• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.59 no.11
• /
• pp.2073-2082
• /
• 2010

The 4-th generation of mobile communication aims to realize global, fast and mobile communication service. The satellite communication charges a key role in this field. In this study, an OTM(On-The-Move) antenna which is mounted on ground vehicles and is used for mobile communication between vehicle and satellite was addressed. Since vehicles move during communication, active antenna line-of-sight stabilization is a core technology to guarantee high satellite communication quality. Stabilization of a satellite tracking antenna which consists of 2-DOF gimbals, an elevation gimbal over an azimuth gimbal, was considered in this study. Various disturbance torques such as static and dynamic mass imbalance torques, variation of moment of inertia according to elevation angle, friction torque related to vehicle motion, equivalent disturbance torque due to antenna roll motion, etc. were analyzed. As a robust stabilization control, rate feedback with sliding mode control and position feedback with proportional+integral control was suggested. To compensate antenna roll motion, a supplementary roll rate feed forward control was included beside of the feedback control loop. The feasibility of the analysis and the proposed control design were verified along with some simulation results.

• Journal of Microbiology and Biotechnology
• /
• v.22 no.7
• /
• pp.939-946
• /
• 2012

Antibiotic production with Streptomyces sindenensis MTCC 8122 was optimized under submerged fermentation conditions by artificial neural network (ANN) coupled with genetic algorithm (GA) and Nelder-Mead downhill simplex (NMDS). Feed forward back-propagation ANN was trained to establish the mathematical relationship among the medium components and length of incubation period for achieving maximum antibiotic yield. The optimization strategy involved growing the culture with varying concentrations of various medium components for different incubation periods. Under non-optimized condition, antibiotic production was found to be $95{\mu}g/ml$, which nearly doubled ($176{\mu}g/ml$) with the ANN-GA optimization. ANN-NMDS optimization was found to be more efficacious, and maximum antibiotic production ($197{\mu}g/ml$) was obtained by cultivating the cells with (g/l) fructose 2.7602, $MgSO_4$ 1.2369, $(NH_4)_2PO_4$ 0.2742, DL-threonine 3.069%, and soyabean meal 1.952%, for 9.8531 days of incubation, which was roughly 12% higher than the yield obtained by ANN coupled with GA under the same conditions.

• Journal of Electrical Engineering and Technology
• /
• v.9 no.3
• /
• pp.1060-1071
• /
• 2014

Signals of the Electroencephalogram (EEG) can reflect the electrical background activity of the brain generated by the cerebral cortex nerve cells. This has been the mostly utilized signal, which helps in effective analysis of brain functions by supervised learning methods. In this paper, an approach for improving the accuracy of EEG signal classification is presented to detect epileptic seizures. Moreover, Independent Component Analysis (ICA) is incorporated as a preprocessing step and Short Time Fourier Transform (STFT) is used for denoising the signal adequately. Feature extraction of EEG signals is accomplished on the basis of three parameters namely, Standard Deviation, Correlation Dimension and Lyapunov Exponents. The Artificial Neural Network (ANN) is trained by incorporating Levenberg-Marquardt(LM) training algorithm into the backpropagation algorithm that results in high classification accuracy. Experimental results reveal that the methodology will improve the clinical service of the EEG recording and also provide better decision making in epileptic seizure detection than the existing techniques. The proposed EEG signal classification using feed forward Backpropagation Neural Network performs better than to the EEG signal classification using Adaptive Neuro Fuzzy Inference System (ANFIS) classifier in terms of accuracy, sensitivity, and specificity.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.18 no.6
• /
• pp.128-135
• /
• 2004

The author present a modified multi-loop algorithm including feedforward for controlling a 55kW step down chopper in the power supply of Maglev. The control law for the duty cycle consists of three terms. The first is the feedforward term. which compensates for variations in the input voltaga. The second term consists of the difference between the slowly moving inductor current and output current. The third term consists of proportional and integral terms involving the perturbation in the output voltage. This perturvation is derived by subtracting the desired output voltage from the actual output voltage. The proportional and integral action stabilizes the system and minimizes output voltage error. In order to verify the validity of the proposed multi-loop controller, simulation study was tried using Matlab simulink

• Journal of Power Electronics
• /
• v.15 no.2
• /
• pp.469-478
• /
• 2015

In order to promote the application of switched reluctance machines (SRM) in electric vehicles (EVs), the braking torque closed-loop control of a SRM is proposed. A hysteresis current regulator with the soft chopping mode is employed to reduce the switching frequency and switching loss. A torque estimator is designed to estimate the braking torque online and to achieve braking torque feedback. A feed-forward plus saturation compensation torque regulator is designed to decrease the dynamic response time and to improve the steady-state accuracy of the braking torque. The turn-on and turn-off angles are optimized by a genetic algorithm (GA) to reduce the braking torque ripple and to improve the braking energy feedback efficiency. Finally, a simulation model and an experimental platform are built. The simulation and experimental results demonstrate the correctness of the proposed control strategy.

