• Korean Journal of Child Studies
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• v.26 no.5
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• pp.297-309
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• 2005

This study investigated the relationship between young children's narrative representation of mother and their self-concept. Subjects were 120 children of 5 years 01d(60 boys and 60 girls) at three nursery centers located in Daejon city. The children's narrative representation of their mother was examined using the tool of Ryu & Lee(200l) based upon MSSB made by Bretherton et al(1990). And the children's self-concept was examined using the tool of Lim(1995) based upon 'I feel${\cdots}$Me feel' made by Bently & Yeatts(1974). The data were analysed by frequency, two independent t-test and Pearson's correlation with SPSS program. The young children's narrative representation of mother was positive. And there were significant correlations between the young children's narrative representations of mothers and their self-concept.

• Journal of the military operations research society of Korea
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• v.23 no.2
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• pp.85-102
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• 1997

There have been several research efforts to support interoperability among multiple databases. In integrating multiple databases, we must resolve schema conflicts due to the heterogeneity in databases. To resolve these conflicts, not only meta-data for database schemas but also general knowledge expressing the real world meanings associated with the database schemas are required. This paper presents a uniform representation method for relational schema and general knowledge base that is composed, among other things, of concept hierarchy and thematic roles in relationship, using the knowledge representation language Lk. This representation method has a flexible descriptive power which facilitates concepts to be expressed at different levels of granularity and can describe knowledge expressed in Lk are used for input of the next step, such as conflict resolution and query processing of multiple database.

• Korean Journal of Child Studies
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• v.30 no.2
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• pp.195-209
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• 2009

This study analyzed aspects of multiple intelligences related to rhythm, melody, understanding and representation of traditional Korean music. Subjects were 60 4-to 6-years-old children. Instruments were the Children's Korean Traditional Music (KTM) Ability Test (Park 2006)and Korean Multiple Intelligence Development Assessment Scale-My Young Child (MIDAS-MYC, Shearer, 1996). Data were analyzed by correlations and t-test. Findings were that (1) average scores on KTM rhythm and understandings were higher than melody and representation. (2) Traditional rhythm ability correlated most with linguistic intelligence. (3) Multiple intelligences by representation ability for KTM differed significantly in Linguistic intelligence and relationships to Naturalist, Musical, Logical-mathematical, Interpersonal, and Bodily-Kinesthetic intelligences.

• Journal of Computing Science and Engineering
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• v.3 no.1
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• pp.15-26
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• 2009

Given an n-length text T over a $\sigma$-size alphabet, we present a compressed representation of T which supports retrieving queries of rank/select/access and updating queries of insert/delete. For a measure of compression, we use the empirical entropy H(T), which defines a lower bound nH(T) bits for any algorithm to compress T of n log $\sigma$ bits. Our representation takes this entropy bound of T, i.e., nH(T) $\leq$ n log $\sigma$ bits, and an additional bits less than the text size, i.e., o(n log $\sigma$) + O(n) bits. In compressed space of nH(T) + o(n log $\sigma$) + O(n) bits, our representation supports O(log n) time queries for a log n-size alphabet and its extension provides O(($1+\frac{{\log}\;{\sigma}}{{\log}\;{\log}\;n}$) log n) time queries for a $\sigma$-size alphabet.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.12
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• pp.1687-1696
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• 1995

An integrated fault diagnosis system for heterogeneous manufacturing environments is developed. This system has a contrast with existing diagnosis systems in the respect that they are mostly for diagnosing faults on individual machines. In addition to the usual (e.g., audio, electrical) diagnostic signals, the characteristics of products from the machines are considered as the unifying diagnostic parameters among heterogeneous machines in the diagnosis. The system is composed of a knowledge representation scheme and a diagnostic query processing mechanism. Its knowledge representation scheme allows the diagnostic knowledges from heterogeneous unit diagnostic systems to be uniformly expressed in terms of the causal relations among relevant data items. It is flexible in the sense that causes for one relation can be effects for another may be reflected on our knowledge representation scheme. The diagnosis mechanism is based on a probabilistic inferencing method. This probablistic diagnosis mechanism provides more general diagnosis than existing ones in that it accommodates multiple causes and takes complication among causes into account. These scheme and mechanism are applied to a typical example to demonstrate how our system works.

