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Proceedings of the 2025 CHI Conference on Human Factors in Computing systems

作者： Yankai Zhao Jin Zhang Jiao Li Tao Sun Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

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Simulation and analysis of a tomographic imaging system for cloud field measurement in rectangular icing wind tunnels

Simulation and analysis of a tomographic imaging system for ...

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2024 International Conference on Optoelectronic Materials and Devices, ICOMD 2024

作者： Zhang, Quan Qing, Chao Zuo, Chenglin Hu, Jiahui Li, Yan School of Computer Science and Software Engineering Southwest Petroleum University Chengdu610500 China Low Speed Aerodynamics Institute China Aerodynamics Research and Development Center Sichuan Mianyang621000 China Key Laboratory of Icing and Anti/De-Icing China Aerodynamics Research and Development Center Mianyang621000 China Intelligent Oil and Gas Laboratory Southwest Petroleum University Chengdu610500 China

ISBN: (数字)9781510689046

ISBN: (纸本)9781510689039

The uniformity of cloud distribution is a critical metric for evaluating the performance of icing wind tunnels, directly affecting the accuracy of experimental results. To address the challenge of measuring cloud distribution in wind tunnels, this paper proposes a solution based on extinction tomography. A rectangular cross-sectional imaging system was developed specifically for rectangular wind tunnel structures, overcoming the limitations of traditional ring-shaped systems. To resolve the issue of insufficient projections, an iterative reconstruction algorithm combined with regularization techniques was employed for accurate image reconstruction. Additionally, the BM3D denoising algorithm was applied to enhance image quality. The results of simulation experiments demonstrate that the system effectively improves the PSNR and SSIM of reconstructed images under both Gaussian and speckle noise conditions, significantly enhancing the accuracy of cloud distribution measurements. © 2025 SPIE.

关键词： Image reconstruction

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Generalized Simultaneous Perturbation-Based Gradient Search With Reduced Estimator Bias

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IEEE Transactions on Automatic Control 2025年

作者： Pachalyl, Soumen Bhatnagarz, Shalabh Prashanth, L.A. TCS Research Department of Computer Science and Engineering Indian Institute of Technology Madras Chennai India Indian Institute of Science Department of Computer Science and Automation The Robert Bosch Centre for Cyber Physical Systems Bangalore India Indian Institute of Technology Madras Department of Computer Science and Engineering Chennai India

We present a family of generalized simultaneous perturbation-based gradient search (GSPGS) estimators that use noisy function measurements. The number of function measurements required by each estimator is guided by the desired level of accuracy. We first present in detail unbalanced generalized simultaneous perturbation stochastic approximation (GSPSA) estimators and later present the balanced versions (B-GSPSA) of these. We extend this idea further and present the generalized smoothed functional (GSF) and generalized random directions stochastic approximation (GRDSA) estimators, respectively, as well as their balanced variants. We show that estimators within any specified class requiring more number of function measurements result in lower estimator bias. We present a detailed analysis of both the asymptotic and non-asymptotic convergence of the resulting stochastic approximation schemes. We further present a series of experimental results with the various GSPGS estimators on the Rastrigin and quadratic function objectives. Our experiments are seen to validate our theoretical findings. © 1963-2012 IEEE.

关键词： Stochastic systems

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Drosophila Larvae-Inspired Soft Crawling Robot with Multimodal Locomotion and Versatile Applications

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research 2025年第1期7卷 35-46页

作者： Qin Fang Jingyu Zhang Yinhui He Nenggan Zheng Yue Wang Rong Xiong Zhefeng Gong Haojian Lu State Key Laboratory of Industrial Control and Technology Zhejiang UniversityHangzhou 310027China Institute of Cyber-Systems and Control Zhejiang UniversityHangzhou 310027China Medical School of Zhejiang University Hangzhou 310027China College of Computer Science and Technology Zhejiang UniversityHangzhou 310027China Stomatology Hospital School of StomatologyZhejiang University School of MedicineZhejiang Provincial Clinical Research Center for Oral DiseasesKey Laboratory of Oral Biomedical Research of Zhejiang ProvinceCancer Center of Zhejiang UniversityEngineering Research Center of Oral Biomaterials and Devices of Zhejiang ProvinceHangzhou 310000China

