• Journal of Digital Convergence
• /
• v.17 no.12
• /
• pp.413-425
• /
• 2019

This study was conducted to identify factors affecting depression in junior nursing students(JNS). The number of the participants was 144 at a college in S and G city. The data were collected using questionnaires about somatic symptoms(SS), sleep quality, stress, adaptation to college life(AC), depression. Mean score of depression was 18.89, 58.3% are experiencing depression that requires clinical treatment. The significant predictors of JNS were levels of depression AC(β=-.503, p=.000) and SS(β=.263, p=.000) respectively, explaining 58.9% of variance. In order to control the depressive symptoms of JNS, it is necessary to diagnose basic learning ability from the beginning of admission and provide guidance management plans to help students adapt to academic activities by providing customized programs for each level to improve learning ability. It is necessary to develop and apply various intervention programs to alleviate physical symptoms such as fatigue/low energy experienced by JNS.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.8
• /
• pp.3984-4005
• /
• 2017

Image recognition has become an increasingly important topic for its wide application. It is highly challenging when facing to large-scale database with large variance. The recognition systems rely on a key component, i.e. the low-level feature or the learned mid-level feature. The recognition performance can be potentially improved if the data distribution information is exploited using a more sophisticated way, which usually a function over hidden variable, model parameter and observed data. These methods are called generative score space. In this paper, we propose a discriminative extension for the existing generative score space methods, which exploits class label when deriving score functions for image recognition task. Specifically, we first extend the regular generative models to class conditional models over both observed variable and class label. Then, we derive the mid-level feature mapping from the extended models. At last, the derived feature mapping is embedded into a discriminative classifier for image recognition. The advantages of our proposed approach are two folds. First, the resulted methods take simple and intuitive forms which are weighted versions of existing methods, benefitting from the Bayesian inference of class label. Second, the probabilistic generative modeling allows us to exploit hidden information and is well adapt to data distribution. To validate the effectiveness of the proposed method, we cooperate our discriminative extension with three generative models for image recognition task. The experimental results validate the effectiveness of our proposed approach.

• Smart Media Journal
• /
• v.10 no.3
• /
• pp.60-67
• /
• 2021

This study attempted to examine the effects of SW education using EPL and coding robots on computing thinking and problem-solving skills among various teaching and learning methods of software education. To this end, a survey was conducted on 350 students from middle schools A and B in Gwangju and Jeollanam-do, and the difference in mean was analyzed through t-verification and one-way analysis of variance in order to investigate the relationship between variables. Based on the research results obtained through this, we will identify the effects, strengths, and weaknesses of SW education using EPL and coding robots, provide basic data and information for efficient teaching and learning methods of SW education, and further suggest better directions in terms of academic and practical aspects.

• Journal of Digital Convergence
• /
• v.19 no.7
• /
• pp.441-449
• /
• 2021

This study was conducted to identify the factors affecting the SDLA of nursing students who experienced online-class due to COVID-19. The subjects of this study were 115 nursing students who had experienced online-class due to COVID-19 for more than one semester at a university. Data were collected through online surveys from November 18 to December 1, 2020. Factors that have a statistically significant influenced on SDLA in nursing students were 'use of emotion' in emotional intelligence (β=.42, p<.001), 'control' in resilience (β=.28, p=.001), and 'objectivity' in critical thinking disposition (β=.27, p<.001), which accounted for 62.0% of the total variance. Research and intervention to improve the emotional intelligence of nursing students were constantly needed, and it is suggested that this be considered when producing educational contents.

• Korean Journal of Agricultural Science
• /
• v.47 no.4
• /
• pp.1109-1122
• /
• 2020

