• Genomics & Informatics
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• v.1 no.1
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• pp.32-39
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• 2003

We present a latent variable model-based approach to the analysis of gene expression patterns, coupled with topographic clustering. Aspect model, a latent variable model for dyadic data, is applied to extract latent patterns underlying complex variations of gene expression levels. Then a topographic clustering is performed to find coherent groups of genes, based on the extracted latent patterns as well as individual gene expression behaviors. Applied to cell cycle­regulated genes of the yeast Saccharomyces cerevisiae, the proposed method could discover biologically meaningful patterns related with characteristic expression behavior in particular cell cycle phases. In addition, the display of the variation in the composition of these latent patterns on the cluster map provided more facilitated interpretation of the resulting cluster structure. From this, we argue that latent variable models, coupled with topographic clustering, are a promising tool for explorative analysis of gene expression data.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.2
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• pp.125-134
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• 2012

In this paper, we propose a method to analyze M:N corresponding relations in semantic matching, especially focusing on feature class matching. Similarities between any class pairs are measured by spatial objects which coexist in the class pairs, and corresponding classes are obtained by clustering with these pairwise similarities. We applied a graph embedding method, which constructs a global configuration of each class in a low-dimensional Euclidean space while preserving the above pairwise similarities, so that the distances between the embedded classes are proportional to the overall degree of similarity on the edge paths in the graph. Thus, the clustering problem could be solved by employing a general clustering algorithm with the embedded coordinates. We applied the proposed method to polygon object layers in a topographic map and land parcel categories in a cadastral map of Suwon area and evaluated the results. F-measures of the detected class pairs were analyzed to validate the results. And some class pairs which would not detected by analysis on nominal class names were detected by the proposed method.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.20 no.1
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• pp.1-11
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• 2002

This study set up a purpose in the efficient utilization of security target objects. This purpose is the following: Firstly, this study analyzed problem about deleted areas for security described on aerial photography image. Secondly, this study made clustering and labeling to recognize censored areas of image. Finally, this study tried to maximize various utilizability of digital image data through postprocessing algorithm. Based on these courses, the results of this study appeared that brightness value of image increased depending on topography and quantities of topographic features. It was estimated that these was able to utilized by useful estimative data in judging information of topography and topographic features included in the total image. Besides, in the image recognition and postprocessing, the better result value was not elicited than in a mountainous region. Because it was included that a lots of topography and topographic features was similarly recognized with the process for deletion of the existing security target objects in urban and suburb region. This result appeared that the topography and quantities of topographic features absolutely affected the recognition and processing of image.

• Journal of The Korean Society of Agricultural Engineers
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• v.56 no.3
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• pp.55-64
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• 2014

While utilizing high resolution satellite image for land use classification has been popularized, object-oriented classification has been adapted as an affordable classification method rather than conventional statistical classification. The aim of this study is to extract the paddy field area using object-oriented classification with time series NDVI from high-resolution satellite images, and the RapidEye satellite images of Jaeryung-gun in North Korea were used. For the implementation of object-oriented classification, creating objects by setting of scale and color factors was conducted, then 3 different land use categories including paddy field, forest and water bodies were extracted from the objects applying the variation of time-series NDVI. The unclassified objects which were not involved into the previous extraction classified into 6 categories using unsupervised classification by clustering analysis. Finally, the unsuitable paddy field area were assorted from the topographic factors such as elevation and slope. As the results, about 33.6 % of the total area (32313.1 ha) were classified to the paddy field (10847.9 ha) and 851.0 ha was classified to the unsuitable paddy field based on the topographic factors. The user accuracy of paddy field classification was calculated to 83.3 %, and among those, about 60.0 % of total paddy fields were classified from the time-series NDVI before the unsupervised classification. Other land covers were classified as to upland(5255.2 ha), forest (10961.0 ha), residential area and bare land (3309.6 ha), and lake and river (1784.4 ha) from this object-oriented classification.

