• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.939-951
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• 2022

The most important factor in designing autonomous driving systems is to recognize the exact location of the vehicle within the surrounding environment. To date, various sensors and navigation systems have been used for autonomous driving systems; however, all have limitations. Therefore, the need for high-definition (HD) maps that provide high-precision infrastructure information for safe and convenient autonomous driving is increasing. HD maps are drawn using three-dimensional point cloud data acquired through a mobile mapping system (MMS). However, this process requires manual work due to the large numbers of points and drawing layers, increasing the cost and effort associated with HD mapping. The objective of this study was to improve the efficiency of HD mapping by segmenting semantic information in an MMS point cloud into six classes: roads, curbs, sidewalks, medians, lanes, and other elements. Segmentation was performed using various machine learning techniques including random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and gradient-boosting machine (GBM), and 11 variables including geometry, color, intensity, and other road design features. MMS point cloud data for a 130-m section of a five-lane road near Minam Station in Busan, were used to evaluate the segmentation models; the average F1 scores of the models were 95.43% for RF, 92.1% for SVM, 91.05% for GBM, and 82.63% for KNN. The RF model showed the best segmentation performance, with F1 scores of 99.3%, 95.5%, 94.5%, 93.5%, and 90.1% for roads, sidewalks, curbs, medians, and lanes, respectively. The variable importance results of the RF model showed high mean decrease accuracy and mean decrease gini for XY dist. and Z dist. variables related to road design, respectively. Thus, variables related to road design contributed significantly to the segmentation of semantic information. The results of this study demonstrate the applicability of segmentation of MMS point cloud data based on machine learning, and will help to reduce the cost and effort associated with HD mapping.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.2
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• pp.91-108
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• 2022

In most cases, optical images have been used as training data of DL (Deep Learning) models for object detection, recognition, identification, classification, semantic segmentation, and instance segmentation. However, properties of 3D objects in the real-world could not be fully explored with 2D images. One of the major sources of the 3D geospatial information is DSM (Digital Surface Model). In this matter, characteristic information derived from DSM would be effective to analyze 3D terrain features. Especially, man-made objects such as buildings having geometrically unique shape could be described by geometric elements that are obtained from 3D geospatial data. The background and motivation of this paper were drawn from concept of the intrinsic image that is involved in high-level visual information processing. This paper aims to extract buildings after classifying terrain features by training DL model with DSM-derived information including slope, aspect, and SRI (Shaded Relief Image). The experiments were carried out using DSM and label dataset provided by ISPRS (International Society for Photogrammetry and Remote Sensing) for CNN-based SegNet model. In particular, experiments focus on combining multi-source information to improve training performance and synergistic effect of the DL model. The results demonstrate that buildings were effectively classified and extracted by the proposed approach.

• Design Convergence Study
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• v.14 no.4
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• pp.181-192
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• 2015

The layout includes various functions not only arrangement, connection, division of information but also affordance and information recognition as the important element that determine the whole structure of information. Recently, the layout of web interface design was gradually changing from the concept of the original Z-layout to F-layout, whereas, it is being changed again into the split layout as the transform of Z-layout. However, there can be lack of grounds as to the reason of the change of layout. Therefore, it needs to study on how the functional difference between the F-layout and Z-layout affects user experience. In this study, we conduct the study on the effect of each layout type on the user experience focusing on the product description of web-page by user test. The user test have shown that the user has better feeling on the split layout based on the Z-layout than F-layout. The Z-layout also has more advantages on the formation of the favorable attitude of user in efficiency, accessibility and concentration. This result is consistent with the trend of web-design to change into the split layout based on the Z-layout and we provided the objective reasons for the effective web-design layout by user test.

• Journal of the Korea institute for structural maintenance and inspection
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• v.12 no.1
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• pp.81-90
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• 2008

This study deals with a method to identify structural damage using the combined finite element method (FEM) and the advanced damage search technique. The novelty of this study is the application of plates with arbitrary damage shapes and their response due to the anomalies in a structure subjected to impact loading. The technique described in this paper may allow us not only to detect the stiffness distribution of the damaged areas but also to find locations and the extent of damage. To demonstrate the feasibility of the method, the algorithm is applied to a steel thin plate structures with an arbitrary damage shape. The results demonstrate the excellencies of the method from the standpoints of computation efficiency as well as its ability to investigate the arbitrary stiffness reductions.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.271-280
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• 1999

