This study proposes a lightweight convolutional neural network with an octave-like convolution mixed block, called OLCMNet, for detecting driver distraction under a limited computational budget. The OLCM block uses point-wise convolution (PC) to expand feature maps into two sets of branches. In the low-frequency branches, we perform average pooling, depth-wise convolution (DC), and upsampling to obtain a low-resolution low-frequency feature map, reducing spatial redundancy and connection density. In the high-frequency branches, the expanded feature map with the original resolution is fed to the DC operator, gaining an apposite receptive field to capture fine details. The feature concatenation of the low-frequency and high-frequency branches is encoded sequentially by a squeeze-and-excitation (SE) module and PC operator, realizing feature global information fusion. Introducing another SE module at the last stage, the OLCMNet facilitates further sensitive information exchange between layers. In addition, with an augmented reality head-up display (ARHUD) platform, we create a Lilong Distracted Driving Behavior (LDDB) Dataset through a series of on-road experiments. Such a dataset contains 14808 videos collected from an infrared camera, covering six driving behaviors of 2468 participants. We manually annotate these videos at five frames per second, obtaining a total of 267378 images. Compared with the existing methods, the embedded hardware platform experiments indicate that OLCMNet hits acceptable trade-offs, namely, 89.53% accuracy for StateFarm Dataset and 95.98% accuracy LDDB Dataset when the latency is 32.8 ± 4.6ms.

When using the belief rule base (BRB) methodology to deal with the nonlinear causal inference problems, combinatorial explosion often occurs due to overnumbered antecedent attributes, resulting in poor performance. Therefore, this paper proposes a novel nonlinear causal inference approach based on vector-based BRB. In the modeling process of BRB, the original attributes are ranked by contribution rate and transformed into attribute vectors. Meanwhile, combined with the k-means method, appropriate referential vectors are obtained. Thereby a vector-based BRB can be established. In the inference process of BRB, the idea of full activation of vector-based rules is presented. By calculating the spatial matching degree of the testing sample and the referential vectors, activation weights of the rules which are used in the evidential reasoning algorithm are acquired. Experimental results of a nonlinear function with four-dimensional input and the pipeline leakage detection data show the effectiveness and superiority of the proposed approach.

Disease diagnosis can be performed based on fusing the data acquired by multiple medical sensors from patients, and it is a crucial task in sensor-based e-healthcare systems. However, it is a challenging problem that there are few effective diagnosis methods based on sensor data fusion for atrial hypertrophy disease. In this article, we propose a novel multi-sensor data fusion method for atrial hypertrophy diagnosis, namely, characterized support vector hyperspheres (CSVH). Instead of constructing a hyperplane, as a traditional support vector machine does, the proposed method generates “hyperspheres” to collect the discriminative medical information, since a hypersphere is more powerful for data description than a hyperplane. In detail, CSVH constructs two characterized hyperspheres for the classes of patient and healthy subject, respectively. The hypersphere for the patient class is developed in a weighted version so as to take the diversity of patient instances into consideration. The hypersphere for the class of healthy people keeps furthest away from the patient class in order to achieve maximum separation from the patient class. A query is labelled by membership functions defined based on the two hyperspheres. If the query is rejected by the two classes, the angle information of the query to outliers and overlapping-region data is investigated to provide the final decision. The experimental results illustrate that the proposed method achieves the highest diagnosis accuracy among the state-of-the-art methods.

It is necessary to switch the control strategies for propulsion system frequently according to the changes of sea states in order to ensure the stability and safety of the navigation. Therefore, identifying the current sea state timely and effectively is of great significance to ensure ship safety. To this end, a reasoning model that is based on maximum likelihood evidential reasoning (MAKER) rule is developed to identify the propeller ventilation type, and the result is used as the basis for the sea states identification. Firstly, a data-driven MAKER model is constructed, which fully considers the interdependence between the input features. Secondly, the genetic algorithm (GA) is used to optimize the parameters of the MAKER model in order to improve the evaluation accuracy. Finally, a simulation is built to obtain experimental data to train the MAKER model, and the validity of the model is verified. The results show that the intelligent sea state identification model that is based on the MAKER rule can identify the propeller ventilation type more accurately, and finally realize intelligent identification of sea states.

Echo-state networks (ESNs) are a distinct architecture for recurrent neural networks (RNNs). The great advantage of ESN is that they offer an easy way to train the RNN. To make full use of ESN, one needs to first identify their global (hyper) parameters. These are input scaling, leaking rate (for leaky ESN), spectral radius, and the size of the ESN. The most recommended way to get their optimal (or suboptimal) values is by trial-and-error. However, in practice, this method has a very low efficiency. In order to tackle this problem, we propose a novel “zoom-in-zoom-out” (ZIZO) algorithm for generating the global parameters automatically. The proposed technique consists of two major parts. First, we generate random ranges for the parameters of ESNs. Then, based on bootstrap-sampling, we search the optimal solution within the fixed specific ranges. To evaluate the proposed method, we use two different data sets, which are collected from the literature. The obtained results demonstrate the efficiency and accuracy of ZIZO.