KJC Korea
Journal Central

This paper presents the control strategy of Auto-leveling equipment (ALE) of Multi-function radar (MFR) for vehicle using Embedded System. MFR implements surveillance patrol missions such as surface-to-air missiles and fighters with constant rotation. ALE consists of 4 Auto-leveling modules (ALM) and retains the stability with maintaining level. The gradient of vehicle can be measured and controlled by embedded systems. This paper contributes for improvement the system design with the ALM 1 set modeling. The validity of the modeling is verified using MATLAB/Simulink.

Metaverse is a modern new technology that is advancing quickly. The goal of this study is to investigate this technique from the perspective of computer vision as well as general perspective. A thorough analysis of computer vision related Metaverse topics has been done in this study. Its history, method, architecture, benefits, and drawbacks are all covered. The Metaverse's future and the steps that must be taken to adapt to this technology are described. The concepts of Mixed Reality (MR), Augmented Reality (AR), Extended Reality (XR) and Virtual Reality (VR) are briefly discussed. The role of computer vision and its application, advantages and disadvantages and the future research areas are discussed.

In this paper, we propose a model that can perform human pose estimation through a MobileViT-based model with fewer parameters and faster estimation. The based model demonstrates lightweight performance through a structure that combines features of convolutional neural networks with features of Vision Transformer. Transformer, which is a major mechanism in this study, has become more influential as its based models perform better than convolutional neural network-based models in the field of computer vision. Similarly, in the field of human pose estimation, Vision Transformer-based ViTPose maintains the best performance in all human pose estimation benchmarks such as COCO, OCHuman, and MPII. However, because Vision Transformer has a heavy model structure with a large number of parameters and requires a relatively large amount of computation, it costs users a lot to train the model. Accordingly, the based model overcame the insufficient Inductive Bias calculation problem, which requires a large amount of computation by Vision Transformer, with Local Representation through a convolutional neural network structure. Finally, the proposed model obtained a mean average precision of 0.694 on the MS COCO benchmark with 3.28 GFLOPs and 9.72 million parameters, which are 1/5 and 1/9 the number compared to ViTPose, respectively.

This paper studied the 3D modeling process for the restoration of the 'Three-story Stone Pagoda of Bulguksa Temple in Gyeongju', a stone pagoda from the Unified Silla Period, using artificial intelligence (AI). Existing 3D modeling methods generate numerous verts and faces, which takes a considerable amount of time for AI learning. Accordingly, a method of performing more efficient 3D modeling by lowering the number of verts and faces is required. To this end, in this study, the structure of the stone pagoda was deeply analyzed and a modeling method optimized for AI learning was studied. In addition, it is meaningful to propose a new 3D modeling methodology for the restoration of stone pagodas in Korea and to secure a data set necessary for artificial intelligence learning.

In this study, we propose a Dual Approach methodology to enhance the accuracy of document classifiers by utilizing both contextual and keyword information. Firstly, contextual information is extracted using Google's BERT, a pre-trained language model known for its outstanding performance in various natural language understanding tasks. Specifically, we employ KoBERT, a pre-trained model on the Korean corpus, to extract contextual information in the form of the CLS token. Secondly, keyword information is generated for each document by encoding the set of keywords into a single vector using an Autoencoder. We applied the proposed approach to 40,130 documents related to healthcare and medicine from the National R&D Projects database of the National Science and Technology Information Service (NTIS). The experimental results demonstrate that the proposed methodology outperforms existing methods that rely solely on document or word information in terms of accuracy for document classification.

Infants utilize crying as a non-verbal means of communication [1]. However, deciphering infant cries presents challenges. Extensive research has been conducted to interpret infant cry audios [2,3]. This paper proposes the classification of infant cries using 3D feature vectors augmented with various audio data techniques. A total of 5 classes (belly pain, burping, discomfort, hungry, tired) are employed in the study dataset. The data is augmented using 5 techniques (Pitch, Tempo, Shift, Mixup-noise, CutMix). Tempo, Shift, and CutMix augmentation techniques demonstrated improved performance. Ultimately, applying effective data augmentation techniques simultaneously resulted in a 17.75% performance enhancement compared to models using single feature vectors and original data.

In this paper, we present a method that utilizes geometry-based sound generation techniques to efficiently handle multiple sound sources, wind turbulence, and temperature-dependent interactions. Recently, a method based on reduced raytracing has been proposed to update the sound position and efficiently calculate sound propagation and diffraction without recursive reflection/refraction of many rays, but this approach only considers the propagation characteristics of sound and does not consider the interaction of multiple sound sources, wind currents, and temperature. These limitations make it difficult to create sound scenes in a variety of virtual environments because they only generate static sounds. In this paper, we propose a method for efficiently constructing a sound map in a situation where multiple sounds are placed, and a method for efficiently controlling the movement of an agent through it. In addition, we propose a method for controlling sound propagation by considering wind currents and temperature. The method proposed in this paper can be utilized in various fields such as metaverse environment design and crowd simulation, as well as games that can improve content immersion based on sound.

