• Journal of the Korean Society of Radiology
• /
• v.10 no.8
• /
• pp.645-652
• /
• 2016

In this study, based on the analysis of texture feature values of statistical properties. And we examined the normal and the applicability of the computer-aided diagnosis of cerebral infarction in the brain computed tomography images. The experiment was analyzed to evaluate the ROC curve recognition rate of disease using six parameters representing the feature values of the texture. As a result, it showed average mean 88%, variance 92%, relative smoothness 94%, uniformity of 88%, a high disease recognition rate of entropy 84%. However, it showed a slightly lower disease recognition rate and 58% for skewness. In the analysis using ROC curve, the area under the curve for each parameter indicates 0.886 (p = 0.0001) or more, resulted in a meaningful recognition of the disease. Further, to determine the cut-off values for each parameter are determined to be the prediction of disease through the computer-aided diagnosis.

• Journal of Internet Computing and Services
• /
• v.15 no.4
• /
• pp.9-20
• /
• 2014

Making good predictions about the outcome of one's actions would seem to be essential in the context of social interaction and decision-making. This paper proposes a computational model for learning articulated motion patterns for action recognition, which mimics biological-inspired visual perception processing of human brain. Developed model of cortical architecture for the unsupervised learning of motion sequence, builds upon neurophysiological knowledge about the cortical sites such as IT, MT, STS and specific neuronal representation which contribute to articulated motion perception. Experiments show how the model automatically selects significant motion patterns as well as meaningful static snapshot categories from continuous video input. Such key poses correspond to articulated postures which are utilized in probing the trained network to impose implied motion perception from static views. We also present how sequence selective representations are learned in STS by fusing snapshot and motion input and how learned feedback connections enable making predictions about future input sequence. Network simulations demonstrate the computational capacity of the proposed model for motion recognition.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2014.07a
• /
• pp.21-24
• /
• 2014

본 연구는 뇌손상(뇌졸중, 외상성 뇌손상, 뇌성마비 등)으로 인하여 손의 능동적 움직임이 결여되어 발생하는 관절의 구축, 근육의 단축, 근육의 탄력성 저하 등의 문제점을 분석하여 인체 역학적 모델에 따른 과학적 설계를 기반으로 환자의 손 기능 회복을 위하여 로봇 기술과 스마트폰의 융합을 통한 재활 로봇 보조 치료기를 설계하고 구현하였다. 제안된 시스템은 일반적인 근 경직을 치료하는 방법을 응용하여 IT 기술과 로봇기술을 융합하여 치료사들의 부담을 덜어 주고, 환자들에게 오랫동안 정확한 운동을 반복적으로 할 수 있도록 하는데 목적이 있다. 하나의 구동기로 2자유도의 움직임을 조절 할 수 있는 링크 매커니즘과 링크의 길이를 조절하여 신전(extension)과 과신전(Hyperextension)의 범위 조절이 가능하도록 로봇 플랫폼을 설계하였다. 또한 환자의 재활정도 및 상태에 적합한 운동속도, 운동반복횟수 등을 손쉽게 조작할 수 있는 등의 개인 맞춤형 재활훈련이 가능한 사용자 인터페이스를 설계 및 구현하였다.

• The KIPS Transactions:PartB
• /
• v.10B no.3
• /
• pp.339-346
• /
• 2003

In this paper, we propose a new method in which interpolation images are created by using a small number of axiai T2-weighted images instead of using many sectional images for 3D visualization of brain MR images. For image Interpolation, an important part of this process, we first segment a region of interest (ROI) that we wish to apply 3D reconstruction and extract the boundaries of segmented ROIs and MBR information. After the image size of interpolation layer is determined according to the changing rate of MBR size between top slice and bottom slice of segmented ROI, we find the corresponding pixels in segmented ROI images. Then we calculate a pixel's intensity of interpolation image by assigning to each pixel intensity weights detected by cube interpolation method. Finally, 3D reconstruction is accomplished by exploiting feature points and 3D voxels in the created interpolation images.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2018.05a
• /
• pp.424-424
• /
• 2018

인공신경망(Artificial Neural Network; ANN)은 뇌에 존재하는 생물학적 신경세포와 이들의 신호처리 과정을 수학적으로 묘사하여 뇌가 나타내는 지능적 형태의 반응을 구현한 것이다. 인공신경망은 학습(training)을 통해 입력과 출력으로 구성되는 하나의 시스템을 병렬적이고 비선형적으로 구축할 수 있으며, 유연한 모델링 특성으로 인하여 시스템 예측, 패턴인식, 분류 및 공정제어 등의 다양한 분야에서 활용되고 있다. 인공신경망에 대한 최초의 이론은 Muculloch and Pitts(1943)가 제안한 Perceptron에서 시작 되었으며, 기본적인 학습기법인 오차역전파 기법(back-propagation Algorithm) 이 1980년대에 들어 수학적으로 정립된 이후 여러 분야에서 활용되기 시작하였다). 본 연구에서는 하도추적, 구체적으로는 상류단의 복수의 수위관측을 이용하여 하류단의 수위를 예측하기 위하여 인공신경망 모델을 구성하였다. 대상하도는 금강유역의 용담댐과 대청댐 사이의 본류이며, 상류단 입력자료로써 본류에 있는 수통, 호탄 관측소 관측수위와 지류인 송천 관측소 관측수위를 고려하였다. 출력 값으로는 하류단의 옥천 관측소 수위를 3시간 및 6시간의 선행시간으로 예측하도록 인공신경망 모형을 구성하였다. 인공신경망의 학습(testing), 시험(testing), 검증(validation)을 위해 2000년부터 2012년까지 13년간의 시수위자료를 이용하여 학습을 진행하였으며, 2013년부터 2014년의 2년간의 수위자료를 이용한 시험을 통해 최적의 모형을 선정하였다. 또한 선정된 최적의 모형을 이용하여 2015년부터 2016년까지의 수위예측을 수행하였다.

