• International Journal of Advanced Culture Technology
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• v.9 no.4
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• pp.329-344
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• 2021

AI assistant, a software interface designed to interact with a user in a natural way and perform specific tasks on the user's behalf, receives increasing attention from both scholars and practitioners. While most of the literatures explain about technical aspects, little is known about the social and psychological factors that intimately influence consumers when using it. This study sheds light on the reason people use AI assistant and how perceived values influence on intention of continuous usage. A total of 361 AI assistant users participated in an online survey, and all were recruited from a major online panel in South Korea. The results from the principal component analysis suggest five social and psychological motives: self-expression, quality of life, entertainment, information, and compatibility. In addition, perceived values, informativeness, entertainment, and trustworthiness, positively predict the intention to use AI assistant. This research provides theoretical contributions from finding motivations of AI assistant usage and from the effects of perceived values on the intention to use it. Practical implications should not be overlooked in this ever-expanding AI industry.

• Electronics and Telecommunications Trends
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• v.37 no.4
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• pp.70-80
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• 2022

With the rapid growth in media service platforms providing broadcast programs or content services, content production has become more important and competitive. As a strategy to meet the diverse needs of global consumers for a variety of content and to retain them as long-term repeat customers, global over-the-top service providers are increasing not only the number of content productions but also their production efficiency. Moreover, a considerable amount of scene composition in the flow of content production work appears to be combined with rendering technology from a game engine and converted to object-based computer programming, thereby enhancing the creativity, diversity, quality, and efficiency of content production. This study examines the latest technology trends in content production such as virtual studio technology, which has emerged as the center of content production, the use cases in various fields of artificial intelligence, and the metadata standards for content search or scene composition. This study also examines the possibility of using object-based computer programming as one of the future candidate technologies for content production.

• Geomechanics and Engineering
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• v.27 no.5
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• pp.511-525
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• 2021

In the design of underground excavation, the shear strength (SS) is a key characteristic. It describes the way the rock material resists the shear stress-induced deformations. In general, the measurement of the parameters related to rock shear strength is done through laboratory experiments, which are costly, damaging, and time-consuming. Add to this the difficulty of preparing core samples of acceptable quality, particularly in case of highly weathered and fractured rock. This study applies rock index test to the indirect measurement of the SS parameters of shale. For this aim, two efficient artificial intelligence methods, namely (1) adaptive neuro-fuzzy inference system (ANFIS) implemented by subtractive clustering method (SCM) and (2) support vector regression (SVR) optimized by Harmony Search (HS) algorithm, are proposed. Note that, it is the first work that predicts the SS parameters of shale through ANFIS-SCM and SVR-HS hybrid models. In modeling processes of ANFIS-SCM and SVR-HS, the results obtained from the rock index tests were set as inputs, while the SS parameters were set as outputs. By reviewing the obtained results, it was found that both ANFIS-SCM and SVR-HS models can provide acceptable predictions for interlocking and friction angle parameters, however, ANFIS-SCM showed a better generalization capability.

• KEPCO Journal on Electric Power and Energy
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• v.8 no.2
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• pp.127-135
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• 2022

With the advent of high technologies such as the 4th Industrial Revolution and artificial intelligence and big data, efforts are being made to solve urban problems and improve the quality of life by applying new technologies in the smart city field. In addition, as carbon neutrality has emerged as an important issue due to global warming, smart city energy platform technologies such as urban energy management, efficiency improvement, and carbon reduction are in the spotlight. In order to effectively manage urban energy, energy resource information such as electricity, water, gas, hot water, heating, etc. must be collected from the management system of various energy utilities and managed on the central platform. The centrally integrated data is delivered to external city management systems that require city energy information through an energy platform. This study developed a CIM profile for smart city energy monitoring required to provide energy data to external systems. Electric data model were designed using the CIM class of IEC 61970, and water, gas, and heat data model were designed in compliance with the UML-based design ideas of IEC 61970.

• International journal of advanced smart convergence
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• v.11 no.3
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• pp.132-141
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• 2022

We look at the rocket life stages of NaverZ's metaverse platform scaling and investigate the ignition and scale-up stage of its metaverse platform brand, Zepeto based on the Rocket Model (RM). The results are derived as follows: Firstly, NaverZ shows the event strategy by collaborating with K-pops, the piggybacking strategy by utilizing other SNSs, and the VIP strategy by investing in game and entertainment content genres in the 'attract' function. In the second 'match' function, based on the matching rule of Zepeto, the users can generate their own characters and "World" with Zepeto Studio. However, for strengthening the matching quality, NaverZ is investing in the artificial intelligence (AI) based companies consistently. In the 'connect' function, NaverZ's maximization of the positive interaction is possible by inducing feed activities in Zepeto & other SNSs and by uploading attractive content for viral effects in the ignition. For facilitating this, NaverZ expands the scale to other continents like Southeast Asia and Middle East with the localization strategy inclusive investment. Lastly, in the 'transact' function, based on three monetization experiments like Coin & ZEM, user generated content (UGC) fee, and advertising revenue in the ignition, NaverZ starts to invest in NFT platforms and abroad blockchain companies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4326-4344
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• 2021

