• Korean Chemical Engineering Research
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• v.47 no.1
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• pp.72-78
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• 2009

For the electro-discharge machining of an electro-conductive anisotropic composite, an unsteady state formulation was established and solved by Galerkin's finite element method. The distribution of temperature on work piece, the shape of the crater and the material removal rate were obtained in terms of the process parameters. The $12{\times}12$ irregular mesh that was chosen as the optimum in the previous analysis was used for computational accuracy and efficiency. A material having the physical properties of alumina/titanium carbide composite was selected and an electricity with power of 51.4 V and current of 7 A was applied, assuming the removal efficiency of 10 % and the thermal anisotropic factors of 2 and 3. As the spark was initiated the workpiece immediately started to melt and the heat affected zone was formed. The moving boundary of the crater was also identified with time. When the radial and axial conductivities were increased separately, the temperature distribution and the shape of the crater were shifted in the radial and axial directions, respectively. The material removal rate was found to be higher when the conductivity was increased in the radial direction rather than in the axial direction.

• The KIPS Transactions:PartB
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• v.13B no.3 s.106
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• pp.345-352
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• 2006

The practical English-Korean machine translation system should be able to translate long sentences quickly and accurately. The intra-sentence segmentation method has been proposed and contributed to speeding up the syntactic analysis. This paper proposes the syntactic category prediction method using decision trees for getting accurate parsing results. In parsing with segmentation, the segment is separately parsed and combined to generate the sentence structure. The syntactic category prediction would facilitate to select more accurate analysis structures after the partial parsing. Thus, we could improve the parsing accuracy by the prediction. We construct features for predicting syntactic categories from the parsed corpus of Wall Street Journal and generate decision trees. In the experiments, we show the performance comparisons with the predictions by human-built rules, trigram probability and neural networks. Also, we present how much the category prediction would contribute to improving the translation quality.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.2
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• pp.163-171
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• 2012

High resolution satellite images have been used for building and road system analysis, landscape analysis, and ecological assessment for several years. However, in high resolution satellite images, shadows are necessarily cast by manmade objects such as buildings and over-pass bridges. This paper develops the shadow extraction procedures in urban area including various land-use classes, and the extracted shadow areas are evaluated by a manually digitized shadow map. For the shadow extraction, the Canny edge operator and the dilation filter are applied to make building edge buffer area. Also, the object-based segmentation was performed using Gram-Schmitt fusion image, and spectral and spatial parameters are calculated from the segmentation results. Finally, we proposed appropriate parameters and extraction rules for the shadow extraction. The accuracy of the shadow extraction results from the various assessment indices is 80% to 90%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.1
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• pp.147-156
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• 2011

With the increasing utility of the recent information system, the methods to pick up necessary products rapidly by using a lot of data has been studied. Association rule search methods to find hidden patterns has been drawing much attention, and the Apriori algorithm is a major method. However, the Apriori algorithm increases search time due to its repeated scans. This paper proposes an algorithm to reduce searching time of frequent items. The proposed algorithm creates matrix using transaction database and search for frequent items using the mean number of items of transactions at matrix and a defined minimum support. The mean number of items of transactions is used to reduce the number of transactions, and the minimum support to cut down on items. The performance of the proposed algorithm is assessed by the comparison of search time and precision with existing algorithms. The findings from this study indicated that the proposed algorithm has been searched more quickly and efficiently when extracting final frequent items, compared to existing Apriori and Matrix algorithm.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.3
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• pp.159-168
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• 2017

Smartphone malware has increased because Smartphone users has increased and smartphones are widely used in everyday life. Since 2012, Android has been the most mobile operating system. Owing to the open nature of Android, countless malware are in Android markets that seriously threaten Android security. Most of Android malware detection program does not detect malware to which bypass techniques apply and also does not detect unknown malware. In this paper, we propose lightweight method for detection of Android malware using static analysis and deep learning techniques. For experiments we crawl 7,000 apps from the Google Play Store and collect 6,120 malwares. The result show that proposed method can achieve 98.05% detection accuracy. Also, proposed method can detect about unknown malware families with good performance. On smartphones, the method requires 10 seconds for an analysis on average.

