• Journal of Korea Society of Industrial Information Systems
• /
• v.8 no.2
• /
• pp.45-53
• /
• 2003

The significances of this paper are of two points. One is that this research develops the query expansion search by adding the related terms based on the dictionary to the original query terms. This method shortens the process of the conventional model of query expansion utilizing the feedback data of the search. The other is that this research tries to find out the optimal point of precisions and recalls by differentiating the weight ratio between original quay and additional terms. This method shows that the efficiency and precision of query expansion search increase.

• Journal of Korean Society of Transportation
• /
• v.21 no.1
• /
• pp.103-113
• /
• 2003

This paper shows alternative route search and preference route search for the traffic route search, and proposes the use of the fuzzy non-additive controller by the application of AHP(analytic hierarchy process). It is different from classical route search and notices thinking method of human. Appraisal element, weight of route is extracted from basic of the opinion gathering for the driving expert and example of route model was used for the finding of practice utility. Model evaluation was performed attribute membership function making of estimate element, estimate value setting, weight define by the AHP, non additive presentation of weight according to $\lambda$-fuzzy measure and Choquet fuzzy integral. Finally, alternative route search was possible to real time traffic route search for the well variable traffic environment, and preference route search showed reflection of traffic route search disposition for the driver individual. This paper has five important meaning. (1)The approach is similar to the driver's route selection decision process. (2)The approach is able to control of route appraisal criteria for the multiple attribute. (3)The approach makes subjective judgement objective by a non additive. (4)The approach shows dynamic route search for the alternative route search. (5)The approach is able to consider characteristics of individual drivers attributed for the preference route search.

• Journal of Korea Multimedia Society
• /
• v.20 no.7
• /
• pp.986-993
• /
• 2017

Object Tracking is a technique for tracking moving objects over time in a video image. Using object tracking technique, many research are conducted such a detecting dangerous situation and recognizing the movement of nearby objects in a smart car. However, it still remains a challenging task such as occlusion, deformation, background clutter, illumination variation, etc. In this paper, we propose a novel deep visual object tracking method that can be operated in robust to many challenging task. For the robust visual object tracking, we proposed a Convolutional Neural Network(CNN) which shares weight of the convolutional layers. Input of the CNN is a three; first frame object image, object image in a previous frame, and current search frame containing the object movement. Also we propose a method to consider the motion of the object when determining the current search area to search for the location of the object. Extensive experimental results on a authorized resource database showed that the proposed method outperformed than the conventional methods.

• Journal of the Korea Society of Computer and Information
• /
• v.22 no.12
• /
• pp.49-54
• /
• 2017

Pathfinding for pedestrians provided by various navigation programs is based on a shortest path search algorithm. There is no big difference in their guide results, which makes the path quality more important. Multiple criteria should be included in the search cost to calculate the path quality, which is called a multi-criteria pathfinding. In this paper we propose a user adaptive pathfinding algorithm in which the cost function for a multi-criteria pathfinding is defined as a weighted sum of multiple criteria and the weights are learned automatically by Perceptron learning. Weight learning is implemented in two ways: short-term weight learning that reflects weight changes in real time as the user moves and long-term weight learning that updates the weights by the average value of the entire path after completing the movement. We use the weight update method with momentum for long-term weight learning, so that learning speed is improved and the learned weight can be stabilized. The proposed method is implemented as an app and is applied to various movement situations. The results show that customized pathfinding based on user preference can be obtained.

• Health Policy and Management
• /
• v.28 no.2
• /
• pp.119-127
• /
• 2018

Background: This study aimed to examine (1) the patterns of online health information search with respect to seeking and scanning, and (2) how online search, along with eHealth literacy, predicts perceived information usefulness in the context of diet and weight control. Methods: Online survey was conducted with 299 adults from the consumer panel recruited for the purpose of quality assessment of the Korean National Health Information Portal in 2016. We conducted paired sample t-test and multiple logistic regression to address the research questions. Data analysis was performed using IBM SPSS Statistics ver. 24.0 (IBM Corp., Armonk, NY, USA) and SAS ver. 9.3 (SAS Institute Inc., Cary, NC, USA). Results: Of the respondents, 38.8% were 'high seek-high scanners,' 35.8% were 'low seek-low scanners,' 13.0% were 'high seek-low scanners,' and 12.4% were 'low seek-high scanners.' eHealth literacy was a significant, positive predictor of online information scanning (odds ratio [OR], 2.46; 95% confidence interval [CI], 1.41-4.29), but not for online information seeking (OR, 1.75; 95% CI, 1.00-3.05). With respect to perceived usefulness of online information seeking, online seeking (OR, 4.90; 95% CI, 2.19-11.00) and eHealth literacy (OR, 2.30; 95% CI, 1.11-4.75) were significant predictors. Perceived usefulness of online scanning had a significant association with online scanning (OR, 2.38; 95% CI, 1.08-5.22), but not with eHealth literacy. Conclusion: To increase the effectiveness of the health policy for online information search and related outcomes in the context of diet and weight control, it is important to develop education programs promoting eHealth literacy.

