• Journal of Power Electronics
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• v.2 no.1
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• pp.11-18
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• 2002

The development of the solar and the wind energy is necessary since the future alternative energies that have no pollution and no limitation are restricted. Currently MW Class power generation system has been developed, but it still has a few faults with the weather condition. In order to solve these existing problems, combined generation system of photovoltaic and wind power was suggested. It combines wind power energy and solar energy to have the supporting effect from each other. However, since even combined generation system cannot always generate stable output with everchanging weather condition, power storage apparatus that uses elastic energy of spiral spring to combined generation system was also added for the present study.

• Atmosphere
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• v.23 no.4
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• pp.401-412
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• 2013

East Asia experienced extremely cold weather in January 2011, while the previous December and the following February had normal winter temperature. In this study National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis data are used to investigate the characteristic features observed in the meteorological fields such as temperature, sea-level pressure, geopotential height, and wind during this winter period. In January the planetary-wave pattern is dominated by stationary-wave form in the mid-to-high latitude region, while transient waves are significant in the previous month. To understand the planetary-wave features quantitatively, harmonic analyses have been done for the 500-hPa geopotential height field. In the climatological-mean geopotential heights the wave numbers 1, 2, and 3 are dominant during the whole winter. In January 2011 the waves of number 1, 2, and 3 are dominant and stationary as in the climatological-mean field. In December 2010 and February 2011, however, the waves of number 4, 5, and 6 play a major role and show a transient pattern. In addition to the distinctive features in each month the planetary-wave patterns dependent on the latitude are also discussed.

• Atmosphere
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• v.31 no.4
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• pp.445-459
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• 2021

This study uses a cloud-resolving storm simulator (CReSS) to understand the individual effect of determinant meteorological factors on snowfall characteristics in the Yeongdong region based on the rawinsonde soundings for two snowfall cases that occurred on 23 February (Episode 1) and 13 December (Episode 2) 2016; one has a single-layered cloud and the other has two-layered cloud structure. The observed cloud and precipitation (snow crystal) features were well represented by a CReSS model. The first ideal experiment with a decrease in low-level temperature for Episode 1 indicates that total precipitation amount was decreased by 19% (26~27% in graupel and 53~67% in snow) compared with the control experiment. In the ideal experiment that the upper-level wind direction was changed from westerly to easterly, although total precipitation was decreased for Episode 1, precipitation was intensified over the southwestern side (specifically in terrain experiment) of the sounding point (128.855°E, 37.805°N). In contrast, the precipitation for Episode 2 was increased by 2.3 times greater than the control experiment under terrain condition. The experimental results imply that the low-level temperature and upper-level dynamics could change the location and characteristics of precipitation in the Yeongdong region. However, the difference in precipitation between the single-layered experiment and control (two-layered) experiment for Episode 2 was negligible to attribute it to the effect of upper-level cloud. The current results could be used for the development of guidance of snowfall forecast in this region.

• Ecology and Resilient Infrastructure
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• v.11 no.2
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• pp.55-64
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• 2024

The disaster impact assessment system was introduced in 2005 as a disaster prevention procedure for comprehensive and systematic developmental projects. However, according to the 'Practical Guidelines for Disaster Impact Assessment', Jeju Island's unique hydrogeological features necessitate the calculation of isohyetal-based probabilistic rainfall, which can reflect altitude, when estimating probabilistic rainfall for flood volume determination, rather than using conventional methods. Despite Jeju Island being centered around Hallasan, there are three Automatic Weather Stations (AWS) located at the summit of Hallasan, making weather stations denser than in other cities and provinces. Therefore, it is judged that there would be no difficulty in applying conventional methods, such as utilizing the probabilistic rainfall data from the weather stations or employing the Thiessen method, to estimate flood volumes for small-scale project areas. Accordingly, this study conducts a comparative analysis of the impact of applying general probabilistic rainfall from weather stations and isohyetal-based probabilistic rainfall in site in the context of Jeju Island's disaster impact assessment system.

