• Applied Biological Chemistry
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• v.7
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• pp.15-19
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• 1966

In order to investigate the stability of the major component of malathion E.C. product, dimethyl S-(1, 2-dicarboxyethoxyethyl) dithiophosphate, toward the quality of glasswares as container, the amount of extractable inorganic components, change of pH and decomposition of the major component of the product were examined during the storage in brown-colored bottles of 100 ml. volume from 3 different companies in comparison with that in a Pyrex flask. 1. Malathion E.C. product was put in three containers A,B and C, and any changes occurring in storage were analyzed at three intervals of 60, 120 and 240 days. 2. It was shown that the amounts of Si, Mg, K, Ca, and Na extracted during these periods of storage differed markedly depending on the qualify of container. Container A revealed ten times higher extraction of Na and Ca than container B and C in a 8-month period. 3. Three commercial containers revealed the shift of pH from 6.5 to alkaline reaction in the storage whereas the Pyrex flask did not show any detectable change. In particular, the pH in container A changed to 9.2 in 60 days and 9.9 in 240 days. 4. The decomposition of malathion was the greatest in container A which showed the decomposition of 7.37% in 240 days. On the other hand, 0.5% was decomposed in the Pyrex flask. 5. The decemposition of malathion had a high correlation with the change of pH of water· in the same container, $r^2$ being 0.899. From the above results, it is concluded that about 10% of malathion E.C. product is decomposed in a year due to the alkaline metallic salts extracted from the container when it is stored in glassware bottles of lower quality.

• Korean Journal of Agricultural and Forest Meteorology
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• v.6 no.4
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• pp.250-255
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• 2004

This study was carried out to investigate the chemical properties of rainwater in the Suwon and Taean areas. Rainwater was collected during the farming seasons of 2002 and 2003. The number of samples collected in Suwon and Taean were 69 and 71, respectively. These were analyzed for chemical composition. The pH of samples collected in April was higher than those collected after June. The most common range of rainwater pH was 5.0-5.6 in Suwon and 4.5-5.0 in Taean during investigation periods. The neutralization capacity of rainwater acidity by $Ca^{2+}$ and N $H_4$$^{+}$ was decreased during the rainy season. The EC of rainwater was lower during the rainy season. Cation concentrations in rainwater were N $H_4$$^{+}$ > $H^{+}$ > $Ca^{2+}$ > $Mg^{2+}$ > $K^{+}$ in Suwon and $Ca^{2+}$ > N $H_4$$^{+}$ > $H^{+}$ > $K^{+}$ > $Mg^{2+}$ in Taean. In the case of anion, the order was sol > N $O_3$$^{[-10]}$ > C $I^{[-10]}$ in Suwon and S $O_4$$^{2-}$ > C $I^{[-10]}$ > N $O_3$$^{[-10]}$ in Taean. The mean values of sulfate in rainwater were 130 $\mu$eq $L^{-1}$ in Suwon and 117 $\mu$eq $L^{-1}$ in Taean. The ratio of non-sea salt sulfate to sulfate (nss-S $O_4$$^{2-}$ > S $O_4$$^{2-}$) was 89% and 88%. This implies that the major origin of sulfate in rainwater might be anthropogenic.ht be anthropogenic..

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.161-177
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• 2015

