The World Health Organization (WHO) reported 1.25 million deaths yearly due to road traffic accidents worldwide and the number has been continuously increasing over the last few years. Nearly fifth of these accidents are caused by distracted drivers. Existing work of distracted driver detection is concerned with a small set of distractions (mostly, cell phone usage). Unreliable ad-hoc methods are often used. In this paper, we present the first publicly available dataset for driver distraction identification with more distraction postures than existing alternatives. In addition, we propose a reliable deep learning-based solution that achieves a 90% accuracy. The system consists of a genetically-weighted ensemble of convolutional neural networks, we show that a weighted ensemble of classifiers using a genetic algorithm yields in a better classification confidence. We also study the effect of different visual elements in distraction detection by means of face and hand localizations, and skin segmentation. Finally, we present a thinned version of our ensemble that could achieve 84.64% classification accuracy and operate in a real-time environment.
This work presents a design for nuclear radiation detection and monitoring system in a nuclear facility based on wireless sensor networks (WSNs). Energy efficiency is a critical factor in designing WSNs where a sensor node is small with limited power resources. A reliable WSN must be energy efficient to maximize its lifetime. Media access control (MAC) protocols are essential for the energy-efficiency objectives of WSNs as they directly control the most energy consuming part of a sensor node communications over the shared medium. Different MAC protocols for WSNs are presented. This search will explain the important role of MAC protocols for energy saving and why currently conventional protocols don't fit for the actual requirements. A comparison between two MAC protocols, IEEE802.11 and sensor MAC (SMAC), is presented using network simulator-NS-2.35. Then their performance will be compared to each other. SMAC outperforms IEEE802.11 in total energy conservation by approximately 21%. Power saving is the main aim in the design of nuclear radiation WSN to guarantee more life time for the network.
Worldwide, Nuclear Power Plants (NPPs) must have higher security protection and precise fault detection systems, especially underground power cable faults, to avoid causing national disasters and keep on safe national ratios of electricity production. Hence, this paper proposes an automatic, effective, and accurate Deep Learning (DL)-based fault classification and location technique for these cables via a One-dimensional Convolutional Neural Network (1D-CNN) and a Binary Support Vector Machine (BSVM). The proposed approach includes four main steps: data collection, feature extraction and reduction, fault detection, and fault classification and location. Signal collection from the underground cable's sending end is performed via the Alternating Transient Program/Electromagnetic Transient Program (ATP/EMTP). Feature extraction and reduction are performed via Fractional Discrete Cosine Transform (FrDCT) and Singular Value Decomposition (SVD) methods. Fault detection is performed through leveraging BSVM with the linear Kernel method in the third step. Finally, this permits 1D-CNN to classify the fault type and locate it. Simulation results confirmed the efficiency of our proposed method, especially for 11kV underground cable faults, including different fault resistances and inception angles. Moreover, the proposed technique is applicable in real-time scenarios with a 99.6% accuracy rate, 0.15sec lowest execution time, and 0.095% maximum error rate for fault location at fractional factor (α) equals to 0.8.
Parallax error decreases the accuracy of the Positron Emission Tomography (PET) scanner. One of suitable solutions to reduce this error is to gain the depth of interaction information in a PET which uses the phoswich detectors. The pulse shape of the scintillator material can identify the corresponding layer of interaction within the phoswich detector by using Pulse Shape Discrimination (PSD) methods. In this work, we propose the PSD based on a Discrete Fractional Fourier Transform (DFRFT) to extract the features of the scintillation pulses. Then, we use the Support Vector Machine (SVM) to classify these features. A data set consists of 100 000 scintillation pulses for LSO and LuYAP crystals are discriminated using the proposed method. Different fraction factors of the DFRFT are studied to select the optimum one. Also, the SVM is applied using linear, radial basis function, or quadratic kernel. The highest efficiency of the proposed PSD is 96.3% when the applied fraction factor is 0.8 and a quadratic SVM kernel is used.
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