• Journal of Christian Education in Korea
• /
• v.64
• /
• pp.145-171
• /
• 2020

Christian communities have long sought to find what type of moral judgment is appropriate and what the Christian behavior is, by taking the church's ethical norms and behavior patterns as objects of reflection. In the same context, Christian education also tried to base the psychological rationalism of J. Piaget and L. Kohlberg, but the reason-centered structural development theory was not the answer. In fact, the structural development theory, which emphasized autonomy while excluding emotions from the moral judgment process, over-emphasizing cognition or reason, eventually led to moral relativism, unlike what was intended. In addition, it was criticized for not being able to adequately elucidate the gap between human moral reasoning and behavior, and for attempting to interpret morality excessively within the context of social culture. Recently, these limitations of structural developmental theory have been reinterpreted by neuroethics, especially moral psychology theories, which claim that moral judgment ability is physically wired in the brain and relies heavily on networks between cortical and limbic system. The purpose of this paper is to review some of the newly emerged research themes of neuroethics, and then to discuss two main theories that explain morality in the perspective of neuroethics and the implications that Christian education should pay attention to.

• Science of Emotion and Sensibility
• /
• v.14 no.1
• /
• pp.101-116
• /
• 2011

Current researches on emotion detection classify emotions by using the information from facial, vocal, and bodily expressions, or physiological responses. This study was to review three representative emotion recognition methods, which were based on psychological theory of emotion. Firstly, literature review on the emotion recognition methods based on facial expressions was done. These studies were supported by Darwin's theory. Secondly, review on the emotion recognition methods based on changes in physiology was conducted. These researches were relied on James' theory. Lastly, a review on the emotion recognition was conducted on the basis of multimodality(i.e., combination of signals from face, dialogue, posture, or peripheral nervous system). These studies were supported by both Darwin's and James' theories. In each part, research findings was examined as well as theoretical backgrounds which each method was relied on. This review proposed a need for an integrated model of emotion recognition methods to evolve the way of emotion recognition. The integrated model suggests that emotion recognition methods are needed to include other physiological signals such as brain responses or face temperature. Also, the integrated model proposed that emotion recognition methods are needed to be based on multidimensional model and take consideration of cognitive appraisal factors during emotional experience.

• Proceedings of the Korea Contents Association Conference
• /
• 2018.05a
• /
• pp.167-168
• /
• 2018

The importance of business problem-solving creativity (BPSC) becomes crucial much more as competitive situations go on in the market. However, how to assess the BPSC remains an unsolved research issue yet in the literature. In this sense, this study proposes an exploratory analysis of the BPSC from the view of neuro-science experiments called fNIRS. The fNIRS represents a functional near-infrared spectroscopy, a new type of neuro-science research paradigm. This study proposes an exploratory level of how to conduct the fNIRS-based experiments to analyze the BPSC.

• Journal of Digital Convergence
• /
• v.13 no.8
• /
• pp.289-294
• /
• 2015

This paper proposes an autonomous machine learning method applicable to the BCI(Brain-Computer Interface) is based on the self-organizing Kohonen method, one of the exemplary method of unsupervised learning. In addition we propose control method of learning region and self machine learning rule using an interactive function. The learning region control and machine learning was used to control the side effects caused by interaction function that is based on the self-organizing Kohonen method. After determining the winner neuron, we decided to adjust the connection weights based on the learning rules, and learning region is gradually decreased as the number of learning is increased by the learning. So we proposed the autonomous machine learning to reach to the network equilibrium state by reducing the flow toward the input to weights of output layer neurons.

• Journal of Adhesion and Interface
• /
• v.25 no.2
• /
• pp.43-49
• /
• 2024

In recent years, the flexible organic synaptic transistor (FOST) has garnered attention for its flexibility, biocompatibility, ease of processability, and reduced complexity, which arise from using organic semiconductors as channel layers. These transistors can emulate the plasticity of the human brain with a simpler structure and lower fabrication costs compared to conventional inorganic synaptic devices. This makes them suitable for applications in next-generation wearable devices and soft robotics technologies. In FOST, the organic substrate is sensitive to the device preparation temperature; high-temperature treatment processes can cause thermal deformation of the organic substrate. Therefore, low-temperature solution-based processing techniques are essential for fabricating high-performance devices. This review summarizes the current research status of low-temperature solution-based FOST devices and presents the problems and challenges that need to be addressed.