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arXiv 2025年

作者： Niazi, M.B. Umar Cao, John Barreau, Matthieu Johansson, Karl Henrik Division of Decision and Control Systems Digital Futures KTH Royal Institute of Technology StockholmSE-100 44 Sweden Laboratory for Information and Decision Systems Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering Stanford University StanfordCA94305 United States

This paper proposes a novel learning approach for designing Kazantzis-Kravaris/Luenberger (KKL) observers for autonomous nonlinear systems. The design of a KKL observer involves finding an injective map that transforms the system state into a higher-dimensional observer state, whose dynamics is linear and stable. The observer’s state is then mapped back to the original system coordinates via the inverse map to obtain the state estimate. However, finding this transformation and its inverse is quite challenging. We propose to sequentially approximate these maps by neural networks that are trained using physics-informed learning. We generate synthetic data for training by numerically solving the system and observer dynamics. Theoretical guarantees for the robustness of state estimation against approximation error and system uncertainties are provided. Additionally, a systematic method for optimizing observer performance through parameter selection is presented. The effectiveness of the proposed approach is demonstrated through numerical simulations on benchmark examples and its application to sensor fault detection and isolation in a network of Kuramoto oscillators using learned KKL observers. © 2025, CC BY.

关键词： State estimation

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Global prescribed-time control of a class of uncertain nonholonomic systems by smooth time-varying feedback

arXiv

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arXiv 2024年

作者： Zhang, Kang-Kang Zhou, Bin Fan, Chenchen Lam, James Department of Mechanical Engineering University of Hong Kong Hong Kong Department of Computer Science KU Leuven HeverleeB-3001 Belgium Center for Control Theory and Guidance Technology Harbin Institute of Technology Harbin150001 China Department of Rehabilitation Sciences The Hong Kong Polytechnic University Hong Kong

This paper investigates the prescribed-time smooth control problem for a class of uncertain nonholonomic systems. With a novel smooth time-varying state transformation, the uncertain chained nonholonomic system is reformulated as an uncertain linear time-varying system. By fully utilizing the properties of a class of parametric Lyapunov equations and constructing time-varying Lyapunov-like functions, smooth timevarying high-gain state and output feedback controllers are designed. The states and controllers are proven to converge to zero at any prescribed time. The proposed smooth time-varying method combines the advantage of a time-varying high-gain function, which enhances control performance, and a smooth time-varying function that can drive the states to zero at the prescribed time. The effectiveness of the proposed methods is verified by a numerical example. Copyright © 2024, The Authors. All rights reserved.

关键词： Lyapunov functions

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Fast Image Processing Approach for the Reliable Quantification of Intracranial Hematoma Volume in Brain CT Images

Fast Image Processing Approach for the Reliable Quantificati...

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control Instrumentation System Conference (CISCON)

作者： Vidhya V. Anjan Gudigar Raghavendra U. Akanksha Shetty Lekha Shree B. T. Parvathy Nair K. Ajay Hegde Girish R. Menon Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Instrumentation and Control Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Neurosurgery Manipal Hospitals Karnataka India Department of Neurosurgery Kasturba Medical College Manipal Academy of Higher Education Manipal India

