KJC Korea
Journal Central

Recently, NAND flash memories have replaced magnetic disks due to non-volatility, high capacity and high resistance, in various computer systems but it has disadvantages which are the limited lifespan and imbalanced operation latency. Therefore, many page replacement policies have been studied to overcome the disadvantages of NAND flash memories. Although it is clear that these policies reflect execution characteristics of various environments and applications, researches on the foundation-policy decision for disk buffer management are insufficient. Thus, in this paper, we propose a foundation-policy recommendation model, called FRM for effectively utilizing NAND flash memories. FRM proposes a suitable page replacement policy by classifying and analyzing characteristics of workloads through machine learning. As an implementation case, we introduce FRM with a disk buffer management policy and in experiment results, prediction accuracy and weighted average of FRM shows 92.85% and 88.97%, by training dataset and validation dataset for foundation disk buffer management policy, respectively.

In this paper, it is confirmed that the metrics used to evaluate image quality can be applied to low-light images. Due to the nature of low-illumination images, factors related to light create various noise patterns, and the smaller the amount of light, the more severe the noise. Therefore, in situations where it is difficult to obtain a clean image without noise, the quality of a low-illuminance image from which noise has been removed is often judged by the human eye. In this paper, noise in low-illuminance images for which ground truth cannot be obtained is removed using Noise2Noise, and spatial resolution and radial resolution are evaluated using ISO 12233 charts and colorchecker as metrics such as MTF and SNR. It can be shown that the quality of the low-illuminance image, which has been evaluated mainly for qualitative evaluation, can also be evaluated quantitatively.

The study proposes a model that utilizes Python-based deep learning text classification techniques to detect the legality of illegal financial advertising posts on the internet. These posts aim to promote unlawful financial activities, including the trading of bank accounts, credit card fraud, cashing out through mobile payments, and the sale of personal credit information. Despite the efforts of financial regulatory authorities, the prevalence of illegal financial activities persists. By applying this proposed model, the intention is to aid in identifying and detecting illicit content in internet-based illegal financial advertisining, thus contributing to the ongoing efforts to combat such activities. The study utilizes convolutional neural networks(CNN) and recurrent neural networks(RNN, LSTM, GRU), which are commonly used text classification techniques. The raw data for the model is based on manually confirmed regulatory judgments. By adjusting the hyperparameters of the Korean natural language processing and deep learning models, the study has achieved an optimized model with the best performance. This research holds significant meaning as it presents a deep learning model for discerning internet illegal financial advertising, which has not been previously explored. Additionally, with an accuracy range of 91.3% to 93.4% in a deep learning model, there is a hopeful anticipation for the practical application of this model in the task of detecting illicit financial advertisements, ultimately contributing to the eradication of such unlawful financial advertisements.

Style transfer is one of deep learning-based image processing techniques that has been actively researched recently. These research efforts have led to significant improvements in the quality of result images. Style transfer is a technology that takes a content image and a style image as inputs and generates a transformed result image by applying the characteristics of the style image to the content image. It is becoming increasingly important in exploiting the diversity of digital content. To improve the usability of style transfer technology, ensuring stable performance is crucial. Recently, in the field of natural language processing, the concept of Transformers has been actively utilized. Attention maps, which forms the basis of Transformers, is also being actively applied and researched in the development of style transfer techniques. In this paper, we analyze the representative techniques SANet and AdaAttN and propose a novel attention map-based structure which can generate improved style transfer results. The results demonstrate that the proposed technique effectively preserves the structure of the content image while applying the characteristics of the style image.

This study was conducted to provide a solution to the problem of sizing errors occurring in online purchases due to discrepancies and non-standardization in clothing sizes. This paper discusses an implementation approach for a machine learning-based recommender system capable of providing personalized sizes to online consumers. We trained multiple validated collaborative filtering algorithms including Non-Negative Matrix Factorization (NMF), Singular Value Decomposition (SVD), k-Nearest Neighbors (KNN), and Co-Clustering using purchasing data derived from online commerce and compared their performance. As a result of the study, we were able to confirm that the NMF algorithm showed superior performance compared to other algorithms. Despite the characteristic of purchase data that includes multiple buyers using the same account, the proposed model demonstrated sufficient accuracy. The findings of this study are expected to contribute to reducing the return rate due to sizing errors and improving the customer experience on e-commerce platforms.

