• Journal of the Korea Computer Graphics Society
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• v.30 no.3
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• pp.9-22
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• 2024

Mobility data is important to understand social phenomena and problem. Previous studies have utilized Origin-Destination (OD) visualization methods to represent human's mobility. However, the effectiveness of visualization tools as a method for understanding social phenomena remains unexplored. Therefore, in this study, we developed a visualization tool called SeoulOD-Vis to facilitate understanding social issues. It included three different modules: map visualization, condition selection, and detailed information presentation. We recruited 28 participants to evaluate the SeoulOD-Vis and compared it with a publicly available visualization tool. The results suggested that the SeoulOD-Vis had higher usability and problem-solving performances. Interview results suggested that it attributed to its 'condition selection' and 'detailed information presentation' modules. Our results will contribute to develop visualization tools to solve social problems using mobility data.

• Journal of the Korea Computer Graphics Society
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• v.30 no.3
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• pp.71-79
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• 2024

This study organizes an immersive content production pipeline using Meta Quest Pro as an asymmetric virtual environment where mixed reality(MR) and virtual reality(VR) users participate and interact together. Based on this, we compare and analyze the differences in presence and experience provided by the user's experience environment. The proposed production process is to build an integrated development environment using Meta XR All-in-One SDK based on the Unity 3D engine. This includes a real space analysis method using the Room Model function for organic and accurate interaction between MR users in the real world and VR users based on virtual scenes at fixed coordinates. Based on this, this study produces immersive table tennis content where MR and VR users participate together. Finally, we conduct survey experiments to compare and analyze the effects of differences in platform and participation methods on presence and experience using the produced content. As a result, this study confirmed that all users can feel a satisfactory presence and experience within an experience environment where real and virtual correspond.

• Journal of the Korea Computer Graphics Society
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• v.30 no.3
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• pp.81-86
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• 2024

The Image Signal Processor (ISP) converts RAW images captured by the camera sensor into user-preferred sRGB images. While RAW images contain more meaningful information for image processing than sRGB images, RAW images are rarely shared due to their large sizes. Moreover, the actual ISP process of a camera is not disclosed, making it difficult to model the inverse process. Consequently, research on learning the conversion between sRGB and RAW has been conducted. Recently, the ParamISP[1] model, which directly incorporates camera parameters (exposure time, sensitivity, aperture size, and focal length) to mimic the operations of a real camera ISP, has been proposed by advancing the simple network structures. However, existing studies, including ParamISP[1], have limitations in modeling the camera ISP as they do not consider the degradation caused by lens shading, optical aberration, and lens distortion, which limits the restoration performance. This study introduces Positional Encoding to enable the camera ISP neural network to better handle degradations caused by lens. The proposed positional encoding method is suitable for camera ISP neural networks that learn by dividing the image into patches. By reflecting the spatial context of the image, it allows for more precise image restoration compared to existing models.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.693-700
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• 2024

As of the end of March 2022, the total area of domestic industrial complexes is 606 km2, which is only about 0.6% of the total land area. However, as of 2018, the annual energy consumption of domestic industrial complexes is 110,866.1 thousand TOE, accounting for 53.5% of the country's total energy consumption and 83.1% of the entire industrial sector energy consumption. In addition, industrial complexes have a significant impact on the environment, accounting for 45.1% of the country's total greenhouse gas emissions and 76.8% of industrial sector greenhouse gas emissions. Under this background, in this study, in order to contribute to the energy efficiency of industrial complexes, a prediction study on energy demand and supply for an industrial complex in Korea using machine learning was conducted. In addition, a simulator UI screen was designed to more efficiently convey information on energy demand/supply prediction results and energy consumption status. Among the machine learning algorithms, Multi-Layer Perceptron (MLP) was used, and Bayesian Optimization was applied as an optimization technique for the prediction model. The energy prediction model for the industrial complex built in this study showed a prediction accuracy of 87.90% for compressed air demand and 99.54% for the flow rate available for the public air compressor.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.383-388
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• 2024

This study conducted voluntary focus group user observations to empirically validate the efficacy of the self-developed psychological support mobile application, "Mindful Now". The app is structured as an interactive game format, enabling individuals to activate self-awareness of mindfulness states anytime, anywhere. It consists of a 3-step process of sensory/emotional/consciousness awareness, facilitating the expression of non-judgmental awareness. To demonstrate the effectiveness of this mindful activation in enhancing psychological well-being such as happiness and stress reduction, voluntary mindfulness mobile app usage was tracked among 49 university students. The results revealed significant improvements, with a 14.4% increase in SWLS happiness index and a 17.1% decrease in PSS-10 stress levels among 12 users who used the app continuously for over 60 days to practice mindfulness awareness. Particularly, higher app engagement was observed among students who initially reported relatively lower indices before using the app. The utilization of mobile apps that promote mindful activation aligns with various therapeutic paradigms based on mindfulness and meditation, contributing to advancements in digital therapeutic interventions for psychological support.