• Journal of Science Education
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• v.34 no.1
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• pp.12-22
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• 2010

The purpose of this study was to investigate the effect of concept sketches on the understanding and scientific attitude in high school student's learning of weather change. Among the various fields of meteorology, especially in weather change, we often deal with the change of the spatiotemporal change in an abstract way. So making use of 'Concept Sketches'- simplified sketches which represent the main features, principles, processes and interrelationships of the learning contents using some concise explanations, signs and terms - could help the students learn the phenomena of weather change efficiently. This study's aim was to check up the effect and analyze the results of the lesson including the concept sketches. As a result of this study, concept sketches group showed significant improvement compared to the other groups in understanding of weather change and in scientific attitude, too. In students' recognition research of concept sketches showed that students found the class more interesting with improved concentration and had a chance to review through concept sketching, which is helpful for their learning. Considering the above research results, the study which applies concept sketching required the students to actively process their knowledge, and had a positive effect on the understanding of weather changes. Most of all, drawing the pictures which is a familiar activity helped the students to take part in the class eagerly.

• Journal of Species Research
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• v.1 no.2
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• pp.257-261
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• 2012

In Irkutsk region the plants of spring wheat (Tr. aestivum) grow in three agro-ecological zones: steppe, forest-steppe and subtaiga. Due to this reason, the paper determines the coefficients of correlation between the indicators field germination of seeds, plant safety, productivity, temperature and moisture content of the plant habitat for each zone. The zonal moisture saving features of soil treatment for growing wheat plants (Tr. aestivum) are discussed on the basis of these data.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.169-172
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• 2005

Modem SAR interferometry (IFSAR) sensors delivering intensity images and corresponding digital terrain model (DTM) allow for a thorough surface lineament interpretation with the all-weather day-night applicability. In this paper, an automatic linear-feature detection algorithm for high-resolution SAR images acquired in Taiwan is proposed. Methodologies to extract linear features consist of several stages. First, the image denoising techniques are used to remove the speckle noise on the raw image. In this stage, the Lee filter has been chosen because of its superior performance. After denoising, the Coefficient of Variation Detector is performed on the result images for edge enhancements and detection. Dilation and erosion techniques are used to reconnect the fragmented lines. The Hough transform, which is a special case of a more general transform known as Radon transform, is a suitable method for line detection in our analysis. Finally, linear features are extracted from the binary edge image. The last stage contains many substeps such as edge thinning and curve pruning.

• Journal of Electrical Engineering and Technology
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• v.13 no.3
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• pp.1377-1382
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• 2018

In the marine environment, many clutters have similar features with the marine targets due to the diverse changes of the air temperature, water temperature, various weather and seasons. Also, the clutters in the ground environment have similar features due to the same reason. In this paper, we proposed a robust Hybrid Machine Character (HMC) method to classify the targets from the clutters in the infrared images for the various environments. The proposed HMC method adopts human's multiple personality utilization and the CNN ensemble method to classify the targets in the ground and marine environments. This method uses an advantage of the each environmental training model. Experimental results demonstrate that the proposed method has better success rate to classify the targets and clutters than previously proposed CNN classification method.

• Journal of Korea Multimedia Society
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• v.24 no.10
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• pp.1319-1325
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• 2021

It is important to remove fog for accurate object recognition and detection during preprocessing because images taken in foggy adverse weather suffer from poor quality of images due to scattering and absorption of light, resulting in poor performance of various vision-based applications. This paper proposes an end-to-end deep learning-based single image de-fogging method using U-Net architecture. The loss function used in the algorithm is a loss function based on Mahalanobis distance with fog features, which solves the problem of domain shifts, and demonstrates superior performance by comparing qualitative and quantitative numerical evaluations with conventional methods. We also design it to generate fog through the VGG19 loss function and use it as the next training dataset.

• Journal of Information Processing Systems
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• v.17 no.2
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• pp.411-425
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• 2021

In this paper, a vehicle recognition algorithm based on deep convolutional neural network and compression dictionary is proposed. Firstly, the network structure of fine vehicle recognition based on convolutional neural network is introduced. Then, a vehicle recognition system based on multi-scale pyramid convolutional neural network is constructed. The contribution of different networks to the recognition results is adjusted by the adaptive fusion method that adjusts the network according to the recognition accuracy of a single network. The proportion of output in the network output of the entire multiscale network. Then, the compressed dictionary learning and the data dimension reduction are carried out using the effective block structure method combined with very sparse random projection matrix, which solves the computational complexity caused by high-dimensional features and shortens the dictionary learning time. Finally, the sparse representation classification method is used to realize vehicle type recognition. The experimental results show that the detection effect of the proposed algorithm is stable in sunny, cloudy and rainy weather, and it has strong adaptability to typical application scenarios such as occlusion and blurring, with an average recognition rate of more than 95%.