• Title/Summary/Keyword: Cognitive Network

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Mathematical thinking, its neural systems and implication for education (수학적 사고에 동원되는 두뇌 영역들과 이의 교육학적 의미)

  • Kim, Yeon Mi
    • The Mathematical Education
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    • v.52 no.1
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    • pp.19-41
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    • 2013
  • What is the foundation of mathematical thinking? Is it logic based symbolic language system? or does it rely more on mental imagery and visuo-spatial abilities? What kind of neural changes happen if someone's mathematical abilities improve through practice? To answer these questions, basic cognitive processes including long term memory, working memory, visuo-spatial perception, number processes are considered through neuropsychological outcomes. Neuronal changes following development and practices are inspected and we can show there are neural networks critical for the mathematical thinking and development: prefrontal-anterior cingulate-parietal network. Through these inquiry, we can infer the answer to our question.

A Study on High Temperature Low Cycle Fatigue Crack Growth Modelling by Neural Networks (신경회로망을 이용한 고온 저사이클 피로균열성장 모델링에 관한 연구)

  • Ju, Won-Sik;Jo, Seok-Su
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.20 no.4
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    • pp.2752-2759
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    • 1996
  • This paper presents crack growth analysis approach on the basis of neural networks, a branch of cognitive science to high temperature low cycle fatigue that shows strong nonlinearity in material behavior. As the number of data patterns on crack growth increase, pattern classification occurs well and two point representation scheme with gradient of crack growth curve simulates crack growth rate better than one point representation scheme. Optimal number of learning data exists and excessive number of learning data increases estimated mean error with remarkable learning time J-da/dt relation predicted by neural networks shows that test condition with unlearned data is simulated well within estimated mean error(5%).

Perspectives : Understanding the Pathophysiology of Intraventricular Hemorrhage in Preterm Infants and Considering of the Future Direction for Treatment

  • Young Soo Park
    • Journal of Korean Neurosurgical Society
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    • v.66 no.3
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    • pp.298-307
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    • 2023
  • Remarkable advances in neonatal care have significantly improved the survival of extremely low birth weight infants in recent years. However, intraventricular hemorrhage (IVH) continues to be a major complication in preterm infants, leading to a high incidence of cerebral palsy and cognitive impairment. IVH is primarily caused by disruption of the fragile vascular network of the subependymal germinal matrix, and subsequent ventricular dilatation adversely affects the developing infant brain. Based on recent research, periventricular white matter injury is caused not only by ischemia and morphological distortion due to ventricular dilatation but also by free iron and inflammatory cytokines derived from hematoma and its lysates. The current guidelines for the treatment of posthemorrhagic hydrocephalus (PHH) in preterm infants do not provide strong recommendations, but initiating treatment intervention based on ultrasound measurement values before the appearance of clinical symptoms of PHH has been proposed. Moreover, in the past decade, therapeutic interventions that actively remove hematomas and lysates have been introduced. The era is moving beyond cerebrospinal fluid shunt toward therapeutic goals aimed at improving neurodevelopmental outcomes.

Sharing Cognition LMS: an Alternative Teaching and Learning Environment for Enhancing Collaborative Performance

  • NGUYEN, Hoai Nam;KIM, Hoisoo;JO, Yoonjeong;DIETER, Kevin
    • Educational Technology International
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    • v.16 no.1
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    • pp.1-30
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    • 2015
  • The purpose of this research is to propose a novel social LMS developed for group collaborative learning with a think-aloud tool integrated for sharing cognitive processes in order to improve group collaborative learning performance. In this developmental research, the system was designed with three critical elements: the think-aloud element supports learners through shared cognition, the social network element improves the quality of collaborative learning by forming a structured social environment, and the learning management element provides a understructure for collaborative learning for student groups. Moreover, the three critical elements were combined in an educational context and applied in three directions.