• Korean Journal of Human Ecology
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• v.13 no.4
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• pp.521-535
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• 2004

The purpose of this study is to investigate the formation of parental beliefs. For this study, 154 unmarried university students were asked to fill in a packet of survey questionnaire about parental beliefs, the internal representation of childhood, and communication with their parents. Data collected were analyzed using correlation and multiple regression by SPSS PC program. It was reported that the internal representation of parenting formed in childhood influences the development of rational authoritative beliefs, distancing beliefs, and direct authoritative beliefs. Also, the open communication with parents affected the distancing beliefs and rational authoritative beliefs.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.4
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• pp.240-248
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• 2014

We present a robust speaker identification algorithm that uses novel features based on soft bag-of-word representation and a simple Naive Bayes classifier. The bag-of-words (BoW) based histogram feature descriptor is typically constructed by summarizing and identifying representative prototypes from low-level spectral features extracted from training data. In this paper, we define a generalization of the standard BoW. In particular, we define three types of BoW that are based on crisp voting, fuzzy memberships, and possibilistic memberships. We analyze our mapping with three common classifiers: Naive Bayes classifier (NB); K-nearest neighbor classifier (KNN); and support vector machines (SVM). The proposed algorithms are evaluated using large datasets that simulate medical crises. We show that the proposed soft bag-of-words feature representation approach achieves a significant improvement when compared to the state-of-art methods.

• Korean Journal of Child Studies
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• v.21 no.2
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• pp.171-188
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• 2000

The level of representation, exploration and artistry by age and sex was examined in twenty children in each of 4-, 5-, and 6-year old groups attending an early education facility in Pundang City, Kyong'gi Province. Evaluation was by the scoring system used in the Project Spetrum(Krechevsky, 1994) with children's drawings of an animal, person, imaginary animal and 3D work with clay. Data were analyzed by 2-way ANOVA and Duncan test. The level of representation was lower than exploration and artistry in the 4-year-old groups. Level of artistry was lower than representation and exploration in the 5-and 6-year-old groups. The older children and girls showed higher scores than the younger children and boys in representation and exploration. There were differences between 4-and 5-year old children in artistry development.

• Journal of the Korean Chemical Society
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• v.56 no.4
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• pp.500-508
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• 2012

The purpose of this study was to investigate the impact of multimodal representation-based lesson on embeddedness of multimodal representation in high school students' writing. The participants in this study were two groups of second-year science-track students (74 students) at an academic high school in a metropolitan city. One group (41 students) was assigned to the experimental group, the other group (33 students) was assigned to the comparative group. Data analysis showed that the students of the experimental group were better at utilizing and embedding multimodal representations. Thus, the conclusion was drawn that multimodal representation-based lesson had an effect on high school students' embeddedness of multimodal representation.

• Smart Structures and Systems
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• v.29 no.1
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• pp.93-103
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• 2022

With the wider availability of sensor technology through easily affordable sensor devices, several Structural Health Monitoring (SHM) systems are deployed to monitor vital civil infrastructure. The continuous monitoring provides valuable information about the health of the structure that can help provide a decision support system for retrofits and other structural modifications. However, when the sensors are exposed to harsh environmental conditions, the data measured by the SHM systems tend to be affected by multiple anomalies caused by faulty or broken sensors. Given a deluge of high-dimensional data collected continuously over time, research into using machine learning methods to detect anomalies are a topic of great interest to the SHM community. This paper contributes to this effort by proposing a relatively new time series representation named "Shapelet Transform" in combination with a Random Forest classifier to autonomously identify anomalies in SHM data. The shapelet transform is a unique time series representation based solely on the shape of the time series data. Considering the individual characteristics unique to every anomaly, the application of this transform yields a new shape-based feature representation that can be combined with any standard machine learning algorithm to detect anomalous data with no manual intervention. For the present study, the anomaly detection framework consists of three steps: identifying unique shapes from anomalous data, using these shapes to transform the SHM data into a local-shape space and training machine learning algorithms on this transformed data to identify anomalies. The efficacy of this method is demonstrated by the identification of anomalies in acceleration data from an SHM system installed on a long-span bridge in China. The results show that multiple data anomalies in SHM data can be automatically detected with high accuracy using the proposed method.