• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.609-614
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• 1989

A collision avoidance algorithm based on a heuristic graph search and subgoals is presented. The joint angle space is quantized into cells. The evaluation function for a heuristic search is defined by the sum of the distance between the links of a manipulator and middle planes among the obstables and the distance between the end-effector and the subgoals on desired trajectory. These subgoals reduce the combinatorial explosion in the search space. This method enables us to avoid a dead-lock in searching. Its effectiveness has been verified by simulation studies.

• Journal of Korean Institute of Industrial Engineers
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• v.22 no.4
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• pp.579-588
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• 1996

The purpose of this paper is to develop a method of Collision-Free Path Planning (CFPP) for an articulated robot. First, the configuration of the robot is built by a set of robot joint angles derived from robot inverse kinematics. The joint space, that is made of the joint angle set, forms a Configuration space (Cspcce). Obstacles in the robot workcell are also transformed into the Cobstacles using slice projection method. Actually the Cobstacles means the configurations of the robot causing collision with obstacles. Secondly, a connected graph, a kind of roadmap, is constructed by the free configurations in the Cspace, where the free configurations are randomly sampled from a free Cspace immune from the collision. Thirdly, robot paths are optimally determinant in the connected graph. A path searching algorithm based on $A^*$ is employed in determining the paths. Finally, the whole procedures for the CFPP method are shown for a proper articulated robot as an illustrative example.

• The KIPS Transactions:PartB
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• v.18B no.2
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• pp.57-66
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• 2011

To prevent cardiac diseases, quantifying cardiac function is important in routine clinical practice by analyzing blood volume and ejection fraction. These works have been manually performed and hence it requires computational costs and varies depending on the operator. In this paper, an automatic left ventricle segmentation algorithm is presented to segment left ventricle on cardiac magnetic resonance images. After coil sensitivity of MRI images is compensated, a K-mean clustering scheme is applied to segment blood area. A graph searching scheme is employed to correct the segmentation error from coil distortions and noises. Using cardiac MRI images from 38 subjects, the presented algorithm is performed to calculate blood volume and ejection fraction and compared with those of manual contouring by experts and GE MASS software. Based on the results, the presented algorithm achieves the average accuracy of 6.2mL${\pm}$5.6, 2.9mL${\pm}$3.0 and 2.1%${\pm}$1.5 in diastolic phase, systolic phase and ejection fraction, respectively. Moreover, the presented algorithm minimizes user intervention rates which was critical to automatize algorithms in previous researches.

• Journal of Korea Multimedia Society
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• v.19 no.12
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• pp.1927-1935
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• 2016

This paper propose a framework for human body parts in RGB-D image. We conduct tasks of obtaining person area, finding candidate areas and local detection in order to detect hand, foot and head which have features of long accumulative geodesic distance. A person area is obtained with background subtraction and noise removal by using depth image which is robust to illumination change. Finding candidate areas performs construction of graph model which allows us to measure accumulative geodesic distance for the candidates. Instead of raw depth map, our approach constructs graph model with segmented regions by quadtree structure to improve searching time for the candidates. Local detection uses HOG based SVM for each parts, and head is detected for the first time. To minimize false detections for hand and foot parts, the candidates are classified with upper or lower body using the head position and properties of geodesic distance. Then, detect hand and foot with the local detectors. We evaluate our algorithm with datasets collected Kinect v2 sensor, and our approach shows good performance for head, hand and foot detection.

• Journal of the Korean Society for Precision Engineering
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• v.25 no.7
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• pp.103-110
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• 2008

This paper presents a new approach of recognizing a 3D object using a single camera, based on the extended convex hull of its silhouette. It aims at minimizing the DB size and simplifying the processes for matching and feature extraction. For this purpose, two concepts are introduced: extended convex hull and measurable region. Extended convex hull consists of convex curved edges as well as convex polygons. Measurable region is the cluster of the viewing vectors of a camera represented as the points on the orientation sphere from which a specific set of surfaces can be measured. A measurable region is represented by the extended convex hull of the silhouette which can be obtained by viewing the object from the center of the measurable region. Each silhouette is represented by a relation graph where a node describes an edge using its type, length, reality, and components. Experimental results are included to show that the proposed algorithm works efficiently even when the objects are overlapped and partially occluded. The time complexity for searching the object model in the database is O(N) where N is the number of silhouette models.

