Browsing by Author "Lai, Chien-Hsiang"

Now showing 1 - 3 of 3

Exploring Multi-View Learning for Activity Inferences on Smartphones
(2016-01-01)
Njoo, Gunarto Sindoro
;
Lai, Chien-Hsiang
;
Hsu, Kuo-Wei
;
交大名義發表
;
National Chiao Tung University
Inferring activities on smartphones is a challenging task. Prior works have elaborated on using sensory data from built-in hardware sensors in smartphones or taking advantage of location information to understand human activities. In this paper, we explore two types of data on smartphones to conduct activity inference: 1) Spatial-Temporal: reflecting daily routines from the combination of spatial and temporal patterns, 2) Application: perceiving specialized apps that assist the user's activities. We employ multi-view learning model to accommodate both types of data and use weighted linear kernel model to aggregate the views. Note that since resources of smartphones are limited, activity inference on smartphones should consider the constraints of resources, such as the storage, energy consumption, and computation power. Finally, we compare our proposed method with several classification methods on a real dataset to evaluate the effectiveness and performance of our method. The experimental results show that our approach outperforms other methods regarding the balance between accuracy, running time, and storage efficiency.
Mining Top-K Relevant Stay Regions from Historical Trajectories
(2014-01-01)
Lin, Yung-Hsiang
;
Lai, Chien-Hsiang
;
Lei, Po-Ruey
;
資訊工程學系
;
Department of Computer Science
With increasingly prevalent mobile positioning devices, such as GPS loggers, smart phones, and GPS navigation devices, a huge amount of trajectories data is collected. Users are able to obtain the various location-based services by uploading their trajectories. In this paper, we address that a user\'s movement behavior is able to discover by their similar shape trajectories and resulted in some regions frequently stay in common, called relevant stay regions. Once a set of stay regions discovered, we can predict the next region where the user intends to go and provide location-based information of the next stay in advance, such as traffic status, targeted advertises, sightseeing recommendations, and so on. Prior works have elaborated on discovering stay region from the whole crowd trajectories and then exploring the relations between the regions to describe the movement patterns for location prediction. However, the trajectories pass the same region may not have the similar movement behavior. Thus, we propose a framework to discover stay regions relevant to the specific movement behavior and then applied in location prediction, called Region Modeling and Mobility Prediction. The proposed framework includes two modules: region modeling and mobility prediction. In the region modeling module, we develop shape clustering method to group the similar trajectories from historical data and then explore the stay region model from trajectory clusters. Based on the discovered region model, the mobility prediction module provide a cluster selection algorithm and several prediction strategies to generate the top-k relevant stay regions. Experiments results on real datasets demonstrate the effectiveness and accuracy of our proposed model on detecting next stay region, comparing with other baseline methods.
RouteMiner: Mining Ship Routes from a Massive Maritime Trajectories
(2014-01-01)
Wen, Yu-Ting
;
Lai, Chien-Hsiang
;
Lei, Po-Ruey
;
Peng, Wen-Chih
;
資訊工程學系
Mining trajectory data has been attracting significant interest in the last years. By analyzing trajectory data, we are able to discover the movement behavior and location-aware knowledge, and then develop many interesting applications such as movement behavior discovery, location prediction, traffic analysis, and so on. However, trajectory data mining is a challenge task because of the trajectory data is available with uncertainty. Furthermore, discovering the valuable knowledge from maritime trajectory is made even more difficult due to the maritime area is a free moving space. Unlike the vehicles\' movements are constrained by road networks, there is no such a sea route for ships to follow in maritime area. A ship\'s movement may not exactly repeat the same trajectory even the ship has the similar movement behavior with others. In this work, RouteMiner system provides a framework of ship route mining for maritime traffic analysis. Given a set of ship trajectories in a maritime area, RouteMiner explore the movement behavior from those massive trajectories in a free moving space. Then, ship routes are detected based on those behavioral pattern. Finally, the system generates a set of ships routes to provide operators a better understanding from ship trajectory data. We conduct the experiments on real maritime trajectories to show the effectiveness of proposed RouteMiner. In the future, RouteMiner is going to serve as the phototype for exploring the solutions of the challenges those related to anomaly detection and traffic management in the maritime domain.