• Korean Journal of Soil Science and Fertilizer
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• v.44 no.5
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• pp.717-721
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• 2011

In case of plastic film greenhouses cultivating fresh vegetables on paddy soil, soil characteristics must be considered as more important factor than any other factors. Generally after the four years of cultivation, soils tend to increase electrical conductivity value, nutrient unbalance and soil pests. As a result, degradation of agricultural products occurred, therefore it is necessary to improve soil conditions. In this study, yield and economic cost of cucumber were analyzed. The best soil conditions for cucumber cultivation were alluvial or valley in soil topology, moderately or poorly drainage in soil drainage classes, coarse loamy soil in texture. In addition, rich-sunlight and-deep groundwater would be proper for the cucumber cultivation. Good environmental managements of plastic film greenhouse were as follows. The temperature needed to be adjusted three times. The optimal daytime temperature could be $22{\sim}28^{\circ}C$, the one from 12 until night could be $14{\sim}15^{\circ}C$, and the temperature from 24 to sunrise could be $10{\sim}12^{\circ}C$. During plant growth period, soil moisture content was as low as 10~15%, and it needed to be maintained as 15~20% during reproductive growth period. To control pests, catch crop cultivation and solar treatment were carried out, after those EC was reduced and the root-knot nematode was controled too. Cucumber yield from the plot with improved soil managements increased to $158.4Mg\;ha^{-1}$, but plot with successive cropping injury yielded $140.3Mg\;ha^{-1}$. The income from the plot with improved soil managements was 215,676 thousand won $ha^{-1}$, the plot with successive cropping injury was 131,649 thousand won $ha^{-1}$. Income rate of each plot was 51.8% and 38.4%, respectively.

• Journal of Korea Water Resources Association
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• v.54 no.12
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• pp.1243-1254
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• 2021

For water resources operation or agricultural water management, it is important to accurately predict evapotranspiration for a long-term future over a seasonal or monthly basis. In this study, reference evapotranspiration forecast (up to 12 months in advance) was performed using statistically predicted monthly temperatures and temperature-based Hamon method for the Han River basin. First, the daily maximum and minimum temperature data for 15 meterological stations in the basin were derived by spatial-temporal downscaling the monthly temperature forecasts. The results of goodness-of-fit test for the downscaled temperature data at each site showed that the percent bias (PBIAS) ranged from 1.3 to 6.9%, the ratio of the root mean square error to the standard deviation of the observations (RSR) ranged from 0.22 to 0.27, the Nash-Sutcliffe efficiency (NSE) ranged from 0.93 to 0.95, and the Pearson correlation coefficient (r) ranged from 0.97 to 0.98 for the monthly average daily maximum temperature. And for the monthly average daily minimum temperature, PBIAS was 7.8 to 44.7%, RSR was 0.21 to 0.25, NSE was 0.94 to 0.96, and r was 0.98 to 0.99. The difference by site was not large, and the downscaled results were similar to the observations. In the results of comparing the forecasted reference evapotranspiration calculated using the downscaled data with the observed values for the entire region, PBIAS was 2.2 to 5.4%, RSR was 0.21 to 0.28, NSE was 0.92 to 0.96, and r was 0.96 to 0.98, indicating a very high fit. Due to the characteristics of the statistical models and uncertainty in the downscaling process, the predicted reference evapotranspiration may slightly deviate from the observed value in some periods when temperatures completely different from the past are observed. However, considering that it is a forecast result for the future period, it will be sufficiently useful as information for the evaluation or operation of water resources in the future.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.275-290
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• 2021

Sulfur dioxide (SO2) in the atmosphere is mainly generated from anthropogenic emission sources. It forms ultra-fine particulate matter through chemical reaction and has harmful effect on both the environment and human health. In particular, ground-level SO2 concentrations are closely related to human activities. Satellite observations such as TROPOMI (TROPOspheric Monitoring Instrument)-derived column density data can provide spatially continuous monitoring of ground-level SO2 concentrations. This study aims to propose a 2-step residual corrected model to estimate ground-level SO2 concentrations through the synergistic use of satellite data and numerical model output. Random forest machine learning was adopted in the 2-step residual corrected model. The proposed model was evaluated through three cross-validations (i.e., random, spatial and temporal). The results showed that the model produced slopes of 1.14-1.25, R values of 0.55-0.65, and relative root-mean-square-error of 58-63%, which were improved by 10% for slopes and 3% for R and rRMSE when compared to the model without residual correction. The model performance by country was slightly reduced in Japan, often resulting in overestimation, where the sample size was small, and the concentration level was relatively low. The spatial and temporal distributions of SO2 produced by the model agreed with those of the in-situ measurements, especially over Yangtze River Delta in China and Seoul Metropolitan Area in South Korea, which are highly dependent on the characteristics of anthropogenic emission sources. The model proposed in this study can be used for long-term monitoring of ground-level SO2 concentrations on both the spatial and temporal domains.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.3
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• pp.21-36
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• 2019

