• The Journal of the Convergence on Culture Technology
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• v.5 no.1
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• pp.361-369
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• 2019

With the commercialization of the ADAS functions, the need for the experience of the autonomous driving system is increasing, and the role of the artificial intelligence agent is attracting attention. This study is an autonomous driving experience experiment that verifies the effect of self-efficacy and agent embodiment. Through a simulator experiment, we measured the effect of existence of self-efficacy and agent embodiment on social presence, perceived risk, and perceived ease of use. Results show that self-efficacy had a positive effect on social presence and perceived risk, and agent embodiment negatively affected perceived ease of use. Based on the results of the study, we proposed guidelines for agent design that can increase the acceptance of the semi-autonomous driving system.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.5
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• pp.915-932
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• 2022

According to the Personal Information Protection Act and Pseudonymization Guidelines, the mapping is processed to the hash value of the combination key generation items including Salt value when different combination applicants wish to combine. Example of combination key generation items may include personal information like name, phone number, date of birth, address, and so on. Also, due to the properties of the hash functions, when different applicants store their items in exactly the same form, the combination can proceed without any problems. However, this method is vulnerable to combination in scenarios such as address changing and renaming, which occur due to different database update times of combination applicants. Therefore, we propose a privacy preserving combination key generation scheme robust to updates of items used to generate combination key even in scenarios such as address changing and renaming, based on the thresholds through probabilistic record linkage, and it can contribute to the development of domestic Big Data and Artificial Intelligence business.

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.87-99
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• 2024

This study aims to identify the competencies required for webtoon artists to survive in the future era of AI commercialization. It explores the current and future use of AI in webtoons, and predicts the role of artists in the future webtoon industry. The study finds that AI will replace human workers in some areas, but human empathy-related fields can be sustained. Artist roles like story projectors, Visual directors, and AI editors were identified as potential models for the changing role of artists. To address terminology ambiguity, a three-step AI categorization mechanical type AI, humanoid type AI, and transcendent type AI was proposed for a more realistic separation of AI capabilities. The researcher suggested these findings as guidelines for developing skills in emerging artists or re-skilling existing ones, emphasizing collaboration with AI for mutual growth rather than a negative acceptance of new technology.

• The Transactions of the Korea Information Processing Society
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• v.13 no.7
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• pp.311-318
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• 2024

As the amount of data increases due to the development of artificial intelligence and the Internet, data management is becoming increasingly important, and the efficient utilization of data retrieval and memory space is crucial. In this study, we investigate how to optimize search speed and memory efficiency by analyzing data structure based on metadata. As a research method, we compared and analyzed the performance of the array, association list, dictionary binary tree, and graph data structures using metadata of photographic images, focusing on temporal and space complexity. Through experimentation, it was confirmed that dictionary data structure performs best in collection speed and graph data structure performs best in search speed when dealing with large-scale image data. We expect the results of this paper to provide practical guidelines for selecting data structures to optimize search speed and memory efficiency for the images data.

• Journal of the Korean Institute of Gas
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• v.18 no.2
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• pp.1-9
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• 2014

To develop a coalbed methane(CBM) reservoir, it is important to apply production methods such as drilling, completion, and stimulation which coincide with coal properties. However, the reliability of the selected resulted in most of CBM field is not enough to accept because the selection of production method has been done by empirical decision. As the result, the empirical decision show inaccurate results and need to prove using simulation whether it was true exactly. In this study, the intelligent system has been developed to assist the selection of CBM production method using artificial neural network(ANN). Before the development of the system, technical screening guideline was analyzed by literature survey and the system to select drilling and completion method, and hydraulic fracture fluid was developed by utilizing the guideline. The result as a validation of the developed system showed a high accuracy. In conclusion, it has been confirmed that the developed system can be utilized as a effective tool to select production method in CBM reservoir.

• Journal of the Korea Convergence Society
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• v.13 no.4
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• pp.7-15
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• 2022

With the aim of an intelligent world in the age of individual customization through decentralization of information and technology, sharing/opening, and connection, we often see a tendency to cross expectations and concerns in the technological discourse and interest in artificial intelligence more than ever. Recently, it is easy to find claims by futurists that AI singularity will appear before and after 2045. Now, as part of preparations to create a paradigm of coexistence that coexists and prosper with AI in the coming age of artificial intelligence, a standard framework for setting up more correct AI ethics and regulations is required. This is because excluding the risk of omission of setting major guidelines and methods for evaluating reasonable and more reasonable guideline items and evaluation standards are increasingly becoming major research issues. In order to solve these research problems and at the same time to develop continuous experiences and learning effects on AI ethics and regulation setting, we collect guideline data on AI ethics and regulation of international organizations / countries / companies, and research and suggest ways to set up a standard framework (SF: Standard Framework) through a setting research model and text mining exploratory analysis. The results of this study can be contributed as basic prior research data for more advanced AI ethics and regulatory guidelines item setting and evaluation methods in the future.