With the rapid evolution of technology, the size, number, and the type of databases has increased concomitantly, so data mining approaches face many challenging applications from databases. One such application is discovery of fraud patterns from agricultural product wholesale transaction instances. The agricultural product wholesale market in Korea is huge, and vast numbers of transactions have been made every day. The demand for agricultural products continues to grow, and the use of electronic auction systems raises the efficiency of operations of wholesale market. Certainly, the number of unusual transactions is also assumed to be increased in proportion to the trading amount, where an unusual transaction is often the first sign of fraud. However, it is very difficult to identify and detect these transactions and the corresponding fraud occurred in agricultural product wholesale market because the types of fraud are more intelligent than ever before. The fraud can be detected by verifying the overall transaction records manually, but it requires significant amount of human resources, and ultimately is not a practical approach. Frauds also can be revealed by victim's report or complaint. But there are usually no victims in the agricultural product wholesale frauds because they are committed by collusion of an auction company and an intermediary wholesaler. Nevertheless, it is required to monitor transaction records continuously and to make an effort to prevent any fraud, because the fraud not only disturbs the fair trade order of the market but also reduces the credibility of the market rapidly. Applying data mining to such an environment is very useful since it can discover unknown fraud patterns or features from a large volume of transaction data properly. The objective of this research is to empirically investigate the factors necessary to detect fraud transactions in an agricultural product wholesale market by developing a data mining based fraud detection model. One of major frauds is the phantom transaction, which is a colluding transaction by the seller(auction company or forwarder) and buyer(intermediary wholesaler) to commit the fraud transaction. They pretend to fulfill the transaction by recording false data in the online transaction processing system without actually selling products, and the seller receives money from the buyer. This leads to the overstatement of sales performance and illegal money transfers, which reduces the credibility of market. This paper reviews the environment of wholesale market such as types of transactions, roles of participants of the market, and various types and characteristics of frauds, and introduces the whole process of developing the phantom transaction detection model. The process consists of the following 4 modules: (1) Data cleaning and standardization (2) Statistical data analysis such as distribution and correlation analysis, (3) Construction of classification model using decision-tree induction approach, (4) Verification of the model in terms of hit ratio. We collected real data from 6 associations of agricultural producers in metropolitan markets. Final model with a decision-tree induction approach revealed that monthly average trading price of item offered by forwarders is a key variable in detecting the phantom transaction. The verification procedure also confirmed the suitability of the results. However, even though the performance of the results of this research is satisfactory, sensitive issues are still remained for improving classification accuracy and conciseness of rules. One such issue is the robustness of data mining model. Data mining is very much data-oriented, so data mining models tend to be very sensitive to changes of data or situations. Thus, it is evident that this non-robustness of data mining model requires continuous remodeling as data or situation changes. We hope that this paper suggest valuable guideline to organizations and companies that consider introducing or constructing a fraud detection model in the future.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.157-175
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• 2012

It is difficult to evaluate the performance of process innovation of e-procurement which has large scale and complex processes. The existing evaluation methods for measuring the effects of process innovation have been mainly done with statistically quantitative methods by analyzing operational data or with qualitative methods by conducting surveys and interviews. However, these methods have some limitations to evaluate the effects because the performance evaluation of e-procurement process innovation should consider the interactions among participants who are active either directly or indirectly through the processes. This study considers the e-procurement process as a complex system and develops a simulation model based on MAS(Multi-Agent System) to evaluate the effects of e-procurement process innovation. Multi-agent based simulation allows observing interaction patterns of objects in virtual world through relationship among objects and their behavioral mechanism. Agent-based simulation is suitable especially for complex business problems. In this study, we used Netlogo Version 4.1.3 as a MAS simulation tool which was developed in Northwestern University. To do this, we developed a interaction model of agents in MAS environment. We defined process agents and task agents, and assigned their behavioral characteristics. The developed simulation model was applied to G2B system (KONEPS: Korea ON-line E-Procurement System) of Public Procurement Service (PPS) in Korea and used to evaluate the innovation effects of the G2B system. KONEPS is a successfully established e-procurement system started in the year 2002. KONEPS is a representative e-Procurement system which integrates characteristics of e-commerce into government for business procurement activities. KONEPS deserves the international recognition considering the annual transaction volume of 56 billion dollars, daily exchanges of electronic documents, users consisted of 121,000 suppliers and 37,000 public organizations, and the 4.5 billion dollars of cost saving. For the simulation, we analyzed the e-procurement of process of KONEPS into eight sub processes such as 'process 1: search products and acquisition of proposal', 'process 2 : review the methods of contracts and item features', 'process 3 : a notice of bid', 'process 4 : registration and confirmation of qualification', 'process 5 : bidding', 'process 6 : a screening test', 'process 7 : contracts', and 'process 8 : invoice and payment'. For the parameter settings of the agents behavior, we collected some data from the transactional database of PPS and some information by conducting a survey. The used data for the simulation are 'participants (government organizations, local government organizations and public institutions)', 'the number of bidding per year', 'the number of total contracts', 'the number of shopping mall transactions', 'the rate of contracts between bidding and shopping mall', 'the successful bidding ratio', and the estimated time for each process. The comparison was done for the difference of time consumption between 'before the innovation (As-was)' and 'after the innovation (As-is).' The results showed that there were productivity improvements in every eight sub processes. The decrease ratio of 'average number of task processing' was 92.7% and the decrease ratio of 'average time of task processing' was 95.4% in entire processes when we use G2B system comparing to the conventional method. Also, this study found that the process innovation effect will be enhanced if the task process related to the 'contract' can be improved. This study shows the usability and possibility of using MAS in process innovation evaluation and its modeling.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.163-179
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• 2010

