• The Journal of the Korea Contents Association
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• v.10 no.4
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• pp.10-18
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• 2010

This paper presents a optimization of sigmoid activation function parameter using genetic algorithms and pattern recognition analysis in input space of two spirals benchmark problem. To experiment, cascade correlation learning algorithm is used. In the first experiment, normal sigmoid activation function is used to analyze the pattern classification in input space of the two spirals problem. In the second experiment, sigmoid activation functions using different fixed values of the parameters are composed of 8 pools. In the third experiment, displacement of the sigmoid function to determine the value of the three parameters is obtained using genetic algorithms. The parameter values applied to the sigmoid activation functions for candidate neurons are used. To evaluate the performance of these algorithms, each step of the training input pattern classification shows the shape of the two spirals.

• Journal of Korean Society of Transportation
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• v.17 no.1
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• pp.105-112
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• 1999

Image Processing Technique has been used as an efficient method to collect traffic information on the road such as vehicle counts, speed, queues, congestion and incidents. Most of the current methods which have been used to detect vehicles by the image processing are based on point processing, dealing with the local gray level of each pixel in the small window. However, these methods have some drawbacks. Firstly, detection is restricted by image quality. Secondly, they can not deal with occlusion and perspective projection problems, In this research, a new method which possibly deals with occlusion and perspective problems will be proposed. It extracts spatial information such as the position, the relationship of vehicles in 3-dimensional space, as well as vehicle detection in the image. The main algorithm used in this research is based on an extension of the Hough Transform. The Hough Transform which is proposed to estimates parameters of vertices and directed edges analytically on the Hough Space, is a valuable method for the 3-dimensional analysis of static scenes, motion detection and the estimation of viewing parameters.

• Journal of Internet Computing and Services
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• v.20 no.3
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• pp.33-41
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• 2019

In this paper, we propose a method to learn the redesigned class with background and object for semantic segmentation of indoor scene image. Semantic image segmentation is a technique that divides meaningful parts of an image, such as walls and beds, into pixels. Previous work of semantic image segmentation has proposed methods of learning various object classes of images through neural networks, and it has been pointed out that there is insufficient accuracy compared to long learning time. However, in the problem of separating objects and backgrounds, there is no need to learn various object classes. So we concentrate on separating objects and backgrounds, and propose method to learn after class simplification. The accuracy of the proposed learning method is about 5 ~ 12% higher than the existing methods. In addition, the learning time is reduced by about 14 ~ 60 minutes when the class is configured differently In the same environment, and it shows that it is possible to efficiently learn about the problem of separating the object and the background.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.375-383
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• 2021

Due to COVID-19, people are building new lifestyles such as online shopping, online travel, and video conferencing by limiting going out and gatherings. Such rapid social change is causing new problems and deepening existing problems at the same time. In particular, as online consumption increases significantly, traffic congestion, air pollution, and the heavy workload of delivery drivers are deepening in the daily logistics industry, and face-to-face delivery is emerging as a new problem. With the advent of the 4th industrial revolution, unmanned delivery using drones, artificial intelligence, and autonomous driving is emerging as an alternative to the existing logistics industry. However, space for logistics facilities and securing additional logistics sites due to drone flight are emerging as new problems to be solved. Therefore, it is intended to link additional services such as logistics movement, storage, and delivery by utilizing the existing transportation business, the subway, as a space for a logistics facility for drones that can solve existing problems and new problems.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.12
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• pp.5015-5022
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• 2010

Most of the existing pattern mining techniques are capable of searching patterns according to the continuous change of the spatial information of an object but there is no constraint on the spatial information that must be included in the extracted pattern. Thus, the existing techniques are not applicable to the optimal path search between specific nodes or path prediction considering the nodes that a moving object is required to round during a unit time. In this paper, the precision of the path search according to the spatial hierarchy is analyzed using the Spatial-Temporal Optimal Moving Pattern(with Frequency & Weight) (STOPM(FW)) algorithm which searches for the optimal moving path by considering the most frequent pattern and other weighted factors such as time and cost. The result of analysis shows that the database retrieval time is minimized through the reduction of retrieval range applying with the spatial constraints. Also, the optimal moving pattern is efficiently obtained by considering whether the moving pattern is included in each hierarchical spatial scope of the spatial hierarchy or not.

