No abstract

Background: The purpose of this study is to explore the differences and similarities of EEG -based neural emotional response toward flower arrangements (FAs) between the normal elderly (NE) and cognitively impaired elderly (CIE) in arranging flowers. Methods: The study participants included 16 elderly individuals: eight elderly people with normal cognitive function and eight elderly people with cognitive dysfunction. They were divided into two groups to arrange flowers, and six mood indicators (Engagement, Excitation, Focus, Interest, Relaxation and Stress) were measured with EEG before and after the experiment. Results: The similarities were that there was no significant difference in Excitement, Relaxation and Stress between pre-test and post-test for NE and CIE. The differences were that there was a significant difference on Engagement and Interest in CIE, and they both increased, but there was no difference with respect to them in NE. While there was a significant difference on the Focus of NE, it was decreased, but there was no difference for it with respect to CIE. Conclusions: A similarity on EEG-Based Neural Emotional Responses to flower arrangements between NE and CIE was that they both felt relaxation. The differences were that the Focus of NE decreased and the Interest and Engagement of CIE increased. CIEs were more interested and engaged in FAs.

Rural communities have become a hot topic in academic circles because of their graceful natural environment and great healing potential. However, existing research still lacks attention to the street space in rural communities and rarely considers its integrated visual and soundscape design in terms of their effect on public health. As a result, the healing potential of rural community streets cannot be fully used in design practice. Relevant audiovisual materials were collected from a field investigation in four rural communities in southwestern China. Based on these data, the subjective and objective healing index data of subjects under comprehensive audiovisual conditions were collected and analyzed through laboratory experiments. The results revealed that type of street space affects healing potential, and the artificial–natural enclosed and natural semi-enclosed streets are the street types with the best healing effect. When the total sound pressure level was 55dB(A), the sound combination with birdsong accounting for 70% had a significant positive effect on improving the healing effect of rural community streets. In contrast, the sound combination with birdsong accounting for 50% or less had no significant effect on improving healing. The subjective healing perception of street space in rural communities was significantly positively correlated with aesthetic preferences. There was also a significant correlation between subjective healing perception and physiological index data in the audiovisual combination. This research explored the impact of different types of street space and sound combinations on the healing effect of rural community streets in an integrated audiovisual environment and provided a scientific basis for the healing landscape design of rural community streets in an integrated audiovisual environment. It was expected to provide new ideas for the construction of rural community landscapes, including acoustic landscapes, to promote physical and mental healing.

In recent years, the Internet of vehicles (IOV) with intelligent networked automobiles as terminal node has gradually become the development trend of automotive industry and research hot spot in related fields. This is due to its characteristics of intelligence, networking, low-carbon and energy saving. Real time emotion recognition for drivers and pedestrians in the community can be utilized to prevent fatigue driving and malicious collision, keep safety verification and pedestrian safety detection. This paper mainly studies the face emotion recognition model that can be utilized for IOV. Considering the fluctuation of image acquisition perspective and image quality in the application scene of IOV, the natural scene video similar to vehicle environment and its galvanic skin response (GSR) are utilized to make the testing set of emotion recognition. Then an expression recognition model combining codec and Support Vector Machine classifier is proposed. Finally, emotion recognition testing is completed on the basis of Algorithm 1. The matching accuracy between the emotion recognition model and GSR is 82.01%. In the process of model testing, 189 effective videos are involved and 155 are correctly identified.

Intelligent surveillance is an important management method for the construction and operation of power stations such as wind power and solar power. The identification and detection of equipment, facilities, personnel, and behaviors of personnel are the key technology for the ubiquitous electricity The Internet of Things. This paper proposes a video solution based on support vector machine and histogram of oriented gradient (HOG) methods for pedestrian safety problems that are common in night driving. First, a series of image preprocessing methods are used to optimize night images and detect lane lines. Second, an image is divided into intelligent regions to be adapted to different road environments. Finally, the HOG and support vector machine methods are used to optimize the pedestrian image on a Linux system, which reduces the number of false alarms in pedestrian detection and the workload of the pedestrian detection algorithm. The test results show that the system can successfully detect pedestrians at night. With image preprocessing optimization, the correct rate of nighttime pedestrian detection can be significantly improved, and the correct rate of detection can reach 92.4%. After the division area is optimized, the number of false alarms decreases significantly, and the average frame rate of the optimized video reaches 28 frames per second.