• Journal of Power Electronics
• /
• v.15 no.4
• /
• pp.1066-1073
• /
• 2015

This paper proposes a control strategy based on the frequency detection method, comprising a current control and a feed-forward voltage control loop, is proposed for grid-interactive power conditioning systems (PCS). For continuous provision of power to critical loads, PCS should be able to check grid outages instantaneously. Hence, proposed in the present paper are a frequency detection method for detecting abnormal grid conditions and a controller, which consists of a current controller and a feedforward voltage controller, for different operation modes. The frequency detection method can detect abnormal grid conditions accurately and quickly. The controller which has current and voltage control loops rapidly helps in load voltage regulation when grid fault occurs by changing reference and control modes. The proposed seamless transfer control strategy is confirmed by experimental results.

• Proceedings of the KIPE Conference
• /
• 2012.07a
• /
• pp.359-360
• /
• 2012

This paper proposes a scheme of auto-tuning fuzzy PI controller and input voltage feed forward to control the output voltage of a three-phase Z-source inverter (ZSI). The proposed scheme adjusts the ts (Kp and Ki) in real time in order to find the most suitable Kp and Ki for PI controller and to simplify the controller design. The proposed scheme is verified the validity by experiment and co-simulation in PSIM and MATLAB/SIMULINK both load step change and input DC voltage variation in Z-source inverter, and has compared with the conventional PID control scheme. The experiment results involve of three-phase output voltage, Z-network capacitor voltage and dc-link peak voltage value. By those analysis and comparison, the availability of the proposed method in output voltage transient response quality improving has been verified. Compared with conventional PID method, the proposed method showed a more effective and robust control performance for coping with the severe disturbance conditions.

• Proceedings of the KIEE Conference
• /
• 2001.07d
• /
• pp.2286-2288
• /
• 2001

In this paper, a drive system of induction motor with high performance is realized on the viewpoint of the design and experiment, using the DSP (TMS320F240). The speed controller for induction motor drive system is designed on the basis of a neuro-fuzzy network. The neuro-fuzzy controller acts as a feed-forward controller that provides the right control input for the plant and accomplishes error back-propagation algorithm through the network. The proposed network is used to achieve the high speedy calculation of the space vector PWM (Pulse Width Modulation) and to build the neuro-fuzzy control algorithm, for the real-time control. The proposed neuro-fuzzy algorithm on the basis of DSP shows that experimental results have good performance for the precise speed control of an induction motor drive system. It is confirmed that the proposed controller could provide more improved control performance than conventional v/f vector controllers through the experiment.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2012.05a
• /
• pp.195-195
• /
• 2012

인공신경망 모형은 복잡하고 비선형의 입력과 출력 관계를 잘 반영할 수 있어서 유출 모델링에 널리 적용되어 왔다. 그러나 인공신경망 모형은 강우나 유역특성의 공간적 분포를 반영하는 것이 어려우며 물리적 개념이 결여되어 있는 단점이 있다. 본 연구에서는 유역특성과 물리적 개념을 반영할 수 있는 물리기반 모형과 인공신경망 모형의 장점들을 조합하여 물리기반 모형의 일 유출량 해석 능력을 향상하기 위하여 SWAT 모형과 인공신경망(ANN)을 연계하였다. SWAT-ANN 연계모형은 두 단계로 구성되어 진다. 첫 번째 단계에서는 관측 자료를 이용하여 SWAT 모형을 보정한다. 두 번째 단계에서는 첫 번째 단계에서 계산한 소유역별 SWAT 모형의 유출결과를 ANN의 입력자료로 이용하여 SWAT-ANN 연계모형을 구축한다. SCE-UA 최적화 방법을 적용하여 SWAT 모형의 매개변수들을 보정하였고, ANN 학습은 3층의 feed-forward 역전파 알고리즘에 기초한 Bayesian Regularization 방법을 적용하였다. ANN 은닉층의 뉴런 및 전달함수는 시행착오를 통하여 적절한 ANN 구조를 설정하여 SWAT-ANN 연계모형의 일유출량을 모의하였다. 여러 가지 통계적 오차기준을 이용하여 보청천 유역에서 SWAT-ANN 연계모형의 결과와 SWAT 단독 모형의 결과를 비교하였다. SWAT-ANN 연계모형이 SWAT 단독 모형보다 더 우수한 결과를 나타내어 일 유출량 해석을 위한 SWAT-ANN 연계모형의 유용성을 확인할 수 있었다.

• Korean Journal of Remote Sensing
• /
• v.18 no.3
• /
• pp.155-162
• /
• 2002

Neural networks have been focused on as a robust classifier for the remotely sensed imagery due to its statistical independency and teaming ability. Also the artificial neural networks have been reported to be more tolerant to noise and missing data. However, unlike the conventional statistical classifiers which use the statistical parameters for the classification, a neural network classifier uses individual training sample in teaming stage. The training performance of a neural network is know to be very sensitive to the discrepancy of the number of the training samples of each class. In this paper, the effect of the population discrepancy of training samples of each class was analyzed with three layered feed forward network. And a method for reducing the effect was proposed and experimented with Landsat TM image. The results showed that the effect of the training sample size discrepancy should be carefully considered for faster and more accurate training of the network. Also, it was found that the proposed method which makes teaming rate as a function of the number of training samples in each class resulted in faster and more accurate training of the network.