Providing security to the Supervisory Control and Data Acquisition (SCADA) systems is one of the demanding and crucial tasks in recent days, due to the different types of attacks on the network. For this purpose, there are different types of attack detection and classification methodologies have been developed in the conventional works. But it limits with the issues like high complexity in design, misclassification results, increased error rate, and reduced detection efficiency. In order to solve these issues, this paper aims to develop an advanced machine learning models for improving the SCADA security. This work comprises the stages of preprocessing, clustering, feature selection, and classification. At first, the Markov Chain Clustering (MCC) model is implemented to cluster the network data by normalizing the feature values. Then, the Rapid Probabilistic Correlated Optimization (RPCO) mechanism is employed to select the optimal features by computing the matching score and likelihood of particles. Finally, the Block Correlated Neural Network (BCNN) technique is employed to classify the predicted label, where the relevancy score is computed by using the kernel function with the feature points. During experimentation, there are different performance indicators have been used to validate the results of proposed attack detection mechanisms. Also, the obtained results are compared with the RPCO-BCNN mechanism for proving the superiority of the proposed attack detection system.
The majority of the accidents were happening perpetually due to driver drowsiness over the decades. Automation has been playing key role in many fields to provide conformity and improve the quality of life of the users. Though various drowsiness detection systems have been developed during last decade based on many factors, still the systems were demanding an improvement in terms of efficiency, accuracy, cost, speed, and availability, etc. In this paper, proposed an integrated approach depends on the Eye and mouth closure status (PERCLOS) along with the calculation of the new proposed vector FAR (Facial Aspect Ratio) similarly to EAR and MAR. This helps to find the status of the closed eyes or opened mouth like yawning, and any frame finds that has hand gestures like nodding or covering opened mouth with hand as innate nature of humans when trying to control the sleepiness. The system also integrated the methods and textural-based gradient patterns to find the driver's face in various directions identify the sunglasses on the driver's face and the scenarios like hands-on eyes or mouth while nodding or yawning were also recognized and addressed. The proposed work tested on datasets such as NTHU-DDD, YawDD, and a proposed dataset EMOCDS (Eye and Mouth Open Close Data Set) and proved better in terms of accuracy and provides results in general by considering various circumstances.INDEX TERMS: EAR (Eye Aspect Ratio), MAR (Mouth Aspect Ratio), FAR (Face Aspect Ratio), advanced driver movement tracking system, Spatio-temporal interest points, eye gaze tracking, deep neural networks.
This article presents a new cascaded control strategy to control the power flow in a renewable-energy-based microgrid operating in grid-connected mode. The microgrid model is composed of an AC utility grid interfaced with a multi-functional grid interactive converter (MF-GIC) acting as a grid-forming converter, a photovoltaic (PV) power-generation system acting as grid-feeding distributed generation unit, and various sensitive/non-sensitive customer loads. The proposed control strategy consists of a fractional order PI (FO-PI) controller to smoothly regulate the power flow between the utility grid, distributed generation unit, and the customers. The proposed controller exploits the advantages of FO (Fractional Order) calculus in improving the steady-state and dynamic performance of the renewable-energy-based microgrid under various operating conditions and during system uncertainties. To tune the control parameters of the proposed controller, a recently developed evaporation-rate-based water-cycle algorithm (ERWCA) is utilized. The performance of the proposed control strategy is tested under various operating conditions to show its efficacy over the conventional controller. The result shows that the proposed controller is effective and robust in maintaining all the system parameters within limits under all operating conditions, including system uncertainties.
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Chemotherapy is a widely used cancer treatment method globally. However, cancer cells can develop resistance towards single-drug-based chemotherapy if it is infused for extended periods, resulting in treatment failure in many cases. To address this issue, oncologists have progressed towards using multi-drug chemotherapy (MDC). This method considers different drug concentrations for cancer treatment, but choosing incorrect drug concentrations can adversely affect the patient’s body. Therefore, it is crucial to recognize the trade-off between drug concentrations and their adverse effects. To address this issue, a closed-loop multi-drug scheduling based on Fractional Order Internal-Model-Control Proportional Integral (IMC-FOPI) Control is proposed. The proposed scheme combines the benefits of fractional PI and internal model controllers. Additionally, the parameters of IMC-FOPI are optimally tuned using a random walk-based Moth-flame optimization. The performance of the proposed controller is compared with PI and Two degrees of freedom PI (2PI) controllers for drug concentration control at the tumor site. The results reveal that the proposed control scheme improves the settling time by 43% and 21% for VX, 54% and 48 % for VY, and 48% and 40% for VZ, respectively, compared to PI and 2PI. Therefore, it can be concluded that the proposed control scheme is more efficient in scheduling multi-drug than conventional controllers.
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