• Convergence Security Journal
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• v.23 no.1
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• pp.147-159
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• 2023

Recently, Chat GPT artificial intelligence (AI) is of keen interest to all governments, companies, and military sectors around the world. In the existing era of literacy AI, it has entered an era in which communication with humans is possible with generative AI that creates words, writings, and pictures. Due to the complexity of the current laws and ordinances issued during the recent national crisis in Korea and the ambiguity of the timing of application of laws and ordinances, the golden time of situational measures was often missed. For these reasons, it was not able to respond properly to every major disaster and military conflict with North Korea. Therefore, the purpose of this study was to revise the National Crisis Management Basic Act, which can act as a national tower in the event of a national crisis, and to promote artificial intelligence governance by linking artificial intelligence technology with the civil, government, military, and police.

• Journal of the Korean Society of Food Science and Nutrition
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• v.42 no.5
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• pp.784-791
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• 2013

Four different yeasts (Fermivin (FM), Saf-instant yeast red (SI), Angest wine active dry yeast (AW) and Angest instant yeast high sugar (AI)) were used for the fermentation of black raspberry wine. The amount of reducing sugars in FM (2.7%) and AI (2.8%) were higher than those in SI (2.4%) and AW (2.5%). The amount of glucose (the major free sugar) was higher in AW (2.57 mg/mL) and AI (2.50 mg/mL) than FM (2.03 mg/mL) and SI (1.75 mg/mL). AW (11.95%) had the highest alcohol content, while SI (11.75%) had the lowest. The pH of FM (pH 3.73) was the lowest, and there were no significant differences in total acidity among the samples. The major organic acid in the wines was citric acid (6.71~8.18 mg/mL) and the amount of organic acids depended on the type of yeasts. The amount of malic acid was highest in SI (2.92 mg/mL), and lowest in AI (1.83 mg/mL). The Hunter color test showed that SI was highest in lightness, redness and yellowness, whereas AI was lowest. There were no differences in turbidity between the samples. There were no significant differences in total phenolic contents (TPC) and total anthocyanin contents (TAC). However, the TPC and TAC of black raspberry wines were higher than those in commercial red wines. The antioxidant activities of wines (determined by ABTS and FRAP) increased in the order of FM, AI, AW and SI. It could not be concluded which yeast is adequate for the fermentation of black raspberry wine because any of the tested yeasts showed the best in all the quality characteristics of the wines.

• Science of Emotion and Sensibility
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• v.22 no.2
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• pp.49-58
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• 2019

In the era of the 4th Industrial Revolution, artificial intelligence (AI) has become one of the core technologies in terms of the business strategy among information technology companies. Both international and domestic major portal companies are launching AI search services. These AI search services utilize voice, images, and other unstructured data to provide different experiences from existing text-based search services. An unfamiliar experience is a factor that can hinder the usability of the service. Therefore, the usability testing of the AI search services is necessary. This study examines the usability of the AI search service on the Naver App 8.9.3 beta version by comparing it with the search services of the current Naver App and targets 30 people in their 20s and 30s, who have experience using Naver apps. The usability of Smart Lens, Smart Voice, Smart Around, and AiRS, which are the Naver App beta versions of their artificial intelligence search service, is evaluated and statistically significant usability changes are revealed. Smart Lens, Smart Voice, and Smart Around exhibited positive changes, whereas AiRS exhibited negative changes in terms of usability. This study evaluates the change in usability according to the application of the artificial intelligence search services and investigates the correlation between the evaluation factors. The obtained data are expected to be useful for the usability evaluation of services that use AI.

• Journal of Practical Engineering Education
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• v.15 no.3
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• pp.605-614
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• 2023

In In the era of digital transition, AI-based personalized services are emerging in the field of education. This research aims to examine the development strategies for implementing AI-based learning services in school. Focusing on AI-based math learning service "Math Cell" developed by i-Scream Edu, this study surveyed the functional requirements from the perspective of an educator. The results were analyzed for importance and suitability using IPA, and expert opinions were surveyed to explore specific development directions for the service. Consequently, importance in all areas such as diagnosis, learning, evaluation, and management averaged 4.82 and performance averaged 4.56, showing excellent results in most questions, and in particular, importance was higher than performance. Among certain detailed functions, concept learning, customized task presentation, evaluation result analysis function, dashboard-related functions, and learning materials in the dashboard were not intuitive for students to understand and had to be supplemented. This study provides meaningful insights by summarizing expert opinions on AI-based personalized mathematics learning services, thereby contributing to the exploration of the development strategies for "Math Cell".

