• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제13권1호
• /
• pp.12-18
• /
• 2013

Motor imagery classification in electroencephalography (EEG)-based brain-computer interface (BCI) systems is an important research area. To simplify the complexity of the classification, selected power bands and electrode channels have been widely used to extract and select features from raw EEG signals, but there is still a loss in classification accuracy in the state-of- the-art approaches. To solve this problem, we propose a discriminative feature extraction algorithm based on power bands with principle component analysis (PCA). First, the raw EEG signals from the motor cortex area were filtered using a bandpass filter with ${\mu}$ and ${\beta}$ bands. This research considered the power bands within a 0.4 second epoch to select the optimal feature space region. Next, the total feature dimensions were reduced by PCA and transformed into a final feature vector set. The selected features were classified by applying a support vector machine (SVM). The proposed method was compared with a state-of-art power band feature and shown to improve classification accuracy.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
• /
• 제16권4호
• /
• pp.477-481
• /
• 2010

본 연구에서는 무선 센서 네트워크를 기반으로 침대 주변에서 사람의 행동유형을 분류하고자 한다. 침대 주변에서 사람의 다섯가지 행동유형과 세가지 상태들을 정의하고, 이들을 상태기계로 표현하였다. 움직임 감지 및 진동센서들을 통해 행동유형 관련 데이터들을 수집하고 이로 부터 특정벡터를 추출하였다. 행동유형별 특징벡터와 상태기계를 기초로 행동유형 모델을 정립하였고, 정립된 모델의 유효성 검증을 위해 실험을 실시한 후 행동유형 모델을 보정하였다. 이러한 실험결과들은 침대 주변에서 사람들이 행하는 행동유형들이 잘 분류될 수 있음을 보여준다.

• 한국멀티미디어학회논문지
• /
• 제20권9호
• /
• pp.1509-1518
• /
• 2017

Although several methods have been used to assess the pain levels, few practical methods for classifying presence or absence of the pain using pattern classifiers have been suggested. The aim of this study is to design an pattern classifier that classifies the presence or absence of the pain using electrocardiogram (ECG). We measured the ECG signal from 10 subjects with the painless state and the pain state(Induced by mechanical stimulation). The 10 features of heart rate variability (HRV) were extracted from ECG - MeanRRI, SDNN, rMSSD, NN50, pNN50 in the time domain; VLF, LF, HF, Total Power, LF/HF in the frequency domain; and we used the features as input vector of the pattern classifier's artificial neural network (ANN) / support vector machine (SVM) for classifying the presence or absence of the pain. The study results showed that the classifiers using ANN / SVM could classify the presence or absence of the pain with accuracies of 81.58% / 81.84%. The proposed classifiers can be applied to the objective assessment of pain level.

• Journal of Information Processing Systems
• /
• 제12권3호
• /
• pp.436-454
• /
• 2016

This research presents the battery discharge rate models for the energy consumption of mobile phone batteries based on machine learning by taking into account three usage patterns of the phone: the standby state, video playing, and web browsing. We present the experimental design methodology for collecting data, preprocessing, model construction, and parameter selections. The data is collected based on the HTC One X hardware platform. We considered various setting factors, such as Bluetooth, brightness, 3G, GPS, Wi-Fi, and Sync. The battery levels for each possible state vector were measured, and then we constructed the battery prediction model using different regression functions based on the collected data. The accuracy of the constructed models using the multi-layer perceptron (MLP) and the support vector machine (SVM) were compared using varying kernel functions. Various parameters for MLP and SVM were considered. The measurement of prediction efficiency was done by the mean absolute error (MAE) and the root mean squared error (RMSE). The experiments showed that the MLP with linear regression performs well overall, while the SVM with the polynomial kernel function based on the linear regression gives a low MAE and RMSE. As a result, we were able to demonstrate how to apply the derived model to predict the remaining battery charge.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
• /
• 제5B권4호
• /
• pp.350-357
• /
• 2005

This paper presents a mechanical sensorless vector-controlled system with parameter identification by the aid of image processor. Based on the flux observer and the model reference adaptive system method, the proposed sensorless system includes rotor speed estimation and stator resistance identification using flux errors. Since the mathematical model of this system is constructed in a synchronously rotating reference frame, a linear model is easily derived for analyzing the system stability, including motor operating state and parameter variations. Because it is difficult to identify rotor resistance simultaneously while estimating rotor speed, a low-accuracy image processor is used to measure the mechanical axis position for calculating the rotor speed at a steady-state operation. The rotor resistance is identified by the error between the estimated speed using the estimated flux and the calculated speed using the image processor. Finally, the validity of this proposed system has been proven through experimentation.

