• Journal of Korea Spatial Information System Society
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• v.10 no.1
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• pp.29-40
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• 2008

With the spread of mobile devices and advances in communication techknowledges, the needs of application which uses the movement patterns of moving objects in history trajectory data of moving objects gets Increasing. Especially, to design public transportation route or road network of the new city, we can use the similar patterns in the trajectories of moving objects that move on the spatial network such as road and railway. In this paper, we propose a spatio-temporal similar trajectory search algorithm for moving objects on road network. For this, we define a spatio-temporal similarity measure based on the real road network distance and propose a grid-based index structure for similar trajectory search. Finally, we analyze the performance of the proposed similar trajectory search algorithm in order to show its efficiency.

• Journal of Information Processing Systems
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• v.6 no.4
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• pp.521-536
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• 2010

Spatio-temporal patterns extracted from historical trajectories of moving objects reveal important knowledge about movement behavior for high quality LBS services. Existing approaches transform trajectories into sequences of location symbols and derive frequent subsequences by applying conventional sequential pattern mining algorithms. However, spatio-temporal correlations may be lost due to the inappropriate approximations of spatial and temporal properties. In this paper, we address the problem of mining spatio-temporal patterns from trajectory data. The inefficient description of temporal information decreases the mining efficiency and the interpretability of the patterns. We provide a formal statement of efficient representation of spatio-temporal movements and propose a new approach to discover spatio-temporal patterns in trajectory data. The proposed method first finds meaningful spatio-temporal regions and extracts frequent spatio-temporal patterns based on a prefix-projection approach from the sequences of these regions. We experimentally analyze that the proposed method improves mining performance and derives more intuitive patterns.

• Journal of Korea Spatial Information System Society
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• v.11 no.1
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• pp.127-136
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• 2009

This paper proposes an on-line mining algorithm of moving trajectory patterns in RFID data streams considering changing characteristics over time and constraints of single-pass data scan. Since RFID, sensor, and mobile network technology have been rapidly developed, many researchers have been recently focused on the study of real-time data gathering from real-world and mining the useful patterns from them. Previous researches for sequential patterns or moving trajectory patterns based on stream data have an extremely time-consum ing problem because of multi-pass database scan and tree traversal, and they also did not consider the time-changing characteristics of stream data. The proposed method preserves the sequential strength of 2-lengths frequent patterns in binary relationship table using the time-evolving graph to exactly reflect changes of RFID data stream from time to time. In addition, in order to solve the problem of the repetitive data scans, the proposed algorithm infers candidate k-lengths moving trajectory patterns beforehand at a time point t, and then extracts the patterns after screening the candidate patterns by only one-pass at a time point t+1. Through the experiment, the proposed method shows the superior performance in respect of time and space complexity than the Apriori-like method according as the reduction ratio of candidate sets is about 7 percent.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5782-5799
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• 2018

With the development of GPS and the popularity of mobile devices with positioning capability, collecting massive amounts of trajectory data is feasible and easy. The daily trajectories of moving objects convey a concise overview of their behaviors. Different social roles have different trajectory patterns. Therefore, we can identify users or groups based on similar trajectory patterns by mining implicit life patterns. However, most existing daily trajectories mining studies mainly focus on the spatial and temporal analysis of raw trajectory data but missing the essential semantic information or behaviors. In this paper, we propose a novel trajectory semantics calculation method to identify groups that have similar behaviors. In our model, we first propose a fast and efficient approach for stay regions extraction from daily trajectories, then generate semantic trajectories by enriching the stay regions with semantic labels. To measure the similarity between semantic trajectories, we design a semantic similarity measure model based on spatial and temporal similarity factor. Furthermore, a pruning strategy is proposed to lighten tedious calculations and comparisons. We have conducted extensive experiments on real trajectory dataset of Geolife project, and the experimental results show our proposed method is both effective and efficient.

