Earticle

본 논문에서는 고속전송을 위해 넓은 대역폭을 사용하는 OFDM (Orthogonal Frequency Division Multiflex)시스템에서 심각한 주파수 선택적 페이딩 환경에서도 강한 특성을 갖는 FE(Frequency Estimation) 채널 추정 기법을 제안하였다. 시간적으로 천천히 변화하는 채널에서 주로 사용하는 채널추정기법은 상관도가 높은 심볼간 데이터를 이용하는 DDCE(Decision Directed Channel Estimation)기법이 있다. DDCE는 주파수 비선택적인 환경에서는 높은 이득을 보이지만 시스템 대역폭이 증가함에 따라 심각한 주파수 선택적 페이딩 환경이 되어 데이터의 신뢰도가 떨어지면 에러 플로우 현상이 일어나게 된다. 제안한 방법인 FE 기법은 주파수 선택적 페이딩 채널에서 OFDM의 부반송파간의 상관관계를 이용하여 목표 주파수에서의 채널 추정감을 인접 부반송파의 채널 추정값들의 평균으로 적응시켜주어 이득을 얻는다. FE 기법은 DDCE처럼 데이터를 이용하지 않고 프리엠블만을 이용하기 때문에 데이터의 신뢰도와 관계된 전송율과 다중 경로 수에 독립적이다 본 논문에서 제안한 FE 기법은 DDCE의 에러 플로우가 발생한 넓은 대역폭을 가진 시스템에서도 이득을 얻을 수 있어 심각한 주파수 선택적 페이딩 환경에서 유용하다.

This paper proposes the channel estimation method robust to severe frequency selective fading channels in O%M system using wide bandwidth for the high data rate transmission. DDCE, which uses data between the high correlated symbols, is usually used for channel estimation in the slow fading channels. DDCE can get high gains in the non-selective channels. As the bandwidth of system gets wider, it becomes more severe frequency selective fading environments so that the reliability of data becomes lower and error flow is occurred. FE method, this paper proposed, uses the relation between sub-carriers of OFDM in frequency selective fading channels so FE method gets some gains by adapting the power value at a target frequency to the mean value of channel estimated values of adjacent sub-carriers. Because FE uses only preamble unlike DDCE using data, it is independent of data rate related to the reliability of data and the number of multipath. Consequently, FE can obtain considerable gains in the wideband systems where the errorflow of DDCE is occurred, and FE is applicable to frequency selective fading environments.

To improve the performance of sensorless induction motor (IM) drives, a novel speed estimation method based on the real-time adaptive extended Kalman filter (RAEKF) is proposed in this paper. In this algorithm, the fuzzy factor is introduced to tune the measurement covariance matrix online by the degree of mismatch between the actual innovation and the theoretical. Simultaneously, the fuzzy factor can be continuously self-tuned tuned by the fuzzy logic reasoning system based on Takagi-Sugeno (T-S) model. Therefore, the proposed method improves the model adaptability to the actual systems and the environmental variations, and reduces the speed estimation error. Furthermore, a simple exponential function based on the fuzzy theory is used to reduce the computational burden, and the real-time performance of the system is improved. The correctness and the effectiveness of the proposed method are verified by the simulation and experimental results.

To improve the performance of sensorless induction motor (IM) drives, an adaptive speed estimation method based on a strong tracking extended Kalman filter with a least-square algorithm (LS-STEKF) for induction motors is proposed in this paper. With this method, a fading factor is introduced into the covariance matrix of the predicted state, which forces the innovation sequence orthogonal to each other and tunes the gain matrix online. In addition, the estimation error is adjusted adaptively and the mutational state is tracked fast. Simultaneously, the fading factor can be continuously self-tuned with the least-square algorithm according to the innovation sequence. The application of the least-square algorithm guarantees that the information in the innovation sequence is extracted as much as possible and as quickly as possible. Therefore, the proposed method improves the model adaptability in terms of actual systems and environmental variations, and reduces the speed estimation error. The correctness and the effectiveness of the proposed method are verified by experimental results.

