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Is Almost Sure Asymptotic Stabilizability Sufficient for Input Delay Tolerability of Nonlinear Stochastic systems?

IEEE Transactions on Automatic Control 2025年

作者： Yu, Xin Lin, Wei Jiangsu Normal University School of Electrical Engineering and Automation Xuzhou221116 China Case Western Reserve University Dept. of Electrical Computer and Systems Engineering ClevelandOH44106 United States

This article considers the problem of input delay tolerance (IDT) for stochastic control systems that are almost surely asymptotically stabilizable but inherently nonlinear (e.g., neither locally Lipschitz continuous nor linear growth and non-smoothly stabilizable). Using the notions of almost sure forward completeness [42] and weighted homogeneity, together with the existence of the solutions of continuous time-delay stochastic systems, we prove under Lyapunov-like conditions that : i) almost sure asymptotic stabilizability by nonsmooth homogeneous state feedback and dominated homogeneity of degree zero imply input delay tolerability of stochastic nonlinear systems, and ii) almost sure asymptotic stabilizability by homogeneous output feedback also implies input delay tolerability of the same class of multi-input/multi-output stochastic nonlinear systems. Our results contain not only the existing IDT results for the global Lipschitz or linear growth stochastic systems as special cases, but also important applications to significant classes of stochastic control systems with unstabilizable and undetectable linearization. © 1963-2012 IEEE.

关键词： Stochastic control systems

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Learning Adaptive Safety for Multi-Agent systems

arXiv

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arXiv 2023年

作者： Berducci, Luigi Yang, Shuo Mangharam, Rahul Grosu, Radu Institute of Computer Engineering TU Wien Austria Dept. of Electrical and Systems Engineering University of Pennsylvania United States

Ensuring safety in dynamic multi-agent systems is challenging due to limited information about the other agents. Control Barrier Functions (CBFs) are showing promise for safety assurance but current methods make strong assumptions about other agents and often rely on manual tuning to balance safety, feasibility, and performance. In this work, we delve into the problem of adaptive safe learning for multi-agent systems with CBF. We show how emergent behavior can be profoundly influenced by the CBF configuration, highlighting the necessity for a responsive and dynamic approach to CBF design. We present ASRL, a novel adaptive safe RL framework, to fully automate the optimization of policy and CBF coefficients, to enhance safety and long-term performance through reinforcement learning. By directly interacting with the other agents, ASRL learns to cope with diverse agent behaviours and maintains the cost violations below a desired limit. We evaluate ASRL in a multi-robot system and a competitive multi-agent racing scenario, against learning-based and control-theoretic approaches. We empirically demonstrate the efficacy and flexibility of ASRL, and assess generalization and scalability to out-of-distribution scenarios. Code and supplementary material are public online1 © 2023, CC BY-NC-SA.

关键词： Multi agent systems

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Federated Learning Model for Early Detection of Dementia Using Blood Biosamples

Federated Learning Model for Early Detection of Dementia Usi...

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2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2023

作者： Elsersy, Mohamed Sherif, Ahmed Imam, Ahmad Abdel-Aliem Khan Mamun, Mohammad M. R. Khalil, Kasem Haitham, Mohamed Higher Colleges of Technology Computer and Information Systems Dept. Abu Dhabi United Arab Emirates University of Southern Mississippi School of Computing Sciences and Computer Engineering MS United States William Carey University College of Osteopathic Medicine Mississippi MS United States Tennessee Technological University Electrical and Computer Engineering Department TN United States University of Mississippi Electrical and Computer Engineering Department MS United States Cairo Egypt

ISBN: (纸本)9798350322347

Alzheimer's disease (AD) is a serious, long-term health problem that causes much pain and loss for the person with it and their family. Its early and accurate detection might result in a substantial reduction of the disease outcomes and consequences. Blood biosamples are a simple and inexpensive technique in medical testing. This paper proposes diagnostic models for blood biosamples based on federated learning (FL) and its modifications to detect AD early. Our experiments used blood biosample data sets from the ADNI website to evaluate our models. Our performance analysis indicates that our algorithms are more accurate and achieve an accuracy of 87% for early detection. © 2023 IEEE.

关键词： Neurodegenerative diseases

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Instructors' Perspectives on Capstone Courses in Computing Fields: A Mixed-Methods Study 2024

Instructors' Perspectives on Capstone Courses in Computing F...

