• Proceedings of the KSRS Conference
• /
• 2005.10a
• /
• pp.317-320
• /
• 2005

The LBS (Location-Based Services) are valuable information services combined the location of moving object with various contents such as map, POI (point of Interest), route and so on. The must general service of LBS is route determination service and its applicable parts are FMS (Fleet Management System), travel advisory system and mobile navigation system. The core function of route determination service is determination of optimal route from source to destination in various environments. The MODB (Moving Object Database) system, core part of LBS composition systems, is able to manage current or past location information of moving object and massive trajectory information stored in MODB is value-added data in CRM, ERP and data mining part. Also this past trajectory information can be helpful to determine optimal route. In this paper, we suggest methods to determine optimal route by querying past trajectory information in MODB system and verify the effectiveness of suggested method.

• International Journal of Computer Science & Network Security
• /
• v.21 no.6
• /
• pp.207-212
• /
• 2021

Clustering Unlabeled Spatial-datasets to convert them to Labeled Spatial-datasets is a challenging task specially for geographical information systems. In this research study we investigated the NYC Taxi Limousine Commission dataset and discover that all of the spatial-temporal trajectory are unlabeled Spatial-datasets, which is in this case it is not suitable for any data mining tasks, such as classification and regression. Therefore, it is necessary to convert unlabeled Spatial-datasets into labeled Spatial-datasets. In this research study we are going to use the Clustering Technique to do this task for all the Trajectory datasets. A key difficulty for applying machine learning classification algorithms for many applications is that they require a lot of labeled datasets. Labeling a Big-data in many cases is a costly process. In this paper, we show the effectiveness of utilizing a Clustering Technique for labeling spatial data that leads to a high-accuracy classifier.

• Journal of Intelligence and Information Systems
• /
• v.10 no.2
• /
• pp.15-37
• /
• 2004

With the growth in the size of datasets, data mining has recently become an important research topic. Especially, interests about spatio-temporal data mining has been increased which is a method for analyzing massive spatio-temporal data collected from a wide variety of applications like GPS data, trajectory data of surveillance system and earth geographic data. In the former approaches, conventional clustering algorithms are applied as spatio-temporal data mining techniques without any modification. In this paper, we focused to SOM that is the most common clustering algorithm applied to clustering analysis in data mining wet and develop the spatio-temporal data mining module based on it. In addition, we analyzed the clustering results of developed SOM module and compare them with those of K-means and Agglomerative Hierarchical algorithm in the aspects of homogeneity, separation, separation, silhouette width and accuracy. We also developed specialized visualization module fur more accurate interpretation of mining result.

• Asian Journal of Atmospheric Environment
• /
• v.5 no.1
• /
• pp.47-55
• /
• 2011

Variations in ambient atmospheric carbon monoxide(CO) observed at an inland mining site in the Indo-Gangetic plains, Jaduguda ($22^{\circ}38'N$, $86^{\circ}21'E$, 122m MSL, ~75 km away from the coast of the Bay of Bengal) during the Tsunami of 26 December 2004 were monitored. CO mixing ratio over this site was measured using a non-dispersive infrared analyzer (Monitor Europe Model 9830 B). Back trajectory analysis data obtained using NOAA Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) Model was also used for this study. Variations in CO mixing ratio at a coastal site, Thiruvananthapuram ($8^{\circ}29'N$, $76^{\circ}57'E$, located ~2 km from the Arabian Sea coast) have also been investigated using CO data retrieved from the Measurement Of Pollution In The Troposphere (MOPITT) instrument. Ground-based measurements indicated abnormal variations in CO mixing ratio at Jaduguda from 25 December 2004 evening (previous day of the Tsunami). MOPITT CO data showed an enhancement in CO mixing ratio over Thiruvananthapuram on the Tsunami day. Back trajectory analyses over Thiruvananthapuram and Jaduguda for a period of 10 days from $21^{st}$ to $30^{th}$ December 2004 depicted that there were unusual vertical movements of air from high altitudes from 25 December 2004 evening. CO as well as the back trajectory analyses data showed that the variations in the wind regimes and consequently wind driven transport are the most probable reasons for the enhancement in CO observed at Jaduguda and Thiruvananthapuram during the Tsunami.

