• Journal of Korean Society of Environmental Engineers
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• v.35 no.12
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• pp.889-896
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• 2013

Advanced Oxidation Processes (AOP) have been interested for removing micropollutants in water. Most of water treatment plants (WTPs) located along the lower part of Nakdong River have adopted the $O_3/BAC$ process and have interesting in peroxone process a kind of AOP. This study evaluated the removal characteristics of residual hydrogen peroxide ($H_2O_2$) combining with the biofiltration process in the next BAC process when the hydrogen peroxide is applied for the WTP operating $O_3/BAC$ process. In the experiment, changing the temperature and the concentration of $H_2O_2$ of influent, the biofiltration process showed rapidly dropped the biodegradability when the $H_2O_2$ concentration was increased and lowered water temperature while BAC process maintained relatively stable efficiency. The influent fixed at $20^{\circ}C$ and the concentration of $H_2O_2$ at 300 mg/L was continuously input for 78 hours. Most of the $H_2O_2$ in the influent did not remove at the biofiltration process controlled 5 to 15 minutes EBCT condition after 24~71 hours operating time while BAC process controlled 5 to 15 minutes EBCT showed 38~91% removal efficiency condition after 78 hours operating time. Besides, after 78 hours continuously input experiment, the biomass and activity of attached bacterial on the biofilter and BAC were $6.0{\times}10^4CFU/g$, $0.54mg{\cdot}C/m^3{\cdot}hr$ and $0.4{\times}10^8CFU/g$, $1.42mg{\cdot}C/m^3{\cdot}hr$ respectively. These biomass and activity values were decreased 99% and 72% in biofilter and 68% and 53% in BAC compared with initial condition. The biodegradation rate constant ($k_{bio}$) and half-life ($t_{1/2}$) in BAC were decreased from $1.173min^{-1}$ to $0.183min^{-1}$ and 0.591 min to 3.787 min respectively according to increasing the $H_2O_2$ concentration from 10 mg/L to 300 mg/L at $5^{\circ}C$ water temperature and the $k_{bio}$ and $t_{1/2}$ were $1.510min^{-1}$ to $0.498min^{-1}$ and 0.459 min to 1.392 min at $25^{\circ}C$ water temperature. By increasing the water temperature from $5^{\circ}C$ to $15^{\circ}C$ or $25^{\circ}C$, the $k_{bio}$ were increased 1.1~2.1 times and 1.3~4.4 times. If a water treatment plant operating $O_3/BAC$ process is considering the hydrogen peroxide for the peroxone process, post BAC could effectively decrease the residual $H_2O_2$, moreover, in case of spilling the $H_2O_2$ into the water process line, these spilled $H_2O_2$ concentration can be able to decrease by increasing the EBCT at the BAC process.

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.721-732
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• 2017

With the increasing socio-economic importance of rice as a global staple food, several models have been developed for rice yield estimation by combining remote sensing data with carbon cycle modelling. In this study, we aimed to estimate rice yield in Korea using such an integrative model using satellite remote sensing data in combination with a biophysical crop growth model. Specifically, daily meteorological inputs derived from MODIS (Moderate Resolution imaging Spectroradiometer) and radar satellite products were used to run a light use efficiency based crop growth model, which is based on the MODIS gross primary production (GPP) algorithm. The modelled biomass was converted to rice yield using a harvest index model. We estimated rice yield from 2003 to 2014 at the county level and evaluated the modelled yield using the official rice yield and rice straw biomass statistics of Statistics Korea (KOSTAT). The estimated rice biomass, yield, and harvest index and their spatial distributions were investigated. Annual mean rice yield at the national level showed a good agreement with the yield statistics with the yield statistics, a mean error (ME) of +0.56% and a mean absolute error (MAE) of 5.73%. The estimated county level yield resulted in small ME (+0.10~+2.00%) and MAE (2.10~11.62%),respectively. Compared to the county-level yield statistics, the rice yield was over estimated in the counties in Gangwon province and under estimated in the urban and coastal counties in the south of Chungcheong province. Compared to the rice straw statistics, the estimated rice biomass showed similar error patterns with the yield estimates. The subpixel heterogeneity of the 1 km MODIS FPAR(Fraction of absorbed Photosynthetically Active Radiation) may have attributed to these errors. In addition, the growth and harvest index models can be further developed to take account of annually varying growth conditions and growth timings.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.127-141
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• 2017