Effect of Constraint and Supporting Features of Mobile Social Networking Games(SNGs) on User's Satisfaction (모바일 소셜 네트워크 게임의 제약과 지원 기능이 사용자의 만족에 미치는 영향)

  • IM, Chae-Rin;SHIN, Young-soo;KIM, Jin-woo;LEE, In-seong
    • The Journal of the Korea Contents Association
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    • v.15 no.7
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    • pp.353-367
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    • 2015
  • From the era of feature phone to the current smart phone which bloomed Social Network Games(SNGs), mobile games have held the top position as killer contents of mobile market. Mobile game has many constraints such as specification of device, network status, and external environment. Based on common perspective of game research, these factors have negative influences on flow of user. Even with these opinions, most mobile gamers have felt satisfaction. To explain this phenomenon, our research focuses on the constraints. Referring to theoretical concepts, we attempt to clarify the relationship between game features and flow through survey methodology. Our finding shows that the constraints have a positive effect on flow even though it disaccords with previous studies. Therefore, we argue that users of mobile game have sense of satisfaction not a cognitive overload as discomforts.

Using 3D Deep Convolutional Neural Network with MRI Biomarker patch Images for Alzheimer's Disease Diagnosis (치매 진단을 위한 MRI 바이오마커 패치 영상 기반 3차원 심층합성곱신경망 분류 기술)

  • Yun, Joo Young;Kim, Kyeong Tae;Choi, Jae Young
    • Journal of Korea Multimedia Society
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    • v.23 no.8
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    • pp.940-952
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    • 2020
  • The Alzheimer's disease (AD) is a neurodegenerative disease commonly found in the elderly individuals. It is one of the most common forms of dementia; patients with AD suffer from a degradation of cognitive abilities over time. To correctly diagnose AD, compuated-aided system equipped with automatic classification algorithm is of great importance. In this paper, we propose a novel deep learning based classification algorithm that takes advantage of MRI biomarker images including brain areas of hippocampus and cerebrospinal fluid for the purpose of improving the AD classification performance. In particular, we develop a new approach that effectively applies MRI biomarker patch images as input to 3D Deep Convolution Neural Network. To integrate multiple classification results from multiple biomarker patch images, we proposed the effective confidence score fusion that combine classification scores generated from soft-max layer. Experimental results show that AD classification performance can be considerably enhanced by using our proposed approach. Compared to the conventional AD classification approach relying on entire MRI input, our proposed method can improve AD classification performance of up to 10.57% thanks to using biomarker patch images. Moreover, the proposed method can attain better or comparable AD classification performances, compared to state-of-the-art methods.

Development of Information Technology for Smart Defense (Smart Defense 를 위한 IT 기술 개발)

  • Chung, Kyo-Il;Lee, So Yeon;Park, Sangjoon;Park, Jonghyun;Han, Sang-Cheol
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.38 no.3
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    • pp.323-328
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    • 2014
  • Recently, there has been demand for the convergence of IT (Information and communication Technologies, ICT) with defense, as has already been achieved in civilian fields such as healthcare and construction. It is expected that completely new and common requirements would emerge from the civilian and military domains and that the shape of war field would change rapidly. Many military scientists forecast that future wars would be network-centric and be based on C4I(Command, Control, Communication & Computer, Intelligence), ISR(Intelligence, Surveillance & Reconnaissance), and PGM(Precision Guided Munitions). For realizing the smart defense concept, IT should act as a baseline technology even for simulating a real combat field using virtual reality. In this paper, we propose the concept of IT-based smart defense with a focus on accurate detection in real and cyber wars, effective data communication, automated and unmanned operation, and modeling and simulation.

