• Journal of KIISE:Software and Applications
• /
• v.32 no.9
• /
• pp.920-926
• /
• 2005

Nearest neighbor algorithms classify an unseen input instance by selecting similar cases and use the discovered membership to make predictions about the unknown features of the input instance. The usefulness of the nearest neighbor algorithms have been demonstrated sufficiently in many real-world domains. In nearest neighbor algorithms, it is an important issue to assign proper weights to the attributes. Therefore, in this paper, we propose a new method which can automatically assigns to each attribute a weight of its importance with respect to the target attribute. The method has been implemented as a computer program and its effectiveness has been tested on a number of machine learning databases publicly available.

• The Journal of Korean Institute of Next Generation Computing
• /
• v.15 no.3
• /
• pp.16-24
• /
• 2019

Recently, research on emotion recognition based on EEG has attracted great interest from human-robot interaction field. In this paper, we propose a method of labeling using image-based EEG topography instead of evaluating emotions through self-assessment and annotation labeling methods used in MAHNOB HCI. The proposed method evaluates the emotion by machine learning model that learned EEG signal transformed into topographical image. In the experiments using MAHNOB-HCI database, we compared the performance of training EEG topography labeling models of SVM and kNN. The accuracy of the proposed method was 54.2% in SVM and 57.7% in kNN.

• Sleep Medicine and Psychophysiology
• /
• v.25 no.1
• /
• pp.15-20
• /
• 2018

Objectives: This study was designed to assess the change of heart rate variability (HRV) at resting, upright, and psychological stress states in depressive disorder patients. Methods: HRV was measured at resting, upright, and psychological stress states in 62 depressive disorder patients. We used visual analogue scale (VAS) score to assess tension and stress severity. Beck depression inventory (BDI) and state trait anxiety inventories I and II (STAI-I and II) were used to assess depression and anxiety severity, respectively. Differences between HRV indices and VAS score were evaluated using paired t-tests. Gender difference analysis was conducted with ANCOVA. Results: SDNN (standard deviation of normal to normal intervals), LF/HF (low frequency/high frequency), and VLF (very low frequency) were significantly increased, while NN50 and pNN50 were significantly decreased in the upright position compared to resting state. SDNN, RMSSD (root mean square of the differences of successive normal to normal intervals), and VLF were significantly increased, while pNN50 was significantly decreased in the psychological stress state compared to resting state. SDNN, NN50, and pNN50 were significantly lower in an upright position compared to a state of psychological stress, and LF, HF, and LF/HF showed no significant differences Conclusion: The LF/HF ratio was significantly increased after physical stress in depressive disorder. However, the LF/HF ratio was not significantly increased after psychological stress, and the change in LF/HF ratio after physical stress and psychological stress did not significantly differ from each other. Significant increase in SDNN, NN50, and pNN50 in an upright posture compared to psychological stress suggests that depressive patients react more sensitively to physical stress than psychological stress.

• 한국생물공학회:학술대회논문집
• /
• 2000.04a
• /
• pp.173-176
• /
• 2000

A neural network based controller (NN controller) was studied for the control of ammonia concentrations in biological processes. An ammonia FIA has been employed to on-line monitor the concentrations of ammonia in a bioreactor. The optimal neural network structure was investigated by computer simulation and found to be a 3(inputlayer)-2(hidden layer)-1(output layer). The NN controller had advantage over the PID controller, even though the former is more time consuming. The 3-2-1 NN controller has been used to control the ammonia concentrations in a simulated bioprocess and also in a real cultivation process of yeast, and its performance were investigated.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2000.11a
• /
• pp.133-136
• /
• 2000

우리는 인터넷을 통한 사용자의 선호도(preference)를 분석하고 협력적 여과 기술을 학습하여 유머문서를 추천하는 MrHumor 시스템을 구축하였다. MrHumor에서는 사용자집합이 유머문서 집합에 대하여 보여준 등급매김값을 토대로 사용집합의 백터공간(vector space)를 설정하고 노이즈에 강하면서 효율적인 학습을 위해 선형 PCA를 이용하여 축소된 2차원 공간상에서 유머문서의 통계적 특성을 반영하여 적응형 k-NN으로 지엽성을 적적히 조절하여 새로운 문서에 대한 선호도를 추정하게 된다.

