• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.20 no.1
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• pp.1-15
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• 2015

Impacts of Sea Surface Temperature (SST) assimilation to the prediction of upper ocean temperature is investigated by using a regional ocean forecasting system, in which 3-dimensional optimal interpolation is applied. In the present study, Sea Surface Temperature and Sea Ice Analysis (OSTIA) dataset is adopted for the daily SST assimilation. This study mainly compares two experimental results with (Exp. DA) and without data assimilation (Exp. NoDA). When comparing both results with OSTIA SST data during Sept. 2011, Exp. NoDA shows Root Mean Square Error (RMSE) of about $1.5^{\circ}C$ at 24, 48, 72 forecast hour. On the other hand, Exp. DA yields the relatively lower RMSE of below $0.8^{\circ}C$ at all forecast hour. In particular, RMSE from Exp. DA reaches $0.57^{\circ}C$ at 24 forecast hour, indicating that the assimilation of daily SST (i.e., OSTIA) improves the performance in the early SST prediction. Furthermore, reduction ratio of RMSE in the Exp. DA reaches over 60% in the Yellow and East seas. In order to examine impacts in the shallow costal region, the SST measured by eight moored buoys around Korean peninsula is compared with both experiments. Exp. DA reveals reduction ratio of RMSE over 70% in all season except for summer, showing the contribution of OSTIA assimilation to the short-range prediction in the coastal region. In addition, the effect of SST assimilation in the upper ocean temperature is examined by the comparison with Argo data in the East Sea. The comparison shows that RMSE from Exp. DA is reduced by $1.5^{\circ}C$ up to 100 m depth in winter where vertical mixing is strong. Thus, SST assimilation is found to be efficient also in the upper ocean prediction. However, the temperature below the mixed layer in winter reveals larger difference in Exp. DA, implying that SST assimilation has still a limitation to the prediction of ocean interior.

• Korean Journal of Remote Sensing
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• v.31 no.5
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• pp.441-448
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• 2015

Lack of agricultural information for food supply and demand in Democratic People's republic Korea(DPRK) make people sometimes confused for right and timely decision for policy support. We carried out a study to estimate paddy rice yield in DPRK using MODIS NDVI reflecting rice growth and climate data. Mean of MODIS $NDVI_{max}$ in paddy rice over the country acquired and processed from 2002 to 2014 and accumulated rainfall collected from 27 weather stations in September from 2002 to 2014 were used to estimated paddy rice yield in DPRK. Coefficient of determination of the multiple regression model was 0.44 and Root Mean Square Error(RMSE) was 0.27 ton/ha. Two-way analysis of variance resulted in 3.0983 of F ratio and 0.1008 of p value. Estimated milled rice yield showed the lowest value as 2.71 ton/ha in 2007, which was consistent with RDA rice yield statistics and the highest value as 3.54 ton/ha in 2006, which was not consistent with the statistics. Scatter plot of estimated rice yield and the rice yield statistics implied that estimated rice yield was higher when the rice yield statistics was less than 3.3 ton/ha and lower when the rice yield statistics was greater than 3.3 ton/ha. Limitation of rice yield model was due to lower quality of climate and statistics data, possible cloud contamination of time-series NDVI data, and crop mask for rice paddy, and coarse spatial resolution of MODIS satellite data. Selection of representative areas for paddy rice consisting of homogeneous pixels and utilization of satellite-based weather information can improve the input parameters for rice yield model in DPRK in the future.

• Journal of Korean Society of Environmental Engineers
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• v.28 no.10
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• pp.1016-1023
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• 2006

The water content of dewatered cake produced from belt press dewatering equipment was about 75 wt% which was some high to handle it, so the equipment contained a limit at the economical and environmental aspect. The thermal dewatering equipment built as an alternative to overcome several problems was set up at the sludge treatment field and estimated some feasibility as comparison with the dewatering performance of belt press. First, dewatering properties of waterworks sludge was analyzed by monthly. The sludge of a water shortage season contained a high organic content which led to be difficult to dewater the cake, the other side the sludge of rainwater season was ease to dewater because of low organic content. According to the results to analysis the water content of dewatered cake produced from two equipments on the base of the seasonal dewatering properties, the water content of dewatered cake produced from thermal dewatering for sludge of water shortage season was $41.6{\sim}48.3$ wt% and $71{\sim}84$ wt% from belt press. In the case of rainwater season, the water content of dewatered cake produced from thermal dewatering was $34{\sim}37.7$ wt% and $57{\sim}70$ wt% from belt press. It was understood that thereduction of water content of cake by thermal dewatering was larger than belt press. The economical aspect for two equipments was evaluated on considering the reduction of cake treatment amount as the decrease of water content of cake. When putting the cost index of thermal dewatering into 100, belt press was 121. This meant that thermal dewater was more economical than belt press by about 20% in the side of construction and operation. In conclusion, thermal dewatering equipment was estimated by producing the low water content dewatered cake as well as being operated with low coat.

