• Journal of Industrial Convergence
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• v.21 no.11
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• pp.1-12
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• 2023

With the recent explosive popularity of SNS, it is increasingly important to utilize SNS marketing, and in this process, the importance of image and caption order in SNS layout is also growing. This research aims to analyze the impact of SNS layouts (Image First vs. Caption First) on the user's attitude toward SNS shopping. A survey was conducted targeting 350 general public and college(graduate) students living in Daegu City and Gyeongbuk Province. The data was analyzed using PROCESS, regression analysis, and t-test by SPSS 21.0 program. The result of this study, it was confirmed that the Image First was more accessible than the Caption First. The Caption First was confirmed to be more diagnostic than the Image First. Moreover, from three specific mediation paths, only two were confirmed, named is through diagnosticity and usefulness, and through accessibility, diagnosticity, and usefullness. The path through diagnosticity and usefulness were stronger than another. Additionally, the impact of accessibility on diagnosticity was found to be higher when involvement was high rather than when involvement was low.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.2
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• pp.119-125
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• 2023

In this study, we photograph driving obstacle objects such as crosswalks, side spheres, manholes, braille blocks, partial ramps, temporary safety barriers, stairs, and inclined curb that hinder or cause inconvenience to the movement of the vulnerable using electric mobility aids. We develop an optimal AI model that classifies photographed objects and automatically recognizes them, and implement an algorithm that can efficiently determine obstacles in front of electric mobility aids. In order to enable object detection to be AI learning with high probability, the labeling form is labeled as a polygon form when building a dataset. It was developed using a Mask R-CNN model in Detectron2 framework that can detect objects labeled in the form of polygons. Image acquisition was conducted by dividing it into two groups: the general public and the transportation weak, and image information obtained in two areas of the test bed was secured. As for the parameter setting of the Mask R-CNN learning result, it was confirmed that the model learned with IMAGES_PER_BATCH: 2, BASE_LEARNING_RATE 0.001, MAX_ITERATION: 10,000 showed the highest performance at 68.532, so that the user can quickly and accurately recognize driving risks and obstacles.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1321-1330
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• 2023

This study attempts to address the problem of 3D pose estimation for multiple human objects through a single image generated during the character development process that can be used in augmented reality. In the existing top-down method, all objects in the image are first detected, and then each is reconstructed independently. The problem is that inconsistent results may occur due to overlap or depth order mismatch between the reconstructed objects. The goal of this study is to solve these problems and develop a single network that provides consistent 3D reconstruction of all humans in a scene. Integrating a human body model based on the SMPL parametric system into a top-down framework became an important choice. Through this, two types of collision loss based on distance field and loss that considers depth order were introduced. The first loss prevents overlap between reconstructed people, and the second loss adjusts the depth ordering of people to render occlusion inference and annotated instance segmentation consistently. This method allows depth information to be provided to the network without explicit 3D annotation of the image. Experimental results show that this study's methodology performs better than existing methods on standard 3D pose benchmarks, and the proposed losses enable more consistent reconstruction from natural images.

• Journal of Advanced Technology Convergence
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• v.3 no.3
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• pp.17-27
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• 2024

This study focuses on the development and evaluation of a chatbot system based on the International Classification of Functioning, Disability, and Health (ICF) framework to enhance community participation among elderly individuals with mild dementia in South Korea. The study involved 12 elderly participants who were living alone and had been diagnosed with mild dementia, along with 15 caregivers who were actively involved in their daily care. The development process included a comprehensive needs assessment, system design, content creation, natural language processing using Transformer Attention Algorithm, and usability testing. The chatbot is designed to offer personalized activity recommendations, reminders, and information that support physical, social, and cognitive engagement. Usability testing revealed high levels of user satisfaction and perceived usefulness, with significant improvements in community activities and social interactions. Quantitative analysis showed a 92% increase in weekly community activities and an 84% increase in social interactions. Qualitative feedback highlighted the chatbot's user-friendly interface, relevance of suggested activities, and its role in reducing caregiver burden. The study demonstrates that an ICF-based chatbot system can effectively promote community participation and improve the quality of life for elderly individuals with mild dementia. Future research should focus on refining the system and evaluating its long-term impact.

