• The Journal of Fisheries Business Administration
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• v.4 no.1_2
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• pp.19-57
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• 1973

Laver io one of the most necessary and seasonal items in Korean food from oldtimes. Laver is lagely eaten in dried form, and its supply depends entirely upon culture weeds. The history of laver culture in Korea about sixty or seventy years is older than in Japan. Significance of laver culture is divided into two aspects, one is food supply in the nation, and the other is export to other countries. Houses engaged in laver culture are about foully thousands, and laver production in 1972 is estimated as 1, 3 bitten sheets. (1 sheet is a dried laver of 20 cm sq, in the shape of paper) Especcially meaning of layer production is the concentration of labour input, and systematic management of labour. From around 1920, the method of laver culture was introduced by Japanese Imperialism for mono culture in shallow seas, and mass products of laver is provided to Japan market, DOMESTIC MARKET Fundamental consume function calculates at below, $D_{(68_71)}$=16354 $Y^{0.471}$ $P^{-1.0662}$ where D is total layer demand, Y income variable, P price variable. It means income elasticity is 476. in the whole country, and price elasticity is 1, 07. But generally income elasticity is higher in urban area than in rural area, as shown at 1, 3 in Seoul city. Expence of laver in house expenditure is mutually correlated with another expence, See Table 12 about the relative function. See Table 14 and 16 about the relation between the gathering and the changes of price in auction, wholesale and retail price support system is for two effects, one of which is constraint of the upper price, the other is rise of the lower price. Before the system control, the equation in three year average calculated as below, $Y_{b}$ ＝18, 907.7455＋15435.9364 t (r=0.89) where the origin t＝0 is the November and the units are month. Post the system control, $Y_{p}$ =30, 047.9636+1, 631.1721t (r＝0.97) therefore, this system has an effect only on the rise of lower price, Average annual margins of laver products at four market levels according to the consumer spent is below. EXPORTING MARKET Japanese demand function of laver products is, Log D=5, 289+1, 108 Log Y-1, 395 Log P (r=0.987) where D is Japanese laver demand, Y income variable, P price variable. according to which income elasticity is 1. 1 and price elasticity is 1.4. Laver production in 1970 tile highest record till then, is estimated as six billion sheets. But the recent improvement of laver culture techniques, the production of seeds and freezing storage of seeds has been stabilized. Futher new culture farms have been developed by means of break- water fences or by floating culture method. These improvements have been backed up with increased demand of laver products. Import quantity and price of Korean laver products are restrained by three organizations, that is producer, distributor and consumer. This relationship calculated by regression equation shows that import is influenced only producer organization, at the sacrifice of consumer profit. For increase to export of laver products, we urgently require to open foreign trade of laver products for Japanese consumer, .and Japan has political responsibility to solve Korean laver structure. But with long run timeseries, as regards Japanese production and import quantity, importing function shows increasing trend as below, 250 million sheets ＜3, 947.1674+0.005 $L_{g}$ ＞) 600 million sheets where $L_{q}$ is relative production quantity of laver in Japan. (unit; 100 thousand sheets) Our Export effort should be put on the highly processed products whithin the restraind quote.ote.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.27 no.4
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• pp.413-433
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• 1994

Jinhae Bay once was a productive area of fisheries. It is, however, now notorious for its red tides; and oxygen deficient water-masses extensively develop at present in summer. Therefore the shellfish production of the bay has been decreasing and mass mortality often occurs. Under these circumstances, the three-dimensional numerical hydrodynamic and the material cycle models, which were developed by the Institute for Resources and Environment of Japan, were applied to analyze the processes affecting the oxygen depletion and also to evaluate the environment capacity for the reception of pollutant loads without dissolved oxygen depletion. In field surveys, oxygen deficient water-masses were formed with concentrations of below 2.0mg/l at the bottom layer in Masan Bay and the western part of Jinhae Bay during the summer. Current directions, computed by the $M_2$ constituent, were mainly toward the western part of Jinhae Bay during flood flows and in opposite directions during ebb flows. Tidal currents velocities during the ebb tide were stronger than that of the flood tide. The comparision between the simulated and observed tidal ellipses showed fairly good agreement. The residual currents, which were obtained by averaging the simulated tidal currents over 1 tidal cycle, showed the presence of counterclockwise eddies in the central part of Jinhae Bay. Density driven currents were generated southward at surface and northward at the bottom in Masan Bay and Jindong Bay, where the fresh water of rivers entered. The material cycle model was calibrated with the data surveyed in the field of the study area from June to July, 1992. The calibrated results are in fairly good agreement with measured values within relative error of $28\%$. The simulated dissolved oxygen distributions of bottom layer were relatively high with the concentration of $6.0{\sim}8.0mg/l$ at the boundaries, but an oxygen deficient water-masses were formed within the concentration of 2.0mg/l at the inner part of Masan Bay and the western part of Jinhae Bay. The results of sensitivity analyses showed that sediment oxygen demand(SOD) was one of the most important influence on the formation of oxygen depletion. Therefore, to control the oxygen deficient water-masses and to conserve the coastal environment, it is an effective method to reduce the SOD by improving the polluted sediment. As the results of simulations, in Masan Bay, oxygen deficient water-masses recovered to 5.0mg/l when the $50\%$ reduction in input COD loads from Masan basin and $70\%$ reduction in SOD was conducted. In the western part of Jinhae Bay, oxygen deficient water-masses recovered to 5.0mg/l when the $95\%$ reduction in SOD and $90\%$ reduction in culturing ground fecal loads was conducted.

