• KOREAN JOURNAL OF CROP SCIENCE
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• v.64 no.4
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• pp.299-310
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• 2019

The eating quality of rice is one of the main concerns of rice breeding programs in many countries, especially in japonica rice cultivation areas. To select new resources with high eating quality from Korean native japonica rice, we evaluated a total of 76 varieties, including 47 native rice resources (26 landraces + 21 weedy rice) of Korea. In this study, all eating quality traits varied widely among the native resources, and some of the native resources revealed a high evaluation score in the palatability, expected eating quality, and physicochemical traits among the tested whole-plant materials. From the results, we selected two landraces (Sangdo and Waebyeo) and three weedy rice varieties (Hoengseongaengmi3, Namjejuaengmi6, and Wandoaengmi6) as promising resources for improvement of rice eating quality. Specifically, Wandoaengmi6 presented potential as a key breeding material for improving the eating quality of Korean rice cultivars, having the best evaluation results in palatability score (PS 0.83) from the sensory test and glossiness value (GV 81.8) from the Toyo taste meter of cooked rice. Given the urgent need to overcome the constraint of the narrow genetic background of Korean japonica rice, the results could be a practical solution for exploring new opportunities for improving rice eating quality through the expansion of genetic resources.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.64 no.4
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• pp.414-421
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• 2019

Pod shattering during the maturing stage causes a serious yield loss in soybean. It is the main limiting factor of soybean cultivation and mechanization. It is important to develop varieties suitable for mechanical harvesting and to develop energy-efficient agricultural machinery to save labor and costs. 'Daewonkong,' developed by the National Institute of Crop Science (NICS) in 1997, is an elite cultivar that occupies more than 80% of the soybean cultivation area in Korea because of its strong tolerance to pod shattering. The objectives of this study were to investigate the variation in pod shattering degree in a RIL population developed from a 'Daewonkong' parent and to select promising lines with pod shattering tolerance. 'Daewonkong' demonstrated a high level of tolerance to pod shattering compared to the 'Tawonkong' and 'Saeolkong' varieties, with no shattered pods after 72 hours of drying. Screening of pod shattering showed a clear distinction between the tolerant and susceptible varieties. Also, the distribution of shattering pod ratio in the two populations showed a similar pattern for three years. The promising lines with pod shattering tolerance included 27 lines in the 'Daewonkong'×'Tawonkong' population and 21 lines in the 'Daewonkong'×'Saeolkong' population. The promising lines are expected to be widely used as breeding parents for creating soybean cultivars with pod shattering tolerance.

• Journal of Korean Society of Environmental Engineers
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• v.32 no.10
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• pp.916-927
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• 2010

In general water treatment process, the disinfection process by chlorine is used to prevent water borne disease and microbial regrowth in water distribution system. Because chlorines were reacted with organic matter, carcinogens such as disinfection by-products (DBPs) were produced in drinking water. Therefore, a suitable injection of chlorine is need to decrease DBPs. Rechlorination in water pipelines or reservoirs are recently increased to secure the residual chlorine in the end of water pipelines. EPANET 2.0 developed by the U.S. Environmental Protection Agency (EPA) is used to compute the optimal chlorine injection in water treatment plant and to predict the dosage of rechlorination into water distribution system. The bulk decay constant ($k_{bulk}$) was drawn by bottle test and the wall decay constant ($k_{wall}$) was derived from using systermatic analysis method for water quality modeling in target region. In order to predict water quality based on hydraulic analysis model, residual chlorine concentration was forecasted in water distribution system. The formation of DBPs such as trihalomethanes (THMs) was verified with chlorine dosage in lab-scale test. The bulk decay constant ($k_{bulk}$) was rapidly decreased with increasing temperature in the early time. In the case of 25 degrees celsius, the bulk decay constant ($k_{bulk}$) decreased over half after 25 hours later. In this study, there were able to calculate about optimal rechlorine dosage and select on profitable sites in the network map.

• Analytical Science and Technology
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• v.11 no.1
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• pp.62-72
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• 1998

This study was performed to characterize the trace organic pollutants in the industrial wastewater and to establish the database of the trace organic pollutants. The four manufacturing industries, which are refined petroleum, industrial chemicals, rubber & plastics and fabricated metals, were surveyed. The wastewater and discharging water of these 30 factories are analyzed to characterize the trace organic pollutants. In industrial chemicals, the kinds of products and organic pollutants are very various. Therefore to select the characteristic organic pollutants in this categories are also very difficult. In industrial chemicals, the gas chromatograpic peak patterns of wastewater are represented the various type according to their products, therefore the typical patterns of the characteristic organic pollutants could not be obtained because the kinds of manufactured goods and organic pollutants are very various. In refined petroleum, the effluent is discharged in the distillatory process of atmosphere pressure and contained the saturated hydrocarbons, phenol compounds, benzene compounds and naphtalene compounds. The saturated hydrocarbons peaks from $C_{15}$ to $C_{35}$ are represented the typical oil patterns by the uniform intervals therefore the peak can be easily distinguished. In rubber & plastics, the wastewater is discharged in the washing process which contains the additives. The problem of wastewater is not serious because the manufacturing process is not produced the effluent or the produced cooling water is recycled in that process.

