• Journal of Korean Neurosurgical Society
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• v.59 no.5
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• pp.485-491
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• 2016

Objective : The aims of the Korean Neuro-Trauma Data Bank System (KNTDBS) are to evaluate and improve treatment outcomes for brain trauma, prevent trauma, and provide data for research. Our purpose was to examine the mortality rates following traumatic brain injury (TBI) in a retrospective study and to investigate the sociodemographic variables, characteristics, and causes of TBI-related death based on data from the KNTDBS. Methods : From 2010 to 2014, we analyzed the data of 2617 patients registered in the KNTDBS. The demographic characteristics of patients with TBI were investigated. We divided patients into 2 groups, survivors and nonsurvivors, and compared variables between the groups to investigate variables that are related to death after TBI. We also analyzed variables related to the interval between TBI and death, mortality by region, and cause of death in the nonsurvivor group. Results : The frequency of TBI in men was higher than that in women. With increasing age of the patients, the incidence of TBI also increased. Among 2617 patients, 688 patients (26.2%) underwent surgical treatment and 125 patients (4.7%) died. The age distributions of survivors vs. nonsurvivor groups and mortality rates according the severity of the brain injury, surgical treatment, and initial Glasgow Coma Scale (GCS) scores were statistically significantly different. Among 125 hospitalized nonsurvivors, 70 patients (56%) died within 7 days and direct brain damage was the most common cause of death (80.8%). The time interval from TBI to death differed depending on the diagnosis, surgical or nonsurgical treatment, severity of brain injury, initial GCS score, and cause of death, and this difference was statistically significant. Conclusion : Using the KNTDBS, we identified epidemiology, mortality, and various factors related to nonsurvival. Building on our study, we should make a conscious effort to increase the survival duration and provide rapid and adequate treatment for TBI patients.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.3
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• pp.9-15
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• 1998

This paper presents an adaptive learning control method for unknown nonlinear systems by combining neuro control and iterative learning control techniques. In the present control system, an iterative learning controller (ILC) is used for a process of short term memory involved in a temporary adaptive and learning manipulation and a short term storage of a specific temporary action. The learning gain of the iterative learning law is estimated by using a neural network for an unknown system except relative degrees. The control informations obtained by ILC are transferred to a long term memory-based feedforward neuro controller (FNC) and accumulated in it in addition to the previously stored infonnations. This scheme is applied to a two link robot manipulator through simulations.

• The Korean Journal of Pain
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• v.8 no.1
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• pp.131-134
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• 1995

Hiccup is characterized by a myoclonus in the diaphragm, resulting in a sudden inspiration associated with an audible closure of the glottis. The reflex arc in hiccups comprises three pars: an afferent, a central and an efferent part. The afferent portion of the neural pathway of hiccup formation is composed of the vagus nerve, the phrenic nerve, and the sympathetic chain arising from T6 to T12. The hiccup center is localised in the brain stem and the efferent limb comprises phrenic pathways. All stimuli affecting the above mentioned reflex arc may produce hiccups. The pathogenesis of persistent hiccups is not known. Hiccup can present a symptom of a subphrenic abscess or gastric distention, and metabolic alterations may also cause hiccups. Numerous treatment modalities have been tried but with questionable success. We describe a patient whose persistant hiccups was treated successfully by a cervical epidural block.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.693-695
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• 2004

Among modem control method, the observer is being used widely because it has the advantage of the guarantee of reliability on financial problem, over heating, and physical shock. However, an existing state observer and a sliding observer have such problems that an experimenter needs to know dynamics and parameters of the system. And also, the high gain observer has such a problem that it has transient state at the beginning of the observation. In this paper, the neuro observer is proposed to improve these problems. The proposed observer complement a problem that occur from increase of gain of High-gain observer in proportion to the square number of observable state variables. And also, the proposed observer can tune the gain obtained by differentiating observational error at transient state automatically by using the backpropagation training method to stabilize the observational speed. To prove a performance of the proposed observer, it is simulated that the comparison between the state estimate performance of the proposed observer and that of sliding, high gain observer is made by using a sinusoidal input to the observer which consists of four layers in stable 2nd order system.