Advances in Internet technologies and the proliferation of mobile devices enabled consumers to approach a wide range of goods and services, while causing an adverse effect that they have hard time reaching their congenial items even if they devote much time to searching for them. Accordingly, businesses are using the recommender systems to provide tools for consumers to find the desired items more easily. Association Rule Mining (ARM) technology is advantageous to recommender systems in that ARM provides intuitive form of a rule with interestingness measures (support, confidence, and lift) describing the relationship between items. Given an item, its relevant items can be distinguished with the help of the measures that show the strength of relationship between items. Based on the strength, the most pertinent items can be chosen among other items and exposed to a given item's web page. However, the diversity of the measures may confuse which items are more recommendable. Given two rules, for example, one rule's support and confidence may not be concurrently superior to the other rule's. Such discrepancy of the measures in distinguishing one rule's superiority from other rules may cause difficulty in selecting proper items for recommendation. In addition, in an online environment where a web page or mobile screen can provide a limited number of recommendations that attract consumer interest, the prudent selection of items to be included in the list of recommendations is very important. The exposure of items of little interest may lead consumers to ignore the recommendations. Then, such consumers will possibly not pay attention to other forms of marketing activities. Therefore, the measures should be aligned with the probability of consumer's acceptance of recommendations. For this reason, this study proposes a model-based approach to combine those measures into one unified measure that can consistently determine the ranking of recommended items. A regression model was designed to describe how well the measures (independent variables; i.e., support, confidence, and lift) explain consumer's acceptance of recommendations (dependent variables, hit rate of recommended items). The model is intuitive to understand and easy to use in that the equation consists of the commonly used measures for ARM and can be used in the estimation of hit rates. The experiment using transaction data from one of the Korea's largest online shopping malls was conducted to show that the proposed model can improve the hit rates of recommendations. From the top of the list to 13th place, recommended items in the higher rakings from the proposed model show the higher hit rates than those from the competitive model's. The result shows that the proposed model's performance is superior to the competitive model's in online recommendation environment. In a web page, consumers are provided around ten recommendations with which the proposed model outperforms. Moreover, a mobile device cannot expose many items simultaneously due to its limited screen size. Therefore, the result shows that the newly devised recommendation technique is suitable for the mobile recommender systems. While this study has been conducted to cover the cross-selling in online shopping malls that handle merchandise, the proposed method can be expected to be applied in various situations under which association rules apply. For example, this model can be applied to medical diagnostic systems that predict candidate diseases from a patient's symptoms. To increase the efficiency of the model, additional variables will need to be considered for the elaboration of the model in future studies. For example, price can be a good candidate for an explanatory variable because it has a major impact on consumer purchase decisions. If the prices of recommended items are much higher than the items in which a consumer is interested, the consumer may hesitate to accept the recommendations.