Dead fuel moisture content is a key variable in fire danger rating as it affects fire ignition and behavior. This study evaluates simple regression models estimating the moisture content of standardized 10-h fuel stick (10-h FMC) at three sites with different characteristics(urban and outside/inside the forest). Equilibrium moisture content (EMC) was used as an independent variable, and in-situ measured 10-h FMC was used as a dependent variable and validation data. 10-h FMC spatial distribution maps were created for dates with the most frequent fire occurrence during 2013-2018. Also, 10-h FMC values of the dates were analyzed to investigate under which 10-h FMC condition forest fire is likely to occur. As the results, fitted equations could explain considerable part of the variance in 10-h FMC (62~78%). Compared to the validation data, the models performed well with R2 ranged from 0.53 to 0.68, root mean squared error (RMSE) ranged from 2.52% to 3.43%, and bias ranged from -0.41% to 1.10%. When the 10-h FMC model fitted for one site was applied to the other sites, $R^2$ was maintained as the same while RMSE and bias increased up to 5.13% and 3.68%, respectively. The major deficiency of the 10-h FMC model was that it poorly caught the difference in the drying process after rainfall between 10-h FMC and EMC. From the analysis of 10-h FMC during the dates fire occurred, more than 70% of the fires occurred under a 10-h FMC condition of less than 10.5%. Overall, the present study suggested a simple model estimating 10-h FMC with acceptable performance. Applying the 10-h FMC model to the automatic mountain weather observation system was successfully tested to produce a national-scale 10-h FMC spatial distribution map. This data will be fundamental information for forest fire research, and will support the policy maker.

• The Journal of Korean Philosophical History
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• no.59
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• pp.261-286
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• 2018

It is possible to communicate with objects in various styles, but especially the poem[詩], the ode[賦], the inscription[銘] are remarkable. The word is not the mouth, but the mind and the soul. Therefore, if a person is in a relationship with an object that defines the person, what conversation with the object is the person's inner. So if you know what a person has been with things, you can imagine his outer surface, and you can get inner if you know what you talked about. Seo Kyung-duk who lived a poverty life, but can not live without things, so his things are not a thing, Seo Kyung-duk also recorded especially about the dojookjang[bamboo cane], the buchae[fan], and the $k{\breve{o}}mungo$[Korean lute] Seo Kyung-duk with the buchae, Seo Kyung-duk with the dojookjang, and Seo Kyung-duk with the $k{\breve{o}}mungo$. These are the pictures we can imagine. And I can draw Seo Kyung-duk to talk with those things. Seo Kyung-duk, who is reflected in the dojookjang, shows the reality of participating in the rescue of the people's hardships and the stubborn world. Seo Kyung-duk, who is reflected in the buchae, is a philosopher who explores the origin of existence with the appearance of realistic preachers who have to wash away the difficulties of the people. Seo Kyung-duk, who is reflected in the $k{\breve{o}}mungo$, is a philosopher who grasps Ki(氣) the phenomenon and the source, the immaterial and the material, the type and the intangible. Both the strings and non-strings are $k{\breve{o}}mungos$. The $k{\breve{o}}mungo$ is strong in ideology symbolizing the Confucianism ideological ideals, and Seok Kyung-duk is also in the extension line. Seo Gyeong-deok, who has seen through the dojookjang, the buchae, and the $k{\breve{o}}mungo$ has a realistic sense of realizing that he should worry about the pain of the world and fulfill a good world. He is a philosopher who pierces the root of existence and can be governed by the logic of Ki(氣).

• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.1-15
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• 2021