• 한국통신학회논문지
• /
• 제33권10A호
• /
• pp.1006-1012
• /
• 2008

Duvall 구조에 기초하여 코히런트(coherent), 인코히런트(incoherent) 간섭을 제거하는 효율적인 적응 빔 형성방법을 제시한다. 하나의 상관벡터를 이용하는 기존방식과 달리, 제안된 방법에서는 여러 개의 상관벡터를 이용하여 가중벡터의 차원을 크게 한다. 가중벡터 차원의 증가로 빔 형성기의 SINR(signal-to-interference plus noise ratio) 성능을 개선할 수 있으며, 더 많은 간섭을 제거 할 수 있다. 시뮬레이션 결과에 따르면, 제안방식은 기존방식에 비해 빠른 수렴특성, 우수한 정상상태(steady-state)에서의 SINR 성능을 보여준다.

• 대한전기학회논문지
• /
• 제35권7호
• /
• pp.261-271
• /
• 1986

This paper describes a method for the state estimation considering current measurement component and detection of the bad data. The state values are estimated by weighted least square method in which measurement vector included bus injection current and line current. The bad data are detected using standardized variable of normal distribution and identified using sensitivity coefficients. When the bad data were occured by the bad measurement values. The results of the application to the model power system reveal the effectiveness of the presented algorithms.

• 전력전자학회:학술대회논문집
• /
• 전력전자학회 1999년도 전력전자학술대회 논문집
• /
• pp.282-286
• /
• 1999

This paper presents a vector control of parallel drive, a beatless control and a low switching PWM technique for the propulsion system of Electric car as transient state which are included interrupting line voltage, changing line voltage slowly, suddenly, regenerating light load and starting from backward running. Improved performance and a validation of proposed method is shown by the experimental results using a 1.65MVA IGBT VVVF inverter and inertia load equivalent to 160 tons electric cars through a running and transient state test.

• 전자공학회논문지B
• /
• 제31B권8호
• /
• pp.39-46
• /
• 1994

In this paper a state reconstruction filter for linear discrete-time stochastic systems with unknown inputs and noises is presented. The suggested filter can estimate the system state vector and the unknown inputs simultaneously As an extension of the filter a fault diagnosis filter for linear discrete-time stochastic systems with unknown inputs and noises is presented for each filters the optimal gain determination methods which minimize the variance of the state reconstruction errorare presented. Finally the usability of the filtersis shown via numerical examples.

• Bulletin of the Korean Chemical Society
• /
• 제33권12호
• /
• pp.4161-4164
• /
• 2012

Density functional MO calculations for the methylsilole anion of $[C_4H_4SiMe]^-$ and methylgermole anion of $[C_4H_4SiMe]^-$ at the B3LYP (full)/6-311+$G^*$ level (GAUSSIAN 94) were carried out and characterized by frequency analysis. The ground state structure for the methylsilole anion and methylgermole anion is that the methyl group is pyramidalized with highly localized structure. The difference between the calculated $C_{\alpha}-C_{\beta}$ and $C_{\beta}-C_{\beta}$ distances are 9.4 and 11.5 pm, respectively. The E-Me vector forms an angle of $67.9^{\circ}$ and $78.2^{\circ}$ with the $C_4E$ plane, respectively. The optimized structures of the saddle point state for the methylsilole anion and methylgermole anion have been also found as a planar with highly delocalized structure. The optimized $C_{\alpha}-C_{\beta}$ and $C_{\beta}-C_{\beta}$ distances are nearly equal for both cases. The methyl group is located in the plane of $C_4E$ ring and the angle between the E-Me vector and the $C_4E$ plane for the methylsilole anion and methylgermole anion is $2.0^{\circ}$ and $2.3^{\circ}$, respectively. The energy difference between the ground state structure and the transition state structure is only 5.1 kcal $mol^{-1}$ for the methylsilole anion. However, the energy difference of the methylgermole anion is 14.9 kcal $mol^{-1}$, which is much higher than that for the corresponding methylsilole monoanion by 9.8 kcal $mol^{-1}$. Based on MO calculations, we suggest that the head-to-tail dimer compound, 4, result from [2+2] cycloaddition of silicon-carbon double bond character in the highly delocalized transition state of 1. However, the inversion barrier for the methylgermole anion is too high to dimerize.