Investigating the Hotspot and Evolution Path in the Field of Art Design: A Social Network Analysis Approach

With the purpose of finding out the development trend of Chinese art design discipline, a key network in the field of art design was constructed using social network analysis method to perform an in-depth investigation of the evolution path of China’s research hotspots in art design. This paper employs the Netdraw and the Bicomb software to extract high-frequency key themes and to draw the key themes co-occurrence social network in the field of art design, respectively. The key themes in the field of art design were classified and summarized into three stages from 2003 to 2007, 2008 to 2010 and from 2011 to 2016. The results showed a diversified research direction of China’s art design, and many fields such as traditional art, environmental art, and information art design were beginning to receive great attention. However, teaching and innovation have been the focus of attention in the field of art design. In addition, the correlation between research directions in the field of Chinese art design is gradually increasing and the key network shows a special small-world effect. But, there was yet to be any significant alliance among the research topics. Keywords: Art design; Social network analysis; Research hotspot; Evolution pat

Photometric Calibration and Image Stitching for a Large Field of View Multi-Camera System

A new compact large field of view (FOV) multi-camera system is introduced. The camera is based on seven tiny complementary metal-oxide-semiconductor sensor modules covering over 160° × 160° FOV. Although image stitching has been studied extensively, sensor and lens differences have not been considered in previous multi-camera devices. In this study, we have calibrated the photometric characteristics of the multi-camera device. Lenses were not mounted on the sensor in the process of radiometric response calibration to eliminate the influence of the focusing effect of uniform light from an integrating sphere. Linearity range of the radiometric response, non-linearity response characteristics, sensitivity, and dark current of the camera response function are presented. The R, G, and B channels have different responses for the same illuminance. Vignetting artifact patterns have been tested. The actual luminance of the object is retrieved by sensor calibration results, and is used to blend images to make panoramas reflect the objective luminance more objectively. This compensates for the limitation of stitching images that are more realistic only through the smoothing method. The dynamic range limitation of can be resolved by using multiple cameras that cover a large field of view instead of a single image sensor with a wide-angle lens. The dynamic range is expanded by 48-fold in this system. We can obtain seven images in one shot with this multi-camera system, at 13 frames per second

A Collision Relationship-Based Driving Behavior Decision-Making Method for an Intelligent Land Vehicle at a Disorderly Intersection via DRQN

An intelligent land vehicle utilizes onboard sensors to acquire observed states at a disorderly intersection. However, partial observation of the environment occurs due to sensor noise. This causes decision failure easily. A collision relationship-based driving behavior decision-making method via deep recurrent Q network (CR-DRQN) is proposed for intelligent land vehicles. First, the collision relationship between the intelligent land vehicle and surrounding vehicles is designed as the input. The collision relationship is extracted from the observed states with the sensor noise. This avoids a CR-DRQN dimension explosion and speeds up the network training. Then, DRQN is utilized to attenuate the impact of the input noise and achieve driving behavior decision-making. Finally, some comparative experiments are conducted to verify the effectiveness of the proposed method. CR-DRQN maintains a high decision success rate at a disorderly intersection with partially observable states. In addition, the proposed method is outstanding in the aspects of safety, the ability of collision risk prediction, and comfort

MIXING CONSUMERS’ RATIONALITY AND SOCIALITY: EFFECTS OF PRODUCT QUANTITY AND POPULARITY INFORMATION ON ONLINE SHOPPING

Classical economical theories assume whatever information is available about products will be fully and efficiently processed by consumers, thus the increase of information amount would lead to greater diversity of decisions. However, consumers’ information processing capacity is limited. A large amount of product alternatives that requests consumers’ intensive cognitive effort will activate their social learning by switching decision strategy and relying on the information of product popularity. Thus, this experimental study attempts to discover the mixture of consumers’ rationality and sociality in online shopping. A 2*2 factorial design was conducted to tease out the effects of choice set and product popularity on individuals’ cognitive efforts and preference reversal, as well as the effects on the market structures that are aggregated from individual decisions. The experimental results demonstrate that, despite a large amount of product alternatives, participants may not always invest abundant cognitive efforts and the market is more concentrated with the presence of product popularity. Theoretical and practical implications are discussed

