• Journal of Korean Academy of Nursing
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• v.46 no.4
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• pp.490-500
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• 2016

Purpose: This study was conducted to develop assertiveness training applying Dongsasub training for junior nursing students, and to verify effectiveness of the training on assertiveness behavior, self-esteem, clinical practice stress, and clinical competence. Methods: The study design was a non-equivalent control group non-synchronized design. Participants were 63 nursing students in clinical training (31 students in the experimental group and 32 students in the control group). The assertiveness training applying Dongsasub training consisted of four sessions. Outcome variables included assertiveness behavior, self-esteem, clinical practice stress, and clinical competence. Data were analyzed using Chi-square, Fisher's exact test and independent samples t-test with SPSS/WIN 21.0. Results: Scores of assertiveness behavior (t=-2.49, p=.015), self-esteem (t=-4.80, p <.001) and clinical competence (t=-2.33, p=.023) were significantly higher and clinical practice stress (t=4.22, p <.001) was significantly lower in the experimental group compared to the control group. Conclusion: Results indicate that the assertiveness training applying Dongsasub training can be used as a nursing intervention to lower clinical practice stress and improve the clinical competence of nursing students.

• Smart Media Journal
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• v.9 no.3
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• pp.80-89
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• 2020

Out - of - distribution (OOD) samples are frequently encountered when deploying a classification model in plenty of real-world machine learning-based applications. Those samples are normally sampling far away from the training distribution, but many classifiers still assign them high reliability to belong to one of the training categories. In this study, we address the problem of removing OOD examples by estimating marginal density estimation using variational autoencoder (VAE). We also investigate other proper methods, such as temperature scaling, Gaussian discrimination analysis, and label smoothing. We use Chonnam National University (CNU) weeds dataset as the in - distribution dataset and CIFAR-10, CalTeach as the OOD datasets. Quantitative results show that the proposed framework can reject the OOD test samples with a suitable threshold.

• Journal of the Korean Society of Tobacco Science
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• v.17 no.1
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• pp.5-14
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• 1995

Efficient algorithms for the automatic classification of flue-cured tovacco ripeness and grading have been developed The ripeness of the tobacco was classified into 4 levels vased on the color. The lab-built simple RGB color measuring system was utilized for detecting the light reflectance of the tobacco leaves. The measured data were used far training the artificial neural network The performance of the trained network was also tested far the untrained samples. The spectrophotometer was used to detect the light reflectance and absorption of the graded tobacco leaves in the frequency ranges of the visible light The measured data and the statistical analysis was performed to investigate the light characteristics of the graded samples. The measured data were obtained from samples of 5 different grades directly without considering the leaf positions. Those data were used far training the artificial neural network The performance of the trained network was also tested far the untrained samples. The neural network based sensor information processing showed successful results for grading of tobacco leaves.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.5
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• pp.1851-1855
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• 2015

Background: There are increasing data about microRNAs (miRNA) in the literature, providing abundant evidence that they play important roles in pathogenesis and development of colorectal cancer. In this study, we aimed to investigate the miRNA expression profiles in surgically resected specimens of patients with recurrent and non-recurrent colorectal cancer. Materials and Methods: The study population included 40 patients with stage II colorectal cancer (20 patients with recurrent tumors, and 20 sex and age matched patients without recurrence), who underwent curative colectomy between 2004 and 2011 without adjuvant therapy. Expression of 16 miRNAs (miRNA-9, 21, 30d, 31, 106a, 127, 133a, 133b, 135b, 143, 145, 155, 182, 200a, 200c, 362) was verified by quantitative real-time polymerase chain reaction (qRT-PCR) in all resected colon cancer tissue samples and in corresponding normal colonic tissues. Data analyses were carried out using SPSS 15 software. Values were statistically significantly changed in 40 cancer tissues when compared to the corresponding 40 normal colonic tissues (p<0.001). MiR-30d, miR-133a, miR-143, miR-145 and miR-362 expression was statistically significantly downregulated in 40 resected colorectal cancer tissue samples (p<0.001). When we compared subgroups, miRNA expression profiles of 20 recurrent cancer tissues were similar to all 40 cancer tissues. However in 20 non-recurrent cancer tissues, miR-133a expression was not significantly downregulated, moreover miR-133b expression was significantly upregulated (p<0.05). Conclusions: Our study revealed dysregulation of expression of ten miRNAs in Turkish colon cancer patients. These miRNAs may be used as potential biomarkers for early detection, screening and surveillance of colorectal cancer, with functional effects on tumor cell behavior.

