• The Journal of Korean Association of Computer Education
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• v.6 no.2
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• pp.61-70
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• 2003

Currently many companies and cyber-universities are investing large amount of time and efforts to develop a standard for Web-based learning contents. Among various standards proposed, SCORM(Sharable Content Object Reference Model) has been especially interested regarding web contents and LCMS(Learning Content Management System), In contrast with corporate and adult education, many seem to be skeptical that SCORM could be applied to K-12 education. In the study, opportunities and limitations of the concept for the learning object in 'SCORM' are examined through analyzing relevant studies and cases. In addition, this study examines the learning object in the pedagogical Perspective, and derives suggestions for applying them to K-12 education.

• Journal of Information Processing Systems
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• v.16 no.5
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• pp.1214-1222
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• 2020

Pervasive augmented reality (AR) technology can be used to efficiently search for the required information regarding products in stores through text augmentation in an Internet of Things (IoT) environment. The evolution of context awareness and image processing technologies are the main driving forces that realize this type of AR service. One of the problems to be addressed in the service is that augmented objects are fixed and cannot be replaced efficiently in real time. To address this problem, a real-time mobile AR framework is proposed. In this framework, an optimal object to be augmented is selected based on object similarity comparison, and the augmented objects are efficiently managed using distributed metadata servers to adapt to the user requirements, in a given situation. To evaluate the feasibility of the proposed framework, a prototype system was implemented, and a qualitative evaluation based on questionnaires was conducted. The experimental results show that the proposed framework provides a better user experience than existing features in smartphones, and through fast AR service, the users are able to conveniently obtain additional information on products or objects.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.3
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• pp.79-87
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• 2000

In this paper, we propose a model based moving object tracking algorithm In dynamic scenes To adapt shape change of the moving object, the Hausdorff distance is applied as the measurement of similarity between model and image To reduce processing time, 2D logarithmic search method is applied for locate the position of moving object Experiments on a running vehicle and motorcycle, the result showed that the mean square error of real position and tracking result is 1150 and 1845; matching times are reduced average 1125times and 523 times than existing algorithm for vehicle image and motorcycle image, respectively It showed that the proposed algorithm could track the moving object accurately.

• Journal of Korea Spatial Information System Society
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• v.7 no.3 s.15
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• pp.67-81
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• 2005

Recently, the interest and research on the development of new application services such as the Location Based Service and Telemetics providing the emergency service, neighbor information search, and route search according to the development of the Geographic Information System have been increasing. User's search in the spatio-temporal database which is used in the field of Location Based Service or Telemetics usually fixes the current time on the time axis and queries the spatial and aspatial attributes. Thus, if the range of query on the time axis is extensive, it is difficult to efficiently deal with the search operation. For solving this problem, the snapshot, a method to summarize the location data of moving objects, was introduced. However, if the range to store data is wide, more space for storing data is required. And, the snapshot is created even for unnecessary space that is not frequently used for search. Thus, non storage space and memory are generally used in the snapshot method. Therefore, in this paper, we suggests the Hash-based Spatio-Temporal Clustering Algorithm(H-STCA) that extends the two-dimensional spatial hash algorithm used for the spatial clustering in the past to the three-dimensional spatial hash algorithm for overcoming the disadvantages of the snapshot method. And, this paper also suggests the knowledge extraction algorithm to extract the knowledge for the path search of moving objects from the past location data based on the suggested H-STCA algorithm. Moreover, as the results of the performance evaluation, the snapshot clustering method using H-STCA, in the search time, storage structure construction time, optimal path search time, related to the huge amount of moving object data demonstrated the higher performance than the spatio-temporal index methods and the original snapshot method. Especially, for the snapshot clustering method using H-STCA, the more the number of moving objects was increased, the more the performance was improved, as compared to the existing spatio-temporal index methods and the original snapshot method.

