• International Journal of Advanced Culture Technology
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• v.7 no.2
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• pp.209-217
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• 2019

Due to the development of artificial intelligence and image recognition technology that play important roles in the field of 4th industry, office automation systems and unmanned automation systems are rapidly spreading in human society. The proposed algorithm first finds the variances of the differences between the tile values constituting the learning characters and the experimental character and then recognizes the experimental character according to the distribution of the three learning characters with the smallest variances. In more detail, for 100 learning data characters and 10 experimental data characters, each character is defined as the number of black pixels belonging to 15 tile areas. For each character constituting the experimental data, the variance of the differences of the tile values of 100 learning data characters is obtained and then arranged in the ascending order. After that, three learning data characters with the minimum variance values are selected, and the final recognition result for the given experimental character is selected according to the distribution of these character types. Moreover, we compare the recognition result with the result made by a neural network of basic structure. It is confirmed that satisfactory recognition results are obtained through the processes that subdivide the learning characters and experiment characters into tile sizes and then select the recognition result using variances.

• KEPCO Journal on Electric Power and Energy
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• v.7 no.1
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• pp.115-123
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• 2021

Through machine learning-based load prediction, it is possible to prevent excessive power generation or unnecessary economic investment by estimating the appropriate amount of facility investment in consideration of the load that will increase in the future or providing basic data for policy establishment to distribute the maximum load. However, in order to secure the reliability of the developed load prediction model in the field, the performance comparison verification between the distribution line load prediction models must be preceded, but a comparative performance verification system between the distribution line load prediction models has not yet been established. As a result, it is not possible to accurately determine the performance excellence of the load prediction model because it is not possible to easily determine the likelihood between the load prediction models. In this paper, we developed a reliability verification system for load prediction models including a method of comparing and verifying the performance reliability between machine learning-based load prediction models that were not previously considered, verification process, and verification result visualization methods. Through the developed load prediction model reliability verification system, the objectivity of the load prediction model performance verification can be improved, and the field application utilization of an excellent load prediction model can be increased.

• Journal of Distribution Science
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• v.20 no.11
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• pp.121-129
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• 2022

Purpose: Artificial intelligence is widely used, particularly in the popular neural network theory called Deep learning. The improvement of computing speed and capability expedited the progress of Deep learning applications. The application of Deep learning in education has various effects and possibilities in creating and managing educational content and services that can replace human cognitive activity. Among Deep learning, Deep fake technology is used to combine and synchronize human faces with voices. This paper will show how to develop e-Learning content videos using those technologies and open-source tools. Research design, data, and methodology: This paper proposes 4 step development process, which is presented step by step on the Google Collab environment with source codes. This technology can produce various video styles. The advantage of this technology is that the characters of the video can be extended to any historical figures, celebrities, or even movie heroes producing immersive videos. Results: Prototypes for each case are also designed, developed, presented, and shared on YouTube for each specific case development. Conclusions: The method and process of creating e-learning video contents from the image, video, and audio files using Deep fake open-source technology was successfully implemented.

• Journal of Distribution Science
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• v.20 no.9
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• pp.39-46
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• 2022

Purpose: At the industrial sites of aviation enterprises there is a significant optimization of the main production processes through the use of advanced digital technologies. The most promising are the latest technologies of industrial Internet of Things, active use of big data and practical application of artificial intelligence in production. Research design, data and methodology:The process of creating a competitive product in the high-tech aviation sector is actively linked to the investment appeal of aircraft and helicopter construction products, which is built on the basis of reducing production and time costs through the creation of an effective digital system. Results: The aviation cluster of Rostec State Corporation is currently being transformed in a significant way. The leading enterprises of the Russian aviation industry are actively mastering cooperation schemes using integrated digital management principles and the widespread introduction of digital products from leading Russian vendors. Conclusions: Following the transition to electronic aircraft design technologies and modern materials in the production of aircraft, UAC continues to improve all production processes through robotization and optimization of technological processes, due to the introduction of aircraft assembly technology in accordance with digital models.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.737-745
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• 2023

This paper discusses a study on developing an artificial intelligence model to detect incidents of fires in distribution switchboard using thermal images. The objective of the research is to preprocess collected thermal images into suitable data for object detection models and design a model capable of determining the occurrence of fires within distribution panels. The study utilizes thermal image data from AI-HUB's industrial complex for training. Two CNN-based deep learning object detection algorithms, namely Faster R-CNN and RetinaNet, are employed to construct models. The paper compares and analyzes these two models, ultimately proposing the optimal model for the task.

