• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.200-209
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• 2019

When forecasting future values, a model estimated after minimizing training errors can yield test errors higher than the training errors. This result is the over-fitting problem caused by an increase in model complexity when the model is focused only on a given dataset. Some regularization and resampling methods have been introduced to reduce test errors by alleviating this problem but have been designed for use with only a given dataset. In this paper, we propose a new optimization approach to reduce test errors by transforming a test error minimization problem into a training error minimization problem. To carry out this transformation, we needed additional data for the given dataset, termed pseudo data. To make proper use of pseudo data, we used three types of missing data imputation techniques. As an optimization tool, we chose the least squares method and combined it with an extra pseudo data instance. Furthermore, we present the numerical results supporting our proposed approach, which resulted in less test errors than the ordinary least squares method.

• Journal of Auto-vehicle Safety Association
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• v.15 no.1
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• pp.55-62
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• 2023

In this study, a chest deflection is predicted by introducing a deep learning technique with the results of the frontal impact of the USNCAP conducted for 110 car models from MY2018 to MY2020. The 120 data are divided into training data and test data, and the training data is divided into training data and validation data to determine the hyperparameters. In this process, the deceleration data of each vehicle is averaged in units of 10 ms from crash pulses measured up to 100 ms. The performance of the deep learning model is measured by the indices of the mean squared error and the mean absolute error on the test data. A DNN (Deep Neural Network) model can give different predictions for the same hyperparameter values at every run. Considering this, the mean and standard deviation of the MSE (Mean Squared Error) and the MAE (Mean Absolute Error) are calculated. In addition, the deep learning model performance according to the inclusion of CVW (Curb Vehicle Weight) is also reviewed.

• ETRI Journal
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• v.17 no.1
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• pp.11-22
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• 1995

This paper proposes a modified error function to improve the error back-propagation (EBP) algorithm for multi-Layer perceptrons (MLPs) which suffers from slow learning speed. It can also suppress over-specialization for training patterns that occurs in an algorithm based on a cross-entropy cost function which markedly reduces learning time. In the similar way as the cross-entropy function, our new function accelerates the learning speed of the EBP algorithm by allowing the output node of the MLP to generate a strong error signal when the output node is far from the desired value. Moreover, it prevents the overspecialization of learning for training patterns by letting the output node, whose value is close to the desired value, generate a weak error signal. In a simulation study to classify handwritten digits in the CEDAR [1] database, the proposed method attained 100% correct classification for the training patterns after only 50 sweeps of learning, while the original EBP attained only 98.8% after 500 sweeps. Also, our method shows mean-squared error of 0.627 for the test patterns, which is superior to the error 0.667 in the cross-entropy method. These results demonstrate that our new method excels others in learning speed as well as in generalization.

• International Journal of Contents
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• v.13 no.4
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• pp.23-28
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• 2017

Of the total economic loss caused by disasters, 40% are due to floods and floods have a severe impact on human health and life. So, it is important to monitor the water level of a river and to issue a flood warning during unfavorable circumstances. In this paper, we propose a modified error function to improve a hydrological modeling using a multi-layer perceptron (MLP) neural network. When MLP's are trained to minimize the conventional mean-squared error function, the prediction performance is poor because MLP's are highly tunned to training data. Our goal is achieved by preventing overspecialization to training data, which is the main reason for performance degradation for rare or test data. Based on the modified error function, an MLP is trained to predict the water level with rainfall data at upper reaches. Through simulations to predict the water level of Nakdong River near a UNESCO World Heritage Site "Hahoe Village," we verified that the prediction performance of MLP with the modified error function is superior to that with the conventional mean-squared error function, especially maximum error of 40.85cm vs. 55.51cm.

• Korean Journal of Adult Nursing
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• v.25 no.6
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• pp.633-643
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• 2013

Purpose: The objective of this study was to identify the moderating and mediating effects of transformational-leadership in the relationship between medication error management climate and error reporting intention. Methods: Participants in this study were 118 nurses from 11 hospitals in Korea. The scales of medication error management climate, transformational-leadership and error reporting intention of nurses were used in this study. Descriptive statistics, t-test, ANOVA, partial Pearson correlation coefficient, and stepwise multiple regression were used for data analysis. Results: Higher transformational leadership group members had higher error management climate (t=3.88~4.64, p<.001) and higher intention to error reporting (t=2.49, p=.014). There were significant positive correlations between subcategories of medication error management climate and transformational leadership (r=.37~.51, p<.001). But error reporting intention was related to the transformational leadership (r=.28 p=.002), two subcategories such as 'learn from error' (r=.26, p=.004) and 'medication error competence' (r=.25, p=.008) of medication error management climate. Transformational-leadership was a moderator and a mediator between medication error management climate and error reporting intention. Conclusion: Based on the results of this study, transformational-leadership promotion training program to construct medication error management climate and to improve error reporting intention should be needed.

