• Journal of Korean Society of Environmental Engineers
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• v.32 no.1
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• pp.33-46
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• 2010

We investigated and estimated at the characteristics of decomposition and by-products of N-Nitrosodimethylamine (NDMA) using a design of experiment (DOE) based on the Box-Behken design in an UV process, and also the main factors (variables) with UV intensity($X_2$) (range: $1.5{\sim}4.5\;mW/cm^2$), NDMA concentration ($X_2$) (range: 100~300 uM) and pH ($X_2$) (rang: 3~9) which consisted of 3 levels in each factor and 4 responses ($Y_1$ (% of NDMA removal), $Y_2$ (dimethylamine (DMA) reformation (uM)), $Y_3$ (dimethylformamide (DMF) reformation (uM), $Y_4$ ($NO_2$-N reformation (uM)) were set up to estimate the prediction model and the optimization conditions. The results of prediction model and optimization point using the canonical analysis in order to obtain the optimal operation conditions were $Y_1$ [% of NDMA removal] = $117+21X_1-0.3X_2-17.2X_3+{2.43X_1}^2+{0.001X_2}^2+{3.2X_3}^2-0.08X_1X_2-1.6X_1X_3-0.05X_2X_3$ ($R^2$= 96%, Adjusted $R^2$ = 88%) and 99.3% ($X_1:\;4.5\;mW/cm^2$, $X_2:\;190\;uM$, $X_3:\;3.2$), $Y_2$ [DMA conc] = $-101+18.5X_1+0.4X_2+21X_3-{3.3X_1}^2-{0.01X_2}^2-{1.5X_3}^2-0.01X_1X_2+0.07X_1X_3-0.01X_2X_3$ ($R^2$= 99.4%, 수정 $R^2$ = 95.7%) and 35.2 uM ($X_1$: 3 $mW/cm^2$, $X_2$: 220 uM, $X_3$: 6.3), $Y_3$ [DMF conc] = $-6.2+0.2X_1+0.02X_2+2X_3-0.26X_1^2-0.01X_2^2-0.2X_3^2-0.004X_1X_2+0.1X_1X_3-0.02X_2X_3$ ($R^2$= 98%, Adjusted $R^2$ = 94.4%) and 3.7 uM ($X_1:\;4.5\;$mW/cm^2$, $X_2:\;290\;uM$, $X_3:\;6.2$) and $Y_4$ [$NO_2$-N conc] = $-25+12.2X_1+0.15X_2+7.8X_3+{1.1X_1}^2+{0.001X_2}^2-{0.34X_3}^2+0.01X_1X_2+0.08X_1X_3-3.4X_2X_3$ ($R^2$= 98.5%, Adjusted $R^2$ = 95.7%) and 74.5 uM ($X_1:\;4.5\;mW/cm^2$, $X_2:\;220\;uM$, $X_3:\;3.1$). This study has demonstrated that the response surface methodology and the Box-Behnken statistical experiment design can provide statistically reliable results for decomposition and by-products of NDMA by the UV photolysis and also for determination of optimum conditions. Predictions obtained from the response functions were in good agreement with the experimental results indicating the reliability of the methodology used.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.79-96
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• 2012

