International Journal of System Assurance Engineering and Management最新文献

The main purpose of this study is to propose a decision support system that deals with the uncertainties in a model of atmospheric dispersion and in meteorological data (speed and direction of wind), which may negatively affect the model accuracy. This later helps the safety agencies in making decisions and allocating necessary materials and human resources to handle potential disastrous events. In order to investigate the aforementioned issues and provide a more reliable data we propose the adaptive Neuro-Fuzzy inference (ANFIS) system enhanced by the mean particle swarm optimization (PSO) to predict the concentration of Sulfur Dioxide release in the atmosphere. This method takes the advantages of fuzzy logic system to address the uncertainties and the ability of neural network to learn from the data. Furthermore our study attempts to estimate the severity index of the released material with the help of fuzzy logic. The result of our study shows that the presented method is successfully applied and it can be a powerful alternative to deal with Sulfur Dioxide release.

The Industrial Internet of Things (IoT) comes together with different services, industrial applications, sensors, machines, and databases. Industrial IoT is improving the lives of the people in various ways such as smart cities, e-healthcare, and agriculture etc. Although Industrial IoT shares some characteristics with customer IoT, for both networks, separate cybersecurity techniques are used. Industrial IoT solutions are more likely to be incorporated into broader operational systems than customer IoT solutions, which are utilized by the single user for a particular purpose. As a result, Industrial IoT security solutions necessitate more preparation and awareness in order to ensure the system’s security and privacy. In this research paper, a random subspace and blockchain based technique is proposed. PCA is used as a preprocessing technique to preprocess the data. Furthermore, all the communication and node details are shared through blockchain to provide more secure communication. The integration of the blockchain in the existing approach gives better results in comparison to the other methods. The proposed methodology achieves better results in comparison to the previous techniques. The proposed methodology improves attack detection efficiency in comparison to the state-of-the-art machine learning techniques for IoT security.

In this work, a novel SRF-PLL and DSOGI-PLL with the JAYA based optimization approach is presented herein for the control of a unified power quality conditioner (UPQC) system. The proposed UPQC system is linked to a three-phase distribution system that has nonlinear loads. Increased use of non-linear loads has contributed to harmonic effluence in power distribution systems and thus power quality issues have been elevated which is essential to be efficiently addressed. Since, the UPQC consists of a shunt and a series filters therefore, it is a most promising custom power device to mitigate power quality issues of instance voltage swell, sag, phase unbalance, current and voltage harmonics, DC-link voltage regulation, reactive power compensation etc. SRF-PLL and DSOGI-PLL perform grid synchronization and reference signal generation simultaneously in a single platform. Additionally, JAYA based optimization has been employed for determination of PI controller gains of both the controller. To validate the performance of UPQC and its controller, the complete UPQC system has been developed and fabricated in MATLAB/ Simulink as well as in hardware platform. The accuracy of simulation as well as hardware outcomes and their comparative power quality investigation is found to be satisfactory.

Data-driven electricity theft detection (ETD) based on machine learning and deep learning has the advantages of automation, real-time performance, and efficiency while requiring a large amount of labeled data to train models. However, the imbalance ratio between positive and unlabeled samples has reached 1:200, which significantly limits the accuracy of the ETD model. In cases like this, we refer to it as positive-unlabeled learning. Down-sampling wastes a large amount of negative samples, while up-sampling will result in the ETD model not being robust. Both can lead to ETD models performing well in experimental environments but poorly in production environments. In this context, this paper proposes a semi-supervised electricity theft detection algorithm based on fuzzy c-means and logistic regression cross detection (FCM-LR). Firstly, a statistical feature set based on business data and load data is proposed to depict the profile of electricity users, which can achieve the effect of reducing the complexity of data structure. Furthermore, by using the FCM-LR method, the utilization of unlabeled data can be maximized, and new electricity theft patterns can be discovered. The simulation results show that the theft detection effect of this method is significant, with Precision, Recall, F1, and Area under Curve all approaching 99%.