• Journal of Mechanical Science and Technology
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• v.16 no.4
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• pp.454-467
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• 2002

It has been established that a crack has an important effect on the dynamic behavior of a structure. This effect depends mainly on the location and depth of the crack. Toidentifythelocation and depth of a crack in a structure, a method is presented in this paper which uses neuro-fuzzy-evolutionary technique, that is, Adaptive-Network-based Fuzzy Inference System (ANFIS) solved via hybrid learning algorithm (the back-propagation gradient descent and the least-squares method) and Continuous Evolutionary Algorithms (CEAs) solving sir ale objective optimization problems with a continuous function and continuous search space efficiently are unified. With this ANFIS and CEAs, it is possible to formulate the inverse problem. ANFIS is used to obtain the input(the location and depth of a crack) - output(the structural Eigenfrequencies) relation of the structural system. CEAs are used to identify the crack location and depth by minimizing the difference from the measured frequencies. We have tried this new idea on beam structures and the results are promising.

• Korean Journal of Cognitive Science
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• v.17 no.4
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• pp.375-398
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• 2006

Humans learn by observing, hearing, imitating, doing, and feeling. The brain(cortex) is the central tore of this process. The recent rapid progress of brain science and the active interdisciplinary collaboration between brain science and cognitive science opens a new possibility. That is a new research Held called 'Brain-Based learning Science', 'Edutational Neuroscienre', or 'NeuroEduration' This study reviews the nature and basic assumptions of brain-based learning science, current directions in educational neuroscience research, the neuro-myths, educational implications of neuroscience, and a possibility of making a meaningful connection between brain science and education. Also the future prospects and limitations of the brain-based learning science are discussed.

• Molecular & Cellular Toxicology
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• v.5 no.2
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• pp.147-152
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• 2009

This study was conducted to evaluate the inflammatory mechanisms of LPS-stimulated BV-2 microglial cells. The inflammation mechanism was evaluated in BV-2 cells with or without LPS treated using the Affymetrix microarray analysis system. The microarray analysis revealed that B cell receptor signaling pathway, cytokine-cytokine receptor interaction, Jak-STAT signaling pathway, MAPK signaling pathway, Neuro-active ligand-receptor interaction, TLR signaling path-way, and T cell receptor signaling pathway-related genes were up-regulated in LPS stimulated BV-2 cells. Selected genes were validated using real time RTPCR. These results can help an effective therapeutic approach to alleviating the progression of neuro-in-flammatory diseases.

• Annual Conference on Human and Language Technology
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• 1993.10a
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• pp.317-328
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• 1993

본 논문은 신경망과 퍼지 이론을 결합한 한국어 철자 교정기 KSCNN(Korean Spelling Corrector using Neural Network)에 대하여 기술한다. KSCNN은 퍼셉트론(perceptron) 학습을 이용한 연상 메모리(associative memory)로 구성되며 자판 배열 특성을 고려한 퍼지 멤버쉽 함수에 의해 신경망의 입력값을 정한다. 본 철자 교정기의 장점은 인지적인 방법으로 철자를 교정하기 때문에 기존의 VA나 BNA와는 달리 오류의 종류에 영향을 받지 않으며 교정된 철자나 후보자들에 대한 견인값(attraction value)을 측정하여 시스템의 신뢰도를 높일 수 있다는 데 있다. 또한, 본 논문은 실험을 통해서 퍼지 멤버쉽 함수에 의한 입력 노드의 활성화가 자판 배열특성을 고려할 수 있기 때문에 시스템의 성능을 향상시킨다는 사실을 보여준다.

• Journal of Korean Academy of Nursing
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• v.48 no.3
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• pp.298-310
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• 2018

Purpose: The purpose of this study was to examine the effects of the laughter therapy program on perceived stress and psycho-neuro-endocrine-immune responses in obese women. Methods: A nonequivalent control group with a pretest-posttest design was used. The participants (n=60), whose age ranged from 30 to 50 years (pre-menopausal and body mass index of over $25kg/m^2$), were assigned to the experimental group (n=24) or control group (n=26). The experimental group was provided with the laughter therapy program (12 sessions) for 6 weeks. Results: There were significant differences in perceived stress, psychological stress response, fasting blood sugar, interleukin-6, and tumor necrosis factor alpha between the two groups after the program. However, there were no significant differences in normalized low frequency (norm LF), normalized high frequency (norm HF), LF/HF ratio, and cortisol between the two groups after the program. Conclusion: It was found that the laughter therapy program had positive effects on some variables in terms of perceived stress and psycho-neuro-endocrine-immuno responses. It is suggested that the laughter therapy in this study can provide the direction for developing a program for obese women.

• Steel and Composite Structures
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• v.34 no.1
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• pp.155-170
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• 2020

Different parameters potentially affect the properties of corroded reinforced concrete beams. However, the high number of these parameters and their dependence cause that the effectiveness of the parameters could not be simply identified. In this study, an adaptive neuro-fuzzy inference system (ANFIS) was employed to determine the most influencing parameters on the properties of the corrosion-damaged reinforced concrete beams. 207 ANFIS models were developed to analyze the collected data from 107 reinforced concrete (RC) beams. The impact of 23 input parameters on nine output factors was investigated. The results of the paper showed the order of influence of each input parameter on the outputs and revealed that the input parameters regarding the uncorroded properties of concrete beams are the most influencing factors on the corresponding corroded properties of the beams.