Dropouts of students not only cause financial loss to the university, but also have negative impacts on individual students and society together. To resolve this issue, various studies have been conducted to predict student dropout using machine learning. This paper presents a model implemented using DNN (Deep Neural Network) and LGBM (Light Gradient Boosting Machine) to predict dropout of university students and compares their performance. The academic record and grade data collected from 20,050 students at A University, a small and medium-sized 4-year university in Seoul, were used for learning. Among the 140 attributes of the collected data, only the attributes with a correlation coefficient of 0.1 or higher with the attribute indicating dropout were extracted and used for learning. As learning algorithms, DNN (Deep Neural Network) and LightGBM (Light Gradient Boosting Machine) were used. Our experimental results showed that the F1-scores of DNN and LGBM were 0.798 and 0.826, respectively, indicating that LGBM provided 2.5% better prediction performance than DNN.

The Golden Cross is commonly seen as a buy signal in financial markets, but its reliability for predicting stock price movements is limited due to market volatility. This paper introduces a time-invariant approach that considers the Golden Cross as a singular event. Utilizing LSTM neural networks, we forecast significant stock price changes following a Golden Cross occurrence. By comparing our approach with traditional time series analysis and using a confusion matrix for classification, we demonstrate its effectiveness in predicting post-event stock price trends. To conclude, this study proposes a model with a precision of 83%. By utilizing the model, investors can alleviate potential losses, rather than making buy decisions under all circumstances following a Golden Cross event.

Stock price prediction is an important topic extensively discussed in the financial market, but it is considered a challenging subject due to numerous factors that can influence it. In this research, performance was compared and analyzed by applying time series prediction models (LSTM, GRU) and non-time series prediction models (RF, SVR, KNN, LGBM) that do not take into account the temporal dependence of data into stock price prediction. In addition, various data such as stock price data, technical indicators, financial statements indicators, buy sell indicators, short selling, and foreign indicators were combined to find optimal predictors and analyze major factors affecting stock price prediction by industry. Through the hyperparameter optimization process, the process of improving the prediction performance for each algorithm was also conducted to analyze the factors affecting the performance. As a result of feature selection and hyperparameter optimization, it was found that the forecast accuracy of the time series prediction algorithm GRU and LSTM+GRU was the highest.

The background of this study lies in the increasing economic value of Korean fonts and the necessity for font development focused on small character design suitable for the mobile environment. The objective of this study is to analyze and propose strategies to improve readability on mobile screens. The research was conducted by applying eight attributes that could enhance readability according to Tim Ahrens to the design process of a Korean mobile font, adjusting Google's Noto Sans Korean for print/publishing and for small sizes. The results of the study indicate that 1. type width should be increased, 2. open counter (interior space) should be increased, 3. closed counter should be decreased, 4. font weight should be increased, 5. stroke contrast should be decreased, 6. spacing between characters should be increased. Therefore, distinct font families should be provided, differentiating between print/publishing and mobile use, as well as varying font weights and sizes, applying readability and legibility enhancement techniques for Korean fonts.

In this paper, we propose a system that accurately and efficiently determines forward head posture based on network learning by analyzing the user's skeletal posture. Forward head posture syndrome is a condition in which the forward head posture is changed by keeping the neck in a bent forward position for a long time, causing pain in the back, shoulders, and lower back, and it is known that daily posture habits are more effective than surgery or drug treatment. Existing methods use convolutional neural networks using webcams, and these approaches are affected by the brightness, lighting, skin color, etc. of the image, so there is a problem that they are only performed for a specific person. To alleviate this problem, this paper extracts the skeleton from the image and learns the data corresponding to the side rather than the frontal view to find the forward head posture more efficiently and accurately than the previous method. The results show that the accuracy is improved in various experimental scenes compared to the previous method.

Colmap is one of the innovative artificial intelligence technologies, highly effective as a tool in 3D reconstruction tasks. Moreover, it excels at constructing intricate 3D models by utilizing images and corresponding metadata. Colmap generates 3D models by merging 2D images, camera position data, depth information, and so on. Through this, it achieves detailed and precise 3D reconstructions, inclusive of objects from the real world. Additionally, Colmap provides rapid processing by leveraging GPUs, allowing for efficient operation even within large data sets. In this paper, we have presented a method of collecting 2D images of traditional Korean towers and reconstructing them into 3D models using Colmap. This study applied this technology in the restoration process of traditional stone towers in South Korea. As a result, we confirmed the potential applicability of Colmap in the field of cultural heritage restoration.