ISBN: (数字)9798350375480

ISBN: (纸本)9798350375497

Traumatic brain injury (TBI) is a neurological condition that can cause severe physical and mental disabilities or death. One of the serious consequences of TBI is intracranial hematoma that can be diagnosed through early and proper analysis of the brain CT images. Proper estimation of hematoma volume is essential for envisaging the 30-day outcome. The Tada formula is the gold standard used by radiologists to quantify the hematoma volume in CT images which will provide accurate results only for symmetrical hematoma configurations. Manual volume assessment is further subjective and time-consuming, especially in real-time acute clinical care set-up. Hence a computer-aided scheme that uses image processing techniques for rapid, precise, and reliable assessment of hematoma volume is developed. The CT images are pre-processed using histogram equalization and gray matter is further extracted using thresholding and morphological operations. Then the hematoma regions are extracted using thresholding and the 3D hematoma volume for the entire CT scan is computed by multiplying the image width and the total area of hematoma in all CT images. The obtained hematoma volumes are clinically validated by radiologist and the proposed approach achieved an average accuracy of 89.51%. A 3D modeling of the hematoma regions in a CT scan was further generated to provide an extensive visual aid for improved hematoma interpretation and strategic analysis. The entire process of automated hematoma volume computation and generation of 3D volume in a CT scan took around 13 seconds using standalone system. The outcomes of the experiment show that the proposed method can be used as a medical support system for rapid and accurate diagnosis of hematoma.

关键词： Visualization Three-dimensional displays Accuracy Thresholding (Imaging) Computed tomography Estimation Real-time systems Reliability Brain injuries Morphological operations

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Task Scheduling and Resource Allocation for Compressed Sensing in IoT-Edge-Cloud Systems

Task Scheduling and Resource Allocation for Compressed Sensi...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Jingyu Zhang Yiqin Deng Haixia Zhang Yuguang Fang School of Control Science and Engineering Shandong University Jinan Shandong China Shandong Key Laboratory of Wireless Communication Technologies Shandong University Jinan Shandong China Department of Computer Science City University of Hong Kong Hong Kong China

Compressed sensing (CS) has emerged as a promising technique for reducing transmission data volume. Despite its significance, achieving a balance between the delay, energy consumption and data distortion caused by CS and transmission remains an understudied area in resource-constrained IoT systems. The emergence of multi-access edge computing provides a potential solution to the aforementioned issue by enabling the strategic implementation of CS either at IoT devices or an edge server (ES), depending on both bandwidth resources and computing resources at ES. In this paper, we investigate where to perform CS computation and how to determine the compression ratio and bandwidth allocation to minimize the weighted energy and distortion cost (WEDC) of all devices under latency requirements. We formulate a WEDC minimizing problem by jointly optimizing the task scheduling, compression ratio, and bandwidth allocation. Since the formulated problem is a mixed-integer and nonlinear programming, which is typically NP-hard, we decompose the original problem into two sub-problems and then develop an iterative algorithm to find the suboptimal solution. Extensive numerical results demonstrate the superiority of the proposed algorithm in reducing WEDC of all devices under delay constraints.

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Policy and Value Deep RL for Temporal Language-Agnostic Street Image Captioning 3

Policy and Value Deep RL for Temporal Language-Agnostic Stre...

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3rd IEEE Eurasia Conference on Biomedical engineering, Healthcare and Sustainability, ECBIOS 2021

作者： Chan, Christopher Chun Ki Chen, Chih-Cheng Delaney, Steven Yang, Cheng-Fu Department of Information Management Chaoyang University of Technology Taichung Taiwan Department of Automatic Control Engineering Feng Chia University Taichung40724 Taiwan Department of Aeronautical Engineering Chaoyang University of Technology Taichung Taiwan Department of Computer Science Ryerson University Canada Department of Chemical and Materials Engineering National University of Kaohsiung Kaohsiung Taiwan

ISBN: (纸本)9781728193045

Dashcams and street cameras capture a huge amount of street and traffic video data. Temporal image captioning for such video data has been approached with an encoder-decoder framework and achieved substantial success in captioning accuracy. Most recently, policy and value deep reinforcement learning (PVRL) emerged as an outperformer over other decision-making frameworks for image captioning. In this paper, we design a framework that utilizes PVRL on an in-house dataset containing temporal images of East Asia streets as a step towards designing a language-agnostic street image captioning framework that is capable of captioning temporal images of any street regardless of location. For language-invariance, the framework includes cross-modal retrieval at the character level so that similar words in different languages but in the same word-embedding space are grouped together. Our results show that PVRL can be applied successfully to temporal video captured the streets and achieve natural semantic captions;preliminary studies on our dataset suggest that the framework is capable of use in multi-language scenarios. © 2021 ECBIOS 2021. All rights reserved.