• Journal of Bio-Environment Control
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• v.29 no.2
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• pp.161-170
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• 2020

There is systematic spatial variations in environmental properties due to sensitive reaction to external conditions at plastic greenhouse occupied 99.2% of domestic agricultural facilities. In order to construct 3 dimensional distribution of temperature, relative humidity, CO₂ and illuminance, measurement matrix as 3 by 3 by 5 in direction of width, height and length, respectively, dividing indoor space of greenhouse was designed and tested at experimental site. Linear regression analysis was conducted to evaluate optimal estimation method in terms with horizontal and vertical variations. Even though sole measurement point for temperature and relative humidity could be feasible to assess indoor condition, multiple measurement matrix is inevitably required to improve spatial precision at certain time domain such as period of sunrise and sunset. In case with CO₂, multiple measurement matrix could not successfully improve the spatial predictability during a whole experimental period. In case with illuminance, prediction performance was getting smaller after a time period of sunrise due to systematic interference such as indoor structure. Thus, multiple sensing methodology was proposed in direction of length at higher height than growing bed, which could compensate estimation error in spatial domain. Appropriate measurement matrix could be constructed considering the transition of stability in indoor environmental properties due to external variations. As a result, optimal measurement matrix should be carefully designed considering flexibility of construction relevant with the type of property, indoor structure, the purpose of crop and the period of growth. For an instance, partial cooling and heating system to save a consumption of energy supplement could be successfully accomplished by the deployment of multiple measurement matrix.

• Korean Journal of Remote Sensing
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• v.31 no.6
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• pp.531-548
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• 2015

Atmospheric Motion Vector (AMV) from satellite images have shown Slow Speed Bias (SSB) in comparison with rawinsonde. The causes of SSB are originated from tracking, selection, and height assignment error, which is known to be the leading error. However, recent works have shown that height assignment error cannot be fully explained the cause of SSB. This paper attempts a new approach to examine the possibility of SSB reduction of COMS AMV by using a new target tracking algorithm. Tracking error can be caused by averaging of various wind patterns within a target and changing of cloud shape in searching process over time. To overcome this problem, Gaussian Mixture Model (GMM) has been adopted to extract the coldest cluster as target since the shape of such target is less subject to transformation. Then, an image filtering scheme is applied to weigh more on the selected coldest pixels than the other, which makes it easy to track the target. When AMV derived from our algorithm with sum of squared distance method and current COMS are compared with rawindsonde, our products show noticeable improvement over COMS products in mean wind speed by an increase of $2.7ms^{-1}$ and SSB reduction by 29%. However, the statistics regarding the bias show negative impact for mid/low level with our algorithm, and the number of vectors are reduced by 40% relative to COMS. Therefore, further study is required to improve accuracy for mid/low level winds and increase the number of AMV vectors.

• Laboratory Medicine Online
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• v.7 no.1
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• pp.13-19
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• 2017

Background: We evaluated a sensitive and quantitative method utilizing fragment analysis of the fms-like tyrosine kinase 3-internal tandem duplication (FLT3-ITD), simultaneously measuring mutant allele burden and length, and verified the analytical performance. Methods: The number and allelic burden of FLT3-ITD mutations was determined by fragment analysis. Serial mixtures of mutant and wild-type plasmid DNA were used to calculate the limit of detection of fragment analysis, conventional PCR, and Sanger sequencing. Specificity was evaluated using DNA samples derived from 50 normal donors. Results of fragment analysis were compared to those of conventional PCR, using 481 AML specimens. Results: Defined mixtures were consistently and accurately identified by fragment analysis at a 5% relative concentration of mutant to wild-type, and at 10% and 20% ratios by conventional PCR and direct sequencing, respectively. No false positivity was identified. Among 481 AML specimens, 40.1% (193/481) had FLT3-ITD mutations. The mutant allele burden (1.7-94.1%; median, 28.2%) and repeated length of the mutation (14-153 bp; median, 49 bp) were variable. The concordance rate between fragment analysis and conventional PCR was 97.7% (470/481). Fragment analysis was more sensitive than conventional PCR and detected 11 additional cases: seven had mutations below 10%, three cases represented conventional PCR failure, and one case showed false negativity because of short ITD length (14 bp). Conclusions: The new fragment analysis method proved to be sensitive and reliable for the detection and monitoring of FLT3-ITD in patients with AML. This could be used to simultaneously assess ITD mutant allele burden and length.