• Journal of Platform Technology
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• v.12 no.1
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• pp.91-105
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• 2024

As the number of cases of applying IT systems to the existing isolated ICS (Industrial Control System) network environment continues to increase, security threats in the ICS environment have rapidly increased. Security threat scenarios help to design security strategies in cybersecurity training, including analysis, prediction, and response to cyberattacks. For successful cybersecurity training, research is needed to develop valid and reliable security threat scenarios for meaningful training. Therefore, this paper proposes a case-based security threat scenario development methodology for cybersecurity training in the ICS environment. To this end, we develop a methodology consisting of five steps based on analyzing actual cybersecurity incident cases targeting ICS. Threat techniques are standardized in the same form using objective data based on the MITER ATT&CK framework, and then a list of CVEs and CWEs corresponding to the threat technique is identified. Additionally, it analyzes and identifies vulnerable functions in programming used in CWE and ICS assets. Based on the data generated up to the previous stage, develop security threat scenarios for cybersecurity training for new ICS. As a result of verification through a comparative analysis between the proposed methodology and existing research confirmed that the proposed method was more effective than the existing method regarding scenario validity, appropriateness of evidence, and development of various scenarios.

• Journal of Internet Computing and Services
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• v.15 no.3
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• pp.45-52
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• 2014

The Ubiquitous-City (U-City) is a smart or intelligent city to satisfy human beings' desire to enjoy IT services with any device, anytime, anywhere. It is a future city model based on Internet of everything or things (IoE or IoT). It includes a lot of video cameras which are networked together. The networked video cameras support a lot of U-City services as one of the main input data together with sensors. They generate huge amount of video information, real big data for the U-City all the time. It is usually required that the U-City manipulates the big data in real-time. And it is not easy at all. Also, many times, it is required that the accumulated video data are analyzed to detect an event or find a figure among them. It requires a lot of computational power and usually takes a lot of time. Currently we can find researches which try to reduce the processing time of the big video data. Cloud computing can be a good solution to address this matter. There are many cloud computing methodologies which can be used to address the matter. MapReduce is an interesting and attractive methodology for it. It has many advantages and is getting popularity in many areas. Video cameras evolve day by day so that the resolution improves sharply. It leads to the exponential growth of the produced data by the networked video cameras. We are coping with real big data when we have to deal with video image data which are produced by the good quality video cameras. A video surveillance system was not useful until we find the cloud computing. But it is now being widely spread in U-Cities since we find some useful methodologies. Video data are unstructured data thus it is not easy to find a good research result of analyzing the data with MapReduce. This paper presents an analyzing system for the video surveillance system, which is a cloud-computing based video data management system. It is easy to deploy, flexible and reliable. It consists of the video manager, the video monitors, the storage for the video images, the storage client and streaming IN component. The "video monitor" for the video images consists of "video translater" and "protocol manager". The "storage" contains MapReduce analyzer. All components were designed according to the functional requirement of video surveillance system. The "streaming IN" component receives the video data from the networked video cameras and delivers them to the "storage client". It also manages the bottleneck of the network to smooth the data stream. The "storage client" receives the video data from the "streaming IN" component and stores them to the storage. It also helps other components to access the storage. The "video monitor" component transfers the video data by smoothly streaming and manages the protocol. The "video translator" sub-component enables users to manage the resolution, the codec and the frame rate of the video image. The "protocol" sub-component manages the Real Time Streaming Protocol (RTSP) and Real Time Messaging Protocol (RTMP). We use Hadoop Distributed File System(HDFS) for the storage of cloud computing. Hadoop stores the data in HDFS and provides the platform that can process data with simple MapReduce programming model. We suggest our own methodology to analyze the video images using MapReduce in this paper. That is, the workflow of video analysis is presented and detailed explanation is given in this paper. The performance evaluation was experiment and we found that our proposed system worked well. The performance evaluation results are presented in this paper with analysis. With our cluster system, we used compressed $1920{\times}1080(FHD)$ resolution video data, H.264 codec and HDFS as video storage. We measured the processing time according to the number of frame per mapper. Tracing the optimal splitting size of input data and the processing time according to the number of node, we found the linearity of the system performance.

