• Journal of Digital Policy
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• v.3 no.1
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• pp.1-10
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• 2024

The purpose of this study was to identify the predictors of self-regulated learning strategies among nursing university students. Data were collected from 212 nursing university students in G metropolitan city and K city. The SPSS WIN 23.0 version program was used for data analysis. The data were analyzed using Pearson's correlation coefficient and multiple regression. There were significant correlations between media multitasking and self-regulated learning strategies(r=.45, p<.001), learning flow and self-regulated learning strategies(r=.59, p<.001), and media multitasking and learning flow(r=.32, p<.001). Friendship satisfaction, media multitasking and learning flow explained 45% of the variance for self-regulated learning strategies. To increase the self-regulated learning strategies among nursing university students, it is necessary to develop multiple interventions that enhance friendship satisfaction, media multitasking and learning flow.

• Journal of the Korea Society of Computer and Information
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• v.15 no.7
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• pp.11-17
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• 2010

This paper proposes an improved SMO solving a quadratic optmization problem for class imbalanced learning. The SMO algorithm is aproporiate for solving the optimization problem of a support vector machine that assigns the different regularization values to the two classes, and the prosoposed SMO learning algorithm iterates the learning steps to find the current optimal solutions of only two Lagrange variables selected per class. The proposed algorithm is tested with the UCI benchmarking problems and compared to the experimental results of the SMO algorithm with the g-mean measure that considers class imbalanced distribution for gerneralization performance. In comparison to the SMO algorithm, the proposed algorithm is effective to improve the prediction rate of the minority class data and could shorthen the training time.

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.2107-2115
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• 2018

Over recent years, high-concentration of particulate matters (e.g., a.k.a. fine dust) in South Korea has increasingly evoked considerable concerns about public health. It is intractable to track and report $PM_{10}$ measurements to the public on a real-time basis. Even worse, such records merely amount to averaged particulate concentration at particular regions. Under this circumstance, people are prone to being at risk at rapidly dispersing air pollution. To address this challenge, we attempt to build a predictive model via deep learning to the concentration of particulates ($PM_{10}$). The proposed method learns a binary decision rule on the basis of video sequences to predict whether the level of particulates ($PM_{10}$) in real time is harmful (>$80{\mu}g/m^3$) or not. To our best knowledge, no vision-based $PM_{10}$ measurement method has been proposed in atmosphere research. In experimental studies, the proposed model is found to outperform other existing algorithms in virtue of convolutional deep learning networks. In this regard, we suppose this vision based-predictive model has lucrative potentials to handle with upcoming challenges related to particulate measurement.

• Journal of Information Processing Systems
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• v.18 no.2
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• pp.245-257
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• 2022

In the Internet of Things (IoT) era, the types of devices used by one user are becoming more diverse and the number of devices is also increasing. However, a forensic investigator is restricted to exploit or collect all the user's devices; there are legal issues (e.g., privacy, jurisdiction) and technical issues (e.g., computing resources, the increase in storage capacity). Therefore, in the digital forensics field, it has been a challenge to acquire information that remains on the devices that could not be collected, by analyzing the seized devices. In this study, we focus on the fact that multiple devices share data through account synchronization of the online platform. We propose a novel way of identifying the user's interest through analyzing the remnants of targeted advertising which is provided based on the visited websites or search terms of logged-in users. We introduce a detailed methodology to pick out the targeted advertising from cache data and infer the user's interest using deep learning. In this process, an improved learning model considering the unique characteristics of advertisement is implemented. The experimental result demonstrates that the proposed method can effectively identify the user interest even though only one device is examined.

• Journal of the Korean Society for Heat Treatment
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• v.36 no.6
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• pp.396-401
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• 2023

Aus-tempering heat treatment is suitable for thin and small-sized in precision parts. However, the heat treatment process relies on the experience and skill of the operator, making it challenging to produce precision parts due to the cold forging process. The aims of this study is to explore suitable machine learning models using data from the aus-tempering heat treatment process and analyze the factors that significantly impact the mechanic properties (e.g. hardness). As a result, the study analyzed, from a machine learning perspective, how hardness prediction varies based on the quenching temperature, carbon (C), and copper (Cu) contents.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1171-1176
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• 2023

In this paper, we predicts the estimated price using the DNBP (Deep learning Network to predict Budget Price) model with bidding data obtained from the bidding websites, ElecNet and OK EMS. We use the DNBP model to predict four lottery preliminary price, calculate their arithmetic mean, and then estimate the expected budget price ratio. We evaluate the model's performance by comparing it with the actual expected budget price ratio. We train the DNBP model by removing some of the 15 input nodes. The prediction results showed the lowest RMSE of 0.75788% when the model had 6 input nodes (a, g, h, i, j, k).