인공신경망을 시계열예측에 적용하는 경우에 고려되어야 할 문제중, 특히 모형에 적합한 입력변수의 생성이 중요시되고 있는데, 이러한 분야는 인공신경망의 모형생성과정에서 입력변수에 대한 전처리기법으로써 다양하게 제시되어 왔다. 가장 최근의 입력변수 전처리기법으로써 제시되고 있는 신호처리기법은 전통적 주기분할처리방법인 푸리에변환기법(Fourier transforms)을 비롯하여 이를 확장시킨 개념인 웨이블릿변환기법(wavelet transforms) 등으로 대별될 수 있다. 이는 기본적으로 시계열이 다수의 주기(cycle)들로 구성된 상이한 시계열들의 집합이라는 가정에서 출발하고 있다. 전통적으로 이러한 시계열은 전기 또는 전자공학에서 주파수영역분할, 즉 고주파 및 저주파수를 분할하기 위한 기법에 적용되어 왔다. 그러나, 최근에는 이러한 연구가 다양한 분야에 활발하게 응용되기 시작하였으며, 그 중의 대표적인 예가 바로 경영분야의 재무시계열에 대한 분석이다 전통적으로 재무시계열은 장, 단기의사결정을 가진 시장참여자들간의 거래특성이 시계열에 각기 달리 가격으로 반영되기 때문에 이러한 상이한 집단들의 고유한 거래움직임으로 말미암아 예를 들어, 주식시장이 프랙탈구조를 가지고 있다고 보기도 한다. 이처럼 재무시계열은 다양한 사회현상의 집합체라고 볼 수 있으며, 그만큼 예측모형을 구축하는데 어려움이 따른다. 본 연구는 이러한 시계열의 주기적 특성에 기반을 둔 신호처리분석으로서 기존의 시계열로부터 노이즈를 줄여 주면서 보다 의미 있는 정보로 변환시켜 줄 수 있는 웨이블릿분석 방법론을 새로운 필터링기법으로 사용하여 현재 많은 연구가 진행되고 있는 인공신경망과의 모형결합을 통해 기존연구와는 다른 새로운 통합예측방법론을 제시하고자 한다. 본 연구에서 제시하는 통합방법론은 크게 2단계 과정을 거쳐 예측모형으로 완성이 된다. 즉, 1차 모형단계에서 원시 재무시계열은 먼저 웨이블릿분석을 통해서 노이즈가 필터링 되는 동시에, 과거 재무시계열의 프랙탈 구조, 즉 비선형적인 움직임을 보다 잘 반영시켜 주는 다차원 주기요소를 가지는 시계열로 분해, 생성되며, 이렇게 주기에 따라 장단기로 분할된 시계열들은 2차 모형단계에서 신경망의 새로운 입력변수로서 사용되어 최종적인 인공 신경망모델을 구축하는 데 반영된다.

• Journal of the Korea Computer Graphics Society
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• v.17 no.4
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• pp.11-21
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• 2011

This paper proposes a method for decomposing a Hangul glyph of outline fonts into its initial, medial and final components using statistical-structural information. In a font family, the positions of components are statistically consistent and the stroke relationships of a Hangul character reflect its structure. First, we create the component histograms that accumulate the shapes and positions of the same components. Second, we make pixel clusters from character image based on pixel direction probabilities and extract the candidate strokes using position, direction, size of clusters and adjacencies between clusters. Finally, we find the best structural match between candidate strokes and predefined character model by relaxation labeling. The proposed method in this paper can be used for a study on formative characteristics of Hangul font, and for a font classification/retrieval system.

• Journal of KIISE:Software and Applications
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• v.28 no.11
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• pp.795-813
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• 2001

Sophisticated geometric structure analysis must be preceded to create electronic document from logical components extracted from document image. this paper presents a knowledge-based method for sophisticated geometric structure analysis of technical journal pages. The proposed knowledge base encodes geometric characteristics that are not only common in technical journals but also publication-specific in the form rules. The method takes the hybrid of top-down and bottom-up techniques and consists of two phases: region segmentation and identification. Generally, the result of segmentation process does not have a one-to-one matching with composite layout components. Therefore, the proposed method identifies non-text objects such as image, drawing and table, as well as text objects such as text line and equation by splitting or grouping segmented regions into composite layout components. Experimental results with 372 images scanned from the IEEE Transactions on Pattern Analysis and Machine Intelligence show that the proposed method has performed geometrical structure analysis successfully on more than 99% of the test images, resulting in sophisticated performance compared with previous works.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.4
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• pp.430-441
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• 2002

