• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.439-442
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• 1999

It is important to analyze the seasonal change profiles of land cover type in large scale for establishing preservation strategy and environmental monitoring. Because the NOAA-AVHRR data sets provide global data with high temporal resolution, it is suitable for the land cover classification of the large area. The objectives of this study were to classify land cover of Korea, to investigate the phenological profiles of land cover. The NOAA-AVHRR data from Jan. 1998 to Dec. 1998 were received by Korea Ocean Research & Development Institute(KORDI) and were used for this study. The NDVI data were produced from this data. And monthly maximum value composite data were made for reducing cloud effect and temporal classification. And the data were classified using the method of supervised classification. To label the land cover classes, they were classified again using generalized vegetation map and Landsat-TM classified image. And the profiles of each class was analyzed according to each month. Results of this study can be summarized as follows. First, it was verified that the use of vegetation map and TM classified map was available to obtain the temporal class labeling with NOAA-AVHRR. Second, phenological characteristics of plant communities of Korea using NOAA-AVHRR was identified. Third, NDVI of North Korea is lower on Summer than that of South Korea. And finally, Forest cover is higher than another cover types. Broadleaf forest is highest on may. Outline of covertype profiles was investigated.

• Korean Journal of Food Science and Technology
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• v.24 no.5
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• pp.492-496
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• 1992

In order to compare the quality of canned pork products which are called collectively as luncheon meat, residual nitrite, sodium, collagen, total heme pigments and chemical composition were analyzed in 12 products of 8 companies from 4 countries. Also, the proteins of products were compared with that of pork by SDS-PAGE analysis. The level of residual nitrite was low in all the products and sodium levels were similar except in one or two products. As for collagen and total heme pigments content, among imported products luncheon meats were different from chopped meat products while domestic products were similar regardless of label distinction. Collagen contents of domestic products were similar to those of imports but total heme pigments contents were much higher Densitometer scans of gel electrophoretograms of chopped meat were more similar to that of pork than those of luncheon meat. In terms of chemical composition, luncheon meat had more carbohydrate regardless of whether they are domestics or imports. The quality of domestic luncheon meat appears to be the composite of those of imported luncheon meat and chopped meat. Accordingly, the quality standard for luncheon meat as a cheap product should be established in Korea to enable the domestic products to have a competitive power in price.

• Journal of Food Hygiene and Safety
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• v.32 no.2
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• pp.83-88
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• 2017

We introduced disposable amperometric immunosensors for the detection of Sildenafil and Vardenafil (SDF/VDF) based on screen printed carbon electrodes. The developed immunosensors were used as a non-competitive sandwich-type enzyme immunoassay with a horseradish peroxidase label. The sensors were constructed on screen printed carbon electrodes by the simple electrochemical deposition of a reduced graphene oxide and chitosan (ErGO-CS) composite. To evaluate the sensing chemistry and optimize the sensor characteristics, a series of electrochemical experiments were carried out including electrochemical impedance spectroscopy, cyclic voltammetry and amperometry. The sensors showed a linear response to SDF/VDF concentrations in a range from 100 pg/mL to 300 ng/mL. The lower detection limit was calculated to be 55 pg/mL, the sensitivity was calculated to be $1.02{\mu}Ang/mL/cm^2$, and the sensor performance exhibited good reproducibility with a relative standard deviation (RSD) of 7.1%. The proposed sensing chemistry strategy and the sensor format can be used as a simple, cost-effective, and feasible method for the in-field analysis of SDF/VDF in functional or health supplement food samples.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.967-977
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• 2022

The occurrence and intensity of wildfires are increasing with climate change. Emissions from forest fire smoke are recognized as one of the major causes affecting air quality and the greenhouse effect. The use of satellite product and machine learning is essential for detection of forest fire smoke. Until now, research on forest fire smoke detection has had difficulties due to difficulties in cloud identification and vague standards of boundaries. The purpose of this study is to detect forest fire smoke using Level 1 and Level 2 data of Geostationary Environment Monitoring Spectrometer (GEMS), a Korean environmental satellite sensor, and machine learning. In March 2022, the forest fire in Gangwon-do was selected as a case. Smoke pixel classification modeling was performed by producing wildfire smoke label images and inputting GEMS Level 1 and Level 2 data to the random forest model. In the trained model, the importance of input variables is Aerosol Optical Depth (AOD), 380 nm and 340 nm radiance difference, Ultra-Violet Aerosol Index (UVAI), Visible Aerosol Index (VisAI), Single Scattering Albedo (SSA), formaldehyde (HCHO), nitrogen dioxide (NO₂), 380 nm radiance, and 340 nm radiance were shown in that order. In addition, in the estimation of the forest fire smoke probability (0 ≤ p ≤ 1) for 2,704 pixels, Mean Bias Error (MBE) is -0.002, Mean Absolute Error (MAE) is 0.026, Root Mean Square Error (RMSE) is 0.087, and Correlation Coefficient (CC) showed an accuracy of 0.981.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.27-40
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• 2023

With the increase in the spread of smart devices and the impact of COVID-19, the consumption of media contents through smart devices has significantly increased. Along with this trend, the amount of media contents viewed through OTT platforms is increasing, that makes contents recommendations on these platforms more important. Previous contents-based recommendation researches have mostly utilized metadata that describes the characteristics of the contents, with a shortage of researches that utilize the contents' own descriptive metadata. In this paper, various text data including titles and synopses that describe the contents were used to recommend similar contents. KLUE-RoBERTa-large, a Korean language model with excellent performance, was used to train the model on the text data. A dataset of over 20,000 contents metadata including titles, synopses, composite genres, directors, actors, and hash tags information was used as training data. To enter the various text features into the language model, the features were concatenated using special tokens that indicate each feature. The test set was designed to promote the relative and objective nature of the model's similarity classification ability by using the three contents comparison method and applying multiple inspections to label the test set. Genres classification and hash tag classification prediction tasks were used to fine-tune the embeddings for the contents meta text data. As a result, the hash tag classification model showed an accuracy of over 90% based on the similarity test set, which was more than 9% better than the baseline language model. Through hash tag classification training, it was found that the language model's ability to classify similar contents was improved, which demonstrated the value of using a language model for the contents-based filtering.