• Journal of Korea Spatial Information System Society
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• v.8 no.2 s.17
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• pp.109-120
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• 2006

This paper addresses techniques for predicting a future path of an object moving on a road network. Most prior methods for future prediction mainly focus their attention on objects moving in Euclidean space. A variety of applications such as telematics, however, deal with objects that move only over road networks in most cases, thereby requiring an effective method of future prediction of moving objects on road networks. In this paper, we propose a novel method for predicting a future path of an object by analyzing past trajectories whose changing pattern is similar to that of a current trajectory of a query object. We devise a new function that measures a similarity between trajectories by reflecting the characteristics of road networks. By using this function, we predict a future path of a given moving object as follows: First, we search for candidate trajectories that contain subtrajectories similar to a given query trajectory by accessing past trajectories stored in moving object databases. Then, we predict a future path of a query object by analyzing the moving paths along with a current position to a destination of candidate trajectories thus retrieved. Also, we suggest a method that improves the accuracy of path prediction by regarding moving paths that have just small differences as the same group.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.720-723
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• 1997

This paper presents a cooperative control method for multiple robots. This method is based on local sensors. The proposed method integrates all information obtained by local perception through a set of sensors and generates commands without logical conflicts in designing control logic. To control multiple robots effectively, a global control strategy is proposed. These methods are constructed by using AND/OR logic and transition firing sequences in Petri nets. To evaluate these methods, the object-searching task is introduced. This task is to search an object like a box by two robots and consists of two sub-tasks, i.e., a wall tracking task and a robot tracking task. Simulation results for the object-searching task and the wall tracking task are presented to show the effectiveness of the method.

• International Journal of Control, Automation, and Systems
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• v.5 no.5
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• pp.570-576
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• 2007

This paper presents a hexagon-based Q-leaning algorithm to find a hidden targer object with multiple robots. An experimental environment was designed with five small mobile robots, obstacles, and a target object. Robots went in search of a target object while navigating in a hallway where obstacles were strategically placed. This experiment employed two control algorithms: an area-based action making (ABAM) process to determine the next action of the robots and hexagon-based Q-learning to enhance the area-based action making process.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.264-266
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• 2009

A skill is defined as the special ability to do something well, especially as acquired by learning and practice. To learn a skill, a Bayesian network model for representing the skill is first learned. We will regard the Bayesian network for a skill as an affordance. We propose a soft Behavior Motivation(BM) switch as a method for ordering affordances to accomplish a task. Then, a skill is constructed as a combination of an affordance and a soft BM switch. To demonstrate the validity of our proposed method, some experiments were performed with GENIBO(Pet robot) performing a task using skills of Search-a-target-object, Approach-a-target-object, Push-up-in front of -a-target-object.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.259-262
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• 1997

This paper presents a visual servoing of RV-M2 robot manipulators to track and grasp moving object, using pruned dynamic recurrent neural networks(DRNN). The object is stationary in the robot work space and the robot is tracking and grasping the object by using CCD camera mounted on the end-effector. In order to optimize the structure of DRNN, we decide the node whether delete or add, by mutation probability, first in case of delete node, the node which have minimum sum of input weight is actually deleted, and then in case of add node, the weight is connected according to the number of case which added node can reach the other nodes. Using evolutionary programming(EP) that search the struture and weight of the DRNN, and evolution strategies(ES) which train the weight of neuron, we pruned the net structure of DRNN. We applied the DRNN to the Visual Servoing of a robot manipulators to control position and orientation of end-effector, and the validity and effectiveness of the pro osed control scheme will be verified by computer simulations.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.8
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• pp.114-123
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• 1996

In this paper, we propose an automatic recognition algorithm using the object centroid of a facial image. First, we separate the facial image from the background image using the chroma-key technique and we find the centroid of the separated facial image. Second, we search nose in the facial image based on knowledge of human faces and the coordinate of the object centroid and, we calculate 17 feature parameters automatically. Finally, we recognize the facial image by using feature parameters in the neural networks which are trained through error backpropagation algorithm. It is illustrated by experiments by experiments using the proposed recogniton system that facial images can be recognized in spite of the variation of the size and the position of images.