• Korean Journal of Remote Sensing
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• v.30 no.2
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• pp.241-249
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• 2014

The continuous observations for atmospheric aerosol were carried out during 3 years (2009-2011) by using a multi-wavelength Raman lidar at the Gwangju Institute of Science and Technology (GIST), Korea ($35.11^{\circ}N$, $126.54^{\circ}E$). The particle depolarization ratios were retrieved from the observations in order to distinguish the Asian dust layer. The vertical information of Asian dust layers were used as input parameter for the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model for analysis of its backward trajectories. The source regions and transport pathways of the Asian dust layer were identified. The most frequent source region of Asian dust in Korea was Gobi desert during observation period in this study. The statistical analysis on the particle depolarization ratio of Asian dust was conducted according to their transport route in order to retrieve the variation of optical properties of Asian dust during long-range transport. The transport routes were classified into the Asian dust which was transported to observation site directly from the source regions, and the Asian dust which was passed over pollution regions of China. The particle depolarization ratios of Asian dust which were transported via industrial regions of China was ranged 0.07-0.1, whereas, the particle depolarization ratio of Asian dust which was transported directly from the source regions to observation site were comparably higher and ranged 0.11-0.15. It is considered that the pure Asian dust particle from source regions were mixed with pollution particles, which is likely to spherical particle, during transportation so that the values of particle depolarization of Asian dust mixed with pollution was decreased.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.2 no.2
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• pp.151-159
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• 1997

Benthic respiration and chemical fluxes were measured at the sediment-water interface underlying the marine fish cages floating on the open coastal waters off Tong-Young, the South Coast of Korea. The effects of cage farming on coastal benthic environment and on mass balance of organic carbon in the benthic boundary layer under the marine fish cages are addressed. In a growing season of caged fishes of June, 1995, benthic chambers and sediment traps were deployed on the sediment-water interfaces of the two sites chosen for this study: 1) Cage Site, directly underlying the fish cages of the farm at 18 m water depth, and 2) Control Site, about 100 m away from the farm at 32 m water depth. Benthic respiration rates and chemical fluxes were calculated from the evolution of dissolved oxygen and chemicals in the chamber water, and mass balance of organic carbon in the benthic boundary layer was constructed based on the vertical flux of particulate organic matter (POM) and chemical fluxes out of the sediment. High organic dumping (6400 mg C $m^{-2}d^{-1}$) and high benthic respiration (230 mmol $O_2\;m^{-2}d^{-1}$) were observed at the Cage Site. Equivalent to 40% of vertical flux of organic carbon into the Cage Site seemed to be decomposed concurrently and released back to overlying waters (2400 mg C $m^{-2}d^{-1}$). Consequently, up to 4000 mg C $m^{-2}d^{-1}$ of organic carbon could be buried into the farm sediment (equivalent to 60% of organic carbon flux into the Cage Site). At the Control Site, relatively less input of organic carbon (4000 mg C $m^{-2}d^{-1}$) and low benthic respiration rate (75 mmol $O_2\;m^{-2}d^{-1}$) were observed despite short distance away from the cages. The influence of cage farming on benthic chemical fluxes might be restricted and concentrated in the sea bottom just below the fish cages in spite of massive organic dumping and high current regime around the fish cage farm.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.18 no.2
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• pp.53-64
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• 2013

