• PNF and Movement
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• v.4 no.1
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• pp.51-62
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• 2006

Purpose : The purposes of this study were to test the effect of proprioceptive and vestibular sensory input on expression of BDNF after traumatic brain injury in the rat. Subject : The control group was sacrificed at 24 hours after traumatic brain injury. The experimental group I was housed in standard cage for 7 days. The experimental group II was housed in standard cage after intervention to proprioceptive and vestibular sensory(balance training) for 7 days. Method : Traumatic brain injury was induced by weight drop model and after operation they were housed in individual standard cages for 24 hours. After 7th day, rats were sacrificed and cryostat coronal sections were processed individual1y in goat polyclonal anti-BDNF antibody. The morphologic characteristics and the BDNF expression were investigated in injured hemisphere section and contralateral brain section from immunohistochemistry using light microscope. Result : The results of this experiment were as follows: 1. In control group, cell bodies in lateral nucleus of cerebellum, superior vestibular nucleus, purkinje cell layer of cerebellum and pontine nucleus changed morphologically. 2. The expression of BDNF in contralateral hemisphere of group II were revealed. 3. On 7th day after operation, immunohistochemical response of BDNF in lateral nucleus, superior vestibular nucleus, purkinje cell layer and pontine nucleus appeared in group II. Conclusion : The present results revealed that intervention to proprioceptive and vestibular sensory input is enhance expression of BDNF and it is useful in neuronal reorganization improvement after traumatic brain injury.

• International Journal of Quality Innovation
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• v.3 no.2
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• pp.113-131
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• 2002

The relationships among multi-dimensional data (such as medical examination data) with ambiguity and variation are difficult to explore. The traditional approach to building a data classification system requires the formulation of rules by which the input data can be analyzed. The formulation of such rules is very difficult with large sets of input data. This paper first describes two classification approaches using back-propagation (BP) neural network and Mahalanobis distance (MD) classifier, and then proposes two classification approaches for multi-dimensional feature selection. The first one proposed is a feature selection procedure from the trained back-propagation (BP) neural network. The basic idea of this procedure is to compare the multiplication weights between input and hidden layer and hidden and output layer. In order to simplify the structure, only the multiplication weights of large absolute values are used. The second approach is Mahalanobis-Taguchi system (MTS) originally suggested by Dr. Taguchi. The MTS performs Taguchi's fractional factorial design based on the Mahalanobis distance as a performance metric. We combine the automatic thresholding with MD: it can deal with a reduced model, which is the focus of this paper In this work, two case studies will be used as examples to compare and discuss the complete and reduced models employing BP neural network and MD classifier. The implementation results show that proposed approaches are effective and powerful for the classification.

• Journal of Ocean Engineering and Technology
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• v.35 no.5
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• pp.336-346
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• 2021

The prediction of wave conditions is crucial in the field of marine and ocean engineering. Hence, this study aims to predict the significant wave height through machine learning (ML), a soft computing method. The adopted metocean data, collected from 2012 to 2020, were obtained from the Korea Institute of Ocean Science and Technology. We adopted the feedforward neural network (FNN) and long-short term memory (LSTM) models to predict significant wave height. Input parameters for the input layer were selected by Pearson correlation coefficients. To obtain the optimized hyperparameter, we conducted a sensitivity study on the window size, node, layer, and activation function. Finally, the significant wave height was predicted using the FNN and LSTM models, by varying the three input parameters and three window sizes. Accordingly, FNN (W48) (i.e., FNN with window size 48) and LSTM (W48) (i.e., LSTM with window size 48) were superior outcomes. The most suitable model for predicting the significant wave height was FNN(W48) owing to its accuracy and calculation time. If the metocean data were further accumulated, the accuracy of the ML model would have improved, and it will be beneficial to predict added resistance by waves when conducting a sea trial test.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.2
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• pp.115-120
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• 1996

In this paper, a sliding mode controller which is characterized by high accuracy, fast response and robustness is applied to speed control of AC-SERVO motor. The control input is changed to the continuous one in the boundary layer to reduce the chattering phenomenon, and the boundary layer converges to zero when the state variables of system reach to steady state values. The integral compensator is added to reduce steady state error and to provide the continuous torque reference. The acceleration which is necessary for the sliding plane is estimated by an obsever. Sliding surface is included in control input to enhance the robustness and transient response without increasing sliding mode controller gain. The proposed controller is implemented by DSP(digital signal processor). The effectiveness of the proposed scheme is demonstrated through experimental works.

