Study on Enterprises’ Internet Public Opinion Area Hotspots Based on Social Network Analysis

With the rapid development of Web 2.0, online public opinion has become an issue in the companies’ development process. With numerous user-generated contents about real-world events generated almost in real-time, monitoring, evolution and management of online public opinion play the critical role for the healthy development of enterprises. By collecting articles about public opinion on the corporate network from CNKI and using Citespace based on social network analysis, we have combed the context of current research in this area, analyzed the characteristics of the current research on this topic, excavated research rules in this field and summarized research results to provide references for further study

Cloud-structural health monitoring based on smartphone

Smartphone, integrated with CPU, sensors, network, and storage capability, is developed rapidly in recent years. A cloud-structural health monitoring method based on smartphone was proposed, and a structural health monitoring system Orion-CC, which integrates functions of data acquisition, data analysis and data upload, was developed on smartphone to perform structural health monitoring without any other professional devices. And the feasibility of Orion-CC was proved by the cable force test. A cloud-SHM data sharing website was built to make the data synchronization between smartphone and website, and realize data uploading and sharing, which can improve the efficiency of monitoring and big data integration, make the possibility for big data collection and quick structural safety evaluation

Smartphone based public participant emergency rescue information platform for earthquake zone – “E-Explorer”

Devastating earthquake can often cause the disaster area communication interrupt, traffic paralysis, etc. It is difficult for the emergency rescue force to get the disaster area in time. Therefore, active local participation in the quake-hit areas to aid each other appeals extremely important. The paper is based on a self-developed smartphone software called “E-Explorer”, study its significance and its working methods to help the public participate in the earthquake rescue actively when external network are cut off. “E-Explorer” can help deliver important information for personal survival, let rescue workers locate the positions of survivors trapped, creating an efficient self-help and mutual rescue platform for the earthquake-stricken people

Fabrication and properties of PVA-TiO2 hydrogel composites

AbstractThe preparation and properties of PVA-TiO2 hydrogel composites have been studied in this research. The results show that tensile strength and compression modulus of PVA-TiO2 hydrogel composites increased significantly, but the elongation did not changed obviously compared with the PVA hydrogel, indicating the good interaction between inorganic nanoparticles and organic polymer. Friction performance of the hydrogels was discussed, the friction of PVA-TiO2 hydrogel composites decrease than the PVA hydrogel and friction coefficient up to 0.001. The possible mechanism of friction was discussed

Fault classification in dynamic processes using multiclass relevance vector machine and slow feature analysis

This paper proposes a modifed relevance vector machine with slow feature analysis fault classification for industrial processes. Traditional support vector machine classification does not work well when there are insufficient training samples. A relevance vector machine, which is a Bayesian learning-based probabilistic sparse model, is developed to determine the probabilistic prediction and sparse solutions for the fault category. This approach has the benefits of good generalization ability and robustness to small training samples. To maximize the dynamic separability between classes and reduce the computational complexity, slow feature analysis is used to extract the inner dynamic features and reduce the dimension. Experiments comparing the proposed method, relevance vector machine and support vector machine classification are performed using the Tennessee Eastman process. For all faults, relevance vector machine has a classification rate of 39%, while the proposed algorithm has an overall classification rate of 76.1%. This shows the efficiency and advantages of the proposed method