• The Journal of the Convergence on Culture Technology
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• 제9권6호
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• pp.217-227
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• 2023

With the advent of the metaverse era, understanding individuals who engage in metaverse activities has become an important task for businesses and marketing practitioners. This study aimed to compare the socio-demographic and personality factors of metaverse non-users and users, and to ascertain the impact of these individual characteristics on use of metaverse. Cross-sectional data from 9,686 respondents aged 13 and above that extracted from the 2022 Korean Media Panel Survey were analyzed, revealing significant differences between metaverse non-users and users in terms of gender, age, education level, income level, marital status, employment status, openness to experience, extraversion, and conscientiousness. Gender, age, openness to experience, and neuroticism influenced the possibility of metaverse use. Age, education level, and openness to experience were found to impact the extent of metaverse use. These findings are expected to serve as foundational insights for businesses and marketing practitioners aiming to formulate strategies in utilizing the metaverse.

• Land and Housing Review
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• 제15권1호
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• pp.157-165
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• 2024

As major public owner agencies in Korea have improved the application level of BIM, many design and construction companies are paying more attention to ways to improve actual work productivity in the BIM execution process. In this study, we introduce a method to automate the placement of BIM libraries, a recurring task in the BIM-based design process that serves as a prime example of BIM design automation methodologies. In particular, we classify the target surfaces for placement of BIM libraries into straight lines, curves, vertical planes, and surfaces. For each target surface, we implement a BIM library automatic placement model in Dynamo, considering the spacing and alignment according to the distance between the centers of two objects and the linear length. The results of case studies confirm that the proposed method can be employed according to various placement environments and conditions with the working time shortened. The study proposes that various objects and structures that need to be placed according to the terrain characteristics can be placed accurately, and work productivity can be significantly improved through the automation of placement.

• The Journal of Korea Robotics Society
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• 제19권2호
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• pp.176-187
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• 2024

Recently, automated solutions using collaborative robots have been emerging in various industries. Their primary functions include Pick & Place, Peg in the Hole, fastening and assembly, welding, and more, which are being utilized and researched in various fields. The application of these robots varies depending on the characteristics of the grippers attached to the end of the collaborative robots. To grasp a variety of objects, a gripper with a high degree of freedom is required. In this paper, we propose a service automation system using a multi-degree-of-freedom gripper, collaborative robots, and vision sensors. Assuming various products are placed at a checkout counter, we use three cameras to recognize the objects, estimate their pose, and create grasping points for grasping. The grasping points are grasped by the multi-degree-of-freedom gripper, and experiments are conducted to recognize barcodes, a key task in service automation. To recognize objects, we used a CNN (Convolutional Neural Network) based algorithm and point cloud to estimate the object's 6D pose. Using the recognized object's 6d pose information, we create grasping points for the multi-degree-of-freedom gripper and perform re-grasping in a direction that facilitates barcode scanning. The experiment was conducted with four selected objects, progressing through identification, 6D pose estimation, and grasping, recording the success and failure of barcode recognition to prove the effectiveness of the proposed system.

• The Journal of the Acoustical Society of Korea
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• 제43권1호
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• pp.19-28
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• 2024

This paper proposes a method to improve the performance of ship identification through lofargram analysis of ship noise by applying the Hough Transform to a Convolutional Neural Network (CNN) model. When processing the signals received by a passive sonar, the time-frequency domain representation known as lofargram is generated. The machinery noise radiated by ships appears as tonal signals on the lofargram, and the class of the ship can be specified by analyzing it. However, analyzing lofargram is a specialized and time-consuming task performed by well-trained analysts. Additionally, the analysis for target identification is very challenging because the lofargram also displays various background noises due to the characteristics of the underwater environment. To address this issue, the Hough Transform is applied to the lofargram to add lines, thereby emphasizing the tonal signals. As a result of identification using CNN models on both the original lofargrams and the lofargrams with Hough transform, it is shown that the application of the Hough transform improves lofargram identification performance, as indicated by increased accuracy and macro F1 scores for three different CNN models.

