• Journal of Power Electronics
• /
• v.8 no.4
• /
• pp.354-362
• /
• 2008

This paper presents neural load torque compensation method which is composed of a deadbeat load torque observer and gains compensation by a parameter estimator. As a result, the response of the PMSM (permanent magnet synchronous motor) obtains better precision position control. To reduce the noise effect, the post-filter is implemented by a MA (moving average) process. The parameter compensator with an RLSM (recursive least square method) parameter estimator is adopted to increase the performance of the load torque observer and main controller. The parameter estimator is combined with a high performance neural load torque observer to resolve problems. The neural network is trained in online phases and it is composed by a feed forward recall and error back-propagation training. During normal operation, the input-output response is sampled and the weighting value is trained multi-times by the error back-propagation method at each sample period to accommodate the possible variations in the parameters or load torque. As a result, the proposed control system has a robust and precise system against load torque and parameter variation. Stability and usefulness are verified by computer simulation and experiment.

• Journal of Information Processing Systems
• /
• v.10 no.3
• /
• pp.443-458
• /
• 2014

An extreme learning machine (ELM) is a recently proposed learning algorithm for a single-layer feed forward neural network. In this paper we studied the ensemble of ELM by using a bagging algorithm for facial expression recognition (FER). Facial expression analysis is widely used in the behavior interpretation of emotions, for cognitive science, and social interactions. This paper presents a method for FER based on the histogram of orientation gradient (HOG) features using an ELM ensemble. First, the HOG features were extracted from the face image by dividing it into a number of small cells. A bagging algorithm was then used to construct many different bags of training data and each of them was trained by using separate ELMs. To recognize the expression of the input face image, HOG features were fed to each trained ELM and the results were combined by using a majority voting scheme. The ELM ensemble using bagging improves the generalized capability of the network significantly. The two available datasets (JAFFE and CK+) of facial expressions were used to evaluate the performance of the proposed classification system. Even the performance of individual ELM was smaller and the ELM ensemble using a bagging algorithm improved the recognition performance significantly.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.48 no.2
• /
• pp.107-114
• /
• 1999

This paper presents an application of artificial neural networks(ANN) to assess the dynamic security of power systems. The basic role of ANN is to provide assessment of the system's stability based on training samples from off-line analysi. The critical clearing time(CCT) is an attribute which provides significant information about the quality of the post-fault system behaviour. The function of ANN is a mapping of the pre-fault, fault-on, and post-fault system conditions into the CCT's. In previous work, a feed forward neural network is used to learn this mapping by using the generation outputs during the fault as the input data. However, it takes significant calculation time to make the input data through the network reduction at a fault as the input data. However, it takes significant calculation time to make the input data through the network reduction at a fault considered. In order to enhance the speed of security assessment, the bus data and line powers are used as the input data of the ANN in thil paper. Test results show that the proposed neural networks have the reasonable accuracy and can be used in on-line security assenssment efficiently.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.9 no.6
• /
• pp.605-614
• /
• 1999

This paper addresses analysis and design of a class of complex single-input single-output fuzzy control systems. In the proposed method, the fuzzy model, which represents the local dynamic behavior of the given nonlinear system, is utilized to construct the controller. The overall controller consists of the local compensators which compensate the local dynamic linear model and the feed-forward controller which is designed via sliding mode control theory. Therefore, the globally stable fuzzy controller is designed without finding a common Lyapunov matrix. and shows improved perfonnance and tracking results by taking the advantages of fuzzy-model-based control theory and sliding mode control theory. Furthennore, stability analysis is conducted not Ibr the fuzzy model but for the real underlying nonlinear system. Two numerical examples are included to show the effcctiveness and feasibility of the proposed fuzzy control method.

• Journal of the Korean Society for Precision Engineering
• /
• v.34 no.4
• /
• pp.265-272
• /
• 2017

Velocity ripple in manufacturing processes reduces productivity and limits the precision of the product. In practice, the frequency and phase of velocity ripples always change minutely, which makes it impossible to compensate for the ripple by simply inserting an opposite feed-forward signal in the system. In this study, an active-phase compensation algorithm was developed to enable the velocity-ripple controller to track the phase change of the ripples in real time. The proposed controller can compensate for the velocity ripple whatever its cause, including disturbance by the torque ripple. The algorithm consists of three functional modules: the velocity-ripple extractor, the synchronized integrator, and the phase shifter. Experimental results showed that the proposed controller clearly reduces velocity ripples with phase variation.