The aim of this study was to develop a marbling classification and prediction model using small parts of sirloin images based on a deep learning algorithm, namely, a convolutional neural network (CNN). Samples were purchased from a commercial slaughterhouse in Korea, images for each grade were acquired, and the total images (n = 500) were assigned according to their grade number: 1++, 1+, 1, and both 2 & 3. The image acquisition system consists of a DSLR camera with a polarization filter to remove diffusive reflectance and two light sources (55 W). To correct the distorted original images, a radial correction algorithm was implemented. Color images of sirloins of Hanwoo (mixed with feeder cattle, steer, and calf) were divided and sub-images with image sizes of 161 × 161 were made to train the marbling prediction model. In this study, the convolutional neural network (CNN) has four convolution layers and yields prediction results in accordance with marbling grades (1++, 1+, 1, and 2&3). Every single layer uses a rectified linear unit (ReLU) function as an activation function and max-pooling is used for extracting the edge between fat and muscle and reducing the variance of the data. Prediction accuracy was measured using an accuracy and kappa coefficient from a confusion matrix. We summed the prediction of sub-images and determined the total average prediction accuracy. Training accuracy was 100% and the test accuracy was 86%, indicating comparably good performance using the CNN. This study provides classification potential for predicting the marbling grade using color images and a convolutional neural network algorithm.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.21 no.1
• /
• pp.87-92
• /
• 2021

Bias, variance, error and learning are important factors for performance in modeling a big data based recommendation system. The recommendation model in this system must reduce complexity while maintaining the explanatory diagram. In addition, the sparsity of the dataset and the prediction of the system are more likely to be inversely proportional to each other. Therefore, a product recommendation model has been proposed through learning the similarity between products by using a factorization method of the sparsity of the dataset. In this paper, the generalization ability of the model is improved by applying the max-norm regularization as an optimization method for the loss function of this model. The solution is to apply a stochastic projection gradient descent method that projects a gradient. The sparser data became, it was confirmed that the propsed regularization method was relatively effective compared to the existing method through lots of experiment.

• The Journal of Korean Academic Society of Nursing Education
• /
• v.27 no.3
• /
• pp.227-239
• /
• 2021

Purpose: This study used an exploratory sequential approach (mixed methods) design to explore essential meaning through comparing and analyzing the experiences of nursing students in virtual simulation practice and high fidelity simulation practice education in parallel. Methods: The study participants were 20 nursing students, and data were collected through focus group meetings from July 17 to August 5, 2020, and via online quantitative data from November 10 to November 15, 2020. The qualitative data were analyzed using Giorgi's phenomenological method, and the quantitative data were analyzed using descriptive statistics, the Mann-Whitney U test, Kruskal-Wallis H test analysis of variance and Spearman's ρ correlation. Results: The comparison between the two simulation training experiences was shown in five contextual structures, as follows: (1) reflection of the clinical field, (2) thinking theorem vs. thinking expansion, (3) individual-centered learning vs. team-centered learning, (4) attitudes toward participating in practical training, (5) metacognition of personal competency as a prospective nurse, and (6) revisiting the method of practice training. There was a positive correlation between satisfaction with the practice and the clinical judgment ability of high fidelity simulation, which was statistically significant (r=.47, p=.036). Conclusion: Comparing the experiences between virtual simulation practice training and high fidelity simulation practice training, which has increased in demand due to the Coronavirus Disease-2019 pandemic, is meaningful as it provides practical data for introspection and reflection on in-campus clinical education.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.150-150
• /
• 2023

