• Korean Journal of Cognitive Science
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• v.24 no.3
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• pp.271-300
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• 2013

Human and animals are born with an intuitive ability to determine approximate numerosity. This ability is termed approximate number sense (hereafter, number sense). Evolutionarily, number sense is thought to be an essential ability for hunting, gathering and survival. According to previous research, children with mathematical learning disability have impaired number sense. On the other hand, individuals with more accurate number sense have higher mathematical achievement. These results support the hypothesis that number sense provides a basis for the development of mathematical cognition. Recently, researchers have been examining whether number sense training can lead to enhancement in mathematical achievement and changes in brain activity in relation to mathematical problem solving. Numerosity which basically represents discontinuous quantity is expected to be closely related to continuous quantity such as representations of space and time. A theory of magnitude (ATOM) states that processing of number, space and time is based on a common magnitude system in the posterior parietal cortex, especially the intraparietal sulcus. The present paper introduces current literature and future directions for the study of the common magnitude system.

• Korean Journal of Cognitive Science
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• v.25 no.2
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• pp.159-187
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• 2014

Approximate number sense(hereafter, ANS) is the ability to compare and operate upon numerosity information. The numerosity comparison task is used to measure ANS. However, there is considerable variance among previous reports of ANS acuity which may be related to different task formats used. Here, we aim to investigate whether the format of the numerosity comparison task influences measurements of ANS acuity. We compared two task formats; 1) an intermixed format presenting two intermixed arrays of black and white dots, and 2) a side-by-side format showing two arrays of dots side by side. The intermixed format likely makes additional demands on general cognitive resources for inhibitory control, selective attention, or visuospatial working memory. The performance on the intermixed format was significantly lower than that of the side-by-side format resulting in an underestimation of ANS acuity compared to the expected trajectory of ANS development. In addition, the ANS acuity measured from only the side-by-side format was correlated with children's mathematical achievement and age. Our results demonstrate that measurement of ANS from the side-by-side format has higher construct and predictive validity compared to that of the intermixed format.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.384-384
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• 2000

In adaptive neuro-control, neural networks are used to approximate the unknown plant nonlinearities. Until now, most of the papers in the field of controller design fur nonlinear system using neural networks considers the affine system with fixed number of neurons. This paper considers nonaffne nonlinear systems and dynamic variation of the number of neurons. Control laws and adaptive laws for weights are established so that the whole system is stable in the sense of Lyapunov.

• Korean Journal of Child Studies
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• v.38 no.1
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• pp.117-126
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• 2017

Objective: The purpose of this study was to investigate young children's nonsymbolic magnitude comparison ability according to ratio limit and task condition. Methods: The participants included 40 3-year-old children, 42 4-year-old children, and 41 5-year-old children recruited from 4 childcare centers located in Seoul, Korea. All magnitude comparison tasks were composed of image material tasks and concrete material tasks. In addition, each magnitude comparison task varied with the ratio of the two quantities; 0.5 ratio, 0.67 ratio, 0.75 ratio. Results and Conclusion: The results revealed that 3-, 4-, and 5-year-old children could perform nonsymbolic magnitude comparison tasks without learning experiences. Also, 3-, 4-, and 5-year-old children could perform concrete material tasks better than image material tasks in nonsymbolic magnitude comparison tasks. Furthermore, children's performance on nonsymbolic magnitude comparison tasks indicated the ratio signature of the approximate number system. Children have a degree of numerical capacity prior to formal mathematics instruction. Also, children were influenced by task conditions or sense stimulus when they processed numerical information. Furthermore, the approximate number system can be used in understanding the ordinality of number.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.8
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• pp.647-655
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• 2001

In adaptive neuro-control, neural networks are used to approximate unknown plant nonlinearities. Until now, most of the studies in the field of controller design for nonlinear system using neural network considers the affine system with fixed number of neurons. This paper considers nonaffine nonlinear systems and on-line variation of the number of neurons. A control law and adaptive laws for neural network weights are established so that the whole system is stable in the sense of Lyapunov. In addition, at the expense of th input, tracking error converges to the arbitrary small neighborhood of the origin. The efficiency of the proposed scheme is shown through simulations ofa simple nonaffine nonlinear system.

• Journal of the Korea Society for Simulation
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• v.3 no.1
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• pp.17-28
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• 1994

A statistical procedure is developed to estimate the relative difference between two parameters each obtained from either true model or approximate model. Double sample procedure is applied to find the additional number of simulation runs satisfying the preassigned absolute precision of the confidence interval. Two types of parameters, mean and standard deviation, are considered as the performance measures and tried to show the validity of the model by examining both queues and inventory systems. In each system it is assumed that there are three distinct means and their own standard deviations and they form the simultaneous confidence intervals but with control in the sense that the absolute precision for each confidence interval is bounded on the limits with preassigned confidence level. The results of this study may contribute to some situations, for instance, first, we need a statistical method to compare the effectiveness between two alternatives, second, we find the adquate number of replications with any level of absolute precision to avoid the unrealistic cost of running simulation models, third, we are interested in analyzing the standard deviation of the output measure, ..., etc.

