• 한국신재생에너지학회:학술대회논문집
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• 2010.11a
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• pp.141-141
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• 2010

과학기술은 기술영역간의 관계를 통해 고유한 연구영역을 그리며 발달하며, 이는 학제의 다양성을 바탕으로 기존 기술들이 독특한 결합과정의 형성을 의미한다. 기술의 결합과정은 지식의 결합과정 관점으로 이해할 수 있다. 본 연구에서는 기존의 에너지산업과 다른 특성을 갖는 신 재생에너지의 기술발전경로를 '지식흐름'의 관점에서 탐색하여 신 재생에너지 연구의 학제 구조 및 다양성을 살펴보고자 한다. 계량서지학적 분석은 데이터 수집의 간편성 및 초기 연구결과물 분석에 적용할 수 있다는 장점에 의해 여러 분야에서 폭넓게 응용되어 왔다. 특히, A.L Porter(1984)에 의해 'citation'을 이용한 학제간관계 측정에 적용하여 계량서지학적 방법을 바탕으로 지식흐름을 관찰하는데 선구적인 방법을 제시하였다. 또한 Tijsen(1992)은 동시분류분석방법을 적용하여 네덜란드 에너지 연구분야의 학제구조를 분석하였고, Kajikawa(2007)은 에너지분야의 신기술인 태양전지와 연료전지에 한정하여 인용네트워크 분석을 수행하여 연구발전의 경향을 알아보았다. 이에 본 연구에서는 계량 서지학적 방법의 하나인 co-classification 방법론을 적용하여 태양광 분야 중 태양전지에 초점을 맞추어 학제 간 다양성 분석 연구를 수행 하였다. 태양광은 신 재생에너지 중 전후방연관 파급효과가 가장 큰 분야이며, 반도체 기반기술을 바탕으로 그 기술을 전개할 수 있기에 국내 산업과의 연관도가 높은 산업이다. 태양전지의 연구 동향 파악 및 고유의 연구영역을 도출을 분석하기 위한 기본 자료는 ISI의 'Web of Science'를 기반하여 수집하였다. 또한 태양광 연구의 연구구조 파악을 위하여 계량서지학분석의 하나인 'co-classification' 방법론을 추가적으로 적용하여 학제간의 분석을 수행하였다. 분석결과 1979-2009년까지의 태양전지 연구 논문 2,602개를 바탕으로 동시에 2개 이상의 SC를 포함한 논문은 총 논문의 51.8%이며, 출현한 SC는 65개로 분석되었다. 이 중 2개 이상의 SC가 동시에 출연한 횟수는 증가하는 경향을 가짐을 알 수 있었다. 이는 과학기술의 발전이 기술의 결합과정 또는 지식의 결합과정 관점으로 이해할 수 있음을 확인할 수 있었다.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.2
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• pp.70-77
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• 2019

Breast ultrasound readings are very important to diagnose early breast cancer. In Ultrasonic inspection, it shows a significant difference in image quality depending on the ultrasonic equipment, and there is a large difference in diagnosis depending on the experience and skill of the inspector. Therefore, objective criteria are needed for accurate diagnosis and treatment. In this study, we analyzed texture characteristics by applying GLCM (Gray Level Co-occurrence Matrix) algorithm and extracted characteristic parameters and diagnosed breast cancer using neural network classifier. Breast ultrasound images were classified into normal, benign and malignant tumors and six texture parameters were extracted. Fourteen cases of normal, malignant and benign tumor diagnosed by mammography were studied by using the extracted six parameters and learning by multi - layer perceptron neural network back propagation learning method. As a result of classification using 51 normal images, 62 benign tumor images, and 74 malignant tumor images of the learned model, the classification rate was 95.2%.

• Journal of Korean Society for Quality Management
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• v.49 no.1
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• pp.97-112
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• 2021

Purpose: The purpose of this paper is to analyze the technology convergence and its characteristics, focusing on the defense technologies in South Korea. Methods: Patents applied by the Agency for Defense Development (ADD) during 1979~2019 were utilized in this paper. Information Entropy analysis has been conducted on the patents to analyze the usability and potential for development. To analyze the trend of technology convergence in defense technologies, Social Network Analysis(SNA) and Association Rule Mining Analysis were applied to the co-occurrence networks of International Patent Classification (IPC) codes. Results: The results show that sensor, communication, and aviation technologies played a key role in recent development of defense science and technology. The co-occurrence network analysis also showed that the convergence has gradually enhanced over time, and the convergence between different technology sectors largely emerged, showing that the convergence has been diversified. Conclusion: By analyzing the patents of the defense technologies during the last 30 years, this study presents the comprehensive perspectives on trends and characteristics of technology convergence in defense industry. The results of this study are expected to be used as a guideline for decision making in the government's R&D policies in defence industry.

