• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.1449-1450
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• 2013

• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.97-100
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• 2001

In this paper, We propose a new semiautomatic segmentation method using spatio-temporal similarity. In the proposed scheme, segmentation is performed using gradual region merging and hi-direction at spatio-temporal refinement. Simulation results show the efficiency of the proposed method in semantic object extraction.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.1B
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• pp.13-20
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• 2011

The spatio-temporal pattern in precipitation is a significant element in defining characteristics of precipitation. In this study, a new scheme on regionalization utilizing temporal information was introduced on the basis of existing approaches that is mainly based on simple moments of data and geographical information. Given the identified spatio-temporal pattern, this study was extended to characterize regional pattern of annual maximum rainfall over Korea. We have used circular statistics to characterize the temporal distribution on the precipitation, and the circular statistics allow us to effectively assess changes in timing of the extreme rainfall in detail. In this study, a modified K-means method was incorporated with derived temporal characteristics of extreme rainfall in order to better characterize hydrologic pattern for regional frequency analysis. The extreme rainfall was reasonably separated into five categories that considered most attributes in both quantitative and temporal changes in extremes. The results showed that the proposed approach is a promising approach for regionalization in term of physical understanding of extreme rainfall.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.5
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• pp.527-547
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• 2009

Moving object management is widely used in traffic, logistic and data mining applications in ubiquitous environments. It is required to analyze spatio-temporal data and trajectories for moving object management. In this paper, we proposed a novel index structure for spatio-temporal aggregation of trajectory in a constrained network, named aCN-RB-tree. It manages aggregation values of trajectories using a constraint network-based index and it also supports direction of trajectory. An aCN-RB-tree consists of an aR-tree in its center and an extended B-tree. In this structure, an aR-tree is similar to a Min/Max R-tree, which stores the child nodes' max aggregation value in the parent node. Also, the proposed index structure is based on a constrained network structure such as a FNR-tree, so that it can decrease the dead space of index nodes. Each leaf node of an aR-tree has an extended B-tree which can store timestamp-based aggregation values. As it considers the direction of trajectory, the extended B-tree has a structure with direction. So this kind of aCN-RB-tree index can support efficient search for trajectory and traffic zone. The aCN-RB-tree can find a moving object trajectory in a given time interval efficiently. It can support traffic management systems and mining systems in ubiquitous environments.

• Journal of Electrical Engineering and Technology
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• v.8 no.6
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• pp.1615-1625
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• 2013

Modelling and simulating the wind power intermittent behaviour are the basis of the planning and scheduling studies concerning wind power integration. The wind power outputs are evidently correlated in space and time and bring challenges in characterizing their behaviour. This paper provides a methodology to model and simulate the clustered wind power considering its spatio-temporal correlations using the theory of copula. The sampling approach captures the complex spatio-temporal connections among the wind farms by employing a conditional density function calculated using multidimensional copula function. The empirical study of real wind power measurement shows how the wind power outputs are correlated and how these correlations affect the overall uncertainty of clustered wind power output. The case study validates the simulation technique by comparing the simulated results with the real measurements.

• Journal of Advanced Navigation Technology
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• v.14 no.2
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• pp.143-150
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• 2010

Ontologies, which are consist of entities and their relationships, have been used to implement various scenarios of information services. That is because an entity can be understood well when the surrounding entities and the relationships are known. In describing historical events. The spatio-temporal locations connote the historical context comprehensively. Therefore spatio-temporal locations are one of the most important carriers to connect the historical events. In this paper we analyzed the usage scenarios to access and retrieve historical information, and proposed the design of ontologies for historical information services for making historical stories based on spatio-temporal reference frame.

• The KIPS Transactions:PartD
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• v.12D no.1 s.97
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• pp.43-50
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• 2005

The Processing domains of spatio-temporal databases are divided into time-series databases for moving objects and sequence databases for discrete historical objects. Recently the selectivity estimation techniques for query optimization in spatio-temporal databases have been studied, but focused on query optimization in time-series databases. There wat no previous work on the selectivity estimation techniques for sequence databates as well. Therefore, we construct T-Minskew histogram for query optimization In sequence databases and propose a selectivity estimation method using the T-Minskew histogram. Furthermore we propose an effective histogram maintenance technique for food performance of the histogram.

• The Journal of Korean Physical Therapy
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• v.21 no.2
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• pp.79-85
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• 2009

Purpose: This study examined the characteristics of gait in patients with chronic low back pain. Methods: The subjects were out-patients suffering from chronic low back pain at the department of physical therapy, B hospital in Seoul. Gait analysis was performed by dividing the subjects into two groups. The study and control group comprised 15 chronic low back pain patients and 14 healthy people, respectively. Gait analysis was performed using a VICON 512 Motion Analysis System to obtain the spatio-temporal and kinematic parameters. Results: First, there was a significant difference in the spatio-temporal parameters between the two groups (p<0.05). Second, the study group showed significant differences in the kinematic parameters during the stance phase (p<0.05). Third, there were significant differences in kinematic parameters in the study group during the swing phase (p<0.05). Conclusion: The gait pattern of patients with chronic low back pain is characterized by more rigid patterns. Compared to the control group, there was a decrease in the spatio-temporal parameters and kinematic parameters in patients with chronic low back pain. These findings are expected to play a role as basic data and to form a rehabilitation program for low back pain patients.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.4
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• pp.909-916
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• 2010

In ubiquitous GIS services is possible to use the spatio-temporal data using a mobile device at anytime. Also, client is transmitted latest spatio-temporal data from server. But traditional systems have a problem that the time of transmitting latest information from server to client takes long time because of collecting data periodically. In this paper, we proposed Two-way Synchronization system supporting field update to solve the existing problem. This system uses mobile device for collecting changed data in the real world and sending collected data to server.

• Journal of Information Processing Systems
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• v.19 no.6
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• pp.830-841
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• 2023

Tokenization is the process of segmenting the input text into smaller units of text, and it is a preprocessing task that is mainly performed to improve the efficiency of the machine learning process. Various tokenization methods have been proposed for application in the field of natural language processing, but studies have primarily focused on efficiently segmenting text. Few studies have been conducted on the Korean language to explore what tokenization methods are suitable for document classification task. In this paper, an exploratory study was performed to find the most suitable tokenization method to improve the performance of a representative spatio-temporal document classifier in Korean. For the experiment, a convolutional neural network model was used, and for the final performance comparison, tasks were selected for document classification where performance largely depends on the tokenization method. As a tokenization method for comparative experiments, commonly used Jamo, Character, and Word units were adopted. As a result of the experiment, it was confirmed that the tokenization of word units showed excellent performance in the case of representative spatio-temporal document classification task where the semantic embedding ability of the token itself is important.