• Journal of Mechanical Science and Technology
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• 제19권7호
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• pp.1424-1431
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• 2005

In this study, head injury by impact force was evaluated by numerical analysis with 3-dimensional finite element (FE) model. Brain deformation by frontal head impact was analyzed to evaluate traumatic brain injury (TBI). The variations of head acceleration and intra-cranial pressure (ICP) during the impact were analyzed. Relative displacement between the skull and the brain due to head impact was investigated from this simulation. In addition, pathological severity was evaluated according to head injury criterion (HIC) from simulation with FE model. The analytic result of brain damage was accorded with that of the cadaver test performed by Nahum et al.(1977) and many medical reports. The main emphasis of this study is that our FE model was valid to simulate the traumatic brain injury by head impact and the variation of the HIC value was evaluated according to various impact conditions using the FE model.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 하계종합학술대회 논문집(3)
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• pp.139-142
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• 2002

The brain of the human is the best model for the artificial intelligence and is studied by many natural, medical scientists and engineers. In the engineering department, the brain model becomes a main subject in the area of development of a system that can represent and think like human. In this paper, we approach and define the function of the brain biologically and especially, make a model for the function of cerebral cortex, known as a part that performs behavior inference and decision for sensitive information from the thalamus. Therefore, we try to make a model for the transfer process of the brain. The brain takes the sensory information from sensory organ, proceeds behavior inference and decision and finally, commands behavior to the motor nerves. We use the modular neural network in this model. finally, we would like to design the intelligent system that can sense, recognize, think and decide like the brain by learning the information process in the brain with the modular neural network.

• 한국수정란이식학회지
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• 제31권1호
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• pp.97-107
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• 2016

Although many diseases could be treated by the development of modern medicine, there are some incurable diseases including brain cancer, Alzheimer disease, etc. To study human brain cancer, various animal models were reported. Among these animal models, mouse models are valuable tools for understanding brain cancer characteristics. In spite of many mouse brain cancer models, it has been difficult to find a new target molecule for the treatment of brain cancer. One of the reasons is absence of large animal model which makes conducting preclinical trials. In this article, we review a recent study of molecular characteristics of human brain cancer, their genetic mutation and comparative analysis of the mouse brain cancer model. Finally, we suggest the need for development of large animal models using somatic cell nuclear transfer in translational research.

• Journal of Chest Surgery
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• 제28권5호
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• pp.437-442
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• 1995

We developed an experimental model of brain death using dogs. Brain death was caused by increasing the intracranial pressure[ICP suddenly by injecting saline to an epidural Foley catheter in five female mongrel dogs[weight, 20-25Kg .Hemodynamic and electrocardiographic changes were evaluated continuously during the process of brain death. 1. Abrupt rise of ICP after each injection of saline followed by a rapid decline to a new steady-state level within 15 minutes and the average volume required to induce brain death was 7.6$\pm$0.8ml.2. Body temperature, heart rate, mean pulmonary arterial pressure, left ventricular[LV enddiastolic pressure and cardiac output was not changed significantly during the process of brain death, but there was an increasing tendency.3. Mean arterial pressure and LV maximum +dP/dt increased significantly at the time of brain death.4. Hemodynamic collapse was developed within 140 minutes after brain death.5. Marked sinus bradycardia followed by junctional rhythm was seen in two dogs and frequent VPB`s with ventricular tachycardia was observed in one dog at the time of brain death. Hyperdynamic state develops and arrhythmia appears frequently at the time of brain death. Studies on the effects of brain death on myocardium and its pathophysiologic mechanism should be followed in the near future.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제5권10호
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• pp.1770-1782
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• 2011

A brain-computer interface (BCI) is a communication system that translates brain activity into commands for computers or other devices. In other words, BCIs create a new communication channel between the brain and an output device by bypassing conventional motor output pathways consisting of nerves and muscles. This is particularly useful for facilitating communication for people suffering from paralysis. Due to the low bit rate, it takes much more time to translate brain activity into commands. Especially it takes much time to input characters by using BCI-based typewriters. In this paper, we propose a brain-operated typewriter which is accelerated by a language prediction model. The proposed system uses three kinds of strategies to improve the entry speed: word completion, next-syllable prediction, and next word prediction. We found that the entry speed of BCI-based typewriter improved about twice as much through our demonstration which utilized the language prediction model.

• 한국방사선학회논문지
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• 제16권1호
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• pp.25-34
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• 2022

뇌 전산화단층촬영은 비침습성, 3차원 영상 제공, 저방사선량 등의 장점 때문에 뇌출혈과 같은 질병 진단을 위해 시행된다. 하지만 뇌 전산화단층영상 판독을 위한 전문의의 인력 공급 부족 및 막대한 업무량으로 인해 수많은 판독 오류 및 오진이 발생하고 있다. 이와 같은 문제를 해결하기 위해 객체 검출을 위한 다양한 인공지능 기술이 개발되고 있다. 본 연구에서는 뇌 전산화단층영상으로부터 뇌출혈 검출을 위한 딥러닝 기반 YOLOv5s 모델의 적용 가능성을 확인하였다. 또한 YOLOv5s 모델 학습 시 초매개변수를 변화시켜 학습된 모델의 성능을 평가하였다. YOLOv5s 모델은 backbone, neck 및 output 모듈로 구성하였고, 입력 CT 영상 내 뇌출혈로 의심되는 부위를 검출하여 출력할 수 있도록 하였다. YOLOv5s 모델 학습 시 활성화함수, 최적화함수, 손실함수 및 학습 횟수를 변화시켰고, 학습된 모델의 뇌출혈 검출 정확도 및 학습 시간을 측정하였다. 연구결과 학습된 YOLOv5s 모델은 뇌출혈로 의심되는 부위에 대한 경계 박스 및 해당 경계박스에 대한 정확도를 출력할 수 있음을 확인하였다. Mish 활성화함수, stochastic gradient descent 최적화함수 및 completed intersection over union 손실함수 적용 시 YOLOv5s 모델의 뇌출혈 검출 정확도 향상 및 학습 시간이 단축되는 결과를 확인하였다. 또한 YOLOv5s 모델의 뇌출혈 검출 정확도 및 학습 시간은 학습 횟수에 비례하여 증가하는 결과를 확인하였다. 따라서 YOLOv5s 모델은 뇌 전산화단층영상을 이용한 뇌출혈 검출을 위해 활용할 수 있으며, 최적의 초매개변수 적용을 통해 성능을 향상 시킬 수 있다.

