• Korean Journal of Cognitive Science
• /
• v.25 no.4
• /
• pp.343-384
• /
• 2014

In order to unravel the problems of the mind, today's cognitive science has expanded its perspective from the narrow framework of the past computer model or neuronal network model to the wider frameworks of interaction with the brain in interaction with the body in interaction with their environments. The theories of 'the extended mind', 'embodied mind', or 'enactive mind' appeared through such processes are working on a way to move into the environments while the problem to unravel the complex process of interactions between the mind, the body and the environments are left alone. This problem can be traced back as far as to Gibson and Maturana & Varela who tried at first to unravel the problem of the mind in terms of interaction between the brain, the body and there environments in 1960~70s. It's because Gibson stressed the importance of the 'affordance' provided by the environment while Maturana & Varela emphasized the 'autonomy' of auto-poiesis of life. However, it will be proper to say that there are invariants in the affordances provided by the environment as well as the autonomy of life in the state of structural coupling of the environment's variants and life's openness toward the environment. In this case, the confrontational points between Gibson and Maturana & Varela will be resolved. In this article, I propose Benjamin's theory of mimesis as a mediator of both theories. Because Benjamin's concept of mimesis has the process of making a constellation of the embodiment of the affordance and the enaction of new affordance into the environment at the same time, Gibson's concept of the affordance and Maturana & Varela's concept of embodiment and enaction will be so smoothly interconnected to circulate through the medium of Benjamin's concept of mimesis.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.17 no.11
• /
• pp.271-284
• /
• 2016

This study was carried out to evaluate the efficacy of CBT for perinatal depression through systematic literature review and meta-analysis. The following databases were used to search the literature: CINAHL, PubMed, EMBASE, Koreamed, Library of Korean Congress, KISS, and Korean Academic Publication Database. Keywords included 'perinatal depression,' 'pregnant women,' and 'cognitive behavioral therapy,' and the evaluated articles were published up to May 2016. Using the R program, the effect size of perinatal depression and anxiety were calculated by random-effects model. The heterogeneity of the effect size was analyzed by data moderator analysis using the meta-ANOVA. Furthermore, the funnel plot, Egger's regression test, fail-safe N, trim-and-fill test, and publication bias analysis were conducted and used to verify the results. Out of the 180 selected articles, 16 clinical trial studies were meta-analyzed. Each articles were evaluated for the risk of bias by the checklist of SIGN; the overall risk of bias was low. The effect size of CBT for perinatal depression was Hedges' g=-0.55 (95% CI: -0.76~-0.33), which was a moderate level, while for anxiety reduction, Hedges' g=-0.20 (95% CI: -0.48~-0.08) and it was not statistically significant. Heterogeneity or risk of publication bias were low. This meta-analytic study found that CBT is moderately effective in reducing perinatal depression in pregnant women.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.14 no.1
• /
• pp.179-187
• /
• 2014

The underlay protocol is a cognitive radio method in which secondary or cognitive users use the same frequency without affecting the quality of service (QoS) for the primary users. In addition, because of the broadcast characteristics of the wireless environment, some nodes, which are called eavesdropper nodes, want to illegally receive information that is intended for other communication links. Hence, Physical Layer Security is applied considering the achievable secrecy rate (ASR) to prevent this from happening. In this paper, a performance analysis of the amplify-and-forward scheme under an interference constraint and Physical Layer Security is investigated in the cooperative communication mode. In this model, the relays use an amplify-and- forward method to help transmit signals from a source to a destination. The best relay is chosen using an opportunistic relay selection method, which is based on the end-to-end ASR. The system performance is evaluated in terms of the outage probability of the ASR. The lower and upper bounds of this probability, based on the global statistical channel state information (CSI), are derived in closed form. Our simulation results show that the system performance improves when the distances from the relays to the eavesdropper are larger than the distances from the relays to the destination, and the cognitive network is far enough from the primary user.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.36 no.4A
• /
• pp.369-379
• /
• 2011

During the routing process between nodes on the CR(Cognitive Radio) network conducting for efficient use of limited frequency resources, spectrum handover process due to the appearance of the PU occupies most of the routing latency, and also decreases the reliability of the path. In this paper, a cooperative routing protocol in a multi-channel environment is proposed. The source node broadcasts a message with available channel lists and probability of PU appearance during its route guidance. The intermediate nodes re-transmit the message, received from the source node, and update and maintain the information, status table of the path. The destination node determines the optimal path and sends a reply message to the selected path after it receives the messages from the intermediate nodes. The average probability of the PU appearance and the average time of the PU appearance are updated while transferring data. During data transmission the channel with the lowest probability of appearance of the PU is selected dynamically and if a PU appears on the current channel partial repairment is performed. It is examined that reliability of the selected path considerably is improved and the routing cost is reduced significantly compared to traditional routing methods.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.11
• /
• pp.2924-2944
• /
• 2023

Alzheimer's disease (AD) is a neurological condition that is recognized as one of the primary causes of memory loss. AD currently has no cure. Therefore, the need to develop an efficient model with high precision for timely detection of the disease is very essential. When AD is detected early, treatment would be most likely successful. The most often utilized indicators for AD identification are the Mini-mental state examination (MMSE), and the clinical dementia. However, the use of these indicators as ground truth marking could be imprecise for AD detection. Researchers have proposed several computer-aided frameworks and lately, the supervised model is mostly used. In this study, we propose a novel 3D Convolutional Neural Network Multilayer Perceptron (3D CNN-MLP) based model for AD classification. The model uses Attention Mechanism to automatically extract relevant features from Magnetic Resonance Images (MRI) to generate probability maps which serves as input for the MLP classifier. Three MRI scan categories were considered, thus AD dementia patients, Mild Cognitive Impairment patients (MCI), and Normal Control (NC) or healthy patients. The performance of the model is assessed by comparing basic CNN, VGG16, DenseNet models, and other state of the art works. The models were adjusted to fit the 3D images before the comparison was done. Our model exhibited excellent classification performance, with an accuracy of 91.27% for AD and NC, 80.85% for MCI and NC, and 87.34% for AD and MCI.