Recentlythe concept of fast fashion is drawing attention as customer needs are diversified and supply lead time is getting shorter in fashion industry. It is emphasized as one of the critical success factors in the fashion industry how quickly and efficiently to satisfy the customer needs as the competition has intensified. Because the fast fashion is inherently susceptible to trend, it is very important for fashion retailers to make quick decisions regarding items to launch, quantity based on demand prediction, and the time to respond. Also the planning decisions must be executed through the business processes of procurement, production, and logistics in real time. In order to adapt to this trend, the fashion industry urgently needs supports from intelligent quick response(QR) system. However, the traditional functions of QR systems have not been able to completely satisfy such demands of the fast fashion industry. This paper proposes an intelligent quick response system for the fast fashion(IQRS-FF). Presented are models for QR process, QR principles and execution, and QR quantity and timing computation. IQRS-FF models support the decision makers by providing useful information with automated and rule-based algorithms. If the predefined conditions of a rule are satisfied, the actions defined in the rule are automatically taken or informed to the decision makers. In IQRS-FF, QRdecisions are made in two stages: pre-season and in-season. In pre-season, firstly master demand prediction is performed based on the macro level analysis such as local and global economy, fashion trends and competitors. The prediction proceeds to the master production and procurement planning. Checking availability and delivery of materials for production, decision makers must make reservations or request procurements. For the outsourcing materials, they must check the availability and capacity of partners. By the master plans, the performance of the QR during the in-season is greatly enhanced and the decision to select the QR items is made fully considering the availability of materials in warehouse as well as partners' capacity. During in-season, the decision makers must find the right time to QR as the actual sales occur in stores. Then they are to decide items to QRbased not only on the qualitative criteria such as opinions from sales persons but also on the quantitative criteria such as sales volume, the recent sales trend, inventory level, the remaining period, the forecast for the remaining period, and competitors' performance. To calculate QR quantity in IQRS-FF, two calculation methods are designed: QR Index based calculation and attribute similarity based calculation using demographic cluster. In the early period of a new season, the attribute similarity based QR amount calculation is better used because there are not enough historical sales data. By analyzing sales trends of the categories or items that have similar attributes, QR quantity can be computed. On the other hand, in case of having enough information to analyze the sales trends or forecasting, the QR Index based calculation method can be used. Having defined the models for decision making for QR, we design KPIs(Key Performance Indicators) to test the reliability of the models in critical decision makings: the difference of sales volumebetween QR items and non-QR items; the accuracy rate of QR the lead-time spent on QR decision-making. To verify the effectiveness and practicality of the proposed models, a case study has been performed for a representative fashion company which recently developed and launched the IQRS-FF. The case study shows that the average sales rateof QR items increased by 15%, the differences in sales rate between QR items and non-QR items increased by 10%, the QR accuracy was 70%, the lead time for QR dramatically decreased from 120 hours to 8 hours.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.9
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• pp.4602-4609
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• 2013