An improved fuzzy Kalman filtering speed estimation scheme was proposed by means of measuring stator side voltage and current value based on vector control state equation of induction motor. The designed fuzzy adaptive controller conducted recursive online correction of measurement noise covariance matrix by monitoring the ratio of theory residuals and actual residuals to make it approach real noise level gradually, allowing the filter to perform optimal estimation to improve estimation accuracy of EKF. Meanwhile, co-simulation scheme based on MATLAB and Ansoft was proposed in order to improve simulation accuracy. Field-circuit coupling problems of induction motor under the action of vector control were solved and the parameter optimization accuracy was improved dramatically. The simulation and experimental results show that this algorithm has a strong ability to inhibit the random measurement noise. It is able to estimate motor speed accurately, and has superior static and dynamic characteristics.

This paper describes an approach to a speed estimation method to remove speed sensor of underwater robot's AC drive systems. AC motors have been widely used in the field of underwater robot's manipulator or propulsion system. Most of these AC motors for underwater use have usually filled oil to compensate the high pressure in deep-sea operation, where a resolver is adopted to feed back the speed of rotor But this kind of speed feedback devices gives rise to some defects arising from their mechanical complexity and numerous signal lines; a resolver needs 6 or 7 signal lines for proper operation. This paper presents a speed estimation method to improve these problems of induction motor, which is adopted as a prototype of AC motor. The proposed speed estimation method is based on the RFO(rotor flux orientation) vector control method of voltage-fed AC drives. Using the controller of voltage-fed AC drives, it is unnecessary to measure the voltage for the estimation of rotor speed, which reduces the effects of measurement error Numerical simulation is carried out to investigate the validity of the method and the effects of rotors resistance variation.

본 논문에서는 유기랭킨 사이클(organic rankine cycle: ORC) 발전 시스템에서 터빈과 발전기의 속도를 추정하는 방법을 제안한다. 다이오드 정류기에 의해 왜곡된 발전기 3상 단자 전압은 PLL 기법을 이용한 속도추정의 성능을 저하시키므로, 정상분 추출과 맥동성분 제거를 위한 상태관측기와 주파수 가변형 PR 제어기를 동기좌표계 PLL 기법에 적용하여 발전기의 속도를 추정한다. 제안하는 터빈과 발전기의 속도추정 방법은 실험 결과를 통해 그 성능을 검증한다.

This paper proposes a new speed and position estimation method for PMSM(Permanent Magnet Synchronous Motor) using low-resolution hall-effect sensors. In general, there are a variety of sensors to estimate rotor position for PMSM such as resolvers, and encoders. Position detection using hall-effect sensors that detect the flux of the rotor for rotor position is excellent method in terms of cost and space, but has low-resolution. To overcome this problem, this paper proposes a new speed and position estimation observer. The performance of the observer has been verified by simulations carried out using Matlab/Simulink.

풍력 터빈은 정격 풍속 미만일 때는 최대 출력을 내기위한 제어를 한다. MPC(Maximun Power Control)제어방법은 발전기의 회전속도를 제어하여 최대 출력을 내는 방법인데 풍속을 알아야한다. 하지만 여러 가지 이유로 풍속을 실측하기보다는 풍속을 예측하여 사용하는 방법들이 제안되고 있다. 풍속예측은 TSR(Tip Speed Ratio)을 알아야 하는 데, TSR은 복잡한 관계식을 풀어야 한다. 그래서 TSR을 구하기 위해 다양한 방법들이 소개되고 있다. 본 논문에서는 TSR을 쉽고 빠르게 구하기 위하여 복잡한 관계식을 리그레션을 이용하여 간단한 방정식으로 근사화하는 방법을 제안하였다. 제안된 방법은 Matlab/Simulink 시뮬레이션을 통하여 제대로 작동하는 것을 확인하였다.