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29th Working Group Reports on Innovation and Technology in computer Science Education, ITiCSE WGR 2024

作者： Hooshangi, Sara Shakil, Asma Dasgupta, Subhasish Davis, Karen C.C. Farghally, Mohammed Fitzpatrick, KellyAnn Gutica, Mirela Hardt, Ryan Riddle, Steve Seyam, Mohammed Department of Computer Science Virginia Tech BlacksburgVA United States School of Computer Science University of Auckland Auckland New Zealand Info Systems and Tech Mgmt Dept George Washington University WashingtonDC United States Computer Science and Software Engineering Dept. Miami University OxfordOH United States School of Computing Instruction Georgia Institute of Technology AtlantaGA United States School of Computing & Academic Studies British Columbia Institute of Technology BurnabyBC Canada Computer & Information Science University of St. Thomas Saint PaulVA United States School of Computing Newcastle University Newcastle upon Tyne United Kingdom

ISBN: (纸本)9798400712081

Team-based capstone courses are integral to many undergraduate and postgraduate degree programs in the computing field. They are designed to help students gain hands-on experience and practice professional skills such as communication, teamwork, and self-reflection as they transition into the real world. Prior research on capstone courses has focused primarily on the experiences of students. The perspectives of instructors who teach capstone courses have not been explored comprehensively. However, an instructor's experience, motivation, and expectancy can have a significant impact on the quality of a capstone course. In this working group, we used a mixed methods approach to understand the experiences of capstone instructors. Issues such as class size, industry partnerships, managing student conflicts, and factors influencing instructor motivation were examined using a quantitative survey and semi-structured interviews with capstone teaching staff from multiple institutions across different continents. Our findings show that there are more similarities than differences across various capstone course structures. Similarities include team size, team formation methodologies, duration of the capstone course, and project sourcing. Differences in capstone courses include class sizes and institutional support. Some instructors felt that capstone courses require more time and effort than regular lecture-based courses. These instructors cited that the additional time and effort is related to class size and liaising with external stakeholders, including industry partners. Some instructors felt that their contributions were not recognized enough by the leadership at their institutions. Others acknowledged institutional support and the value that the capstone brought to their department. Overall, we found that capstone instructors were highly intrinsically motivated and enjoyed teaching the capstone course. Most of them agree that the course contributes to their professional de

关键词： Students

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Single-Phase Unidirectional Multilevel Rectifiers Based on Cascaded Cells

Single-Phase Unidirectional Multilevel Rectifiers Based on C...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Amanda Pereira Monteiro Cursino Brandão Jacobina Filipe Antônio Da Costa Bahia Reuben Palmer Rezende de Sousa Jean Torelli Cardoso Alan Robson Andrade Pinto Mechanical Engineering Dept. Federal University of Ceará Fortaleza Brazil Electrical Engineering Dept. Federal University of Campina Grande Campina Grande Brazil Electrical and Computer Engineering Dept. Federal University of Bahia Salvador Brazil Electronics and Systems Dept. Federal University of Pernambuco Recife Brazil

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

This paper proposes and investigates two new single-phase unidirectional multilevel rectifiers based on cascaded cells. Compared to conventional topology, as the number of cascaded cells increases, proposed ones employ, proportionally, a reduced controlled switch count and driver circuits, which may reduce control complexity and increase reliability. The dc-link voltage ratios are calculated for asymmetrical operation to provide the maximum number of voltage levels generated in the ac side of the rectifiers, ensuring a high power quality. Besides, the proposed configurations can synthesize a higher number of voltage levels by employing the same controlled switch count as the conventional one so that it is possible to provide a higher levels-per-switch ratio. Simulation and experimental results are presented to validate the performance and feasibility of the proposed topologies.

关键词： Reactive power Power quality Rectifiers Switches Control systems Reliability engineering Topology Power system reliability Voltage control Switching circuits

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International Conference on Communications, Information System and computer engineering (CISCE)

作者： Jiayuan Jin Xiantao Jiang Tian Song Fei. Richard Yu WeiLi Jin Liu Dept. Information Engineering Shanghai Maritime University Shanghai China dept. Electrical and Electronics Engineering Tokushima University Tokushima Japan dept. Systems and Computer Engineering Carleton University Ottawa Canada

A new Quadtree with nested multi-type tree(QTMT) partitioning method is adopted in the latest video coding standard Versatile Video Coding(VVC) to achieve effective encoding. Compared with Quadtree(QT) in HEVC, QTMT can provide enhanced encoding performance. However, this improved coding performance is obtained at the cost of additional computational load from recursive and nested searches for the best CU structure. A context similarity-enabled CU partitioning algorithm is proposed to achieve a balance between coding efficiency and compression quality in VVC. According to the difference between frames, the method of frame difference algorithm is used to calculate the possibility that the current block can follow the same subblock division mode of the previous frame, so as to skip the unnecessary division selection process. The experimental results show that the proposed method can save the encoding time up to 18.22% with about 0.91% BD-rate degradation on average compared with VVC software reference VTM-9.3

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SCC: Efficient Error Correction Codes for MLC PCM

SCC: Efficient Error Correction Codes for MLC PCM

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International SoC Design Conference, ISOCC

作者： Yujin Lim Dongwhee Kim Jungrae Kim Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Dept. of Semiconductor and Display Engineering Sungkyunkwan University Suwon Korea Dept. of Semiconductor Systems Engineering Sungkyunkwan University Suwon Korea

Phase Change Memory (PCM) has presented as a potential replacement of DRAM due to its better scalability. However, MLC PCM encounters reliability problems such as resistance drift error and cell wear-out. To improve reliability, implementing strong Error Correction Codes (ECC) is required but this incurs substantial overhead to memory system. Existing ECC schemes have trade-offs between error correction capability and overhead. This paper proposes Single Cell Correction (SCC) codes, an efficient ECC scheme which can correct single cell errors in MLC PCM with small overhead.

