• The Journal of Korean Physical Therapy
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• v.29 no.2
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• pp.74-79
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• 2017

Purpose: The purpose of this study was to compare the effects of the FES-gait with augmented feedback training to the FES alone on the gait and functional performance in individuals with chronic stroke. Methods: This study used a pretest and posttest randomized control design. The subjects who signed the agreement were randomly divided into 12 experimental groups and 12 control groups. The experimental groups performed two types of augmented feedback training (knowledge of performance and knowledge of results) together with FES, and the control group performed FES on the TA and GM without augmented feedback and then walked for 30 minutes for 40 meters. Both the experimental groups and the control groups received training five times a week for four weeks. Results: The groups that received the FES with augmented feedback training significantly showed a greater improvement in single limb support (SLS) and gait velocity than the groups that received FES alone. In addition, timed up and go (TUG) test and six minute walk test (6MWT) showed a significant improvement in the groups that received FES with augmented feedback compared to the groups that received FES alone. Conclusion: Compared with the existing FES gait training, augmented feedback showed improvements in gait parameters, walking ability, and dynamic balance. The augmented feedback will be an important method that can provide motivation for motor learning to stroke patients.

• The Journal of Korea Robotics Society
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• v.16 no.3
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• pp.276-282
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• 2021

The system for recognizing text in natural scenes has been applied in various industries. However, due to the change in brightness that occurs in nature such as light reflection and shadow, the text recognition performance significantly decreases. To solve this problem, we propose an adversarial shadow generation and training algorithm that is robust to shadow changes. The adversarial shadow generation and training algorithm divides the entire image into a total of 9 grids, and adjusts the brightness with 4 trainable parameters for each grid. Finally, training is conducted in a adversarial relationship between the text recognition model and the shaded image generator. As the training progresses, more and more difficult shaded grid combinations occur. When training with this curriculum-learning attitude, we not only showed a performance improvement of more than 3% in the ICDAR2015 public benchmark dataset, but also confirmed that the performance improved when applied to our's android application text recognition dataset.

• Nuclear Engineering and Technology
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• v.56 no.1
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• pp.114-122
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• 2024

Irradiation-induced defects and the precipitates in the wrapper material of the Indian Fast Breeder Test Reactor (FBTR), SS 316 are analyzed using the synchrotron source-based Angle Dispersive X-Ray Diffraction (ADXRD) technique with X-rays of energy 17.185 keV (wavelength ~0.72146 Å). The differences and similarities in the high displacement damage samples as a function of dpa (displacement per atom) and dpa rate in the range of 2.9 × 10^-7- 9 × 10^-7 dpa/s are studied. Ferrite and M₂₃C₆ are commonly observed in the present set of high displacement damage 40-74 dpa SS 316 samples irradiated at temperatures in the range of 400-483 ℃. Also, the dislocation density has increased as a function of the irradiation dose. The X-ray diffraction peak profile parameters quantified such as peak shift and asymmetry show that the irradiation-induced defects are sensitive to the dpa rate-irradiation temperature combinations. The increase in yield strength as a function of displacement damage is also correlated to the dislocation density.

• IE interfaces
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• v.10 no.1
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• pp.135-143
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• 1997

The importance of training for NC, CNC and Machining Center has been greatly increased. This paper presents implementation of a DNC(Direct Numerical Control) operating software for educational system. This system is able to connect 8-32 CNCs to Control PC with RS232 multi-port serial card. Therefore, it allows much efficiency in training even after costs are considered. The KISCO DNC S/W for above system includes various communication functions, communication parameters setting, program editor and user-friendly environment. This software was developed with C and Windows programming. It was proved in function and stability by iterative field tests.

