Chi-Hua Chen

Designing intelligent disaster prediction models and systems for debris-flow disasters in Taiwan

Effective disaster prediction relies on using correct disaster decision model to predict the disaster occurrence accurately. This study proposes three effective debris-flow prediction models and an inference engine to predict and decide the debris-flow occurrence in Taiwan. The proposed prediction models are based on linear regression, multivariate analysis, and back-propagation networks. To create a practical simulation environment, the decision database is the pre-analyzed 181 potential debris-flows in Taiwan. According to the simulation results, the prediction model based on back-propagation networks predicted the debris flow most accurately. Moreover, a Real-timeMobileDebrisFlowDisasterForecastSystem (RM(DF)^2) was implemented as a three-tier architecture consisting of mobile appliances, intelligent situation-aware agents and decision support servers based on the wireless/mobile Internet communications. The RM(DF)^2 system provides real-time communication between the disaster area and the rescue-control center, and effectively prevents and manages debris-flow disasters.

An intelligent slope disaster prediction and monitoring system based on WSN and ANP

Taiwan generally has large-scale landslides and torrential rainfall during the typhoon season. As Wireless Sensor Networks (WSN) and mobile communication technologies advance rapidly, state-of-the-art technologies are adopted to build a model to reliably predict and monitor disasters, as well as accumulate environmental variation-related information. By integrating WSN and Analytic Network Process (ANP), this study evaluates the weight of disaster factors that adopt the consistency index of pair comparisons on hillslopes. The weight estimation and classification of disaster factors are based on the K-means model to build the hillslope prediction model. The Portrait-based Disaster Alerting System (PDAS) is designed and implemented using the proposed disaster prediction model. The PDAS adopts Web-GIS to visualize the environmental information. Evaluation results of the system indicate that the proposed prediction model achieves more accurate disaster determination than the conventional method.

Traffic speed estimation based on normal location updates and call arrivals from cellular networks

Information and communication technologies have improved the quality of Intelligent Transportation Systems (ITS). By estimating from Cellular Floating Vehicle Data (CFVD) is more cost-effective, and easier to acquire than traditional ways. In this paper, this study proposes a novel approach to evaluate the relation of normal location update, call arrivals, traffic flow, and traffic density. Moreover, the traffic speed is estimated by the proposed approach according to CFVD. In the simulation, this study compares the real traffic information with the estimated traffic information by Vehicle Detector (VD). The experiment results show that the accuracy of traffic speed estimation is 92.92%. Therefore, the proposed approach can be used to estimate traffic speed from CFVD for ITS.

The Optimal Sampling Period of a Fingerprint Positioning Algorithm for Vehicle Speed Estimation

Using cellular floating vehicle data is a crucial technique for measuring and forecasting real-time traffic information based on anonymously sampling mobile phone positions for intelligent transportation systems (ITSs). However, a high sampling frequency generates a substantial load for ITS servers, and traffic information cannot be provided instantly when the sampling period is long. In this paper, two analytical models are proposed to analyze the optimal sampling period based on communication behaviors, traffic conditions, and two consecutive fingerprint positioning locations from the same call and estimate vehicle speed. The experimental results show that the optimal sampling period is 41.589 seconds when the average call holding time was 60 s, and the average speed error rate was only 2.87%. ITSs can provide accurate and real-time speed information under lighter loads and within the optimal sampling period. Therefore, the optimal sampling period of a fingerprint positioning algorithm is suitable for estimating speed information immediately for ITSs.

A green positioning algorithm for Campus Guidance System

In this paper, we propose a new CGS, called the Green Campus Guidance System (GCGS). The GCGS employs the Fingerprint Position Algorithm (FPA) using Received Signal Strength (RSS) from mobile stations/user equipment through cellular networks and Location-Based Services (LBSs) for user reference. In experiments, the results show that the error of location estimation using FPA is about 9.92 m. As to power consumption, the cost per sample of GPS is 27.68 times of that of FPA. Consequently, the FPA exerts itself as a green positioning algorithm, and thus can be used to support GCGS.