关键词： Deep learning

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BigFeat: Scalable and Interpretable Automated Feature engineering Framework

BigFeat: Scalable and Interpretable Automated Feature Engine...

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IEEE International Conference on Big Data

作者： Hassan Eldeeb Shota Amashukeli Radwa ElShawi Institute of Computer Science University of Tartu Tartu Estonia Computers and Automatic Control Department Faculty of Engineering Tanta University Tanta Egypt.

ISBN: (纸本)9781665480468

Feature engineering is a crucial step in building well-performing machine learning pipelines. However, manually constructing highly predictive features is time-consuming and requires domain knowledge. Although the research area of automated feature engineering has attracted much interest lately, both in academia and industry, the scalability and efficiency of the existing systems and tools are still practically unsatisfactory. This paper presents a scalable and interpretable automated feature engineering framework, BigFeat, that optimizes input features’ quality to maximize the predictive performance according to a user-defined metric. BigFeat employs a dynamic feature generation and selection mechanism that constructs a set of expressive features that improve the prediction performance while retaining interpretability. Extensive experiments are conducted, and the results show that BigFeat provides superior performance compared to the state-of-the-art automated feature engineering framework, AutoFeat, on a wide range of datasets. We show that BigFeat significantly improves the F1-Score of 8 classifiers by 4.59%, on average. In addition, the performance improvement achieved by integrating BigFeat into different AutoML frameworks is higher than that achieved by integrating AutoFeat into the same frameworks. Besides, the scalability of BigFeat is confirmed by its linear complexity, parallel design, and execution time which is, on average, 22x faster than AutoFeat.

关键词： Measurement Industries Scalability Pipelines Buildings Machine learning Big Data

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Resource Allocation for RIS-Assisted Device-to-Device Communications in Heterogeneous Cellular Networks

arXiv

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arXiv 2023年

作者： Ao, Shaoyou Niu, Yong Han, Zhu Ai, Bo Zhong, Zhangdui Wang, Ning Qiao, Yuanyuan The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of The School of Information Engineering Zhengzhou University Zhengzhou450001 China

In recent years, with the explosive growth of data traffic, communication base stations (BSs) need to serve more and more users. Offloading traffic from BSs has become an efficient way to reduce the burden on BSs. Device-to-Device (D2D) communications have emerged to improve spectrum utilization by reusing the frequency spectrum of the cellular frequency band. In the general environment, Heterogeneous Cellular Networks (HCNs) including millimeter wave (mm-wave) have appeared. Since the D2D link allows to share of spectrum resources with the cellular user, it will bring potential interference to the cellular user. Fortunately, an emerging technology called Reconfigurable Intelligent Surface (RIS) can mitigate the severe interference caused by D2D links by shaping the incident beam and improving the multipath phase shift. In this paper, we study the resource allocation scheme to maximize the system sum rate, in the RIS-assisted single-cell heterogeneous D2D communication scenario. To solve the Block Coordinate Descent (BCD) problem, the problem of maximizing the sum rate is decomposed into three sub-problems. The resource allocation sub-problem is solved by a coalitional game method based on the game theory. The power allocation problem of the coalition converts the concave function into a convex optimization by mathematical transformation. The problem is solved by the gradient descent method. The local search method is adopted to find the optimum for the phase conversion problem. Then iterate until the difference of sum rate is less than the threshold. The simulation results show that the designed algorithm is superior to other benchmark schemes in the literature. Copyright © 2023, The Authors. All rights reserved.

关键词： Resource allocation

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Design and Deployment of a Simulation Platform: Case Study of an Agent-Based Model for Youth Suicide Prevention

Design and Deployment of a Simulation Platform: Case Study o...

