• Korean Journal of Cognitive Science
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• v.23 no.3
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• pp.367-388
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• 2012

The centennial of Alan Mathison Turing(23 June 1912 - 7 June 1954) is an appropriate occasion on which to assess his profound influence on the development of cognitive science. His contributions to and attitudes toward that field are discussed from the metamathematical perspective. This essay addresses (i)Turing's mathematical analysis of cognition, (ii)universal Turing machines, (iii)the limitations of universal Turing machines, (iv)oracle Turing machine beyond universal Turing machine, and (v)Turing test for cognitive science. Turing was a ground-breaker, eager to move on to new fields. He actually opened wider the scientific windows to the mind. The results show that first, by means of mathematical logic Turing discovered a new bridge between the mind and the physical world. Second, Turing gave a new formal analysis of operations of the mind. Third, Turing investigated oracle Turing machines and connectionist network machines as new models of minds beyond the limitations of his own universal machines. This paper explores why the cognitive scientist would be ever expecting a new Turing Test on the shoulder of Alan Turing.

• Korean Journal of Culture and Social Issue
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• v.17 no.4
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• pp.485-503
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• 2011

This study was to develope a structural model for risk perception and individual response against terrorism, including several psychological factors - cognitive, social and emotional factors. In this model we measured perceived probability of terrorism, perceived seriousness of the aftermath, and perceived coping(cognitive factors), trust in authorities, in expert group and in preparedness of institutions(social factors), fear and worry(emotional factors), individual preparedness, information seeking, information analysis, and checking relational network(individual behavior responses). Major finding was that cognitive and social factors influenced on emotional factors and then emotional factors influenced on the individual responses. The perceived coping, which one of cognitive factors was linked with individual responses directly and indirectly via emotion factors. We discussed the importance of perceived coping in preparing for terrorism.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37B no.11
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• pp.993-1003
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• 2012

In this paper, we propose a method that wireless mobile nodes can obtain high throughput in heterogeneous wireless networks using multiple connections and it has low packet loses under handover situation. Currently, a mobile node exchanges data with server for one network connection. The proposed method can use high throughput because it doesn't only use one network(WLAN, 3G, etc.) but also use multiple wireless networks. When mobile nodes move to area to use multiple connection, mobile nodes request heterogeneous wireless networks using multiple connections message from the server and the server transmit packets using multiple connections. Also, this method doesn't disconnect previous networks, so packets losses are decreased. Using the NS-2 simulation, we verify that the propose method enhances throughput.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.11
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• pp.1037-1053
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• 1991

Flooding is known to be an effective routing mechanism not only in circuit switched networks but also packet switched networks since it minimizes the time required for up calls and does not have to maintain routing tables or distance tables by periodically intercommunicating among all the network nodes. However, one main drawback of it is the overhead on the control message signaling channels, In the paper, we propose an overlaying scheme on the existing mechanism that retains the desired properties of flooding, yet provides congestion control to the network. This is done by utilizing the wasted search messages to inform the network of the conditions of each path from one node to another. The overhead is to maintain a path table in each node. However, this overhead is not so critical. Also, this scheme can take advantages further by limiting the extent of message propagation through the network and by incorporating trunk reservation schemes additionally.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.10B
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• pp.981-987
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• 2009

Recently, the study on wireless body area network for providing ubiquitous healthcare services has been actively done, including the standardization of the IEEE and others. Wireless body area network is usually configured in tree format using multi-hop communication mode due to the power limitation and the characteristics of human body. In this case, differently from existing sensor network, the wireless body area network tends to be disconnected due to the frequent movement of human body. The number of connections which can be supported at each node has some limitations due to the constraint imposed on power consumption. In this paper, we have proposed a heuristic algorithm for optimal selection of parent node with guaranteed QoS for a disconnected node, which considers the priority on packet transmission. Simulation has been performed to evaluate the performance of the proposed algorithm.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.8
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• pp.393-400
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• 2016

Recently, there have been various researches on efficient and automatic analysis on urban environment methods that utilize the computer vision and machine learning technology. Among many new analyses, urban safety analysis has received a major attention. In order to predict more accurately on safety score and reflect the human visual perception, it is necessary to consider the generic and local information that are most important to human perception. In this paper, we use Double-column Convolutional Neural network consisting of generic and local columns for the prediction of urban safety. The input of generic and local column used re-sized and random cropped images from original images, respectively. In addition, a new learning method is proposed to solve the problem of over-fitting in a particular column in the learning process. For the performance comparison of our Double-column Convolutional Neural Network, we compare two Support Vector Regression and three Convolutional Neural Network models using Root Mean Square Error and correlation analysis. Our experimental results demonstrate that our Double-column Convolutional Neural Network model show the best performance with Root Mean Square Error of 0.7432 and Pearson/Spearman correlation coefficient of 0.853/0.840.