• Knowledge Management Research
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• v.25 no.3
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• pp.47-71
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• 2024

AI-driven news recommendation systems are widely used today, providing personalized news consumption experiences. However, there are significant concerns that these systems might increase users' information bias by mainly showing information from limited perspectives. This lack of diverse information access can prevent users from forming well-rounded viewpoints on specific issues, leading to social problems like Filter bubbles or Echo chambers. These issues can deepen social divides and information inequality. This study aims to explore how AI-based news recommendation services affect users' perceived information bias and to create a foundation for ethical principles in AI services. Specifically, the study looks at the impact of ethical principles like accountability, the right to explanation, the right to choose, and privacy protection on users' perceptions of information bias in AI news systems. The findings emphasize the need for AI service providers to strengthen ethical standards to improve service quality and build user trust for long-term use. By identifying which ethical principles should be prioritized in the design and implementation of AI services, this study aims to help develop corporate ethical frameworks, internal policies, and national AI ethics guidelines.

• Knowledge Management Research
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• v.21 no.3
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• pp.249-269
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• 2020

Recently, as the online market has matured, it is facing many problems to prevent the growth. The most common problem is the homogenization of online products, which fails to increase the number of customers any more. Moreover, although the portion of the online market has increased significantly, it now becomes essential to expand offline for further development. In response, many online firms have recently sought to expand their businesses and marketing channels by securing offline spaces that can complement the limitations of online platforms, on top of their existing advantages of online channels. Based on their competitive advantage in terms of analyzing large volumes of customer data utilizing information technologies (e.g., big data and artificial intelligence), they are reinforcing their offline influence as well through this online for offline (O4O) business model. On the other hand, most of the existing research has primarily focused on online to offline (O2O) business model, and there is still a lack of research on O4O business models, which have been actively attempted in various industrial fields in recent years. Since a few of O4O-related studies have been conducted only in an experience marketing setting following a case study method, it is critical to conduct an empirical study on O4O selection attributes and their impact on customer satisfaction and loyalty. Therefore, focusing on China's representative O4O business model, 'Fresh Hema,' this study attempts to identify some key selection attributes specialized for O4O services from the customers' viewpoint and examine the impact of these attributes on customer satisfaction and loyalty. The results of the structural equation modeling (SEM) with 300 O4O (Fresh Hema) experienced customers, reveal that, out of seven O4O selection attributes, four (mobile app quality, mobile payment, product quality, and store facilities) have an impact on customer satisfaction, which also leads to customer loyalty (reuse intention, recommendation intention, and brand attachment). This study would help managers in an O4O area well adapt to rapidly changing customer needs and provide them with some guidelines for enhancing both customer satisfaction and loyalty by allocating more resources to more significant selection attributes, rather than less significant ones.

• Journal of the Korean Institute of Landscape Architecture
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• v.52 no.2
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• pp.96-109
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• 2024

The purpose of this study is to propose a method for evaluating the similarity of Show gardens using Deep Learning models, specifically VGG-16 and ResNet50. A model for judging the similarity of show gardens based on VGG-16 and ResNet50 models was developed, and was referred to as DRG (Deep Recognition of similarity in show Garden design). An algorithm utilizing GAP and Pearson correlation coefficient was employed to construct the model, and the accuracy of similarity was analyzed by comparing the total number of similar images derived at 1st (Top1), 3rd (Top3), and 5th (Top5) ranks with the original images. The image data used for the DRG model consisted of a total of 278 works from the Le Festival International des Jardins de Chaumont-sur-Loire, 27 works from the Seoul International Garden Show, and 17 works from the Korea Garden Show. Image analysis was conducted using the DRG model for both the same group and different groups, resulting in the establishment of guidelines for assessing show garden similarity. First, overall image similarity analysis was best suited for applying data augmentation techniques based on the ResNet50 model. Second, for image analysis focusing on internal structure and outer form, it was effective to apply a certain size filter (16cm × 16cm) to generate images emphasizing form and then compare similarity using the VGG-16 model. It was suggested that an image size of 448 × 448 pixels and the original image in full color are the optimal settings. Based on these research findings, a quantitative method for assessing show gardens is proposed and it is expected to contribute to the continuous development of garden culture through interdisciplinary research moving forward.

• Journal of the Korea Computer Graphics Society
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• v.30 no.3
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• pp.125-132
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• 2024

Recently, with immersive virtual reality(IVR) technologies, various services such as education, training, entertainment, industry, healthcare and remote collaboration have been applied. In particular, researches are actively being studied to visualize and interact with virtual humans, research on virtual pets in IVR is also emerging. For interaction with the virtual pet, similar to real-world interaction scenarios, the most important thing is to provide physical contact such as haptic and non-verbal interaction(e.g., gesture). This paper investigates the effects on factors (e.g., shape and texture) of passive haptic feedbacks using mapping physical props corresponding to the virtual pet. Experimental results show significant differences in terms of immersion, co-presence, realism, and friendliness depending on the levels of texture elements when interacting with virtual pets by passive haptic feedback. Additionally, as the main findings of this study by statistical interaction between two variables, we found that there was Uncanny valley effect in terms of friendliness. With our results, we will expect to be able to provide guidelines for creating interactive contents with the virtual pet in immersive VR environments.