• 간호행정학회지
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• 제6권3호
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• pp.375-388
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• 2000

This study was attempted to help in explore new direction about Clinical Classification System of the pediatric patients visiting emergency center. Data were collected from 276 patients who visited emergency center of E University Hospital during 3 months period form March 1, to May 31, 1999. The results were as follows: 1. Distribution of pediatric patients according to Clinical Classification System, class I(59.9%) topped followed by class II(23.9%), class III(14.1%), class IV(2.0%). Average score of pediatric patients according to Clinical Classification System showed class I.00, class II .02, class III .05, class IV .07. and total mean score of items lowed averaged .01. 2. With the resepect to the Clinical Classification System according to the pediatric patients visiting emergency center, there were stastically significant difference in visiting time($x^2=27.839$, P=.023), experience of admission($x^2=11.365$, p=.010), disease classification($x^2=89.998$, p=.000), state of airway patency($x^2=18.781$, p=.000), consciousness level($x^2=59.774$, p=.000), period of symptom manifestation($x^2=34.112$, p=.000), pediatric patients protector's thinking about pediatric patients state($x^2=49.998$, p=.000), treatment outcome($x^2=72.278$, p=.000), duration of stay at emergency center($x^2=103.062$, p=.000). 3. There were significant correlation between the state of pediatric patients and Clinical Classification System(r=.530, p=.000).

• 한국물환경학회지
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• 제35권4호
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• pp.340-351
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• 2019

In this study, the relationship between trophic state indices was analyzed based on the monthly or weekly water quality data of 81 lakes (mostly man-made) in Korea between 2013-2017. Carlson's $TSI_C$ and Aizaki's $TSI_m$ were calculated using the summer (Jun.-Sep.) average data at the upper water layer. The previous Korean trophic state index ($TSI_{KO}$) and the newly suggested index ($TSI_{KON}$) was calculated using the annual average data at the whole layer and at the upper layer, respectively. While previous trophic state index (TSI) such as Carlson's TSI included logarithmic function, we devised newly Monod-type $TSI_{KON}$(Chl) that is 50 when half-saturation concentration of chlorophyll ${\alpha}$ ($Chl.{\alpha}$) measured by UNESCO-method is $10{\mu}gL^{-1}$. MMF-type $TSI_{KON}$(TP) was derived based on the relationship between TP and $Chl.{\alpha}$. A comprehensive $TSI_{KON}$ was decided as the larger one of the two $TSI_{KON}$ values. The range of previous TSI was usually 40-50 for the mesotrophic state, which seemed narrow to discriminate trophic characteristics of the class. The upper limits of $TSI_{KON}$ for oligotrophic, mesotrophic, and eutrophic state were set to 23, 50 and 75, respectively. Classification by $TSI_C$ and $TSI_m$ showed higher frequency of eutrophic class compared to $TSI_{KO}$ and $TSI_{KON}$. This means that the estimation by TSIs developed in foreign natural lakes can lead to distorted results in the classification of the trophic state of Korean lakes. This is due to the decrease of transparency by non-algal material and the reduction in phosphorus availability to algal growth, particularly in Monsoon period.

• 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.

• 기록학연구
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• 제7호
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• pp.75-105
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• 2003

Russian State Historical Archive (Rossiiskii gosudarstvennyi istoricheskii arkhiv - RGIA) received its present name in June 1992, and before 1961 was known as TsGIA. RGIA holds the major records of high-level and central state and administrative institutions and agencies of tsarist Russia from the eighteenth century to 1910s (except the records of the Army, Navy, and the Ministry of Foreign Affairs), as well as fonds of social organizations, institutions, and individuals of prerevolutionary Russia. The main goal of this article is to assist understanding russian principle on arrangement and classification archives with its historical development focusing on one of the biggest historical archive in Russia. The primary set of historical records in RGIA remain arranged in 3-steps classification system: fond (collection) -- opis' (inventory) -- delo (file). In this general survey of RGIA author offers detailed information on the collection of archives and the system for classification of its huge amounts of primary sources in connection with influence upon historical studies. Despite the general economic crisis Russian archives are struggling to keep their doors open for public research and are exerting their energies in present electronic information to scholars and other researchers from throughout the world. The result, however, is not rewarded enough, considering the effort involved.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.682-685
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• 2004

This paper investigated a method for classifying emotional states by using pulse wave signal. It focused on finding effective features for emotional state classification. The emptional states considered here consisted of interest and neutral. Classification experiments utilized 65 and 60 samples of interest and neutral states respectively. We have investigated 19 features derived from pulse wave signals by using both time domain and frequency domain analysis methods with 2 classifiers of minimum distance (normalized Euclidean distanece) and ${\kappa}$-Nearest Neighbour. The Leave-one-out cross validation was used as an evaluation mehtod. Based on experimental results, the most efficient features were a combination of 4 features consisting of (i) the mean of the first differences of the smoothed pulse rate time series signal, (ii) the mean of absolute values of the second differences of thel normalized interbeat intervals, (iii) the root mean square successive difference, and (iv) the power in high frequency range in normalized unit, which provided 80.8% average accuracy with ${\kappa}$-Nearest Neighbour classifier.