If it is difficult for us to make decisions, we ask for advice from friends or people around us. When we decide to buy products online, we read anonymous reviews and buy them. With the advent of the Data-driven era, IT technology's development is spilling out many data from individuals to objects. Companies or individuals have accumulated, processed, and analyzed such a large amount of data that they can now make decisions or execute directly using data that used to depend on experts. Nowadays, the recommender system plays a vital role in determining the user's preferences to purchase goods and uses a recommender system to induce clicks on web services (Facebook, Amazon, Netflix, Youtube). For example, Youtube's recommender system, which is used by 1 billion people worldwide every month, includes videos that users like, "like" and videos they watched. Recommended system research is deeply linked to practical business. Therefore, many researchers are interested in building better solutions. Recommender systems use the information obtained from their users to generate recommendations because the development of the provided recommender systems requires information on items that are likely to be preferred by the user. We began to trust patterns and rules derived from data rather than empirical intuition through the recommender systems. The capacity and development of data have led machine learning to develop deep learning. However, such recommender systems are not all solutions. Proceeding with the recommender systems, there should be no scarcity in all data and a sufficient amount. Also, it requires detailed information about the individual. The recommender systems work correctly when these conditions operate. The recommender systems become a complex problem for both consumers and sellers when the interaction log is insufficient. Because the seller's perspective needs to make recommendations at a personal level to the consumer and receive appropriate recommendations with reliable data from the consumer's perspective. In this paper, to improve the accuracy problem for "appropriate recommendation" to consumers, the recommender systems are proposed in combination with context-based deep learning. This research is to combine user-based data to create hybrid Recommender Systems. The hybrid approach developed is not a collaborative type of Recommender Systems, but a collaborative extension that integrates user data with deep learning. Customer review data were used for the data set. Consumers buy products in online shopping malls and then evaluate product reviews. Rating reviews are based on reviews from buyers who have already purchased, giving users confidence before purchasing the product. However, the recommendation system mainly uses scores or ratings rather than reviews to suggest items purchased by many users. In fact, consumer reviews include product opinions and user sentiment that will be spent on evaluation. By incorporating these parts into the study, this paper aims to improve the recommendation system. This study is an algorithm used when individuals have difficulty in selecting an item. Consumer reviews and record patterns made it possible to rely on recommendations appropriately. The algorithm implements a recommendation system through collaborative filtering. This study's predictive accuracy is measured by Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Netflix is strategically using the referral system in its programs through competitions that reduce RMSE every year, making fair use of predictive accuracy. Research on hybrid recommender systems combining the NLP approach for personalization recommender systems, deep learning base, etc. has been increasing. Among NLP studies, sentiment analysis began to take shape in the mid-2000s as user review data increased. Sentiment analysis is a text classification task based on machine learning. The machine learning-based sentiment analysis has a disadvantage in that it is difficult to identify the review's information expression because it is challenging to consider the text's characteristics. In this study, we propose a deep learning recommender system that utilizes BERT's sentiment analysis by minimizing the disadvantages of machine learning. This study offers a deep learning recommender system that uses BERT's sentiment analysis by reducing the disadvantages of machine learning. The comparison model was performed through a recommender system based on Naive-CF(collaborative filtering), SVD(singular value decomposition)-CF, MF(matrix factorization)-CF, BPR-MF(Bayesian personalized ranking matrix factorization)-CF, LSTM, CNN-LSTM, GRU(Gated Recurrent Units). As a result of the experiment, the recommender system based on BERT was the best.

• Journal of the Korean earth science society
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• v.41 no.6
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• pp.617-629
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• 2020

Sea surface temperature (SST), which plays an important role in climate change and global environmental change, can be divided into skin sea surface temperature (SSST) observed by satellite infrared sensors and the bulk temperature of sea water (BSST) measured by instruments. As sea surface temperature products distributed by many overseas institutions represent temperatures at different depths, it is essential to understand the relationship between the SSST and the BSST. In this study, we constructed an observation system of infrared radiometer onboard a marine research vessel for the first time in Korea to measure the SSST. The calibration coefficients were prepared by performing the calibration procedure of the radiometer device in the laboratory prior to the shipborne observation. A series of processes were applied to calculate the temperature of the layer of radiance emitted from the sea surface as well as that from the sky. The differences in skin-bulk temperatures were investigated quantitatively and the characteristics of the vertical structure of temperatures in the upper ocean were understood through comparison with Himawari-8 geostationary satellite SSTs. Comparison of the skin-bulk temperature differences illustrated overall differences of about 0.76℃ at Jangmok port in the southern coast and the offshore region of the eastern coast of the Korean Peninsula from 21 April to May 6, 2020. In addition, the root-mean-square error of the skin-bulk temperature differences showed daily variation from 0.6℃ to 0.9℃, with the largest difference of 0.83-0.89℃ at 1-3 KST during the daytime and the smallest difference of 0.59℃ at 15 KST. The bias also revealed clear diurnal variation at a range of 0.47-0.75℃. The difference between the observed skin sea surface temperature and the satellite sea surface temperature showed a mean square error of approximately 0.74℃ and a bias of 0.37℃. The analysis of this study confirmed the difference in the skin-bulk temperatures according to the observation depth. This suggests that further ocean shipborne infrared radiometer observations should be carried out continuously in the offshore regions to understand diurnal variation as well as seasonal variations of the skin-bulk SSTs and their relations to potential causes.