• KEPCO Journal on Electric Power and Energy
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• v.5 no.3
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• pp.209-213
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• 2019

Recently, demand for high energy density and long cycling stability of energy storage system has increased for application using with frequency regulation (F/R) in power grid. Supercapacitor have long lifetime and high charge and discharge rate, it is very adaptable to apply a frequency regulation in power grid. Supercapacitor can complement batteries to reduce the size and installation of batteries. Because their utilization in a system can potentially eliminate the need for short-term frequent replacement as required by batteries, hence, saving the resources invested in the upkeep of the whole system or extension of lifecycle of batteries in the long run of power grid. However, low energy density in supercapacitor is critical weakness to utilization for huge energy storage system of power grid. So, it is still far from being able to replace batteries and struggle in meeting the demand for a high energy density. But, today, LIC (Lithium Ion Capacitor) considered as an attractive structure to improve energy density much more than EDLC (Electric double layer capacitor) because LIC has high voltage range up to 3.8 V. But, many aspects of the electrochemical performance of LIC still need to be examined closely in order to apply for commercial use. In this study, in order to improve the capacitance of LIC related with energy density, we designed new method of pre-doping in anode electrode. The electrode in cathode were fabricated in dry room which has a relative humidity under 0.1% and constant electrode thickness over $100{\mu}m$ was manufactured for stable mechanical strength and anode doping. To minimize of contact resistance, fabricated electrode was conducted hot compression process from room temperature to $65^{\circ}C$. We designed various pre-doping method for LIC structure and analyzing the doping mechanism issues. Finally, we suggest new pre-doping method to improve the capacitance and electrochemical stability for LIC.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.175-197
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• 2021

Recently, with the development of deep learning technology, research on unstructured data analysis is being actively conducted, and it is showing remarkable results in various fields such as classification, summary, and generation. Among various text analysis fields, text classification is the most widely used technology in academia and industry. Text classification includes binary class classification with one label among two classes, multi-class classification with one label among several classes, and multi-label classification with multiple labels among several classes. In particular, multi-label classification requires a different training method from binary class classification and multi-class classification because of the characteristic of having multiple labels. In addition, since the number of labels to be predicted increases as the number of labels and classes increases, there is a limitation in that performance improvement is difficult due to an increase in prediction difficulty. To overcome these limitations, (i) compressing the initially given high-dimensional label space into a low-dimensional latent label space, (ii) after performing training to predict the compressed label, (iii) restoring the predicted label to the high-dimensional original label space, research on label embedding is being actively conducted. Typical label embedding techniques include Principal Label Space Transformation (PLST), Multi-Label Classification via Boolean Matrix Decomposition (MLC-BMaD), and Bayesian Multi-Label Compressed Sensing (BML-CS). However, since these techniques consider only the linear relationship between labels or compress the labels by random transformation, it is difficult to understand the non-linear relationship between labels, so there is a limitation in that it is not possible to create a latent label space sufficiently containing the information of the original label. Recently, there have been increasing attempts to improve performance by applying deep learning technology to label embedding. Label embedding using an autoencoder, a deep learning model that is effective for data compression and restoration, is representative. However, the traditional autoencoder-based label embedding has a limitation in that a large amount of information loss occurs when compressing a high-dimensional label space having a myriad of classes into a low-dimensional latent label space. This can be found in the gradient loss problem that occurs in the backpropagation process of learning. To solve this problem, skip connection was devised, and by adding the input of the layer to the output to prevent gradient loss during backpropagation, efficient learning is possible even when the layer is deep. Skip connection is mainly used for image feature extraction in convolutional neural networks, but studies using skip connection in autoencoder or label embedding process are still lacking. Therefore, in this study, we propose an autoencoder-based label embedding methodology in which skip connections are added to each of the encoder and decoder to form a low-dimensional latent label space that reflects the information of the high-dimensional label space well. In addition, the proposed methodology was applied to actual paper keywords to derive the high-dimensional keyword label space and the low-dimensional latent label space. Using this, we conducted an experiment to predict the compressed keyword vector existing in the latent label space from the paper abstract and to evaluate the multi-label classification by restoring the predicted keyword vector back to the original label space. As a result, the accuracy, precision, recall, and F1 score used as performance indicators showed far superior performance in multi-label classification based on the proposed methodology compared to traditional multi-label classification methods. This can be seen that the low-dimensional latent label space derived through the proposed methodology well reflected the information of the high-dimensional label space, which ultimately led to the improvement of the performance of the multi-label classification itself. In addition, the utility of the proposed methodology was identified by comparing the performance of the proposed methodology according to the domain characteristics and the number of dimensions of the latent label space.

