• Journal of Mechanical Science and Technology
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• v.19 no.7
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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.

• Proceedings of the IEEK Conference
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• 2002.06c
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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.

• Journal of Embryo Transfer
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• v.31 no.1
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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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• v.28 no.5
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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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• v.5 no.10
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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.

• Journal of the Korean Society of Radiology
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• v.16 no.1
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• pp.25-34
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• 2022

Brain computed tomography (CT) is useful for brain lesion diagnosis, such as brain hemorrhage, due to non-invasive methodology, 3-dimensional image provision, low radiation dose. However, there has been numerous misdiagnosis owing to a lack of radiologist and heavy workload. Recently, object detection technologies based on artificial intelligence have been developed in order to overcome the limitations of traditional diagnosis. In this study, the applicability of a deep learning-based YOLOv5s model was evaluated for brain hemorrhage detection using brain CT images. Also, the effect of hyperparameters in the trained YOLOv5s model was analyzed. The YOLOv5s model consisted of backbone, neck and output modules. The trained model was able to detect a region of brain hemorrhage and provide the information of the region. The YOLOv5s model was trained with various activation functions, optimizer functions, loss functions and epochs, and the performance of the trained model was evaluated in terms of brain hemorrhage detection accuracy and training time. The results showed that the trained YOLOv5s model is able to provide a bounding box for a region of brain hemorrhage and the accuracy of the corresponding box. The performance of the YOLOv5s model was improved by using the mish activation function, the stochastic gradient descent (SGD) optimizer function and the completed intersection over union (CIoU) loss function. Also, the accuracy and training time of the YOLOv5s model increased with the number of epochs. Therefore, the YOLOv5s model is suitable for brain hemorrhage detection using brain CT images, and the performance of the model can be maximized by using appropriate hyperparameters.

• Journal of Korean Elementary Science Education
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• v.24 no.1
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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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• v.38 no.3
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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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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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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• v.22 no.5
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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.