• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.55 no.4
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• pp.402-410
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• 2019

This paper deals with the disturbance observer (DOB) based sliding mode control (SMC) for a DC motor to control motor rotating speed precisely and to ensure strong robustness against disturbance including load torque and parameter variation. The reason of steady state error in speed on conventional SMC without DOB is analyzed in detail. Especially, the suggested DOB is designed to prevent measuring noise and harmonics caused by derivative operation on rotating speed. The control performance of the DOB based SMC is evaluated by the various simulations. The simulation results showed that the DOB based SMC had more robust performance than the SMC system without DOB. Especially, precise speed control was possible even though motor parameter variation and load torque was added to the system.

• Proceedings of the KIEE Conference
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• 1994.07a
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• pp.252-254
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• 1994

This paper describes the control method in order that output current of a voltage source inverter tracks reference sinusoidal current so that its harmonic current components are reduced. Operating character of this inverter is analyzed with normalized values of parameter. And the method that apply multilayed feedfoeward neural networks, which play excellent steady state operation in control system, to inverter control system and training method are presented. Then, the output current of inverter which is driven by the proposed method. is considered throughout computer simulation and safe operating range of inverter parameter is resented.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1995.10a
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• pp.95-102
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• 1995

Procedures for estimation of axial or flexural rigidities of bridge members by neural networks are shown. To treat large scale structures containing many unkwon parameters, substructuring concept is introduced. The measurement points are selected considering the sensitivity of the element stiffnesses of interest. Utilization of relative mode vectors is found to be very effective for the local parameter estimation. Then, the study focuses on the method to obtain the training set enough to represent structures. It is shown that noise injection is effective to reduce the estimation errors caused by measurement noise. Verification of the present method is carried out using a cable-stayed bridge model.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.179-182
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• 1997

In a training simulator for power plant, operator's action in the MCR(Main Control Room) are given to plant process and computer system model as an inputs, and the same response as in real power plant is provided in real time. Inter-process communication and synchronization are especially important among various inputs. In the plant simulator, to simulate the digital control system such as FOXBORO SPEC-200 Micro control system, modification and adaptation of control card(CCC) and its continuous display station(CDS) is necessary. This paper describes the modeling and simulation of FOXBORO SPEC-200 Micro control system applied to Younggwang nuclear power plant unit #3 & 4, and its integration process to the full-scope replica type training simulator. In a simulator, display station like CDS of FOXBORO SPEC-200 Micro control system is classified as ITI(Intelligent Type Instrument), which has a micro processor inside to process information and the corresponding alphanumeric display, and the stimulation of ITI limits the important functions in a training simulator such as backtrack, replay, freeze and IC reset. Therefore, to achieve the better performance of the simulator, modification of CDS and special firmware is developed to simulate the FOXBORO SPEC-200 Micro control system. Each control function inside control card is modeled and simulated in generic approach to accept the plant data and control parameter conveniently, and debugging algorithms are applied for massive coding developed in short period.

• Phonetics and Speech Sciences
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• v.1 no.2
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• pp.37-42
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• 2009

The purpose of the present study was to determine the effect of the management program known as vocal function exercise (VFE) on voice quality. Typical VFE was modified and applied to patients with vocal nodules by controlling intensity of voice and relieving the vocal fold to solve hyperfunctional problems in VFE. Eight female subjects aged between 28 and 54 who had been diagnosed with vocal nodules took part in the study. The patients performed VFEs once a week for eight weeks. Vocal function exercises consist of voice hygiene, respiratory training, phonation training, and glide training. The subjects' voices were analyzed pre and post therapy on the aspects of acoustics, maximum phonation time (MPT), GRBAS, and voice handicap index (VHI). As a result, it was found that fundamental frequency ($F_o$) was significant increased, shimmer decreased remarkably and that noise to harmonic ratio (NHR) lowered obviously in the acoustic parameter. In addition, MPT was increased significantly. The scale of GRBAS indicated significant improvement in grade, roughness, and strained voice. VHI indicated significant improvement in an emotional part. In conclusion, VFE was effective in improving voice quality for patients with vocal nodules.

• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.7
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• pp.1322-1331
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• 1992

The conventional neural networks are built without considering the underlying structure of the problems. Hence, they usually contain redundant weights and require excessive training time. A novel neural network structure is proposed for symmetric problems, which alleviate some of the aforementioned drawback of the conventional neural networks. This concept is expanded to that of the constrained neural network which may be applied to general structured problems. Because these neural networks can not be trained by the conventional training algorithm, which destroys the weight structure of the neural networks, a proper training algorithm is suggested. The illustrative examples are shown to demonstrate the applicability of the proposed idea.

• Journal of the Korean Society of Safety
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• v.30 no.3
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• pp.123-134
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• 2015

This study was conducted with the following three study objectives. First, effects of safety awareness level of lab researchers to the improvement of safety culture in the organization Second, effects of organizational safety behaviors to the improvement of safety culture Third, test of mediating effects of organizational safety behaviors in the relationship between safety awareness level and the improvement of safety culture. The results show that organizational safety behavior is an indispensable factor for the improvement level of safety culture. Especially, the factors in safety training activities, safety compliance and management system are mediating variables which affect the safety awareness level and improvement level of safety culture, which shows these variables are very important factors in reducing safety accidents through the improvement of safety culture. Therefore, safety behaviors in the organization should be considered with priority. If the organization leads to improve safety awareness through regular safety training and rewards and punishes according to the test results, safety awareness could be improved. This study was conducted to identify the necessary factors to improve the overall safety culture in the organization and contribute to the diffusion of safety culture by improving the safety training awareness of the researchers.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.6C
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• pp.343-348
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• 2011

This paper introduces a design for real time wireless headset using ANC (active noise control) system based on NFxLMS adaptive filter algorithm. The training time of the proposed system is significantly reduced by using the RMS delay spread of a channel as an error correction parameter, and convergence rate of the FxLMS filter has been improved with updating the coefficients of the NFxLMS filter, which we have got during the training process. Our system has shorter training time and better convergence rate at the same noise reduction level than the conventional system under real noisy environment.

• PNF and Movement
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• v.4 no.1
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• pp.37-44
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• 2006

Purpose : Improved walking is a common goal after stroke. Although the neurodevelopmental intervention(PNF) is the most widely used approach in the walking training of hemiparetic subjects. There is little neurophysiological evidence for its presumed effects on gait symmetry and facilitation of paretic muscles during the therapeutic intervention. The study, therefore, investigated the immediate effects of gait entrainment by a PNF techniques. Methods : Included persons with stroke who were living in the community. Sixteen subjects were assigned to the experimental group participated in a measures design that evaluated the subjects with pre-treatment, post-treatment(8 weeks). Temporal-spatial parameter of gait were analysed for using the computerized GAITRite system. Intervention : Training for the experimental group was carried out 3 times a week for 8 weeks. The training sessions were comprised of 50 minutes of walking with pattern and techniques in PNF. Results : The experimental group had improvements in the functional walking ability after 8 weeks treatment and Post-treatment test scores were more significant than the pre-treatment score. The treatment group demonstrated significantly post-treatment test improvement in gait velocity, cadence and FAP. Post-treatment test scores were more significant than the pre-treatment score(p<0.05). Conclusion : The results of this study showed that the PNF exercise intervention can improve functional gait ability. This study provides evidence for the efficacy of PNF treatment at improving locomotor function in chronic stroke.

• Design & Manufacturing
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• v.14 no.3
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• pp.1-7
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• 2020

This paper predicts the mass and the length of injection-molded products through the Artificial Neural Network (ANN) method. The ANN was implemented with 5 input parameters and 2 output parameters(mass, length). The input parameters, such as injection time, melt temperature, mold temperature, packing pressure and packing time were selected. 44 experiments that are based on the mixed sampling method were performed to generate training data for the ANN model. The generated training data were normalized to eliminate scale differences between factors to improve the prediction performance of the ANN model. A random search method was used to find the optimized hyper-parameter of the ANN model. After the ANN completed the training, the ANN model predicted the mass and the length of the injection-molded product. According to the result, average error of the ANN for mass was 0.3 %. In the case of length, the average deviation of ANN was 0.043 mm.