• Korean Journal of Ecology and Environment
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• v.44 no.1
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• pp.9-21
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• 2011

This study is conducted to know the change in water environment of Lake Hwajinpo from 2000 to 2008 with physico-chemical parameters; salinity, dissolved oxygen, total phosphorus and total nitrogen and others. And zooplanktons and phytoplanktons were studied from 2007 to 2008. From the water quality data of Lake Hwajinpo from 2000 to 200S; water temperature, salinity, transparency, chemical oxygen demand and dissolved oxygen ranges are $2.8{\sim}29.4^{\circ}C$, 0.23~33.2‰, $0.2{\sim}1.8\;m$, $0.2{\sim}20.2\;mg\;L^{-1}$ and $0.1{\sim}17.4\;mg\;L^{-1}$ and the average values are $18.0^{\circ}C$, 15.7‰, 0.7 m, $5.7\;mg\;L^{-1}$ and $8.0\;mg\;L^{-1}$, respectively. Total phosphorus (TP) and total nitrogen (TN) ranges are $0.024{\sim}0.869\;mg\;L^{-1}$ (average 0.091) and $0.240{\sim}5.310\;mg\;L^{-1}$ (average 1.235). Average TN/TP ratio is 16.4. The annual variations in COD, TP, TN and Chl.${\alpha}$ are compared. COD in 2000 is $4.83\;mg\;L^{-1}$ and 2008 is $1.80\;mg\;L^{-1}$ which is reduced by $0.34\;mg\;L^{-1}$ every year. TP in 2000 is $0.07\;mg\;L^{-1}$ and 2008 is $0.05\;mg\;L^{-1}$ reduced gradually. Yearly reduction in TN is $0.09\;mg\;L^{-1}$, in 2000 and 2008 the values are $1.54\;mg\;L^{-1}$ and $0.77\;mg\;L^{-1}$ respectivly. Chl.${\alpha}$ in 2000 is $46.30\;{\mu}g\;L^{-1}$ and $5.78\;{\mu}g\;L^{-1}$ in 2008; yearly reduction is $4.50\;{\mu}g\;L^{-1}$. The tropic state index (TSI) in south and north parts of Lake Hwajinpo in 2000 are 67 and 63 which are reduced to 63 and 59 in 2008 respectively. North and south part of Lake Hwajinpo have 67 species of phytoplankton under 47 families in 2007 and 2008. Dominant species in south part in 2007 are; Asterococcus superbus in May, Lyngbya sp. in September and Trachelomonas spp. in November and in 2008 Anabaena spiroides in August are abundant and varies with time. Zooplankton species in Lake Hwajinpo are 25 of 25 families. Dominant species in south part in May and August 2007 and May and November in 2008 Copepoda larvae and in September 2007 Protozoa spp. of Protozoan and Brachionus plicatilis and Brachionus urceolaris of Cladocera in August 2008. Dominant species in north part Asplanchna sp. of Cladecera in August and November 2007 and rest of the time are larvae of Copepoda. In this way, the water quality of Lake Hwajinpo is changing with slow rate in the long period specially nutrients concentration (TP, TN etc) is decreasing.

• Journal of Intelligence and Information Systems
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• v.20 no.3
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• pp.93-108
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• 2014