• Proceedings of the KSCN Conference
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• 2004.12a
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• pp.66-67
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• 2004

• Journal of Korean Library and Information Science Society
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• v.53 no.1
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• pp.231-263
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• 2022

The understanding and capability to utilize artificial intelligence (AI) incorporated technology has become a required basic skillset for the people living in today's information age, and various members of the university have also increasingly become aware of the need for AI education. Amidst such shifting societal demands, both domestic and international university libraries have recognized the users' need for educational content centered on AI, but a user-centered service that aims to provide personalized recommendations of digital AI educational content is yet to become available. It is critical while the demand for AI education amongst university students is progressively growing that university libraries acquire a clear understanding of user intention towards an AI educational content recommender system and the potential factors contributing to its success. This study intended to ascertain the factors affecting acceptance of such system, using the Extended Technology Acceptance Model with added variables - innovativeness, self-efficacy, social influence, system quality and task-technology fit - in addition to perceived usefulness, perceived ease of use, and intention to use. Quantitative research was conducted via online research surveys for university students, and quantitative research was conducted through written interviews of university librarians. Results show that all groups, regardless of gender, year, or major, have the intention to use the AI-related Educational Content Recommendation System, with the task suitability factor being the most dominant variant to affect use intention. University librarians have also expressed agreement about the necessity of the recommendation system, and presented budget and content quality issues as realistic restrictions of the aforementioned system.

• Journal of the Korean Society for Heat Treatment
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• v.5 no.3
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• pp.179-185
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• 1992

The shape memory effect results from the martensite transfomation of each individual grain. Thus it is necessary to study the texture and its variation. In this study the change of texture during thermal cycling and it's effect on shape memory ability are investigated. The major component of the rolling texture in the parent phase is identified (001) [110], and minor components are (112) [110], (111) [112], {hkl}<100> fiber texture is developed at $45^{\circ}$ from rolling direction. In the case of martensite phase, it is estimated that the major component is (011) [100] and the minor components are (105) [501], (010) [101] and (100) [001]. According to thermal cycling. severity of texture, especially (001) [110] component in parent phase and (011) [100] component in martensite phase are increased. The shape memory ability is increased with increase of thermal cycles and also increased as the direction of specimen approach to $45^{\circ}$ from rolling direction. After first thermal cycling the temperature of transformation can be define clearly and Ms and As are raised by thermal cycling.

• Journal of Korea Multimedia Society
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• v.24 no.2
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• pp.178-185
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• 2021

Three-dimensional high-resolution magnetic resonance imaging (MRI) provides fine-level anatomical information for disease diagnosis. However, there is a limitation in obtaining high resolution due to the long scan time for wide spatial coverage. Therefore, in order to obtain a clear high-resolution(HR) image in a wide spatial coverage, a super-resolution technology that converts a low-resolution(LR) MRI image into a high-resolution is required. In this paper, we propose a super-resolution technique through filter learning based on information on the surrounding gradient information in 3D space from 3D MRI images. In the learning step, the gradient features of each voxel are computed through eigen-decomposition from 3D patch. Based on these features, we get the learned filters that minimize the difference of intensity between pairs of LR and HR images for similar features. In test step, the gradient feature of the patch is obtained for each voxel, and the filter is applied by selecting a filter corresponding to the feature closest to it. As a result of learning 100 T1 brain MRI images of HCP which is publicly opened, we showed that the performance improved by up to about 11% compared to the traditional interpolation method.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.206-214
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• 2022

The way to measure the extent of damage from floods and droughts is to identify changes in the extent of water systems. In order to effectively grasp this at a glance, satellite images are used. KOMPSAT-5 uses Synthetic Aperture Radar (SAR) to capture images regardless of weather conditions such as clouds and rain. In this paper, various deep learning models are applied to perform semantic segmentation of the water system in this SAR image and the performance is compared. The models used are U-net, V-Net, U2-Net, UNet 3+, PSPNet, Deeplab-V3, Deeplab-V3+ and PAN. In addition, performance comparison was performed when the data was augmented by applying elastic deformation to the existing SAR image dataset. As a result, without data augmentation, U-Net was the best with IoU of 97.25% and pixel accuracy of 98.53%. In case of data augmentation, Deeplab-V3 showed IoU of 95.15% and V-Net showed the best pixel accuracy of 96.86%.

• Annual Conference of KIPS
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• 2020.11a
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• pp.842-845
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• 2020

본 연구는 시각-관성 기반의 딥러닝 학습으로 자유분방하게 움직이는 드론의 주행기록을 정확하게 추정하는 것을 목표로 한다. 드론의 비행주행은 드론의 온보드 센서와 조정값을 이용하는 것이 일반적이다. 본 연구에서는 이 온보드 센서 데이터를 학습에 사용하여 비행주행의 위치추정을 실험하였다. 선행연구로써 DeepVO[1]룰 구현하여 KITTI[3] 데이터와 Midair[4] 데이터를 비교, 분석하였다. 3D 좌표면에서의 위치 추정에 선행연구 모델의 한계가 있음을 확인하고 IMU를 Feature로써 사용하였다. 본 모델은 FlowNet[2]을 모방한 CNN 네트워크로부터 Optical Flow Feature에 IMU 데이터를 더해 RNN으로 학습을 진행하였다. 본 연구를 통해 주행기록 예측을 다소 정확히 했다고 할 수 없지만, IMU Feature를 통해 주행기록의 예측이 가능함을 볼 수 있었다. 본 연구를 통해 시각-관성 분야에서 사람의 지식이나 조정이 들어가는 센서를 융합하는 기존의 방식에서 사람의 제어가 들어가지 않는 End-to-End 방식으로 인공지능을 학습했다. 또한, 시각과 관성 데이터를 통해 주행기록을 추정할 수 있었고 시각적으로 그래프를 그려 정답과 얼마나 차이 있는지 확인해보았다.