Confrontation Training Research Articles

Optimizing the quality of Fourier single-pixel imaging via generative adversarial network

Fourier single-pixel imaging (FSI) usually achieves an enhanced imaging speed via undersampling; however, many details are lost in the reconstructed image obtained by frequency truncation due to the lack of the high-frequency part, and the image contains ringing artefacts due to the lack of an expression ability, thus reducing the image quality. To improve the quality of real-time imaging, a fast image reconstruction framework based on the Wasserstein generative adversarial network (WGAN) and gradient penalty (GP) is proposed. Under the common constraints of the resistance loss and content loss, confrontation training is first conducted between the generator and the discriminator in the framework, and then, an additional generator is connected to improve the fidelity of the reconstructed image based on the generation of the confrontation network. The model can be used to restore high-frequency details and denoise low-quality images that are undersampled. The simulation and experimental results show that the FSI using the GAN method, which achieved a compression rate of 95–98 %, is superior to conventional imaging in terms of the quality at a low sampling rate; therefore, the proposed scheme has potential practical applications.

Evaluation of a Vaccine-Communication Tool for Physicians

To evaluate a Kaiser Permanente Northern California physician training tool entitled "Effective Communication without Confrontation" aimed at improving communication with vaccine-hesitant parents, building trust, and alleviating physician stress surrounding vaccination visits. Trainings were held May to July 2015. Pre- and post-training surveys assessed physician comfort and perceived effectiveness in communicating with vaccine-hesitant parents. We measured vaccination coverage at the 2-, 4-, and 6-month well-child visits, and days undervaccinated at 9months of age. We compared vaccination rates before and after the training. Of 415 physicians who received training, 249 completed post-training surveys. Physicians reported that the training helped them feel "much more or more" comfortable talking with parents who are unsure (72.3%), want to delay (73.9%), or refuse (63.5%) vaccinations and "much more or more" effective at persuading parents who are unsure (67.5%) or want to delay vaccinations (61.4%). They reported feeling "the same or less" effective persuading parents who refuse vaccinations (66.3%). Vaccine coverage remained unchanged and high from before to after the training (95%-96%), as did parent satisfaction with his or her child's provider (4.73/5.00). The Effective Communication without Confrontation training did not increase vaccine coverage, but did improve physicians' comfort and perceived effectiveness communicating with most vaccine-hesitant parents and may help to ease potentially stressful vaccination visits.

Ripple-GAN: Lane Line Detection With Ripple Lane Line Detection Network and Wasserstein GAN

With artificial intelligence technology being advanced by leaps and bounds, intelligent driving has attracted a huge amount of attention recently in research and development. In intelligent driving, lane line detection is a fundamental but challenging task particularly under complex road conditions. In this paper, we propose a simple yet appealing network called Ripple Lane Line Detection Network (RiLLD-Net), to exploit quick connections and gradient maps for effective learning of lane line features. RiLLD-Net can handle most common scenes of lane line detection. Then, in order to address challenging scenarios such as occluded or complex lane lines, we propose a more powerful network called Ripple-GAN, by integrating RiLLD-Net, confrontation training of Wasserstein generative adversarial networks, and multi-target semantic segmentation. Experiments show that, especially for complex or obscured lane lines, Ripple-GAN can produce a superior detection performance to other state-of-the-art methods.

Wasserstein Generative Adversarial Network and Convolutional Neural Network (WG-CNN) for Bearing Fault Diagnosis

In recent years, intelligent fault diagnosis technology with deep learning algorithms has been widely used in industry, and they have achieved gratifying results. Most of these methods require large amount of training data. However, in actual industrial systems, it is difficult to obtain enough and balanced sample data, which pose challenges in fault identification and classification. In order to solve the problems, this paper proposes a data generation strategy based on Wasserstein generative adversarial network and convolutional neural network (WG-CNN), which uses generator and discriminator to conduct confrontation training, expands a small sample set into a high-quality dataset, and uses one-dimensional convolutional neural network (1D-CNN) to learn sample characteristics and classify different fault types. Experimental results over the standard Case Western Reserve University (CWRU) bearing fault diagnosis benchmark dataset showed that the proposed method has obvious and satisfactory fault diagnosis effect with 100% classification accuracy for few-shot learning. In different noise environments, this method also has excellent performance.