Metaverse is a modern new technology that is advancing quickly. The goal of this study is to investigate this technique from the perspective of computer vision as well as general perspective. A thorough analysis of computer vision related Metaverse topics has been done in this study. Its history, method, architecture, benefits, and drawbacks are all covered. The Metaverse's future and the steps that must be taken to adapt to this technology are described. The concepts of Mixed Reality (MR), Augmented Reality (AR), Extended Reality (XR) and Virtual Reality (VR) are briefly discussed. The role of computer vision and its application, advantages and disadvantages and the future research areas are discussed.

This paper describes a level of detail (LOD) based bending spring structure and damping technique that can reliably represent strain-based dynamics (SBD) on a triangular mesh. SBD models elastic energy using strain instead of energy based on the edge length of a triangular mesh. However, when a large external force occurs, the process of calculating the elastic energy based on edges results in a degenerate triangle, which stretches in the wrong direction because it calculates an unstable strain. In this paper, we introduce an LOD-based bending spring generation and energy calculation method that can efficiently handle this problem. As a result, the technique proposed in this paper can reliably and efficiently handle SBD based on bending springs, which can provide a stable representation of cloth simulation.

A privacy model is a technique that quantitatively restricts the possibility and degree of privacy breaches through privacy attacks. Representative models include k-anonymity, l-diversity, t-closeness, and differential privacy. While many privacy models have been studied, research on selecting the most suitable model for a given dataset has been relatively limited. In this study, we develop a system for recommending the suitable privacy model to prevent privacy breaches. To achieve this, we analyze the data features that need to be considered when selecting a model, such as data type, distribution, frequency, and range. Based on privacy model background knowledge that includes information about the relationships between data features and models, we recommend the most appropriate model. Finally, we validate the feasibility and usefulness by implementing a recommendation prototype system.

In modern society, the service method is tended to prefer the non-face-to-face method rather than the face-to-face method. However, services that recommend products such as shoes will inevitably be face-to-face method. In this paper, for the purpose of non-face-to-face service, a system that a foot size is automatically measured and some shoes are recommended based on the measurement result is proposed. To analyze the performance of the proposed method, size measurement error rate and recommendation performance were analyzed. In the recommendation performance experiments, a total of 10 methods for similarity calculation were used and the recommendation method with the best performance among them was applied to the system. From the experiments, the error rate the foot size was small and the recommendation performance was possible to derive significant results. The proposed method is at the laboratory level and needs to be expanded and applied to the real environment. Also, the recommendation method considering design could be needed in the future work.

Currently, most of content restoration using artificial intelligence learning is 2D learning. However, 3D form of artificial intelligence learning is in an incomplete state due to the disadvantage of requiring a lot of computation and learning speed from the existing 2 axes (X, Y) to 3 axes (X, Y, Z). The purpose of this paper is to secure a data-set for artificial intelligence learning by analyzing and 3D modeling the stone pagodas of ourinari by era based on the two-dimensional information (image) of cultural assets. In addition, we analyzed the differences and characteristics of towers in each era in Korea, and proposed a feature modeling method suitable for artificial intelligence learning. Restoration of cultural properties relies on a variety of materials, expert techniques and historical archives. By recording and managing the information necessary for the restoration of cultural properties through this study, it is expected that it will be used as an important documentary heritage for restoring and maintaining Korean traditional pagodas in the future.

The purpose of this study was conducted to provide scientific data supporting the development of health programs for enhancing adolescents’ health, by understanding factors influencing their self-rated health. It conducted a composite sample χ2 test of 54,848 adolescents, to understand differences in the self-rated health depending on their sociodemographic characteristics, stress and depression levels, and also implemented a logistic regression analysis, to verify the factors influencing their sense of self-rated health. As a result of the study, Male students were healthier than female students; students with greater scholarly attainments and higher economic levels were more healthier; and students who had less stress and experienced no depression showed higher sense of self-rated health. Therefore, in order to improve the self-rated health of adolescents, it is necessary to continuously manage through the establishment of a customized health promotion program.

To increase the lifetime of the network in wireless sensor networks, energy harvesting from the surrounding environment or wireless power transfer is being used. In addition, to reduce the energy imbalance and increase the amount of data gathered, a method using mobile sink nodes that visit sensor nodes to gather data has been used. In this paper, we propose a technique to reduce the load on the relay node and collect a lot of data evenly in this environment. In the proposed scheme, sensor nodes construct Minimum Depth Trees (MDTs) considering the network environment and energy, and allocate the data collection amount. Simulation results show that the proposed technique effectively suppresses energy depletion and collects more data compared to existing techniques.

This study is to investigate the effect of CPR education on the knowledge, attitude, and self-efficacy of kindergarten, elementary, and secondary teachers. For the study, a total of 176 kindergartens, elementary schools, and secondary school teachers in J province who received CPR education at an Emergency Medical Education Center from March 21 to June 20, 2023 were surveyed. For the collected data, SPSS WIN 20.0 was used to analyze general characteristics, knowledge, attitude, self-efficacy, and educational program effects. As a result of the study, the knowledge score after education (t=-15.93, p<.001), attitude score (t=-5.11, p<.001), self-efficacy score (t=-12.52, p<.001) was significantly increased. After the education by experts, knowledge, attitude, and self-efficacy improved, and it is thought that the increase in knowledge increased attitude and self-efficacy. This is a result proving that the CPR education of the Emergency Medical Education Center was effective. Therefore, it is necessary to find ways for effective CPR education.