• Journal of the Korean Geosynthetics Society
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• v.20 no.4
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• pp.85-93
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• 2021

The seventy percentage of Korean Peninsular is covered by the mountainous area, and the depth of west sea and south sea is relatively shallow. Therefore, a large scale land reclamation from the sea has been implemented for the construction of industrial complex, residental area, and port and airport facilities. The common problem of reclaimed land is consisted of soft ground, and hence it has low load bearing capacity as well as excessive settlement upon loading on the ground surface. The hollow concrete block has been used to reinforce the loose and soft foundation soil where the medium-high apartment or one-story industrial building is being planned to be built. Recently the earthquakes with the magnitude of 4.0~5.0 have been occurred in the west coastal and southeast coastal areas. Lee (2019) reported the advantages of hollow concrete block reinforced shallow foundation through the static laboratory bearing capacity tests. In this study, the dynamic behavior of hollow concrete block reinforced sandy ground with filling the crushed stone in the hollow space has been investigated by the means of shaking table test with the size of shaking table 1000 mm × 1000 mm. Three types of seismic wave, that is, Ofunato, Hachinohe, Artificial, and two different accelerations (0.154 g, 0.22 g) were applied in the shaking table tests. The horizontal displacement of structure which is situated right above the hollow concrete block reinforced ground was measured by using the LVDT. The relative density of soil ground are varied with 45%, 65%, and 85%, respectively, to investigate the effectiveness of reinforcement by hollow block and measured the magnitude of lateral movement, and compared with the limit value of 0.015h (Building Earthquake Code, 2019). Based on the results of shaking table test for hollow concrete block reinforced sandy ground, honeycell type hollow block gives a large interlocking force due to the filling of crushed stone in the hollow space as well as a great interface friction force by the confining pressure and punching resistance along the inside and outside of hollow concrete block. All these factors are contributed to reduce the great amount of horizontal displacement during the shaking table test. Finally, hollow concrete block reinforced sandy ground for shallow foundation is provided an outstanding reinforced method for medium-high building irrespective of seismic wave and moderate accelerations.

• Journal of Korea Water Resources Association
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• v.54 no.11
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• pp.903-913
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• 2021

In the flood season, the measurement of the river discharge has many restrictions due to reasons such as budget, manpower, safety, convenience in measurement and so on. In particular, when heavy rain events occur due to typhoons, etc., it is difficult to measure the amount of flood due to the above problems. In order to improve this problem, in this study, a method was developed that can measure the river discharge in a flood season simply and safely in a short time with minimal manpower by combining the functions of a drone and a surface velocity doppler radar. To overcome the mechanical limitations of drones caused by weather issues such as wind and rainfall derived from the measurement of the river discharge using the conventional drone, we developed a drone with P56 grade dustproof and waterproof performance, stable flight capability at a wind speed of up to 36 km/h, and a payload weight of up to 10 kg. Further, to eliminate vibration which is the most important constraint factor in the measurement with a surface velocity doppler radar, a damper plate was developed as a device that combines a drone and a surface velocity Doppler radar. The velocity meter DSVM (Dron and Surface Veloctity Meter using doppler radar) that combines the flight equipment with the velocity meter was produced. The error of ±3.5% occurred as a result of measuring the river discharge using DSVM at the point of Geumsan-gun (Hwangpunggyo) located at Bonghwang stream (the first tributary stream of the Geum River). In addition, when calculating the mean velocity from the measured surface velocity, the measurement was performed using ADCP simultaneously to improve accuracy, and the mean velocity conversion factor (0.92) was calculated by comparing the mean velocity. In this study, the discharge measured by combining a drone and a surface velocity meter was compared with the discharge measured using ADCP and floats, so that the application and utility of DSVM was confirmed.