• Journal of the Korean Society of Propulsion Engineers
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• v.13 no.6
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• pp.1-7
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• 2009

The gas turbine engine performance is greatly relied on its component performance characteristics. Generally, acquisition of component maps is not easy for engine purchasers because it is an expensive intellectual property of gas turbine engine supplier. In the previous work, the maps were inversely generated from engine performance deck data, but this method is limited to obtain the realistic maps due to calculated performance deck data. Therefore this work proposes newly to generate more realistic compressor map from experimental performance test data. And then a realistic compressor map can be generated form those processed data using the proposed extended scaling method at each rotational speed. Evaluation can be made through comparison between performance analysis results using the performance simulation program including the generated compressor map and on-condition monitoring performance data.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.6
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• pp.617-625
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• 2017

In this paper, an effective reversible data embedding algorithm was proposed to embed secrete data into image. In the proposed algorithm, prediction image is generated by accurately predicting pixel values using local similarity existing in image, difference sequence is generated using the generated prediction image and original cover image, and then histogram shift technique is applied to create a stego-image with secrete data hidden. Applying the proposed algorithm, secrete data can be extracted from the stego-image and the original cover image can be restored without loss. Experimental results show that it is possible to embed more secrete data into cover image than APD algorithm by applying the proposed algorithm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.285-287
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• 2015

Recently, the development of sensors and the mobile communication device offers a number of possibilities in the medical and related fields. However, this data is generated, it is difficult to match the metadata and standard units. The data integration is required to use the data generated by the different specifications of the sensor efficiently. Accordingly, in this paper we propose a method using an ontology as a method to integrate the data generated by the existing sensors and the new sensor. The ontology is mapping to the standard item and sensors, also include a type and structural difference. The mapping is comprised of two : data mapping, and metadata mapping. There are standard items that are created in this way, type of data exchange between services. This can solve the heterogeneous problem generated by sensors.

• Magazine of the Korean Society of Agricultural Engineers
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• v.36 no.1
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• pp.63-72
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• 1994

The objective of the study is to develop weather generators for daily rainfall and small pan evaporation and to test the applicability with recorded data. Daily rainfall forecasting model(DRFM) was developed that uses a first order Markov chain to describe rainfall seque- nces and applies an incomplete Gamma function to predict the amount of precipitation. Daily evaporation forecasting model(DEFM) that adopts a normal distribution function to generate the evaporation for dry and wet days was also formulated. DRFM and DEFM were tested with twenty year weather data from eleven stations using Chi-square and Kolmogorov and Smirnov goodness of fit tests. The test results showed that the generated sequences of rainfall occurrence, amount of rainfall, and pan evaporation were statistically fit to recorded data from eleven, seven, and seven stations at the 5% level of significance. Generated rainfall data from DRFM were very close in frequency distri- bution patterns to records for stations all over the country. Pan evaporation for rainy days generated were less accurate than that for dry days. And the proposed models may be used as tools to provide many mathematical models with long-term daily rainfall and small pan evaporation data. An example is an irrigation scheduling model, which will be further detailed in the paper.

• Journal of Korean Medical classics
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• v.35 no.4
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• pp.1-9
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• 2022

Objectives : This study aims to increase efficiency of the prescription similarity analysis method that uses drug composition ratio. Methods : The controlled experiment compared result generation time, generated data quantity, and accuracy of results between previous and new analysis method on the 12,598 formulas and 61 prescription groups. Results : The control group took 346 seconds on average and generated 768,478 results, while the test group took 24 seconds and generated 241,739 results. The test group adopted a selective calculation method that only used overlapping data between two formulas instead of analyzing all number of cases. It simplified the data processing process, reducing the quantity of data that is required to be processed, leading to better system speed, as fast as 14.47 times more than previous analysis method with equal results. Conclusions : Efficiency for similarity analysis could be improved by reducing data span and simplifying the calculation processes.

• Advances in Energy Research
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• v.7 no.2
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• pp.123-134
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• 2020

Photovoltaic systems are progressively attached importance and their installed capacity increases day by day because of their reliability, decremented installation and operating cost and simple construction structure. Generated power obtained from a photovoltaic system changes depending upon regional distinctness, and It can be estimated approximately by taking into consideration mean global radiation amount, temperature and humidity. However, there may be different regional negative or positive factors like dust, air pollution, desert powder which affect generated power. The best reliable data for a region can be obtained from the existing photovoltaic system in the region. For this purpose, a monitoring system for 1000W monocrystalline photovoltaic system constructed at Kocaeli University Uzunciftlik Nuh Cimento Vocational High Scholl is prepared. The installed monitoring system shows and records real values generated from the photovoltaic system and environmental data. In the study, Instantaneous data obtained from the monitoring system for October 2018 and 7th October 2018 is given within figures. Additionally, daily and monthly total energy productions of the photovoltaic system are given for October 2018 and date interval between July 2018 and March 2018, respectively.