Wind turbines, in the case of less than rated wind speed, is controlled to achieve maximum power. MPC(Maximun Power Control) method, by controlling the rotational speed of the generator, is a method to achieve maximum power but should know the wind speed. However, for several reasons, there have been proposed methods of estimating the wind speed rather than measuring wind speed. TSR(Tip Speed Ratio) is needed to know to estimate the wind speed. However, a complex interaction formula has to be solved to find a TSR. Therefore, many methods have been suggested to solve a complex interaction formula. In this paper, the new method has been proposed to simplify the complicated interaction formula by using the regression method. Matlab/Simulink is used to simulate and to verify the proposed method.

The rotor speed flux information most important in the speed sensorless vector c So in the paper used current voltage model for flux information in a induction motor. Voltage realized low pass filter insted of integrator, c model realized used of current equation. And cur voltage model estimated flux compoed of Pl cont For conpensation of estimated flux error conpansation algorithm using exactly, rapidly flux obtained for conpensation of estimated flux Proposed control system used TMS320C31 DS high speed processing. The effectiveness of proposed method is verified by simulation experimental results. This method shows h characteristic speed estimation highly flux esti and stable, robust character of load regulation.

Servomotors, especially linear brushless servomotors have numerous advantages over ball screws, timing belts, rack/pinion drives and friction drives compared wi th rotary servomotors This paper proposes an linear regression method as the estimated ion of unknown parameters from the linear brushless DC motor The estimated parameters are used to tune the gain of controller. In order to agree with this purpose, Digital Signal Processor (TMS320F240), developed for implementation of the motor control, is adopted in this study. The processor playing an important role in controller has A/D converters, PWM generators, riched I/O ports internally.

In this paper, a new speed estimation method of induction motors based on the very quick torque control is proposed to realize speed sensorless control. The proposed method can be realized very simply by detecting primary motor current and voltage command at every sampling time. As the method need not the differential value of primary current in a arithmetic of voltage command, it can be expected to promote the precision of speed estimation in low speed area, especially. Through the numerical simulation, the validity of the proposed method was successfully confirmed.

Due to the acceleration of an aging society, the need for lower limb exoskeletons to assist gait is increasing. And for use in daily life, it is essential to have technology that can accurately estimate gait phase even in the walking environment and walking speed of the wearer that changes frequently. In this paper, we implement an LSTM-based gait phase estimation learning model by collecting gait data according to changes in gait speed in outdoor level ground and stair environments. In addition, the results of the gait phase estimation error for each walking environment were compared after learning for both max hip extension (MHE) and max hip flexion (MHF), which are ground truth criteria in gait phase divided in previous studies. As a result, the average error rate of all walking environments using MHF reference data and MHE reference data was 2.97% and 4.36%, respectively, and the result of using MHF reference data was 1.39% lower than the result of using MHE reference data.

OFDM을 위한 기존의 채널추정기법들은 다양한 이동체의 속도에 대해 강건한 특성을 지니지 못 한다. 기존의 기법들에서 이런 단점을 극복하기 위해 타 기법들에 비해 우수한 성능 및 양호한 연산량을 보이는 기법을 제안한다. 제안된 기법은 PSAM이나 ESAE와 같은 기법들과 달리 전체 채널 내에 블록 프레임 구조를 이용한다. 이러한 개념은 발산을 하거나 나쁜 성능을 보이는 것을 막기 위한 것이며 성능 분석을 통해 제안된 기법이 기존의 기법들에 비해 모든 래일라이 페이딩 환경에서 MSE나 BER측면에서 우수한 성능을 보이는 것을 확인할 수 있었다. 실험 성능 및 연산량 비교를 통해 제안한 기법은 기존의 기법들보다 우수한 성능을 보였으며 매우 심한 페이딩 환경에서도 오차 마루를 보이지 않는 특성을 보였다.

Conventional channel estimation methods for OFDM system have not the robust characteristics relating to various mobile speed. To solve this drawback in conventional methods, we propose the channel estimation method for OFDM, which has a good performance and computational complexity in consideration of other methods. Proposed method uses a block frame structure within a whole channel in contrast with conventional methods such as PSAM and ESAE. This concept prevents overall performance from diverging or showing a poor one. The analytical results demonstrate the proposed method outperforms PSAM and ESAE in terms of MSE and BER performance under all Rayleigh fading environment. Considering the simulation performance and computational complexity, proposed method shows better characteristics than conventional methods for OFDM and has not an error floor even in a severe Rayleigh fading environment.