• Journal of Information Processing Systems
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• v.13 no.5
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• pp.1158-1167
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• 2017

Many real-world applications information are organized and represented with graph structure which is often used for representing various ubiquitous networks, such as World Wide Web, social networks, and protein-protein interactive networks. In particular, similarity evaluation between graphs is a challenging issue in many fields such as graph searching, pattern discovery, neuroscience, chemical compounds exploration and so forth. There exist some algorithms which are based on vertices or edges properties, are proposed for addressing this issue. However, these algorithms do not take both vertices and edges similarities into account. Towards this end, this paper pioneers a novel approach for similarity evaluation between graphs based on formal concept analysis. The feature of this approach is able to characterize the relationships between nodes and further reveal the similarity between graphs. Therefore, the highlight of our approach is to take vertices and edges into account simultaneously. The proposed algorithm is evaluated using a case study for validating the effectiveness of the proposed approach on detecting and measuring the similarity between graphs.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.7
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• pp.1868-1887
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• 2024

Recommendation systems research is a subfield of information retrieval, as these systems recommend appropriate items to users during their visits. Appropriate recommendation results will help users save time searching while increasing productivity at work, travel, or shopping. The problem becomes more difficult when the items are geographical locations on the ground, as they are associated with a wealth of contextual information, such as geographical location, opening time, and sequence of related locations. Furthermore, on social networking platforms that allow users to check in or express interest when visiting a specific location, their friends receive this signal by spreading the word on that online social network. Consideration should be given to relationship data extracted from online social networking platforms, as well as their impact on the geolocation recommendation process. In this study, we compare the similarity of geographic locations based on their distance on the ground and their correlation with users who have checked in at those locations. When calculating feature embeddings for users and locations, social relationships are also considered as attention signals. The similarity value between location and correlation between users will be exploited in the overall architecture of the recommendation model, which will employ graph convolution networks to generate recommendations with high precision and recall. The proposed model is implemented and executed on popular datasets, then compared to baseline models to assess its overall effectiveness.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.5
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• pp.654-659
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• 2007

This paper proposes the effective method for classifying the working spates and modelling the environments, when complex working environments of AGVS(Automated Guided Vehicle System) ate changed. And, we propose the shortest path searching algorithm using the A* algorithm of graph search method. Also, we propose the methods for finding each AGV's travel time of shortest path. Finally, a simple example is included for visualizing the feasibility of the proposed methods.

• Educational Technology International
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• v.7 no.1
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• pp.21-37
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• 2006

This paper focuses on finding out visualization process by means of VTA(Visual Task Analysis) of expert teachers' simple task. Findings indicate teachers have coding schema of performing visual task as such; (a) the analyzing by reading and some activities in the task text, (b) conceptualizing or understanding in his/her own way, (c) the explaining of the action or product, (d) the searching by thinking or finding, (e) the decision of visualizing of the task. Expert teachers tried to visualize in the form of abstract graph, and to omit the object which was not directly related to the topic at the pilot study. VAT based on ground theory and protocol analysis was developed and performed. This case study suggests that an additional study for searching a guide and method might be beneficial for conducting a visual task analysis.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.7
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• pp.159-172
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• 2016

This paper presents a novel algorithm for searching evacuation paths in indoor disaster environments. The proposed method significantly improves the time complexity to find the paths to the evacuation exit by introducing a light-weight Disaster Evacuation Graph (DEG) for a building in terms of the size of the graph. With the DEG, the method also considers load balancing and bottleneck capacity of the paths to the evacuation exit simultaneously. The behavior of the algorithm consists of two phases: horizontal tiering (HT) and vertical tiering (VT). The HT phase finds a possible optimal path from anywhere of a specific floor to the evacuation stairs of the floor. Thus, after finishing the HT phases of all floors in parallel the VT phase begins to integrate all results from the previous HT phases to determine a evacuation path from anywhere of a floor to the safety zone of the building that could be the entrance or the roof of the building. It should be noted that the path produced by the algorithm. And, in order to define the range of graph to process, tiering scheme is used. In order to test the performance of the method, computing times and evacuation times are compared to the existing path searching algorithms. The result shows the proposed method is better than the existing algorithms in terms of the computing time and evacuation time. It is useful in a large-scale building to find the evacuation routes for evacuees quickly.