• Journal of Korea Multimedia Society
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• v.21 no.2
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• pp.130-137
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• 2018

Recently, human life is getting longer due to change of living environment and development of medical technology, and silver medical technology has been in the limelight. Geriatric skin disease is difficult to detect early, and when it is missed, it becomes a malignant disease and is difficult to treatment. Melanoma is one of the most common diseases of geriatric skin disease and initially has a similar modality with the nevus. In order to overcome this problem, we attempted to perform a feature analysis in order to attempt automatic detection of melanoma-like lesions. In this paper, one is first order analysis using information of pixels in radiomic feature. The other is a gray-level co-occurrence matrix and a gray level run length matrix, which are feature extraction methods for converting image information into a matrix. The features were extracted through these analyses. And classification is implemented by SVM.

• Ocean Systems Engineering
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• v.4 no.1
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• pp.53-62
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• 2014

The detection of ship wakes image can demonstrate substantial information regarding on a ship, such as its tonnage, type, direction, and speed of movement. Consequently, the wake target recognition is a favorable way for ship identification. This paper proposes a Local Binary Pattern (LBP) approach to extract image features (wakes) for training an Improved Back Propagation Artificial Neural Network (IBPANN) to identify ship speed. This method is applied to sort and recognize the ship wakes of five different speeds images, the result shows that the detection accuracy is satisfied as expected, the average correctness rates of wakes target recognition at the five speeds may be achieved over 80%. Specifically, the lower ship's speed, the better accurate rate, sometimes it's accuracy could be close to 100%. In addition, one significant feature of this method is that it can receive a higher recognition rate than the nearest neighbor classification method.

• The Research Journal of the Costume Culture
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• v.11 no.6
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• pp.835-851
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• 2003

This study analyses patent information of apparel design using computer technology and researches the trend of patent application focused on International Patent Classification. In terms of trend by filling data, Patent application started first in 1974 and increased sharply in 1993 with 14 cases and increased to 25 cases in 2000. In case of Korea, they began somewhat late in 1996, but reached a similar level with the leading country in 2000. In terms of trend by applicant, Gerber Garment Technology, Inc. filed 7 cases TORAY IND INC, filed 6 cases Levi Strauss & Co. filed 4 cases, NEC HOME ELECTRONICS LTD filed 3 cases, TOYOBO CO LTD filed 3 cases. Japanese companies occupied 52% and United States's companies occupied 48%. In terms of trend by country, foreigner occupied 47% of the patents filed by United State. Japanese take up 10% of total patent of United States. Korean occupied 84% of total patent of Korea and foreigner, american occupied 16% of the patents filed by Korea. In regared to International Patent Classification, in the section level G filed 92 cases(53%). In class level, G06 marked the first place in United States, Japan, and Korea. In subclass level, G06F marksed the first place with 74 cases. G06T and A61B were regarded as the new technologies. The new technologies are representing the dimensions of garment or computer-rendered model, providing the virtual reality through the texture mapping, digital dressing room or virtual dressing, and performing or retriving display on a screen for the result of changing pattern ao dress design, The technologies of core patent are designing or producing custom manufactured item, providing or prealtering the data for pattern making and visually displaying, interactively generating or previewing of various articles.