• Journal of Korean Society of Water and Wastewater
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• v.21 no.3
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• pp.305-314
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• 2007

Forecasting of water quality variation is not an easy process due to the complicated nature of various water quality factors and their interrelationships. The objective of this study is to test the applicability of neural network models to the forecasting of the water quality at Dalchun station in Han River. Input data is consist of monthly data of concentration of DO, BOD, COD, SS and river flow. And this study selected optimal neural network model through changing the number of hidden layer based on input layer(n) from n to 6n. After neural network theory is applied, the models go through training, calibration and verification. The result shows that the proposed model forecast water quality of high efficiency and developed web-based water quality forecasting system after extend model

• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.900-904
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• 1991

In this paper, our study for the recognition of Hand-written Korean characters, Arabic numbers and alphabets by neural netwoks. This System extracts feature of character by using the MESH feature point of handwritten character, Arabic numbers and alphabets. To reduce the input image data, features are extracted from each input images. A MLP(multi-layer perceptron) with one hidden layer was trained with a modified BEP(back error propagation) algorithm. This method extracts feature sets of the characters directly from the scanner and can enhance computation speed without using the special preprocesses such as size normalization, smoothing, and thinning.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.26 no.4
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• pp.402-409
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• 2017

Selection of optimal cutting conditions is important for improving productivity and implementing efficient process control in metal machining. In this study, improvement of cutting conditions in machining using end-mills is studied by using deep-layered neural networks, which comprise an input layer, output layer, and two hidden layers. System networks are designed with inputs as cutting conditions, and they output the cutting force. A pseudo-inverse network is designed that has the adjustable cutting condition as output and cutting force and other cutting conditions as input. The combination of the system network and pseudo-inverse network enables selection or improvement of cutting conditions that results in the expected cutting force.

• Wind and Structures
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• v.15 no.3
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• pp.247-261
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• 2012

Some numerical investigations are presented concerning the response of a given plate under turbulence driven excitations. Three different input loads are simulated according to the wall pressure distributions derived from the models proposed by Corcos, Efimtsov and Chase, respectively. Modal solutions (finite element based) are used for building the modal stochastic responses in the sub-critical aerodynamic frequency range. The parametric investigations concern two different values of the structural damping and three values of the boundary layer thickness. A final comparison with available experimental data is also discussed. The results demonstrate that the selection of the adequate TBL input model is still the most critical step in order to get a good prediction.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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• pp.587-591
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• 1996

Usually triangular patches are used to transfer geometric shape in Rpaid Prototyping CAM system. STL, a list of triangles, is de facto in RP industry. Because STL has no topology data, it can cause errornous results. So, STL should be verified before using. After adding support structures to anchor the part to the platform and to prevent sagging or distortion, slicing and layer by layer manufacturing process are done. But triangular patch is surface model and cannot provide dufficient information on geometry in the above processes. So, geometric modeling is necessary in verifying STL, adding support structures, and slicing. It is natural that triangle based modeling is the best when traingular patches are used as input. Considering support structures, solid and faces coexist in RP process. Therefore non-manifold modeler is required. In this study, triangle based non-manifold geometric modeling is proposed for RP system consitent with STL input.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.215-218
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• 1995

In this paper, a sliding mode controller (SMC) which can be characterized by high accuracy, fast response and robustness is applied to speed control of AC-SERVO motor. The control input is changed to continuous one in the boundary layer to reduce the chattering phenomenon, and the boundary layer converges to zero when the state variables of system reach to steady state values. The integral compensator is added to reduce steady state error and to provide the continuous torque reference. The acceleration which is necessary to get the sliding plane is estimated by an observer. Sliding surface is included in control input to enhance the robustness and transient response without increasing sliding mode controller gain. The proposed controller is implemented by DSP(digital signal processor). The effectiveness of the proposed control scheme for speed controller is shown by the real-time experimental results in the paper.