• Journal of Platform Technology
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• 제11권6호
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• pp.79-88
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• 2023

Recently, industrial accidents have continued to increase due to the industrialization, and worker safety management is recognized as essential to reduce losses due to hazardous factors at work places. To manage the safety of workers, it is required to apply customized safety management artificial intelligence technology that takes into account the characteristics of industrial sites, and a service for real-time risk detection and response to workers depending on the situation based on safety accident types and risk analysis for each task and process. The proposed safety management service consists of worker devices to acquire sensor data, edge devices to collect from IoT-based sensors, and a voice chatbot to support workers' disaster response. The voice chatbot plays a major role in interacting with workers at disaster sites to respond to risks. This paper focuses on real-time risk response using an IoT-based system and voice chatbot on a server for work safety according to the worker's situation. A Scenario-based voice chatbot is used to process responses at the edge level to provide safety management services.

• Geophysics and Geophysical Exploration
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• 제27권3호
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• pp.171-180
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• 2024

The processing of seismic data involves analyzing earthquake wave data to understand the internal structure and characteristics of the Earth, which requires high computational power. Recently, machine learning (ML) techniques have been introduced to address these challenges and have been utilized in various tasks such as noise reduction and velocity model construction. However, most studies have focused on specific seismic data processing tasks, limiting the full utilization of similar features and structures inherent in the datasets. In this study, we compared the efficacy of using receiver-wise time-series data ("receiver array") and synchronized receiver signals ("time array") from shotgathers for pretraining a Bidirectional Encoder Representations from Transformers (BERT) model. To this end, shotgather data generated from a synthetic model containing faults was used to perform noise reduction, velocity prediction, and fault detection tasks. In the task of random noise reduction, both the receiver and time arrays showed good performance. However, for tasks requiring the identification of spatial distributions, such as velocity estimation and fault detection, the results from the time array were superior.

• Journal of the Institute of Convergence Signal Processing
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• 제25권1호
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• pp.7-14
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• 2024

This study conducted a comparative experiment on the continuity of time series data and the classification performance of artificial intelligence models. In BCI research using EEG signals, the performance of behavior and thought classification improved as the continuity of the data decreased. In particular, LSTM achieved a high performance of 0.8728 on data with low continuity, and DNN showed a performance of 0.9178 when continuity was not considered. This suggests that data without continuity may perform better. Additionally, data without continuity showed better performance in task classification. These results suggest that BCI research based on EEG signals can perform better by showing various data characteristics through shuffling rather than considering data continuity.

• Journal of KIISE:Software and Applications
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• 제30권1_2호
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• pp.117-128
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• 2003

Software usability is a characteristic of the software that is decided based on learnability, effectiveness, and satisfaction when it is evaluated. The usability is a main factor of the software quality. A software has to be continuously improved by taking guidelines that comes from the usability evaluation. Usability factors may vary among the different software products and even for the same factor, the users may have different opinions according to their experience and knowledge. Therefore, a usability evaluation process must be developed with the consideration of many factors like various applications and users. Existing systems such as satisfaction evaluation and performance evaluation only evaluate the result and do not perform cause analysis. And also unified evaluation items and contents do not reflect the characteristics of the products. To address these problems, this paper presents a evaluation model that is based on the mental model of user and the problems, this paper presents a evaluation model that is based on the mental model of user and the emotion of users. This model uses evaluation factors of the user task which are extracted by analyzing usage of the target product. In the mental model approach, the conceptual model of designer and the mental model of the user are compared and the differences are taken as a gap also reported as a part to be improved in the future. In the emotional factor approach, the emotional factors are extracted for the target products and evaluated in terms of the emotional factors. With this proposed method, we can evaluate the software products with customized attributes of the products and deduce the guidelines for the future improvements. We also takes the GUI framework as a sample case and extracts the directions for improvement. As this model analyzes tasks of users and uses evaluation factors for each task, it is capable of not only reflecting the characteristics of the product, but exactly identifying the items that should be modified and improved.