• The Journal of Korean Physical Therapy
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• v.35 no.6
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• pp.213-219
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• 2023

Purpose: In this study, based on the error augmentation, we performed walking training with increased rhythmic auditory stimulation speed on the affected side (IRAS) and walking training with decreased rhythmic auditory stimulation speed on the unaffected side (DRAS). The purpose of this study was to verify whether motor learning was effective in improving balance ability. Methods: Twenty-eight subjects with chronic stroke were recruited from a rehabilitation center. The subjects were divided into three groups: an IRAS group (10 subjects), a DRAS group (9 subjects), and control group (9 subjects). They received 30minutes of neuro-developmental therapy and walking training for 30minutes, five times a week for three weeks. Static and functional balance ability were measured before and after the training period. Static balance was measured by balancia software. Functional balance was measured by the timed up and go test (TUG) and the berg balance scale (BBS). Results: After the training periods, the IRAS group showed a significant improvement in TUG, BBS, area 95% COP, and weight distribution on the affected side when compared to both the DRAS group and control group (p<0.05). Conclusion: Based on the results of this study, it is possible to consider error augmentation methods of motor learning if rhythmic auditory stimulation is applied to stroke patients in clinical practice. If the affected side is shorter than the unaffected side, the affected side should be adjusted to the increased rhythmic auditory stimulation speed, which is considered to be an effective intervention to improve balance ability.

• Journal of the Korea Institute of Military Science and Technology
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• v.26 no.2
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• pp.139-148
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• 2023

Safety standards for long-range artillery ammunition test and training sites follow the US artillery shooting range safety zone standards. Although the South Korean geographical conditions of shooting ranges are different from those of the United States, there is no safety standard reflecting the South Korean topographical characteristics. Probable error associated with the shooting range, trajectory should be considered in establishing the safety standards. In this study, we present the safety standards for the ammunition testing site suitable for the Korean situation, with applying a concept of trajectory and probable error differed by ammunition type, which are currently confirmed by the South Korean Army's artillery shooting.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.4
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• pp.100-106
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• 2008

In this paper we apply a discriminative weight training employing power spectral flatness measure (PSFM) to a statistical model-based voice activity detection (VAD) in various noise environments. In our approach, the VAD decision rule is expressed as the geometric mean of optimally weighted likelihood ratio test (LRT) based on a minimum classification error (MCE) method which is different from the previous works in th at different weights are assigned to each frequency bin and noise environments depending on PSFM. According to the experimental results, the proposed approach is found to be effective for the statistical model-based VAD using the LRT.

• International Journal of Aerospace System Engineering
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• v.5 no.1
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• pp.9-18
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• 2018

The longitudinal flight parameters of a light airplane are estimated from flight test data by use of the output error method. The reliability of the flight test measurement is examined in engineering judgment, scatter and Cramer-Rao bound, which turns out to be satisfactory with minor defects. Estimated parameter values are validated by comparing the simulated responses with the ones from actual flight tests. The FTD(Flight Training Device) of a light airplane turns out to satisfy the qualification of FAA Level 5 FTD in longitudinal motion. All the necessary practices for generation of high-fidelity data in longitudinal motion of a light aircraft are successfully performed in this study.

• Phonetics and Speech Sciences
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• v.6 no.2
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• pp.29-34
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• 2014

In the Vector Taylor Series (VTS)-based noisy speech recognition methods, Hidden Markov Models (HMM) are usually trained with clean speech. However, better performance is expected by training the HMM with noisy speech. In a previous study, we could find that Minimum Mean Square Error (MMSE) estimation of the training noisy speech in the log-spectrum domain produce improved recognition results, but since the proposed algorithm was done in the log-spectrum domain, it could not be used for the HMM adaptation. In this paper, we modify the previous algorithm to derive a novel mathematical relation between test and training noisy speech in the cepstrum domain and the mean and covariance of the Multi-condition TRaining (MTR) trained noisy speech HMM are adapted. In the noisy speech recognition experiments on the Aurora 2 database, the proposed method produced 10.6% of relative improvement in Word Error Rates (WERs) over the MTR method while the previous MMSE estimation of the training noisy speech produced 4.3% of relative improvement, which shows the superiority of the proposed method.