• Journal of Korea Water Resources Association
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• v.57 no.3
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• pp.151-164
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• 2024

In water treatment plants supplying potable water, the management of chlorine concentration in water treatment processes involving pre-chlorination or intermediate chlorination requires process control. To address this, research has been conducted on water quality prediction techniques utilizing AI technology. This study developed an AI-based predictive model for automating the process control of chlorine disinfection, targeting the prediction of residual chlorine concentration downstream of sedimentation basins in water treatment processes. The AI-based model, which learns from past water quality observation data to predict future water quality, offers a simpler and more efficient approach compared to complex physicochemical and biological water quality models. The model was tested by predicting the residual chlorine concentration downstream of the sedimentation basins at Plant, using multiple regression models and AI-based models like Random Forest and LSTM, and the results were compared. For optimal prediction of residual chlorine concentration, the input-output structure of the AI model included the residual chlorine concentration upstream of the sedimentation basin, turbidity, pH, water temperature, electrical conductivity, inflow of raw water, alkalinity, NH₃, etc. as independent variables, and the desired residual chlorine concentration of the effluent from the sedimentation basin as the dependent variable. The independent variables were selected from observable data at the water treatment plant, which are influential on the residual chlorine concentration downstream of the sedimentation basin. The analysis showed that, for Plant, the model based on Random Forest had the lowest error compared to multiple regression models, neural network models, model trees, and other Random Forest models. The optimal predicted residual chlorine concentration downstream of the sedimentation basin presented in this study is expected to enable real-time control of chlorine dosing in previous treatment stages, thereby enhancing water treatment efficiency and reducing chemical costs.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.37-51
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• 2015

Due to the development of internet technology and the rapid increase of internet data, various studies are actively conducted on how to use and analyze internet data for various purposes. In particular, in recent years, a number of studies have been performed on the applications of text mining techniques in order to overcome the limitations of the current application of structured data. Especially, there are various studies on sentimental analysis to score opinions based on the distribution of polarity such as positivity or negativity of vocabularies or sentences of the texts in documents. As a part of such studies, this study tries to predict ups and downs of stock prices of companies by performing sentimental analysis on news contexts of the particular companies in the Internet. A variety of news on companies is produced online by different economic agents, and it is diffused quickly and accessed easily in the Internet. So, based on inefficient market hypothesis, we can expect that news information of an individual company can be used to predict the fluctuations of stock prices of the company if we apply proper data analysis techniques. However, as the areas of corporate management activity are different, an analysis considering characteristics of each company is required in the analysis of text data based on machine-learning. In addition, since the news including positive or negative information on certain companies have various impacts on other companies or industry fields, an analysis for the prediction of the stock price of each company is necessary. Therefore, this study attempted to predict changes in the stock prices of the individual companies that applied a sentimental analysis of the online news data. Accordingly, this study chose top company in KOSPI 200 as the subjects of the analysis, and collected and analyzed online news data by each company produced for two years on a representative domestic search portal service, Naver. In addition, considering the differences in the meanings of vocabularies for each of the certain economic subjects, it aims to improve performance by building up a lexicon for each individual company and applying that to an analysis. As a result of the analysis, the accuracy of the prediction by each company are different, and the prediction accurate rate turned out to be 56% on average. Comparing the accuracy of the prediction of stock prices on industry sectors, 'energy/chemical', 'consumer goods for living' and 'consumer discretionary' showed a relatively higher accuracy of the prediction of stock prices than other industries, while it was found that the sectors such as 'information technology' and 'shipbuilding/transportation' industry had lower accuracy of prediction. The number of the representative companies in each industry collected was five each, so it is somewhat difficult to generalize, but it could be confirmed that there was a difference in the accuracy of the prediction of stock prices depending on industry sectors. In addition, at the individual company level, the companies such as 'Kangwon Land', 'KT & G' and 'SK Innovation' showed a relatively higher prediction accuracy as compared to other companies, while it showed that the companies such as 'Young Poong', 'LG', 'Samsung Life Insurance', and 'Doosan' had a low prediction accuracy of less than 50%. In this paper, we performed an analysis of the share price performance relative to the prediction of individual companies through the vocabulary of pre-built company to take advantage of the online news information. In this paper, we aim to improve performance of the stock prices prediction, applying online news information, through the stock price prediction of individual companies. Based on this, in the future, it will be possible to find ways to increase the stock price prediction accuracy by complementing the problem of unnecessary words that are added to the sentiment dictionary.

