• 인터넷정보학회논문지
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• 제22권3호
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• pp.9-16
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• 2021

This study aims to develop a human activity recognition (HAR) system as a Deep-Learning (DL) classification model, distinguishing various human activities. We solely rely on the signals from a wristband accelerometer worn by a person for the user's convenience. 3-axis sequential acceleration signal data are gathered within a predefined time-window-slice, and they are used as input to the classification system. We are particularly interested in developing a Deep-Learning model that can outperform conventional machine learning classification performance. A total of 13 activities based on the laboratory experiments' data are used for the initial performance comparison. We have improved classification performance using the Convolutional Neural Network (CNN) combined with an auto-encoder feature reduction and parameter tuning. With various publically available HAR datasets, we could also achieve significant improvement in HAR classification. Our CNN model is also compared against Recurrent-Neural-Network(RNN) with Long Short-Term Memory(LSTM) to demonstrate its superiority. Noticeably, our model could distinguish both general activities and near-identical activities such as sitting down on the chair and floor, with almost perfect classification accuracy.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2008년도 춘계종합학술대회 A
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• pp.229-232
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• 2008

A Neural network classification of human activity data is presented. The data acquisition system involves a tri-axial accelerometer in wireless sensor network environment. The wireless ad-hoc system has the advantage of small size, convenience for wearability and cost effectiveness. The system can further improve the range of user mobility with the inclusion of ad-hoc environment. The classification is based on the frequencies of the involved activities. The most significant Fast Fourier coefficients, of the acceleration of the body movement, are used for classification of the daily activities like, Rest walk and Run. A supervised learning approach is used. The work presents classification accuracy with the available fast batch training algorithms i.e. Levenberg-Marquardt and Resilient back propagation scheme is used for training and calculation of accuracy.

• 한국음향학회지
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• 제39권4호
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• pp.286-291
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• 2020

본 논문에서는 템포 기반의 음악 분류와 센서 기반의 인간 행동 인식을 통한 음악을 추천하는 시스템을 제안한다. 제안하는 방식은 템포 기반의 음악 분류를 통해 음악 파일을 색인하고, 인식된 행동에 따라 적합한 음악을 추천한다. 정확한 음악 분류를 위해 변조 스펙트럼 기반의 동적 분류기와 멜 스펙트로그램 기반의 시퀀스 분류기가 함께 사용된다. 또한, 간단한 스마트폰 가속도계, 자이로스코프 센서 데이터가 심층 스파이킹 신경망에 적용되어 행동 인식 성능을 향상시킨다. 마지막으로 인식된 행동과 색인된 음악 파일의 관계를 고려한 매핑 테이블을 통해 음악 추천이 수행된다. 실험 결과는 제안된 시스템이 음악 플레이어가 있는 실제 모바일 장치에 사용하기에 적합하다는 것을 보여준다.

• 한국초등과학교육학회지:초등과학교육
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• 제24권3호
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• pp.281-291
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• 2005

The purpose of this study was to investigate elementary school pupil's classification ability that appears in classification activity. For this study, we developed 2 suitable tools in classification activity achievement. One is artificial stimulus card that comes into view clearly. The other is natural stimulus card that does not come into view well. The test was administrated to 376 pupils of 2, 4, and 6 grade in D elementary School in Yeongdeungpo-gu, Seoul. The result proved in this study was as following. First, elementary school pupil's classification ability showed the developmental change as the grade level rises. Second, there was no statistical difference between boys and girls. Third, there was high correlation between sort artificial category and natural category in their ability. Fourth, classification achievement rate of constant level by grade was seen regardless of the items. The findings above gives following guidance in science classification learning. First, if teacher understands the development of students' classification ability, more effective classification guidance is available. Second, to cultivate students' classification ability, we should devise and apply program depending on their classification ability by grade.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권4호
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• pp.1887-1898
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• 2018

In this paper, a method to classify insider threat activity is introduced. The internal threats help detecting anomalous activity in the procedure performed by the user in an organization. When an anomalous value deviating from the overall behavior is displayed, we consider it as an inside threat for classification as an inside intimidator. To solve the situation, Markov Chain Model is employed. The Markov Chain Model shows the next state value through an arbitrary variable affected by the previous event. Similarly, the current activity can also be predicted based on the previous activity for the insider threat activity. A method was studied where the change items for such state are defined by a transition probability, and classified as detection of anomaly of the inside threat through values for a probability variable. We use the properties of the Markov chains to list the behavior of the user over time and to classify which state they belong to. Sequential data sets were generated according to the influence of n occurrences of Markov attribute and classified by machine learning algorithm. In the experiment, only 15% of the Cert: insider threat dataset was applied, and the result was 97% accuracy except for NaiveBayes. As a result of our research, it was confirmed that the Markov Chain Model can classify insider threats and can be fully utilized for user behavior classification.

