• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1144-1148
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• 2010

오염원이 집중되는 도시지역에서의 비점오염원에 대한 관리대책은 점오염원에 비하여 미비한 실정이다. 따라서 도시 지역의 비점오염원 부하량의 합리적인 조사, 비점오염물질 저감을 위한 관리기술 개발과 아울러 정책의 개발 등이 필요하며, 도시지역에서의 장기적인 비점오염물질 유출에 관한 모니터링을 통한 비점오염물질 원단위 조사가 절실히 요구되고 있는 실정이다. 따라서 본 연구에서는 상업 및 위락시설지역의 비점오염원 유출특성을 분석하기 위해 2008년 4월부터 2009년 10월까지 실측에 의해 측정된 강우량과 유출량 자료를 이용하여 비점오염원 유출특성을 분석하였다. 또한 본 연구에서는 자기 조직화 특성지도(Self-Organizing Feature Map: SOFM) 이론을 적용하여 측정된 유출 및 수질자료에 대해 패턴분류를 수행하여 분할구역별 자료의 특성분석을 통해 초기강우 특성이 구분되어짐을 확인 할 수 있었다. 그러나 현재 축적된 자료에 대한 양적인 한계로 인해 명확한 구분이 이루어지지 않는 항목도 있었으나, 향후 지속적인 모니터링을 통해 충분한 자료가 축적될 경우 초기강우 기준을 위한 새로운 접근방법으로 제시될 수 있을 것으로 기대된다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.23 no.3
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• pp.221-231
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• 2005

Though Digital Map Ver. 2.0 is adequate to GIS, the possibility to gain information from its external form and the ease of producing paper map had retrogressed. In this research, concerned with the problems, we plan to make Digital Map Ver. 2.0, which satisfies the conditions of GIS. It will have geometrical and logical data structure, and also possess informative ability as much as that of Ver. 1.0. For the study, the process to analyze the topographic code, color, code priority order, etc. of paper relief map, digital map Ver. 1.0, and digital map Ver. 2.0 was taken. For the topographical feature with diverse expression, we changed the portrayal of digital map Ver. 2.0 to make it fit the regulations of map portrayal. At the point of topographic code priority order, the rule is to arrange them in the same order as the real territory. However, we made a special code in the case of any change of the locational order. According to the property of this study, we observe the regulations of map portrayal, for the elements related to subjective sense, such as colors. And we give priority on the data construction when the portrayal of topographical feature and the schema of GIS database system are contradictory.

• IEIE Transactions on Smart Processing and Computing
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• v.2 no.5
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• pp.277-281
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• 2013

Modern speaker verification systems based on support vector machines (SVMs) use Gaussian mixture model (GMM) supervectors as their input feature vectors, and the maximum a posteriori (MAP) adaptation is a conventional method for generating speaker-dependent GMMs by adapting a universal background model (UBM). MAP adaptation requires the appropriate amount of input utterance due to the number of model parameters to be estimated. On the other hand, with limited utterances, unreliable MAP adaptation can be performed, which causes adaptation noise even though the Bayesian priors used in the MAP adaptation smooth the movements between the UBM and speaker dependent GMMs. This paper proposes a sparse MAP adaptation method, which is known to perform well in the automatic speech recognition area. By introducing sparse MAP adaptation to the GMM-SVM-based speaker verification system, the adaptation noise can be mitigated effectively. The proposed method utilizes the L0 norm as a regularizer to induce sparsity. The experimental results on the TIMIT database showed that the sparse MAP-based GMM-SVM speaker verification system yields a 42.6% relative reduction in the equal error rate with few additional computations.

• Journal of the Optical Society of Korea
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• v.17 no.2
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• pp.188-199
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• 2013

This paper presents a novel algorithm for nighttime detection of the lane markers painted on a road at night. First of all, the proposed algorithm uses neighborhood average filtering, 8-directional Sobel operator and thresholding segmentation based on OTSU's to handle raw lane images taken from a digital CCD camera. Secondly, combining intensity map and gradient map, we analyze the distribution features of pixels on boundaries of lanes in the nighttime and construct 4 feature sets for these points, which are helpful to supply with sufficient data related to lane boundaries to detect lane markers much more robustly. Then, the searching method in multiple directions- horizontal, vertical and diagonal directions, is conducted to eliminate the noise points on lane boundaries. Adapted Hough transformation is utilized to obtain the feature parameters related to the lane edge. The proposed algorithm can not only significantly improve detection performance for the lane marker, but it requires less computational power. Finally, the algorithm is proved to be reliable and robust in lane detection in a nighttime scenario.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.4
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• pp.451-455
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• 2009

This paper provides a comparison of global path planning method in single string by using pulled and pushed SOFM (Self-Organizing Feature Map) which is a method among a number of neural network. The self-organizing feature map uses a randomized small valued initial-weight-vectors, selects the neuron whose weight vector best matches input as the winning neuron, and trains the weight vectors such that neurons within the activity bubble are move toward the input vector. On the other hand, the modified SOFM method in this research uses a predetermined initial weight vectors of the one dimensional string, gives the systematic input vector whose position best matches obstacles, and trains the weight vectors such that neurons within the activity bubble are move toward or reverse the input vector, by rising a pulled- or a pushed-SOFM. According to simulation results one can conclude that the modified neural networks in single string are useful tool for the global path planning problem of a mobile robot. In comparison of the number of iteration for converging to the solution the pushed-SOFM is more useful than the pulled-SOFM in global path planning for mobile robot.

