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arXiv 2020年

作者： Basiri, Mohammad Hossein Ghojogh, Benyamin Azad, Nasser L. Fischmeister, Sebastian Karray, Fakhri Crowley, Mark The Department of Systems Design Engineering University of Waterloo ON Canada The Department of Electrical and Computer Engineering University of Waterloo ON Canada The Smart Hybrid and Electric Vehicles Systems Lab The Real-Time Embedded Software Lab The Machine Learning Lab The Centre for Pattern Analysis and Machine Intelligence

Vehicle platooning has been shown to be quite fruitful in the transportation industry to enhance fuel economy, road throughput, and driving comfort. Model Predictive Control (MPC) is widely used in literature for platoon control to achieve certain objectives, such as safely reducing the distance among consecutive vehicles while following the leader vehicle. In this paper, we propose a Distributed Nonlinear MPC (DNMPC), based upon an existing approach, to control a heterogeneous dynamic platoon with unidirectional topologies, handling possible cut-in/cut-out maneuvers. The introduced method addresses a collision-free driving experience while tracking the desired speed profile and maintaining a safe desired gap among the vehicles. The time of convergence in the dynamic platooning is derived based on the time of cut-in and/or cut-out maneuvers. In addition, we analyze the improvement level of driving comfort, fuel economy, and absolute and relative convergence of the method by using distributed metric learning and distributed optimization with Alternating Direction Method of Multipliers (ADMM). Simulation results on a dynamic platoon with cut-in and cut-out maneuvers and with different unidirectional topologies show the effectiveness of the introduced method. Copyright © 2020, The Authors. All rights reserved.

关键词： Topology

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Robust software Rejuvenation for CPS with State Estimation and Disturbances

Robust Software Rejuvenation for CPS with State Estimation a...

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American Control Conference (ACC)

作者： Raffaele Romagnoli Bruce H. Krogh Bruno Sinopoli Dept. of Electrical and Computer Engineering Carnegie Mellon University (CMU) Pittsburgh PA USA Software Engineering Institute CMU Department of Electrical and Systems Engineering Washington University St. Louis MO USA

ISBN: (数字)9781538682661

ISBN: (纸本)9781538682678

software rejuvenation has been proposed and demonstrated as a strategy to protect cyber-physical systems (CSPs) against unanticipated and undetectable cyber attacks, but the supporting theory has neglected the effects of modeling uncertainties and disturbances and has assumed the availability of perfect state information from the sensor measurements. This paper addresses these issues by providing sufficient conditions for the successful implementation of software rejuvenation in the face of these real-world considerations. The results are illustrated for a simple position control system.

关键词： software Ellipsoids Safety Symmetric matrices Noise measurement State estimation Cyberattack

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Multi-objective Optimal Control of Dynamic Integrated Model of Climate and Economy: Evolution in Action

arXiv

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arXiv 2020年

作者： Heris, Mostapha Kalami Rahnamayan, Shahryar Intelligent Systems Lab. Research and Development Department FaraDars Online Education Organization Tehran Iran Lab Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa Canada

One of the widely used models for studying economics of climate change is the Dynamic Integrated model of Climate and Economy (DICE), which has been developed by Professor William Nordhaus, one of the laureates of the 2018 Nobel Memorial Prize in Economic Sciences. Originally a single-objective optimal control problem has been defined on DICE dynamics, which is aimed to maximize the social welfare. In this paper, a bi-objective optimal control problem defined on DICE model, objectives of which are maximizing social welfare and minimizing the temperature deviation of atmosphere. This multi-objective optimal control problem solved using Non-Dominated Sorting Genetic Algorithm II (NSGA-II) also it is compared to previous works on single-objective version of the problem. The resulting Pareto front rediscovers the previous results and generalizes to a wide range of non-dominant solutions to minimize the global temperature deviation while optimizing the economic welfare. The previously used single-objective approach is unable to create such a variety of possibilities, hence, its offered solution is limited in vision and reachable performance. Beside this, resulting Pareto-optimal set reveals the fact that temperature deviation cannot go below a certain lower limit, unless we have significant technology advancement or positive change in global conditions. Copyright © 2020, The Authors. All rights reserved.