• Korean Journal of Culture and Social Issue
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• v.26 no.1
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• pp.25-45
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• 2020

Intentionality judgement in criminal cases is a core area of fact finding that is root of guilty and sentencing judgment on the defendant. However, the third party is not sure the intentionality because it reflects subjective aspect of agent. Thus, mechanism behind intentionality judgment is an important factor to be properly understood by the academia and the criminal justice system. However, previous studies regarding intentionality judgment models have shown inconsistent results. Mental-state models proposed foreseeability(belief) and desire of agent at the time of the offence as key factors in intentionality judgment. These factors consistent with central things on intentionality judgment in criminal law. However, key factors in moral-evaluation models are blameworthiness of agent and badness of outcome reflected on the consequent aspect of act. Recently, deep-self concordance model emerged suggesting important factors on intentionality judgment are not mental states and moral evaluations but individual's deep-self. However, these models are limited in that they do not consider the important features of criminal cases, that the consequence of the case is inevitably negative, and therefore the actor who is a party to legal punishment rarely expresses his or her mental state at the time of the act. Therefore, this study suggests that, based on the existing intentionality judgment studies and the characteristics of the criminal case, the inference about who the agent was originally will play a key role in judging the intentionality in the criminal case. This is the moral-character model. Futhermore, In this regard, this study discussed what the media and criminal justice institutions should keep in mind and the directions for future research.

• Journal of Korea Entertainment Industry Association
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• v.15 no.3
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• pp.75-88
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• 2021

The film won the Golden Palm Award at the Cannes Film Festival, and received the Academy Award for a non-English-speaking film in February 2020, respectively. It has received a monumental evaluation in the world film history. Overall, this film is about class conflict, and critics evaluate the theme of the film as "badly twisted class gap" and "anger from class." The film expresses an intrinsic conflict embodied in culture as a "tragedy in which no bad person appears," rather than the dichotomous composition of the classical class struggle from Marxism. In other words, this can be seen as expressing the substrated class relationship of the modern society that Pierre Bourdieu had argued. This film has been focused as a controversial target under Korea society with excess of ideology. Politics used to adopt the keyword, 'parasite', for political disputes not only in culture contents world. Paradoxically socialism China did not allow to release film 'Parasite.' On the other hand, Lee O-Yong argues that the movie "Parasite" does not look at social phenomena through a dichotomous perspective, but is viewed through a "double perspective" and evaluates that it does not lose eyes looking at humans through tension. This view is based upon 'Vital Capitalism'. Lee. O-Yong looks at the movie "Parasite" from the perspective of "Vital Capitalism". The theory of Vital Capitalism does not seek to find the root of historical development in class struggle conflicts, but rather figuring out history and society pays attention onto the intrinsic characteristics of life, Topophilia, Neophilia, and Biophilia. Lee Eo-ryeong argues that the development of civilization theory evolved from the stage of Hobbes' Darwinism or predatism to the stage of host vs. parasite of Michel Serres, and onto the stage of Margulis's 'Win-Win (inter-dependence)'. In this paper, after overview of vital capital concept and preceeding research, re-interpretations were tried onto scenes based upon fields from habitus, culture capital. This exploration looks for a alternative for excess of ideology in Korea society.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1723-1735
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• 2022

Landslides are one of the most prevalent natural disasters, threating both humans and property. Also landslides can cause damage at the national level, so effective prediction and prevention are essential. Research to produce a landslide susceptibility map with high accuracy is steadily being conducted, and various models have been applied to landslide susceptibility analysis. Pixel-based machine learning models such as frequency ratio models, logistic regression models, ensembles models, and Artificial Neural Networks have been mainly applied. Recent studies have shown that the kernel-based convolutional neural network (CNN) technique is effective and that the spatial characteristics of input data have a significant effect on the accuracy of landslide susceptibility mapping. For this reason, the purpose of this study is to analyze landslide vulnerability using a pixel-based deep neural network model and a patch-based convolutional neural network model. The research area was set up in Gangwon-do, including Inje, Gangneung, and Pyeongchang, where landslides occurred frequently and damaged. Landslide-related factors include slope, curvature, stream power index (SPI), topographic wetness index (TWI), topographic position index (TPI), timber diameter, timber age, lithology, land use, soil depth, soil parent material, lineament density, fault density, normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were used. Landslide-related factors were built into a spatial database through data preprocessing, and landslide susceptibility map was predicted using deep neural network (DNN) and CNN models. The model and landslide susceptibility map were verified through average precision (AP) and root mean square errors (RMSE), and as a result of the verification, the patch-based CNN model showed 3.4% improved performance compared to the pixel-based DNN model. The results of this study can be used to predict landslides and are expected to serve as a scientific basis for establishing land use policies and landslide management policies.