To support business decision making, interests and efforts to analyze and use transaction data in different perspectives are increasing. Such efforts are not only limited to customer management or marketing, but also used for monitoring and detecting fraud transactions. Fraud transactions are evolving into various patterns by taking advantage of information technology. To reflect the evolution of fraud transactions, there are many efforts on fraud detection methods and advanced application systems in order to improve the accuracy and ease of fraud detection. As a case of fraud detection, this study aims to provide effective fraud detection methods for auction exception agricultural products in the largest Korean agricultural wholesale market. Auction exception products policy exists to complement auction-based trades in agricultural wholesale market. That is, most trades on agricultural products are performed by auction; however, specific products are assigned as auction exception products when total volumes of products are relatively small, the number of wholesalers is small, or there are difficulties for wholesalers to purchase the products. However, auction exception products policy makes several problems on fairness and transparency of transaction, which requires help of fraud detection. In this study, to generate fraud detection rules, real huge agricultural products trade transaction data from 2008 to 2010 in the market are analyzed, which increase more than 1 million transactions and 1 billion US dollar in transaction volume. Agricultural transaction data has unique characteristics such as frequent changes in supply volumes and turbulent time-dependent changes in price. Since this was the first trial to identify fraud transactions in this domain, there was no training data set for supervised learning. So, fraud detection rules are generated using outlier detection approach. We assume that outlier transactions have more possibility of fraud transactions than normal transactions. The outlier transactions are identified to compare daily average unit price, weekly average unit price, and quarterly average unit price of product items. Also quarterly averages unit price of product items of the specific wholesalers are used to identify outlier transactions. The reliability of generated fraud detection rules are confirmed by domain experts. To determine whether a transaction is fraudulent or not, normal distribution and normalized Z-value concept are applied. That is, a unit price of a transaction is transformed to Z-value to calculate the occurrence probability when we approximate the distribution of unit prices to normal distribution. The modified Z-value of the unit price in the transaction is used rather than using the original Z-value of it. The reason is that in the case of auction exception agricultural products, Z-values are influenced by outlier fraud transactions themselves because the number of wholesalers is small. The modified Z-values are called Self-Eliminated Z-scores because they are calculated excluding the unit price of the specific transaction which is subject to check whether it is fraud transaction or not. To show the usefulness of the proposed approach, a prototype of fraud transaction detection system is developed using Delphi. The system consists of five main menus and related submenus. First functionalities of the system is to import transaction databases. Next important functions are to set up fraud detection parameters. By changing fraud detection parameters, system users can control the number of potential fraud transactions. Execution functions provide fraud detection results which are found based on fraud detection parameters. The potential fraud transactions can be viewed on screen or exported as files. The study is an initial trial to identify fraud transactions in Auction Exception Agricultural Products. There are still many remained research topics of the issue. First, the scope of analysis data was limited due to the availability of data. It is necessary to include more data on transactions, wholesalers, and producers to detect fraud transactions more accurately. Next, we need to extend the scope of fraud transaction detection to fishery products. Also there are many possibilities to apply different data mining techniques for fraud detection. For example, time series approach is a potential technique to apply the problem. Even though outlier transactions are detected based on unit prices of transactions, however it is possible to derive fraud detection rules based on transaction volumes.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.109-125
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• 2020

The Ministry of National Defense is pushing for the Defense Acquisition Program to build strong defense capabilities, and it spends more than 10 trillion won annually on defense improvement. As the Defense Acquisition Program is directly related to the security of the nation as well as the lives and property of the people, it must be carried out very transparently and efficiently by experts. However, the excessive diversification of laws and regulations related to the Defense Acquisition Program has made it challenging for many working-level officials to carry out the Defense Acquisition Program smoothly. It is even known that many people realize that there are related regulations that they were unaware of until they push ahead with their work. In addition, the statutory statements related to the Defense Acquisition Program have the tendency to cause serious issues even if only a single expression is wrong within the sentence. Despite this, efforts to establish a sentence comparison system to correct this issue in real time have been minimal. Therefore, this paper tries to propose a "Comparison System between the Statement of Military Reports and Related Laws" implementation plan that uses the Siamese Network-based artificial neural network, a model in the field of natural language processing (NLP), to observe the similarity between sentences that are likely to appear in the Defense Acquisition Program related documents and those from related statutory provisions to determine and classify the risk of illegality and to make users aware of the consequences. Various artificial neural network models (Bi-LSTM, Self-Attention, D_Bi-LSTM) were studied using 3,442 pairs of "Original Sentence"(described in actual statutes) and "Edited Sentence"(edited sentences derived from "Original Sentence"). Among many Defense Acquisition Program related statutes, DEFENSE ACQUISITION PROGRAM ACT, ENFORCEMENT RULE OF THE DEFENSE ACQUISITION PROGRAM ACT, and ENFORCEMENT DECREE OF THE DEFENSE ACQUISITION PROGRAM ACT were selected. Furthermore, "Original Sentence" has the 83 provisions that actually appear in the Act. "Original Sentence" has the main 83 clauses most accessible to working-level officials in their work. "Edited Sentence" is comprised of 30 to 50 similar sentences that are likely to appear modified in the county report for each clause("Original Sentence"). During the creation of the edited sentences, the original sentences were modified using 12 certain rules, and these sentences were produced in proportion to the number of such rules, as it was the case for the original sentences. After conducting 1 : 1 sentence similarity performance evaluation experiments, it was possible to classify each "Edited Sentence" as legal or illegal with considerable accuracy. In addition, the "Edited Sentence" dataset used to train the neural network models contains a variety of actual statutory statements("Original Sentence"), which are characterized by the 12 rules. On the other hand, the models are not able to effectively classify other sentences, which appear in actual military reports, when only the "Original Sentence" and "Edited Sentence" dataset have been fed to them. The dataset is not ample enough for the model to recognize other incoming new sentences. Hence, the performance of the model was reassessed by writing an additional 120 new sentences that have better resemblance to those in the actual military report and still have association with the original sentences. Thereafter, we were able to check that the models' performances surpassed a certain level even when they were trained merely with "Original Sentence" and "Edited Sentence" data. If sufficient model learning is achieved through the improvement and expansion of the full set of learning data with the addition of the actual report appearance sentences, the models will be able to better classify other sentences coming from military reports as legal or illegal. Based on the experimental results, this study confirms the possibility and value of building "Real-Time Automated Comparison System Between Military Documents and Related Laws". The research conducted in this experiment can verify which specific clause, of several that appear in related law clause is most similar to the sentence that appears in the Defense Acquisition Program-related military reports. This helps determine whether the contents in the military report sentences are at the risk of illegality when they are compared with those in the law clauses.