Design and application of augmented reality query-answering system in mobile phone information navigation

An augmented reality query-answering system (AR-QAS) is designed and implemented in this study.The average question classification accuracy in an artificial neural network was 98.76%.AR-QAS can provide a fast and convenient mobile information navigation service for users.Language variety, timely feedback, and personal focus improve behavioral intentions to us AR-QAS.This study confirms that the new model combining TAM and MRT can be applied to relevant AR research. This study proposed an augmented reality query-answering system (AR-QAS) based on mobile cloud computing to provide natural language informational navigation services. Empirical research was performed to examine the effectiveness of the system in actual use. This study confirms that the new model developed by combining technology acceptance model (TAM), media richness theory, and the factors of self-efficacy can be applied to relevant AR research. The experiment results revealed that the average question classification accuracy of QAS when combined with artificial neural network and ontology was 98.76%. Moreover, the perceived media richness was found to be positively related to self-efficacy, perceived usefulness, perceived ease of use, user attitude, and use intention. Furthermore, this study reveals that combining the TAM and media richness theory provides a stronger explanation than does the TAM alone. Before new systems are created, designers are suggested to consider the four factors of media richness theory (i.e., multiple cues, language variety, timely feedback, and personal focus), to greatly improve user attitudes toward and behavioral intentions to use new technologies.

A Real-Time Pothole Detection Approach for Intelligent Transportation System

In recent years, fast economic growth and rapid technology advance have led to significant impact on the quality of traditional transport system. Intelligent transportation system (ITS), which aims to improve the transport system, has become more and more popular. Furthermore, improving the safety of traffic is an important issue of ITS, and the pothole on the road causes serious harm to drivers’ safety. Therefore, drivers’ safety may be improved with the establishment of real-time pothole detection system for sharing the pothole information. Moreover, using the mobile device to detect potholes has been more popular in recent years. This approach can detect potholes with lower cost in a comprehensive environment. This study proposes a pothole detection method based on the mobile sensing. The accelerometer data is normalized by Euler angle computation and is adopted in the pothole detection algorithm to obtain the pothole information. Moreover, the spatial interpolation method is used to reduce the location errors from global positioning system (GPS) data. In experiments, the results show that the proposed approach can precisely detect potholes without false-positives, and the higher accuracy is performed by the proposed approach. Therefore, the proposed real-time pothole detection approach can be used to improve the safety of traffic for ITS.

The analysis of speed-reporting rates from a cellular network based on a fingerprint-positioning algorithm

The collection of real-time traffic information is an important part of intelligent transportation systems. In particular, cellular floating vehicle data (CFVD) technology has become increasingly widespread, and more and more popular for measuring and forecasting real-time traffic information, based on anonymous sampling of the positions of mobile phones. This study proposes an analytical model to analyse the speed-reporting rates – based on communication behaviour, traffic conditions, and the two consecutive fingerprintpositioning locations from the call arrival and call completion signals of the same call – for a feasibility evaluation of CFVD.

An Intelligent Embedded Marketing Service System based on TV apps: Design and implementation through product placement in idol dramas

Due to the increase in advertising requirements for various multi-media services, two studies were conducted to first propose an Intelligent Embedded Marketing Service System (IEMSS) and then to use this IEMSS to implement product placement strategies for idol dramas using interactive television. In study 1, the IEMSS combines TV apps, multiple agents, and multi-document summarization technologies to retrieve and store information and comments about merchandise from search engines, blogs, and forums. The IEMSS involves a multi-document summarization technique that uses the TF-IDF (term frequency-inverse document frequency), the position and an artificial neural network (ANN) to automatically generate and transmit key positive comments to the user via TV apps. The experimental results show that the IEMSS has 100% accuracy, indicating that the IEMSS is capable of helping users understand the merchandise and improving purchase intentions. In study 2, a 2 (product description messages: shown vs. not shown)x2 (online reviews: shown vs. not shown) between-subjects design was conducted to examine the effectiveness of the IEMSS in an actual application. The results of this empirical research reveal that the display of reviews of the embedded products obtained from the Internet using the IEMSS functionality provides the viewing audience of idol dramas with the opinions of others who have used the embedded product, thereby improving attitudes toward the brand and product placement and stimulating purchase intentions. In sum, the IEMSS can be successfully applied to automatic summarization for advertising. Furthermore, this approach can be considered an extension of eWOM marketing and an application of Media Richness Theory that increases the effectiveness of product placement.

A Traffic Information Estimation Model Using Periodic Location Update Events from Cellular Network

In recent years considerable concerns have arisen over building Intelligent Transportation System (ITS) which focuses on efficiently managing the road network. One of the important purposes of ITS is to improve the usability of transportation resources so as extend the durability of vehicle, reduce the fuel consumption and transportation times. Before this goal can be achieved, it is vital to obtain correct and real-time traffic information, so that traffic information services can be provided in a timely and effective manner. Using Mobile Stations (MS) as probe to tracking the vehicle movement is a low cost and immediately solution to obtain the real-time traffic information. In this paper, we propose a model to analyze the relation between the amount of Periodic Location Update (PLU) events and traffic density. Finally, the numerical analysis shows that this model is feasible to estimate the traffic density.