• Korean Journal of Radiology
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• v.25 no.7
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• pp.613-622
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• 2024

Objective: In Korea, radiology has been positioned towards the early adoption of artificial intelligence-based software as medical devices (AI-SaMDs); however, little is known about the current usage, implementation, and future needs of AI-SaMDs. We surveyed the current trends and expectations for AI-SaMDs among members of the Korean Society of Radiology (KSR). Materials and Methods: An anonymous and voluntary online survey was open to all KSR members between April 17 and May 15, 2023. The survey was focused on the experiences of using AI-SaMDs, patterns of usage, levels of satisfaction, and expectations regarding the use of AI-SaMDs, including the roles of the industry, government, and KSR regarding the clinical use of AI-SaMDs. Results: Among the 370 respondents (response rate: 7.7% [370/4792]; 340 board-certified radiologists; 210 from academic institutions), 60.3% (223/370) had experience using AI-SaMDs. The two most common use-case of AI-SaMDs among the respondents were lesion detection (82.1%, 183/223), lesion diagnosis/classification (55.2%, 123/223), with the target imaging modalities being plain radiography (62.3%, 139/223), CT (42.6%, 95/223), mammography (29.1%, 65/223), and MRI (28.7%, 64/223). Most users were satisfied with AI-SaMDs (67.6% [115/170, for improvement of patient management] to 85.1% [189/222, for performance]). Regarding the expansion of clinical applications, most respondents expressed a preference for AI-SaMDs to assist in detection/diagnosis (77.0%, 285/370) and to perform automated measurement/quantification (63.5%, 235/370). Most respondents indicated that future development of AI-SaMDs should focus on improving practice efficiency (81.9%, 303/370) and quality (71.4%, 264/370). Overall, 91.9% of the respondents (340/370) agreed that there is a need for education or guidelines driven by the KSR regarding the use of AI-SaMDs. Conclusion: The penetration rate of AI-SaMDs in clinical practice and the corresponding satisfaction levels were high among members of the KSR. Most AI-SaMDs have been used for lesion detection, diagnosis, and classification. Most respondents requested KSR-driven education or guidelines on the use of AI-SaMDs.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.2
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• pp.173-182
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• 2024

In recent, there has been a trend toward primarily utilizing data-driven models employing artificial intelligence technologies, such as machine learning, for flood prediction. These data-driven models offer the advantage of utilizing pre-training results, significantly reducing the required simulation time. However, it remains that a considerable amount of flood data is necessary for the pre-training in data-driven models, while the available observed data for application is often insufficient. As an alternative, validated simulation results from physically-based models are being employed as pre-training data alongside observed data. In this context, we developed a flood mapping accelerator to generate flood maps for pre-training. The proposed accelerator automates the entire process of flood mapping, i.e., estimating flood discharge using HEC-1, calculating water surface levels using HEC-RAS, simulating channel overflow and generating flood maps using RAS Mapper. With the accelerator, users can easily prepare a database for pre-training of data-driven models from hundreds to tens of thousands of rainfall scenarios. It includes various convenient menus containing a Graphic User Interface(GUI), and its practical applicability has been validated across 26 test-beds.

• Journal of the Korean Society for Library and Information Science
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• v.31 no.4
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• pp.289-318
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• 1997