Soft crawling robots have been widely studied and applied because of their excellent environmental adaptability and flexible ***,most existing soft crawling robots typically exhibit a single-motion mode and lack diverse *** by Drosophila larvae,this paper proposes a compact soft crawling robot(weight,13 g;length,165 mm;diameter,35 mm)with multimodal locomotion(forward,turning,rolling,and twisting).Each robot module uses 4 sets of high-power-density shape memory alloy actuators,endowing it with 4 degrees of motion *** analyze the mechanical characteristics of the robot modules through experiments and simulation *** plug-and-play modules can be quickly assembled to meet different motion and task *** soft crawling robot can be remotely operated with an external controller,showcasing multimodal motion on various material *** a narrow maze,the robot demonstrates agile movement and effective maneuvering around *** addition,leveraging the inherent bistable characteristics of the robot modules,we used the robot modules as anchoring units and installed a microcamera on the robot's head for pipeline *** robot completed the inspection in horizontal,vertical,curved,and branched pipelines,adjusted the camera view,and twisted a valve in the pipeline for the first *** research highlights the robot's superior locomotion and application capabilities,providing an innovative strategy for the development of lightweight,compact,and multifunctional soft crawling robots.

关键词： multimodal locomotion versatile applications Drosophila larvae soft crawling robot weight soft crawling robots shape memory alloy actuators

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Improved Feed-Forward Control of Active Power Decoupler for PV Microinverter With Non-Sinusoidal Power Decoupling Capability

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IEEE Transactions on Power Electronics 2025年

作者： Shen, Yidi Hasnain, Arafat Zakzewski, Daniel Resalayyan, Rakesh Khaligh, Alireza University of Maryland Department of Electrical and Computer Engineering Maryland Power Electronics Laboratory Institute for Systems Research College ParkMD20742 United States

In single-phase power conversion systems, there is an inherent difference between the dc-side constant and ac-side oscillating power, and power decoupling is required especially in applications like photovoltaic (PV) inverters. Active power decoupling (APD) converters are often used to balance instantaneous power while reducing the size of dc link capacitors and improving the system reliability. Conventionally, APD converters are controlled to decouple only sinusoidal power at double-line frequency. However, the control is further complicated when non-sinusoidal power is delivered due to non-linear circuit components, non-linear loads, or grid distortions. This paper proposes an improved feed-forward APD control scheme with the capability of decoupling non-sinusoidal power, which has application in single-phase systems, particularly all types of PV microinverters. The proposed feed-forward control includes the capacitor voltage calculation under arbitrary power harmonics and a dead-time compensation to increase the accuracy of duty prediction. The proposed algorithm can work as open-loop control to eliminate APD sensors and can serve as a feed-forward term in closed-loop control to improve power decoupling performance, thereby simplifying the controller design. The proposed approach is experimentally verified using a dc-parallel APD prototype, a 50 V 250 W microinverter with non-linear ac capacitor, and a diode rectifier load. © 1986-2012 IEEE.

关键词： Closed loop control systems

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MESSI: Task Mapping and Scheduling Strategy for FPGA-based Heterogeneous Real-Time systems

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ACM Transactions on Design Automation of Electronic systems 2025年第3期30卷 1-29页

作者： Ahmadi-Pour, Sallar Saha, Sangeet Mcdonald-Maier, Klaus Drechsler, Rolf Institute of Computer Science University of Bremen Faculty 3 Mathematics and Computer Science Bremen Bremen Germany Robotics and Embedded Systems Research Group University of Essex School of Computer Science and Electronic Engineering Colchester United Kingdom Cyber-Physical Systems DFKI Rheinland-Pfalz Kaiserslautern Germany

