The Impact of Resource Sharers’ Personal Descriptive Information on Sharing Effect in the Sharing Economy

The sharing economy not only gets more and more people\u27s attention, but also subverts the traditional economic model with its revolutionary power. This paper aims to study the impact of resource sharers’ personal descriptive information on the sharing effect in the sharing economy. The author collected the data of sharers’ personal descriptive information through the Python program from XiaoZhu short rental website, and analyzed the impact of breadth and depth of sharers’ personal descriptive information on sharing effect using regression analysis and content analysis methods. The results show that the breadth and depth of the information have a significant positive impact on the sharing effect, and credit level has a negative moderating effect on the impact of information breadth on sharing effect. Finally, according to the results, it provides some reference for trust building between resource sharers and consumers

An Experimental and Modelling Study on the Adsorption Characteristics of Activated Carbon under Different Challenge Concentration Levels

Applying air cleaning devices is an effective approach to control targeted indoor gaseous pollutants. It is important to understand the adsorption characteristics of filter media (e.g, activated carbon) at typical indoor application conditions as well as standard test conditions. Tests per ASHRAE Standard 145.1 for filter media performance evaluation can provide a relative comparison among different media. However, as the tests are conducted at elevated gas concentrations (1~100 ppm), they do not represent the media performance under lower concentrations typical of indoor applications

Lamb wave-based probabilistic fatigue life prediction for riveted lap joints

This study presents a Lamb wave-based probabilistic fatigue life prediction for riveted lap joints. First, a brief introduction is given for the experiment of Lamb wave-based damage detection. Three damage sensitive features (correlation coefficient, amplitude change and phase change) are employed to correlate the fatigue crack size with Lamb wave signal. Then the probability of detection (POD) method is used to couple the actual crack size with the model predictions using Lamb wave signal. Considering the uncertainties of the initial crack size and crack growth parameters, Bayesian method and Markov Chain Monte Carlo (MCMC) simulation are applied to obtain the probabilistic fatigue life. In order to verify the reliability of the proposed probabilistic fatigue life prediction procedure, one set of experimental data is used for validation purpose

Probabilistic inference of fatigue damage propagation with limited and partial information

AbstractA general method of probabilistic fatigue damage prognostics using limited and partial information is developed. Limited and partial information refers to measurable data that are not enough or cannot directly be used to statistically identify model parameter using traditional regression analysis. In the proposed method, the prior probability distribution of model parameters is derived based on the principle of maximum entropy (MaxEnt) using the limited and partial information as constraints. The posterior distribution is formulated using the principle of maximum relative entropy (MRE) to perform probability updating when new information is available and reduces uncertainty in prognosis results. It is shown that the posterior distribution is equivalent to a Bayesian posterior when the new information used for updating is point measurements. A numerical quadrature interpolating method is used to calculate the asymptotic approximation for the prior distribution. Once the prior is obtained, subsequent measurement data are used to perform updating using Markov chain Monte Carlo (MCMC) simulations. Fatigue crack prognosis problems with experimental data are presented for demonstration and validation

Indoor pedestrian dead reckoning calibration by visual tracking and map information

Currently, Pedestrian Dead Reckoning (PDR) systems are becoming more attractive in market of indoor positioning. This is mainly due to the development of cheap and light Micro Electro-Mechanical Systems (MEMS) on smartphones and less requirement of additional infrastructures in indoor areas. However, it still faces the problem of drift accumulation and needs the support from external positioning systems. Vision-aided inertial navigation, as one possible solution to that problem, has become very popular in indoor localization with satisfied performance than individual PDR system. In the literature however, previous studies use fixed platform and the visual tracking uses feature-extraction-based methods. This paper instead contributes a distributed implementation of positioning system and uses deep learning for visual tracking. Meanwhile, as both inertial navigation and optical system can only provide relative positioning information, this paper contributes a method to integrate digital map with real geographical coordinates to supply absolute location. This hybrid system has been tested on two common operation systems of smartphones as iOS and Android, based on corresponded data collection apps respectively, in order to test the robustness of method. It also uses two different ways for calibration, by time synchronization of positions and heading calibration based on time steps. According to the results, localization information collected from both operation systems has been significantly improved after integrating with visual tracking data