• Journal of the Society of Disaster Information
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• v.17 no.4
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• pp.817-828
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• 2021

Purpose: In this paper, we propose a common dataset structure which includes the incidents investigation information and features data for machine learning. Most of the data is from the incidents reports of the governmental part and restricts on the social disaster and safety areas. Method: Firstly, we extract basic incidents data from the several incident investigation reports. The data includes the cause, damage, date, classification of the incidents and additionally considers the feature data for the machine learning. All data is represented by XML standard notation. Result: We defined the standard XML schema and the example for the incidents investigation information. Conclusion: We defined the common incidents dataset structure for the machine learning. It may play roles of the common infrastructure for the disaster and safety applications areas

• KOREAN JOURNAL OF CROP SCIENCE
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• v.41 no.3
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• pp.302-307
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• 1996

Seed coat anthocyanin can be purified by soaking 3 times in methanol solution supplemented with one percent of HCl. Anthocyanin content was very wide range in collected lines and average anthocyanin content of black seed coat lines was 15.07 permillage, but that of white mottled on brown seed coat lines was 0.31 permillage. In black seed coat lines green seed embryo type has more anthocyanin in amount compare to yellow seed embryo. Anthocyanin accumulation was promoted in late maturing lines compare to early maturing lines. Positive correlations were observed among 100 seed weight, days to flowering, days to growing and anthocyanin content, but negative correlation between days from flowering to maturity and anthocyanin content. Collected black seed coat lines were divided into two maturity groups. Group VI which has longer than group V in days to maturity accumulated more anthocyanin compare to group V. When the seeding date was May 15, highest anthocyanin content was observed.

• Journal of Industrial Convergence
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• v.21 no.1
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• pp.103-110
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• 2023

In this study, clustering analysis was performed to find out the influence of the maturity level of Industry 4.0 of SMEs in Korea, index factors of clustering, and major factors on the self-evaluation of companies. When 80 domestic SMEs were classified into 4 categories, it was found that there was a significant positive correlation between process, technology and organization. In addition, the majority of the 80 companies tested according to the maturity model appear to be immature or partially mature, and many improvements and re-evaluation of innovation strategies related to Industry 4.0 are needed. Finally, it was concluded that the Singapore Smart Industry Readiness Index is suitable for conducting self-assessment in domestic SMEs. These conclusions can serve as useful maturity and grouping guidelines for practitioners and researchers.

• Journal of the Korean Society for information Management
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• v.40 no.3
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• pp.55-76
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• 2023

This study analyzed academic information seeking behavior, focusing on university students, the main users of the university library, to derive considerations for the development and improvement of the subject guide of the university library. As a result of the analysis, university students highly evaluated their subjective information seeking ability, but it was found that it was difficult to set specific search terms. The purpose of using academic information is specific, and it has been shown that there is a tendency to perform all information search activities in one database. In addition, when selecting information resources, reliability, suitability, and recency are primarily taken into consideration. Awareness of university libraries and subject guides was generally low, but their reliability was found to be high. Based on this, it is necessary to consider the classification of information sources according to specific information seeking purposes, the composition of information resources, explanatory element technology related to information resource selection criteria, comprehensive database, topic keyword recommendation, library marketing, and close cooperation with internal institutions.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.8
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• pp.243-252
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• 2023

Satellite imageries are at a greatest importance for land cover examining. Numerous studies have been conducted with satellite images and uses semantic segmentation techniques to extract information which has higher altitude viewpoint. The device which is taking these images must employee wireless communication links to send them to receiving ground stations. Wireless communications from a satellite are inevitably affected due to transmission errors. Evidently images which are being transmitted are distorted because of the information loss. Current semantic segmentation techniques are not made for segmenting distorted images. Traditional image enhancement methods have their own limitations when they are used for satellite images enhancement. This paper proposes an auto-encoder based image pre-enhancing method for satellite images. As a distorted satellite images dataset, images received from a real radio transmitter were used. Training process of the proposed auto-encoder was done by letting it learn to produce a proper approximation of the source image which was sent by the image transmitter. Unlike traditional image enhancing methods, the proposed method was able to provide more applicable image to a segmentation model. Results showed that by using the proposed pre-enhancing technique, segmentation results have been greatly improved. Enhancements made to the aerial images are contributed the correct assessment of land resources.