• Korean Journal of Soil Science and Fertilizer
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• v.45 no.5
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• pp.853-860
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• 2012

To estimate potential use of fly ash in reducing $CH_4$ and $CO_2$ emission from soil, $CH_4$ and $CO_2$ fluxes from a paddy soil mixed with fly ash at different rate (w/w; 0, 5, and 10%) in the presence and absence of fertilizer N ($(NH_4)_2SO_4$) addition were investigated in a laboratory incubation for 60 days under changing water regime from wetting to drying via transition. The mean $CH_4$ flux during the entire incubation period ranged from 0.59 to $1.68mg\;CH_4\;m^{-2}day^{-1}$ with a lower rate in the soil treated with N fertilizer due to suppression of $CH_4$ production by $SO_4^{2-}$ that acts as an electron acceptor, leading to decreases in electron availability for methanogen. Fly ash application reduced $CH_4$ flux by 37.5 and 33.0% in soils without and with N addition, respectively, probably due to retardation of $CH_4$ diffusion through soil pores by addition of fine-textured fly ash. In addition, as fly ash has a potential for $CO_2$ removal via carbonation (formation of carbonate precipitates) that decreases $CO_2$ availability that is a substrate for $CO_2$ reduction reaction (one of $CH_4$ generation pathways) is likely to be another mechanisms of $CH_4$ flux reduction by fly ash. Meanwhile, the mean $CO_2$ flux during the entire incubation period was between 0.64 and $0.90g\;CO_2\;m^{-2}day^{-1}$, and that of N treated soil was lower than that without N addition. Because N addition is likely to increase soil respiration, it is not straightforward to explain the results. However, it may be possible that our experiment did not account for the substantial amount of $CO_2$ produced by heterotrophs that were activated by N addition in earlier period than the measurement was initiated. Fly ash application also lowered $CO_2$ flux by up to 20% in the soil mixed with fly ash at 10% through $CO_2$ removal by the carbonation. At the whole picture, fly ash application at 10% decreased global warming potential of emitted $CH_4$ and $CO_2$ by about 20%. Therefore, our results suggest that fly ash application can be a soil management practice to reduce green house gas emission from paddy soils. Further studies under field conditions with rice cultivation are necessary to verify our findings.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.179-196
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• 2019