This study analyzed the ADCP records along with wind by KMA and discharge records at Seosan A-, B-district tide embankment by KRC for 33 days obtained in the Chunsu Bay, Yellow Sea, Korea spanning from July 29 to August 30, 2010. Various analyses include descriptive statistics, harmonic analysis of tidal constituents, spectra and coherence, complex correlation, progressive vector diagram and cumulative curves to understand the tidal and sub-tidal current characteristics caused by local wind and discharge effect. Observed current speed ranges from -30 to 40 (cm/sec), with standard deviation from 1.7 (cm/sec) at bottom to 18.7 (cm/sec) at surface. According to the harmonic analysis results, the tidal current direction show NNW-SSE. The magnitudes of semi-major axes range from 9.4 to 14.8 (cm/sec) for M2 harmonic constituent and from 4.4 to 7.0 (cm/sec) for S2, respectively. And the magnitudes of semi-minor axes range from 0.1 to 0.5 (cm/sec) for M2 and from 0.4 to 1.4 (cm/sec) for S2, respectively. In the spectral analysis results in the frequency domain, we found 3~6 significant spectral peaks for band-passed wind and residual current of all depth. These peak periods represent various periodicities ranging from 2 to 8 (days). In the coherency analysis results between band-passed wind and residual current of all depth, several significant coherencies could be resolved in 3~5 periodicities within 2.8 (days). Highest coherency peak occurred at 4.6 (day) with 1.2-day phase lag of discharge to band-passed residual current. The progressive vector of wind and residual current travelled to northward at all layers, and the travel distance at middle layer was greater than surface layer distance. The Northward residual current was caused by a seasonal southern wind, and the density-driven current formed by fresh water input effected southward residual current. The sub-tidal current characteristics is determined by seasonal wind force and fresh water inflow in the Chunsu Bay, Yellow Sea, Korea.

• Journal of the Korean Society of Radiology
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• v.6 no.1
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• pp.73-77
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• 2012

Recently, Interest to the photoconductor, which is used to flat form X-ray detector such as a-Se, $HgI_2$, PbO, CdTe, $PbI_2$ etc. is increasing. In this study, the film layer by using the photoconductive material with particle sedimentation was fabricated and evaluated. The quantization efficiency of the continuous X-ray with the 70 kVp energy bandwidth was analyzed by using the Monte Carlo simulation. With the results, the thickness of film with 64 % quantization efficiency was 180 ${\mu}m$ which is similar to the efficiency of 500 ${\mu}m$ a-Se film. And $HIg_2$ film has the high quantization efficiency of 74 % on 240 ${\mu}m$ thickness. The electrical characteristics of the 239 ${\mu}m$ $Hgl_2$ films produced by particle sedimentation were shown as very low dark current(under 10 $pA/mm^2$), and high sensitivity(19.8 mC/mR-sec) with 1 $V/{\mu}m$ input voltage. The SNR, which is influence to the contrast of X-ray image, was shown highly as 3,125 in low driving voltage on 0.8 $V/{\mu}m$. With the results of this study, the development of the low-cost, high-performance image detector with film could be possible by replacing the film produced by particle sedimentation instead to a-Se detector.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.8 no.4
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• pp.357-368
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• 2003

Time-series CTDT (Conductivity/Temperature/Depth/Transmissivity) were obtained at one point near tidewater glacier of Marian Cove (King George Islands, Antarctica) to present water column properties and SPM (suspended particulate matter) dispersal pattern in relation with tide, current, meteorological data, and SPM concentration. Four layers were divided from the water column characteristics measured in the interval of an hour for about 2 days: 1) cold, fresh, and turbid surface mixed layer between 0-20 m in water depth, 2) warm, saline, and relatively clean Maxwell Bay inflow between 20-40 m in water depth, 3) turbid/cold tongue of subglacial discharges compared with the ambient waters between 40-70 m in water depth, and 4) cold, saline, and clean bottom water beneath 70 m in water depth. Surface plume, turbid freshwater at coastal/cliff area in late summer (early February), had the characteristic temperature and SPM concentration according to morphology, glacial condition, and composition of sediments. The restrict dispersion only over the input source of meltwater discharges was due to calm wether condition. Due to strong wind-induced surface turbulence, fresh and turbid surface plume, englacial upwelling cold water, glacier-contact meltwater, and Maxwell Bay inflow was mixing at ice-proximal zone and the consequent mixed layer deepened at the surface. Large amount of precipitation, the major controlling factor for increasing short-term glacial discharges, was accompanied by the apparent development of subglacial discharge that resulted in the rapid drop of salinity below the mid depth. Although amount of subglacial discharge and englacial upwelling may be large, however, their low SPM concentration would have small influence on bottom deposition of terrigenous sediments.

