• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.219-219
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• 2022

Granule-bound starch synthase I (GBSSI), encoded by the waxy gene, is responsible for the accumulation of amylose during the development of starch granules in rice endosperm. Despite many findings on waxy alleles, the genetic diversity and evolutionary studies are still not fully explored regarding their functional effects. Comprehensive evolutionary analyses were performed to investigate the genetic variations and relatedness of the GBSSI gene in 374 rice accessions composed of 54 wild accessions and 320 bred cultivars (temperate japonica, tropical japonica, indica, aus, aromatic, and admixture). GBSS1 coding regions were analyzed from a VCF file retrieved from whole-genome resequencing data, and eight haplotypes were identified in the GBSSI coding region of 320 bred cultivars. The genetic diversity indices revealed the most negative Tajima's D value in the tropical-japonica, followed by the aus and temperate-japonica, while Tajima's D values in indica were positive, indicating balancing selection. Diversity reduction was noticed in temperate japonica (0.0003) compared to the highest one (wild, 0.0044), illustrating their higher genetic differentiation by F_ST-value (0.604). The most positive Tajima's D value was observed in indica (0.5224), indicating the GBSSI gene domestication signature under balancing selection. In contrast, the lowest and negative Tajima's D value was found in tropical japonica (-0.5291), which might have experienced a positive selection and purified due to the excess of rare alleles. Overall, our study offers insights into haplotype diversity and evolutionary fingerprints of GBSSI. It ako provides genomic information to increase the starch content of cooked rice.

• Journal of Practical Engineering Education
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• v.14 no.3
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• pp.499-504
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• 2022

Artificial intelligence is expanding its reach throughout our society, and education is no exception to its scope of application. In line with this trend, we conducted a computer programming class for teachers in graduate school. The final purpose of this class is to develop the programming skills of teachers who teach students to code artificial intelligence programs. This paper studies how the logical thinking and mental consistency of teachers, who are learners, are related to programming aptitude and describes education environmental factors of the class. It was confirmed that logical thinking and mental consistency were proportional to the programming score. This proportional relationship is expected to apply to students learning programming languages. When team formation is required in programming classes, it is expected that better learning effects will be achieved if students with excellent logical thinking and mental consistency are included in each team.

• Journal of IKEEE
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• v.27 no.4
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• pp.601-609
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• 2023

In this paper, we propose an artificial intelligence-based high-speed fault diagnosis method using an FPGA in the event of a ground fault in a DC system. When applying artificial intelligence algorithms to fault diagnosis, a substantial amount of computation and real-time data processing are required. By employing an FPGA with AI-based high-speed fault diagnosis, the DC breaker can operate more rapidly, thereby reducing the breaking capacity of the DC breaker. therefore, in this paper, an intelligent high-speed diagnosis algorithm was implemented by collecting fault data through fault simulation of a DC system using Matlab/Simulink. Subsequently, the proposed intelligent high-speed fault diagnosis algorithm was applied to the FPGA, and performance verification was conducted.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.761-768
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• 2023

In the 4th Industrial Revolution, AI(Artificial Intelligence) and IoT(Internet of Things) technologies are being applied to across various fields, with particularly prominence in asset management, disaster management, and meteorological observation. In these fields, it is necessary to accurately determine the real-time and precise tracking of the object's location and status, and to collect various data even in situations that are difficult to detect with existing sensors. In order to address these demands, the use of GNSS(Global Navigation Satellite System) is essential, and this technology enables the efficient management of assets, disaster prevent and response, and accurate weather forecasting. In this paper, we provide the investigated results for the latest trends in the application of GNSS in the fields of asset management, disaster management, and weather observation, among various fields incorporating AI and IoT and analyze them.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.821-830
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• 2023

The advancement of Internet network technology has triggered the emergence of new OTT video content platforms, increasing demand for content and altering consumption patterns. This trend is bringing positive changes to the South Korean animation industry, where diverse and high-quality animation content is becoming increasingly important. As investment in technology grows, video production technology continues to advance. Specifically, 3D animation and VFX production technologies are enabling effects that were previously unthinkable, offering detailed and realistic graphics. The Fourth Industrial Revolution is providing new opportunities for this technological growth. The rise of Artificial Intelligence (AI) is automating repetitive tasks, thereby enhancing production efficiency and enabling innovations that go beyond traditional production methods. Cutting-edge technologies like 3D animation and VFX are being continually researched and are expected to be more actively integrated into the production process. Digital technology is also expanding the creative horizons for artists. The future of AI and advanced technologies holds boundless potential, and there is growing anticipation for how these will elevate the video content industry to new heights.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.9-16
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• 2023