Reconstruction of shale image based on Wasserstein Generative Adversarial Networks with gradient penalty

Generative Adversarial Networks (GANs), as most popular artificial intelligence models in the current image generation field, have excellent image generation capabilities. Based on Wasserstein GANs with gradient penalty, this paper proposes a novel digital core reconstruction method. First, a convolutional neural network is used as a generative network to learn the distribution of real shale samples, and then a convolutional neural network is constructed as a discriminative network to distinguish reconstructed shale samples from real ones. Through this confrontation training method, realistic digital core samples of shale can be reconstructed. The paper uses two-point covariance function, Frechet Inception Distance and Kernel Inception Distance, to evaluate the quality of digital core samples of shale reconstructed by GANs. The results show that the covariance function can test the similarity between generated and real shale samples, and that GANs can efficiently reconstruct digital core samples of shale with high-quality. Compared with multiple point statistics, the new method does not require prior inference of the probability distribution of the training data, and directly uses noise vector to generate digital core samples of shale without using constraints of hard data in advance. It is easy to produce an unlimited number of new samples. Furthermore, the training time is also shorter, only 4 hours in this paper. Therefore, the new method has some good points compared with current methods. Cited as : Zha, W., Li, X., Xing, Y., He, L., Li, D. Reconstruction of shale image based on Wasserstein Generative Adversarial Networks with gradient penalty. Advances in Geo-Energy Research, 2020, 4(1): 107-114, doi: 10.26804/ager.2020.01.10

Research on Air Confrontation Maneuver Decision-Making Method Based on Reinforcement Learning

With the development of information technology, the degree of intelligence in air confrontation is increasing, and the demand for automated intelligent decision-making systems is becoming more intense. Based on the characteristics of over-the-horizon air confrontation, this paper constructs a super-horizon air confrontation training environment, which includes aircraft model modeling, air confrontation scene design, enemy aircraft strategy design, and reward and punishment signal design. In order to improve the efficiency of the reinforcement learning algorithm for the exploration of strategy space, this paper proposes a heuristic Q-Network method that integrates expert experience, and uses expert experience as a heuristic signal to guide the search process. At the same time, heuristic exploration and random exploration are combined. Aiming at the over-the-horizon air confrontation maneuver decision problem, the heuristic Q-Network method is adopted to train the neural network model in the over-the-horizon air confrontation training environment. Through continuous interaction with the environment, self-learning of the air confrontation maneuver strategy is realized. The efficiency of the heuristic Q-Network method and effectiveness of the air confrontation maneuver strategy are verified by simulation experiments.

Research on Underwater Image Enhancement Technology Based on Generative Adversative Net work s

Under the influence of light refraction and particle scattering, the underwater image has a low contrast and color attenuation . This paper proposes an underwater image enhancement method based on Generative Adversative Net work s (GANs). The main principle is to apply the GANs to learn the pixel transformation between images through con frontation training, so as to achieve the purpose of image enhancement. In this paper, we select the real underwater image as the data set and degrade it before inputting the image into the neural network. Then we input the corresponding degenerate image , so that the neural network can learn the mapping from input image to real image and realize the color reduction and enhancement of underwater image. Experiments show that this method has strong robustness and can be applied to underwater shooting and under water navigation.

EAP Referral Training and Supervisor's Beliefs About Troubled Workers

This study was designed to investigate the relationship between EAP referral training and supervisors' interactions with troubled workers. Using a field survey, self-report, retrospective methodology, 224 industrial supervisors' beliefs about impaired employees were examined in connection with the former group's participation in constructive confrontation training and their attitudes toward EAPs. In general, based on Bayer and Gerstein's (1988a) Bystander-Equity Model of Supervisory Helping Behavior, it was predicted that supervisors who had attended training and/or held positive attitudes toward their EAP would report more welldeveloped beliefs about troubled workers than persons who had not received training and/or held negative attitudes toward their EAP. Overall, our results provided support for the idea that constructive confrontation training enhances supervisors' recognition of impaired employees. While we anticipated this finding, we did not expect supervisors to report that their attitudes toward...

Helping Counselors Learn to Confront

This article presents a model for training counselors to confront empathetically and presents a confrontation training scale that includes the inadequate responses of acquiescing and scolding and the progressively more effective responses of recognizing ineffective behavior, realizing its negative consequences, and committing to change. The authors contend that counselors who confront tend to become more empathic but also more able to help others and themselves face their problems responsibly.