• The Korean Journal of Pesticide Science
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• v.4 no.3
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• pp.19-26
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• 2000

In order to select potent bioactive isolates, 70 Fusarium isolates obtained from soil and 21 plant species were screened by antifungal, insecticidal, herbicidal, and duckweed bioassays after culturing in potato dextrose broth and rice solid media. Eight (11.4%) of the 70 liquid broth cultures showed disease-controlling activities more than 80% against at least one of the 6 plant diseases tested. Fusarium sp. FO-68 isolate exhibited the most potent antifungal activity; it controlled rice blast, wheat leaf rust, and barley powdery mildew with control values more than 95%. Out of 70 solid cultures, 21 (30.0%) controlled at least one plant disease more than 80% and F. equiseti FO-68 isolate showed disease-controlling activities more than 95% against 3 plant diseases such as rice blast, tomato late blight, and wheat leaf rust. As for tile insecticidal activities, 2 liquid and 1 solid cultures showed potent insecticidal activities against pest insects more than 80%, Liquid cultures of F. oxysporum FO-61 and Fusarium sp. FO-80 isolates exhibited insecticidal activities more than 80% against green peach aphid and diamondback moth, respectively. The solid culture of Fusarium sp. FO-510 isolate had 80% insecticidal activity against green peach aphid. However, none of liquid and solid cultures of the 70 Fusarium isolates showed potent herbicidal activities against 10 upland weeds. As the results of duckweed assay, 3 liquid cultures showed 70% growth inhibitory activity at concentrations less than 1.25% of culture supernatants and 9 solid cultures had a potent inhibitory activity against duckweed growth. On the other hand, there was a significant correlation between antifungal activities and herbicidal activities against duckweed of both liquid and solid cultures of tile 70 Fusarium isolates.

• The Korean Journal of Pesticide Science
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• v.4 no.3
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• pp.40-46
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• 2000

To select the effective fungicides for the control of leaf spot disease of jujube tree (Zizyphus jujuba) caused by Phoma sp., inhibitory effects of 26 fungicides for mycelial growth were investigated at $250{\mu}g\;a.i./m{\ell}$. In the test, eight fungicides were selected and minimum inhibitory concentration (MIC) for mycelial growth and an inhibitory effect for spore germination were investigated. Among the fungicides, myclobutanil, hexaconazole, and triflumizole were excluded in control effect tests because of their relatively high MICs. MICs were ranged $10-50{\mu}g\;a.i./m{\ell}$ for benomyl, carbendazim + kasugamycin (CK), and thiophanate-methyl. triflumizole (TT), and $50-250{\mu}g\;a.i./m{\ell}$ for iprodione + propineb (IT) and iminoctadine-triacelate (IT). However, benomyl and IP showed very low inhibitory effect on conidial germination. When the fungicides were sprayed on the seedlings before the leaves were inoculated with conidial suspension of Phoma sp., the protective values of CK and TT were around 70% at 1,000 ppm and around 90% at 2,000 ppm. The protective values were around 70% at 2,000 ppm (benomyl), 4,000 ppm (IP), and 8,000 ppm (IT). When the fungicides were sprayed after inoculation, benomyl showed the highest curative values of over 90% at 1,000 ppm and the values of CK and TT ranged $70{\sim}80%$ at 1,000 ppm. However, IP and IT had little or no effect on therapy of the disease. IT caused necrotic phytotoxicity on the leaves of jujube seedlings. As results, the best fungicides for the protection of jujube trees from leaf spot disease were CK (2,000 ppm) and TT (2,000 ppm) and for the remedy of the tree, benomyl (1,000 ppm) was the best. Therefore, alternate application of benomyl and CK or TT will be effective in the disease control.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.65-82
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• 2015