• Applied Biological Chemistry
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• v.50 no.4
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• pp.295-303
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• 2007

A preliminary diet experiment utilizing Jerusalem artichoke's inulin, lotus leaf powder, nettle powder and eucalyptus powder extract indicated that combining all four elements gave the most effective result. Therefore, a study was conducted to evaluate the effectiveness of combined diet for weight loss. In this study, Sprague-Dawley, male white rats about 200 g in weight was fed with high fat diet for 8 weeks in order to induce obesity followed by 4 week administration of combined diet to look into the effect of the diet. After a total of 12 weeks of feeding, factors relevant to weight, blood, and lipid metabolism by liver in the body were researched and histologic change was examined with optical microscope. In terms of weight change, both high fat diet group and regular diet group gained weight from high fat diet for 8 weeks compared to normal group. Then, for another 4 weeks, while normal group and high fat diet group kept gaining weight, combined diet group which was provided with high fat diet for 8 weeks, lost weight to the normal group level after 3 week administration of diet. However, after the 4th week of administration, the group weighted significantly less than the normal group and the efficiency of diet also significantly dropped. In the biochemical analysis of blood, total cholesterol, LDL-cholesterol, triglyceride, glucose, GOT, GPT, ${\gamma}-GTP$ and creatine showed significant increase in high fat diet group and there was no significant difference between diet group and normal group except for GPT, ${\gamma}-GTP$ and creatine. In the biochemical analysis of liver, there was significant increase in LDL-cholesterol, triglyceride of high fat diet group compared to normal group, while there was no significant difference in term of total cholesterol and HDL-cholesterol. Compared to normal group, diet group had higher HDL-cholesterol, while total cholesterol dropped significantly. There was no significant difference in terms of LDL-cholesterol and triglyceride. Besides, in high fat diet group, observation of histologic change in liver and change in ultrastructure showed volume increase of hepatic cell and severe fatty degeneration in hepatic cell around hepatic vein. However in diet group, like normal group, no pathological change was observed in terms of cytoplasm, nucleus and capillary in hepatocyte and the alignment of hepatocyte had regularity thanks to the administration of combined diet. Therefore, combined diet utilizing Jerusalem artichoke's inulin, lotus leaf powder, nettle powder and eucalyptus powder was proven to be an effective measure to prevent and improve obesity as a result of abnormal adipose deposition.

• Journal of Technology Innovation
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• v.26 no.3
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• pp.37-68
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• 2018

Since the 20th century, automobiles, which are the most common means of transportation, have been evolving as the use of electronic control devices and automotive semiconductors increases dramatically. Automotive semiconductors are a key component in automotive electronic control devices and are used to provide stability, efficiency of fuel use, and stability of operation to consumers. For example, automotive semiconductors include engines control, technologies for managing electric motors, transmission control units, hybrid vehicle control, start/stop systems, electronic motor control, automotive radar and LIDAR, smart head lamps, head-up displays, lane keeping systems. As such, semiconductors are being applied to almost all electronic control devices that make up an automobile, and they are creating more effects than simply combining mechanical devices. Since automotive semiconductors have a high data rate basically, a microprocessor unit is being used instead of a micro control unit. For example, semiconductors based on ARM processors are being used in telematics, audio/video multi-medias and navigation. Automotive semiconductors require characteristics such as high reliability, durability and long-term supply, considering the period of use of the automobile for more than 10 years. The reliability of automotive semiconductors is directly linked to the safety of automobiles. The semiconductor industry uses JEDEC and AEC standards to evaluate the reliability of automotive semiconductors. In addition, the life expectancy of the product is estimated at the early stage of development and at the early stage of mass production by using the reliability test method and results that are presented as standard in the automobile industry. However, there are limitations in predicting the failure rate caused by various parameters such as customer's various conditions of use and usage time. To overcome these limitations, much research has been done in academia and industry. Among them, researches using data mining techniques have been carried out in many semiconductor fields, but application and research on automotive semiconductors have not yet been studied. In this regard, this study investigates the relationship between data generated during semiconductor assembly and package test process by using data mining technique, and uses data mining technique suitable for predicting potential failure rate using customer bad data.