The SinGAN is one of generative adversarial networks that can be trained on a single nature image. It has poor ability to learn more global features from nature image, and losses much local detail information when it generates arbitrary size image sample. To solve the problem, a non-linear function is firstly proposed to control downsampling ratio that is ratio between the size of current image and the size of next downsampled image, to increase the ratio with increase of the number of downsampling. This makes the low-resolution images obtained by downsampling have higher proportion in all downsampled images. The low-resolution images usually contain much global information. Therefore, it can help the model to learn more global feature information from downsampled images. Secondly, the attention mechanism is introduced to the generative network to increase the weight of effective image information. This can make the network learn more local details. Besides, in order to make the output image more natural, the TVLoss function is introduced to the loss function of SinGAN, to reduce the difference between adjacent pixels and smear phenomenon for the output image. A large number of experimental results show that our proposed model has better performance than other methods in generating random samples with fixed size and arbitrary size, image harmonization and editing.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.43-46
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• 2022

The optimal operation of the water purification plant can be carried out only when the required flow rate is supplied in a timely manner using the minimum electrical energy by accurately predicting the pattern and amount of tap water used in the consumer. In order to ensure the stability of tap water production and supply, a system that can be pre-verified before applying AI-based composite sensors to the water purification plant was established to derive complementary matters through the pre-verification model for each composite sensor and improve the quality and operation stability of the composite sensor data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1597-1610
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• 2022

At present, the main method of high-speed train chassis detection is using computer vision technology to extract keypoints from two related chassis images firstly, then matching these keypoints to find the pixel-level correspondence between these two images, finally, detection and other steps are performed. The quality and accuracy of image matching are very important for subsequent defect detection. Current traditional matching methods are difficult to meet the actual requirements for the generalization of complex scenes such as weather, illumination, and seasonal changes. Therefore, it is of great significance to study the high-speed train image matching method based on deep learning. This paper establishes a high-speed train chassis image matching dataset, including random perspective changes and optical distortion, to simulate the changes in the actual working environment of the high-speed rail system as much as possible. This work designs a convolutional neural network to intensively extract keypoints, so as to alleviate the problems of current methods. With multi-level features, on the one hand, the network restores low-level details, thereby improving the localization accuracy of keypoints, on the other hand, the network can generate robust keypoint descriptors. Detailed experiments show the huge improvement of the proposed network over traditional methods.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.45-46
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• 2021

의료영상이란 의료영상장비로부터 DICOM이라는 의료영상표준에 따라 저장되며, 의료영상관리 시스템인 PACS를 통해 관리된다. 이러한, 의료영상장비 ICT기술이 융합되어 급격하게 발전되고 있으며 다양한 의료영상장치가 개발되어지고 있다. 하지만, 기술력은 높아지고 있으나 개발된 의료영상장비로부터 촬영된 영상품질관리에 대한 문제점이 제기되고 있다. 이와 관련하여 다기관의 의료영상장비 개발과 해당 기기로부터 수집된 의료영상에 대한 품질을 관리할 필요성이 증가하고 있다. 따라서 코로나 19와 같은 상황에서 의료기기 개발 지원과 관리를 비대면 관리서비스 시스템 개발과 의료영상장치 개발 정도를 관리할 수 있을 뿐만 아니라 의료영상에 대한 품질까지 모니터링하여 및 개선 할 수 있는 시스템을 제안하고자 한다.

• International Journal of Advanced Culture Technology
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• v.11 no.2
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• pp.409-414
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• 2023

In recent years, the emergence of a number of AI image generation software represented by [Midjourney] has brought great impetus to the development of the field of AI-assisted art creation. Compared with the traditional hand-painted digital painting with the aid of electronic equipment, broke the traditional sense of animation character creation logic.This paper analyzes the application of AI technology in the field of animation character design through the practice of two-dimensional animation character . This is having a significant impact on the productivity and innovation of animation design and character modeling. The key results of the analysis indicate that AI technology, particularly through the utilization of "Midjourney,"enables the automation of certain design tasks, provides innovative approaches, and generates visually appealing and realistic characters. In conclusion, the integration of AI technology, specifically the application of "Midjourney," brings a new dimension to animation character design. The utilization of AI image generation software facilitates streamlined workflows, sparks creativity, and improves the overall quality of animated characters. As the animation industry continues to evolve, AI-assisted tools like "Midjourney" hold great potential for further advancement and innovation.