• The KIPS Transactions:PartA
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• v.19A no.4
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• pp.187-194
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• 2012

This paper proposes a string analysis based system for classifying Android Apps that may access so called harmful sites, and shows an experiment result for real Android apps on the market. The system first transforms Android App binary codes into Java byte codes, it performs string analysis to compute a set of strings at all program points, and it classifies the Android App as bad ones if the computed set contains URLs that are classified because the sites provide inappropriate contents. In the proposed approach, the system performs such a classification in the stage of distribution before installing and executing the Apps. Furthermore, the system is suitable for the automatic management of Android Apps in the market. The proposed system can be combined with the existing methods using DNS servers or monitoring modules to identify harmful Android apps better in different stages.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.413-416
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• 1998

In this paper we first show that uniform PR systems and half independent PR systems have same dynamics, and then an important property of this two kinds of systems is derived. The most important property of uniform PR systems is that they have the ability of classifying m-dimensional problabilistic vector into in classes. The significance of studying the dynamics of uniform PR systems are tried from the beginning with a uniform PR system.

• Proceedings of the Optical Society of Korea Conference
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• 1991.06a
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• pp.35-40
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• 1991

In this paper, a new adaptive learning algorithm, Training by Adaptive Gain (TAG) for optical implementation of large-sized neural networks has been developed and its electro-optical implementation for 2-dimensional input and output neurons has been demostrated. The 4-dimensional global fixed interconnections and 2-dimensional adaptive gain-controls are implemented by multi-facet computer generated holograms and LCTV spatial light modulators, respectively. When the input signals pass through optical system to the output classifying layer, the TAG adaptive learning algorithm is implemented by a personal computer. The system classifies three 5$\times$5 input patterns correctly.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2000.11a
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• pp.161-175
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• 2000

This study includes the fundamental planning of the sectoral management technology in energy distribution system so that the industry itself would be willing to participate for the effective energy conservation in its own sector. Furthermore guidlines for the effective energy management techniques have been presented by first analyzing the energy consumption structures, the energy loss problems, the energy conservation status and the technology demands, and second classifying, according to the energy distribution system, the energy source management, the energy generation & conversion facilities, the energy transportation ＆ storage facilities, the energy consumming facilities and the waste energy management, etc. in the industrial sector.

• Proceedings of the KSR Conference
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• 2004.10a
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• pp.240-245
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• 2004

It is necessary to analyze the railway accidents and incidents for the purpose of understanding present safety state and enhancing its system. Korea National Railroad has its accident/incident reporting codes, but it is relatively not sufficient for detail classification and investigation of accident and incident compared with foreign countries. This paper suggests how to classify the railway accident/incident, and describes the analysis result for domestic recent railway accidents and incidents according to the new suggested classifying system.

• Proceedings of the KSR Conference
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• 2003.05a
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• pp.450-455
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• 2003

This paper is to discuss the hardware redundancy architecture of fault-tolerance system with using redundancy. Each architecture will be studied to implement fault-tolerance in classifying hardware redundancy architecture as passive, active and hybrid hardware redundancy. Therefore Fault-Masking and Fault-Detecting Techniques in each redundancy architecture is studied.

• Journal of Biomedical Engineering Research
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• v.24 no.3
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• pp.193-201
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• 2003

In this paper, a classifier of urine analysis system was designed using preprocessing and fuzzy algorithm. Preprocessing were processed by normalizing data of strip using calibration curve composed of achromatic colors value and by calculating three stimulus. FUZZY classifier capable of analyzing a qualitative concentration of test items was composed of fuzzifier by gaussian shaped membership function, inference of MIN method, and defuzzifier of centroid method through verification by measuring standard solution and by classifying concentration classes. After tuning membership function according to relating standard solution with urinalysis sample, the possibility to adapt classifier designed for urine analysis system near a bed was verified as classifying measured urinalysis samples and observing classified result. Of all test items, experimental results showed a satisfactory agreement with test results of reference system.

• Journal of Physiology & Pathology in Korean Medicine
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• v.23 no.1
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• pp.97-103
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• 2009

The status and the property of a tongue are the important indicators to diagnose one's health like physiological and clinicopathological changes of inner organs. However, the tongue diagnosis is affected by examination circumstances like a light source, patient's posture, and doctor's condition. To develop an automatic tongue diagnosis system for an objective and standardized diagnosis, classifying tongue coating is inevitable but difficult since the features like color and texture of the tongue coatings and substance have little difference, especially in the neighborhood on the tongue surface. The proposed method has two procedures; the first is to acquire the color table to classify tongue coatings and substance by automatically separating coating regions marked by oriental medical doctors, decomposing the color components of the region into hue, saturation and brightness and obtaining the 2nd order discriminant with statistical data of hue and saturation corresponding to each kind of tongue coatings, and the other is to apply the tongue region in an input image to the color table, resulting in separating the regions of tongue coatings and classifying them automatically. As a result, kinds of tongue coatings and substance were segmented from a face image corresponding to regions marked by oriental medical doctors and the color table for classification took hue and saturation values as inputs and produced the classification of the values into white coating, yellow coating and substance in a digital tongue diagnosis system. The coating regions classified by the proposed method were almost the same to the marked regions. The exactness of classification was 83%, which is the degree of correspondence between what Oriental medical doctors diagnosed and what the proposed method classified. Since the classified regions provide effective information, the proposed method can be used to make an objective and standardized diagnosis and applied to an ubiquitous healthcare system. Therefore, the method will be able to be widely used in Oriental medicine.

• Journal of the Korea Society of Computer and Information
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• v.16 no.1
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• pp.175-182
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• 2011

This study proposes CAOPI(Computer Aided Organ Prediction Index) system based on APACHE II(Acute Physiology And Chronic Health Evaluation) for classifying disease severity and predicting the conditions of patients' major organs. The existing ICU disease severity evaluation is mostly about calculating risk scores using patients' data at certain points, which has limitations on making precise treatments. CAOPI system is designed to provide personalized treatments by classifying accurate severity degrees of emergency patients, predicting patients' mortality rate and scoring the conditions of certain organs.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.203-214
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• 2023

The Korea Customs Service is efficiently handling business with an electronic customs system that can effectively handle one-stop business. This is the case and a more effective method is needed. Import and export require HS Code (Harmonized System Code) for classification and tax rate application for all goods, and item classification that classifies the HS Code is a highly difficult task that requires specialized knowledge and experience and is an important part of customs clearance procedures. Therefore, this study uses various types of data information such as product name, product description, and product image in the item classification request form to learn and develop a deep learning model to reflect information well based on Multimodal representation learning. It is expected to reduce the burden of customs duties by classifying and recommending HS Codes and help with customs procedures by promptly classifying items.