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检索条件"任意字段=10th International Conference on Tools and Algorithms for the Construction and Analysis of Systems"

共 1703 条记录，以下是121-130 订阅

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A modeling method for cyber-physical attacks on distributed photovoltaic systems based on probabilistic colored Petri nets 10

A modeling method for cyber-physical attacks on distributed ...

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10th international conference on Energy Materials and Electrical Engineering, ICEMEE 2024

作者： Di, Li Zhang, Weijian Chen, Cen Xi, Qingyu State Grid Henan Electric Power Company Electric Power Science Research Institute Henan Zhengzhou450000 China Nanjing University of Information Science and Technology School of Computer Science Jiangsu Nanjing210044 China

ISBN: (纸本)9781510686243

As distributed photovoltaic systems (DPS) are widely adopted in power grids, their cyber-physical security has become an increasingly pressing concern. this paper proposes a modeling method based on Probabilistic Colored Logical Petri Nets (PCLPN) to describe and analyze cyber-physical attacks in DPS. First, we introduce the basic concepts and formal definition of PCLPN, detailing its advantages in handling the complexities of randomness, colored tokens, and logical conditions in such systems. We then construct a basic cyber-physical attack model. By introducing attack nodes, we simulate various cyber-physical attack scenarios, such as cyber-attacks and physical disruptions. In the model building process, we use dependency relationship matrices and logical conditions to describe the dynamic changes of attack behaviors, leverage colored tokens to differentiate attack types, and employ a mixed-strategy game to calculate the probabilities of nodes being attacked, thereby quantifying the propagation probabilities of attacks along different paths. Finally, through simulation analysis, we verify the effectiveness of the proposed method in capturing the complex dynamic behaviors of DPS and evaluating system vulnerabilities. this research provides new ideas and tools for the cyber-physical security assessment and protection strategy development of DPS. © 2024 SPIE.

关键词： Cyber attacks

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Efficient analysis of Cyclic Redundancy Architectures via Boolean Fault Propagation 28th

Efficient Analysis of Cyclic Redundancy Architectures via Bo...

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28th international conference on tools and algorithms for the construction and analysis of systems (TACAS) Held as Part of the 25th European Joint conferences on theory and Practice of Software (ETAPS)

作者： Bozzano, Marco Cimatti, Alessandro Griggio, Alberto Jonas, Martin Fdn Bruno Kessler Trento Italy

ISBN: (纸本)9783030995270;9783030995263

Many safety critical systems guarantee fault-tolerance by using several redundant copies of their components. When designing such redundancy architectures, it is crucial to analyze their fault trees, which describe combinations of faults of individual components that may cause malfunction of the system. State-of-the-art techniques for fault tree computation use first-order formulas with uninterpreted functions to model the transformations of signals performed by the redundancy system and an AllSMT query for computation of the fault tree from this encoding. Scalability of the analysis can be further improved by techniques such as predicate abstraction, which reduces the problem to Boolean case. In this paper, we show that as far as fault trees of redundancy architectures are concerned, signal transformation can be equivalently viewed in a purely Boolean way as fault propagation. this alternative view has important practical consequences. First, it applies also to general redundancy architectures with cyclic dependencies among components, to which the current state-of-the-art methods based on AIISMT are not applicable, and which currently require expensive sequential reasoning. Second, it allows for a simpler encoding of the problem and usage of efficient algorithms for analysis of fault propagation, which can significantly improve the runtime of the analyses. A thorough experimental evaluation demonstrates the superiority of the proposed techniques.

关键词： Fault tolerance

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Towards Handling Bias in Intelligence analysis with Twitter 10

Towards Handling Bias in Intelligence Analysis with Twitter

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10th IEEE international conference on Data Science and Advanced Analytics, DSAA 2023

作者： Karakikes, Alexandros Alexiadis, Panagiotis theocharopoulos, theocharis Skoulidas, Nikolaos Spiliotopoulos, Dimitrios Kotis, Konstantinos University of the Aegean Intelligent Systems Lab Dept. of Cultural Technology and Communication Mytilene Greece University of the Aegean Faculty of Humanities Rhodes Greece University of the Peloponnese Department of Management Science and Technology Tripoli Greece