• Journal of the Society of Naval Architects of Korea
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• v.33 no.2
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• pp.44-55
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• 1996

Since the use of computer for the ship structural design around mid 1960``s, specially many researches on the midship section optimum design were carried out from 1980. For a rule-based optimum design case, there has been a problem of handling a discrete design variable such as plate thickness for a practical use. To deal with the discrete design variable problems and to develop an effective new method using artificial neural network for the ship structural design applications, Neuro-Optimizer combing Hopfield Neural Network and other Simulated Annealing is proposed as a new optimization method and then applied to the fundamental skeletal structures and Midship section of Tanker. From the numerical results, it is confirmed that Neuro-Optimizer could be used effectively as a new optimization method for the structural design.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.2
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• pp.203-208
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• 1998

This paper presents an adaptive fuzzy controller using an neural network and adaptation algorithm. Reference-model following neuro-fuzzy controller(RMFNFC) is invesgated in order to overcome the difficulty of rule selecting and defects of the membership function in the general fuzzy logic controller(FLC). RMFNFC is developed to tune various parameter of the fuzzy controller which is used for the discrete nonlinear system control. RMFNFC is trained with the identification information and control closed loop error. A closed loop error is used for design criteria of a fuzzy controller which characterizes and quantize the control performance required in the overall control system. A control system is trained up the controller with the variation of the system obtained from the identifier and closed loop error. Numerical examples are presented to control of the discrete nonlinear system. Simulation results show the effectiveness of the proposed controller.

• The KIPS Transactions:PartD
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• v.8D no.2
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• pp.175-180
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• 2001

This paper presents the optimal fraction of validation set to obtain a prediction accuracy of software failure count or failure time in the future by a neuro-fuzzy system. Given a fixed amount of training data, the most popular effective approach to avoiding underfitting and overfitting is early stopping, and hence getting optimal generalization. But there is unresolved practical issues : How many data do you assign to the training and validation set\ulcorner Rules of thumb abound, the solution is acquired by trial-and-error and we spend long time in this method. For the sake of optimal fraction of validation set, the variant specific fraction for the validation set be provided. It shows that minimal fraction of the validation data set is sufficient to achieve good next-step prediction. This result can be considered as a practical guideline in a prediction of software reliability by neuro-fuzzy system.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.17 no.4
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• pp.57-63
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• 2003

Aluminum Stranded Conductors Steel Reinforced (ACSR) in overhead transmission lines have slowly degraded due to pollutants in the air for a long period of time, so in the 2000, a number of them has been exceeded over their forecasted useful life. Since most of them are faced with assessment their present conditions in regard to economical maintenance, in this paper, we have suggested a method in order to evaluate the current condition of aged conductors by using dominant parameters such as elapsed years, environment index, and conductor configuration. A diagnostic system for predicting the deterioration grades corresponding to the lifetime of aged conductors is described, which is designed as an Adaptive Neuro-fuzzy Inference System (ANFIS) based on knowledge and experiences of experts. Applying this diagnostic system to practical transmission lines in domestic, it is shown that the system can be effectively used as a guide to perform nondestructive diagnosis and economical operation for old ACSR conductors.

• Journal of Environmental Science International
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• v.15 no.2
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• pp.141-155
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• 2006

This study is aimed at the development of a runoff forecasting model to solve the uncertainties occurring in the process of rainfall-runoff modeling and improve the modeling accuracy of the stream runoff forecasting, The study area is the downstream of Naeseung-chun. Therefore, time-dependent data was obtained from the Wolpo water level gauging station. 11 and 2 out of total 13 flood events were selected for the training and testing set of model. The model performance was improved as the measuring time interval$(T_m)$ was smaller than the sampling time interval$(T_s)$. The Neuro-Fuzzy(NF) and TANK models can give more accurate runoff forecasts up to 4 hours ahead than the Feed Forward Multilayer Neural Network(FFNN) model in standard above the Determination coefficient$(R^2)$ 0.7.

• Computers and Concrete
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• v.31 no.2
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• pp.85-95
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• 2023

Natural pozzolans are used as additives in cement to develop more durable and high-performance concrete. Pozzolanic activity index (PAI) is important for assessing the performance of a pozzolan as a binding material and has an important effect on the compressive strength, permeability, and chemical durability of concrete mixtures. However, the determining of the 28 days (short term) and 90 days (long term) PAI of concrete mixtures is a time-consuming process. In this study, to reduce extensive experimental work, it is aimed to predict the short term and long term PAIs as a function of the chemical compositions of various natural pozzolans. For this purpose, the chemical compositions of various natural pozzolans from Central Anatolia were determined with X-ray fluorescence spectroscopy. The mortar samples were prepared with the natural pozzolans and then, the short term and the long term PAIs were calculated based on compressive strength method. The effect of the natural pozzolans' chemical compositions on the short term and the long term PAIs were evaluated and the PAIs were predicted by using multiple linear regression (MLR) and adaptive neuro-fuzzy inference system (ANFIS) model. The prediction model results show that both reactive SiO₂ and SiO₂+Al₂O₃+Fe₂O₃ contents are the most effective parameters on PAI. According to the performance of prediction models determined with metrics such as root mean squared error (RMSE) and coefficient of correlation (R²), ANFIS models are more feasible than the multiple regression model in predicting the 28 days and 90 days pozzolanic activity. Estimation of PAIs based on the chemical component of natural pozzolana with high-performance prediction models is going to make an important contribution to material engineering applications in terms of selection of favorable natural pozzolana and saving time from tedious test processes.