Continuous demands for improved performance within constrained resource budgets are driving a move from homogeneous to heterogeneous processing platforms for the implementation of today's Real-Time (RT) embedded systems. The applications executing on such systems are typically represented as a Precedence Task Graph (PTG), where a node represents a task or algorithm for one functionality and edges represent the complex interactions between multiple functionalities. Due to RT constraints, the task graph needs to be executed within a specified deadline. Although some existing studies have looked into solving this challenge, comprehensive studies that combine the theoretical features of RT task-graph mapping and scheduling with practical runtime architectural characteristics have mostly been ignored to date. Hence, in this article, we consider the challenge of scheduling an RT application modeled as a single PTG, with the objective of minimizing the overall execution time under Hardware (HW) resource and deadline constraints for heterogeneous Central Processing Unit (CPU) + Field Programmable Gate Array (FPGA) architectures. First, we introduce an optimal solution using Integer Linear Programming (ILP). However, this ILP-based optimal solution suffers from computational complexity and does not scale well even for moderately large problem sizes. Hence, we additionally propose heuristic algorithms for task mapping and scheduling. The efficiency of the proposed scheme, named MESSI, has been evaluated through experiments using PTG on a practical CPU+FPGA system regarding current technology restrictions. Our experiments demonstrate that performance gains of 55.6% and area usage reductions of 46.3% are possible compared to full Software (SW) and HW execution, respectively. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： FPGA heuristic HW SW co-design integer linear programming real-time scheduling Task graph

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Deep learning and ensemble methods for anomaly detection in ICS security

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International Journal of Information Technology (Singapore) 2025年第3期17卷 1761-1775页

作者： Hossain, Md. Alamgir Hasan, Tahmid Karovic, jr, Vincent Abdeljaber, Hikmat A. M. Haque, Md. Alimul Ahmad, Sultan Zafar, Aasim Nazeer, Jabeen Mishra, B.K. Department of Computer Science and Engineering State University of Bangladesh South Purbachal Kanchan Dhaka 1461 Bangladesh Department of Computer Science and Engineering Prime University Dhaka Bangladesh Department of Information Systems Faculty of Management Comenius Bratislava Odbojárov 10 Bratislava 25 82005 Slovakia Department of Computer Science Faculty of Information Technology Applied Science Private University Amman 11931 Jordan MEU Research Unit Middle East University Amman 11831 Lebanon Department of Computer Science Veer Kunwar Singh University Bihar Ara India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Gharuan Punjab Mohali 140413 India Department of Computer Science Aligarh Muslim University U.P Aligarh 202002 India P.G. Department of Physics Veer Kunwar Singh University Bihar Ara India

This research addresses the escalating threats to industrial control systems by introducing a novel approach that combines deep learning for feature selection with a robust ensemble-based classification technique to enhance anomaly detection. Our method utilizes a tailored autoencoder architecture to efficiently select features, followed by a Random Forest classifier to ensure reliable and generalizable detection of anomalies. Evaluated on the HAI Security Dataset, the proposed approach demonstrates exceptional performance, with significant improvements in detecting anomalous activities in ICS. The results highlight the method’s potential to enhance ICS security by providing a scalable and adaptable framework that evolves with emerging threats. This study not only advances cybersecurity methodologies for industrial systems but also lays the groundwork for future research, emphasizing the need for innovative and effective security solutions in the face of evolving cyber challenges. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Anomaly detection in ICS Cyber-resilience in critical infrastructure Deep learning for feature selection Industrial control systems security Random forest classifier for ICS

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Probabilistic Health Monitoring and Maintenance Scheduling for Nuclear Power systems

Probabilistic Health Monitoring and Maintenance Scheduling f...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Amer, Ahmad Tsilifis, Panagiotis Saxena, Abhinav Baglietto, Emilio Khan, Genghis Probabilistic Design & Material Informatics GE Aerospace Research Niskayuna NY12309 United States Robotics AI & Computer Vision GE Vernova Advanced Research Niskayuna NY12309 United States AI & Computer Vision GE Aerospace Research San RamonCA94583 United States Department of Nuclear Science & Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Sustainable Systems GE Aerospace Research Niskayuna NY12309 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

Operations and Maintenance (O&M) cost optimization in the nuclear energy industry is an imperative task for developing sustainable systems and efficient renewable technologies. We present a modular probabilistic framework for addressing maintenance scheduling challenges for small modular nuclear reactors using a Dynamic Bayesian Network (DBN) approach that monitors fatigue and performance in multiple subsystems of the reactors and provides a probabilistic assessment for potential failure events. The DBN framework fuses information from potential digital twins that assimilate real time data from several sensors with different time granularities in order to monitor and update thecurrentstate of knowledge aboutthestatus of several fatigue or performance criteria. Comprised of multi-modal digital twins (empirical, data- and physics-based twins), this DBN serves as a predictive framework forecasting the future state of specific reactor subsystems and enhancing decision-making capabilities associated with preventing system failure and minimizing the time and cost of maintenance events. The work presented in this paper is a holistic approach for monitoring and safeguarding complex, multi-component engineering systems where maintenance scheduling is critical and does not restrict itself to nuclear environments. However, our goal is to illustrate how such a framework can be adjusted to small modular water reactors. © 2025, American institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Nuclear energy

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Poster: Reporting Unique-Cause MC/DC Score Using Formal Verification

Poster: Reporting Unique-Cause MC/DC Score Using Formal Veri...

