Underwater enhanced images (UEIs) are affected by not only the color cast and haze effect due to light attenuation and scattering, but also the over-enhancement and texture distortion caused by enhancement algorithms. However, existing underwater image quality assessment (UIQA) methods mainly focus on the inherent distortion caused by underwater optical imaging, and ignore the widespread artificial distortion, which leads to poor performance in evaluating UEIs. In this paper, a novel mapping-based underwater image quality representation is proposed. We divide underwater enhanced images into different domains and utilize a feature vector to measure the distance from the raw image domain to each enhanced image domain. The length and direction of the vector are defined as the enhancement degree and enhancement direction of the image. We construct a best enhancement direction and map other vectors to this direction to obtain the corresponding quality representation. Based on this, a novel network, called generation-based joint luminance-chrominance underwater image quality evaluation (GLCQE), is proposed, which is mainly divided into three parts: bi-directional reference generation module (BRGM), chromatic distortion evaluation network (CDEN), and sharpness distortion evaluation network (SDEN). BRGM is designed to generate two reference images about the unenhanced and the optimal enhanced versions of input UEI. In addition, the distortions in the luminance and chrominance domains of the UEI are analyzed. The luminance and chrominance channels of images are separated and input to SDEN and CDEN respectively to detect different distortions. A multi-scale feature mapping module is proposed in CDEN and SDEN to extract the feature representation of quality in chrominance and luminance of these images respectively. Moreover, a parallel spatial attention module is designed to focus on distortions in structural space by utilizing the different receptive fields of the convolution layer, due to the diverse manifestations of structural loss in the image. Finally, the mapped features extracted by two collaborative networks help the model evaluate the quality of underwater images more accurately. Extensive experiments demonstrate the superiority of our model against other representative state-of-the-art models.

Named entity recognition (NER) is an essential part of natural language processing tasks. Chinese NER task is different from the many European languages due to the lack of natural delimiters. Therefore, Chinese Word Segmentation (CWS) is usually regarded as the first step of processing Chinese NER. However, the word-based NER models relying on CWS are more vulnerable to incorrectly segmented entity boundaries and the presence of out-of-vocabulary (OOV) words. In this paper, we propose a novel character-based Gated Convolutional Recurrent neural network with Attention called GCRA for Chinese NER task. In particular, we introduce a hybrid convolutional neural network with gating filter mechanism to capture local context information and a highway neural network after LSTM to select characters of interest. The additional gated self-attention mechanism is used to capture the global dependencies from different multiple subspaces and arbitrary adjacent characters. We evaluate the performance of our proposed model on three datasets, including SIGHAN bakeoff 2006 MSRA, Chinese Resume, and Literature NER dataset. The experiment results show that our model outperforms other state-of-the-art models without relying on any external resources like lexicons and multi-task joint training.

In this paper, a novel DRL-based model (VWP, VAE-WGAN-PPOE) is proposed to solve the problem of long training time and unsatisfactory training effect in the end-to-end autonomous driving. The model is optimized from feature extraction and algorithm decision. In feature extraction, we encode the input video by combining variational auto encoder (VAE) with wasserstein generative adversarial network (WGAN). The state dimension is reduced and the problem of mode collapse and gradient disappearance caused by generative adversarial network (GAN) training is solved. In decision algorithm, we formulate a new reward function by analyzing the factors affecting driving performance. Furthermore, we propose an enhanced algorithm PPOE based on the proximal policy optimization (PPO). In the CARLA simulator, compared with CNN and ResNet34, the convergence speed of the DRL model based on VAE-WGAN increases by 26.1% and 20.3%, the navigation task completion rate increases by 18.5% and 9.2%, and the collision rate decreases by 13.6% and 9.4%. Compared with deep deterministic policy gradient (DDPG) decision algorithm, the convergence speed of the DRL model based on PPOE increases by 23.3%, the navigation task completion rate increases by 5.0% in sunny days and 8.4% in severe weather, the collision rate decreases by 3.5% in sunny days and 6.6% in severe weather. Extensive experiments show that the proposed model enables the agent to drive safely along the navigational route in the complex environment with pedestrian and vehicle interaction, even in severe weather.

Turning attention to a particular speaker when many people talk simultaneously is known as the cocktail party problem. It is still a tough task that remained to be solved especially for singlechannel speech separation. Inspired by the physiological phenomenon that humans tend to distinguish some attractive sounds from mixed signals, we propose the multi-head self-attention deep clustering network (ADCNet) for this problem. We creatively combine the widely used deep clustering network with multihead self-attention mechanism and exploit how the number of heads in multi-head self-attention affects separation performance. We also adopt the density-based canopy K-means algorithm to further improve performance. We trained and evaluated our system using the Wall Street Journal dataset (WSJ0) on two and three talker mixtures. Experimental results show the new approach can achieve a better performance compared with many advanced models.

Smart Parking Management Systems (SPMSs) have become a research hotspot in recent years. Many researchers are focused on vehicle detection technology for SPMS which is based on magnetic sensors. Magnetism-based wireless vehicle detectors (WVDs) integrate low-power wireless communication technology, which improves the convenience of construction and maintenance. However, the magnetic signals are not only susceptible to the adjacent vehicles, but also affected by the magnetic signal dead zone of high-chassis vehicles, resulting in a decrease in vehicle detection accuracy. In order to improve the vehicle detection accuracy of the magnetism-based WVDs, the paper introduces an RF-based vehicle detection method based on the characteristics analysis of received signal strengths (RSSs) generated by the wireless transceivers. Since wireless transceivers consume more energy than magnetic sensors, the proposed RF-based method is only activated to extract the data characteristics of RSSs to further judge the states of vehicles when the data feature of magnetic signals is not sufficient to provide accurate judgment on parking space status. The proposed method was evaluated in an actual roadside parking lot and experimental results show that when the sampling rate of magnetic sensor is 1 Hz, the vehicle detection accuracy is up to 99.62%. Moreover, compared with machine-learning-based vehicle detection method, the experimental results show that our method has achieved a good compromise between detection accuracy and power consumption.