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Simulation Winter Conference

作者： Joshua Huddleston Michael C. Galgoczy Kareem A. Ghumrawi Philippe J. Giabbanelli Ketra L. Rice Nisha Nataraj Margaret M. Brown Christopher R. Harper Curtis S. Florence Department of Computer Science & Software Engineering Miami University Oxford OH USA National Center for Injury Prevention and Control Centers for Disease Control and Prevention (CDC) Atlanta GA USA

ISBN: (纸本)9781665476621

Research has examined the process of engaging end-users in co-designing a simulation model and using it within workplace settings. In contrast, few studies document the software development aspect of creating, packaging, and deploying a simulation platform that satisfies end-users' needs. In this case study, we detail these aspects regarding a platform involving Agent-Based Modeling for youth suicide prevention. Goals and constraints are detailed in three categories of needs, each showing how competing demands are addressed via software solutions: 1) data, encompassing data security (e.g., no direct access to individual-level data) and data updates (e.g., changing a model's initialization without coding expertise); 2) deployment, to satisfy security protocols (e.g., secure intranet communications) and the end-users' experience (e.g., ease of installation); 3) accessibility, ensuring that individuals with impairments are not excluded from using simulations. By presenting practical solutions to each category, our case study supports modelers in addressing their own deployment needs.

关键词： Protocols Filtering Employment Ecosystems Packaging Software Data models

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Unsupervised Learning for Wafer Surface Defect Pattern Recognition

Unsupervised Learning for Wafer Surface Defect Pattern Recog...

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2021中国智能自动化大会(CIAC 2021)

作者： Sheng Geng Huaping Liu Feng Wang Shimin Zhao Hu Liu Department of Control Science and Engineering Shanghai Institute of Technology Department of Computer Science and Technology Tsinghua University

With the development of the semiconductor industry, the demand for wafer production has gradually increased. Wafer manufacturing is a very complicated process, and any abnormal fluctuations in each process in this process may cause surface defects on the *** and rapid identification of wafer defect patterns can promptly reflect abnormal problems in the production process. Aiming at such tasks, this paper proposes a pattern recognition method for wafer surface defects based on unsupervised learning. The method is divided into two parts: unsupervised pre-training and classification fine-tuning. In the unsupervised pre-training stage, in order to improve the model's surface defect feature extraction capability, we propose a new wafer-oriented unsupervised sampling method(WaUSM). Our wafer surface defect pattern recognition method uses an unsupervised pre-training model as the initialization to set up a classification model downstream, and fine-tune the classification model through a small number of label images with wafer defect patterns, so that the classification model can more accurately identify various types of defects pattern. The method we proposed was successfully verified by wafer defect pattern recognition on the public dataset WM-811K, and the experimental results fully proved the effectiveness and industrial applicability of the method we proposed.

关键词： Semiconductor Wafer defect Unsupervised learning Pattern recognition

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Effect of lead time prediction accuracy in trust-based resource sharing

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IFAC-PapersOnLine 2021年第1期54卷 1126-1131页

作者： Ádám Szaller Botond Kádár Centre of Excellence in Production Informatics and Control Institute for Computer Science and Control Budapest Hungary Department of Manufacturing Science and Engineering Budapest University of Technology and Economics Budapest Hungary

Nowadays, challenges are changing production facilities: decentralized production networks are replacing centralized organizations to remain competitive. This paper investigates a resource sharing approach where matching resource offers and requests are made by an intermediate platform. One of the main pillars of collaboration is to keep promises, especially about deadlines: in the presented model, facilities could rate each other based on trustfulness and choose from offers based on this setting. Lead time prediction accuracy has a direct effect on the real processing intervals: if the prediction was accurate, the deadline could be met, which results in good ratings and a higher possibility to win more jobs. In the paper, effect of lead time prediction accuracy is investigated in trust-based resource sharing, and the performance of facilities is compared with agent-based simulation.

关键词： Co-operative control/manufacturing Multi-agent simulation Manufacturing System engineering

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