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Understanding the Impact of AI-driven Clinical Decision Support systems 15

Understanding the Impact of AI-driven Clinical Decision Supp...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Mohammed, Abdul Sajid Neravetla, Anuteja Reddy Nomula, Varun Kumar Gupta, Ketan Dhanasekaran, S. University of the Cumberlands Dept of School of Computer and Information Sciences KY United States University of the Cumberlands Department of Information Technology KY United States Georgia Institue of Technology Department of Analytics Industrial & Systems Engineering United States University of The Cumberlands Dept. of Information Technology WilliamsburgKY United States Sri Eshwar College of engineering Department of Electronics and Communication engineering Coimbatore641202 India

ISBN: (纸本)9798350370249

Medical selection aid structures (CDSSs) are facts systems designed to assist healthcare experts in the decision-making manner. Conventional CDSSs rely upon analytical fashions and understanding-based algorithms to guide choices. However, the latest advances have visible the emergence of AI-pushed CDSSs that utilize artificial intelligence (AI) to help clinicians in their selection-making. AI-pushed CDSSs are predicted to enhance selection-making accuracy and results by way of leveraging massive datasets and greater state-of-the-art modeling strategies. But, there is nonetheless a lot to understand about the impact of AI-driven CDSSs on healthcare shipping and results. This paper evaluates the present-day literature on the impact of AI-driven CDSSs, exploring the results of this generation for healthcare experts and the healthcare gadget as an entire. It highlights areas in need of additional research and suggests possibilities for the design and implementation of AI-pushed CDSSs. © 2024 IEEE.

关键词： Decision making

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Prediction of Soil Moisture From Near-Global Cygnss Gnss-Reflectometry Using a Random Forest Machine Learning Model

Prediction of Soil Moisture From Near-Global Cygnss Gnss-Ref...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： M.D. Wilson R. Datta S. Savarimuthu D. Moller C. Ruf Geospatial Research Institute Toi Hangarau University of Canterbury New Zealand Dept. Electrical Computer and Software Engineering University of Auckland New Zealand Dept. Climate and Space Sciences and Engineering University of Michigan United States

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

We developed a random forest model to predict soil moisture at the field scale (∼1 km), combining 6.5 years of CYGNSS Global Navigation Satellite System Reflectometry (GNSS-R) data with observations from 1049 gauges which are part of the International Soil Moisture Network. Several relevant predictor variables were developed, including geophysical parameters related to soil properties, topography, land cover and climatology, and dynamic variables including precipitation from the Global Precipitation Mission and evapotranspiration derived from MODIS temperature. The model achieved good accuracy for predicted soil moisture (R 2 = 0.68; RMSE = 0.064 mm/mm), which was further improved through postprocessing using a linear regression model of the residuals for bias correction (R 2 = 0.82; RMSE = 0.048 mm/mm). Our work demonstrates the potential for field-scale prediction of soil moisture from GNSS-R data, which may be further improved through enhanced co-variates, and is applicable to recent and near-future GNSS-R missions such as Rongowai and HydroGNSS.

关键词： Precipitation Accuracy Temperature Soil moisture Soil properties Forestry Predictive models

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Rain Predictive Model using Machine learning Techniques

Rain Predictive Model using Machine learning Techniques

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Multi-disciplinary Conference in Emerging Research Trends (IMCERT), International

作者： Muhammad Shahbaz Muneer Syed Muhammad Nabeel Mustafa Syeda Sundus Zehra Haniya Maqsood Dpertment of Computational Finance NED University of Engineering and Technology Karachi Pakistan Dept. of Computer Science & I.T NED University of Engineering and Technology Karachi Pakistan Dept. of Software Engineering Sir Syed Uuniversity of Engineering and Technology Karachi Pakistan Department of Computer and Information Systems Engineering NED University of Engineering and Technology Karachi Pakistan

Climate is rapidly changing around the world. Over time, there have been significant changes in the weather. Rainfall is now erratic due to climate change. The frequency of extreme weather events like droughts and floods has increased due to climate change, necessitating the need for more precise and timely rainfall forecasts. For strategic reasons including agriculture, water resource management, and architectural design, rain forecasting is crucial. The naturally occurring non-stationary component in the rainfall time series impairs model performance for practical hydrologists and drought risk assessors. We present a rain predicting model based on machine learning to address the forecasting issue. In our work, we predict the possibility of rain the next day on the basis of last 10 years' data. The variables that were calculated during the experiments were humidity, pressure, evaporation, sunshine, rainfall, and so on. Random Forest gave the 90% accuracy with 0.904 Area under Curve, highest out of all the algorithms. The model's performance will significantly aid in the rain forecast.

关键词： Climate change Rain Machine learning algorithms Droughts Weather forecasting Predictive models Prediction algorithms

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