• Journal of Korean Elementary Science Education
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• v.17 no.1
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• pp.75-87
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• 1998

The problem solving processes of elementary school children who are talented in science have been seldom studied. Researchers often resort to thinking aloud method to collect data of problem solving processes. The major purpose of the study is investigating high ability elementary school students' problem solving processes through the analysis of written responses to science problems in everyday context. 67 elementary students were participated Chungcheongbuk-do Elementary Science Contest held on October, 1997. The written responses of the contest participants to science problems in everyday context were analyzed in terms of problem solving processes. The findings of the research are as follows. (1) High ability elementary students use various concepts about air and water in the process of problem solving. (2) High ability elementary students use content specific problem solving strategies. (3) The problem solving processes of the high ability elementary students consist of problem representation, problem solution, and answer stages. Problem representation stage is further divided into translation and integration phases. Problem solving stage is composed of deciding relevant knowledge, strategy, and info..ins phases. (4) High ability elementary students' problem solving processes could be categorized into 11 qualitatively different groups. (5) Students failures in problem solving are explained by many phases of problem solving processes. Deciding relevant knowledge and inferring phases play major roles in problem solving. (6) The analysis of students' written responses, although has some limitations, could provide plenty of information about high ability elementary students' problem solving precesses.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2709-2729
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• 2016

Representation based classification, kernel method and sparse representation have received much attention in the field of face recognition. In this paper, we proposed an improved kernel principal component analysis method based on sparse representation to improve the accuracy and robustness for face recognition. First, the distances between the test sample and all training samples in kernel space are estimated based on collaborative representation. Second, S training samples with the smallest distances are selected, and Kernel Principal Component Analysis (KPCA) is used to extract the features that are exploited for classification. The proposed method implements the sparse representation under ℓ₂ regularization and performs feature extraction twice to improve the robustness. Also, we investigate the relationship between the accuracy and the sparseness coefficient, the relationship between the accuracy and the dimensionality respectively. The comparative experiments are conducted on the ORL, the GT and the UMIST face database. The experimental results show that the proposed method is more effective and robust than several state-of-the-art methods including Sparse Representation based Classification (SRC), Collaborative Representation based Classification (CRC), KCRC and Two Phase Test samples Sparse Representation (TPTSR).

• MALSORI
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• no.53
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• pp.75-92
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• 2005

Prosody Generation Based on C-ToBI Representation for Text-to-SpeechSeungwon Kim, Yu Zheng, Gary Geunbae Lee, Byeongchang KimProsody modeling is critical in developing text-to-speech (TTS) systems where speech synthesis is used to automatically generate natural speech. In this paper, we present a prosody generation architecture based on Chinese Tone and Break Index (C-ToBI) representation. ToBI is a multi-tier representation system based on linguistic knowledge to transcribe events in an utterance. The TTS system which adopts ToBI as an intermediate representation is known to exhibit higher flexibility, modularity and domain/task portability compared with the direct prosody generation TTS systems. However, the cost of corpus preparation is very expensive for practical-level performance because the ToBI labeled corpus has been manually constructed by many prosody experts and normally requires a large amount of data for accurate statistical prosody modeling. This paper proposes a new method which transcribes the C-ToBI labels automatically in Chinese speech. We model Chinese prosody generation as a classification problem and apply conditional Maximum Entropy (ME) classification to this problem. We empirically verify the usefulness of various natural language and phonology features to make well-integrated features for ME framework.

• Proceedings of the Korea Society of Mathematical Education Conference
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• 1990.07a
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• pp.64-64
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• 1990

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.842-846
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• 1993

Because of complexity, neural network is difficult to learn. So if internal representation[1] can be performed successfully, it is possible to use perceptron learning rule. As a result, learning is easier. Therefore the method of internal representations applied to the "XOR" problem, and the "spirals" problem. And then using the above results, the structure of neural network for computing is embodied.mputing is embodied.

• Journal of International Society for Simulation Surgery
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• v.3 no.2
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• pp.46-48
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• 2016

Purpose In face reocognition area, estimating occlusion in face images is on the rise. In this paper, we propose a new face recognition algorithm based on dual sparse representation to solve this problem. Method Each face image is partitioned into several pieces and sparse representation is implemented in each part. Then, some parts that have large sparse concentration index are combined and sparse representation is performed one more time. Each test sample is classified by using the final sparse coefficient where correlation between the test sample and training sample is applied. Results The recognition rate of the proposed algorithm is higher than that of the basic sparse representation classification. Conclusion The proposed method can be applied in real life which needs to identify someone exactly whether the person disguises his face or not.

