No abstract

Accurately classify teeth category is important in further dental diagnosis. Analyzing huge dental data, that is, identifying the teeth category, is often a hard task. Current automatic methods are based on computer vision and deep learning approaches. In this study, we aimed to classify the teeth category into four classes: incisor, canine, premolar, and molar. Cone beam computed tomography was used to collect the data. We proposed a seven‐layer deep convolutional neural network with global average pooling to identify teeth category. Data augmentation method was used to enlarge the size of training dataset. The results showed the sensitivities of incisor, canine, premolar, and molar teeth are 88%, 86%, 84%, and 90%, respectively. The average sensitivity is 87.0%. We validated max pooling gives better results than average pooling. Our method is better than three state‐of‐the‐art approaches.

Growing evidence shows that consumer choices in real life are mostly driven by unconscious mechanisms rather than conscious. The unconscious process could be measured by behavioral measurements. This study aims to apply automatic facial expression analysis technique for consumers' emotion representation, and explore the relationships between sensory perception and facial responses. Basic taste solutions (sourness, sweetness, bitterness, umami, and saltiness) with 6 levels plus water were used, which could cover most of the tastes found in food and drink. The other contribution of this study is to analyze the characteristics of facial expressions and correlation between facial expressions and perceptive hedonic liking for Asian consumers. Up until now, the facial expression application researches only reported for western consumers, while few related researches investigated the facial responses during food consuming for Asian consumers. Experimental results indicated that facial expressions could identify different stimuli with various concentrations and different hedonic levels. The perceived liking increased at lower concentrations and decreased at higher concentrations, while samples with medium concentrations were perceived as the most pleasant except sweetness and bitterness. High correlations were founded between perceived intensities of bitterness, umami, saltiness, and facial reactions of disgust and fear. Facial expression disgust and anger could characterize emotion "dislike," and happiness could characterize emotion "like," while neutral could represent "neither like nor dislike." The identified facial expressions agree with the perceived sensory emotions elicited by basic taste solutions. The correlation analysis between hedonic levels and facial expression intensities obtained in this study are in accordance with that discussed for western consumers.

With the development of artificial intelligence, multiagent algorithms have been applied to many real-time strategy games. Making plans on the human being is gradually passing away, especially in combat scenarios. Cognitive electronic warfare (CEW) is a complex and challenging work due to the sensitivity of the data sources. There are few studies on CEW. In the past, wargame simulations depended on differential equations and war theory, which resulted in high time and human resource costs. In the future, as other artificial intelligence theories are developed, artificial intelligence will play a more critical role in wargames. The capabilities of multiagent modeling to describe complex systems and predict actions in dynamic environments are superior to those of traditional methods. In this paper, we use a 3D wargame engine from China Aerospace System Simulation Technology Co., Ltd. (Beijing) named All Domain Simulation (ACS), which supports land, sea, and air combat scenarios, to simulate combat. In the simulations, there are several unmanned air vehicles (UAVs) as attackers and several radar stations as defenders, and both have the ability to detect the others. In the game, several UAVs need to learn to detect targets and track targets separately, and we train the UAV's behavior by well-designed reward shaping and multiagent reinforcement learning (MARL) with LSTM. We improved the RDPG [1] algorithm and merged the MADDPG [2] and RDPG algorithms. From the experimental results, we can see that the effectiveness and accuracy of the algorithm have been greatly improved.