Decentralized motion planning for intelligent bus platoon based on hierarchical optimization framework

Intelligent bus platoon effectively meets the high demand for public transport during rush hours. A decentralized motion planning method based on hierarchical optimization framework is proposed for intelligent bus platoon to improve flexibility and efficiency. This method realizes the optimization of the guidance trajectory in the higher layer, and the optimization of the motion trajectory in the lower layer. First, a guidance trajectories generation method is presented to decouple the platoon motion planning. The state and trajectory of the preceding bus are used as a reference to constrain the lateral and longitudinal feasible space for the current bus. Meanwhile, the quintic polynomial is adopted to generate the trajectory cluster and the optimal trajectory is selected using the multi-objective optimization. Second, the joint kinematics model of the bus is built as the prediction model. And independent model predictive control modules are utilized to optimize each guidance trajectory to make them smoother and safer. The obstacle avoidance constraints are reconstructed based on the duality theorem. Finally, simulations demonstrate the advantages of the decentralized motion planning method under different scenarios, and results demonstrate our method improves the flexibility and consistency of the platoon

Non-Invasive Brain Stimulation: Augmenting the Training and Performance Potential in Esports Players

During the last two decades, esports, a highly competitive sporting activity, has gained increasing popularity. Both performance and competition in esports require players to have fine motor skills and physical and cognitive abilities in controlling and manipulating digital activities in a virtual environment. While strategies for building and improving skills and abilities are crucial for successful gaming performance, few effective training approaches exist in the fast-growing area of competitive esports. In this paper, we describe a non-invasive brain stimulation (NIBS) approach and highlight the relevance and potential areas for research while being cognizant of various technical, safety, and ethical issues related to NIBS when applied to esports

Tourist emotion-learning nexus: A case of Sertar, China

This paper discusses the nexus between tourists\u27 emotions and learning at a religious site, Sertar, in China. Based on Kolb\u27s learning cycle, this qualitative study proposes a conceptual framework regarding the connections between tourist attraction stimuli, tourist emotions, and tourist learning outcomes. The findings reveal that tourists have experienced mixed and intense emotions and obtained valuable learning outcomes, including changes in attitudes toward life, changes in attitudes toward others, changes in values and beliefs, and cross-cultural awareness. Tourist emotions play a vital role in triggering reflection and further generating transformative learning. Negative emotions could also result in positive learning and future behavior intentions. Practical implications are offered to destination marketers to design experience products and individuals seeking learning and eudaimonia

Enhancing Stability and Performance in Mobile Robot Path Planning with PMR-Dueling DQN Algorithm

Path planning for mobile robots in complex circumstances is still a challenging issue. This work introduces an improved deep reinforcement learning strategy for robot navigation that combines dueling architecture, Prioritized Experience Replay, and shaped Rewards. In a grid world and two Gazebo simulation environments with static and dynamic obstacles, the Dueling Deep Q-Network with Modified Rewards and Prioritized Experience Replay (PMR-Dueling DQN) algorithm is compared against Q-learning, DQN, and DDQN in terms of path optimality, collision avoidance, and learning speed. To encourage the best routes, the shaped Reward function takes into account target direction, obstacle avoidance, and distance. Prioritized replay concentrates training on important events while a dueling architecture separates value and advantage learning. The results show that the PMR-Dueling DQN has greatly increased convergence speed, stability, and overall performance across conditions. In both grid world and Gazebo environments the PMR-Dueling DQN achieved higher cumulative rewards. The combination of deep reinforcement learning with reward design, network architecture, and experience replay enables the PMR-Dueling DQN to surpass traditional approaches for robot path planning in complex environments