• International Journal of Control, Automation, and Systems
• /
• v.3 no.4
• /
• pp.612-619
• /
• 2005

A robust adaptive speed sensorless induction motor direct torque control (DTC) using a neural network (NN) is presented in this paper. The inherent lumped uncertainties of the induction motor DTC system such as parametric uncertainty, external load disturbance and unmodeled dynamics are approximated by the NN. An additional robust control term is introduced to compensate for the reconstruction error. A control law and adaptive laws for the weights in the NN, as well as the bounding constant of the lumped uncertainties are established so that the whole closed-loop system is stable in the sense of Lyapunov. The effect of the speed estimation error is analyzed, and the stability proof of the control system is also proved. Experimental results as well as computer simulations are presented to show the validity and efficiency of the proposed system.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.581-586
• /
• 2003

This paper presents control designs using neural networks (NN) for a XY positioning table. The proposed neurocontroller is composed of an outer PD tracking loop for stabilization of the fast flexible-mode dynamics and an NN inner loop used to compensate for the system nonlinearities. A tuning algorithm is given for the NN weights, so that the NN compensation scheme becomes adaptive, guaranteeing small tracking errors and bounded weight estimates. Formal nonlinear stability proofs are given to show that the tracking error is small. The proposed neuro-controller is implemented and tested on an IBM PC-based XY positioning table, and is applicable to many precision XY tables. The algorithm, simulation, and experimental results are described. The experimental results are shown to be superior to those of conventional control.

• Smart Structures and Systems
• /
• v.16 no.6
• /
• pp.1133-1145
• /
• 2015

A new neural network (NN) predictive controller (NNPC) algorithm has been developed and tested in the computer simulation of active control of a nonlinear structure. In the present method an NN is used as a predictor. This NN has been trained to predict the future response of the structure to determine the control forces. These control forces are calculated by minimizing the difference between the predicted and desired responses via a numerical minimization algorithm. Since the NNPC is very time consuming and not suitable for real-time control, it is then used to train an NN controller. To consider the effectiveness of the controller on probability of damage, fragility curves are generated. The approach is validated by using simulated response of a 3 story nonlinear benchmark building excited by several historical earthquake records. The simulation results are then compared with a linear quadratic Gaussian (LQG) active controller. The results indicate that the proposed algorithm is completely effective in relative displacement reduction.

• Journal of Advanced Marine Engineering and Technology
• /
• v.28 no.6
• /
• pp.1010-1016
• /
• 2004

In this paper, a scheme for estimating the parameters of solar cells and a NN-based emulator for predicting the maximum power point are presented. The diode model with series and shunt resistors is used to estimate parameters highly affecting its V-I characteristic curve and both a real-coded genetic algorithm and the model adjustment technique are employed. For implementing the emulator, a multi-layered neural network incorporating with the BP algorithm is used. A set of simulation works using both field data and generated data are carried out to demonstrate the effectiveness of the proposed method.

• Journal of Korean Institute of Industrial Engineers
• /
• v.25 no.1
• /
• pp.111-124
• /
• 1999

This evapaper is toluate the forecasting performance of three neural network(NN) approaches against ARIMA model using the famous time series analysis competition data. The first NN approach is to analyze the second Makridakis (M2) Competition Data using Multilayer Perceptron (MLP) that has been the most popular NN model in time series analysis. Since it is recently known that MLP suffers from bias/variance dilemma, two approaches are suggested in this study. The second approach adopts Cascade Correlation Network (CCN) that was suggested by Fahlman & Lebiere as an alternative to MLP. In the third approach, a time series is separated into two series using Noise Filtering Network (NFN) that utilizes autoassociative memory function of neural network. The forecasts in the decomposition analysis are the sum of two prediction values obtained from modeling each decomposed series, respectively. Among the three NN approaches, Decomposition Analysis shows the best forecasting performance on the M2 Competition Data, and is expected to be a promising tool in analyzing socio-economic time series data because it reduces the effect of noise or outliers that is an impediment to modeling the time series generating process.