• Journal of Korea Spatial Information System Society
• /
• v.9 no.1
• /
• pp.59-77
• /
• 2007

Advances in mobile techknowledges and supporting techniques require an effective representation and analysis of moving objects. Similarity search of moving object trajectories is an active research area in data mining. In this paper, we propose a trajectory search algorithm for spatio-temporal similarity of moving objects on road network. For this, we define spatio-temporal distance between two trajectories of moving objects on road networks, and propose a new method to measure spatio-temporal similarity based on the real road network distance. In addition, we propose a similar trajectory search algorithm that retrieves spatio-temporal similar trajectories in the road network. The algorithm uses a signature file in order to retrieve candidate trajectories efficiently. Finally, we provide performance analysis to show the efficiency of the proposed algorithm.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.23 no.1
• /
• pp.109-141
• /
• 1998

Many processes in the industrial realm exhibit sstochastic and nonlinear behavior. Consequently, an intelligent system must be able to nonlinear production processes as well as probabilistic phenomena. In order for a knowledge based system to control a manufacturing processes as well as probabilistic phenomena. In order for a knowledge based system to control manufacturing process, an important capability is that of prediction : forecasting the future trajectory of a process as well as the consequences of the control action. This paper examines the robustness of data mining tools under varying levels of noise while predicting nonlinear processes, includinb chaotic behavior. The evaluated models include the perceptron neural network using backpropagation (BPN), the recurrent neural network (RNN) and case based reasoning (CBR). The concepts are crystallized through a case study in predicting a chaotic process in the presence of various patterns of noise.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.16 no.6
• /
• pp.1-15
• /
• 2017

Recently terrorism targets unspecified masses and causes massive destruction, which is so-called Super Terrorism. Many countries have tried hard to protect their citizens with various preparation and safety net. With inexpensive and advanced technologies of sensors, the surveillance systems have been paid attention, but few studies associated with the classification of the pedestrians' trajectories and the difference among themselves have attempted. Therefore, we collected individual trajectories at Samseoung Station using an analytical solution (system) of pedestrian trajectory by LiDAR sensor. Based on the collected trajectory data, a comprehensive framework of classifying the types of pedestrians' trajectories has been developed with data normalization and "trajectory association rule-based algorithm." As a result, trajectories with low similarity within the very same cluster is possibly detected.

• Journal of the Korea Convergence Society
• /
• v.10 no.2
• /
• pp.217-225
• /
• 2019

The Latent Growth Modeling(LGM) is known as the typical analysis method of longitudinal data and it could be classified into unconditional model and conditional model. It is common to assume that the growth trajectory of unconditional model of LGM is linear. In the case of quasi-linear, the methodology for improving the model fitness using Sequential Pattern of Association Rule Mining is suggested. To do this, we divide longitudinal data into quintiles and extract periodic changes of the longitudinal data in each quintiles and make sequential pattern based on this periodic changes. To evaluate the effectiveness, the LGM module in SPSS AMOS was used and the dataset of the Youth Panel from 2001 to 2006 of Korea Employment Information Service. Our methodology was able to increase the fitness of the model compared to the simple linear growth trajectory.

• Geomechanics and Engineering
• /
• v.37 no.1
• /
• pp.73-84
• /
• 2024

Owing to the high strength and abrasive characteristics of cobble-boulders, cutters are easily worn and damaged during shield tunneling, making construction inefficient. In the present work, the stress on the ripper and scraper on the cutterhead was analyzed by the PFC3D-FLAC3D coupling model of shield tunneling to get insight into the performance of the cutterhead for cutting underground cobble and boulders. The numerical calculation results revealed that the increase in trajectory radius leads to a rising stress on the cutters, and the stress on the front cutting surface is greater than that on the back of the cutters. Moreover, the correlation between cutter wear and stress is revealed based on field measurement data. The distribution of the cutter stress is consistent with the cutter wear and breakage characteristics in actual construction, in which more extensive cutter stress is exhibited, extreme cutter wear appears, and more cutter breakage occurs. Finally, the relationship between the cutterhead opening area's layout and cutter wear distribution was investigated, indicating that the cutter wear extent is the most severe in the region where the radial opening ratio dropped sharply.

• KIPS Transactions on Software and Data Engineering
• /
• v.3 no.8
• /
• pp.321-328
• /
• 2014

In this paper, we present a MapReduce-based mobility pattern mining system which can predict efficiently the next place of mobile users. It learns the mobility pattern model of each user, represented by Hidden Markov Models(HMM), from a large-scale trajectory dataset, and then predicts the next place for the user to visit by applying the learned models to the current trajectory. Our system consists of two parts: the back-end part, in which the mobility pattern models are learned for individual users, and the front-end part, where the next place for a certain user to visit is predicted based on the mobility pattern models. While the back-end part comprises of three distinct MapReduce modules for POI extraction, trajectory transformation, and mobility pattern model learning, the front-end part has two different modules for candidate route generation and next place prediction. Map and reduce functions of each module in our system were designed to utilize the underlying Hadoop infrastructure enough to maximize the parallel processing. We performed experiments to evaluate the performance of the proposed system by using a large-scale open benchmark dataset, GeoLife, and then could make sure of high performance of our system as results of the experiments.