As the pace of competition dramatically accelerates and the complexity of change grows, a variety of research have been conducted to improve firms' short-term performance and to enhance firms' long-term survival. In particular, researchers and practitioners have paid their attention to identify promising technologies that lead competitive advantage to a firm. Discovery of promising technology depends on how a firm evaluates the value of technologies, thus many evaluating methods have been proposed. Experts' opinion based approaches have been widely accepted to predict the value of technologies. Whereas this approach provides in-depth analysis and ensures validity of analysis results, it is usually cost-and time-ineffective and is limited to qualitative evaluation. Considerable studies attempt to forecast the value of technology by using patent information to overcome the limitation of experts' opinion based approach. Patent based technology evaluation has served as a valuable assessment approach of the technological forecasting because it contains a full and practical description of technology with uniform structure. Furthermore, it provides information that is not divulged in any other sources. Although patent information based approach has contributed to our understanding of prediction of promising technologies, it has some limitations because prediction has been made based on the past patent information, and the interpretations of patent analyses are not consistent. In order to fill this gap, this study proposes a technology forecasting methodology by integrating patent information approach and artificial intelligence method. The methodology consists of three modules : evaluation of technologies promising, implementation of technologies value prediction model, and recommendation of promising technologies. In the first module, technologies promising is evaluated from three different and complementary dimensions; impact, fusion, and diffusion perspectives. The impact of technologies refers to their influence on future technologies development and improvement, and is also clearly associated with their monetary value. The fusion of technologies denotes the extent to which a technology fuses different technologies, and represents the breadth of search underlying the technology. The fusion of technologies can be calculated based on technology or patent, thus this study measures two types of fusion index; fusion index per technology and fusion index per patent. Finally, the diffusion of technologies denotes their degree of applicability across scientific and technological fields. In the same vein, diffusion index per technology and diffusion index per patent are considered respectively. In the second module, technologies value prediction model is implemented using artificial intelligence method. This studies use the values of five indexes (i.e., impact index, fusion index per technology, fusion index per patent, diffusion index per technology and diffusion index per patent) at different time (e.g., t-n, t-n-1, t-n-2, ${\cdots}$) as input variables. The out variables are values of five indexes at time t, which is used for learning. The learning method adopted in this study is backpropagation algorithm. In the third module, this study recommends final promising technologies based on analytic hierarchy process. AHP provides relative importance of each index, leading to final promising index for technology. Applicability of the proposed methodology is tested by using U.S. patents in international patent class G06F (i.e., electronic digital data processing) from 2000 to 2008. The results show that mean absolute error value for prediction produced by the proposed methodology is lower than the value produced by multiple regression analysis in cases of fusion indexes. However, mean absolute error value of the proposed methodology is slightly higher than the value of multiple regression analysis. These unexpected results may be explained, in part, by small number of patents. Since this study only uses patent data in class G06F, number of sample patent data is relatively small, leading to incomplete learning to satisfy complex artificial intelligence structure. In addition, fusion index per technology and impact index are found to be important criteria to predict promising technology. This study attempts to extend the existing knowledge by proposing a new methodology for prediction technology value by integrating patent information analysis and artificial intelligence network. It helps managers who want to technology develop planning and policy maker who want to implement technology policy by providing quantitative prediction methodology. In addition, this study could help other researchers by proving a deeper understanding of the complex technological forecasting field.

• Journal of Intelligence and Information Systems
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• v.20 no.3
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• pp.45-58
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• 2014

The Korean Culture Contents spread out to Worldwide, because the Korean wave is sweeping in the world. The contents stand in the middle of the Korean wave that we are used it. Each country is ongoing to keep their Culture industry improve the national brand and High added value. Performing contents is important factor of arousal in the enterprise industry. To improve high arousal confidence of product and positive attitude by populace is one of important factor by advertiser. Culture contents is the same situation. If culture contents have trusted by everyone, they will give information their around to spread word-of-mouth. So, many researcher study to measure for person's arousal analysis by statistical survey, physiological response, body movement and facial expression. First, Statistical survey has a problem that it is not possible to measure each person's arousal real time and we cannot get good survey result after they watched contents. Second, physiological response should be checked with surround because experimenter sets sensors up their chair or space by each of them. Additionally it is difficult to handle provided amount of information with real time from their sensor. Third, body movement is easy to get their movement from camera but it difficult to set up experimental condition, to measure their body language and to get the meaning. Lastly, many researcher study facial expression. They measures facial expression, eye tracking and face posed. Most of previous studies about arousal and interest are mostly limited to reaction of just one person and they have problems with application multi audiences. They have a particular method, for example they need room light surround, but set limits only one person and special environment condition in the laboratory. Also, we need to measure arousal in the contents, but is difficult to define also it is not easy to collect reaction by audiences immediately. Many audience in the theater watch performance. We suggest the system to measure multi-audience's reaction with real-time during performance. We use difference image analysis method for multi-audience but it weaks a dark field. To overcome dark environment during recoding IR camera can get the photo from dark area. In addition we present Multi-Audience Engagement Index (MAEI) to calculate algorithm which sources from sound, audience' movement and eye tracking value. Algorithm calculates audience arousal from the mobile survey, sound value, audience' reaction and audience eye's tracking. It improves accuracy of Multi-Audience Engagement Index, we compare Multi-Audience Engagement Index with mobile survey. And then it send the result to reporting system and proposal an interested persons. Mobile surveys are easy, fast, and visitors' discomfort can be minimized. Also additional information can be provided mobile advantage. Mobile application to communicate with the database, real-time information on visitors' attitudes focused on the content stored. Database can provide different survey every time based on provided information. The example shown in the survey are as follows: Impressive scene, Satisfied, Touched, Interested, Didn't pay attention and so on. The suggested system is combine as 3 parts. The system consist of three parts, External Device, Server and Internal Device. External Device can record multi-Audience in the dark field with IR camera and sound signal. Also we use survey with mobile application and send the data to ERD Server DB. The Server part's contain contents' data, such as each scene's weights value, group audience weights index, camera control program, algorithm and calculate Multi-Audience Engagement Index. Internal Device presents Multi-Audience Engagement Index with Web UI, print and display field monitor. Our system is test-operated by the Mogencelab in the DMC display exhibition hall which is located in the Sangam Dong, Mapo Gu, Seoul. We have still gotten from visitor daily. If we find this system audience arousal factor with this will be very useful to create contents.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.1-20
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• 2013

Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.

• The Korean Journal of Nuclear Medicine Technology
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• v.25 no.1
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• pp.34-40
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• 2021

Purpose If a new test is introduced or reagents are changed in the laboratory of a medical institution, the characteristics of the test should be analyzed according to the procedure and the assessment of reagents should be made. However, several necessary conditions must be met to perform all required comparative evaluations, first enough samples should be prepared for each test, and secondly, various reagents applicable to the comparative evaluations must be supplied. Even if enough comparative evaluations have been done, there is a limit to the fact that the data variation for the new reagent represents the overall patient data variation, The fact puts a burden on the laboratory to the change the reagent. Due to these various difficulties, reagent changes in the laboratory are limited. In order to introduce a competitive bid, the institute conducted a full investigation of Radioimmunoassay(RIA) reagents for each test and established the range of reagents available in the laboratory through comparative evaluations. We wanted to share this process. Materials and Methods There are 20 items of tests conducted in our laboratory except for consignment tests. For each test, RIA reagents that can be used were fully investigated with the reference to external quality control report. and the manuals for each reagent were obtained. Each reagent was checked for the manual to check the test method, Incubation time, sample volume needed for the test. After that, the primary selection was made according to whether it was available in this laboratory. The primary selected reagents were supplied with 2kits based on 100tests, and the data correlation test, sensitivity measurement, recovery rate measurement, and dilution test were conducted. The secondary selection was performed according to the results of the comparative evaluation. The reagents that passed the primary and secondary selections were submitted to the competitive bidding list. In the case of reagent is designated as a singular, we submitted a explanatory statement with the data obtained during the primary and secondary selection processes. Results Excluded from the primary selection was the case where TAT was expected to be delayed at the moment, and it was impossible to apply to our equipment due to the large volume of reagents used during the test. In the primary selection, there were five items which only one reagent was available.(squamous cell carcinoma Ag(SCC Ag), β-human chorionic gonadotropin(β-HCG), vitamin B12, folate, free testosterone), two reagents were available(CA19-9, CA125, CA72-4, ferritin, thyroglobulin antibody(TG Ab), microsomal antibody(Mic Ab), thyroid stimulating hormone-receptor-antibody(TSH-R-Ab), calcitonin), three reagents were available (triiodothyronine(T3), Tree T3, Free T4, TSH, intact parathyroid hormone(intact PTH)) and four reagents were available are carcinoembryonic antigen(CEA), TG. In the secondary selection, there were eight items which only one reagent was available.(ferritin, TG, CA19-9, SCC, β-HCG, vitaminB12, folate, free testosterone), two reagents were available(TG Ab, Mic Ab, TSH-R-Ab, CA125, CA72-4, intact PTH, calcitonin), three reagents were available(T3, Tree T3, Free T4, TSH, CEA). Reasons excluded from the secondary selection were the lack of reagent supply for comparative evaluations, the problems with data reproducibility, and the inability to accept data variations. The most problematic part of comparative evaluations was sample collection. It didn't matter if the number of samples requested was large and the capacity needed for the test was small. It was difficult to collect various concentration samples in the case of a small number of tests(100 cases per month or less), and it was difficult to conduct a recovery rate test in the case of a relatively large volume of samples required for a single test(more than 100 uL). In addition, the lack of dilution solution or standard zero material for sensitivity measurement or dilution tests was one of the problems. Conclusion Comparative evaluation for changing test reagents require appropriate preparation time to collect diverse and sufficient samples. In addition, setting the total sample volume and reagent volume range required for comparative evaluations, depending on the sample volume and reagent volume required for one test, will reduce the burden of sample collection and planning for each comparative evaluation.

