• Journal of Internet Computing and Services
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• v.6 no.5
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• pp.97-110
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• 2005

Recently, many WMS(Web Map Services) and POI(Point of Interest) services come to be in service on the Internet using Web GIS(Geographic Information System) as Information Technology and computer H/W are evolved faster in its speed, network bandwidth and features. The Web GIS is, however, limited and constrained on the specification of its system configuration, the service class provided and the presentation methodology of a map. As the mobile Internet becomes popular in mobile service, Web GIS service on mobile environment is strongly required and to be provided by location based WMS(Web Map Service) on a mobile client such as PDA with location information of the user. In this paper, we made an effort to design and implement a GIS computing environment by thin client for mobile web map service. For implementing the thin client GIS computing environment. we were using NGII's(National Geographic Information Institute's) DXF map, representing the map by SVG(Scalable Vector Graphics) recommended by OGC(OpenGis Consortium), and adapting standard XML web service to provide the thin client GIS service on PDA by applying the location information of the user in realtime with GPS on mobile environment.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.9
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• pp.5446-5451
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• 2014

Because there are some mistakes by hand in processing electronic maps using a navigation terminal, this paper proposes an automatic offline recognition for traffic signs, which are considered ingredient navigation information. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), which have been used widely in the field of 2D face recognition as computer vision and pattern recognition applications, was used to recognize traffic signs. First, using PCA, a high-dimensional 2D image data was projected to a low-dimensional feature vector. The LDA maximized the between scatter matrix and minimized the within scatter matrix using the low-dimensional feature vector obtained from PCA. The extracted traffic signs under a real-world road environment were recognized successfully with a 92.3% recognition rate using the 40 feature vectors created by the proposed algorithm.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.2
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• pp.835-838
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• 2016

Background: Lung cancer remains one of the most common cancers in the world, both in terms of new cases (about 13% of total per year) and deaths (nearly one cancer death in five), because of the high case fatality. Errors in lung cancer type or malignant growth determination lead to degraded treatment efficacy, because anticancer strategy depends on tumor morphology. Materials and Methods: We have made an attempt to evaluate effectiveness of machine learning algorithms in the task of lung cancer classification based on gene expression levels. We processed four publicly available data sets. The Dana-Farber Cancer Institute data set contains 203 samples and the task was to classify four cancer types and sound tissue samples. With the University of Michigan data set of 96 samples, the task was to execute a binary classification of adenocarcinoma and non-neoplastic tissues. The University of Toronto data set contains 39 samples and the task was to detect recurrence, while with the Brigham and Women's Hospital data set of 181 samples it was to make a binary classification of malignant pleural mesothelioma and adenocarcinoma. We used the k-nearest neighbor algorithm (k=1, k=5, k=10), naive Bayes classifier with assumption of both a normal distribution of attributes and a distribution through histograms, support vector machine and C4.5 decision tree. Effectiveness of machine learning algorithms was evaluated with the Matthews correlation coefficient. Results: The support vector machine method showed best results among data sets from the Dana-Farber Cancer Institute and Brigham and Women's Hospital. All algorithms with the exception of the C4.5 decision tree showed maximum potential effectiveness in the University of Michigan data set. However, the C4.5 decision tree showed best results for the University of Toronto data set. Conclusions: Machine learning algorithms can be used for lung cancer morphology classification and similar tasks based on gene expression level evaluation.

• Journal of KIISE:Software and Applications
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• v.29 no.3
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• pp.192-201
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• 2002

Automated text classification is considered as an important method to manage and process a huge amount of documents in digital forms that are widespread and continuously increasing. Recently, text classification has been addressed with machine learning technologies such as k-nearest neighbor, decision tree, support vector machine and neural networks. However, only few investigations in text classification are studied on real problems but on well-organized text corpus, and do not show their usefulness. This paper proposes and analyzes text classification methods for a real application, email document classification task. First, we propose a combining method of multiple neural networks that improves the performance through the combinations with maximum and neural networks. Second, we present another strategy of combining multiple machine learning classifiers. Voting, Borda count and neural networks improve the overall classification performance. Experimental results show the usefulness of the proposed methods for a real application domain, yielding more than 90% precision rates.