• The KIPS Transactions:PartB
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• v.13B no.4 s.107
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• pp.439-448
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• 2006

Previous recognition/clustering algorithms such as Kohonen SOM(Self-Organizing Map), MLP(Multi-Layer Percecptron) and SVM(Support Vector Machine) might not adapt to unexpected input pattern. And it's recognition rate depends highly on the complexity of own training patterns. We could make up for and improve the weak points with lowering complexity of original problem without losing original characteristics. There are so many ways to lower complexity of the problem, and we chose a kernel concepts as an approach to do it. In this paper, using a kernel concepts, original data are mapped to hyper-dimension space which is near infinite dimension. Therefore, transferred data into the hyper-dimension are distributed spasely rather than originally distributed so as to guarantee the rate to be risen. Estimating ratio of recognition is based on a new similarity-probing and learning method that are proposed in this paper. Using CEDAR DB which data is written in cursive letters, 0 to 9, we compare a recognition/clustering performance of kSOM that is proposed in this paper with previous SOM.

• Spatial Information Research
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• v.16 no.4
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• pp.499-511
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• 2008

To solve the emerging geospatial problems, more creative and effective spatial information infrastructures are required. To solve the emerging geospatial problems at a national level, this study assumes that the current Korean NSDI considered descriptive needs to be more prescriptive. The future NSDI will require a more useful integration vehicle for matters and places of national importance such as national security and emergency prevention and management. The purpose of this study is to identify "What can be done for the Korean NSDI to be more prescriptive?" This study reviews previous researches and new SDI concepts, analyzes the Korean NSDI in terms of a descriptive NSDI, and proposes a prescriptive NSDI model for Korean geospatial problems. The model includes new aspects of an advanced NSDI and several tasks for the future prescriptive Korean NSDI.

• The Journal of Korean Association of Computer Education
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• v.4 no.1
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• pp.175-181
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• 2001

WBI provides new opportunities to realize the flexible learning environment based on hypermedia and to support distance learning with a diverse interaction. The instructors or learners in WBI claim to be able to resolve reluctant fluctuations such as disorientation and cognitive overload. To overcome these phenomena, a supplementary tool able to manage a learning space organized by the instructor's or learner's own way and offer effective navigation techniques is presented in this paper. A learning space management and navigation tool called HyperMap dynamically represents the learning space in the form of a two-dimensional labeled graph. This HyperMap also can be used for an instruction design tool, learners portfolio for the exchange of learning experiences, and the assessment of WBI.

• Journal of the Korean Geographical Society
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• v.43 no.1
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• pp.71-86
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• 2008

The ambiguity of spatial language can be a source of communication problems. For instance, the 'the opposite side' in a sentence such as 'where is the opposite side of building X' can mean more than one direction. Research interests are focused on the directions of a spatial language 'the opposite side'. This study also explored the effect of geometric properties such as reference object's shape and distance from the reference object and spatial reference frame in the comprehension of 'the opposite side'. The assessment tasks used consisted of rating how appropriate the sentence 'where is the opposite side of building X' was to describe a series of pictures. The results of experiment suggest that 'the opposite side' means in most cases more than one direction simultaneously. Changing spatial reference frame has significant effects on individuals' rating of the tasks. However, while reference object's shape (prolonged building) has a consistent effect of the ratings given, the distance from the reference object (shortened road width) has limited influence in comprehending the tasks.

• Journal of KIISE:Databases
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• v.32 no.5
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• pp.536-546
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• 2005

In this paper, we focus on the filtering step of candidate objects for spatial join operations on the input tables that none of the inputs is indexed. Over the last decade, several spatial Join algorithms for the input tables with index have been extensively studied. Those algorithms show excellent performance over most spatial data, while little research on solving the performance degradation in the presence of skewed data has been attempted. Therefore, we propose a spatial hash strip join(SHSJ) algorithm that can refine the problem of skewed data in the conventional spatial hash Join(SHJ) algorithm. The basic idea is similar to the conventional SHJ algorithm, but the differences are that bucket capacities are not limited while allocating data into buckets and SSSJ algorithm is applied to bucket join operations. Finally, as a result of experiment using Tiger/line data set, the performance of the spatial hash strip join operation was improved over existing SHJ algorithm and SSSJ algorithm.