In summer and winter, the difference between the temperature during the day and that during the night is high, which leads to various problems during concrete placement, such as cracks and defects in the concrete as well as low durability and strength. Although nuclear power plant concrete is widely used for placement in all seasons, particular attention must be paid to its quality during the summer. Therefore, we evaluated the effects of a cooling method for mixing water, which is a commonly used hot weather precooling method, and the use of a retarder, on the characteristics of Nuclear Power Plant concrete. In the cooling method for mixing water, cold water at 5 was used, with 50% of the water content consisting of ice flakes. The effects of using a retarder were evaluated by reviewing the characteristics of the cement at the unset stage and after hardening. To evaluate the characteristics of the unset cement, we measured the slump, air volumes, setting times, and pressure strengths after hardening. Furthermore, we measured the heat of hydration at different temperatures; the loss of heat was minimized using insulation. Both the slump time and the complete ageing time of the air volume were found to be 120 min at $20^{\circ}C$ and 40 min at $40^{\circ}C$. In the case when the cooling method for mixing water was used and in the case when a retarder was used, the initial and final sets by penetration resistance were delayed, and the delay decreased with increasing air temperature. For the heat of hydration, the cooling method for mixing water not only lowered the maximum temperature but also delayed its attainment. However, the use of a retarder had no effect on the maximum temperature. Moreover, in the early ages (e.g., 3 and 7 days), the pressure strength of the concrete was lower than that of plain cement. However, the strength of 28-day concrete met the standard construction specifications.

• Restorative Dentistry and Endodontics
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• v.34 no.5
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• pp.450-459
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• 2009

The aim of this study was to measure the initial dynamic modulus changes of light cured composites using a custom made rheometer. The custom made rheometer consisted of 3 parts: (1) a measurement unit of parallel plates made of glass rods, (2) an oscillating shear strain generator with a DC motor and a crank mechanism, (3) a stress measurement device using an electromagnetic torque sensor. This instrument could measure a maximum torque of 2Ncm, and the switch of the light-curing unit was synchronized with the rheometer. Six commercial composite resins [Z-100 (Z1), Z-250 (Z2), Z-350 (Z3), DenFil (DF), Tetric Ceram (TC), and Clearfil AP-X (CF)] were investigated. A dynamic oscillating shear test was undertaken with the rheometer. A certain volume ($14.2\;mm^3$) of composite was loaded between the parallel plates, which were made of glass rods (3 mm in diameter). An oscillating shear strain with a frequency of 6 Hz and amplitude of 0.00579 rad was applied to the specimen and the resultant stress was measured. Data acquisition started simultaneously with light curing, and the changes in visco-elasticity of composites were recorded for 10 seconds. The measurements were repeated 5 times for each composite at $25{\pm}0.5^{\circ}C$. Complex shear modulus G*, storage shear modulus G', loss shear modulus G" were calculated from the measured strain-stress curves. Time to reach the complex modulus G* of 10 MPa was determined. The G* and time to reach the G* of 10 MPa of composites were analyzed with One-way ANOVA and Tukey's test ($\alpha$ = 0.05). The results were as follows. 1. The custom made rheometer in this study reliably measured the initial visco-elastic modulus changes of composites during 10 seconds of light curing. 2. In all composites, the development of complex shear modulus G* had a latent period for $1{\sim}2$ seconds immediately after the start of light curing, and then increased rapidly during 10 seconds. 3. In all composites, the storage shear modulus G" increased steeper than the loss shear modulus G" during 10 seconds of light curing. 4. The complex shear modulus of Z1 was the highest, followed by CF, Z2, Z3, TC and DF the lowest. 5. Z1 was the fastest and DF was the slowest in the time to reach the complex shear modulus of 10 MPa.