关键词： Climate change

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Correction to “Performance Evaluation of LoRaWAN for Green Internet of Things”

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IEEE Access 2022年 10卷 125400-125400页

作者： Zulfiqar Ali Shagufta Henna Adnan Akhunzada Mohsin Raza Sung Won Kim Department of Software Engineering Bahria University Islamabad Pakistan Department of Computer Science Bahria University Islamabad Pakistan Telecommunications Software and Systems Group (TSSG) Waterford Institute of Technology (WIT) Waterford Ireland Department of Computer Science COMSATS University Islamabad Islamabad Pakistan Department of Design Engineering and Mathematics Middlesex University London London U.K Department of Information and Communication Engineering Yeungnam University Gyeongsan South Korea

In the above article [1] , we want to change the affiliation of the first author to “department of software engineering, Bahria University, Islamabad, 46000, Pakistan” and add a new affiliation for the second author, which is “department of computer Science, Bahria University, Islamabad, 46000, Pakistan.” The first author (ZULFIQAR ALI; zulfijobs@***) is a Ph.D. student at Bahria University Islamabad, Pakistan, and the second author (SHAGUFTA HENNA; shaguftahenna@***) is his Ph.D. Supervisor.

关键词： Performance evaluation computer science Internet of Things software engineering

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CheXMed: A Multimodal Algorithm to Improve Pneumonia Detection in the Elderly

SSRN

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SSRN 2023年

作者： Hao, Ren Jing, Fengshi Chen, Zhurong Liu, Le Zhou, Jiandong Jing, Ran Lian, Wanmin Tian, Junzhang Xu, Zhongzhi Zhang, Qingpeng Cheng, Weibin Institute for Healthcare Artificial Intelligence Guangdong Second Provincial General Hospital Guangzhou510317 China UNC Project-China UNC Global School of Medicine University of North Carolina Chapel HillNC27599 United States College of Computer Science and Software Engineering Shenzhen University Shenzhen518061 China College of Foreign Languages Jishou University Zhangjiajie427000 China Nuffield Department of Medicine University of Oxford OxfordOX3 9DU United Kingdom Information Department Guangdong Second Provincial General Hospital Guangzhou510317 China School of Public Health Sun Yat-sen University Guangzhou510080 China School of Data Science City University of Hong Kong Hong Kong

Objectives: Pneumonia can be a deadly illness for particular populations, one of which is older adults. While studies have successfully trained artificial intelligent assisted diagnostic tools to detect pneumonia using chest X-ray images, they were targeted to the general population without stratification on age ***: This study investigated the performance disparities between geriatric and younger patients when using chest X-ray images to detect pneumonia, and developed and tested a multimodal algorithm that incorporates clinical notes together with image data to improve pneumonia detection performance for older *** and Methods: Three most used pneumonia diagnostic models, CheXNet, DenseNet121, and ResNet18, were evaluated to test their performance in older persons. Electronic medical records and frontal-view chest X-ray images of 2,572 inpatients were collected in a local hospital data to train and test a multi-input deep neural network framework named CheXMed, for pneumonia detection among older patients. Accuracy, precision, recall, F1-score, and AUC were used for model performance ***: CheXNet, DenseNet121, and ResNet18 achieved 9.5%, 6.0%, and 6.9% of higher AUC in the younger age group than in the elderly group, respectively. The proposed novel model, CheXMed, outperforms CheXNet by 15.3% in accuracy, 9.0% in precision, 38.3% in recall, and 24.0% in ***: Diagnostic models achieving good performance for young adults or the general population do not guarantee that they are useful for the elderly. Chest X-ray images combined with clinical information significantly improve pneumonia detection performance among elderly patients who are more vulnerable while neglected in general models. © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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AI-based Fog and Edge Computing: A Systematic Review, Taxonomy and Future Directions