ISBN: (纸本)9798350345032

Bias identification and mitigation in the Twitter ecosystem has been lately researched towards achieving a more efficient utilization of the application by different stakeholders and for a wide area of purposes. Among these stakeholders, intelligence services worldwide, collectively called the Intelligence Community (IC), tend to use Twitter, supplementarily to their pre-existent disciplines, for monitoring areas of interest and identifying emerging social, political and security trends/threats. Over time, the IC has identified bias as the major obstacle in information analysis, thus it has developed scientific and empirical methods for bias mitigation, in parallel to those developed by the information and communication technology (ICT) and artificial intelligence (AI) community. As it becomes apparent, it is to both communities' interest to accurately trace bias and ideally eradicate or moderate its effects. In this paper we draw systemic parallels between Intelligence analysis (IA) and Twitter Analytics (TA), comparatively examine existing bias mitigating methodologies to pinpoint similarities/dissimilarities, and utterly investigate the feasibility of adapting and adjusting methodologies from the first field to the latter. Furthermore, we propose a novel framework for AI-augmented bias mitigation in the IC. Finally, we propose methods and tools, already adapted by the ICT community, for efficiently supporting bias mitigation methodologies adapted by the IC. © 2023 IEEE.

关键词： Integrated circuits

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Study on the construction Method of Spacecraft System Model Based on Meta-model 10th

Study on the Construction Method of Spacecraft System Model ...

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10th international conference on Signal and Information Processing, Network and Computers, ICSINC 2022

作者： Ruan, Jiangdong Li, C.G. Li, Yuxuan Chen, Xinwu Beijing China Hangzhou China Hangzhou Hope System Technology Co. Ltd Hangzhou China

ISBN: (纸本)9789811999673

Model-based systems engineering approach is a key technology in the transformation of spacecraft digital design. Based on the meta-model theory, this paper proposes a new method of designing spacecraft MBSE system models, which introduces the MOF to ensure the logicality of the domain meta-model through the modeling of the domain ontology concept, explores how to customize the domain meta-model with modeling tools, and realizes the construction of the system model based on the spacecraft domain meta-model. At last, a spacecraft case study is developed to verify the proposed method. It is expected that this paper will provide the necessary reference for other complex equipment to build system models with domain meta-model. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Spacecraft

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Solving Problems of Deformable Solid Mechanics by the Method of Dynamic Particles 10

Solving Problems of Deformable Solid Mechanics by the Method...

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10th international conference on Advances in Machinery, Material Science and Engineering Application, MMSE 2024

作者： Oshkhunov, Muaed M. Dosko, Sergey I. Tlibekov, Aleksey Kh. Kabardino - Balkarian State University Nalchik Russia IDTI of the Russian Academy of Sciences Moscow Russia Machine Tools Department Bauman Moscow State University Moscow Russia

ISBN: (纸本)9781643685465

A general method for solving all types of problems of 'Solid mechanics' is given. A unified algorithm is proposed for solving the main types of problems of deformable solid mechanics: classical theory of elasticity, physically and geometrically nonlinear problems, dynamic problems of elasticity theory. A model of heat transfer in media with finite velocities is also proposed. the essence of the approach is to replace a continuous deformable medium with a system of interacting particles equivalent in physical and mechanical properties. the interaction potential between neighboring particles for an elastic isotropic continuous medium is determined by the Young's modulus, Poisson's ratio, or their analogues for heat transfer problems. the problems that must be solved for the effective use of a new method for solving problems of solid mechanics are outlined. Such a model is similar to dynamic models with concentrated masses, represented by systems of linear (or nonlinear) differential equations, algorithms and numerical methods for solving which are well known. However, it is necessary to ensure the accuracy of numerical integration and reduce the number of computational operations. It is proposed to use neural networks to solve large systems of ordinary differential equations. A mathematical analysis of the methods of separating a continuous medium and placing masses at points in an uneven grid of dynamic particles and a method for selecting a damping coefficient proportional to the velocity of movement of a material point is required. © 2024 the Authors.

关键词： Ordinary differential equations

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MCDA Calculator: A Streamlined Decision Support System for Multi-criteria Decision analysis 10th

MCDA Calculator: A Streamlined Decision Support System for M...