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International Conference on Software Testing, Verification, and Validation, ICST

作者： Monika Rani Golla Sangharatna Godboley Avijit Das P. Radha Krishna Dept. of Computer Science and Engineering National Institute of Technology Warangal Warangal India Dept. of Computer Science and Engineering NITMiner Technologies National Institute of Technology Warangal Warangal India Electronics & Radar Development Establishment Defence Research & Development Organisation Bengaluru India

ISBN: (数字)9798331508142

ISBN: (纸本)9798331508159

Unique-Cause MC/DC (UCM) is the most desired form of MC/DC in many safety-critical applications. For a given predicate, the UCM considers the independent pair of each condition by flipping the corresponding condition and fixing the other conditions. For the given N conditions in a predicate, the existing static symbolic execution tool, CBMC, generates MC/DC (Modified Condition/Decision) goal constraints of size, N + 1 that constitutes its minimal independent pairs. However, we propose a novel UCM Sequence Generator (UCM-Gen) that generates all possible inequality comparisons of the sequences/combinations of the N conditions which helps in computing the independent pairs further. The UCM-Gen outputs UCM Annotated Program which when given to the program verifiers, produces the UCM Score (%). In our work, we have considered CBMC to get the SAT/UNSAT results for each sequence of the UCM Annotated Program. Upon analysing these results, we calculate the total number of independently affected conditions (i.e., I value) for all the predicates in the given program. Furthermore, this work is compared with the CBMC's mode of MC/DC implementation. Interestingly, our proposed approach based UCM score (%) is always greater than the CBMC's MC/DC score (%) and hence claiming that their corresponding test cases contribute in effective bug finding.

关键词： Software testing computer bugs Generators Formal verification

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FIDSUS: Federated Intrusion Detection for Securing UAV Swarms in Smart Aerial Computing

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IEEE Internet of Things Journal 2025年第9期12卷 11312-11328页

作者： Deng, Jiangtao Wang, Wei Wang, Lina Bashir, Ali Kashif Gadekallu, Thippa Reddy Feng, Hailin Lv, Meilei Fang, Kai Zhejiang A&F University College of Mathematics and Computer Science Hangzhou311300 China Quzhou University College of Electrical and Information Engineering Quzhou324000 China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing Shenzhen518172 China Beijing Institute of Technology School of Medical Technology Beijing100081 China Manchester Metropolitan University Department of Computing and Mathematics ManchesterM15 6BX United Kingdom Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Rajpura140401 India Lovely Professional University Division of Research and Development Phagwara144411 India Chitkara University Center of Research Impact and Outcome Rajpura140401 India

The dynamic environment of UAV swarms in forest management is characterized by communication instability, heterogeneous nodes, and frequent topology changes due to challenging terrain. These systems are vulnerable to network attacks, requiring advanced intrusion detection technologies. Traditional methods struggle with rapid changes due to data privacy concerns and centralized computational limits, while existing federated learning (FL) algorithms lack robustness against client heterogeneity and dynamic data distribution, especially in complex forest environments. To address these challenges, we propose federated intrusion detection for securing UAV swarms (FIDSUS). FIDSUS improves intrusion detection systems by leveraging collaborative sensing among UAVs, enabling better monitoring and response to security threats in forestry. By quantifying the similarity between UAVs’ local feature extractors through an affinity matrix, FIDSUS guides the aggregation of feature extractors, improving detection capabilities. It also uses AI-driven aerial and distributed computing to enhance data processing efficiency and decision-making speed. The framework addresses data heterogeneity by cross-round feature fusion, improving detection in dynamic environments. Experimental results on the NSL-KDD and UNSW-NB15 datasets show that FIDSUS outperforms existing FL methods with a 4%–34% accuracy improvement. FIDSUS shows robustness and accuracy in dynamic environments, providing an effective solution for securing UAV swarms in forestry. © 2014 IEEE.

关键词： Network intrusion

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