• Journal of Power Electronics
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• v.8 no.4
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• pp.354-362
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• 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 Power Electronics
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• v.17 no.5
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• pp.1288-1297
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• 2017

The estimation of the remaining useful life (RUL) of lithium-ion (Li-ion) batteries is important for intelligent battery management system (BMS). Data mining technology is becoming increasingly mature, and the RUL estimation of Li-ion batteries based on data-driven prognostics is more accurate with the arrival of the era of big data. However, the support vector machine (SVM), which is applied to predict the RUL of Li-ion batteries, uses the traditional single-radial basis kernel function. This type of classifier has weak generalization ability, and it easily shows the problem of data migration, which results in inaccurate prediction of the RUL of Li-ion batteries. In this study, a novel multi-kernel SVM (MSVM) based on polynomial kernel and radial basis kernel function is proposed. Moreover, the particle swarm optimization algorithm is used to search the kernel parameters, penalty factor, and weight coefficient of the MSVM model. Finally, this paper utilizes the NASA battery dataset to form the observed data sequence for regression prediction. Results show that the improved algorithm not only has better prediction accuracy and stronger generalization ability but also decreases training time and computational complexity.

• Journal of Preventive Medicine and Public Health
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• v.38 no.4
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• pp.379-385
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• 2005

The impact of the next influenza pandemic is difficult to predict. It is dependent on how virulent the virus is, how rapidly it spreads from population to population, and the effectiveness of prevention and response efforts. Despite the uncertainty about the magnitude of the next pandemic, estimates of the health and economic impact remain important to aid public health policy decisions and guide pandemic planning for health and emergency sectors. Planning ahead in preparation for an influenza pandemic, with its potentially very high morbidity and mortality rates, is essential for hospital administrators and public health officials. The estimat ion of pandemic impact is based on the previous pandemics- we had experienced at least 3 pandemics in 20th century. But the epidemiologi cal characteristics - ie, start season, the impact of 1st wave, pathogenicity and virulence of the viruses and the primary victims of population were quite different from one another. I reviewed methodology for estimation and modelling of pandemic impact and described some nations's results using them in their national preparedness plans. And then I showed the estimates of pandemic influenza impact in Korea with FluSurge and FluAid. And, I described the results of pandemic modelling with parameters of 1918 pandemic for the shake of education and training of the first-line responder health officials to the epidemics. In preparing influenza pandemics, the simulation and modelling are the keys to reduce the uncertainty of the future and to make proper policies to manage and control the pandemics.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.4
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• pp.391-397
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• 2011

Control of interior permanent magnet (IPMSM) is difficult because its nonlinearity and parameter uncertainty. In this paper, a fuzzy c-regression models clustering algorithm which is based on T-S fuzzy is used to model IPMSM with a series linear model and weight them by memberships. Lagrangian of constrained function is built for calculating clustering centers where training output data are considered. Based on these clustering centers, least square method is applied for T-S fuzzy linear model parameters. As a result, IPMSM can be modeled as T-S fuzzy model for T-S fuzzy control of them.

• The Journal of Korean Physical Therapy
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• v.27 no.2
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• pp.81-84
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• 2015

Purpose: The purpose of this study was to compare the effect of 'knowledge of result' and 'knowledge of performance', two types of extrinsic feedbacks, during the sit-to-stand movement in children with hemiplegic cerebral palsy. Methods: A total of ten children with hemiplegic cerebral palsy (ages 8 to 12 years) were recruited for the study. Subjects with hemiplegic cerebral palsy performed sit-to-stand movement in front of a mirror. Their performance was supervised and revised for normal movement by a pediatric physiotherapist. In the knowledge of the result, subjects performed sit-to-stand using a chair with an armrest in their mind with normal movement. In the knowledge of performance, subjects performed sit-to-stand under verbal instructions. Randomized cross over trials were used in this study. Main outcome measurements were as follows: mediolateral speed, anteroposterior speed, velocity moment, extent in mediolateral direction, extent in anteroposterior direction, and vertical distance of the center of pressure. Results: The mediolateral speed and extent of center of pressure was higher for 'knowledge of performance' in comparison with the other type of extrinsic feedbacks (p<0.05). The other parameters, including anteroposterior speed and extent, and vertical speed of the center of pressure, did not differ between the two types of extrinsic feedbacks (p>0.05). Conclusion: These findings suggested that training in sit-to-stand movement with 'knowledge of result' may result in better use of extrinsic feedback.