• Journal of Biomedical Engineering Research
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• v.17 no.3
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• pp.313-318
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• 1996

We proposed a new system for the postural balance rehabilitation training. For the purpose, we used the virtual hiking system using virtual reality technology. We evaluated the system by measuring the parameters such as path deviation, path deviation velocity, cycling time, and head movement. From our results, we verified the usefulness of virtual reality technology in rehabilitation. Our results showed that this system was effective postural balance rehabilitation training device and might be useful as the clinical equipment.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.122-125
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• 2000

The paper describes the adaptation of evolutionary fuzzy model ins (EFM) in developing global function approximation tools for use in genetic algorithm based optimization of nonlinear structural systems. EFM is an optimization process to determine the fuzzy membership parameters for constructing global approximation model in a case where the training data are not sufficiently provided or uncertain information is included in design process. The paper presents the performance of EFM in terms of numbers of fuzzy rules and training data, and then explores the EFM based sizing of automotive component for passenger protection.

• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.499-506
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• 2004

The Unscented Kalman Filter is known to outperform the Extended Kalman Filter for the nonlinear state estimation with a significance advantage that it does not require the computation of Jacobian but EKF has a competitive advantage to the UKF on the performance time. We compare both algorithms on training the artificial neural network. The validation data set is used to estimate parameters which are supposed to result in better fitting for the test data set. Experimental results are presented which indicate the performance of both algorithms.

• Speech Sciences
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• v.7 no.3
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• pp.185-195
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• 2000

This paper seeks to compare the feature parameters used in speaker identification systems under noisy environments. The feature parameters compared are LP cepstrum (LPCC), Cepstral mean subtraction(CMS), Pole-filtered CMS(PFCMS), Adaptive component weighted cepstrum(ACW) and Postfilter cepstrum(PF). The GMM-based text independent speaker identification system is designed for this target. Some series of experiments show that the LPCC parameter is adequate for modelling the speaker in the matched environments between train and test stages. But in the mismatched training and testing conditions, modified parameters are preferable the LPCC. Especially CMS and PFCMS parameters are more effective for the microphone mismatching conditions while the ACW and PF parameters are good for more noisy mismatches.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2001.10a
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• pp.156-161
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• 2001

Currently, agricultural facilities are evaluated using either basic inspections or detailed analysis. However, conventional analyses as well as methods based on fuzzy logic and rule of thumb have not been very successful in providing a clear relationship between rating and real state of agricultural facilities, because they can't provide exactly acceptable reliability of degraded structures with manager or supervisor. Therefore, in this stage, we must define probabilistic variables for representing degradation of structures being given damages during a survival time. This paper describes the application of neural network system in developing the relation between subjective ratings and parameters of agricultural reservoir as well as that between subjective and analytical ratings. It is shown that neural networks can be trained and used successfully in estimating a rating based on several parameters. The specific application problem for agricultural reservoir in the rural area of Korea is presented and database is constructed to maintain training data set, the information of inspection and facilities. This study showed that a successful training of a neural network could be useful, especially if the input data set for target problem contains parameters with a diverse combination of inter-correlation coefficients. And the networks had a prediction rating of about $^{\ast}^{\ast}^{\ast}%$. The neural network system is expected to show high performance fairly in estimate than statistical method to use equation that is consisted of very lowly interrelated variables.

• Journal of Korean Society on Water Environment
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• v.21 no.1
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• pp.21-28
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• 2005

The training and prediction modeling using an artificial neural network was implemented to predict the turbidity of treated water as well as to estimate the optimized feed concentration of polyaluminium chloride (PACl) in a water treatment plant. The parameters used in the input layers were pH, temperature, turbidity and alkalinity, while those in output layers were PACl and turbidity of treated water. Levenberg-Marquadt method of feedforward back-propagation perceptron in the neural network toolbox of MATLAB program was used in this study. Correlation coefficients of the training data with the measured data were 0.9997 for PACl and 0.6850 for turbidity and those of the testing data with measured data were 0.9140 for PACl and 0.3828 for turbidity, when four parameters at input layer, 12-12 nodes each at both the first and the second hidden layers, and two parameters(PACl and turbidity) at output layer were used. Although the predictability of PACl was improved, compared to that of the previous studies to use the only coagulant dose as output layer, turbidity in treated water could not be predicted well. Acquisition of more data through several years obtained with the advanced on-line measuring system could make the artificial neural network useful and practical in actual water treatment plants.