• Structural Engineering and Mechanics
• /
• v.29 no.4
• /
• pp.391-410
• /
• 2008

In this article, a harmony search algorithm is presented for optimum design of steel frame structures. Harmony search is a meta-heuristic search method which has been developed recently. It is based on the analogy between the performance process of natural music and searching for solutions of optimization problems. The design algorithms obtain minimum weight frames by selecting suitable sections from a standard set of steel sections such as American Institute of Steel Construction (AISC) wide-flange (W) shapes. Stress constraints of AISC Load and Resistance Factor Design (LRFD) and AISC Allowable Stress Design (ASD) specifications, maximum (lateral displacement) and interstorey drift constraints, and also size constraint for columns were imposed on frames. The results of harmony search algorithm were compared to those of the other optimization algorithms such as genetic algorithm, optimality criterion and simulated annealing for two planar and two space frame structures taken from the literature. The comparisons showed that the harmony search algorithm yielded lighter designs for the design examples presented.

• Proceedings of the IEEK Conference
• /
• 2000.06c
• /
• pp.13-16
• /
• 2000

In this paper, we propose hybrid control method of neural network using the 3-point search algorithm. Proposed control method is searched the weight using the 3-point search algorithm for off-line then control the on-line. In order to verify the usefulness of the proposed method, we simulated the proposed control method with one link manipulator system and confirmed the excellency.

• Structural Engineering and Mechanics
• /
• v.45 no.1
• /
• pp.111-127
• /
• 2013

In this paper, a new version of gravitational search algorithm based on opposition-based learning (OBGSA) is introduced and applied for optimum design of reinforced concrete retaining walls. The new algorithm employs the opposition-based learning concept to generate initial population and updating agents' position during the optimization process. This algorithm is applied to minimize three objective functions include weight, cost and $CO_2$ emissions of retaining structure subjected to geotechnical and structural requirements. The optimization problem involves five geometric variables and three variables for reinforcement setups. The performance comparison of the new OBGSA and classical GSA algorithms on a suite of five well-known benchmark functions illustrate a faster convergence speed and better search ability of OBGSA for numerical optimization. In addition, the reliability and efficiency of the proposed algorithm for optimization of retaining structures are investigated by considering two design examples of retaining walls. The numerical experiments demonstrate that the new algorithm has high viability, accuracy and stability and significantly outperforms the original algorithm and some other methods in the literature.

• Journal of the Korea Society of Computer and Information
• /
• v.22 no.6
• /
• pp.49-55
• /
• 2017

Robots, games and life applications have been developed while computer areas are developed. Moreover, various applications have been utilized for various users including the early childhood. Recently, smart phones have been dramatically used by various users including early childhood. Many applications need to find a path from a starting point to destinations. For example, without using real maps, users can find the direct paths for the destinations in realtime. Specifically, path exploration in game programs is so important to have accurate results. Nowadays, with these techniques, diverse applications for educations of early childhood have been developed. To deal with the functions, necessity of efficient path search programs with high accuracy becomes much higher. In this paper, we design and develop a friendly maze program for early childhood based on a path searching algorithm. Basically, the path of lineal distance from a starting location to destination is considered. Moreover, weight values are calculated by considering heuristic weighted h(x). In our approach, A* algorithm searches the path considering weight values. Moreover, we utilize depth first search approach instead of breadth first search in order to reduce the search space. so it is proper to use A* algorithm in finding efficient paths although it is not optimized paths.

• Steel and Composite Structures
• /
• v.7 no.3
• /
• pp.253-262
• /
• 2007

A methodology to design symmetrically laminated fibre-reinforced structures under transverse loads for minimum weight, with manufacturing uncertainty in the ply angle, is described. The ply angle and the ply thickness are the design variables, and the Tsai-Wu failure criteria is the design constraint implemented. It is assumed that the probability of any tolerance value occurring within the tolerance band, compared with any other, is equal, and thus the approach is a worst-case scenario approach. The finite element method, based on Mindlin plate and shell theory, is implemented, and thus effects like bending-twisting coupling are accounted for. The Golden Section method is used as the search algorithm, but the methodology is flexible enough to allow any appropriate finite element formulation, search algorithm and failure criterion to be substituted. In order to demonstrate the procedure, laminated plates with varying aspect ratios and boundary conditions are optimally designed and compared.