• Journal of Digital Convergence
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• v.18 no.10
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• pp.233-238
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• 2020

In the cloud environment, IoT devices using sensors and wearable devices are being applied in various environments, and technologies that accurately determine the information generated by IoT devices are being actively studied. However, due to limitations in the IoT environment such as power and security, information generated by IoT devices is very weak, so financial damage and human casualties are increasing. To accurately collect and analyze IoT information, this paper proposes a topographic information-based key management technique that considers IoT information errors. The proposed technique allows IoT layout errors and groups topographic information into groups of dogs in order to secure connectivity of IoT devices in the event of arbitrary deployment of IoT devices in the cloud environment. In particular, each grouped terrain information is assigned random selected keys from the entire key pool, and the key of the terrain information contained in the IoT information and the probability-high key values are secured with the connectivity of the IoT device. In particular, the proposed technique can reduce information errors about IoT devices because the key of IoT terrain information is extracted by seed using probabilistic deep learning.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2001.10a
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• pp.165-196
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• 2001

DNA chip 또는 microarray는 다수의 유전자 또는 유전자 조각을 (보통 수천내지 수만 개)칩상에 고정시켜 놓고 DNA hybridization 반응을 이용하여 유전자들의 발현 양상을 분석할 수 있는 기술이다. 이러한 high-throughput기술은 예전에는 생각하지 못했던 여러가지 분자생물학의 문제에 대한 해답을 제시해 줄 수 있을 뿐 만 아니라, 분자수준에서의 질병 진단, 신약 개발, 환경 오염 문제의 해결 등 그 응용 가능성이 무한하다. 이 기술의 실용적인 적용을 위해서는 DNA chip을 제작하기 위한 하드웨어/웻웨어 기술 외에도 이러한 데이터로부터 최대한 유용하고 새로운 지식을 창출하기 위한 bioinformatics 기술이 핵심이라고 할 수 있다. 유전자 발현 패턴을 데이터마이닝하는 문제는 크게 clustering, classification, dependency analysis로 구분할 수 있으며 이러한 기술은 통계학과인공지능 기계학습에 기반을 두고 있다. 주로 사용된 기법으로는 principal component analysis, hierarchical clustering, k-means, self-organizing maps, decision trees, multilayer perceptron neural networks, association rules 등이다. 본 세미나에서는 이러한 기본적인 기계학습 기술 외에 최근에 연구되고 있는 새로운 학습 기술로서 probabilistic graphical model (PGM)을 소개하고 이를 DNA chip 데이터 분석에 응용하는 연구를 살펴본다. PGM은 인공신경망, 그래프 이론, 확률 이론이 결합되어 형성된 기계학습 모델로서 인간 두뇌의 기억과 학습 기작에 기반을 두고 있으며 다른 기계학습 모델과의 큰 차이점 중의 하나는 generative model이라는 것이다. 즉 일단 모델이 만들어지면 이것으로부터 새로운 데이터를 생성할 수 있는 능력이 있어서, 만들어진 모델을 검증하고 이로부터 새로운 사실을 추론해 낼 수 있어 biological data mining 문제에서와 같이 새로운 지식을 발견하는 exploratory analysis에 적합하다. 또한probabilistic graphical model은 기존의 신경망 모델과는 달리 deterministic한의사결정이 아니라 확률에 기반한 soft inference를 하고 학습된 모델로부터 관련된 요인들간의 인과관계(causal relationship) 또는 상호의존관계(dependency)를 분석하기에 적합한 장점이 있다. 군체적인 PGM 모델의 예로서, Bayesian network, nonnegative matrix factorization (NMF), generative topographic mapping (GTM)의 구조와 학습 및 추론알고리즘을소개하고 이를 DNA칩 데이터 분석 평가 대회인 CAMDA-2000과 CAMDA-2001에서 사용된cancer diagnosis 문제와 gene-drug dependency analysis 문제에 적용한 결과를 살펴본다.

• Spatial Information Research
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• v.7 no.1
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• pp.1-12
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• 1999

The DTM(Digital Terrain Model) in GIS(Geographical Information System) shows the elevation from interpolation using data points surveyed. In panoramic flat landform, pixel size, resolution of source data may not be the problem in using DTM However, in mountainous landform like Korea, appropriate resolution accuracy of source data are important factors to represent the topography concerned. In this study, the difference in contour interval of source data, the resolution after interpolation, and different data structures were compared to figure out the accuracy of slope calculation using DTM from the topographic maps of Togyusan National Park Two types of GIS softwares, Idrisi(grid) ver. 2.0 using the altitude matrices and ArcView(TIN) ver. 3.0a using TIN were used for this purpose. After the analysis the conclusions are as follows: 1) The coarser resolution, the more smoothing effect inrepresenting the topography. 2) The coarser resolution the more difference between the grid-based Idrisi and the TIN-based ArcView. 3) Based on the comparison analysis of error for 30 points from clustering, there is not much difference among 10, 20, 30 m resolution in TIM-based Airview ranging from 4.9 to 6.2n However, the coarser resolution the more error for elevation and slope in the grid-based Idrisi. ranging from 6.3 to 10.9m. 4) Both Idrisi and ArcView could net consider breaklines of lanform like hilltops, valley bottoms.