• Korean Chemical Engineering Research
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• v.43 no.1
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• pp.153-160
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• 2005

There have been large areas of soil contaminated with high levels of explosives. For this experimental work, 2,4,6-trinitrotoluene (TNT) was tested as a representative explosive contaminant of concern in both aqueous and soil samples and its removal was evaluated using three different chemical treatment methods: 1) the classical Fenton reaction which utilizes hydrogen peroxide ($H_2O_2$) and soluble iron at pH less than 3; 2) a modified Fenton reaction which utilizes chelating agents, $H_2O_2$, and soluble iron at pH 7; and 3) a Fenton-like process which utilizes iron minerals instead of soluble iron and $H_2O_2$, generating a hydroxyl radical. Using classic Fenton reaction, 93% of TNT was removed in 20 h at pH 3 (soil spiked with 300 mg/L of TNT, 3% $H_2O_2$ and 1mM Fe(III)), whereas 21% removed at pH 7. The modified Fenton reaction, using nitrilotriacetic acid (NTA), oxalate, ethylenediaminetetraacetic acid (EDTA), acetate and citrate as representative chelating agents, was tested with 3% $H_2O_2$ at pH 7 for 24 h. Results showed the TNT removal in the order of NTA, EDTA, oxalate, citrate and acetate, with the removal efficiency of 87%, 71%, 64%, 46%, and 37%, respectively, suggesting NTA as the most effective chelating agent. The Fenton-like reaction was performed with water contaminated with 100 mg/L TNT and soil contaminated with 300 mg/L TNT, respectively, using 3% $H_2O_2$ and such iron minerals as goethite, magnetite, and hematite. In the goethite-water system, 33% of TNT was removed at pH 3 whereas 28% removed at pH 7. In the magnetite-water system, 40% of TNT was removed at pH 3 whereas 36% removed at pH 7. In the hematite-water system, 40% of TNT was removed at pH 3 whereas 34% removed at pH 7. For further experiments combining the modified Fenton reaction with the Fenton-like reaction, NTA, EDTA, and oxalate were selected with the natural iron minerals, magnetite and hematite at pH 7, based on the results from the modified Fenton reaction. As results, in case magnetite was used, 79%, 59%, and 14% of TNT was removed when NTA, oxalate, and EDTA used, respectively, whereas 73%, 25%, and 19% removed in case of hematite, when NTA, oxalate, and EDTA used, respectively.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.1-19
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• 2018

Since stock movements forecasting is an important issue both academically and practically, studies related to stock price prediction have been actively conducted. The stock price forecasting research is classified into structured data and unstructured data, and it is divided into technical analysis, fundamental analysis and media effect analysis in detail. In the big data era, research on stock price prediction combining big data is actively underway. Based on a large number of data, stock prediction research mainly focuses on machine learning techniques. Especially, research methods that combine the effects of media are attracting attention recently, among which researches that analyze online news and utilize online news to forecast stock prices are becoming main. Previous studies predicting stock prices through online news are mostly sentiment analysis of news, making different corpus for each company, and making a dictionary that predicts stock prices by recording responses according to the past stock price. Therefore, existing studies have examined the impact of online news on individual companies. For example, stock movements of Samsung Electronics are predicted with only online news of Samsung Electronics. In addition, a method of considering influences among highly relevant companies has also been studied recently. For example, stock movements of Samsung Electronics are predicted with news of Samsung Electronics and a highly related company like LG Electronics.These previous studies examine the effects of news of industrial sector with homogeneity on the individual company. In the previous studies, homogeneous industries are classified according to the Global Industrial Classification Standard. In other words, the existing studies were analyzed under the assumption that industries divided into Global Industrial Classification Standard have homogeneity. However, existing studies have limitations in that they do not take into account influential companies with high relevance or reflect the existence of heterogeneity within the same Global Industrial Classification Standard sectors. As a result of our examining the various sectors, it can be seen that there are sectors that show the industrial sectors are not a homogeneous group. To overcome these limitations of existing studies that do not reflect heterogeneity, our study suggests a methodology that reflects the heterogeneous effects of the industrial sector that affect the stock price by applying k-means clustering. Multiple Kernel Learning is mainly used to integrate data with various characteristics. Multiple Kernel Learning has several kernels, each of which receives and predicts different data. To incorporate effects of target firm and its relevant firms simultaneously, we used Multiple Kernel Learning. Each kernel was assigned to predict stock prices with variables of financial news of the industrial group divided by the target firm, K-means cluster analysis. In order to prove that the suggested methodology is appropriate, experiments were conducted through three years of online news and stock prices. The results of this study are as follows. (1) We confirmed that the information of the industrial sectors related to target company also contains meaningful information to predict stock movements of target company and confirmed that machine learning algorithm has better predictive power when considering the news of the relevant companies and target company's news together. (2) It is important to predict stock movements with varying number of clusters according to the level of homogeneity in the industrial sector. In other words, when stock prices are homogeneous in industrial sectors, it is important to use relational effect at the level of industry group without analyzing clusters or to use it in small number of clusters. When the stock price is heterogeneous in industry group, it is important to cluster them into groups. This study has a contribution that we testified firms classified as Global Industrial Classification Standard have heterogeneity and suggested it is necessary to define the relevance through machine learning and statistical analysis methodology rather than simply defining it in the Global Industrial Classification Standard. It has also contribution that we proved the efficiency of the prediction model reflecting heterogeneity.