• Analytical Science and Technology
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• v.35 no.2
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• pp.69-81
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• 2022

The method of measuring and classifying the energy category of neutrons directly using raw data acquired through a CZT detector is not satisfactory, in terms of accuracy and efficiency, because of its poor energy resolution and low measurement efficiency. Moreover, this method of measuring and analyzing the characteristics of low-energy or low-activity gamma-ray sources might be not accurate and efficient in the case of neutrons because of various factors, such as the noise of the CZT detector itself and the influence of environmental radiation. We have therefore developed an efficient method of analyzing radiation characteristics using a neutron and gamma-ray analysis algorithm for the rapid and clear identification of the type, energy, and radioactivity of gamma-ray sources as well as the detection and classification of the energy category (fast or thermal neutrons) of neutron sources, employing raw data acquired through a CZT detector. The neutron analysis algorithm is based on the fact that in the energy-spectrum channel of 558.6 keV emitted in the nuclear reaction ¹¹³Cd + ¹n → ¹¹⁴Cd + in the CZT detector, there is a notable difference in detection information between a CZT detector without a PE modulator and a CZT detector with a PE modulator, but there is no significant difference between the two detectors in other energy-spectrum channels. In addition, the gamma-ray analysis algorithm uses the difference in the detection information of the CZT detector between the unique characteristic energy-spectrum channel of a gamma-ray source and other channels. This efficient method of analyzing radiation characteristics is expected to be useful for the rapid radiation detection and accurate information collection on radiation sources, which are required to minimize radiation damage and manage accidents in national disaster situations, such as large-scale radioactivity leak accidents at nuclear power plants or nuclear material handling facilities.

• Journal of Korean Society for Atmospheric Environment
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• v.13 no.1
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• pp.51-63
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• 1997

Total suspended particulate matters in the ambient air were analyzed for eight chemical elements (Ca, Co, Cu, Fe, Mn, Pb, Si, and Zn) using an x-ray fluorescence spectrometry (XRF) at the Kyung Hee University - Suwon Campus during 1989 to 1994. To use these data as basis for source identification study, membership of each sample was selected to represent one of the well defined sample groups. The data sets consisting of 83 objects and 8 variables were initially separated into two groups, fine (d$_{p}$<3.3 ${\mu}{\textrm}{m}$) and coarse particle groups (d$_{p}$>3.3 ${\mu}{\textrm}{m}$). A hierarchical clustering method was examined to obtain possible member of homogeneous sample classes for each of the two groups by transforming raw data and by applying various distances. A disjoint principal component analysis was then used to define homogeneous sample classes after deleting outliers. Each of five homogeneous sample classes was determined for the fine and the coarse particle group, respectively. The data were properly classified via an application of logarithmic transformation and Euclidean distance concept. After determining homogeneous classes, correlation coefficients among eight chemical variables within all the homogeneous classes for calculated and meteorological variables (temperature. relative humidity, wind speed, wind direction, and precipitation) were examined as well to intensively interpret environmental factors influencing the characteristics of each class for each group. According to our analysis, we found that each class had its own distinct seasonal pattern that was affected most sensitively by wind direction.ion.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.29-32
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• 2008

In this paper, we propose one unsupervised classification technique using the degree of polarization (DoP) and the co-polarized phase-difference (CPD) statistics, instead of the entropy and alpha. It is shown that the DoP is closely related to the entropy, and the CPD to the alpha. The DoP explains the feature how much the effect of multiple reflections is contained. Hence, the DoP could be used as an important factor for classifying classes. The CPD can also be computed from the measured Mueller matrix elements. For the smooth surface scattering, the CPD is about $0^{\circ}$, and for dihedral-type scattering, the CPD is about $180^{\circ}$. A DoP-CPD diagram with appropriate boundaries between six different classes is developed based on the SAR image. The classification results are compared with the existing Entropy-alpha diagram as well as the IPL-AirSAR polarimetric data. The technique may have capability to classify an SAR image into six major classes; a bare surface, a village, a crown-layer short vegetation canopy, a trunk-layer short vegetation canopy, a crown-layer forest, and a trunk-dominated forest. Based on the DoP and CPD analysis, a simple three-component decomposition technique was also proposed.

• Journal of the Korea Convergence Society
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• v.6 no.5
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• pp.219-225
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• 2015

Nowadays, technologies are changing through industrial fusion and government & corporates need to predict the flow & direction of technologies. These flow & direction can be grasped through the analysis of patent information. The patent information uses the common classification codes in the world, and it is possible for the quantitative analysis based on objective data with the time information of technical area. The methods of patent analysis analyzed the technology fusion by using citation analysis & simultaneous classification analysis. This research analyzed patent information which used as an index to measure the technical innovation in the society based on knowledge, and would like to analyze technical trends and to describe the way of improvement in the future based on the aviation industry which is the representative fusion/complex industry.