하지 웨어러블 로봇의 근력 보조 성능을 극대화하기 위해서는 착용자의 보행 상태를 인식하는 보행 위상 추정 기술이 필수적으로 요구된다. 본 논문에서는 착용자의 보행 속도 변화 및 착용자 간 보행 특성 차이에도 강인하게 보행 위상을 추정할 수 있는 LSTM 기반 보행 위상 강건 인식 기술을 개발하였다. 웨어러블 고관절 보조 로봇을 착용한 총 5명의 트레드밀 및 실외 overground의 보행 센서 정보를 바탕으로 학습을 수행하였다. 저속 및 고속 보행을 포함한 다양한 보행 속도에서 정밀한 보행 위상 추정이 가능한 웨어러블 센서 조합을 도출하였고, 보행 위상 인식 정밀성은 5-Fold Cross Validation 기준 RMSE 약 1.68% 수준의 결과를 얻을 수 있었다.

본 논문에서는 대기 속도 센서가 없는 항공기에서의 강인 필터 기반의 바람 추정 기법을 제안한다. 바람 속도(wind velocity)는 항공기의 유도 및 제어를 더욱 정밀하게 수행하기 위해 사용되는 정보이다. 일반적으로 바람 속도는, 대기 속도와 지면 속도의 차이를 계산하여 얻을 수 있다. 이때 대기 속도는 피토 튜브와 같은 항공기와 대기의 상대 속도를 측정하는 대기 속도 측정 센서에서 얻을 수 있고, 지면 속도는 항법 시스템으로부터 얻을 수 있다. 그러나 항공기의 구성을 간단하게하기 위하여 대기 속도 측정 센서를 장착하지 않는 경우, 바람 속도를 직접적으로 얻을 수 없기 때문에 필터를 이용한 바람 추정 기법이 필수이다. 이때 난류에 의해 항공기의 공력 계수가 변하게 되는데, 이는 바람 추정 필터의 시스템 모델의 불확실성을 유발하게 되고, 결국 바람 추정 성능이 저하된다. 따라서 본 연구에서는 공력계수 불확실성에 강인함을 확보하기 위해 $H{\infty}$ 필터를 적용한 바람 추정 기법을 제안하였다. 시뮬레이션을 통해 제안하는 기법이 공력계수의 불확실성이 있는 상황에서 성능을 개선하는 것을 확인하였다.

In this paper, a robust filter based wind velocity estimation algorithm without an air velocity sensor in an air vehicle is presented. The wind velocity is useful information for the air vehicle to perform precise guidance and control. In general, the wind velocity can be obtained by subtracting an air velocity which is obtained by an air velocity sensor such as a pitot-tube, and a ground velocity which is obtained by a navigation equipment. However, in order to simplify the configuration of the air vehicle, the wind estimation algorithm is necessary because the wind velocity can not be directly obtained if the air velocity measurement sensor is not used. At this time, the aerodynamic coefficient of the air vehicle changes due to the turbulence, which causes the uncertainty of the system model of the filter, and the wind estimation performance deteriorates. Therefore, in this study, we propose a wind estimation method using $H{\infty}$ filter to ensure robustness against aerodynamic coefficient uncertainty, and we confirmed through simulation that the proposed method improves the performance in the uncertainty of aerodynamic coefficient.