• Annual Conference of KIPS
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• 2023.11a
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• pp.124-125
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• 2023

Defect detection during liquid crystal display (LCD) manufacturing has always been a critical challenge. This study aims to address this issue by proposing a data augmentation method based on generative adversarial networks (GAN) to improve defect identification accuracy in LCD production. By leveraging synthetically generated image data from GAN, we effectively augment the original dataset to make it more representative and diverse. This data augmentation strategy enhances the model's generalization capability and robustness on real-world data. Compared to traditional data augmentation techniques, the synthetic data from GAN are more realistic, diverse and broadly distributed. Experimental results demonstrate that training models with GAN-generated data combined with the original dataset significantly improves the detection accuracy of critical defects in LCD manufacturing, compared to using the original dataset alone. This study provides an effective data augmentation approach for intelligent quality control in LCD production.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.60.2-60.2
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• 2021

In this study, we determinate the differential emission measure(DEM) of solar corona using three SDO/AIA EUV channel images and three AI-generated ones. To generate the AI-generated images, we apply a deep learning model based on multi-layer perceptrons by assuming that all pixels in solar EUV images are independent of one another. For the input data, we use three SDO/AIA EUV channels (171, 193, and 211). For the target data, we use other three SDO/AIA EUV channels (94, 131, and 335). We train the model using 358 pairs of SDO/AIA EUV images at every 00:00 UT in 2011. We use SDO/AIA pixels within 1.2 solar radii to consider not only the solar disk but also above the limb. We apply our model to several brightening patches and loops in SDO/AIA images for the determination of DEMs. Our main results from this study are as follows. First, our model successfully generates three solar EUV channel images using the other three channel images. Second, the noises in the AI-generated EUV channel images are greatly reduced compared to the original target ones. Third, the estimated DEMs using three SDO/AIA images and three AI-generated ones are similar to those using three SDO/AIA images and three stacked (50 frames) ones. These results imply that our deep learning model is able to analyze temperature response functions of SDO/AIA channel images, showing a sufficient possibility that AI-generated data can be used for multi-wavelength studies of various scientific fields. SDO: Solar Dynamics Observatory AIA: Atmospheric Imaging Assembly EUV: Extreme Ultra Violet DEM: Diffrential Emission Measure

• Journal of the Korean Society for Precision Engineering
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• v.19 no.9
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• pp.118-124
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• 2002

The use of ceramic inserts in hot extrusion dies offers significant technical and economic advantages over other forms of manufacture. In this paper, process simulation and stress analysis are thus combined during the design, and a data exchange program has been developed that enables optimal design of the dies taking into account the elastic deflections generated in shrink fitting the die inserts and that caused by the stresses generated in the process. The shrink fit analysis has been performed that enables optimal design of the dies taking into account the elastic deflections which generated in shrink fitting the die inserts and that caused by the stresses generated in the process and by using DEFORM software for process analysis. This data can be processed as load input data for a finite element die-stress analysis. Process simulation and stress analysis are thus combined during the die design. The stress analysis of the dies is used to determine the stress conditions on the ceramic insert by considering contact and interference effects under both mechanical and thermal loads. The results are compared with the experimental ones for verification.

• Smart Structures and Systems
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• v.24 no.2
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• pp.257-266
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• 2019

The benefit of data fusion in improving the performance of Higher Order Sliding Mode (HOSM) observer is brought out in this paper. This improvement in the performance of HOSM observer, resulted in the improvement of active vibration control of a piezo actuated structure, when controlled by a Discrete Sliding Mode Controller (DSMC). The structure is embedded with two piezo sensors for measuring the first two vibrating modes. The fused output of sensors is applied to the HOSM observer for generating state estimates, these states generated are applied to the DSMC, designed for the fourth order linear time invariant model of the structure. In the simulation study, the structure is excited at the first and second mode resonance. It is found that better vibration suppression is obtained, when the states generated by the fused output of sensors is applied as controller input, than the vibration suppression obtained by applying the states generated by using individual sensor output. The closed loop performance of DSMC obtained with HOSM observer is compared with the closed loop performance obtained with the conventional observer. Results obtained shows that better vibration suppression is obtained when the states generated by HOSM observer is applied as controller input.