• 한국연초학회지
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• 제20권1호
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• pp.80-86
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• 1998

Changes in activity and classification of esterase isozymes during the tire cycle or Plodia inteipunctella (Hiibner) were investigated by the native polyacrylamide gel electrophoresis. The stage specificity in esterase activity and isozyme pattern was observed throughout the larvalpupal-adult transformation. The activity esterase was highest at the 3-day old adult stage, and the lowest level at the prepupal stage. A total of 12 esterase bands were identified throughout the development, and the bands showing high enzyme activity was observed in the middle part of gel. Twelve esterases on the basis of inhibition by the three types of inhibitors (organophosphates, eserine sulfate and sulfhydryl reagents) were classified into three class, namely, carboxylesterase (CE), arylesterase (ArE) and cholinesterase (ChE), and these classes contained 7, 3 and 2 isozymes, respectively.

• 한국컴퓨터정보학회논문지
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• 제24권5호
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• pp.59-66
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• 2019

According to the KT telecommunication statistics, people stayed inside their houses on an average of 11.9 hours a day. As well as, according to NSC statistics in the united states, people regardless of age are injured for a variety of reasons in their houses. For purposes of this research, we have investigated an abnormal event detection algorithm to classify infrequently occurring behaviors as accidents, health emergencies, etc. in their daily lives. We propose a fusion method that combines three classification algorithms with vision pattern, audio pattern, and activity pattern to detect unusual user events. The vision pattern algorithm identifies people and objects based on video data collected through home CCTV. The audio and activity pattern algorithms classify user audio and activity behaviors using the data collected from built-in sensors on their smartphones in their houses. We evaluated the proposed individual pattern algorithm and fusion method based on multiple scenarios.

• ETRI Journal
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• 제35권6호
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• pp.1134-1143
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• 2013

The rising pace of technological change in information and communications technology (ICT) has provoked technological convergence by providing a new mode of diversification. This paper investigates the nature of ICT-based converging technologies by examining comparative empirical evidence on converging versus nonconverging technologies in relation to the following issues: patent application trends, concentration across technologies, the concentration of patenting activity across firms, R&D efforts, and a technology impact index. For this study, a new operational definition of ICT-based converging technology is derived, and a massive quantity of patents, up to around 600,000, is analyzed. This study follows the International Patent Classification as well as the modified European Commission's industry classification system for the classification of technologies and industries, respectively.

• Journal of Information Processing Systems
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• 제15권5호
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• pp.1068-1081
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• 2019

Signal complexity is one point of view to analyze the biological signal. It arises as a result of the physiological signal produced by biological systems. Signal complexity can be used as a method in extracting the feature for a biological signal to differentiate a pathological signal from a normal signal. In this research, Hjorth descriptors, one of the signal complexity measurement techniques, were measured on signal sub-band as the features for lung sounds classification. Lung sound signal was decomposed using two wavelet analyses: discrete wavelet transform (DWT) and wavelet packet decomposition (WPD). Meanwhile, multi-layer perceptron and N-fold cross-validation were used in the classification stage. Using DWT, the highest accuracy was obtained at 97.98%, while using WPD, the highest one was found at 98.99%. This result was found better than the multi-scale Hjorth descriptor as in previous studies.

• Communications for Statistical Applications and Methods
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• 제30권6호
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• pp.551-560
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• 2023

Recently, there has been significant research into the recognition of human activities using three-dimensional sequential skeleton data captured by the Kinect depth sensor. Many of these studies employ deep learning models. This study introduces a novel feature selection method for this data and analyzes it using machine learning models. Due to the high-dimensional nature of the original Kinect data, effective feature extraction methods are required to address the classification challenge. In this research, we propose using the first four moments as predictors to represent the distribution of joint sequences and evaluate their effectiveness using two datasets: The exergame dataset, consisting of three activities, and the MSR daily activity dataset, composed of ten activities. The results show that the accuracy of our approach outperforms existing methods on average across different classifiers.

• 한국기록관리학회지
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• 제18권1호
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• pp.155-175
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• 2018

이 연구의 목적은 함평사건희생자유족회의 소장 기록물에 대한 분류체계를 마련하는 데에 있다. 이에 따라 기록물의 맥락을 기능적 출처주의를 통해 구현하며, 기록물을 효과적으로 활용할 수 있도록 유형별 특성과 생산시기별 특성을 반영한 분류표를 제시하였다. 기능분류체계 개발 방법론인 DIRKS를 사용하여 함평사건희생자유족회의 업무분석을 수행함으로써, 업무기능-업무활동-처리행위로 이어지는 업무분류표를 도출한다. 함평사건희생자유족회 소장 기록물을 유형과 생산시기별 특성을 고려하여 그 범주를 결정한다. 기록물 맵핑은 업무분류표에 해당하는 업무분류체계에 1차적으로 실행하고, 2차적으로는 업무분류에 유형분류와 시대분류를 접목한 다중분류체계에 맵핑한다. 업무주제-업무활동-처리행위-유형-시대의 형태로 이어지는 기록물 분류표를 도출한다.