Technological advancement is being advanced in sports such as electronic protection of taekwondo competition and VAR of soccer. However, a person judges and guides the posture by looking at the posture, so sometimes a judgment dispute occurs at the site of the competition in Taekwondo Poomsae. This study proposes an artificial intelligence model that can more accurately judge and evaluate Taekwondo movements using artificial intelligence. In this study, after pre-processing the photographed and collected data, it is separated into train, test, and validation sets. The separated data is trained by applying each model and conditions, and then compared to present the best-performing model. The models under each condition compared the values of loss, accuracy, learning time, and top-n error, and as a result, the performance of the model trained under the conditions using ResNet50 and Adam was found to be the best. It is expected that the model presented in this study can be utilized in various fields such as education sites and competitions.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.17-25
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• 2023

In this paper, we propose a lightweight deep learning architecture tailored for efficient text detection in fashion design sketch images. Given the increasing prominence of Digital Transformation in the fashion industry, there is a growing emphasis on harnessing digital tools for creating fashion design sketches. As digitization becomes more pervasive in the fashion design process, the initial stages of text detection and recognition take on pivotal roles. In this study, a lightweight network was designed by building upon existing text detection deep learning models, taking into consideration the unique characteristics of apparel design drawings. Additionally, a separately collected dataset of apparel design drawings was added to train the deep learning model. Experimental results underscore the superior performance of our proposed deep learning model, outperforming existing text detection models by approximately 20% when applied to fashion design sketch images. As a result, this paper is expected to contribute to the Digital Transformation in the field of clothing design by means of research on optimizing deep learning models and detecting specialized text information.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.37-53
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• 2023

The AI based intelligent command and control system can automatically analyzes the properties of intricate battlefield information and tactical data. In addition, commanders can receive situation analysis results and battlefield awareness through the system to support decision-making. It is necessary to build a battlefield situation analysis dataset similar to the actual battlefield situation for learning AI in order to provide decision-making support to commanders. In this paper, we explain the next step of the dataset construction method of the existing previous research, 'A Virtual Battlefield Situation Dataset Generation for Battlefield Analysis based on Artificial Intelligence'. We proposed a method to build the dataset required for the final battlefield situation analysis results to support the commander's decision-making and recognize the future battlefield. We developed 'Dataset Generator SW', a software tool to build a learning dataset for battlefield situation analysis, and used the SW tool to perform data labeling. The constructed dataset was input into the Siamese Network model. Then, the output results were inferred to verify the dataset construction method using a post-processing ranking algorithm.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.685-686
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• 2023

상수도관의 수질 및 부식도 검사에는 파이프에 손상을 입히지 않고 지속적인 방법이 필요하다. 초음파는 이를 만족하면서 상태를 확인할 수 있고 주파수가 높을수록 해상도가 좋아져 정밀한 측정이 가능하다는 장점이 있다. 이러한 특성을 이용해 상수도관 모니터링 시스템으로 초음파 기반의 Scanning Acoustic Microscopy(SAM)과 Convolutional Neural Network(CNN)을 사용하는 새로운 방법을 제안한다. 기존의 Non-Destructive Testing(NDT)방식의 단점을 보완하면서 더 높은 해상도로 상수도관을 점검하는 방식으로, SAM 을 이용하여 부식으로 인한 파이프 두께 변화와 부유물의 여부 및 수질을 동시에 감지하고 얻은 데이터를 CNN 으로 분석했다. CNN 의 높은 정확도 결과로 이 시스템의 파이프 부식도 및 수질 모니터링에 대한 적합성을 보여주었다.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.41-47
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• 2023

Financial instruments valuation continues to evolve due to various technological changes. Recently, there has been increased interest in valuation using machine learning and artificial intelligence, enabling the financial market to swiftly adapt to changes. This technological advancement caters to the demand for real-time data processing and facilitates accurate and effective valuation, considering the diverse nature of the financial market. Numerical analysis techniques serve as crucial decision-making tools among financial institutions and investors, acknowledged as essential for performance prediction and risk management in investments. This paper analyzes Korean patent trends of numerical analysis-based financial systems, considering the diverse shifts in the financial market and asset data to provide accurate predictions. This study could shed light on the advancement of financial technology and serves as a gauge for technological standards within the financial market.