• Journal of radiological science and technology
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• v.43 no.2
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• pp.65-70
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• 2020

PA(postero-anterior) and AP(antero-posterior) chest projections are the most sought-after types of all kinds of projections. But if a radiological technologist puts wrong information about the position in the computer, the orientation of left and right side of an image would be reversed. In order to solve this problem, we utilized CNN(convolutional neural network) which has recently utilized a lot for studies of medical imaging technology and rule-based system. 70% of 111,622 chest images were used for training, 20% of them were used for testing and 10% of them were used for validation set in the CNN experiment. The same amount of images which were used for testing in the CNN experiment were used in rule-based system. Python 3.7 version and Tensorflow r1.14 were utilized for data environment. As a result, rule-based system had 66% accuracy on evaluating whether the orientation reversal on chest x-ray image. But the CNN had 97.9% accuracy on that. Being overcome limitations by CNN which had been shown on rule-based system and shown the high accuracy can be considered as a meaningful result. If some problems which can occur for tasks of the radiological technologist can be separated by utilizing CNN, It can contribute a lot to optimize workflow.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.2
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• pp.450-460
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• 2017

T wave is cardiac parameters that represent ventricular repolarization, it is very important to diagnose arrhythmia. Several methods for detecting T wave have been proposed, such as frequency analysis and non-linear approach. However, detection accuracy is at the lower level. This is because of the overlap of the P wave and T wave depending on the heart condition. We propose T wave detection algorithm based on target area extraction through QRS cancellation and moving average. For this purpose, we detected Q, R, S wave from noise-free ECG(electrocardiogram) signal through the preprocessing method. And then we extracted P, T target area by applying decision rule for four PAC(premature atrial contraction) pattern another arrhythmia through moving average and detected T wave using RT interval and threshold of RR interval. The performance of T wave detection is evaluated by using 48 record of MIT-BIH arrhythmia database. The achieved scores indicate the average detection rate of 95.32%.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.5
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• pp.319-328
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• 2013

Software maintainers try to comprehend software source code by intensively using source code identifiers. Thus, use of inconsistent identifiers throughout entire source code causes to increase cost of software maintenance. Although participants can adopt peer reviews to handle this problem, it might be impossible to go through entire source code if the volume of code is huge. This paper introduces an approach to automatically detecting inconsistent identifiers of Java source code. This approach consists of tokenizing and POS tagging all identifiers in the source code, classifying syntactic and semantic similar terms, and finally detecting inconsistent identifiers by applying proposed rules. In addition, we have developed tool support, named CodeAmigo, to support the proposed approach. We applied it to two popular Java based open source projects in order to show feasibility of the approach by computing precision.

• Proceedings of the Korea Contents Association Conference
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• 2008.05a
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• pp.823-826
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• 2008

This paper shows linkage management for external services. There are many services for specific entities such as DBLP and OntoWorld. OntoFrame, as a Semantic Web-based research information service portal, aims at one-stop service in ways that it connects external services with hyperlinks. For managing the linkage, linkage rules are manually edited by human administrators and automatically verified and tested by linkage management system. It consists of linkage rule management, linkage rule verification, linkage test, and dynamic link generation. Linkage rule management creates and edits linkage rules to connect external services on the Web. After finished rule editing and verification step, linkage management invokes linkage test with entity list. Only valid links are visible to enable users to click on our system, and thus it increases user's reliability on the system.

• Journal of Navigation and Port Research
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• v.33 no.1
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• pp.65-70
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• 2009

The hybrid numerical model is developed by combining BEM that can calculate the wave motion rapidly under the potential theory and CADMAS-SURF that solves Navier-Stokes equations for the free surface variation near the structure, In the hybrid model the calculation of wave motion in a wide field of wave reflection for deep water area is conducted by BEM but for shallow water area by CADMAS-SURF. Especially the hybrid model can calculate random wave motions for long term period more rapidly with almost similar accuracy than the calculation of wave motion which was carried out by CADMAS-SURF only. In this study the coupling model was applied to the calculation of the strong nonlinear wave motion such as wave runup and overtopping at the coastal structure on the mild-slope bottom and the results of numerical model were compared with the Toyosima's experiments of regular wave runup and Goda's design diagram of ramdom wave overtopping, respectively.