arXiv

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arXiv 2022年

作者： Iftikhar, Sundas Gill, Sukhpal Singh Song, Chenghao Xu, Minxian Aslanpour, Mohammad Sadegh Toosi, Adel N. Du, Junhui Wu, Huaming Ghosh, Shreya Chowdhury, Deepraj Golec, Muhammed Kumar, Mohit Abdelmoniem, Ahmed M. Cuadrado, Felix Varghese, Blesson Rana, Omer Dustdar, Schahram Uhlig, Steve School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom University of Kotli Azad Jammu & Kashmir Azad Kashmir Kotli Pakistan Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Software Systems and Cybersecurity Faculty of Information Technology Monash University Australia Csiro DATA61 Australia Center for Applied Mathematics Tianjin University Tianjin China The Pennsylvania State University PA United States Naya Raipur India Abdullah Gül University Kayseri Turkey Department of Information Technology National Institute of Technology Jalandhar India Spain School of Computer Science University of St Andrews United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria

Resource management in computing is a very challenging problem that involves making sequential decisions. Resource limitations, resource heterogeneity, dynamic and diverse nature of workload, and the unpredictability of fog/edge computing environments have made resource management even more challenging to be considered in the fog landscape. Recently Artificial Intelligence (AI) and Machine Learning (ML) based solutions are adopted to solve this problem. AI/ML methods with the capability to make sequential decisions like reinforcement learning seem most promising for these type of problems. But these algorithms come with their own challenges such as high variance, explainability, and online training. The continuously changing fog/edge environment dynamics require solutions that learn online, adopting changing computing environment. In this paper, we used standard review methodology to conduct this Systematic Literature Review (SLR) to analyze the role of AI/ML algorithms and the challenges in the applicability of these algorithms for resource management in fog/edge computing environments. Further, various machine learning, deep learning and reinforcement learning techniques for edge AI management have been discussed. Furthermore, we have presented the background and current status of AI/ML-based Fog/Edge Computing. Moreover, a taxonomy of AI/ML-based resource management techniques for fog/edge computing has been proposed and compared the existing techniques based on the proposed taxonomy. Finally, open challenges and promising future research directions have been identified and discussed in the area of AI/ML-based fog/edge computing. Copyright © 2022, The Authors. All rights reserved.

关键词： Edge computing

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Reusable garbled turing machines without FHE 3rd

Reusable garbled turing machines without FHE

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3rd International Conference on Codes, Cryptology and Information Security, C2SI 2019

作者： Wang, Yongge Malluhi, Qutaibah M. Department of Software and Information Systems UNC Charlotte 9201 University City Blvd. CharlotteNC28223 United States Department of Computer Science and Engineering Qatar University Doha Qatar

ISBN: (纸本)9783030164577

Since Yao introduced the garbled circuit concept in 1980s, it has been an open problem to design efficient reusable garbled Turing machines/circuits. Recently, Goldwasser et al. and Garg et al. answered this question affirmatively by designing reusable garbled circuits and reusable garbled Turing machines. Both of these reusable garbling schemes use fully homomorphic encryption (FHE) schemes as required building components. Here, we use multilinear maps to design a reusable Turing machine garbling scheme that will not need any FHE schemes. Though it is not clear whether our multilinear map based garbling approach could be more efficient than FHE based garbling approach, the goal of this paper is to develop alternative techniques for resuable garbling schemes to stimulate further research in this direction. © Springer Nature Switzerland AG 2019.

关键词： Turing machines

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An Indoor Localization Approach Based on Deep Learning for Indoor Location-Based Services

An Indoor Localization Approach Based on Deep Learning for I...