• Journal of KIISE:Software and Applications
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• v.30 no.1_2
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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 Korean Home Economics Education Association
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• v.17 no.4 s.38
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• pp.101-115
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• 2005

The purpose of this research was to develop and applicate In based teaching$\cdot$learning process plan for Environmentally friendly Housing. The 3 main stages of process were used: analyses, planning & development, and application & evaluation. Three teaching subjects were selected to teach Environmentally friendly housing in the analysis stage. The webhard learning environment was consisted of contents and various materials such as digital video, PPT, group activity, discussion, individual and group reporting forms, and questions. The number of 101 high school students participated for the application stage during september 22-27, 2003. Most of all students evaluated very positively the various aspects of contents as well as LT cooperative learning methods md the web based learning environment. They strongly expressed to practice the practical ways of Environmentally friendly housing learned in the class in the future. The results imply that the contents and In teaching learning plan developed in this study seem to be adequate to be included in the regular text.

• Journal of Digital Convergence
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• v.15 no.12
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• pp.203-212
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• 2017

This research analyzed the factors that have the influences on the intentions to use the consumer dispute settlement system for the small- and medium-sized corporations. The consumer dispute settlement system is a general Internet information portal service which enables the small- and medium-sized corporations and the small businesses receive the support for the accurate damage handling method and the legal service through the Internet in their disputes with the black consumers or the consumers. With the small- and medium-sized corporation users who use the consumer dispute settlement system as the subjects, the research took a lot at what influences the consumer dispute settlement system has on the quality of the information, the quality of the system, the ease-of-use regarding which the environmental factors are perceived, and the ease that was perceived and, finally, what influences it has on the intention of the use. The accuracy, the convenience, and the costs of the consumer dispute settlement system had the positive influences on the ease-of-use that was perceived and the accuracy and the convenience, also had the positive influences on the usefulness that was perceived. Also, it was verified that the ease-of-use of the consumer dispute settlement system that was perceived and the usefulness of use of the consumer dispute settlement system that was perceived finally had the positive influence relationships with the intention of the use. It is highly expected that if, based on the results of this research, the quality of the consumer dispute settlement system is maintained and supplemented to fit the priority order, there will be the maintenance of, and the development toward, a system that is even more improved than the previously existent system.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.2
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• pp.141-157
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• 2024

This study analyzes the effect of enterepreneurial competencies on start-up performance through network activities for young entrepreneurs. Enterepreneurial competencies are composed of opportunity recognition competencies, marketing competencies, technical competencies, and creative competencies. A total of 354 questionnaires collected from young entrepreneurs residing in the country were used for empirical analysis. SPSS v28.0 and PROCESS macro v4.3 were analyzed based on the research model of a single-parameter single-mediated model. As a result of the analysis, first, it was found that among the enterepreneurial competencies, opportunity recognition competencies, marketing competencies, technical competencies, and creative competencies have a positive (+) significant effect on network activities. Among them, it was found that marketing competence has the greatest effect on network activities and technical competence has the least effect. Second, network activities were found to have a significant effect on start-up performance in a positive (+) direction. Third, among enterepreneurial competencies, opportunity recognition competence, marketing competence, technical competence, and creative competence were found to have a positive (+) effect on start-up performance. Among them, it was found that creative competence had the greatest effect and technical competence had the smallest effect. Fourth, network activities were found to mediate between enterepreneurial competencies and start-up performance. As for the relative effect size of the indirect effects of independent variables, it was found that marketing competence had the greatest effect on start-up performance and technology competence had the smallest effect. The academic implications of this study include investigating the significance and relationship of various variables, providing verification of theoretical frameworks related to entrepreneurship, identifying the main drivers of start-up success, and suggesting the importance of the network between enterepreneurial competencies and start-up performance. In addition, the practical implications of this study suggest the importance of marketing competencies for networking, and suggest differentiation of competencies. It emphasizes the strategic role of creative competence and provides guidance to policymakers for supporting start-ups on customized policies for fostering valuable start-ups.