• Journal of KIISE:Software and Applications
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• v.29 no.10
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• pp.684-693
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• 2002

Electronic Ink is a stored data in the form of the handwritten text or the script without converting it into ASCII by handwritten recognition on the pen-based computers and Personal Digital Assistants(PDAs) for supporting natural and convenient data input. One of the most Important issue is to search the electronic ink in order to use it. We proposed and implemented a script matching algorithm for the electronic ink. Proposed matching algorithm separated the input stroke into a set of primitive stroke using the curvature of the stroke curve. After determining the type of separated strokes, it produced a stroke feature vector. And then it calculated the distance between the stroke feature vector of input strokes and one of strokes in the database using the dynamic programming technique. We did various experiments and our algorithm showed high matching rate over 97.7% for only the Korean script and 94% for the data mixed Korean with the Chinese character.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.2
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• pp.206-218
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• 2002

In this paper, we design and implement a system that presents a method for selecting and providing personalized services independently without unifying the existing system platform with shopping malls joined in the hub site. This system provides a mechanism for gathering information left behind by many clients visiting Web sites for analysis of customers property, vector model for selecting personalized services, and mechanism for providing them to customers who visited in a shopping mall joined to the hub site. In a position of shopping mall site, this kind of personalization system can provide target advertisement, point marketing, and point share service etc. without changing existing shopping mall's environment through wrapper web server. Hub site customers can get personalized services from many shopping mall sites with only once registration for the hub site.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.5
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• pp.1047-1054
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• 2015

This paper presents an acoustic model transform method using untranscribed speech database for improved speech recognition. In the presented model transform method, an adapted GMM is obtained by employing the conventional adaptation method, and the most similar Gaussian component is selected from the adapted GMM. The bias vector between the mean vectors of the clean GMM and the adapted GMM is used for updating the mean vector of HMM. The presented GAMT combined with MAP or MLLR brings improved speech recognition performance in car noise and speech babble conditions, compared to singly-used MAP or MLLR respectively. The experimental results show that the presented model transform method effectively utilizes untranscribed speech database for acoustic model adaptation in order to increase speech recognition accuracy.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.5
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• pp.1077-1083
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• 2015

In this paper, we introduce an energy-efficient opportunistic interference alignment (OIA) scheme that greatly improves the sum-rates in multi-cell uplink networks. Each user employs optimal transmit vector design and power control in the sense of minimizing the amount of generated interference to other-cell base stations while satisfying a required signal quality. As our main result, it is shown that owing to the reduced interference level, the proposed OIA schemes attains larger sum-rates than those of OIA with no power control for almost all signal-to-noise ratio regions. In addition, when both zero-forcing and minimum mean square error (MMSE) detectors are employed at the receiver along with the OIA scheme, it is shown that the OIA scheme with MMSE detection shows superior performance.

• Journal of Korea Multimedia Society
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• v.17 no.3
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• pp.300-311
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• 2014

According to fast change of the environment, the structured study of the ecosystem by analyzing the plant leaves are needed. Expecially, the methodology that searches and classifies the leaves from captured from the smart device have received numerous concerns in the field of computer science and ecology. In this paper, we propose a plant leaf classification technique using shape descriptor by combining Scale Invarinat Feature Transform (SIFT) and Histogram of Oriented Gradient (HOG) from the image segmented from the background via Graphcut algorithm. The shape descriptor is coded in the field of Locality-constrained Linear Coding to optimize the meaningful features from a high degree of freedom. It is connected to Support Vector Machines (SVM) for efficient classification. The experimental results show that our proposed approach is very efficient to classify the leaves which have similar color, and shape.

• KIEAE Journal
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• v.16 no.1
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• pp.89-94
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• 2016

This study is part of three-dimensional(3D) heat transfer analysis program developmental process. The program is being developed without it's own built in 3D-modeller. So 3D-model must be created from another 3D-modeller such as generic CAD programs and imported to the developed program. After that, according to the 3D-geometric data form imported model, 3D-mesh created for numerical calculation. But the 3D-model created from another 3D-modeller is likely to have errors in it's geometric data such as mismatch of position between vertexes or surfaces. these errors make it difficult to create 3D-mesh for calculation. These errors are must be detected and cured in the pre-process before creating 3D-mesh. So, in this study four kinds of filters and functions are developed and tested. Firstly, 'vertex error filter' is developed for detecting and curing for position data errors between vertexes. Secondly, 'normal vector error filter' is developed for errors of surface's normal vector in 3D-model. Thirdly, 'intersection filter' is developed for extracting and creating intersection surface between adjacent objects. fourthly, 'polygon-line filter' is developed for indicating outlines of object in 3D-model. the developed filters and functions were tested on several shapes of 3D-models. and confirmed applicability. these developed filters and functions will be applied to the developed program and tested and modified continuously for less errors and more accuracy.