• Applied Biological Chemistry
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• v.20 no.1
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• pp.66-80
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• 1977

This research was carried as a part of the basic study, in which the aptitude of theKorean oak varieties as barrels for aging apple fine spirits was investigated, and thefollowing results were obtained. 1. Following was the result of the chemical analysis of the fruits which are now mass-produced and can be used as a substitute for raw materials for wine production. Apple (Malus pumila Miller var. domestica Schneider) : Total sugar. total acid, volatile acid and pectin of Jonathan (Hong-og) were 13.95%, 0.46%, 0.012%, 0.20% respectively. Total sugar, total acid, volatile acid and pectin of Ralls (Koog-kwang) were 13.35%, 0.43%, 0.011%, 0.45% respectively. 2. Because of low yield of apple juice due to cellulose, pectin, hemicellulose which are present besides sugars, acids in apples, the apple juice were treated with xylanase of Aspergillus niger SUAFM-430, cellulase and pectinase of Aspergillus niger SUAFM-6. This treatment increased the yield of apple juice. And the apple juice was sterilized by adding potassium metabisulfite $(K_2S_20_5)$ and Saccharomyces cerevisae var. ellipsoideus Rasse Johannisberg II (SUAFM-1018) as a cultivation yeast, which has a strong fermentation power was used to ferment. The yield of apple wine based on raw material was 86-87%. The amount of ethanol, extract and methanol obtained from Jonathan and Ralls were 13.5%, 5.4%, 0.04-0.05% respectively. 3. Wines were distilled for two times by the pot still method to make fine spirits. The yield of fine spirits from apple wine mash was 86.6%, and the pH of fine spirits from Jonathan and Ralls were 4.1, 4.2 respectively. 4. The oak chips made of inner part or outer part of 24 Korean oak varieties were used to select the barrel for aging fine spirits. Two oak chips (one oak chip: $1{\times}1{\times}5cm$) of the inner part or of the outer part of each oak variety were dipped into 300 ml of fine spirits, which was bottled in 640ml beer bottle, and followed aging. The colors, flavors and tastes of the fine spirits were checked during 6 months. A. As a criterion for the first screening of oak barrels for aging fine spirits, the rate five of color extraction was determined. The oak chips showed good results in their order as follows and the best 5 varieties were selected. Gal-cham: Quercus aliena Blume (Inner part), Gul-cham: Quercus variabilis Blume (Outer part), Gal-chain: Quercus aliena Blume (Outer part), Jol-cham: Quercus serrata Thumb (Inner and Outer part). Sin-gal-cham: Quercus mongolica Fisher (Outer and Inner part) Sang-su-ri: Quercus acutissima Carruthers (Outer and Inner part) B. To find out the influence of aging temperature on aging, apple fine spirits were aged by dipping each oak chip at room temperature $(24-25^{\circ}C)$) and $45^{\circ}C$. Aging at $45^{\circ}C$ gave the best result followed aging at $30^{\circ}C$ and then at room temperature. C. Apple fine spirits was aged for six months by dipping oak chips in Erlenmeyer flasks and was irradiated with U.V light. The U.V irradiation enhanced the aging effect by nearly two times, compared with the aging without U.V irradiation. D. In aging apple fine spirits by dipping two oak chips, it was observed that the extent of the extraction of most components of oak chips were strongly dependent upon the pH of fine spirits. E. Oak chips of five selected oak varieties and a Limousin white oak from France as a control were used. Each apple fine spitits was dipped by two oak chips, and was aged at room temperature $(24-25^{\circ}C)$, $30^{\circ}C$, $45^{\circ}C$, and with the U.V irradiation at room temperature shaking every week. After six months of aging, the panel test of these aged fine spirits (Young Brandy) showed the following result. Young brandy of apples aged at $45^{\circ}C$ by dipping oak chips of Gal-chain was almost as the fine spirits which were aged at room temperature by dipping Limousin white oak chips from France. Young brandy of with U.V. irradiation at room temperature which were aged by dipping oak chips of Gal-chain was a little worse than that from the fine spirits aged at room temperature by dipping Limousin white oak chips from France. And so, Korean oak varieties are thought to be able to be used for aging every apple fine spirit which was here investigated.