• Korean Journal of Agricultural Science
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• v.15 no.1
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• pp.47-68
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• 1988

This study was conducted to define the optimal conditions for embryo growth during seed stratification and for breaking dormancy as well as seed germination of stratified ginseng seeds. The experiments were also carried out to detect some materials which were expected to induce seed dormancy in the ginseng seeds. The results summarized as follows; 1. The growth of embryo during seed stratification was significantly inhibited by the existence of endocarp. The fastest embryo growth was resulted at $15^{\circ}C$ and an estimated optimal temperature for embryo growth was about $18^{\circ}C$. 2. There was no significant difference between the embryo growth and germination ratio of ginseng seeds which were sown in seed bed at Aug-5 without seed stratification and that of artificial seed stratification. 3. Embryo growth and germination ratio was significantly inhibited by high temperature treatment at $30^{\circ}C$ for 24 hours or respiration stress by immersing seeds in water for 10 days or more. 4. When the seed stratification was started at $10^{\circ}C$, growth of embryo in the ginseng seeds were almost stopped. But, when the seeds were stratified first at $20^{\circ}C$ for 50 days and next at $10^{\circ}C$ for 50 days, the embryo growth was significantly promoted compared with the embryo growth in the seeds which were stratified at $20^{\circ}C$ for 100 days. 5. The successive embryo growth after seed stratification was significantly accelerated at $10^{\circ}C$ but the seeds chilled at $5^{\circ}C$ for 100 days were resulted in the highest germination ratio as well as the shortest days for germination. 6. The successive embryo growth during chilling treatment and seed germination were significantly inhibited by immersing seeds in water just before chilling treatment or during chilling treatment and by interruption of chilling treatment with raising temperature to $20^{\circ}C$ for 20 days during chilling treatment. 7. The germination ratio of ginseng seeds which finished chilling treatment was highest at $10^{\circ}C$ and 62.5% was the estimated soil moisture for the best germination of ginseng seeds. The ginseng seeds were found to require high amount of oxygen for germination. 8. Only water soluble material in homogenized ginseng seeds showed a significant inhibiting effect on the seed germination of sesame, millet and soybean. Water soluble material dissolved from undehisced ginseng seeds showed stronger inhibiting effect on the seedling growth of sesame than material from dehisced ginseng seeds. Extraction temperature did not influence the inhibiting effect of the material dissolved from ginseng seeds on the seedling growth of sesame. 9. Water soluble materials dissolved from the berry pulps, leaves, fresh roots and dried roots also showed a significant inhibiting effect on the seedling growth of sesame. 10. Water soluble materials dissolved from the ginseng seeds, leaves and fresh roots showed a significant inhibiting effect on the germination of true fungi and the growth of spawn but the growth of phytopathogenic bacteria was not. 11. Among the water soluble materials dissolved from ginseng seeds, the materials of low molecular weight less than 3,000 were resulted a significant inhibiting effect on the seedling growth of sesame and the materials of high molecular weight also showed an inhibiting effect.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.71-88
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• 2017