• Korean Journal of Environmental Biology
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• v.31 no.2
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• pp.78-86
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• 2013

Qualitative and quantitative algal survey was conducted from March 2010 to December 2010 on a man-made artificial beach in the Hwawon Resort Complex in order to understand seasonal changes of algal flora. The seasonal change of algal vegetation was compared with intact natural habitat near from the experimental sites. Total 15 algal species were found at the artificial beach; 8 Chlorophyta, 3 Phaeophyta and 4 Rhodophyta. And 38 algal species were found at the natural habitat; 7 Chlorophyta, 9 Phaeophyta and 22 Rhodophyta. Dominant algal species at the artificial beach were Ulva compressa, U. intestinalis, U. prolifera, U. pertusa in winter and Urospora penicilliformis, U. intestinalis, U. compress in summer. In natural habitat, dominant algal species were U. pertusa, U. compressa in winter and Sargassum thunbergii, Ishige okamurae in summer. (R+C)/P explaining spatial distribution of seaweeds was 3.7~4.0 (warm-temperature) in the artificial beach and 2.6~3.4 (polar-temperate) in the natural habitat, respectively. The flora of artificial beach could be classified into the filamentous form (64.4%), the sheet form (21.9%), and the coarsely branched form (13.7%). There was significant difference from the two habitats representing dominant species, distributions and ratio of functional-form groups.

• Journal of the Korean Solar Energy Society
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• v.32 no.6
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• pp.11-21
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• 2012

In the normal office building, the energy consumption to maintain the reasonable intensity of illumination for the work by using the artificial illumination occupies 30% or greater of the whole building electric energy consumption. If the dependability of the artificial illumination is dropt by positively using the natural lighting from the outside, the large amount of electrical energy can be saved, in addition the more nice visual environment for work can be created. Daylight is lighting source that most closely match visual response of the human, because sunlight and skylight achieve the harmony. For this reason, the daylight of small amount than amount of the artificial lighting source also can give the same effect in work activities of human. In addition, if there is daylight at the window of the building, the energy can be saved by controlling the artificial lighting. In this paper, in the building using the photovoltaic power generation analyze the correlation between the amount of energy generated by photovoltaic and indoor illumination and this was proved through the simulation with Relux 2010. In addition, the amount of daylight inflow in the room and distribution was drawn by the equation and the ratio for the sectional dimming control of each lighting equipment was predicted and the energy saving amount according to this was calculated. As a result, the indoor illumination was satisfied with recommended illumination value of the office and consumption power could be reduced approximately with 20~70%.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.11
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• pp.273-280
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• 2020

Automatic Static Analysis Tools help developers to quickly find potential defects in source code with less effort. However, the tools reports a large number of false positive warnings which do not have to fix. In our study, we proposed an artificial neural network-based warning classification method using topic models of source code blocks. We collect revisions for fixing bugs from software change management (SCM) system and extract code blocks modified by developers. In deep learning stage, topic distribution values of the code blocks and the binary data that present the warning removal in the blocks are used as input and target data in an simple artificial neural network, respectively. In our experimental results, our warning classification model based on neural network shows very high performance to predict label of warnings such as true or false positive.

• Proceedings of KOSOMES biannual meeting
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• 2007.11a
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• pp.9-14
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• 2007

To illusσ'ate the variation of current around artificial upwelling structure which is located in the South sea of Korea, current measurements using ADCP (Acoustic Doppler Current Profiler) during neap and spring tides were carried out on 27th July(summer)， 14th October and 30th November(Autumn), 2006. Current after the set up of artificial upwelling structure were shown different in the upper and lower layer, the boundary between the upper and lower layer was at $27{\sim}30m$ depth in summer. And the boundary layer was formed structure of three layer in Autumn. Upwelling and downwelling flow were occurred around the seamount， and these vertical flows were connected from surface to bottom The distribution of vertical shear and relative vorticity support the vertical flow around the seamount. The strength of vertical shear was higher and the direction of relative vorticity was anticlockwise (+) around the upwelling area.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.2
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• pp.541-551
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• 1996

This study develops the real time ULTC(Under Load Tap Changer) control strategy with LDC setting values moved dynamically using artificial neural networks. The suggested strategy can improve the ULTC voltage compensation capability by building 2 types of neural networks, ANNs and ANNg. ANNs recognizes the uncompensated MTr sending voltage change caused by the receiving voltage variation. And ANNg dynamically determines the most appropriate ULTC setting valtage chanbe caused by the receiving voltage variation. And ANNg dynamically determines the most appropriate ULTC setting values by recognizing the voltage level obtained from ANNs, and the section load pattern for each time period. In order to evaluate the suggested approach, the ULTC voltage compensation strategy are simulated on a 8 feeder distribution system. Artificial neural networks developed in this study are implemented in FORTRAN language on PC 386.