• Proceedings of the KAIS Fall Conference
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• 2009.12a
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• pp.427-431
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• 2009

취업박람회에서 참관객에게 제공되고 있는 박람회의 서비스품질, 가치, 그리고 만족 등의 이론적 배경을 바탕으로 인과관계를 나타내는 구조적 관계를 분석하였다. 개최 도시의 지각된 품질 요인은 정보의 확신성, 인적서비스, 접근성, 물리적 환경 그리고 관광매력성의 총 5개의 요인으로 총 16개의 항목을 Likert 5점 척도로 측정하였으며, 변수들의 집중 타당도와 판별 타당도를 확보하였다. 분석결과 관광매력성, 물리적환경, 인적서비스, 정보의 확신성 순으로 취업박람회 참가자의 지각된 가치에 영향을 미치는 것으로 나타났다. 이러한 연구결과는 박람회 주최자가 사용자 관점에서 좀 더 효율적으로 행사를 준비함과 동시에 참가 업체의 보다 나은 서비스 지원으로 기업의 가치인식 제고에도 기여할 것으로 기대된다.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2406-2410
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• 2004

2족 보행로봇은 그 구조적인 특성상 인간 생활환경에 적용이 용이하며 바퀴형 로봇이 이동하기 어려운 환경에서도 이동이 가능하다. 그러나 2족 보행로봇은 높은 자유도와 직렬형 링크 구조로 인해 안정도 해석과 제어가 어려운 점이 있으며 이는 로봇을 제작하는데 있어 난점으로 작용한다. 본 연구에서는 로봇의 발바닥에 압력센서를 설치하여 ZMP(Zero moment point)를 측정하여 안정도를 판별하고 신경망 이론을 이용하여 보행 안정도를 개선하도록 로봇의 자세를 제어하였다.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2474-2476
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• 2004

지폐 인식 자동화기기가 여러 분야에 보편화되면서 다양한 지폐를 고속으로 처리할 수 있는 고속지폐 인식 자동화 기기가 요구되고 있다. 하지만 대부분의 지폐 인식 자동화 기기가 고속화에 적합하지 않은 구조로 설계되어 있고 신권 추가가 용이하지 않다. 본 논문은 고속 Paper Currency 인식 시스템에 적합한 범용 하드웨어 시스템과 Bayes Rule 기반의 고속 인식 알고리즘을 제안한다. 제안된 범용 하드웨어 구조는 고속의 CIS(Contact Image Sensor)와 DSP(Digital Signal Processor) 그리고 Dual Memory System으로 구성되었다. Bayes Rule에 기초한 고속 인식 알고리즘은 기존의 Paper Currency 인식 시스템에 사용되었던 기계학습 방법에 비해 신권 추가가 쉽고 적은 연산으로 권종을 판별할 수 있어 고속 지폐 인식 자동화기기에 적합하다. 본 논문에서는 제안된 방법들을 실제 자동화기기로 구현하여 그 유용성을 검증한다.

• Journal of the Earthquake Engineering Society of Korea
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• v.4 no.2
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• pp.65-72
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• 2000

콘크리트 구조물이 지진 등으로 손상을 입었을 때 그 내부 상태를 파악하는 일은 구조물의 안전성 판단에 필요한 중요 과정중 하나이다 손상도 파악에 사용되는 비파괴 검사 방법 중 레이더법은 현재 콘크리트 부재의 두께와 매립된 철근 및 공동 탐사에 적용되고 있다 레이더법은 다른 비파괴 검사 방법에서와 마찬가지로 측정된 신호의 처리와 해석에 따라 그 결과가 좌우된다 . 이논문에서는 상용 레이더 시스템에서 얻어지는 화상 데이트터를 개선하는 방법을 개발하여 철근이 매립된 콘크리트 시편에 적용하였다 실험에 사용된 기편의 크기는 1,000mm(길이)$\times$600mm (폭) $\times$140mm(두께) 이고 철근의 매립깊이는 표면으로부터 철근 중심까지 60mm 이다 레이더 실측 실험에서 철근의 수평배근 간격을 60 90, 120, 150 mm 로 변화시켜 간격탐사가능성을 시험하였다 결과적으로 상용 시스템에 비해 샹상된 판별효과를 나타냈으며 배근 간격이 90, 120, 150mm 인 시편에서 그 간격을 정확히 찾아내었다.