• Proceedings of the KSRS Conference
• /
• 2007.10a
• /
• pp.286-289
• /
• 2007

This study is to develop a cloud detection algorit1un for COMS and it is currently tested by using MODIS level 2B and MTSAT-1R satellite radiance data. Unlike many existing cloud detection schemes which use a threshold method and traditional statistical methods, in this study a feed-forward neural network method with back-propagation algorit1un is used. MODIS level 2B products are matched with feature information of five-band MTSAT 1R image data to form the training dataset. The neural network is trained over the global region for the period of January to December in 2006 with 5 km spatial resolution. The main results show that this model is capable to detect complex cloud phenomena. And when it is applied to seasonal images, it shows reliable results to reflect seasonal characteristics except for snow cover of winter. The cloud detection by the neural network method shows 90% accuracy compared to the MODIS products.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2007.06a
• /
• pp.171-174
• /
• 2007

A realization for FF-PID(Feed-Forward PID) controlling system with backward propagation algorithm and image pattern recognition is presented in this paper. The pattern recognition used backward propagation of nervous network is teaming. FF-PID is enhanced the response characteristic of moving image by using the controlling value which is output error for the target value of nervous system. In conclusion of experiment, the system is shown that the response is worked as 2.7sec that is enhanced round 15% in comparison with general difference image algorithm. The system is able to control a moving object with effect.

• Proceedings of the KIEE Conference
• /
• 2004.10a
• /
• pp.160-163
• /
• 2004

A passive lossless turn-on/turn-off snubber network is proposed for the boost PWM converter. Previous AC/DC PFC Boost Converter perceives feed forward signal of output for average current-mode control. Previous Boost Convertor, the Quantity of input current will be decreased by the decrease of output current in light load, and also Power factor comes to be decreased. Also the efficiency of converter will be decreased by the decrease of power factor. The proposed converter presents the good PFC, low line current harmonic distortions and tight output voltage regulations using energy recovery circuit. All of the semiconductor devices in the converter are turned on under exact or near zero voltage switching(ZVS). No additional voltage and current stresses on the main switch and main diode occur. To show the superiority of this converter is verified through the experiment with a 640W, 100kHz prototype converter.

• Animal cells and systems
• /
• v.16 no.2
• /
• pp.121-126
• /
• 2012

In this study, a neural network (NN) modeling approach has been used to predict the mechanical and geometrical behaviors of mouse embryo cells. Two NN models have been implemented. In the first NN model dimple depth (w), dimple radius (a) and radius of the semi-circular curved surface of the cell (R) were used as inputs of the model while indentation force (f) was considered as output. In the second NN model, indentation force (f), dimple radius (a) and radius of the semi-circular curved surface of the cell (R) were considered as inputs of the model and dimple depth was predicted as the output of the model. In addition, sensitivity analysis has been carried out to investigate the influence of the significance of input parameters on the mechanical behavior of mouse embryos. Experimental data deduced by Fl$\ddot{u}$ckiger (2004) were collected to obtain training and test data for the NN. The results of these investigations show that the correlation values of the test and training data sets are between 0.9988 and 1.0000, and are in good agreement with the experimental observations.

• Journal of Power Electronics
• /
• v.16 no.6
• /
• pp.2243-2257
• /
• 2016

This paper presents a two stage three-phase proton exchange membrane fuel cell (PEMFC) generation system. When the system is connected to a three-phase load, it is very sensitive to the characteristics and type of the load. Especially unbalanced three-phase loads, which result in a pulsating power that is twice the output frequency at the inverter output, and cause the dc-link to generate low frequency ripples. This penetrates to the fuel cell side through the front-end dc-dc converter, which makes the fuel cell work in an unsafe condition and degrades its lifespan. In this paper, the generation and propagation mechanism of low frequency ripple is analyzed and its impact on fuel cells is presented based on the PEMFC output characteristics model. Then a novel method to evaluate low frequency current ripple control capability is investigated. Moreover, a control scheme with bandpass filter inserted into the current feed-forward path, and ripple duty ratio compensation based on current mode control with notch filter is also proposed to achieve low frequency ripple suppression and dynamic characteristics improvement during load transients. Finally, different control methods are verified and compared by simulation and experimental results.