• Journal of Gifted/Talented Education
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• v.5 no.2
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• pp.17-36
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• 1995

The present study examined preschooler's (3-5yrs) representation and evaluation skills in a puzzle completion task. The puzzle contained panels of four children dressed for each seacon and the key to success was using a body scheme to reconstruct the panels (head, torso, legs, feet and sky on top). Baseline data (Study 1) revealed a developmental pattern of increasing bydy scheme representation along with more careful attention to season consitent construction. Spontaneous verbalization also shifted from more guiding statements (where'the head?) to move evaluative statements (this isn't right). Study 2 examined different intervention techniques for increasing representation (verbal laveling) and evaluative processes (error detection practice), along with a control group that had unassisted practice. Three year olds benefited from verbal labeling, four year olds from both types of training. Verbalizations also showed appropriated shifts toward increasing evaluation, particularly for the older children. These findings are discussed in terms of a developmental hypothesis that representation precedes evaluation skills and that training techniques should take into account the relative balance between representation and evaluation skills in the individual for the task at hand.

• Korean Journal of Child Studies
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• v.26 no.4
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• pp.247-247
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• 2005

The attachment representation of 40 36- to 52-month-old upper-middle class children was analyzed in relation to their attachment security and mother-child interaction behaviors. Instruments were the Attachment Story Completion Task(Bretherton, Ridgeway, & Cassidy, 1990), the Attachment Q-set, and the Vocabulary test of the K-WIPPSI. Mother-child interaction behaviors were observed at home during semi-structured problem-solving situations. The Attachment Story Completion Task classified Korean preshoolers from these upper-middle class families as securely attached to their mothers. Girls' representation of their attachment relationship was more secure than boys. Preschoolers' attachment representation was significantly related to vocabulary ability. Attachment security correlated positively with preschool children's attachment representation. Mother-child interaction behaviors related significantly to preschool children's attachment representation.

• Journal of the Korean School Mathematics Society
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• v.15 no.3
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• pp.535-563
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• 2012

This study was conducted to confirm the necessity of analogical thinking and to empirically verify the effectiveness of analogical reasoning through the visual representation by analyzing the factors of problem solving depending on analogical conditions. Four conditions (a visual representation mapping condition, a conceptual mapping condition, a retrieval hint condition and no hint condition) were set up for the above purpose and 80 twelfth-grade students from C high-School in Cheong-Ju, Chung-Buk participated in the present study as subjects. They solved the same mathematical problem about sequence of complex numbers in their differed process requirements for analogical transfer. The problem solving rates for each condition were analyzed by Chi-square analysis using SPSS 12.0 program. The results of this study indicate that retrieval of base knowledge is restricted when participants do not use analogy intentionally in problem solving and the mapping of the base and target concepts through the visual representation would be closely related to successful analogical transfer. As the results of this study offer, analogical thinking is necessary while solving mathematical problems and it supports empirically the conclusion that recognition of the relational similarity between base and target concepts by the aid of visual representation is closely associated with successful problem solving.

• Korean Journal of Remote Sensing
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• v.21 no.5
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• pp.393-404
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• 2005

As multi-source spatial data fusion mainly deal with various types of spatial data which are specific representations of real world with unequal reliability and incomplete knowledge, proper data representation and uncertainty analysis become more important. In relation to this problem, this paper presents and applies an advanced data representation methodology for different types of spatial data such as categorical and continuous data. To account for the uncertainties of both categorical data and continuous data, fuzzy boundary representation and smoothed kernel density estimation within a fuzzy logic framework are adopted, respectively. To investigate the effects of those data representation on final fusion results, a case study for landslide hazard mapping was carried out on multi-source spatial data sets from Jangheung, Korea. The case study results obtained from the proposed schemes were compared with the results obtained by traditional crisp boundary representation and categorized continuous data representation methods. From the case study results, the proposed scheme showed improved prediction rates than traditional methods and different representation setting resulted in the variation of prediction rates.