• International Journal of Highway Engineering
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• v.11 no.4
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• pp.69-78
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• 2009

Korea has achieved significant economic growth with building the Gyeongbu Expressway. As the number of new road construction projects has decreased, it becomes more important to maintain optimal status of the current road networks. One of the best ways to accomplish it is weight enforcement as active control measure of traffic load. This study is to develop High-speed Weigh-in-motion System in order to enhance efficiency of weight enforcement, and to analyze patterns of overloaded trucks on highways through the system. Furthermore, it is to review possibilities of developing overweight control system with application of the HS-WIM system. The HS-WIM system developed by this study consists of two sets of an axle load sensor, a loop sensor and a wandering sensor on each lane. A wandering sensor detects whether a travelling vehicle is off the lane or not with the function of checking the location of tire imprint. The sensor of the WIM system has better function of classifying types of vehicles than other existing systems by detecting wheel distance and tire type such as single or dual tire. As a result, its measurement errors regarding 12 types of vehicle classification are very low, which is an advantage of the sensor. The verification tests of the system under all conditions showed that the mean measurement errors of axle weight and gross axle weight were within 15 percent and 7 percent respectively. According to the WIM rate standard of the COST-323, the WIM system of this study is ranked at B(10). It means the system is appropriate for the purpose of design, maintenance and valuation of road infrastructure. The WIM system in testing a 5-axle cargo truck, the most frequently overloaded vehicle among 12 types of vehicles, is ranked at A(5) which means the system is available to control overloaded vehicles. In this case, the measurement errors of axle load and gross axle load were within 8 percent and 5 percent respectively. Weight analysis of all types of vehicles on highways showed that the most frequently overloaded vehicles were type 5, 6, 7 and 12 among 12 vehicle types. As a result, it is necessary to use more effective overweight enforcement system for vehicles which are seriously overloaded due to their lift axles. Traffic volume data depending upon vehicle types is basic information for road design and construction, maintenance, analysis of traffic flow, road policies as well as research.

• Journal of radiological science and technology
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• v.26 no.4
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• pp.21-26
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• 2003

To evaluate the newly designed gastroduodenal coil catheter:in-vitro test. The coil catheter that we made in our laboratory was 150 cm. The coil that is made of stainless steel wire was composed 1.3 mm inner diameter and this coil spring was covered with heat-shrinkable polyethylene tube. To measure the length under fluorocopy, 8 radiopaque marks were attached at 5 cm, 10 cm, 11 cm, 12 cm, 13 cm, 14 cm, 15 cm, 20 cm apart from distal end of the catheter and 6, 2, 1 pores were made at 7 cm, 13 cm, 19 cm apart from the distal end. Radio-opacity and the amount of injected contrast was investigated in formerly used 5 Fr. vessel catheter, which is possible in measuring length, and newly designed coil catheter. Film density was tested for radio-opacity with autodensitometer. For measuring the volume of injected salin, the catheter was located in the acryl box(26 cm, 3 cm, 16 cm) that divided into 4 chambers. After injection 50 cc of contrast with autoinjector, the contrast's quantity in each chamber was measured with and without over the guide wire. Radio-opacity was 0.51 in 5 Fr. vessel catheter, 0.31 in newly made catheter. The amount of injected contrast was measured. In case of 5 Fr. vessel catheter, the amount was 99.5% from the distal part, there was no difference between with and without the guide wire. Otherwise, using a coil catheter, the pacentage the ejected saline was 1.17%, 18.8%, 41.8%, 38.2% from the distal part with the guide wire, 19.5%, 32.6%, 27.7%, 20.3% without the guide wire. Compare with formerly established catheter, this new coil catheter is easy to measure the length thanks to easy confirming under fluoroscopy and excellent in injecting contrast. Therefore, newly designed gastrointestinal catheter seems to be useful in gastrointestinal intervention procedure.