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10th international conference on Decision Support systems Technologies (ICDSST)

作者： Huang, He Burgherr, Peter Paul Scherrer Inst Lab Energy Syst Anal Forschungsstr 111 CH-5232 Villigen Switzerland

ISBN: (纸本)9783031593758;9783031593765

the multi-criteria decision analysis (MCDA) landscape is fraught with complexity and challenges, particularly in diverse decision-making environments. Practitioners often face the challenging tasks of selecting appropriate MCDA methodologies, navigating complicated computational processes, and effectively synthesizing inputs from a variety of stakeholders. the existing landscape of MCDA tools, which are typically limited to specific methodologies, exacerbates these challenges, often resulting in fragmented workflows and steep learning curves. To overcome these hurdles, the MCDA Calculator (https://mcda-calculator. ***) emerges as a novel decision support system (DSS), providing a unified and streamlined platform tailored to increase the efficiency and effectiveness of computational process for experienced practitioners in applying MCDA. the MCDA Calculator features a streamlined computational workflow that blends different MCDA methodologies into a cohesive unit. this approach ensures a consistent and intuitive user experience, effectively eliminating the need for complex, time-consuming configurations. the tool's design philosophy focuses on simplifying the MCDA calculation process. In this paper, we introduce our DSS and detail the workflow of the developed web-based tool. To illustrate the practical benefits and real-world applicability of the MCDA Calculator, the paper presents a numerical example which illustrates the tool's ability to streamline calculation processes, and produce insightful, actionable results.

关键词： Multi-criteria decision analysis Decision support system Software development

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the application of deep learning algorithms in power system load forecasting 6

The application of deep learning algorithms in power system ...

引用

6th international conference on Wireless Communications and Smart Grid, ICWCSG 2024

作者： Wang, Shilin Cui, Wei Yang, Yang Meng, Xiangliang State Grid Information & Telecommunication Group Co. Ltd. No. 10 Xuanwumen Outer Street Xicheng District 95555-0345 Beijing100029 China

ISBN: (纸本)9781510682962

this article explores the application of deep learning (DL) algorithms in power system load forecasting. With the continuous advancement of the construction of new power systems, traditional load forecasting models designed based on time series analysis are no longer in line with the actual needs of the current power system. To improve the accuracy of load forecasting, this paper attempts to transform time series data into images for analysis, and then use deep learning methods widely used in the field of image processing for power load forecasting. Convolutional neural network (CNN), as a commonly used image processing algorithm, has been applied in time series data processing. However, data is still processed as a sequence matrix, and the advantages of CNN algorithm in processing image matrices have not been fully utilized. therefore, this article proposes a CNN algorithm based on sequence to image conversion (STI-CNN). Firstly, the load sequence is converted into a load image. then, a dual branch deep network model is used to accurately cluster the input data. Finally, the STI-CNN model is used for load prediction. Matlab simulation experiments show that the proposed STI-CNN model has excellent performance in different prediction metrics and has higher prediction accuracy. © 2024 SPIE.

关键词： Data modeling Data conversion Image processing systems modeling Convolutional neural networks Evolutionary algorithms Statistical modeling Education and training Support vector machines

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A theoretical analysis of Random Regression Test Prioritization 28th

A Theoretical Analysis of Random Regression Test Prioritizat...

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作者： Yi, Pu Wang, Hao Xie, Tao Marinov, Darko Lam, Wing Peking Univ Beijing Peoples R China Univ Illinois Urbana IL USA George Mason Univ Fairfax VA 22030 USA

ISBN: (纸本)9783030995270;9783030995263

Regression testing is an important activity to check software changes by running the tests in a test suite to inform the developers whether the changes lead to test failures. Regression test prioritization (RTP) aims to inform the developers faster by ordering the test suite so that tests likely to fail are run earlier. Many RTP techniques have been proposed and are often compared with the random RTP baseline by sampling some of the n! different test-suite orders for a test suite with n tests. However, there is no theoretical analysis of random RTP. We present such an analysis, deriving probability mass functions and expected values for metrics and scenarios commonly used in RTP research. Using our analysis, we revisit some of the most highly cited RTP papers and find that some presented results may be due to insufficient sampling. Future RTP research can leverage our analysis and need not use random sampling but can use our simple formulas or algorithms to more precisely compare with random RTP.

关键词： Regression Test Prioritization Random analysis

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Integrated Critical Care Monitoring System for Ventilated Patients: ECG analysis and Air Flow Detection 10

Integrated Critical Care Monitoring System for Ventilated Pa...

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10th IEEE Uttar Pradesh Section international conference on Electrical, Electronics and Computer Engineering, UPCON 2023

作者： Dhanush, D. Prakasham, V.T. Saathappan, S Arun Harissh, G. Ganesan, M. Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham Department of Electronics and Communication Engineering India

ISBN: (纸本)9798350382471

In modern health care, integrating cutting-edge technology has transformed patient care, especially in critical settings. Critical Care Monitoring systems (CCMS) have emerged as vital tools for real-time data acquisition, analysis, and intervention. this paper presents a pioneering critical care monitoring project focused on ventilated patients. the project seamlessly integrates ECG signal analysis and ventilator performance assessment to ensure patient safety and provide data-driven insights to healthcare professionals. the system employs ECG sensors for continuous cardiac monitoring, and abnormal ECG signals trigger an air flow detector for ventilator assessment. the integrated system reduces cognitive load on healthcare providers and enhances patient care. this paper discusses the system's methodology, including instrumentation amplification, filtering, and the use of a driven right leg circuit. Experimental results demonstrate the effectiveness of the system in acquiring accurate ECG data and detecting air flow anomalies. the limitations and future directions of the system are also discussed, pointing towards its potential for revolutionizing critical care practices. this work highlights the promise of integrating ECG analysis and air flow detection in critical care monitoring, contributing to improved patient outcomes and safety. © 2023 IEEE.

关键词： Air-Flow Detection Arduino UNO R3 Driven-Right Leg (DRL) ECG analysis Filters Instrumentation Amplifier

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LEVERAGING AI FOR ENHANCING DISASTER RESILIENCE IN SMART COASTAL CITIES: STRATEGIES FOR TOMORROW'S URBAN CHALLENGES 9

LEVERAGING AI FOR ENHANCING DISASTER RESILIENCE IN SMART COA...

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9th international conference on Big Data Analytics, Data Mining and Computational Intelligence, BigDaCI 2024, the 10th international conference on Connected Smart Cities, CSC 2024 and the 16th international conference on e-Health, EH 2024, Part of the 18th Multi conference on Computer Science and Information systems 2024, MCCSIS 2024

作者： Nazari, Rouzbeh Museru, Mujungu Lawrence Karimi, Maryam Sustainable Smart Cities Research Center University of Alabama BirminghamAL United States Department of Civil Construction and Environmental Engineering University of Alabama BirminghamAL United States Department of Environmental Health Science School of Public Health University of Alabama BirminghamAL United States

ISBN: (纸本)9789898704597

Natural disasters pose significant threats to the sustainability of smart cities, emphasizing the urgent need to re-evaluate and fortify their infrastructural resilience systematically. this necessitates a holistic approach considering the interplay between population dynamics, critical infrastructures, and hazard/disaster risks within communities. To address these challenges, proactive measures, including land use planning, public education, and disaster preparedness, are essential for enhancing resilience. However, there is a critical gap in accessible tools and comprehensive resources for citizens, communities, and decision-makers faced with these complex tasks. this study investigates the economic impacts of hurricanes across small and large counties, aiming to discern disparities in vulnerability and resilience in coastal communities along the Gulf of Mexico and the Atlantic Coast in the United States. Leveraging data from the National Flood Insurance Program (NFIP) and simulated flood hazards, oversampling techniques are employed to enrich flood damage datasets. Ensemble machine learning methods, including random forest, extra tree, extreme gradient boosting, and categorical boosting, are utilized to develop multi-variable flood damage models. Results indicate that extreme gradient boosting outperforms other methods, achieving notable precision, recall, and F1-score metrics, while effectively quantifying relative damage levels. Despite inherent dataset limitations, including SHapley Additive exPlanations offers valuable insights into feature interactions. this research underscores the potential of AI in bolstering disaster resilience strategies and advancing flood damage modeling in coastal regions. © 2024 Big Data Analytics, Data Mining and Computational Intelligence. All Rights Reserved.

关键词： Flood insurance

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