• International Journal of Knowledge Content Development & Technology
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• 제7권2호
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• pp.85-105
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• 2017

Given the variety of approaches to mapping the universe of knowledge that have been presented and discussed in the literature, the purpose of this paper is to systematize their main principles and their applications in the major general modern library classification schemes. We conducted an analysis of the literature on classification and the main classification systems, namely Dewey/Universal Decimal Classification, Cutter's Expansive Classification, Subject Classification of J.D. Brown, Colon Classification, Library of Congress Classification, Bibliographic Classification, Rider's International Classification, Bibliothecal Bibliographic Klassification (BBK), and Broad System of Ordering (BSO). We conclude that the arrangement of the main classes can be done following four principles that are not mutually exclusive: ideological principle, social purpose principle, scientific order, and division by discipline. The paper provides examples and analysis of each system. We also conclude that as knowledge is ever-changing, classifications also change and present a different structure of knowledge depending upon the society and time of their design.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권4호
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• pp.1275-1292
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• 2021

As a fine-grained classification problem, aspect-level sentiment classification predicts the sentiment polarity for different aspects in context. To address this issue, researchers have widely used attention mechanisms to abstract the relationship between context and aspects. Still, it is difficult to effectively obtain a more profound semantic representation, and the strong correlation between local context features and the aspect-based sentiment is rarely considered. In this paper, a hybrid attention capsule network for aspect-level sentiment classification (ABASCap) was proposed. In this model, the multi-head self-attention was improved, and a context mask mechanism based on adjustable context window was proposed, so as to effectively obtain the internal association between aspects and context. Moreover, the dynamic routing algorithm and activation function in capsule network were optimized to meet the task requirements. Finally, sufficient experiments were conducted on three benchmark datasets in different domains. Compared with other baseline models, ABASCap achieved better classification results, and outperformed the state-of-the-art methods in this task after incorporating pre-training BERT.

• 한국통신학회논문지
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• 제41권11호
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• pp.1406-1409
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• 2016

본 논문에서는 송수신단 간 변조기법 및 채널 상태 값이 약속되지 않은 완벽한 블라인드 통신 상황에서 송신측의 변조방식을 알아내기 위해 성좌도 회전 및 확률밀도함수(probability density function : pdf)를 이용한 새로운 자율 변조 구분(Automatic modulation classification : AMC)기법과 경험적 신호 그룹화 알고리즘을 통해 채널 상태 값을 추정하는 방법을 제안한다. 평균제곱근 편차(Root mean square error : RMSE) 및 심볼 오류율(Symbol error rate : SER) 등의 모의실험을 통해 제안된 기법과 기존의 다른 기법간의 채널 상태와 변조 추정 능력을 비교 평가한다.

• Journal of Information Science Theory and Practice
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• 제3권4호
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• pp.62-77
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• 2015

This paper describes qualities of a library classification system that are commonly discussed in the LIS tradition and literature, and explains such a system’s three main functions, namely knowledge mapping, information retrieval, and shelf arrangement. In this vein, the paper states the functional requirements of bibliographic classifications, which broadly are subject collocation and facilitation of browsing the collection. It explains with details the components of a library classification system and their functions. The major components are schedules, notations, and index. It also states their distinguished features, such as generalia class, form divisions, book numbers, and devices for number synthesis which are not required in a knowledge classification. It illustrates with examples from the WebDewey good examples of added features of an online library classification system. It emphasizes that institutional backup and a revision machinery are essential for a classification to survive and remain relevant in the print and e-environment.

• 대한인간공학회지
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• 제31권2호
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• pp.271-279
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• 2012

Objective: This study classified three different emotional states(boredom, pain, and surprise) using physiological signals. Background: Emotion recognition studies have tried to recognize human emotion by using physiological signals. It is important for emotion recognition to apply on human-computer interaction system for emotion detection. Method: 122 college students participated in this experiment. Three different emotional stimuli were presented to participants and physiological signals, i.e., EDA(Electrodermal Activity), SKT(Skin Temperature), PPG(Photoplethysmogram), and ECG (Electrocardiogram) were measured for 1 minute as baseline and for 1~1.5 minutes during emotional state. The obtained signals were analyzed for 30 seconds from the baseline and the emotional state and 27 features were extracted from these signals. Statistical analysis for emotion classification were done by DFA(discriminant function analysis) (SPSS 15.0) by using the difference values subtracting baseline values from the emotional state. Results: The result showed that physiological responses during emotional states were significantly differed as compared to during baseline. Also, an accuracy rate of emotion classification was 84.7%. Conclusion: Our study have identified that emotions were classified by various physiological signals. However, future study is needed to obtain additional signals from other modalities such as facial expression, face temperature, or voice to improve classification rate and to examine the stability and reliability of this result compare with accuracy of emotion classification using other algorithms. Application: This could help emotion recognition studies lead to better chance to recognize various human emotions by using physiological signals as well as is able to be applied on human-computer interaction system for emotion recognition. Also, it can be useful in developing an emotion theory, or profiling emotion-specific physiological responses as well as establishing the basis for emotion recognition system in human-computer interaction.