• The Journal of the Acoustical Society of Korea
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• v.38 no.5
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• pp.601-606
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• 2019

This paper is a study on speech recognition in the Korean using grapheme unit (Cho-sumg [onset], Jung-sung [nucleus], Jong-sung [coda]). Here we make ASR (Automatic speech recognition) system without G2P (Grapheme to Phoneme) process and show that Deep learning based ASR systems can learn Korean pronunciation rules without G2P process. The proposed model is shown to reduce the word error rate in the presence of sufficient training data.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.359-366
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• 2013

Although many research efforts have been conducted to address the effect of crew members' work skills (e.g., technical and planning skills) on work performance (e.g., work duration and quality) in construction projects, the relationship between skill and performance has generated a great deal of controversy in the field of management (Inkpen and Crossan 1995). This controversy can lead to under- or over-estimations of the overall project schedule, and can make it difficult for project managers to implement appropriate managerial policies for enhancing project performance. To address this issue, the following aspects need to be considered: (a) work performances are determined not only by individual-level work skill but also by the group-level work skill affected by work team members, each member's role, and any working behavior pattern; (b) work planning has significant effects on to what extent work skill enhances performance; and (c) different types of activities in construction require different types of work, skill, and team composition. This research, therefore, develops a system dynamics (SD) model to analyze the effects of both individual-and group-level (i.e., multi-level) skill on performances by utilizing the advantages of SD in capturing a feedback process and state changes, especially in human factors (e.g., attitude, ability, and behavior). The model incorporates: (a) a multi-level skill evolution and relevant behavior development mechanism within a work group; (b) the interaction among work planning, a crew's skill-learning, skill manifestation, and performances; and (c) the different work characteristics of each activity. This model can be utilized to implement appropriate work planning (e.g., work scope and work schedule) and crew management policies (e.g., work team composition and decision of each worker's role) with an awareness of crew's skill and work performance. Understanding the different characteristics of each activity can also support project managers in applying strategic work planning and crew management for a corresponding activity, which may enhance each activity's performance, as well as the overall project performance.

• Nutrition Research and Practice
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• v.10 no.2
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• pp.198-205
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• 2016

BACKGROUND/OBJECTIVES: A healthy diet has been reported to be associated with physical development, cognition and academic performance, and personality during adolescence. This study was performed to investigate the relationships among milk consumption and academic performance, learning motivation and strategies, and personality among Korean adolescents. SUBJECTS/METHODS: The study was divided into two parts. The first part was a survey on the relationship between milk consumption and academic performance, in which intakes of milk and milk products and academic scores were examined in percentiles among 630 middle and high school students residing in small and medium-sized cities in 2009. The second part was a survey on the relationships between milk consumption and learning motivation and strategy as well as personality, in which milk consumption habits were collected and Learning Motivation and Strategy Test (L-MOST) for adolescents and Total Personality Inventory for Adolescents (TPI-A) were conducted in 262 high school students in 2011. RESULTS: In the 2009 survey, milk and milk product intakes of subjects were divided into a low intake group (LM: ${\leq}60.2g/day$), medium intake group (MM: 60.3-150.9 g/day), and high intake group (HM: ${\geq}151.0g/day$). Academic performance of each group was expressed as a percentile, and performance in Korean, social science, and mathematics was significantly higher in the HM group (P < 0.05). In the 2011 survey, the group with a higher frequency of everyday milk consumption showed significantly higher "learning strategy total," "testing technique," and "resources management technique" scores (P < 0.05) in all subjects. However, when subjects were divided by gender, milk intake frequency, learning strategy total, class participation technique, and testing technique showed significantly positive correlations (P < 0.05) in boys, whereas no correlation was observed in girls. Correlations between milk intake frequency and each item of the personality test were only detected in boys, and milk intake frequency showed positive correlations with "total agreeability", "organization", "responsibility", "conscientiousness", and "intellectual curiosity" (P < 0.05). CONCLUSION: Intakes of milk and milk products were correlated with academic performance (Korean, social science, and mathematics) in Korean adolescents. In male high school students, particularly, higher milk intake frequency was positively correlated with learning motivation and strategy as well as some items of the personality inventory.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.6
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• pp.1032-1037
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• 2006

The channel is one of the precious and limited resources in wireless networks. There are many researches on the channel management. Recently, the optimization problem of guard channels has been an important issue. In this paper, we propose an intelligent channel management technique based on the neural networks. An SRN channel allocation model is developed to generate the learning data for the neural networks and the performance analysis of system. In the proposed technique, the neural network is trained to generate optimal guard channel number g, using backpropagation supervised learning algorithm. The optimal g is computed using the neural network and compared to the g computed by the SRM model. The numerical results show that the difference between the value of 8 by backpropagation and that value by SRM model is ignorable.