Recently, there has been a surge of interest in finding core issues and analyzing emerging trends for the future. This represents efforts to devise national strategies and policies based on the selection of promising areas that can create economic and social added value. The existing studies, including those dedicated to the discovery of future promising fields, have mostly been dependent on qualitative research methods such as literature review and expert judgement. Deriving results from large amounts of information under this approach is both costly and time consuming. Efforts have been made to make up for the weaknesses of the conventional qualitative analysis approach designed to select key promising areas through discovery of future core issues and emerging trend analysis in various areas of academic research. There needs to be a paradigm shift in toward implementing qualitative research methods along with quantitative research methods like text mining in a mutually complementary manner. The change is to ensure objective and practical emerging trend analysis results based on large amounts of data. However, even such studies have had shortcoming related to their dependence on simple keywords for analysis, which makes it difficult to derive meaning from data. Besides, no study has been carried out so far to develop core issues and analyze emerging trends in special domains like the aviation industry. The change used to implement recent studies is being witnessed in various areas such as the steel industry, the information and communications technology industry, the construction industry in architectural engineering and so on. This study focused on retrieving aviation-related core issues and emerging trends from overall research papers pertaining to aviation through text mining, which is one of the big data analysis techniques. In this manner, the promising future areas for the air transport industry are selected based on objective data from aviation-related research papers. In order to compensate for the difficulties in grasping the meaning of single words in emerging trend analysis at keyword levels, this study will adopt topic analysis, which is a technique used to find out general themes latent in text document sets. The analysis will lead to the extraction of topics, which represent keyword sets, thereby discovering core issues and conducting emerging trend analysis. Based on the issues, it identified aviation-related research trends and selected the promising areas for the future. Research on core issue retrieval and emerging trend analysis for the aviation industry based on big data analysis is still in its incipient stages. So, the analysis targets for this study are restricted to data from aviation-related research papers. However, it has significance in that it prepared a quantitative analysis model for continuously monitoring the derived core issues and presenting directions regarding the areas with good prospects for the future. In the future, the scope is slated to expand to cover relevant domestic or international news articles and bidding information as well, thus increasing the reliability of analysis results. On the basis of the topic analysis results, core issues for the aviation industry will be determined. Then, emerging trend analysis for the issues will be implemented by year in order to identify the changes they undergo in time series. Through these procedures, this study aims to prepare a system for developing key promising areas for the future aviation industry as well as for ensuring rapid response. Additionally, the promising areas selected based on the aforementioned results and the analysis of pertinent policy research reports will be compared with the areas in which the actual government investments are made. The results from this comparative analysis are expected to make useful reference materials for future policy development and budget establishment.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.79-99
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• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.47-60
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• 2012

Video data comes in the form of the unstructured and the complex structure. As the importance of efficient management and retrieval for video data increases, studies on the video parsing based on the visual features contained in the video contents are researched to reconstruct video data as the meaningful structure. The early studies on video parsing are focused on splitting video data into shots, but detecting the shot boundary defined with the physical boundary does not cosider the semantic association of video data. Recently, studies on structuralizing video shots having the semantic association to the video scene defined with the semantic boundary by utilizing clustering methods are actively progressed. Previous studies on detecting the video scene try to detect video scenes by utilizing clustering algorithms based on the similarity measure between video shots mainly depended on color features. However, the correct identification of a video shot or scene and the detection of the gradual transitions such as dissolve, fade and wipe are difficult because color features of video data contain a noise and are abruptly changed due to the intervention of an unexpected object. In this paper, to solve these problems, we propose the Scene Detector by using Color histogram, corner Edge and Object color histogram (SDCEO) that clusters similar shots organizing same event based on visual features including the color histogram, the corner edge and the object color histogram to detect video scenes. The SDCEO is worthy of notice in a sense that it uses the edge feature with the color feature, and as a result, it effectively detects the gradual transitions as well as the abrupt transitions. The SDCEO consists of the Shot Bound Identifier and the Video Scene Detector. The Shot Bound Identifier is comprised of the Color Histogram Analysis step and the Corner Edge Analysis step. In the Color Histogram Analysis step, SDCEO uses the color histogram feature to organizing shot boundaries. The color histogram, recording the percentage of each quantized color among all pixels in a frame, are chosen for their good performance, as also reported in other work of content-based image and video analysis. To organize shot boundaries, SDCEO joins associated sequential frames into shot boundaries by measuring the similarity of the color histogram between frames. In the Corner Edge Analysis step, SDCEO identifies the final shot boundaries by using the corner edge feature. SDCEO detect associated shot boundaries comparing the corner edge feature between the last frame of previous shot boundary and the first frame of next shot boundary. In the Key-frame Extraction step, SDCEO compares each frame with all frames and measures the similarity by using histogram euclidean distance, and then select the frame the most similar with all frames contained in same shot boundary as the key-frame. Video Scene Detector clusters associated shots organizing same event by utilizing the hierarchical agglomerative clustering method based on the visual features including the color histogram and the object color histogram. After detecting video scenes, SDCEO organizes final video scene by repetitive clustering until the simiarity distance between shot boundaries less than the threshold h. In this paper, we construct the prototype of SDCEO and experiments are carried out with the baseline data that are manually constructed, and the experimental results that the precision of shot boundary detection is 93.3% and the precision of video scene detection is 83.3% are satisfactory.

• 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.