기작기반의 역학적 모델과 자료기반의 딥러닝 모델은 수질예측에 다양하게 적용되고 있으나, 각각의 모델은 고유한 구조와 가정으로 인해 장·단점을 가지고 있다. 특히, 딥러닝 모델은 우수한 예측 성능에도 불구하고 훈련자료가 부족한 경우 오차와 과적합에 따른 분산(variance) 문제를 야기하며, 기작기반 모델과 달리 물리법칙이 결여된 예측 결과를 생산할 수 있다. 본 연구의 목적은 주요 상수원인 댐 저수지를 대상으로 수심별 수온과 탁도를 예측하기 위해 기작기반과 자료기반 모델의 장점을 융합한 PGDL(Process-Guided Deep Learninig) 모델을 개발하고, 물리적 법칙 만족도와 예측 성능을 평가하는데 있다. PGDL 모델 개발에 사용된 기작기반 및 자료기반 모델은 각각 CE-QUAL-W2와 순환 신경망 딥러닝 모델인 LSTM(Long Short-Term Memory) 모델이다. 각 모델은 2020년 1월부터 12월까지 소양강댐 댐 앞의 K-water 자동측정망 지점에서 실측한 수온과 탁도 자료를 이용하여 각각 보정하고 훈련하였다. 수온 및 탁도 예측을 위한 PGDL 모델의 주요 알고리즘은 LSTM 모델의 목적함수(또는 손실함수)에 실측값과 예측값의 오차항 이외에 역학적 모델의 에너지 및 질량 수지 항을 제약 조건에 추가하여 예측결과가 물리적 보존법칙을 만족하지 않는 경우 penalty를 부가하여 매개변수를 최적화시켰다. 또한, 자료 부족에 따른 LSTM 모델의 예측성능 저하 문제를 극복하기 위해 보정되지 않은 역학적 모델의 모의 결과를 모델의 훈련자료로 사용하는 pre-training 기법을 활용하여 실측자료 비율에 따른 모델의 예측성능을 평가하였다. 연구결과, PGDL 모델은 저수지 수온과 탁도 예측에 있어서 경계조건을 통한 에너지와 질량 변화와 저수지 내 수온 및 탁도 증감에 따른 공간적 에너지와 질량 변화의 일치도에 있어서 LSTM보다 우수하였다. 또한 역학적 모델 결과를 LSTM 모델의 훈련자료의 일부로 사용한 PGDL 모델은 적은 양의 실측자료를 사용하여도 CE-QUAL-W2와 LSTM 보다 우수한 예측 성능을 보였다. 연구결과는 다차원의 역학적 수리수질 모델과 자료기반 딥러닝 모델의 장점을 결합한 새로운 모델링 기술의 적용 가능성을 보여주며, 자료기반 모델의 훈련자료 부족에 따른 예측 성능 저하 문제를 극복하기 위해 역학적 모델이 유용하게 활용될 수 있음을 시사한다.

• Geomechanics and Engineering
• /
• v.34 no.6
• /
• pp.697-726
• /
• 2023

Brittleness as an important property of rock plays a crucial role both in the failure process of intact rock and rock mass response to excavation in engineering geological and geotechnical projects. Generally, rock brittleness indices are calculated from the mechanical properties of rocks such as uniaxial compressive strength, tensile strength and modulus of elasticity. These properties are generally determined from complicated, expensive and time-consuming tests in laboratory. For this reason, in the present research, an attempt has been made to predict the rock brittleness indices from simple, inexpensive, and quick laboratory test results namely dry unit weight, porosity, slake-durability index, P-wave velocity, Schmidt rebound hardness, and point load strength index using multiple linear regression, exponential regression, support vector machine (SVM) with various kernels, generating fuzzy inference system, and regression tree ensemble (RTE) with boosting framework. So, this could be considered as an innovation for the present research. For this purpose, the number of 39 rock samples including five igneous, twenty-six sedimentary, and eight metamorphic were collected from different regions of Iran. Mineralogical, physical and mechanical properties as well as five well known rock brittleness indices (i.e., B₁, B₂, B₃, B₄, and B₅) were measured for the selected rock samples before application of the above-mentioned machine learning techniques. The performance of the developed models was evaluated based on several statistical metrics such as mean square error, relative absolute error, root relative absolute error, determination coefficients, variance account for, mean absolute percentage error and standard deviation of the error. The comparison of the obtained results revealed that among the studied methods, SVM is the most suitable one for predicting B₁, B₂ and B₅, while RTE predicts B₃ and B₄ better than other methods.

• Journal of the Korea Computer Graphics Society
• /
• v.30 no.3
• /
• pp.141-147
• /
• 2024

Progressive photon mapping is a widely adopted rendering technique that conducts a kernel-density estimation on photons progressively generated from lights. Its hyperparameter, which controls the reduction rate of the density estimation, highly affects the quality of its rendering image due to the bias-variance tradeoff of pixel estimates in photon-mapped results. We can minimize the errors of rendered pixel estimates in progressive photon mapping by estimating the optimal parameters based on gradient-based optimization techniques. To this end, we derived the gradients of pixel estimates with respect to the parameters when performing progressive photon mapping and compared our estimated gradients with finite differences to verify estimated gradients. The gradient estimated in this paper can be applied in an online learning algorithm that simultaneously performs progressive photon mapping and parameter optimization in future work.