Language models were originally developed for speech recognition and language processing. Using a set of example sentences, a language model predicts the next word or character based on sequential input data. N-gram models have been widely used but this model cannot model the correlation between the input units efficiently since it is a probabilistic model which are based on the frequency of each unit in the training set. Recently, as the deep learning algorithm has been developed, a recurrent neural network (RNN) model and a long short-term memory (LSTM) model have been widely used for the neural language model (Ahn, 2016; Kim et al., 2016; Lee et al., 2016). These models can reflect dependency between the objects that are entered sequentially into the model (Gers and Schmidhuber, 2001; Mikolov et al., 2010; Sundermeyer et al., 2012). In order to learning the neural language model, texts need to be decomposed into words or morphemes. Since, however, a training set of sentences includes a huge number of words or morphemes in general, the size of dictionary is very large and so it increases model complexity. In addition, word-level or morpheme-level models are able to generate vocabularies only which are contained in the training set. Furthermore, with highly morphological languages such as Turkish, Hungarian, Russian, Finnish or Korean, morpheme analyzers have more chance to cause errors in decomposition process (Lankinen et al., 2016). Therefore, this paper proposes a phoneme-level language model for Korean language based on LSTM models. A phoneme such as a vowel or a consonant is the smallest unit that comprises Korean texts. We construct the language model using three or four LSTM layers. Each model was trained using Stochastic Gradient Algorithm and more advanced optimization algorithms such as Adagrad, RMSprop, Adadelta, Adam, Adamax, and Nadam. Simulation study was done with Old Testament texts using a deep learning package Keras based the Theano. After pre-processing the texts, the dataset included 74 of unique characters including vowels, consonants, and punctuation marks. Then we constructed an input vector with 20 consecutive characters and an output with a following 21st character. Finally, total 1,023,411 sets of input-output vectors were included in the dataset and we divided them into training, validation, testsets with proportion 70:15:15. All the simulation were conducted on a system equipped with an Intel Xeon CPU (16 cores) and a NVIDIA GeForce GTX 1080 GPU. We compared the loss function evaluated for the validation set, the perplexity evaluated for the test set, and the time to be taken for training each model. As a result, all the optimization algorithms but the stochastic gradient algorithm showed similar validation loss and perplexity, which are clearly superior to those of the stochastic gradient algorithm. The stochastic gradient algorithm took the longest time to be trained for both 3- and 4-LSTM models. On average, the 4-LSTM layer model took 69% longer training time than the 3-LSTM layer model. However, the validation loss and perplexity were not improved significantly or became even worse for specific conditions. On the other hand, when comparing the automatically generated sentences, the 4-LSTM layer model tended to generate the sentences which are closer to the natural language than the 3-LSTM model. Although there were slight differences in the completeness of the generated sentences between the models, the sentence generation performance was quite satisfactory in any simulation conditions: they generated only legitimate Korean letters and the use of postposition and the conjugation of verbs were almost perfect in the sense of grammar. The results of this study are expected to be widely used for the processing of Korean language in the field of language processing and speech recognition, which are the basis of artificial intelligence systems.

• Progress in Medical Physics
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• v.23 no.1
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• pp.15-25
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• 2012

The aim of this study is to evaluate plan quality and dose accuracy for Volumetric Modulated Arc Therapy (VMAT) on the TG-119 and is to investigate the effects on variation of the selectable optimization parameters of VMAT. VMAT treatment planning was implemented on a Varian iX linear accelerator with ARIA record and verify system (Varian Mecical System Palo Alto, CA) and Oncentra MasterPlan treatment planning system (Nucletron BV, Veenendaal, Netherlands). Plan quality and dosimetric accuracy were evaluated by effect of varying a number of arc, gantry spacing and delivery time for the test geometries provided in TG-119. Plan quality for the target and OAR was evaluated by the mean value and the standard deviation of the Dose Volume Histograms (DVHs). The ionization chamber and $Delta^{4PT}$ bi-planar diode array were used for the dose evaluation. For treatment planning evaluation, all structure sets closed to the goals in the case of single arc, except for the C-shape (hard), and all structure sets achieved the goals in the case of dual arc, except for C-shape (hard). For the variation of a number of arc, the simple structure such as a prostate did not have the difference between single arc and dual arc, whereas the complex structure such as a head and neck showed a superior result in the case of dual arc. The dose distribution with gantry spacing of $4^{\circ}$ was shown better plan quality than the gantry spacing of $6^{\circ}$, but was similar results compared with gantry spacing of $2^{\circ}$. For the verification of dose accuracy with single arc and dual arc, the mean value of a relative error between measured and calculated value were within 3% and 4% for point dose and confidence limit values, respectively. For the verification on dose accuracy with the gantry intervals of $2^{\circ}$, $4^{\circ}$ and $6^{\circ}$, the mean values of relative error were within 3% and 5% for point dose and confidence limit values, respectively. In the verification of dose distribution with $Delta^{4PT}$ bi-planar diode array, gamma passing rate was $98.72{\pm}1.52%$ and $98.3{\pm}1.5%$ for single arc and dual arc, respectively. The confidence limit values were within 4%. The smaller the gantry spacing, the more accuracy results were shown. In this study, we performed the VMAT QA based on TG-119 procedure, and demonstrated that all structure sets were satisfied with acceptance criteria. And also, the results for the selective optimization variables informed the importance of selection for the suitable variables according to the clinical cases.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.4 no.2
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• pp.45-53
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• 1984