• Journal of Environmental Impact Assessment
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• v.30 no.5
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• pp.271-296
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• 2021

Since the thermal stratification in a reservoir inhibits the vertical mixing of the upper and lower layers and causes the formation of a hypoxia layer and the enhancement of nutrients release from the sediment, changes in the stratification structure of the reservoir according to future climate change are very important in terms of water quality and aquatic ecology management. This study was aimed to develop a data-driven inflow water temperature prediction model for Daecheong Reservoir (DR), and to predict future inflow water temperature and the stratification structure of DR considering future climate scenarios of Representative Concentration Pathways (RCP). The random forest (RF)regression model (NSE 0.97, RMSE 1.86℃, MAPE 9.45%) developed to predict the inflow temperature of DR adequately reproduced the statistics and variability of the observed water temperature. Future meteorological data for each RCP scenario predicted by the regional climate model (HadGEM3-RA) was input into RF model to predict the inflow water temperature, and a three-dimensional hydrodynamic model (AEM3D) was used to predict the change in the future (2018~2037, 2038~2057, 2058~2077, 2078~2097) stratification structure of DR due to climate change. As a result, the rates of increase in air temperature and inflow water temperature was 0.14~0.48℃/10year and 0.21~0.41℃/10year,respectively. As a result of seasonal analysis, in all scenarios except spring and winter in the RCP 2.6, the increase in inflow water temperature was statistically significant, and the increase rate was higher as the carbon reduction effort was weaker. The increase rate of the surface water temperature of the reservoir was in the range of 0.04~0.38℃/10year, and the stratification period was gradually increased in all scenarios. In particular, when the RCP 8.5 scenario is applied, the number of stratification days is expected to increase by about 24 days. These results were consistent with the results of previous studies that climate change strengthens the stratification intensity of lakes and reservoirs and prolonged the stratification period, and suggested that prolonged water temperature stratification could cause changes in the aquatic ecosystem, such as spatial expansion of the low-oxygen layer, an increase in sediment nutrient release, and changed in the dominant species of algae in the water body.

• 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 Intelligence and Information Systems
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• v.18 no.3
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• pp.185-202
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• 2012

.Since the value of information has been realized in the information society, the usage and collection of information has become important. A facial expression that contains thousands of information as an artistic painting can be described in thousands of words. Followed by the idea, there has recently been a number of attempts to provide customers and companies with an intelligent service, which enables the perception of human emotions through one's facial expressions. For example, MIT Media Lab, the leading organization in this research area, has developed the human emotion prediction model, and has applied their studies to the commercial business. In the academic area, a number of the conventional methods such as Multiple Regression Analysis (MRA) or Artificial Neural Networks (ANN) have been applied to predict human emotion in prior studies. However, MRA is generally criticized because of its low prediction accuracy. This is inevitable since MRA can only explain the linear relationship between the dependent variables and the independent variable. To mitigate the limitations of MRA, some studies like Jung and Kim (2012) have used ANN as the alternative, and they reported that ANN generated more accurate prediction than the statistical methods like MRA. However, it has also been criticized due to over fitting and the difficulty of the network design (e.g. setting the number of the layers and the number of the nodes in the hidden layers). Under this background, we propose a novel model using Support Vector Regression (SVR) in order to increase the prediction accuracy. SVR is an extensive version of Support Vector Machine (SVM) designated to solve the regression problems. The model produced by SVR only depends on a subset of the training data, because the cost function for building the model ignores any training data that is close (within a threshold ${\varepsilon}$) to the model prediction. Using SVR, we tried to build a model that can measure the level of arousal and valence from the facial features. To validate the usefulness of the proposed model, we collected the data of facial reactions when providing appropriate visual stimulating contents, and extracted the features from the data. Next, the steps of the preprocessing were taken to choose statistically significant variables. In total, 297 cases were used for the experiment. As the comparative models, we also applied MRA and ANN to the same data set. For SVR, we adopted '${\varepsilon}$-insensitive loss function', and 'grid search' technique to find the optimal values of the parameters like C, d, ${\sigma}^2$, and ${\varepsilon}$. In the case of ANN, we adopted a standard three-layer backpropagation network, which has a single hidden layer. The learning rate and momentum rate of ANN were set to 10%, and we used sigmoid function as the transfer function of hidden and output nodes. We performed the experiments repeatedly by varying the number of nodes in the hidden layer to n/2, n, 3n/2, and 2n, where n is the number of the input variables. The stopping condition for ANN was set to 50,000 learning events. And, we used MAE (Mean Absolute Error) as the measure for performance comparison. From the experiment, we found that SVR achieved the highest prediction accuracy for the hold-out data set compared to MRA and ANN. Regardless of the target variables (the level of arousal, or the level of positive / negative valence), SVR showed the best performance for the hold-out data set. ANN also outperformed MRA, however, it showed the considerably lower prediction accuracy than SVR for both target variables. The findings of our research are expected to be useful to the researchers or practitioners who are willing to build the models for recognizing human emotions.