Recently, the "Smart Consumer" has been emerging. He or she is increasingly inclined to search for and purchase products by taking into account personal judgment or expert reviews rather than by relying on information delivered through manufacturers' advertising. This is especially true when purchasing cosmetics. Because cosmetics act directly on the skin, consumers respond seriously to dangerous chemical elements they contain or to skin problems they may cause. Above all, cosmetics should fit well with the purchaser's skin type. In addition, changes in global cosmetics consumer trends make it necessary to study this field. The desire to find one's own individualized cosmetics is being revealed to consumers around the world and is known as "Finding the Holy Grail." Many consumers show a deep interest in customized cosmetics with the cultural boom known as "K-Beauty" (an aspect of "Han-Ryu"), the growth of personal grooming, and the emergence of "self-culture" that includes "self-beauty" and "self-interior." These trends have led to the explosive popularity of cosmetics made in Korea in the Chinese and Southeast Asian markets. In order to meet the customized cosmetics needs of consumers, cosmetics manufacturers and related companies are responding by concentrating on delivering premium services through the convergence of ICT(Information, Communication and Technology). Despite the evolution of companies' responses regarding market trends toward customized cosmetics, there is no "Intelligent Data Platform" that deals holistically with consumers' skin condition experience and thus attaches emotions to products and services. To find the Holy Grail of customized cosmetics, it is important to acquire and analyze consumer data on what they want in order to address their experiences and emotions. The emotions consumers are addressing when purchasing cosmetics varies by their age, sex, skin type, and specific skin issues and influences what price is considered reasonable. Therefore, it is necessary to classify emotions regarding cosmetics by individual consumer. Because of its importance, consumer emotion analysis has been used for both services and products. Given the trends identified above, we judge that consumer emotion analysis can be used in our study. Therefore, we collected and indexed data on consumers' emotions regarding their cosmetics experiences focusing on consumers' language. We crawled the cosmetics emotion data from SNS (blog and Twitter) according to sales ranking ($1^{st}$ to $99^{th}$), focusing on the ample/serum category. A total of 357 emotional adjectives were collected, and we combined and abstracted similar or duplicate emotional adjectives. We conducted a "Consumer Sentiment Journey" workshop to build a "Consumer Sentiment Dictionary," and this resulted in a total of 76 emotional adjectives regarding cosmetics consumer experience. Using these 76 emotional adjectives, we performed clustering with the Self-Organizing Map (SOM) method. As a result of the analysis, we derived eight final clusters of cosmetics consumer sentiments. Using the vector values of each node for each cluster, the characteristics of each cluster were derived based on the top ten most frequently appearing consumer sentiments. Different characteristics were found in consumer sentiments in each cluster. We also developed a cosmetics experience pattern map. The study results confirmed that recommendation and classification systems that consider consumer emotions and sentiments are needed because each consumer differs in what he or she pursues and prefers. Furthermore, this study reaffirms that the application of emotion and sentiment analysis can be extended to various fields other than cosmetics, and it implies that consumer insights can be derived using these methods. They can be used not only to build a specialized sentiment dictionary using scientific processes and "Design Thinking Methodology," but we also expect that these methods can help us to understand consumers' psychological reactions and cognitive behaviors. If this study is further developed, we believe that it will be able to provide solutions based on consumer experience, and therefore that it can be developed as an aspect of marketing intelligence.

• Korean Journal of Soil Science and Fertilizer
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• v.5 no.2
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• pp.1-38
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• 1972