• Journal of Integrative Natural Science
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• v.3 no.3
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• pp.143-147
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• 2010

Since it is of great importance to know how a ligand binds to a receptor, there have been a lot of efforts to improve the quality of prediction of docking poses. Earlier efforts were focused on improving search algorithm and scoring function in a docking program resulting in a partial improvement with a lot of variations. Although these are basically very important and essential, more tangible improvements came from the reduction of search space. In a normal docking study, the approximate active site is assumed to be known. After defining active site, scoring functions and search algorithms are used to locate the expected binding pose within this search space. A good search algorithm will sample wisely toward the correct binding pose. By careful study of receptor structure, it was possible to prioritize sub-space in the active site using "receptor-based pharmacophores" or "hot spots". In a sense, these techniques reduce the search space from the beginning. Further improvements were made when the bound ligand structure is available, i.e., the searching could be directed by molecular similarity using ligand information. This could be very helpful to increase the accuracy of binding pose. In addition, if the biological activity data is available, docking program could be improved to the level of being useful in affinity prediction for a series of congeneric ligands. Since the number of co-crystal structures is increasing in protein databank, "Ligand-Guided Docking" to reduce the search space would be more important to improve the accuracy of docking pose prediction and the efficiency of virtual screening. Further improvements in this area would be useful to produce more reliable docking programs.

• The Korean Journal of Applied Statistics
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• v.10 no.1
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• pp.69-84
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• 1997

We consider a linear calibration problem, $y_i = $$\alpha + \beta (x_i - x_0) + \epsilon_i$, $i=1, 2, {\cdot}{\cdot},n$ $y_f = \alpha + \beta (x_f - x_0) + \epsilon, $ where we observe $(x_i, y_i)$'s for the controlled calibration experiments and later we make inference about $x_f$ from a new observation $y_f$. The objective of the calibration design problem is to find the optimal design $x = (x_i, \cdots, x_n$ that gives the best estimates for $x_f$. We compare Kim(1989)'s Bayesian design which minimizes the expected value of the posterior variance of $x_f$ and some optimal designs from literature. Kim suggested the Bayesian optimal design based on the analysis of the characteristics of the expected loss function and numerical must be equal to the prior mean and that the sum of squares be as large as possible. The designs to be compared are (1) Buonaccorsi(1986)'s AV optimal design that minimizes the average asymptotic variance of the classical estimators, (2) D-optimal and A-optimal design for the linear regression model that optimize some functions of $M(x) = \sum x_i x_i'$, and (3) Hunter & Lamboy (1981)'s reference design from their paper. In order to compare the designs which are optimal in some sense, we consider two criteria. First, we compare them by the expected posterior variance criterion and secondly, we perform the Monte Carlo simulation to obtain the HPD intervals and compare the lengths of them. If the prior mean of $x_f$ is at the center of the finite design interval, then the Bayesian, AV optimal, D-optimal and A-optimal designs are indentical and they are equally weighted end-point design. However if the prior mean is not at the center, then they are not expected to be identical.In this case, we demonstrate that the almost Bayesian-optimal design was slightly better than the approximate AV optimal design. We also investigate the effects of the prior variance of the parameters and solution for the case when the number of experiments is odd.

• Journal of Communications and Networks
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• v.10 no.1
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• pp.5-9
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• 2008

Researchers have investigated many upper bound techniques applicable to error probabilities on the maximum likelihood (ML) decoding performance of turbo-like codes and low density parity check (LDPC) codes in recent years for a long codeword block size. This is because it is trivial for a short codeword block size. Previous research efforts, such as the simple bound technique [20] recently proposed, developed upper bounds for LDPC codes and turbo-like codes using ensemble codes or the uniformly interleaved assumption. This assumption bounds the performance averaged over all ensemble codes or all interleavers. Another previous research effort [21] obtained the upper bound of turbo-like code with a particular interleaver using a truncated union bound which requires information of the minimum Hamming distance and the number of codewords with the minimum Hamming distance. However, it gives the reliable bound only in the region of the error floor where the minimum Hamming distance is dominant, i.e., in the region of high signal-to-noise ratios. Therefore, currently an upper bound on ML decoding performance for turbo-like code with a particular interleaver and LDPC code with a particular parity check matrix cannot be calculated because of heavy complexity so that only average bounds for ensemble codes can be obtained using a uniform interleaver assumption. In this paper, we propose a new bound technique on ML decoding performance for turbo-like code with a particular interleaver and LDPC code with a particular parity check matrix using ML estimated weight distributions and we also show that the practical iterative decoding performance is approximately suboptimal in ML sense because the simulation performance of iterative decoding is worse than the proposed upper bound and no wonder, even worse than ML decoding performance. In order to show this point, we compare the simulation results with the proposed upper bound and previous bounds. The proposed bound technique is based on the simple bound with an approximate weight distribution including several exact smallest distance terms, not with the ensemble distribution or the uniform interleaver assumption. This technique also shows a tighter upper bound than any other previous bound techniques for turbo-like code with a particular interleaver and LDPC code with a particular parity check matrix.