고속 수중운동체 능동 탐지는 수중 무기 방어 시스템에 중요한 기술로, 정확한 표적 탐지와 거리 및 속도 추정이 필수적이다. 빔 형성 각도마다 신호를 순차적으로 송신해야 하지만 펄스 분할 송신 방법은 거리 추정의 모호성이 발생한다. 이를 보완하기 위해 다중 주파수 기반 방법이 제안되었지만 대역폭을 분할할 경우 시간-대역폭 곱에서 손실이 발생한다. 이러한 문제를 해결하기 위해, 본 연구에서는 대역폭을 분할하지 않아도 부펄스 간의 상관관계를 낮게 설계할 수 있는 일반화된 사인파 주파수 변조(Generalized Sinusoidal Frequency Modulation, GSFM)를 사용한 능동 고속 탐지 기법을 제안한다. 제안한 방법은 펄스 길이를 최소화시킨 GSFM을 사용함으로써 표적의 거리와 속도를 빠르게 갱신할 수 있다. 제안한 방법의 성능을 검증하기 위해 잔향이 존재하는 모의 환경을 구축하였다. 모의 실험 결과 0.05 s의 선형 주파수 변조 펄스는 한정적인 주파수 대역으로 인해 추정 거리 대비 평균적으로 50 %의 거리 추정 오차와 103 %의 속도 추정오차가 발생하였다. 이에 반해, GSFM은 같은 길이의 펄스를 사용하더라도 추정 거리 대비 거리 추정 오차와 속도 추정 오차가 각각 10 %와 14 %로 표적을 비교적 정확하고 빠르게 추적할 수 있었다. 게다가, GSFM은 방위별로 직교성이 높은 부펄스를 송신하여 표적의 대략적인 방위까지 알 수 있었다.

Underwater active target detection is vital for defense systems, requiring accurate detection and estimation of distance and velocity. Sequential transmission is necessary at each beam angle, but divided pulse length leads to range ambiguity. Multi-frequency transmission results in time-bandwidth product losses when bandwidth is divided. To overcome these problem, we propose a novel method using Generalized Sinusoidal Frequency Modulation (GSFM) for rapid target detection, enabling low-correlation pulses between subpulses without bandwidth division. The proposed method allows for rapid updates of the distance and velocity of target by employing GSFM with minimized pulse length. To evaluate our method, we simulated an underwater environment with reverberation. In the simulation, a linear frequency modulation of 0.05 s caused an average distance estimation error of 50 % and a velocity estimation error of 103 % due to limited frequency band. In contrast, GSFM accurately and quickly tracked targets with distance and velocity estimation errors of 10 % and 14 %, respectively, even with pulses of the same length. Furthermore, GSFM provided approximate azimuth information by transmitting highly orthogonal subpulses for each azimuth.

This study is about fault detection and fault area detection of LV circuit in Cab Cubicle system which have control of train to keep safety in High Speed Train. LV circuit is operated with diagnosis system like safety system. In this paper, we suggest a design and an implementation method to detect fault or to detect fault area automatically about LV circuit. The implemented system is tested successfully after implementation of some function. We expect reduction to diagnosis area or repair time by fault area module

본 논문에서는 기존 RTLS(Real Time Location Service)시스템의 이동개체에 대한 위치추정오차를 개선하기 위하여, 이동개체속도가 증감되는 각각의 구간을 구분하여 속도가 증감된 만큼 전송신호의 주기를 적응적으로 증감시키는 방법을 제안하였다. 보다 정확한 위치추정 값을 얻기 위하여, AercoScout사의 RTLS 시스템을 이용한 Hybrid(TDOA and RSSI) 방식에 의해 각각 측정된 좌표 값과, 계산에 의해 추정된 좌표 값을 최소자승법(Least Square Method)에 대입하여 실제 이동개체의 위치좌표 값과 비교하는 방법을 취하였다. 실제 제안된 방법에 의해 실험을 해본 결과, 이동개체의 실제 위치의 위치추정오차가 개선됨을 알 수가 있었다.

In this paper, in order to improve the location estimation error existing in RTLS(Real Time Location Service) system for the mobility individual, we proposed a method of speed-adaptive location estimation that the transmitting signaling period is adaptively changed according to the changing speed of a mobility individual for each location interval. To get the more accurate location estimation values, we analyzed both the location values measured by Hybrid(TDOA and RSSI) method by using AeroScout TM RTLS system and the estimated value obtained from the theoretical calculation by using the Least Squares Method. Finally, we compared the analyzed values with a real location of mobility individual. From the experimental results based on our proposed method, it can be seen that the location estimation error for the real location of a mobility individual can be improved.