• Journal of The Korean Association For Science Education
• /
• v.36 no.5
• /
• pp.791-800
• /
• 2016

This research aimed to identify core words used as associative words and definitions for expressing 'electricity' terminology and to find how core ones are activated to form a cognitive structure, using network analysis. The participants targeted 83 university freshmen students in the University of Education located in suburbs. Depending on their gender, whether or not they completed physics in high school, the associative words and definitions were analyzed using the network method, classifying two sections: before-lesson and after-lesson. The result is as follows: At before-lesson associative words for 'electricity' terminology, a slightly different network construction was revealed based on their two properties. However, after the class, they showed similar network structure irrespective of their distinctive characteristics. When it comes to other 'electricity' definitions, before taking the course, they had similar network connection across the gender but based on physics education status, there appeared subtle differences. Ultimately, after the class they demonstrated similar network structure regardless of their features. In conclusion, this paper suggests educational implications on network analysis, which covers 'electricity' terminology of university students.

• Journal of the Korean Chemical Society
• /
• v.60 no.2
• /
• pp.137-143
• /
• 2016

Effective teaching and learning is a continuous process of monitoring and re-organization of teaching method, so to benefit both students and educators. Reflective journal writing is an effective method for students to reflect on their learning experience about a new concept or subject taught and at the same time enables educators to improve on their academic skills. In the present paper, we have examined and evaluated the effectiveness of the Uncritical Inference Test (UIT) that was conducted in our basic organic chemistry course through a systematic network built based on students’ reflective writing. From the data analysis, the UIT has benefited students in three dimensions, namely cognitive, affective and group learning domains. Moreover, the UIT activity instilled an active learning environment in organic chemistry classroom and deeper learning among chemistry students as shown in the collected data. In future, this activity could be adapted as a teaching method to enhance students’ critical thinking skills and question-asking capability in other teaching courses.

• World Technopolis Review
• /
• v.5 no.2
• /
• pp.108-118
• /
• 2016

There are very few studies on governance structure for the collaboration between HEIs and science and technology parks until today. Major activities between science parks and HEIs are R&D activities, collaborative researches, technology transfer, space provision for BIs and Technology BIs in the science parks, provision of technical, legal and financial services for start-ups and venture firms. Governance structure for the collaboration of high education institutes with science and technology parks is the handling of complexity and management of dynamic flows of collaboration between two groups. Three models on the governance structure for the collaboration are suggested in this study. The first model is a governance structure that links R&D system such as universities, public research institutes and private research institutes with industrial production cluster such as a group of companies and industrial parks. The second model is a governance structure that has four layers of hierarchy. This hierarchical governance model is composed of four levels of organizations such as central government, three actors, one center for collaboration and many individual research performers. The third model is a governance structure that networks all the stakeholders horizontally. Under this structure, governance is conducted by the network members with no separate and unique governance entity.

• Journal of Institute of Control, Robotics and Systems
• /
• v.13 no.5
• /
• pp.393-398
• /
• 2007

In this paper, we propose the development of Artificial Hippocampus Algorithm(AHA) which remodels a principle of brain of hippocampus. Hippocampus takes charge auto-associative memory and controlling functions of long-term or short-term memory strengthening. We organize auto-associative memory based 4 steps system (EC, DG CA3, and CA1) and improve speed of teaming by addition of modulator to long-term memory teaming. In hippocampus system, according to the 3 steps order, information applies statistical deviation on Dentate Gyrus region and is labeled to responsive pattern by adjustment of a good impression. In CA3 region, pattern is reorganized by auto-associative memory. In CA1 region, convergence of connection weight which is used long-term memory is learned fast a by neural network which is applied modulator. To measure performance of Artificial Hippocampus Algorithm, PCA(Principal Component Analysis) and LDA(Linear Discriminants Analysis) are applied to face images which are classified by pose, expression and picture quality. Next, we calculate feature vectors and learn by AHA. Finally, we confirm cognitive rate. The results of experiments, we can compare a proposed method of other methods, and we can confirm that the proposed method is superior to the existing method.

• Science of Emotion and Sensibility
• /
• v.10 no.1
• /
• pp.23-36
• /
• 2007

This research focused on eliciting and analyzing mental models of mobile device purchasing consumers who are distinguished by their familiarity with information technology. Mental model elicitation processes proceeded by critical decision method. And Pathfinder algorithm and Social Network Analysis were used to analyze the mental models. The results show that IT-familiar consumers have mental models of which elements are more organized and distinctive while IT-unfamiliar consumers have vague and socially affected mental models.