• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.1
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• pp.145-164
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• 2023

Cloud computing offers a platform that is both adaptable and scalable, making it ideal for outsourcing data for sharing. Various organizations outsource their data on cloud storage servers for availing management and sharing services. When the organizations outsource the data, they lose direct control on the data. This raises the privacy and security concerns. Cryptographic encryption methods can secure the data from the intruders as well as cloud service providers. Data owners may also specify access control policies such that only the users, who satisfy the policies, can access the data. Attribute based access control techniques are more suitable for the cloud environment as they cover large number of users coming from various domains. Multi-authority attribute-based encryption (MA-ABE) technique is one of the propitious attribute based access control technique, which allows data owner to enforce access policies on encrypted data. The main aim of this paper is to comprehensively survey various state-of-the-art MA-ABE schemes to explore different features such as attribute and key management techniques, access policy structure and its expressiveness, revocation of access rights, policy updating techniques, privacy preservation techniques, fast decryption and computation outsourcing, proxy re-encryption etc. Moreover, the paper presents feature-wise comparison of all the pertinent schemes in the field. Finally, some research challenges and directions are summarized that need to be addressed in near future.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1497-1503
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• 2023

To address challenges in classifying clouds and snow cover when calculating ground reflectance in Near-UltraViolet (UV) wavelengths, this study introduces a methodology that combines cloud data from the Geostationary Environmental Monitoring Spectrometer (GEMS) and the Advanced Meteorological Imager (AMI)satellites for snow cover analysis. The proposed approach aims to enhance the quality of surface reflectance calculations, and combined cloud data were generated by integrating GEMS cloud data with AMI cloud detection data. When applied to compute GEMS surface reflectance, this fusion approach significantly mitigated underestimation issues compared to using only GEMS cloud data in snow-covered regions, resulting in an approximately 17% improvement across the entire observational area. The findings of this study highlight the potential to address persistent underestimation challenges in snow areas by employing fused cloud data, consequently enhancing the accuracy of other Level-2 products based on improved surface reflectivity.

• Korean Journal of Remote Sensing
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• v.31 no.5
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• pp.371-384
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• 2015

Although GOCI has potential for land surface monitoring, there have been only a few cases for land applications. It might be due to the lack of reliable land products derived from GOCI data for end-users. To use for land applications, it is often essential to provide cloud-free composite over land surfaces. In this study, we proposed a cloud detection method that was very important to make cloud-free composite of GOCI reflectance and vegetation index. Since GOCI does not have SWIR and TIR spectral bands, which are very effective to separate clouds from other land cover types, we developed a multi-temporal approach to detect cloud. The proposed cloud detection method consists of three sequential steps of spectral tests. Firstly, band 1 reflectance threshold was applied to separate confident clear pixels. In second step, thick cloud was detected by the ratio (b1/b8) of band 1 and band 8 reflectance. In third step, average of b1/b8 ratio values during three consecutive days was used to detect thin cloud having mixed spectral characteristics of both cloud and land surfaces. The proposed method provides four classes of cloudiness (thick cloud, thin cloud, probably clear, confident clear). The cloud detection method was validated by the MODIS cloud mask products obtained during the same time as the GOCI data acquisition. The percentages of cloudy and cloud-free pixels between GOCI and MODIS are about the same with less than 10% RMSE. The spatial distributions of clouds detected from the GOCI images were also similar to the MODIS cloud mask products.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.201-206
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• 1998

In this paper, we have considered the method for clustering land cover types over the East Asia from AVHRR data. The feature vectors such that maximum NDVI, amplitude of NDVI, mean NDVI, and NDVI threshold are extracted from the 10-day composite by maximum value composite(MVC) for reducing the effect of cloud contaninations. To find the land cover clusters given by the feature vectors, we are adapted the self-organizing feature map(SOFM) clustering which is the mapping of an input vector space of n-dimensions into a one - or two-dimensional grid of output layer. The approach is to find first the clusters by the first layer SOFM and then merge several clusters of the first layer to a large cluster by the second layer SOFM. In experiments, we were used the 8-km AVHRR data for two years(1992-1993) over the East Asia.

• 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.

• Journal of Korean Society for Atmospheric Environment
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• v.14 no.6
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• pp.545-554
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• 1998

The diurnal variation of O3 concentration shows two peaks, the first peak at noontime and the secondary peak at night. In order to show why the secondary peak, high nocturnal O3 concentration, occurs without sunlight which is a essential factor of a photochemical response, the O3 concentration, several weather elements and synoptic weather map were used for June∼September at 1995, 1996. The mean concentration of high nocturnal O3 concentration days is higher by 5.4 ppb than that of low nocturnal O3 concentration days. The nocturnal O3 concentration is higher than that of diurnal O3 concentration during high nocturnal O3 concentration days, at July, 1995 and June, 1996. The high nocturnal O3 concentration is related to low air pressure, high cloud cover and high wind speed. The correlation coefficient, r. between nocturnal O3 concentration and wind speed, pressure and cloud cover is 0.387, -0.218, and 0.194, respeftiviely. It is interesting that the O3 concentration increases at Pusan when the typhoon passes by. The same result showed at Taegu when the typhoon FAYE passed by. According to the analysis of nocturnal O3 concentration for June∼September at 1995 and 1996, it seems that the high nocturnal O3 concentration relates to the trough and cyclones passing by Pusan.

