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

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Tackling the muon identification in water Cherenkov detectors problem for the future Southern Wide-field Gamma-ray Observatory by means of Machine Learning

arXiv

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arXiv 2021年

作者： González, B.S. Conceição, R. Pimenta, M. Tomé, B. Guillén, A. - Lisbon Av. Prof. Gama Pinto 2 Lisbon1649-003 Portugal Universidade de Lisboa Av. Rovisco Pais 1 Lisbon1049-001 Portugal Computer Architecture and Technology Department University of Granada Granada Spain

This paper presents several approaches to deal with the problem of identifying muons in a water Cherenkov detector with a reduced water volume and 4 PMTs. Different perspectives of information representation are used and new features are engineered using the specific domain knowledge. As results show, these new features, in combination with the convolutional layers, are able to achieve a good performance avoiding overfitting and being able to generalise properly for the test set. The results also prove that the combination of state-of-the-art Machine Learning analysis techniques and water Cherenkov detectors with low water depth can be used to efficiently identify muons, which may lead to huge investment savings due to the reduction of the amount of water needed at high altitudes. This achievement can be used in further research to be able to discriminate between gamma and hadron induced showers using muons as discriminant. © 2021, CC BY-NC-SA.

关键词： Cosmic rays

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