Cortical Thickness of Resting State Networks in the Brain of Male Patients with Alcohol Dependence (남성 알코올 의존 환자 대뇌의 휴지기 네트워크별 피질 두께)

  • Lee, Jun-Ki;Kim, Siekyeong
    • Korean Journal of Biological Psychiatry
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    • v.24 no.2
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    • pp.68-74
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    • 2017
  • Objectives It is well known that problem drinking is associated with alterations of brain structures and functions. Brain functions related to alcohol consumption can be determined by the resting state functional connectivity in various resting state networks (RSNs). This study aims to ascertain the alcohol effect on the structures forming predetermined RSNs by assessing their cortical thickness. Methods Twenty-six abstinent male patients with alcohol dependence and the same number of age-matched healthy control were recruited from an inpatient mental hospital and community. All participants underwent a 3T MRI scan. Averaged cortical thickness of areas constituting 7 RSNs were determined by using FreeSurfer with Yeo atlas derived from cortical parcellation estimated by intrinsic functional connectivity. Results There were significant group differences of mean cortical thicknesses (Cohen's d, corrected p) in ventral attention (1.01, < 0.01), dorsal attention (0.93, 0.01), somatomotor (0.90, 0.01), and visual (0.88, 0.02) networks. We could not find significant group differences in the default mode network. There were also significant group differences of gray matter volumes corrected by head size across the all networks. However, there were no group differences of surface area in each network. Conclusions There are differences in degree and pattern of structural recovery after abstinence across areas forming RSNs. Considering the previous observation that group differences of functional connectivity were significant only in networks related to task-positive networks such as dorsal attention and cognitive control networks, we can explain recovery pattern of cognition and emotion related to the default mode network and the mechanisms for craving and relapse associated with task-positive networks.

Alteration of Functional Connectivity in OCD by Resting State fMRI

  • Kim, Seungho;Lee, Sang Won;Lee, Seung Jae;Chang, Yongmin
    • Journal of Korea Multimedia Society
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    • v.24 no.4
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    • pp.583-592
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    • 2021
  • Obsessive-compulsive disorder (OCD) is a mental disorder in which a person repeated a particular thought or feels. The domain of beliefs and guilt predicted OCD symptoms. Although there were some neuroimaging studies investigating OCD symptoms, resting-state functional magnetic resonance imaging (rs-fMRI) study investigating intra-network functional connectivity associated with guilt for OCD is not reported yet. Therefore, in the current study, we assessed the differences between intra-network functional connectivity of healthy control group and OCD group using independent component analysis (ICA) method. In addition, we also aimed to investigate the correlation between changed functional connectivity and guilt score in OCD. Total 86 participants, which consisted of 42 healthy control volunteers and 44 OCD patients, acquired rs-fMRI data using the 3T MRI. After preprocessing the fMRI data, a functional connectivity was used for group independent component analysis. The results showed that OCD patients had higher score in emotion state in beliefs and lower functional connectivity in fronto-parietal network (FPN) than control group. A decrease of functional connectivity in FPN was negatively correlated with feelings of guilt in OCD. Our results suggest excessive increase in guilt negatively affect to process emotional state and behavior or cognitive processing by influencing intrinsic brain activity.

Performance Evaluation of User Mobility Management Scheme based-on Dwell Time Optimization for Effective Inter-working with Heterogeneous Networks under Cognitive Networking Environments (인지 네트워킹 환경 하에서 체류시간 관리 최적화를 통한 사용자 이동성 모델 기반 이동성 관리방법의 성능평가)

  • Choi, Yu-Mi;Kim, Jeong-Ho
    • Journal of the Institute of Electronics Engineers of Korea TC
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    • v.49 no.5
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    • pp.77-83
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    • 2012
  • The importance of mobility management is becoming to be one of the upcomming issues to be addressed to provide the converged services and the convergence of the heterogeneous network environments. In this paper, the new user mobility management scheme which can be utilized to model the user's mobility behaviors for interworking with heterogeneous overlay convergent networks under the time-varying radio propagation environment has been proposed. Thus user mobility management scheme based on user mobility model is considered in order to optimize the dwell time of users in the overlay convergent networks. This Mobile IP user mobility management will be very useful to model the user mobility behaviors and can be used to estimate the signaling traffic and frequency spectrum demands for massive data transfer for the heterogeneous overlay convergent networks.