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2019 IEEE Jordan International Joint Conference on Electrical engineering and Information Technology, JEEIT 2019

作者： Elbes, Mohammed Almaita, Eyad Alrawashdeh, Thamer Kanan, Tarek Alzurbi, Shadi Hawashin, Bilal Computer Science Department Al-Zaytoonah University of Jordan Amman Jordan Electrical Engineering Department Altafilah Technical University tafila Jordan Software Engineering Department Al-Zaytoonah University of Jordan Amman Jordan Computer Information Systems Al-Zaytoonah University of Jordan Amman Jordan

ISBN: (纸本)9781538679425

The rapid increase in the demand of location based services (LBS) for indoor environments has attracted scholars to indoor localization based on fingerprinting due its high accuracy. In this paper, we propose our novel indoor localization approach based on fingerprints of Received Signal Strength Indicator (RSSI) measurements. We present our approach of fingerprint preparation and setup and how we utilized machine learning techniques using Long Short-Term Memory (LSTM) Neural Networks for location estimation. Our experimental results shows that our localization approach outperforms well-known existing approaches like the KNN and localization techniques. © 2019 IEEE.

关键词： Location based services

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Corrigendum to “LightSOD: Towards lightweight and efficient network for salient object detection” [J. Comput. Vis. Imag. Underst. 249 (2024) 104148]

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computer Vision and Image Understanding 2025年 252卷

作者： Thien-Thu Ngo Hoang Ngoc Tran Md. Delowar Hossain Eui-Nam Huh School of Information Technology Center for Applied Intelligent Systems Research Halmstad University Kristian IV:s väg 3 Halmstad 30118 Halland Sweden Department Computer Science and Engineering KyungHee University Global Campus Deogyeong-daero Yongin-si 17104 Gyeonggi-do Republic of Korea Department of Software Engineering FPT University 600 Nguyen Van Cu Street Ninh Kieu 94000 Can Tho Viet Nam Department of Computer Science and Engineering Hajee Mohammad Danesh Science & Technology University Basherhat Dinajpur 5200 Rangpur Division Bangladesh

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Review of Machine Learning Applications for Predicting the Quality Biomass Briquettes for Sustainable and Low-Carbon Energy Solutions

Green Energy and Resources

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Green Energy and Resources 2025年

作者： Constance Nakato Nakimuli Fred Kaggwa Johan De Greef David Kilama Okot Julien Blondeau Simon Kawuma Department of Information Technology Mbarara University of Science and Technology Uganda Brussels Institute for Thermal-Fluid Systems and Clean Energy(BRITE) Vrije Universiteit Brussel Belgium Thermodynamics and Fluid Mechanics Group Faculty of Engineering Vrije Universiteit Brussel (VUB) Belgium Department of Computer Science Mbarara University of Science and Technology Uganda Department of Materials Engineering – Leuven Group T Campus KU Leuven Leuven Belgium Department of Physics Mbarara University of Science and Technology Uganda Department of Software Engineering Mbarara University of Science and Technology Uganda

This review discusses how Machine Learning is applied to predict the quality of biomass briquettes produced from agricultural and municipal solid organic waste, which are crucial for advancing green and low-carbon solutions. Traditional methods of assessment of briquette quality involve destructive laboratory experiments, do not favor sample reuse, are time-consuming, and labor-intensive, posing barriers to efficient production. This paper has reviewed literature covering various Machine Learning models applied for predicting and optimizing briquette quality parameters including combustion, physical, and emission properties. Several Machine Learning models have shown promising results in predicting and optimizing these key parameters for example Random Forest with R2 of 0.9936 in deformation energy prediction and Artificial Neural Networks with R2 of 0.8936 in the prediction of impact resistance. By enhancing the accuracy and efficiency of briquette quality predictions, Machine Learning algorithms contribute to the development of high-quality biomass briquettes thereby creating sustainable and low-carbon energy systems. This review points to critical literature gaps regarding model generalizability across diverse biomass feedstock and integration of broader quality parameters. Addressing these gaps will advance AI-based solutions, promote greener energy practices, and support sustainable development. The findings are intended to aid researchers, industry professionals, and policy makers in advancing the production of high-quality biomass briquettes for cleaner energy and sustainable development.

关键词： Machine learning briquettes sustainable energy low-carbon solutions agricultural waste municipal waste

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