• Journal of the Mineralogical Society of Korea
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• v.32 no.1
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• pp.63-69
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• 2019

The oxidation states of structural Fe in minerals reflect the paleo-depositional redox conditions for the biologically or abiotically induced mineral formation. Particularly, nano-scale analysis using high-resolution transmission electron microscopy (HRTEM) and electron energy loss spectroscopy (EELS) is necessary to identify evidence for the microbial role in the biomineralization. HRTEM-EELS analysis of oxidation states of structural Fe and carbon bonding structure differentiate biological factors in mineralization by mapping the distribution of Fe(II)/Fe(III) and source of organic C. HRTEM-EELS technique provides geomicrobiologists with the direct nano-scale evidence of microbe-mineral interaction.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.

• Journal of the Korea institute for structural maintenance and inspection
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• v.20 no.4
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• pp.1-8
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• 2016

Recently, buildings tend to be large size, complex shape and functional. As the size of buildings is becoming massive, the need for structural health monitoring(SHM) technique is ever-increasing. Various SHM techniques have been studied for buildings which have different dynamic characteristics and are influenced by various external loads. Generally, the visual inspection and non-destructive test for an accessible point of structures are performed by experts. But nowadays, the system is required which is online measurement and detect risk elements automatically without blind spots on structures. In this study, in order to consider the response of non-linear structures, proposed a signal feature extraction and the adaptive threshold setting algorithm utilized to determine the abnormal behavior by using statistical methods such as control chart, root mean square deviation, generalized extremely distribution. And the performance of that was validated by using the acceleration response of structures during earthquakes measuring system of forced vibration tests and actual operation.

• The KIPS Transactions:PartB
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• v.11B no.3
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• pp.275-280
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• 2004

In this paper, we propose automatic subtraction radiography algorithms to overcome conventional subtraction radiography's defects by applying image processing technique. In order to reach these goals, this paper suggests the image alignment method that is necessary for getting subtraction image and ROI(Region Of Interest) focused on a selection method using the structure characteristics in target images. Therefore, we use these methods because they give accurary, consistency and objective information or data to results. According to the results, easily and visually we can identify fine difference int the affected parts wether they have problems or not.

• Journal of Intelligence and Information Systems
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• v.13 no.1
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• pp.15-27
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• 2007

In Korea, hepatoma is the thirdly frequent cause of death from cancer occupying 17.2% among the whole deaths from cancer and the rate of death from hepatoma comes to about 21's persons per one-hundred thousand ones. This paper proposes an automatic method for the extraction of areas being suspicious as hepatoma from a CT scan and evaluates the availability as an auxiliary tool for the diagnosis of hepatoma. For detecting tumors in the internal of the liver from CT scans, first, an area of the liver is extracted from about $45{\sim}50's$ CT scans obtained by scanning in 2.5-mm intervals starting from the lower part of the chest. In the extraction of an area of the liver, after unconcerned areas outside of the ribs being removed, areas of the internal organs are separated and enlarged by using intensity information of the CT scan. The area of the liver is extracted among separated areas by using information on position and morphology of the liver. Since hepatoma is a hypervascular turner, the area corresponding to hepatoma appears more brightly than the surroundings in contrast-enhancement CT scans, and when hepatoma shows expansile growth, the area has a spherical shape. So, for the extraction of areas of hepatoma, areas being brighter than the surroundings and globe-shaped are selected as candidate ones in an area of the liver, and then, areas appearing at the same position in successive CT scans among the candidates are discriminated as hepatoma. For the performance evaluation of the proposed method, experiment results obtained by applying the proposed method to CT scans were compared with the diagnoses by radiologists. The evaluation results showed that all areas of the liver and liver tumors were extracted exactly and the proposed method has a high availability as an auxiliary diagnosis tools for the discrimination of liver tumors.

• Journal of the Korean Geophysical Society
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• v.2 no.3
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• pp.169-178
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• 1999

Low-field anisotropy of magnetic susceptibility (AMS) was measured with 247 samples from 17 sites of Pre-Cambrian anorthositic rocks in the Hadong-Sanchong area, southwestern part of the Ryongnam Block. Tectonic stress-direction is defined by the minimum susceptibility (k3) direction, and flow-direction by the maximum susceptibility (k1) direction. Five sites rendered self-consistent NW-SE site-mean tectonic stress-direction. Even though a general fold test for every site was not possible due to the homoclinal nature of the bedding attitudes, a site with various bedding attitudes shows far better clustering of the k3-direction before the bedding-tilt correction. The in-situ NW-SE tectonic stress-direction is consistent over the study area and compatible with petrographic foliation observed in metamorphic rocks in and arround the study area, suggesting a regional compressive force acted after the emplacement of the anorthositic rocks. On the other hand, flow-directions obtained from six sites varies from site to site. Strong-field IRM experiments show predominance of titanomagnetites over a small amount of hematite in some samples.