• Nuclear Engineering and Technology
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• v.55 no.6
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• pp.2096-2106
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• 2023

Rotating machinery is widely applied in important equipment of nuclear power plants (NPPs), such as pumps and valves. The research on intelligent fault diagnosis of rotating machinery is crucial to ensure the safe operation of related equipment in NPPs. However, in practical applications, data-driven fault diagnosis faces the problem of small and imbalanced samples, resulting in low model training efficiency and poor generalization performance. Therefore, a deep convolutional conditional generative adversarial network (DCCGAN) is constructed to mitigate the impact of imbalanced samples on fault diagnosis. First, a conditional generative adversarial model is designed based on convolutional neural networks to effectively augment imbalanced samples. The original sample features can be effectively extracted by the model based on conditional generative adversarial strategy and appropriate number of filters. In addition, high-quality generated samples are ensured through the visualization of model training process and samples features. Then, a deep convolutional neural network (DCNN) is designed to extract features of mixed samples and implement intelligent fault diagnosis. Finally, based on multi-fault experimental data of motor and bearing, the performance of DCCGAN model for data augmentation and intelligent fault diagnosis is verified. The proposed method effectively alleviates the problem of imbalanced samples, and shows its application value in intelligent fault diagnosis of actual NPPs.

• Journal of Information Processing Systems
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• v.14 no.5
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• pp.1075-1086
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• 2018

In this paper a cross-validation algorithm for training probabilistic neural networks (PNNs) is presented in order to be applied to automatic face identification. Actually, standard PNNs perform pretty well for small and medium sized databases but they suffer from serious problems when it comes to using them with large databases like those encountered in biometrics applications. To address this issue, we proposed in this work a new training algorithm for PNNs to reduce the hidden layer's size and avoid over-fitting at the same time. The proposed training algorithm generates networks with a smaller hidden layer which contains only representative examples in the training data set. Moreover, adding new classes or samples after training does not require retraining, which is one of the main characteristics of this solution. Results presented in this work show a great improvement both in the processing speed and generalization of the proposed classifier. This improvement is mainly caused by reducing significantly the size of the hidden layer.

• International Journal of Contents
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• v.14 no.4
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• pp.24-29
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• 2018

EEG-based brain-computer interfaces has focused on explicitly expressed intentions to assist physically impaired patients. For EEG-based-computer interfaces to function effectively, it should be able to understand users' implicit information. Since it is hard to gather EEG signals of human brains, we do not have enough training data which are essential for proper classification performance of implicit intention. In this paper, we improve the subject independent classification of implicit intention through the generation of additional training data. In the first stage, we perform the PCA (principal component analysis) of training data in a bid to remove redundant components in the components within the input data. After the dimension reduction by PCA, we train ICA (independent component analysis) network whose outputs are statistically independent. We can get additional training data by adding Gaussian noises to ICA outputs and projecting them to input data domain. Through simulations with EEG data provided by CNSL, KAIST, we improve the classification performance from 65.05% to 66.69% with Gamma components. The proposed sample generation method can be applied to any machine learning problem with fewer samples.

• The Journal of Asian Finance, Economics and Business
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• v.10 no.1
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• pp.169-179
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• 2023

This study aims to determine the factors that influence training programs in increasing entrepreneurial success as a new model for developing entrepreneurship training in a new era. It intended to provide a suggestion for building an entrepreneurship training model for Beginner Young Entrepreneurs (BYE) organized by the Ministry of Youth and Sports of Indonesia. The study used a quantitative method by collecting data through a Google form questionnaire distributed via the WhatsApp group. This study employs samples from 358 BYE training participants for 2017-2020, and data was processed using Amos SEM software to analyze factors that influence the success of entrepreneurship. The results showed that entrepreneurial motivation is a partial mediator in increasing the effect of training on its success by BYE participants. Furthermore, the key factor for increasing entrepreneurial motivation is challenging young people to start businesses. This study recommends that BYE program policymakers build a training model by considering many practical case studies to increase motivation as an important mediator in influencing entrepreneurial success. Meanwhile, to boost the morale of training participants, it is necessary to add significant real challenges for participants to start entrepreneurship. Moreover, future studies should add other independent variables, such as personality.

• Journal of Internet Computing and Services
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• v.17 no.2
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• pp.49-57
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• 2016

Ensemble classification involves combining multiple classifiers to obtain more accurate predictions than those obtained using individual models. Ensemble learning techniques are known to be very useful for improving prediction accuracy. Bagging is one of the most popular ensemble learning techniques. Bagging has been known to be successful in increasing the accuracy of prediction of the individual classifiers. Bagging draws bootstrap samples from the training sample, applies the classifier to each bootstrap sample, and then combines the predictions of these classifiers to get the final classification result. Bootstrap samples are simple random samples selected from the original training data, so not all bootstrap samples are equally informative, due to the randomness. In this study, we proposed a new method for improving the performance of the standard bagging ensemble by optimizing bootstrap samples. A genetic algorithm is used to optimize bootstrap samples of the ensemble for improving prediction accuracy of the ensemble model. The proposed model is applied to a bankruptcy prediction problem using a real dataset from Korean companies. The experimental results showed the effectiveness of the proposed model.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.10 no.4
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• pp.329-336
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• 2016

Deaf people using sign language are experiencing social inequalities and financial losses due to communication restrictions. In this paper, real-time pattern recognition algorithm was applied to distinguish American Sign Language using an armband sensor(8-channel EMG sensors and one IMU) to enable communication between the deaf and the hearing people. The validation test was carried out with 11 people. Learning pattern classifier was established by gradually increasing the number of training database. Results showed that the recognition accuracy was over 97% with 20 training samples and over 99% with 30 training samples. The present study shows that sign language recognition using armband sensor is more convenient and well-performed.