• Journal of Microbiology and Biotechnology
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• 제23권7호
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• pp.932-941
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• 2013

This work aimed to classify Aspergillus (8 species, 28 strains) by using a secondary metabolite profile-based chemotaxonomic classification technique. Secondary metabolites were analyzed by liquid chromatography ion-trap mass spectrometry (LC-IT-MS) and multivariate statistical analysis. Most strains were generally well separated from each section. A. lentulus was discriminated from the other seven species (A. fumigatus, A. fennelliae, A. niger, A. kawachii, A. flavus, A. oryzae, and A. sojae) with partial least-squares discriminate analysis (PLS-DA) with five discriminate metabolites, including 4,6-dihydroxymellein, fumigatin, 5,8-dihydroxy-9-octadecenoic acid, cyclopiazonic acid, and neosartorin. Among them, neosartorin was identified as an A. lentulus-specific compound that showed anticancer activity, as well as antibacterial effects on Staphylococcus epidermidis. This study showed that metabolite-based chemotaxonomic classification is an effective tool for the classification of Aspergillus spp. with species-specific activity.

• 한국과학교육학회지
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• 제32권6호
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• pp.1063-1071
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• 2012

The purpose of this study is to investigate the relation between the classification ability quotient and cortisol-hormonal change of middle school students. Thirty-three students, second graders in middle school, performed the classification task that can be an indicator of students' classification ability. And then amount of the secreted hormone was analyzed during task performance. The study results were as follows: First, the classification methods of students mostly utilized visual, qualitative. Their classification patterns for each subject were static, partial, and non-comparative. Second, the amount of stress-hormone was secreted from students during the experiment decreased in overall after the free classification. It seemed that student-centered activity relieved stress. Third, the classification ability quotient turned out to be significantly correlated to the stress hormone, which means that there was a close relationship between classification ability and stress level. It was also considered that stress had a positive effect on the improvement of classification ability. This study provided physiologically more accurate information on the stress increased in the learning process than other conventional studies based on reports or interviews. Finally, researchers could recognize the effect of stress in the cognitive activity and the need to find an appropriate level of stress in learning processes.

• 한국재난정보학회 논문집
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• 제12권1호
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• pp.19-31
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• 2016

본 연구는 범용 프로젝트 관리 및 운영을 위해 범용 프로젝트 관리 전산시스템에 적용할 수 있는 초기 전산관리용 데이터베이스를 구축하였다. 본 연구에서 제시한 데이터베이스 구축 모형은 시설요소, 구조요소, 공사요소, 자원요소의 조직정보분류체계를 근거로 2교대 3경간의 교량공사를 조직분류체계, 활동, 자원별 활동의 계층으로 표현하여 구축하였다. 본 연구에서 구축된 데이터베이스 모형은 향후 범용 프로젝트 관리 및 운영을 위해서 매우 체계적이고 과학적인 관리로 활용할 수 있을 것으로 사료된다.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권5호
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• pp.361-367
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• 2004

This paper describes design of an algorithm for analyzing human activity using body-fixed 3-axis accelerometer in the small of the back. In the first step, we distinguish static and dynamic activity period using AC signal analysis. Then five postures were classified by applying the threshold in DC signal corresponding to the static activity period. Also, after comparison of average power and taking negative peak signal in the dynamic activity period, the four dynamic activities were classified by adaptive threshold method. To evaluate the performance of the proposed algorithm, the measured signals obtained from six subjects were applied to the proposed algorithm and the results were compared with the simultaneously measured video data. As a result, the activity classification rate of 95.7％ on average was obtained. Overall results show that the proposed classification algorithm has a possibility to be used to analyze the static and dynamic physical activity.

• East Asian mathematical journal
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• 제34권4호
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• pp.371-402
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• 2018

The researchers set up a research question to find out how to teach the concept of a right triangle through classification activities after listening to the conversations of fellow teachers about the recently revised textbooks. First, a questionnaire was created to confirm the objectivity of the research problem, data were collected through online and offline, and interviews were conducted with some of the respondents. As a result, it confirmed that there was a considerable difference in the perception of the research study about the direction of revising the curriculum called 'student participation centered' and 'the possibility of achieving the learning objective'. Then, we analyzed the critical interpretations used in the third grade math textbook Lesson 2. 'Plane Figure' part 4 and 5. Finally, by analyzing the results of the recognition analysis and textbook analysis, we proposed two learning methods which can link the triangle classification activity and the right triangle concept. Based on the results of the research, we obtained suggestions that a teaching should be made regarding that the classification process may be changed according to the student's prior knowledge and the process of classification activities may be different according to the viewpoint and classification criteria.