• Journal of Korea Society of Industrial Information Systems
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• v.14 no.4
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• pp.75-90
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• 2009

Improvement of productivity and efficient process management need to define the problem of the previous work. If it takes long time to gather necessary information, it becomes difficult to continuously manage processes to satisfy customers' needs and to enhance business efficiency. This paper proposes methods and a context awareness system for decision making to solve problems originated in management of manufacturing process through real-time information acquisition. We implement the context awareness by suggesting decision logics that automatically classify works with acquired information. And we also implement a system for case study which makes workers recognize problems and notifies instructions to them. Consistency between real object and stored data and continuous process monitoring with this system could find inefficient resources or delayed works, resolve them and improve processes efficiency.

• 한국체육학회지인문사회과학편
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• v.53 no.5
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• pp.521-531
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• 2014

The purposes of this study were to evaluate the effect of normalization by dimensionless number of Hof(1996) and to analysis the gait pattern for 20s Korean males and females. Subjects are selected in accordance with classification system of Korean standard body figure and age. Experimental equipment is the Motion capture system. Subjects who are walked at a self-selected normal walking speed were photographed using the Motion capture system and analyzed using 3D motion analysis method with OrthoTrak, Cortex, Matlab and SPSS for a statistical test. When used to normalize data, there are no differences of statistical significances between gender in all spatiotemporal variables. I concluded that gait research for mutual comparison requires a normalization by dimensionless number to eliminate the effects of the body size and to accurate statistical analysis.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.428-430
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• 2022

Image processing is playing an important part in automation and artificial intelligence systems, such as object tracking, object recognition and classification, and the importance of IoT technology and automation is emphasizing as interest in automation increases. However, in a system that requires detailed data such as an image boundary, a precise noise removal algorithm is required. Therefore, in this paper, we propose a filtering algorithm based on the pixel value distribution pattern to minimize the information loss in the filtering process. The proposed algorithm finds the distribution pattern of neighboring pixel values with respect to the pixel values of the input image. Then, a weight mask is calculated based on the distribution pattern, and the final output is calculated by applying it to the filtering mask. The proposed algorithm has superior noise removal characteristics compared to the existing method and restored the image while minimizing blurring.

• Environmental and Resource Economics Review
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• v.31 no.4
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• pp.481-503
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• 2022

This study examines how eco-innovation contributes to the exports of environmentally-friendly products using the dynamic panel model. The results reveal that the adjustment in the exports exists to recover the long-run equilibrium with sluggish adjustment speed. In addition, the results show that environmental patent applications and environment-related R&D expenditures are beneficial for enhancing the environmentally-friendly exports. While the environmental patent applications are associated only with an increase in the exports of products for resource management, the environmental R&D expenditures contribute to the exports of pollution management products, cleaner technologies and products, and resource management products. Moreover, as the long-run effects of eco-innovation on the exports become greater than the short-run effects, it appears that public eco-innovation is more likely to support future exports than private eco-innovation.

• Smart Media Journal
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• v.11 no.11
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• pp.25-31
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• 2022

Deep learning-based image processing is essential for autonomous vehicles. To process road images in real-time in a System-on-Chip (SoC) environment, we need to execute deep learning models on a NPU (Neural Procesing Units) specialized for deep learning operations. In this study, we imported seven open-source image processing deep learning models, that were developed on GPU servers, to Texas Instrument Deep Learning (TIDL) NPU environment. We confirmed that the models imported in this study operate normally in the SoC virtual environment through performance evaluation and visualization. This paper introduces the problems that occurred during the migration process due to the limitations of NPU environment and how to solve them, and thereby, presents a reference case worth referring to for developers and researchers who want to port deep learning models to SoC environments.