• Korean Journal of Soil Science and Fertilizer
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• v.36 no.6
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• pp.376-383
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• 2003

To utilize composts more efficiently, combining composts with fertilizer to meet crop requirements is an appealing alternative. A pot experiment was conducted to study the effect of application rate of composted pig manure blended with fertilizer on the availability and loss of fertilizer-N. Chinese cabbage (Brassica campestris L. cv. Samjin) plants were cultivated for 30 and 60 days. 15N-Labeled urea ($5.24\;^{15}N\;atom\;%$) was added to soil at $450mg\;N\;kg^{-1}$, and unlabeled compost ($0.37\;^{15}N\;atom\;%$) was added at 0, 200, 400, and $600mg\;N\;kg^{-1}$. The amount of plant-N derived from urea was not affected by compost application at rate of $200mg\;N\;kg^{-1}$. However, compost application at 400 and $600mg\;N\;kg^{-1}$ significantly (P<0.05) increased plant assimilation of N from urea irrespective of sampling time, probably because of physicochemical changes in the soil properties allowing urea-N to be assimilated more efficiently. The amount of immobilized urea-N increased with increasing rate of compost application at both growth periods, as the results of increased microbial activities using organic C in the compost. Total recovery of urea-N (as percentage of added N) by Chinese cabbage and soil also increased with increasing rate of compost from 71.5 to 95.6% and from 67.0 to 88.2% at the 30- and 60-days of growth, respectively. These results suggest that increasing rate of compost blending increases plant uptake of fertilizer-N and enhances immobilization of fertilizer-N, which leads to decrease in loss of fertilizer-N. However, information about the fate of immobilized N during future crop cultivation is necessary to verify long-term effect of compost blending.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2002.06a
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• pp.148-155
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• 2002

Multithreaded models improve the efficiency of parallel systems by combining inner parallelism, asynchronous data availability and the locality of von Neumann model. This model executes thread code which is generated by compiler and of which quality is given by the method of generation. But multithreaded models have the demerit that execution model is restricted to a specific platform. On the contrary, Java has the platform independency, so if we can translate from threads code to Java bytecode, we can use the advantages of multithreaded models in many platforms. Java executes Java bytecode which is intermediate language format for Java virtual machine. Java bytecode plays a role of an intermediate language in translator and Java virtual machine work as back-end in translator. But, Java bytecode which is translated from multithreaded models have the demerit that it is not secure. This paper, multhithread code whose feature of platform independent can execute in java virtual machine. We design and implement translator which translate from thread code of multithreaded code to Java bytecode and which check secure problems from Java bytecode.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.177-192
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• 2016

Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.