• Journal of Environmental Science International
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• v.10 no.1
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• pp.1-8
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• 2001

This study was carried out estimating the dry deposition flux of $SO_2$at eight urban areas in Korea during one year of 1996. To calculate the deposition flux, deposition velocities were calculated by turbulence parameters estimated from routine meteorological data. Also, hourly averaged $SO_2$concentrations which calculated from air pollution monitoring data of each city were used. The dry deposition velocities were mostly higher in the coastal areas than the other areas, which would be caused by relatively strong wind. And, they were high in the daytime because of turbulence activities. The deposition flux of $SO_2$is mainly related to the atmospheric concentration. The annual average $SO_2$concentration and the deposition flux were 22.62ppb and 1510.52g/$\textrm{km}^2$/hr at Pusan respectively. Also, the flux was higher in winter than other season, which was a significant contribution of exhausted fuel for heating. While the deposition velocity was high to 0.688cm/sec at Yosu in case of strong wind and small cloud cover, the deposition flux was high to 1597.4g/$\textrm{km}^2$/hr at Pusan in case of weak wind and small cloud cover.

• Journal of the Korean Solar Energy Society
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• v.32 no.1
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• pp.8-14
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• 2012

Concerning about global warming due to emission of greenhouse effect gas like C02 and depletion of fossil fuels have been spreading. So the need for solar energy utilization is increased. It is essentially important to make efforts to reduce usage of fossil energy resources. In this study, we analyzed the correlation between climatic elements and the photovoltaic power generation. Cloud cover of the correlation coefficient was 0.93. The order of the correlation coefficient was average temperatures, hours of sunshine duration of sunshine and the humidity. To accurately analyze of the degree of correlation for the photovoltaic power generation, additional research about climatic elements that show a high correlation is needed.

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.1874-1885
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• 2018

The customer side operation is getting more complex in a smart grid environment because of the adoption of renewable resources. In performing energy management planning or scheduling, it is essential to forecast non-controllable resources accurately and robustly. The PV system is one of the common renewable energy resources in customer side. Its output depends on weather and physical characteristics of the PV system. Thus, weather information is essential to predict the amount of PV system output. However, weather forecast usually does not include enough solar irradiation information. In this study, a PV system power output prediction model (PPM) under limited weather information is proposed. In the proposed model, meteorological radiation model (MRM) is used to improve cloud cover radiation model (CRM) to consider the seasonal effect of the target region. The results of the proposed model are compared to the result of the conventional CRM prediction method on the PV generation obtained from a field test site. With the PPM, root mean square error (RMSE), and mean absolute error (MAE) are improved by 23.43% and 33.76%, respectively, compared to CRM for all days; while in clear days, they are improved by 53.36% and 62.90%, respectively.

• Atmosphere
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• v.26 no.4
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• pp.509-522
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• 2016

Heavy snowfall events have occurred frequently in the Yeongdong region but understanding of these events have trouble in lack of snowfall observation in this region because it is composed of complex topography like the "Taebaek mountains" and the "East sea". These problems can be solved by quantitative precipitation estimation technique using remote sensing such as radar, satellite, etc. Two radars which are able to cover over Yeondong region were installed at Gangneung (GNG) and Gwangdeoksan (GDK). This study uses radar and water equivalent of snow cover to investigate the characteristics of radar echoes and the $Z_e-R$ relations associated with the 10 Yeongdong heavy snowfall events during the last 5 years (2010~2014). It was found that the heights which the probability of detection (POD) of snow detection by GNG radar is more than 80% are 3,000 m and 1,500 m in convective cloud and stratiform cloud, respectively. The vertical gradient of radar reflectivity is less decreased in convective cloud than stratiform cloud. However, POD by GDK radar are lower than 80% at all layers because the majority of Yeondong observational stations are more than 100 km away from GDK radar site. Furthermore, we examined $Z_e-R$ relation from the 10 events using GNG radar and compared the "a" and "b" obtained from these examinations at Sokcho (SC) and Daegwallyeong (DG). These "a" and "b" are estimated from radar echo at 500 m (SC) and 1,500 m (DG). The values of "a" differ in their stations such as SC and DG are 30~116 and 6~39, respectively. But "b" is 0.4~1.7 irrespective of stations. Moreover, the value of "a" increased with surface air temperature. Therefore, quantitative precipitation estimation in heavy snowfall events by radar echo using fixed "a" and "b" is difficult because these values changed according to those precipitation characteristics.