• 한국컴퓨터정보학회논문지
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• 제25권2호
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• pp.213-219
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• 2020

본 연구는 스마트폰 과의존을 진단하고 예측하기 위하여 할 수 있는 분류분석 방법과 스마트폰 과의존 분류율에 영향을 미치는 중요변수를 규명하고자 시도되었다. 이를 위해 인공지능의 방법인 기계학습 분석 기법 중 의사결정트리, 랜덤포레스트, 서포트벡터머신의 분류율을 비교하였다. 자료는 한국정보화진흥원에서 제공한 '2018년 스마트폰 과의존 실태조사'에 응답한 25,465명의 데이터였고, R 통계패키지(ver. 3.6.2)를 사용하여 분석하였다. 분석한 결과, 3가지 분류분석 기법은 정분류율이 유사하게 나타났으며, 모델에 대한 과적합 문제가 발생되지 않았다. 3가지 분류분석 방법 중 서포트벡터머신의 분류율이 가장 높게 나타났고, 다음으로 의사결정트리 기법, 랜덤포레스트 기법 순이었다. 스마트폰 이용 유형 중 분류율에 영향을 미치는 상위 3개 변수는 생활서비스형, 정보검색형, 여가추구형이었다.

• 한국멀티미디어학회논문지
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• 제17권4호
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• pp.506-513
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• 2014

In recent years, research on user's activity recognition using a smart phone has attracted a lot of attentions. A smart phone has various sensors, such as camera, GPS, accelerometer, audio, etc. In addition, smart phones are carried by many people throughout the day. Therefore, we can collect log data from smart phone sensors. The log data can be used to analyze user activities. This paper proposes an approach to inferring a user's physical activities based on the tri-axis accelerometer of smart phone. We propose recognition method for four activity which is physical activity; sitting, standing, walking, running. We have to convert accelerometer raw data so that we can extract features to categorize activities. This paper introduces a recognition method that is able to high detection accuracy for physical activity modes. Using the method, we developed an application system to recognize the user's physical activity mode in real-time. As a result, we obtained accuracy of over 80%.

• 한국토양비료학회지
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• 제43권6호
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• pp.837-843
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• 2010

토양광물 종류별 토양의 점토활성도를 구분하기 위하여 우리나라 390개 토양통을 점토광물과 함수산화광물을 기준으로 점토광물 조성이 다른 7개의 토양을 선정하여 토양광물 종류에 따른 점토의 CEC와 비표면적을 비교하였다. 토양 CEC에 대한 점토의 비가 0.7 이상인 토양은 사암을 모재로 Chlorite를 주광물로 하는 토양, 안산암질반암을 모재로 Smectite를 함유한 토양, 화산재를 모재로 Allophane과 Ferrihydrite가 주광물로 이루어진 토양이었으며, 점토활성도 0.3-0.7인 토양은 회장석을 모재로 Kaolin이 주광물 토양, 하성퇴적토를 모재로 Kaolin, Illite, Vermiculite가 혼합된 토양이었다. 또한 점토활성도 0.3이하인 토양은 화강암 및 화강편마암 모재의 Kaolin을 주광물로 Geothite와 Hematite가 함유된 적황색계 토양, 석회암 모재의 Illite와 Vermiculite를 주광물로 Gibbsite, Geothite, Hematite가 함유된 적황색계 토양이었다. 토양의 점토활성도는 점토의 CEC, 점토의 비표면적과 상관이 있어서 점토활성도가 높은 토양에서는 점토의 CEC가 높고 점토의 비표면적이 넓었다. 따라서 토양의 점토활성도는 기존의 점토광물의 정성과 정량분석을 실시하지 않고도 토양의 일반적인 분석을 통하여 토양 중 점토광물의 조성을 추정하고 토양의 물리-화학적 특성을 예측하는데 유용한 기준이 될 것으로 생각된다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권1호
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• pp.454-475
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• 2018

This paper presents a novel Android malware classification model planned to classify and categorize Android malicious code at Drebin dataset. The amount of malicious mobile application targeting Android based smartphones has increased rapidly. In this paper, Restricted Boltzmann Machine and Deep Belief Network are used to classify malware into families of Android application. A texture-fingerprint based approach is proposed to extract or detect the feature of malware content. A malware has a unique "image texture" in feature spatial relations. The method uses information on texture image extracted from malicious or benign code, which are mapped to uncompressed gray-scale according to the texture image-based approach. By studying and extracting the implicit features of the API call from a large number of training samples, we get the original dynamic activity features sets. In order to improve the accuracy of classification algorithm on the features selection, on the basis of which, it combines the implicit features of the texture image and API call in malicious code, to train Restricted Boltzmann Machine and Back Propagation. In an evaluation with different malware and benign samples, the experimental results suggest that the usability of this method---using Deep Belief Network to classify Android malware by their texture images and API calls, it detects more than 94% of the malware with few false alarms. Which is higher than shallow machine learning algorithm clearly.