In this paper, we propose a smart wall switch based on IEEE 802.11b/g/n standard 2.4GHz band communication. As the 4th industrial era evolves, smart home solution development is actively underway, and application cases for smart wall switches are increasing. Most of the Chinese products that preoccupy the market through price competitiveness use Bluetooth and Zigbee communication switches. However, while ZigBee communication is low power, communication speed is slower than Bluetooth and network configuration through a separate hub is additionally required. The Bluetooth method has problems in that the communication range and speed are lower than Wi-Fi communication, the communication standby time is relatively long, and security is weak. In this study, an IEEE 802.11b/g/n smart wall switch applied with Wi-Fi communication technology was developed. In addition, through the two-wire structure, it is designed so that no additional cost is incurred through the construction of a separate neutral line in the building. The result of the study is more than 30% cheaper than the existing wall switch, so it is judged that it will be able to preoccupy the market not only in terms of technological competitiveness but also price competitiveness.

In this paper, we propose a WiFi-based joystick with an acceleration sensor and a vibration sensor that can be used in flight simulators and VR fields. The flight simulator is a technology belonging to the ICT and SW application field and provides a simulation environment that reproduces the aircraft environment. Existing flight simulator control devices are fixed to a specific device and the user's activity area is limited. In this paper, a 3D space manipulation device was implemented for the user's free use of space. In addition, the proposed control device is designed as a WiFi communication board and display that displays information and performs 3-axis sensing for accurate and sophisticated control compared to existing VR equipment controllers. And the applicability was confirmed by implementing a Unity-based virtual environment. As a result of the implementation device verification, it was confirmed that the control device operates normally through the communication interface, It was confirmed that the sensing values in the game and the sensing values measured on the implemented board matched each other. The results of this study can be used for VR and various metaverse related contents in addition to flight simulators.

In this paper, a new model was presented to objectively evaluate the defense ability of defenders in Korean professional baseball. In the proposed model, using Korean professional baseball game data from 2016 to 2019, a representative defender was selected for each team and defensive position to evaluate defensive ability. In order to evaluate the defense ability, a method of calculating the defense range for each position and dividing the calculated defense area was proposed. The defensive range for each position was calculated using the Convex Hull algorithm based on the point at which the defenders in the same position threw out the ball. The out conversion score and victory contribution score for both infielders and outfielders were calculated as basic scores using the defensive range for each position. In addition, double kill points for infielders and extra base points for outfielders were calculated separately and added together.

This study was conducted to identify the effects of mental health problems on stress coping, social support and life satisfaction of nursing students. The collected data were analyzed by t-test, ANOVA, sheffe post hoc verification and Multiple Regression Analysis using the SPSS program. Anxiety (β=-.33, p=.038) and carelessness(β=-.30, p=.003) had a significant negative effect on the problem-focused stress coping(R2=.248). Sensitivity(β=-.33, p=.016) had a negative effect on perception of social support(R2=.083). Depression(β=-.41, p=.003), somatization(β=-.23, p=.047), and alcoholism(β=-.20, p=.029) had significant negative effects on life satisfaction(R2=.355). Considering the results of this study, in order to improve stress coping, social support, and life satisfaction of nursing students, it is necessary to identify mental health problems and take individual approaches accordingly.

One of the methods being suggested as a way to address social issues is the utilization of big data analysis techniques. In this study, we utilized keyword network analysis and CONCOR analysis techniques to analyze the research trends on work-related musculoskeletal disorders. The findings of this study are as follows: Firstly, the number of papers on work-related musculoskeletal disorders has been consistently increasing, with an average of over 33 articles published per year since the investigation of musculoskeletal risk factors in 2003. The publication rate showed an increase from 2007 to 2009. Secondly, the frequency of the top keywords identified through text mining were as follows: work (4,940), musculoskeletal disorders (2,197), symptoms (1,836), related (1,769), musculoskeletal system (1,421). Thirdly, the CONCOR analysis resulted in the formation of four clusters: ' Musculoskeletal disorder treatment', 'Occupational health and safety management', 'Work environment assessment', and ' Workplace environment measurement'. It is expected that this study will contribute to the development of research on musculoskeletal disorders and provide various directions for future studies.

In this research, we applied a problem-based learning(PBL) method for one semester, and measured critical thinking disposition, self efficacy for group work and self confidence of nursing process of Nursing Process and Critical Thinking subjects before and after application. This research is one group pre-test and post-test design. The subjects of this study were collected using an online questionnaire for second-year nursing students located in G city, and 108 students participated in the final. The result of this research showed that critical thinking disposition(p=.000), self efficacy for group work(p=.003) and self confidence of nursing process(p=.000) increased statistically significantly after problem-based learning(PBL). This findings indicate that problem-based learning(PBL) is effective in improving critical thinking disposition, self efficacy for group work and self confidence of nursing process. Therefore, If problem-oriented learning (PBL) is expanded and applied to nursing education, it is believed that it will be effective in enhancing the core competencies of nursing students.