• Journal of Integrative Natural Science
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• v.11 no.4
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• pp.167-183
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• 2018

Electroencephalogram (EEG) recording provides a new way to support human-machine communication. It gives us an opportunity to analyze the neuro-dynamics of human cognition. Machine learning is a powerful for the EEG classification. In addition, machine learning can compensate for high variability of EEG when analyzing data in real time. However, the optimal EEG electrode location must be prioritized in order to extract the most relevant features from brain wave data. In this paper, we propose an intelligent system model for the extraction of EEG data by training the optimal electrode location of EEG in a specific problem. The proposed system is basically a fuzzy system and uses a neural network structurally. The fuzzy clustering method is used to determine the optimal number of fuzzy rules using the features extracted from the EEG data. The parameters and weight values found in the process of determining the number of rules determined here must be tuned for optimization in the learning process. Genetic algorithms are used to obtain optimized parameters. We present useful results by using optimal rule numbers and non - symmetric membership function using EEG data for four movements with the right arm through various experiments.

• Journal of the Korean Society of Safety
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• v.37 no.1
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• pp.78-87
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• 2022

In the risk assessment of urban railway systems, a hazard log is created by identifying hazards from accident and failure data. Then, based on a risk matrix, evaluators analyze the frequency and severity of the occurrence of the hazards, conduct the risk assessment, and then establish safety measures for the risk factors prior to risk control. However, because subjective judgments based on the evaluators' experiences affect the risk assessment results, a more objective and automated risk assessment system must be established. In this study, we propose a risk assessment model in which an adaptive neuro-fuzzy inference system (ANFIS), which is combined in artificial neural networks (ANN) and fuzzy inference system (FIS), is applied to the risk assessment of urban railway systems. The newly proposed model is more objective and automated, alleviating the limitations of risk assessments that use a risk matrix. In addition, the reliability of the model was verified by comparing the risk assessment results and risk control priorities between the newly proposed ANFIS-based risk assessment model and the risk assessment using a risk matrix. Results of the comparison indicate that a high level of accuracy was demonstrated in the risk assessment results of the proposed model, and uncertainty and subjectivity were mitigated in the risk control priority.

• Journal of Korea Entertainment Industry Association
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• v.15 no.5
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• pp.45-53
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• 2021

Digital morph expresses the imaginary beyond the representation of reality by expressing the narrative effect characteristically. In particular, the effect of affect can be considered to be a characteristic of digital cinema as a post-cinema. In (2013), Morphing image prominently shows the characteristics of post-cinema. By actively utilizing software technology, this film gives a shocking effect by expressing the magical image. Paying attention to the post-cinematic characteristics of morphing different from classical film, this article treated the characteristics of digital morphing. The digital morphing presents the flow of affect visualizing uncanny phenomenon of body transformation. This evokes concept of neuro-image which Patricia Pisters distinguished the neuropsychiatric pathology that appears actively on the contemporary digital screen. The Neuro-image goes beyond the temporality of Deleuze's time-image presenting future. Allegedly, the morphing of presents the neuro-images when Nina's body changed to hybrid body with black swan. Digital Morphing technique provides a shocking effect, showing delirium when the body bizarrely deformed while dancing ballet. This is different from the attraction of the morphing in film, it expresses the emotion of the neoliberal era beyond representation. In conclusion, the digital morphing presents the neuro-image system modulating the shock. This shows the characteristics of digital film which interacting and controling the shock effect as post-cinema.

• Korean Journal of Medicinal Crop Science
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• v.27 no.6
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• pp.365-375
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• 2019

Background: Oxidative stress plays an important role in neuro-degenerative disorders such as Alzheimer's disease. Oxidative stress is mediated by reactive oxygen species (ROS), which are implicated in the pathogenesis of numerous diseases, and account for the toxicity of a wide range of compounds. Methods and Results: In order to study the neuro-protective effect of the complex extracts of Aster scaber Thunberg (AS) and Chaenoleles sinensis Koehne (CSK) against hydrogen peroxide in PC12 cells, cell viability was evaluated by the MTT assay using tetrazole, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide and the intracellular ROS levels were determined the by 2',7'-dichlorofluorescein diacetate (DCF-DA) assay. In order to examine the anti-amnesic effects of the complex extracts of AS and CSK, behavioral tests were performed on male ICR mice. The ameliorating effect of the complex extracts against Aβ_1-42-induced learning and memory impairment was analyzed by y-maze and passive avoidance tests. The AS and CSK extracts showed neuro-protective activity both in vitro and in vivo, and the neuro-protective effect of their 60 : 40 (AS : CSK) mixture was better than that of the other mixtures. Moreover, the complex extracts synergistically inhibited acetylcholinesterase activity and rapid peroxidation. Conclusions: A mixture of the AS and CSK extracts could be used to develop functional foods and serve as raw materials for the development of therapeutics against Alzheimer's disease.