• The Journal of Korea Robotics Society
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• v.7 no.2
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• pp.113-119
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• 2012

In this paper we propose the method that detects moving objects in autonomous navigation vehicle using LRF sensor data. Object detection and tracking methods are widely used in research area like safe-driving, safe-navigation of the autonomous vehicle. The proposed method consists of three steps: data segmentation, mobility classification and object tracking. In order to make the raw LRF sensor data to be useful, Occupancy grid is generated and the raw data is segmented according to its appearance. For classifying whether the object is moving or static, trajectory patterns are analysed. As the last step, Markov chain Monte Carlo (MCMC) method is used for tracking the object. Experimental results indicate that the proposed method can accurately detect moving objects.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.3
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• pp.145-153
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• 2009

Trajectory data are ubiquitous in the real world. Recent progress on satellite, sensor, RFID, video, and wireless technologies has made it possible to systematically track object movements and collect huge amounts of trajectory data. Accordingly, there is an ever-increasing interest in performing data analysis over trajectory data. In this paper, we develop a data mining tool for massive trajectory data. This mining tool supports three operations, clustering, classification, and outlier detection, which are the most widely used ones. Trajectory clustering discovers common movement patterns, trajectory classification predicts the class labels of moving objects based on their trajectories, and trajectory outlier detection finds trajectories that are grossly different from or inconsistent with the remaining set of trajectories. The primary advantage of the mining tool is to take advantage of the information of partial trajectories in the process of data mining. The effectiveness of the mining tool is shown using various real trajectory data sets. We believe that we have provided practical software for trajectory data mining which can be used in many real applications.

• Journal of Korea Society of Digital Industry and Information Management
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• v.6 no.1
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• pp.131-142
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• 2010

In this paper, we propose a web GPS based logistics vehicle control management system using MVC design patterns. The proposed system is designed by applying design patterns of object oriented modeling called mini-architecture to enhance reliability of software as well as promote stability of overall system design. In addition, we can get a position information by means of the GPS embedded in PDA and communicate between client and monitoring server using CDMA network so that the position of client can be identified directly by the map service. The system provides an moving object indexing technique which extends the existing TB-tree to manage and retrieve a transporting trajectory of logistics efficiently. Finally, with development of the logistics vehicle control service called WG-LOGICS system, we can verify the usefulness of our system which is able for monitoring a vehicle preparation, allocating registration, loading a burden, transfer path, and destination arrival in real world.

• Journal of the Korea Society of Computer and Information
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• v.11 no.2 s.40
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• pp.101-110
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• 2006

ADevelopment of various geographic observations, GPS, and Wireless Communication technologies make it easy to control many moving objects and to build an intelligent transport system and transport vehicle management system. However it is difficult to make a suitable system in the real world with a variety of tests to evaluate the performance fairly because real vehicle data are not enough as evaluating and testing the transport plan in the system. Therefore some moving object data generator would be used in most researches. However they can not generate vehicle trajectory according to a user scenario defined to be applied to transport plan, because the existing data generators consider only a gauss distribution, road network. In this paper we design and implement a vehicle data generator for creating vehicle trajectory data based on the user-defined scenario. The designed data generator could make the vehicle location depending on user's transport plan. Besides we store the scenario as patterns and reutilize the used scenario.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.4
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• pp.59-66
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• 2016

For the performance experiments of databases systems with moving object databases, we need moving object trajectory data sets. For example, benchmark data sets of moving object trajectories are required for experiments on query processing of moving object databases. For those reasons, several tools have been developed for generating moving objects in Euclidean spaces or road network spaces. Indoor space differs from outdoor spaces in many aspects and moving object generator for indoor space should reflect these differences. Even some tools were developed to produce virtual moving object trajectories in indoor space, the movements generated by them are not realistic. In this paper, we present a moving object generation tool for indoor space. First, this tool generates trajectories for pedestrians in an indoor space. And it provides a parametric generation of trajectories considering not only speed, number of pedestrians, minimum distance between pedestrians but also type of spaces, time constraints, and type of pedestrians. We try to reflect the patterns of pedestrians in indoor space as realistic as possible. For the reason of interoperability, several geospatial standards are used in the development of the tool.

• Journal of Welding and Joining
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• v.14 no.1
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• pp.99-106
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• 1996

Development of an automatic TIG welding system incorporating a vision sensor and torch control mechanism leads to an improved welding quality and greater production efficiency. The automatic welding system should be greatly restricted in its size and weight for the LNG(Liquefied Natural Gas) storage tank and also provide a unique torch rotating mechanism which keeps the torch tip in the constant position while the angle is changed continuously to maintain the welding torch substantially perpendicular to the weld line. The developed system is driven by two translation axes X, Z and one rotational axis. A moving line window method is adopted to the image recognition of the corrugated membranes with specular reflection. This method decides original laser stripe patterns in image which is affected by multi-reflection. A self-teaching algorithm, which guides the automatic welding machine with the information provided by the CCD camera without any previous learning of a reference trajectory, was developed for tracking the corrugated membrane of the LNG tank along the weld line.