This study was conducted to investigate characteristics of nonpoint pollutant discharges and concentrations in runoff from the urban and agricultural areas in Korea. The analytical parameters used for this study were COD, BOD and SS. This study was conducted during the period from May to August 1981. Nonpoint pollutant mass loadings from the urban area were influenced by the rainfall intensity and the duration of rainfall, and etc. The concentrations of pollutants in the first flush was higher as the discharges increased. It was, however, found that the concentrations of pollutants in the heavy storm runoff were decreased due to the dilution effect. When other rainfall followed a peak rainfall, the concentrations of pollutants were lower than expected, because the first flush conveyed the most of pollutants deposited on the combined sewers. However the concentrations were increased in proportion to the increased flow when a rainfall of higher intensity than the first flush was continued. Yearly area yield rates in kg/ha were estimated to be 690.5(489.9~1,328) of COD, 319.7(226.8~614.8) of BOD, and 831.2(589.7~1,598) of SS. Pollutant sources in agricultural area were of the domestic waste water, manure composting stack, and agricultural solid wastes and etc. In the paddy field, yearly area yield rates in kg/ha were estimated to be 623.4(21.7~114) of COD, 18.65(9.53~34.5) of BOD, and 91.9(46.3~171.8) of SS. In the crop land, however, yearly rates in kg/ha were estimated to be 91.9(46.3~171.8) of COD, 23.09(11.7~42.5) of BOD, and 23.09(11.4~43.4) of SS. Pollutant sources in the feedlot area were originating from the feces of cattle, the cleaning water, the wastes spilled from manure composting stack during rain. Yearly area yield rate in kg/ha was estimated to be 3.804(2,489~6,658) of COD, 2.047(464~2,900) of BOD, and 1.149 (729~1,442) of SS. Pollutant discharges in the forest area were resulted from the organic layer like leaves and others deposited on the surface. Yearly area yield rate in kg/ha was estimated to be 9.86(5.45~18.56) of COD, 3.48(1.67~7.54) of BOD, and 4.64(9.74~10.35) of SS.

• Korean Journal of Agricultural and Forest Meteorology
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• v.8 no.1
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• pp.1-9
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• 2006

Regardless of the recent observed warmer winters in Korea, more freeze injuries and associated economic losses are reported in fruit industry than ever before. Existing freeze-frost forecasting systems employ only daily minimum temperature for judging the potential damage on dormant flowering buds but cannot accommodate potential biological responses such as short-term acclimation of plants to severe weather episodes as well as annual variation in climate. We introduce 'dormancy depth', in addition to daily minimum temperature, as a complementary criterion for judging the potential damage of freezing temperatures on dormant flowering buds of grape vines. Dormancy depth can be estimated by a phonology model driven by daily maximum and minimum temperature and is expected to make a reasonable proxy for physiological tolerance of buds to low temperature. Dormancy depth at a selected site was estimated for a climatological normal year by this model, and we found a close similarity in time course change pattern between the estimated dormancy depth and the known cold tolerance of fruit trees. Inter-annual and spatial variation in dormancy depth were identified by this method, showing the feasibility of using dormancy depth as a proxy indicator for tolerance to low temperature during the winter season. The model was applied to 10 vineyards which were recently damaged by a cold spell, and a temperature-dormancy depth-freeze injury relationship was formulated into an exponential-saturation model which can be used for judging freeze risk under a given set of temperature and dormancy depth. Based on this model and the expected lowest temperature with a 10-year recurrence interval, a freeze risk probability map was produced for Hwaseong County, