Reliability
Safety assessment of Cyber-Physical Systems (CPS) requires a tremendous amount of effort as the complexity of cyber-physical systems is increasing. A well-known approach for the safety assessment of CPS is Fault Injection (FI). The goal of fault injection is to find a catastrophic fault that can fail the system by injecting faults into it. These catastrophic faults are less likely to happen, and finding it requires tremendous labor and cost.
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This is a numerical example of parts deterioration monitoring constructed based on the literature "Preventive Maintenance and Joint Optimization of Spare Parts Inventory and Reservation Policy"
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The database contains information about three test systems, including the network connected to Bus 5 of the Roy Billinton Test System and two netwroks with 93 and 135 load nodes.
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README.txt for simulation files IEEE_Collaboration_N_entities_2021.mdl and IEEE_2_Platforms_federation_SF_2021.mdl
This is a README.txt for the model published on the paper titled:
Improving IoT Federation Resiliency with Distributed Ledger Technology, 2021, Elo T, et al.
This readme describes how to replicate the main simulation results from the paper using a Vensim
model file. The model file has been generated using the Vensim DSS Macintosh Version
8.0.7 Double Precision x64.
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Please cite the following paper when using this dataset:
N. Thakur, "Twitter Big Data as a Resource for Exoskeleton Research: A Large-Scale Dataset of about 140,000 Tweets from 2017–2022 and 100 Research Questions", Journal of Analytics, Volume 1, Issue 2, 2022, pp. 72-97, DOI: https://doi.org/10.3390/analytics1020007
Abstract
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This dataset is the experimental data for the manuscript entitled "Standardization Workflow Technology of Software Testing Processes and Its Application to SRGM on RSA Timing Attack Tasks". The software fault data is generated from the testing processes of related software with RSA timing attack tasks, including the test data of three versions of RSA timing attack program and workflow verification system respectively. The attributes of test data contain no. of faults, fault type, interval time between faults and cumulative time, as well as estimate values for different SRGMs' parameters.
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Resource Efficient Real-Time Reliability Model for Multi-Agent IoT Systems
Resource Efficient Real-Time Reliability Model for Multi-Agent IoT Systems is called ERT-CORE. It defines specific input parameters, i.e., system's workload, average request processing time and availability. Defined parameters reflect system's state and react on its changes. Based on these parameters system reliability evaluation is performed.
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these files uploaded mainly used for supporting the research results displayed in paper.
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This dataset contains multimodal sensor data collected from side-channels while printing several types of objects on an Ultimaker 3 3D printer. Our related research paper titled "Sabotage Attack Detection for Additive Manufacturing Systems" can be found here: https://doi.org/10.1109/ACCESS.2020.2971947. In our work, we demonstrate that this sensor data can be used with machine learning algorithms to detect sabotage attacks on the 3D printer.
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MATLAB scripts to generate the Markov models of three-level and four-level ANPC legs and compute their mean time to failure from these models.
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