This study analyzes and verifies the relationship between job satisfaction by factor, organizational commitment, job satisfaction, and overall job satisfaction of working-age workers using a structural equation model. The research data used the 24th data of the Korean Labor and Income Panel Study. The subjects of the study were 8,024 workers in the production age of 15 to 64 as of 2021 among 9,132 people who correctly marked the values of the observation variables. As a result of the study, it was found that job satisfaction by factor had a positive impact on organizational commitment, job satisfaction, and overall work and job satisfaction. In addition, organizational commitment and job satisfaction were found to have a positively significant effect on overall work and job satisfaction. These results emphasize the importance of understanding and solving the unique problems faced by various types of workers in the labor market, ultimately providing important implications for organizations and policy makers to provide workers with an efficient working environment.

This study used Korean work environment surveys data to confirm the relationship between exposure to harmful and risk factors and absenteeism of manufacturing workers according to the survey period. Accordingly, 8,318 workers in the manufacturing industry were analyzed for the same questions of harmful and risk factors in 2011 (3rd), 2017 (5th), and 2020 (6th). Work-related hazards and risks, vibration, noise, high temperature, dust inhalation, vapor inhalation, chemical handling, and infectious material handling were found to affect absenteeism more than 3/4 of the time of exposure in 2020 compared to 2011. In conclusion, it was found that non-exposure to work-related hazards and risk factors increased one survey after another, and that the time of exposure to work-related hazards and risk factors affected absenteeism.

In this paper, we aim to build a gentrification analysis model and examine its characteristics, focusing on the point at which rents rose sharply alongside the recovery of commercial districts after the gradual resumption of daily life. Recently, in Korea, the influence of social distancing measures after the pandemic has led to the formation of small-scale commercial districts, known as ‘hot places’, rather than large-scale ones. These hot places have gained popularity by leveraging various media and social networking services to attract customers effectively. As a result, with an increase in the floating population, commercial districts have become active, leading to a rapid surge in rents. However, for small business owners, coping with the sudden rise in rent even with increased sales can lead to gentrification, where they might be forced to leave the area. Therefore, in this study, we seek to analyze the periods before and after by identifying points where rents rise sharply as commercial districts experience revitalization. Firstly, we collect text data to explore topics related to gentrification, utilizing LDA topic modeling. Based on this, we gather data at the commercial district level and build a gentrification analysis model to examine its characteristics. We hope that the analysis of gentrification through this model during a time when commercial districts are being revitalized after facing challenges due to the pandemic can contribute to policies supporting small businesses.

Recently, a lot of discussions are underway in the field of introducing new technologies about the rapidly growing metaverse. However, the degree of acceptance of metaverse users at the beginning of the introduction is different from expectations, so research should be conducted for the continuous use of current real users and service success. In this study, we would like to investigate the relationship between four experience factors according to Metaverse's experiential economy theory, and perceived value and intention to continue use. A survey was conducted on metaverse real-life veterans, and 177 questionnaires were finally analyzed. The collected data were empirically analyzed using SPSS 25.0 and AMOS 21.0. As a result; First, it was found that all the experience factors of the metaverse had a positive effect on the perceived value. Second, all of the experience factors of metaverse were found to have a positive effect on the intention to continue use. Third, perceived value was found to have a positive effect on the intention to continue use. Based on the analysis results, the implications and limitations of this study were presented. Based on the analysis results, metaverse should provide and develop various experience factors differentiated from reality to users. In addition, providing an experience environment and value that metaverse users can perceive will increase users' intention to continue using it.

In this study, prompt engineering that improves prompts was explored to improve the performance of teaching and learning materials recommendations using generative artificial intelligence such as GPT and Stable Diffusion. Picture materials were used as the types of teaching and learning materials. To explore the impact of the prompt composition, a Zero-Shot prompt, a prompt containing learning target grade information, a prompt containing learning goals, and a prompt containing both learning target grades and learning goals were designed to collect responses. The collected responses were embedded using Sentence Transformers, dimensionalized to t-SNE, and visualized, and then the relationship between prompts and responses was explored. In addition, each response was clustered using the k-means clustering algorithm, then the adjacent value of the widest cluster was selected as a representative value, imaged using Stable Diffusion, and evaluated by 30 elementary school teachers according to the criteria for evaluating teaching and learning materials. Thirty teachers judged that three of the four picture materials recommended were of educational value, and two of them could be used for actual classes. The prompt that recommended the most valuable picture material appeared as a prompt containing both the target grade and the learning goal.