• 한국초등과학교육학회지:초등과학교육
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• 제24권1호
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• pp.86-101
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• 2005

In this study, a brain-basrd model for science teaching and learning was developed based on the natural processes which human acquire knowledge about a natural object or on event, the major domains of science educational objectives of the national curriculum, and the human brain's organizational patterns and functions. In the model, each educational objective domain is related to the brain regions as follows: The affective domain is related to the limbic system, especially amygdala of human brain which is involved in emotions, the psychomotor domain is related to the occipital lobes of human brain which perform visual processing, temporal lobes which perform functions of language generating and understandng, and parietal lobes which receive and process sensory information and execute motor activities of body, and the cognitive domain is related to the frontal and prefrontal lobes which are involved in think-ing, planning, judging, and problem solving. The model is a kind of procedural model which proceed fiom affective domain to psychomotor domain, and to cognitive domain of science educational objective system, and emphasize the order of each step and authentic assessment at each step. The model has both properties of circularity and network of activities. At classrooms, the model can be used as various forms according to subjects and student characteristics. STS themes can be appropriately covered by the model.

• Korean Journal of Acupuncture
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• 제38권3호
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• pp.162-174
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• 2021

Objectives : We aimed to identify the antidepressant effect of liver tonification acupuncture treatment (ACU (LT); KI10, LR8, LU8, LR4) and four gate acupuncture treatment (ACU (FG); LI4, LR3) and its brain neural activity in the normal and chronic restraint stress (CRS)-induced mouse model. Methods : Firstly, normal mice were given ACU (LT) or ACU (FG) and the c-Fos expressions in each brain region were analyzed to examine brain neural activity. Secondly, CRS was administered to mice for 4 weeks, then ACU (LT) or ACU (FG) was performed for 2 weeks. The depression-like behavior was evaluated using open field test (OFT) before and after acupuncture treatment. Then, the c-Fos expressions in each brain region were analyzed to examine brain neural activity. Results : In normal mice, ACU (FG) regulated brain neural activities in the hypothalamus, hippocampus, and periaqueductal gray. ACU (LT) changed more brain regions in the prefrontal cortex, insular cortex, striatum, and hippocampus, including those altered by ACU (FG). In CRS-induced model, ACU (LT) alleviated depression-like behavior more than ACU (FG). Also, brain neural activities in the motor cortex area 2 (M2), agranular ventral part and piriform of insular cortex (AIV and Pir), and cornu ammonis (CA) 1 and CA3 of hippocampus were changed by ACU (LT), and those of AIV and CA3 were also changed by ACU (FG). As in normal mice, ACU (LT) resulted in changes in more brain regions, including those altered by ACU (FG) in CRS model. M2, Pir, and CA1 were only changed by ACU (LT) in depression model, suggesting that these brain regions reflect the specific effect of ACU (LT). Conclusions : ACU (LT) relieved depression-like behavior more than ACU (FG), and this acupuncture effect was associated with modulation of brain neural activities in the motor cortex, insular cortex, and hippocampus.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.181-184
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• 1998

CAM-Brain is a model to develop neural networks based in cellular automata by evolution, and finally aims at a model as and artificial brain,. In order to show the feasibility of evolutionary engineering to develop an artificial brain we have attempted to evolve a module of CAM-Brain for the problem to control a mobile robot, In this paper, we present some recent results obtained by analyzing the behaviors of the evolved neural module. Several experiments reveal a couple of problems that should be solved when CAM-Brain evolves to control a mobile robot. so that some modification of the original model is proposed to solve them. The modified CAM-Brain has evolved to behave well in a simulated environment, and a thorough analysis proves the power of evolution.

• International Journal of Computer Science & Network Security
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• 제22권5호
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• pp.143-148
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• 2022

Brain tumor classification is an important process that allows doctors to plan treatment for patients based on the stages of the tumor. To improve classification performance, various CNN-based architectures are used for brain tumor classification. Existing methods for brain tumor segmentation suffer from overfitting and poor efficiency when dealing with large datasets. The enhanced CNN architecture proposed in this study is based on U-Net for brain tumor segmentation, RefineNet for pattern analysis, and SegNet architecture for brain tumor classification. The brain tumor benchmark dataset was used to evaluate the enhanced CNN model's efficiency. Based on the local and context information of the MRI image, the U-Net provides good segmentation. SegNet selects the most important features for classification while also reducing the trainable parameters. In the classification of brain tumors, the enhanced CNN method outperforms the existing methods. The enhanced CNN model has an accuracy of 96.85 percent, while the existing CNN with transfer learning has an accuracy of 94.82 percent.