• Journal of Korean Society for Geospatial Information Science
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• v.10 no.5 s.23
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• pp.75-80
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• 2002

Depending upon geographical features and surrounding errors in the survey field, inaccurate positioning is inevitable in a kinematic DGPs survey. Therefore, a data inaccuracy detection algorithm and an interpolation algorithm are essential to meet the requirement of a digital map. In this study, GPS characteristics are taken into account to develop the data inaccuracy detection algorithm. Then, the data interpolation algothim is obtained, based on the feature type of the survey. A digital map for 20km of a rural highway is produced by the kinematic DGPS survey and the features of interests are lines associated with the road. Since the vertical variation of GPS data is relatively higher, the trimmed mean of vertical variation is used as criteria of the inaccuracy detection. Four cases of 0.5%, 1%, 2.5% and 5% trimmings have been experimented. Criteria of four cases are 69cm, 65cm, 61cm and 42cm, respectively. For the feature of a curved line, cublic spine interpolation is used to correct the inaccurate data. When the feature is more or less a straight line, the interpolation has been done by a linear polynomial. Difference between the actual distance and the interpolated distance are few centimeters in RMS.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.3
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• pp.714-720
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• 2004

In detecting human faces in color images, additional geometric computation is often necessary for validating the face-candidate regions having various forms. In this paper, we propose a method that detects the facial features using chrominance components of color which do not affected by face occlusion and orientation. The proposed algorithm uses the property that the Cb and Cr components have consistent differences around the facial features, especially eye-area. We designed the Mean-Gray Morphology operator to emphasize the feature areas in the eye-map image which generated by basic chrominance differences. Experimental results show that this method can detect the facial features under various face candidate regions effectively.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.1-11
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• 2023

In this paper, instead of using recurrent neural network, we compare a classification performance of time series imaging algorithms using convolution neural network. There are traditional algorithms that imaging time series data (e.g. GAF(Gramian Angular Field), MTF(Markov Transition Field), RP(Recurrence Plot)) in TSC(Time Series Classification) community. Furthermore, we compare STFT(Short Time Fourier Transform) algorithm that can acquire spectrogram that visualize feature of voice data. We experiment CNN's performance by adjusting hyper parameters of imaging algorithms. When evaluate with GunPoint dataset in UCR archive, STFT(Short-Time Fourier transform) has higher accuracy than other algorithms. GAF has 98~99% accuracy either, but there is a disadvantage that size of image is massive.

• Smart Structures and Systems
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• v.33 no.2
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• pp.105-118
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• 2024

In order to realize tiny bridge crack discovery by UAV-based machine vision, a novel method combining deep learning and tensor voting is proposed. Firstly, the grid images of crack are detected and descripted based on SE-ResNet50 to generate feature points. Then, the probability significance map of crack image is calculated by tensor voting with feature points, which can define the direction and region of crack. Further, the crack detection anchor box is formed by non-maximum suppression from the probability significance map, which can improve the robustness of tiny crack detection. Finally, a case study is carried out to demonstrate the effectiveness of the proposed method in the Xiangjiang-River bridge inspection. Compared with the original tensor voting algorithm, the proposed method has higher accuracy in the situation of only 1-2 pixels width crack and the existence of edge blur, crack discontinuity, which is suitable for UAV-based bridge crack discovery.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.174-179
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• 2003

Through the simultaneous localization and map building (SLAM) technique, a robot can create maps about its unknown environment while it continuously localizes its position. Grid maps and feature maps have been widely used for SLAM together with application of probability methods and POMDP (partially observed Markov decision process). But this approach based on grid maps suffers from enormous computational burden. Topological maps, however, have drawn more attention these days because they are compact, provide natural interfaces, and are easily applicable to path planning in comparison with grid maps. Some topological SLAM techniques like GVG (generalized Voronoi diagram) were introduced, but it enables the robot to decide only whether the current position is part of GVG branch or not in the GVG algorithm. In this paper, therefore, to overcome these problems, we present a method for updating a global topological map from the local topological maps. These local topological maps are created through a labeled Voronoi diagram algorithm from the local grid map built based on the sensor information at the current robot position. And the nodes of a local topological map can be utilized as the features of the environment because it is robust in light of visibility problem. The geometric information of the feature is applied to the extended Kalman filter and the SLAM in the indoor environment is accomplished. A series of simulations have been conducted using a two-wheeled mobile robot equipped with a laser scanner. It is shown that the proposed scheme can be applied relatively well.