• Korean Journal of Cognitive Science
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• v.12 no.1_2
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• pp.77-89
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• 2001

Applying perceptual hierarchy of facial feature points, a neural network model for recognizing facial expressions was designed. Input data were convolution values of 150 facial expression pictures by Gabor-filters of 5 different sizes and 8 different orientations for each of 39 mesh points defined by MPEG-4 SNHC (Synthetic/Natural Hybrid Coding). A set of multiple regression analyses was performed with the rating value of the affective states for each facial expression and the Gabor-filtered values of 39 feature points. The results show that the pleasure-displeasure dimension of affective states is mainly related to the feature points around the mouth and the eyebrows, while a arousal-sleep dimension is closely related to the feature points around eyes. For the filter sizes. the affective states were found to be mostly related to the low spatial frequency. and for the filter orientations. the oblique orientations. An optimized neural network model was designed on the basis of these results by reducing original 1560(39x5x8) input elements to 400(25x2x8) The optimized model could predict human affective rating values. up to the correlation value of 0.886 for the pleasure-displeasure, and 0.631 for the arousal-sleep. Mapping the results of the optimized model to the six basic emotional categories (happy, sad, fear, angry, surprised, disgusted) fit 74% of human responses. Results of this study imply that, using human principles of recognizing facial expressions, a system for recognizing facial expressions can be optimized even with a a relatively little amount of information.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.3
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• pp.115-122
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• 2019

For Korean phoneme recognition, Hidden Markov-Gaussian Mixture model(HMM-GMM) or hybrid models which combine artificial neural network with HMM have been mainly used. However, current approach has limitations in that such models require force-aligned corpus training data that is manually annotated by experts. Recently, researchers used neural network based phoneme recognition model which combines recurrent neural network(RNN)-based structure with connectionist temporal classification(CTC) algorithm to overcome the problem of obtaining manually annotated training data. Yet, in terms of implementation, these RNN-based models have another difficulty in that the amount of data gets larger as the structure gets more sophisticated. This problem of large data size is particularly problematic in the Korean language, which lacks refined corpora. In this study, we introduce CTC algorithm that does not require force-alignment to create a Korean phoneme recognition model. Specifically, the phoneme recognition model is based on convolutional neural network(CNN) which requires relatively small amount of data and can be trained faster when compared to RNN based models. We present the results from two different experiments and a resulting best performing phoneme recognition model which distinguishes 49 Korean phonemes. The best performing phoneme recognition model combines CNN with 3hop Bidirectional LSTM with the final Phoneme Error Rate(PER) at 3.26. The PER is a considerable improvement compared to existing Korean phoneme recognition models that report PER ranging from 10 to 12.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.49 no.6
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• pp.77-82
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• 2012

In this paper, the new location management scheme based on region and movement threshold has been proposed in order to minimize the required cost to handle the resource location management signaling traffics under the wire/wireless convergent networks when the users are moving around the cold and hot regions. In this scheme, the sequential paging is supposed to be performed according to the movement threshold which can be changed in accordance with the number of movements that each mobile user had made while it is moving around the resource regions. With the proposed scheme, the cost of managing the location management traffics and efficient spectrum assignment overhead can be minimized by placing signaling traffics for location managements and also allocating radio spectrum considering individual movement behaviors.

• Korean Journal of Cognitive Science
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• v.19 no.3
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• pp.297-310
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• 2008

Since speech acts implies speakers' intentions, it is essential to determine speakers' speech acts if we want to implement an intelligent dialogue system. We propose a two-step model for effectively determining speakers' speech acts. In the first step, the proposed model returns speech act candidates by using a neural network model based on machine learning and a predictivity model based on statistics, respectively. In the second step, using speech act candidates which are returned by the predictivity model, the proposed model filters out speech act candidates which are returned by the neural network model. Then, the proposed model selects a speech act with maximum output value among the unremoved speech act candidates. In the experiment on a schedule management domain, the proposed two-step modeling method showed better precisions than the previous methods only using a machine learning model or a probability model.