• Journal of applied mathematics & informatics
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• v.37 no.1_2
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• pp.37-52
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• 2019

In this paper we introduce a new concept for Riesz almost lacunary ${\chi}^3$ sequence spaces strong P - convergent to zero with respect to an Orlicz function and examine some properties of the resulting sequence spaces. We introduce and study statistical convergence of Riesz almost lacunary ${\chi}^3$ sequence spaces and some inclusion theorems are discussed.

• Journal of The Korean Association For Science Education
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• v.24 no.2
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• pp.246-257
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• 2004

The purpose of this qualitative study was to identify high school students' conceptual differentiation patterns on human digestion system. The subjects were 124 high school students and this group was guided to independently construct concept maps. Among them, 19 were selected for an in-depth interview and a short test. The concept maps, interview transcripts and the results of short-test were analyzed to identify conceptual differentiation patterns. The results were as follows. Mainly three distinct conceptual differentiation patterns were identified. The first pattern can be named as an 'Free-flow type'. The group belongs to this pattern expressed numerous examples than meaningful concepts with unclear understanding of hierarchial relation between each concepts. Also, this group had difficulties in grasp interrelations of different concepts. The second pattern can be identified as 'Sequence type'. This group constructed concept maps by featuring conceptual sequence. The group applied meaningful learning, yet assembled concept maps primarily according to sequence of learning and exhibited less organized concept maps than hierarchial type. The third pattern can be named as 'Hierarchial type'. All students elaborated concept maps after lessons. The sequence type changed hierarchial type or sequence mixed with hierarchial type but free-flow type was hardly changed.

• Korean Journal of Cognitive Science
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• v.18 no.1
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• pp.1-14
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• 2007

In a goal-oriented dialogue, speakers' intentions can be represented by domain actions that consist of pairs of a speech act and a concept sequence. Therefore, if we plan to implement an intelligent dialogue system, it is very important to correctly infer the domain actions from surface utterances. In this paper, we propose a statistical model to determine speech acts and concept sequences using conditional random fields at the same time. To avoid biased learning problems, the proposed model uses low-level linguistic features such as lexicals and parts-of-speech. Then, it filters out uninformative features using the chi-square statistic. In the experiments in a schedule arrangement domain, the proposed system showed good performances (the precision of 93.0% on speech act classification and the precision of 90.2% on concept sequence classification).

• Journal for History of Mathematics
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• v.15 no.2
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• pp.77-82
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• 2002

This paper is concerned with the mathematical concept displayed in Buddhism, which is reasonable enough to consider as a philosophy and encompasses the concept of infinity as scientific as that of mathematics. The purpose of this paper is to examine the changing process of the Buddhism concept of infinity on the basis of time sequence and to combine this with that of mathematics.

• Journal of The Korean Association For Science Education
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• v.19 no.4
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• pp.645-652
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• 1999

The effects of number, source, and sequence of analogs on middle school students' concept recall and application were investigated. Based on the number (one/two) and source(everyday/science) of analogs, four types of learning materials were developed and pilot-tested. Prior to the treatment the field dependence/independence (FD/l) test was administered and the scores were used as a blocking variable. The learning materials were read by randomly assigned middle school students (N=88), and the concept recall and application test was administered immediately and four weeks later. In the immediate and retention tests, there were no significant main effects of number, source, and sequence of analogs. In the application problems of retention test. however, there were some significant interaction effects with students' FD/I. Field-independent students who learned with two analogs scored significantly higher than those who learned with one analog. In the case of using two analogs, field-dependent students who learned with everyday-analog first scored significantly higher than those who learned with science-analog first.

• Korean Journal of Cognitive Science
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• v.18 no.2
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• pp.179-191
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• 2007

In a goal-oriented dialogue, spoken' intentions can be represented by domain actions that consist of pairs of a speech art and a concept sequence. The domain action prediction of user's utterance is useful to correct some errors that occur in a speech recognition process, and the domain action prediction of system's utterance is useful to generate flexible responses. In this paper, we propose a model to predict a domain action of the next utterance using a neural network. The proposed model predicts the next domain action by using a dialogue history vector and a current domain action as inputs of the neural network. In the experiment, the proposed model showed the precision of 80.02% in speech act prediction and the precision of 82.09% in concept sequence prediction.

• Journal of the Korean School Mathematics Society
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• v.10 no.1
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• pp.91-111
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• 2007

This study investigated how high school students predict the rule, the sum of sequence for the concept of sequence, for the given patterns based on inductive approach using computers that provide dynamic functions and materials that are visual. Students for themselves were able to induce the formula without using the given formula in the textbook. Furthermore, this study examined how these technology and materials affect students' understanding of the concept of actual infinity for those who have the concept of the potential infinity which is the misconception of infinity in a infinity series. This study shows that students made a progress from the concept of potential infinity to that of actual infinity with technology and materials used I this study. Students also became interested in the use of computer and the visualized materials, further there was a change in their attitude toward mathematics.

• Korean Journal of Mathematics
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• v.30 no.4
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• pp.679-686
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• 2022

In this study, the concept of lacunary statistical convergence is studied in G-metric spaces. The G-metric function is based on the concept of distance between three points. Considering this new concept of distance, we examined the relationships between GS, GS_θ, Gσ₁ and GN_θ sequence spaces.

• Communications of the Korean Mathematical Society
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• v.17 no.2
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• pp.215-220
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• 2002

Since P. Erdos introduced the concept of "subset-sum-distinctness", lots of mathematicians have been interested in a "dense" set having distinct subset sums. In this paper, we establish a couple of theorems on maximal subset-sun-distinct sequence with respect to the set inclusion.

• Journal of KIISE:Software and Applications
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• v.35 no.9
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• pp.554-561
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• 2008

Prediction technique of user's intention can be used as a post-processing method for reducing the search space of an automatic speech recognizer. Prediction technique of system's intention can be used as a pre-processing method for generating a flexible sentence. To satisfy these practical needs, we propose a statistical model to predict speakers' intentions that are generalized into pairs of a speech act and a concept sequence. Contrary to the previous model using simple n-gram statistic of speech acts, the proposed model represents a dialogue history of a current utterance to a feature set with various linguistic levels (i.e. n-grams of speech act and a concept sequence pairs, clue words, and state information of a domain frame). Then, the proposed model predicts the intention of the next utterance by using the feature set as inputs of CRFs (Conditional Random Fields). In the experiment in a schedule management domain, The proposed model showed the precision of 76.25% on prediction of user's speech act and the precision of 64.21% on prediction of user's concept sequence. The proposed model also showed the precision of 88.11% on prediction of system's speech act and the Precision of 87.19% on prediction of system's concept sequence. In addition, the proposed model showed 29.32% higher average precision than the previous model.