This article focuses on developing an adaptive fast terminal sliding-mode controller (AFTSMC) with power rate proportional reaching law for the position and altitude tracking of a quadrotor in the presence of parametric uncertainties and bounded external disturbance. A nonlinear fast terminal sliding surface is proposed for the fast and finite-time convergence of the tracking error despite having the system states far away from the equilibrium point. Also, a power rate proportional reaching law has been proposed that ensures fast and finite-time convergence of the sliding manifold while attenuating the chattering phenomena in the sliding phase. To avoid the problem associated with over-estimation of the unknown disturbance bound, which eventually leads to chattering, an adaptive tuning law for gain adaptation is developed based on the Lyapunov's stability theory that completely eradicates the necessity of knowing the upper bound of the disturbance a priori. The finite-time stability of a closed-loop system is investigated using the Lyapunov theory. The effectiveness of the proposed scheme is compared with an adaptive sliding-mode controller (ASMC) using extensive simulation and validated on the DJI Matrice 100 quadrotor as a proof of concept on the hardware platform.

© 2021, King Fahd University of Petroleum & Minerals. Having a full situational awareness while driving is one of the most important perceptions for safe driving which can be reduced by various factors such as in-vehicle infotainment, distraction, or mental load leading. Machine learning methods are being used to optimize for the identification of these inhibiting factors. To do so, three types of data were used: biographic features, physiological signals and vehicle information of 68 participants are being utilized to identify the normal and loaded behaviors. This research, therefore, concentrates on driving behavior analysis using a new automated hybrid framework for detection of performance degradation of drivers due to distraction. The proposed model contains a hybrid of extreme learning neural network, as an ensemble learning method and evolutionary algorithms, to determine the weights of classifiers, for combining several traditional classifiers. The obtained results showcase that the proposed model yields outstanding performance than the other applied methods.

Accurate prediction of solar energy is an important issue for photovoltaic power plants to enable early participation in energy auction industries and cost-effective resource planning. This article introduces a new deep learning-based multistep ahead approach to improve the forecasting performance of global horizontal irradiance (GHI). A deep convolutional long short-term memory is used to extract optimal features for accurate prediction of the GHI. The performance of such deep neural networks directly depends on their architectures. To deal with this problem, a swarm evolutionary optimization method, called the sine-cosine algorithm, is applied and advanced to automatically optimize the network architecture. A three-phase modification model is proposed to increase the diversity of population and avoid premature convergence in the optimization mechanism. The performance of the proposed method is investigated using three datasets collected from three solar stations in the east of the United States. The experimental results demonstrate the superiority of the proposed method in comparison to other forecasting models.

Sudden cardiac death from lethal arrhythmia is a preventable cause of death. Ventricular fibrillation and tachycardia are shockable electrocardiographic (ECG)rhythms that can respond to emergency electrical shock therapy and revert to normal sinus rhythm if diagnosed early upon cardiac arrest with the restoration of adequate cardiac pump function. However, manual inspection of ECG signals is a difficult task in the acute setting. Thus, computer-aided arrhythmia classification (CAAC) systems have been developed to detect shockable ECG rhythm. Traditional machine learning and deep learning methods are now progressively employed to enhance the diagnostic accuracy of CAAC systems. This paper reviews the state-of-the-art machine and deep learning based CAAC expert systems for shockable ECG signal recognition, discussing their strengths, advantages, and drawbacks. Moreover, unique bispectrum and recurrence plots are proposed to represent shockable and non-shockable ECG signals. Deep learning methods are usually more robust and accurate than standard machine learning methods but require big data of good quality for training. We recommend collecting large accessible ECG datasets with a meaningful proportion of abnormal cases for research and development of superior CAAC systems.

Automatic medical image analysis (e.g., medical image classification) is widely used in the early diagnosis of various diseases. The computer-aided diagnosis (CAD) systems enable accurate disease detection and treatment. Nowadays, deep learning (DL)-based CAD systems have been able to achieve promising results in most of the healthcare applications. Also, uncertainty quantification in the existing DL methods have not gained enough attention in the field of medical research. To fill this gap, we propose a novel, simple and effective fusion model with uncertainty-aware module for medical image classification called Binary Residual Feature fusion (BARF). To deal with uncertainty, we applied the Monte Carlo (MC) dropout during inference to obtain the mean and standard deviation of the predictions. The proposed model has two main strategies: direct and cross validated using four different medical image datasets. Our experimental results demonstrate that the proposed model is efficient for medical image classification in real clinical settings.