Video object segmentation is an important component for object-based video coding scheme such as MPEG-4. In this paper, a robust algorithm for segmentation of moving objects in video sequences and VOP(Video Object Planes) extraction is presented. The points of this paper are detection, of an accurate object boundary by associating moving object edge with spatial object edge and generation of VOP. The algorithm begins with the difference between two successive frames. And after extracting difference image, the accurate moving object edge is produced by using the Canny algorithm and morphological operation. To enhance extracting performance, we app]y the morphological operation to extract more accurate VOP. To be specific, we apply morphological erosion operation to detect only accurate object edges. And moving object edges between two images are generated by adjusting the size of the edges. This paper presents a robust algorithm implementation for fast moving object detection by extracting accurate object boundaries in video sequences.

• Journal of Advanced Navigation Technology
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• v.17 no.3
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• pp.279-284
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• 2013

Among the phases of flight operations pilots feel much pressure in landing segment. There is a number of factors affecting landing safety while pilots reduce aircraft speeds and make a touchdown and stop completely. If runway length is sufficient for landing, there maybe is no problem. But it is not the case all the time. So it is necessary to confirm whether landing performance is within limits or not. Required landing distance is actual landing distance demonstrated by flight test pilot plus allowances for average airline pilots. FAR(Federal Aviation Regulations) AFM certification is based upon manual landing for dry and wet runway. Other runway conditions are not the certification basis. JAR dictates even contaminated/slippery runway is included by prescribed allowances. Automatic landing is not certification basis, so actual landing distances are provided. In this paper I would like to analyze distance allowances included in each type of runway condition. In addition there is no regulation about allowances for specific runway condition, I would suggest adequate allowances for that case.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.421-421
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• 2018

효율적인 수자원 관리를 위하여 전국유역조사, 수자원 장기종합계획 등 다양한 사업이 수행되고 있으며, 이를 위하여 유출해석은 필수적인 항목이라 할 수 있다. 유출해석을 위하여 수문모형 또는 관측소의 유량자료가 활용되고 있으나, 이는 기존에 관측된 유량자료를 바탕으로 구축된 수위-유량관계 곡선식(Rating-curve)을 활용하여 재생산된 자료라 할 수 있다. 즉, 수위자료는 매시간 관측소에서 측정이 되지만, 유량자료의 경우 측정이 어려울 뿐만 아니라 변동성 및 불확실성이 크기 때문에 시계열 수위를 곡신식을 통해 유량으로 변환하여 활용하고 있다. 이와 같이 수위-유량관계 곡선식의 정확성이 수문자료 생산에 핵심 요소임에도 불구하고 이에 대한 연구는 제한적이며, 특히 홍수터 등의 영향을 고려하여 분할된 곡선의 천이점 접합시 곡선식의 정확도 향상을 위한 연구도 드문 편이다. 따라서 본 연구에서는 구간 분할된 곡선의 최적 천이점 선정을 위하여 Particle Swarm Optimization(PSO)기법을 활용하였으며, 총 5개 구간까지 구간별 목적함수로 RMSE, RSR, 결정계수 적용시 특성변화에 대한 연구를 수행하였다. 구간에 대하여 절대적인 오차를 산정하는 RMSE를 활용하는 경우 저수위 부분에 대한 오차가 증가하는 것을 확인할 수 있었으며, 상대적인 오차인 RSR, 결정계수를 활용하는 경우 전체 구간에 대한 오차를 보완할 수 있는 것으로 나타났다. PSO기법을 활용하여 도출된 곡선식에 대해서는 구간 및 전체구간에 대한 오차(RMSE, 결정계수, RSR, MAPE)를 활용하여 불확실성을 검토할 수 있도록 하였고, 잔차분석을 통한 이상치 및 회귀곡선에 대한 정규성 검토를 수행할 수 있는 툴을 개발하였다. 레이팅 커브를 작성하는데 있어 최적화 알고리즘을 활용하여 구간분할시 천이점 선정의 자동화로 천이점 선정에 소요되는 시간을 대폭 감축할 수 있을 뿐만 아니라, 구간별 오차를 종합적으로 고려하여 우수한 품질의 레이팅 커브를 도출할 수 있는 기반을 구축하였다.