• Restorative Dentistry and Endodontics
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• v.23 no.2
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• pp.639-646
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• 1998

To evaluate the effect of interface conditions and retention grooves in the Class V composite resin restoration of the maxillary first premolar, the distribution of the values of stress and displacement was analyzed with the two-dimensional finite element method. The results were obtained as follows : 1. Boundary elements and Stiffness values could be used as the interface parameters in the, finite element method. 2. The amount of restriction of the displacement at the cervical margin by placing a retention groove at the cervical wall was about three times as high as that by placing a retention groove at the occlusal wall. 3. Because of the relative amount of tensile components of the stress values in the bucco-lingual direction, the possibility of dislocation of the restoration was much higher at the cervical margin than at the occlusal margin. 4. It might be recommended that both occlusal and cervical retention grooves be used routinely, but if one, it be placed at the cervical wall.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.10
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• pp.2265-2271
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• 2011

This paper presents an u-health Care system based on context-awareness information. Extended project induced an education for creative engineering design. It could occur one of the issue for life care or wellness in recently life. Especially u-health Care system help on achieving this object as an efficient care-training with user blood pressure or with echocardiogram, electromyogram, electroencephalogram, or Electrocardiography. In this paper, development project show an u-health care system based on this parameters of the context-awareness with network service and analysis provide with working memory(DB) for user data base applications.

• Journal of Sensor Science and Technology
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• v.22 no.2
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• pp.156-161
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• 2013

This study was aimed to implement the electroglottography (EGG) system for analyzing fundamental frequency of the phonation. EGG was recorded from the conductance between ring electrodes attached to the neck skin area near thyroid cartilage with high frequency carrier electric signals during vocalization, and voice signal was recorded with microphone simultaneously. EGG and voice signals were transmitted to the audio port in PC and recorded with stereo sound recording program. From the digitized data, several parameters such as pitch, jitter, shimmer, CQ and SQ were analyzed from the vowel sounds. For the voice training, sound fundamental frequency was displayed during the vocalization and singing a song using pitches analyzed from the EGG. The system implemented in this study could be used for vocal exercise.

• Journal of the Korean Data and Information Science Society
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• v.12 no.2
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• pp.11-25
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• 2001

There are several algorithms for classification in modeling relations, patterns, and rules which exist in data. We learn to classify objects on the basis of instances presented to us, not by being given a set of classification rules. The Bayesian learning uses the probability distribution to express our knowledge about unknown parameters and update our knowledge by the law of probability as the evidence gathered from data. Also, the neural network models are designed for predicting an unknown category or quantity on the basis of known attributes by training. In this paper, we compare the misclassification error rates of Bayesian Neural Network method with those of other classification algorithms, CHAID, CART, and QUBST using several data sets.

• Journal of the Society of Naval Architects of Korea
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• v.44 no.6
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• pp.572-578
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• 2007

A Rigid Inflatable Boat (RIB) is now widely used for commercial and military purpose. In this paper, it is supposed that seven-meter-class RIB be used as an unmanned target ship for naval training. In order to develop many tactical maneuvering patterns of a target ship, a simple horizontal maneuvering model of a RIB is needed. Therefore, models of speed and yaw rate are constructed as the first-order differential equations based on Lewandowski#s empirical formula for steady turning circle diameter of a conventional planning hull. Some parameters in the models are determined using the results of sea trial tests. Finally, proposed models are validated through the comparison of the simulation result with the sea trial result for a specific scenario. Even though a simple model does not represent the horizontal motion of a RIB precisely, however, it can be used enough to develop tactical trajectory patterns.