This study investigated the influence of major satisfaction on learning flow and self-efficacy of students majoring in culinary arts at local junior colleges. In the 2022-2 semester, 260 freshmen and sophomore college students majoring in culinary from five junior colleges in the Gwangju and Jeonnam regions were analyzed. For data processing, SPSS Ver. 25.0 was used. The data is used to measure reliability by Cronbach's α, t-test, ANOVA, Pearson's correlation coefficient, and multiple regression analysis. The results of this study are as follows : First, there was a difference in satisfaction between freshmen and sophomores in major satisfaction with cooking related departments at local junior colleges. Second, there was a significant effect of satisfaction with cooking-related majors at local junior colleges on learning immersion. Third, there was a significant effect of satisfaction with cooking-related majors at local junior colleges on self-efficacy. In conclusion, it was found that major satisfaction affects learning immersion and self-efficacy for both students enrolled in cooking-related departments at local junior colleges. In the future, we suggest follow-up research on educational measures to increase learning immersion and self-efficacy for students who are not majoring in cooking in the high school curriculum and students who are insufficient in major classes due to part-time jobs during the semester.

With the global paradigm shift towards climate change, the shipbuilding industry is also considering propulsion systems that utilize eco-friendly fuels various propulsion systems are gaining attention as a result. In conventional propulsion systems, typically consisting of propellers and rudders, have evolved into a diverse range of systems due to the development of a special propulsion system known as the azimuth thruster. While azimuth thrusters were previously commonly installed on tugboats, they are now extensively used on offshore plant operation ships equipped with dynamic positioning systems. However, these azimuth thrusters require different steering methods compared to conventional propulsion systems, leading to a significant learning curve for the crew members boarding such vessels. Furthermore the availability of education related to these special propulsion systems is limited. This study aims to analyze accidents caused by inadequate control of vessels equipped with azimuth thrusters using the STAMP technique. And it proposes the necessity of standard steering commands for the safe operation of vessels equipped with special propellers.

This study aims to analyze the changes in the management efficiency of public institutions in time series, and to examine the relationship with financial performance based on the results of time series changes. Specifically, we classified into upper and lower groups of financial performance based on the government’s management evaluation results, and analyze how the management efficiency of each group changed in the period before the evaluation year. Based on public institutions published in public business information system, DEA(Data Envelopment Analysis) was performed for estimating management efficiency. The results are summarized as follows; First, we find that DEA of the upper group changed in the direction of increasing, but DEA of the lower group changed in the direction of decreasing. Second, we find that there is a significant positive relation between DEA and financial performance. This result means that the higher financial performance, the higher management efficiency. These findings imply that management efficiency can be a factor that improve financial performance in public institutions. The results also suggest that government’s innovation strategies to improve financial stability by enhancing management efficiency were effective.

In this paper, we designed and implemented a graph assessment module that can evaluate graphs in an programming assessment system based on text and numbers. The assessment method of the graph assessment module is self-evaluation that outputs two graphs generated by codes submitted by learners and by answers, automatic-evaluation that converts each graph image into an array, and gives feedback if it is wrong. The data used to generate the graph can be inputted directly or used from external data, and the method of generatng graph that can be evaluated is MATLAB style in matplotlib, and the graph shape that can be evaluated is presented in mathematics and curriculum. Through expert review, it was confirmed that the content elements of the assessment module, the possibility of learning, and the validity of the learner's needs were met. The graph assessment module developed in this study has expanded the evaluation area of the programming automatic asssessment system and is expected to help students learn data visualization.

The purpose of this study is to develop and validate a scale for measuring digital literacy by identifying the factors consisting of digital literacy and extracting items for each factor. Preliminary items for the Delphi study were developed through the analysis of previous literature and the deliberation of the research team. As a result of two rounds of the expert Delphi study, 65 items were selected for the main survey. The validation of the items was carried out in the process of exploratory and confirmatory factor analyses, reliability test, and criterion validity test using the data collected in the main survey. As a result, a 4-factor structure composed of 31 questions(factor 1: digital technology & data literacy- 9 questions, factor 2: digital content & media literacy- 8 questions, factor 3: digital communication & community literacy- 9 questions, factor 4: digital wellness literacy - 5 questions) was confirmed. Also, the goodness of fit indices of the model were found to be good and the result of reliability test revealed the scale had a very appropriate level of Cronbach’s alpha(α=.956). In addition, a statistically significantly positive correlations(p<.001) were found between digital literacy and internet self-efficacy and between digital literacy and self-directed learning ability, which were predicted in the existing evidence, therefore the criterion validity of the developed scale was secured. Finally, practical and academic implications of the study are provided and future study and limitations of the study are discussed.