• Journal of Elementary Mathematics Education in Korea
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• 제13권2호
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• pp.163-192
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• 2009

Currently, our country operates gifted education only as a special curriculum, which results in many problems, e.g., there are few beneficiaries of gifted education, considerable time and effort are required to gifted students, and gifted students' educational needs are ignored during the operation of regular curriculum. In order to solve these problems, the present study formulates the following research questions, finding it advisable to conduct gifted education in elementary regular classrooms within the scope of the regular curriculum. A. To devise a teaching plan for the gifted students on mathematics in the elementary school regular classroom. B. To develop a learning program for the gifted students in the elementary school regular classroom. C. To apply an in-depth learning program to gifted students in mathematics and analyze the effectiveness of the program. In order to answer these questions, a teaching plan was provided for the gifted students in mathematics using a differentiating instruction type. This type was developed by researching literature reviews. Primarily, those on characteristics of gifted students in mathematics and teaching-learning models for gifted education. In order to instruct the gifted students on mathematics in the regular classrooms, an in-depth learning program was developed. The gifted students were selected through teachers' recommendation and an advanced placement test. Furthermore, the effectiveness of the gifted education in mathematics and the possibility of the differentiating teaching type in the regular classrooms were determined. The analysis was applied through an in-depth learning program of selected gifted students in mathematics. To this end, an in-depth learning program developed in the present study was applied to 6 gifted students in mathematics in one first grade class of D Elementary School located in Nowon-gu, Seoul through a 10-period instruction. Thereafter, learning outputs, math diaries, teacher's checklist, interviews, video tape recordings the instruction were collected and analyzed. Based on instruction research and data analysis stated above, the following results were obtained. First, it was possible to implement the gifted education in mathematics using a differentiating instruction type in the regular classrooms, without incurring any significant difficulty to the teachers, the gifted students, and the non-gifted students. Specifically, this instruction was effective for the gifted students in mathematics. Since the gifted students have self-directed learning capability, the teacher can teach lessons to the gifted students individually or in a group, while teaching lessons to the non-gifted students. The teacher can take time to check the learning state of the gifted students and advise them, while the non-gifted students are solving their problems. Second, an in-depth learning program connected with the regular curriculum, was developed for the gifted students, and greatly effective to their development of mathematical thinking skills and creativity. The in-depth learning program held the interest of the gifted students and stimulated their mathematical thinking. It led to the creative learning results, and positively changed their attitude toward mathematics. Third, the gifted students with the most favorable results who took both teacher's recommendation and advanced placement test were more self-directed capable and task committed. They also showed favorable results of the in-depth learning program. Based on the foregoing study results, the conclusions are as follows: First, gifted education using a differentiating instruction type can be conducted for gifted students on mathematics in the elementary regular classrooms. This type of instruction conforms to the characteristics of the gifted students in mathematics and is greatly effective. Since the gifted students in mathematics have self-directed learning capabilities and task-commitment, their mathematical thinking skills and creativity were enhanced during individual exploration and learning through an in-depth learning program in a differentiating instruction. Second, when a differentiating instruction type is implemented, beneficiaries of gifted education will be enhanced. Gifted students and their parents' satisfaction with what their children are learning at school will increase. Teachers will have a better understanding of gifted education. Third, an in-depth learning program for gifted students on mathematics in the regular classrooms, should conform with an instructing and learning model for gifted education. This program should include various and creative contents by deepening the regular curriculum. Fourth, if an in-depth learning program is applied to the gifted students on mathematics in the regular classrooms, it can enhance their gifted abilities, change their attitude toward mathematics positively, and increase their creativity.

• Journal of Intelligence and Information Systems
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• 제26권1호
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.