The purpose of this study is to identify users behavioral characteristics for using the online catalog opened in May 1996 at Duksung Women's University Library. 278 student users were surveyed from October 4th to 8th in 1996. Major findings are as follows. 1. Most users$(87.4\%)$ prefer the online catalog to the card catalog and regard the online catalog easy to use$(89.6\%)$ 2. $(65.8\%)$ of users are active users who frequently use the online catalog at least 10 times or more per semester. 3. $10.4\%$ of users feel the online catalog difficult because they do not know how to use it. 4. Most users prefer the menu search mode among menu, command and fill-in-blank search modes offered by DISCOVER. The most preferred access points are the title for known-item search and subject headings for subject search. 5. User's attitude toward the online catalog is very favorable$(83.5\%)$, however, the search success rate is rather low $(77.0\%)$ compared to that of the card catalog $(87.0\%)$ 6. The title and author are regarded easy to use among access points offered by DISCOVER. Classification numbers and call numbers are the least easy access points to use. 7. Since users show lack of knowledge of how to use the online catalog, education and training programs on the online catalog use for users are needed. 8. Users showed different search patterns for pursuing different search goals. The most preferred access points are the title for known-item search and subject headings for subject search. These search behaviors are different from those in using the card for both the known-item search and subject search was the title.

• Asia pacific journal of information systems
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• v.18 no.3
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• pp.91-121
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• 2008

Electronic commerce, commonly known as e-commerce or eCommerce, has become a major business trend in these days. The amount of trade conducted electronically has grown extraordinarily by developing the Internet technology. Most electronic commerce has being conducted between businesses to customers; therefore, the researches with respect to e-commerce are to find customer's needs, behaviors through statistical methods. However, the statistical researches, mostly based on a questionnaire, are the static researches, They can tell us the dynamic relationships between initial purchasing and repurchasing. Therefore, this study proposes dynamic research model for analyzing the cause of initial purchasing and repurchasing. This paper is based on the System-Dynamic theory, using the powerful simulation model with some restriction, The restrictions are based on the theory TAM(Technology Acceptance Model), PAM, and TPB(Theory of Planned Behavior). This article investigates not only the customer's purchasing and repurchasing behavior by passing of time but also the interactive effects to one another. This research model has six scenarios and three steps for analyzing customer behaviors. The first step is the research of purchasing situations. The second step is the research of repurchasing situations. Finally, the third step is to study the relationship between initial purchasing and repurchasing. The purpose of six scenarios is to find the customer's purchasing patterns according to the environmental changes. We set six variables in these scenarios by (1) changing the number of products; (2) changing the number of contents in on-line shopping malls; (3) having multimedia files or not in the shopping mall web sites; (4) grading on-line communities; (5) changing the qualities of products; (6) changing the customer's degree of confidence on products. First three variables are applied to study customer's purchasing behavior, and the other variables are applied to repurchasing behavior study. Through the simulation study, this paper presents some inter-relational result about customer purchasing behaviors, For example, Active community actions are not the increasing factor of purchasing but the increasing factor of word of mouth effect, Additionally. The higher products' quality, the more word of mouth effects increase. The number of products and contents on the web sites have same influence on people's buying behaviors. All simulation methods in this paper is not only display the result of each scenario but also find how to affect each other. Hence, electronic commerce firm can make more realistic marketing strategy about consumer behavior through this dynamic simulation research. Moreover, dynamic analysis method can predict the results which help the decision of marketing strategy by using the time-line graph. Consequently, this dynamic simulation analysis could be a useful research model to make firm's competitive advantage. However, this simulation model needs more further study. With respect to reality, this simulation model has some limitations. There are some missing factors which affect customer's buying behaviors in this model. The first missing factor is the customer's degree of recognition of brands. The second factor is the degree of customer satisfaction. The third factor is the power of word of mouth in the specific region. Generally, word of mouth affects significantly on a region's culture, even people's buying behaviors. The last missing factor is the user interface environment in the internet or other on-line shopping tools. In order to get more realistic result, these factors might be essential matters to make better research in the future studies.

• Journal of Biosystems Engineering
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• v.16 no.4
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• pp.384-398
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• 1991

A computer program was developed to select the optimum size of farm machine and analyze its operation costs according to various farming conditions. It was written in FORTRAN 77 and BASIC languages and can be run on any personal computer having Korean Standard Complete Type and Korean Language Code. The program was developed as a user-friendly type so that users can carry out easily the costs analysis for the whole farm work or respective operation in rice production, and for plowing, rotarying and pest controlling in upland. The program can analyze simultaneously three different machines in plowing & rotarying and two machines in transplanting, pest controlling and harvesting operations. The input data are the sizes of arable lands, possible working days and number of laborers during the opimum working period, and custom rates varying depending on regions and individual farming conditions. We can find out the results such as the selected optimum combination farm machines, the overs and shorts of working days relative to the planned working period, capacities of the machines, break-even points by custom rate, fixed costs for a month, and utilization costs in a hectare.