This study, designed to establish a classification system of paddy soils and suitability groups on productivity and management of paddy land based on soil characteristics, has been made for the paddy soils on the Gimje-Mangyeong plains. The morphological, physical and chemical properties of the 15 paddy soil series found on these plains are briefly as follows: Ten soil series (Baeggu, Bongnam, Buyong, Gimje, Gongdeog, Honam, Jeonbug, Jisan, Mangyeong and Suam) have a B horizon (cambic B), two soil series (Geugrag and Hwadong) have a Bt horizon (argillic B), and three soil series (Gwanghwal, Hwagye and Sindab) have no B or Bt horizons. Uniquely, both the Bongnam and Gongdeog series contain a muck layer in the lower part of subsoil. Four soil series (Baeggu, Gongdeog, Gwanghwal and Sindab) generally are bluish gray and dark gray, and eight soil series (Bongnam, Buyong, Gimje, Honam, Jeonbug, Jisan, Mangyeong and Suam) are either gray or grayish brown. Three soil series (Geugrag, Hwadong and Hwagye), however, are partially gleyed in the surface and subsurface, but have a yellowish brown to brown subsoil or substrata. Seven soil series (Bongnam, Buyong, Geugrag, Gimje, Gongdeog, Honam and Hwadong) are of fine clayey texture, three soil series (Baeggu, Jeonbug and Jisan) belong to fine loamy and fine silty, three soil series (Gwanghwal, Mangyeong and Suam) to coarse loamy and coarse silty, and two soil series (Hwagye and Sindab) to sandy and sandy skeletal texture classes. The carbon content of the surface soil ranges from 0.29 to 2.18 percent, mostly 1.0 to 2.0 percent. The total nitrogen content of the surface soil ranges from 0.03 to 0.25 percent, showing a tendency to decrease irregularly with depth. The C/N ratio in the surface soil ranges from 4.6 to 15.5, dominantly from 8 to 10. The C/N ratio in the subsoil and substrata, however, has a wide range from 3.0 to 20.25. The soil reaction ranges from 4.5 to 8.0. All soil series except the Gwanghwal and Mangyeong series belong to the acid reaction class. The cation exchange cpacity in the surface soil ranges from 5 to 13 milliequivalents per 100 grams of soil, and in all the subsoil and substrata except those of a sandy texture, from 10 to 20 milliequivalents per 100 grams of soil. The base saturation of the soil series except Baeggu and Gongdeog is more than 60 percent. The active iron content of the surface soil ranges from 0.45 to 1.81 ppm, easily-reduceable manganese from 15 to 148 ppm, and available silica from 36 to 366 ppm. The iron and manganese are generally accumulated in a similar position (10 to 70cm. depth), and silica occurs in the same horizon with that of iron and manganese, or in the deeper horizons in the soil profile. The properties of each soil series extending from the sea shore towards the continental plains change with distance and they are related with distance (x) as follows: y(surface soil, clay content) = $$-0.2491x^2+6.0388x-1.1251$$ y(subsoil or subsurface soil, clay content) = $$-0.31646x^2+7.84818x-2.50008$$ y(surface soil, organic carbon content) = $$-0.0089x^2+0.2192x+0.1366$$ y(subsoil or subsurface soil, pH) = $$-0.0178x^2-0.04534x+8.3531$$ Soil profile development, soil color, depositional and organic layers, soil texture and soil reaction etc. are thought to be the major items that should be considered in a paddy soil classification. It was found that most of the soils belonging to the moderately well, somewhat poorly and poorly drained fine and medium textured soils and moderately deep fine textured soils over coarse materials, produce higher paddy yields in excess of 3,750 kg/ha. and most of the soils belonging to the coarse textured soils, well drained fine textured soils, moderately deep medium textured soils over coarse materials and saline soils, produce yields less than 3,750kg/ha. Soil texture of the profile, available soil depth, salinity and gleying of the surface and subsurface soils etc. seem to be the major factors determining rice yields, and these factors are considered when establishing suitability groups for paddy land. The great group, group, subgroup, family and series are proposed for the classification categories of paddy soils. The soil series is the basic category of the classification. The argillic horizon (Bt horizon) and cambic horizon (B horizon) are proposed as two diagnostic horizons of great group level for the determination of the morphological properties of soils in the classification. The specific soil characteristics considered in the group and subgroup levels are soil color of the profile (bluish gray, gray or yellowish brown), salinity (salic), depositonal (fluvic) and muck layers (mucky), and gleying of surface and subsurface soils (gleyic). The family levels are classified on the basis of soil reaction, soil texture and gravel content of the profile. The definitions are given on each classification category, diagnostic horizons and specific soil characteristics respectively. The soils on these plains are classified in eight subgroups and examined under the existing classification system. Further, the suitability group, can be divided into two major categories, suitability class and subclass. The soils within a suitability class are similar in potential productivity and limitation on use and management. Class 1 through 4 are distinguished from each other by combination of soil characteristics. Subclasses are divided from classes that have the same kind of dominant limitations such as slope(e), wettness(w), sandy(s), gravels(g), salinity(t) and non-gleying of the surface and subsurface soils(n). The above suitability classes and subclasses are examined, and the definitions are given. Seven subclasses are found on these plains for paddy soils. The classification and suitability group of 15 paddy soil series on the Gimje-Mangyeong plains may now be tabulated as follows.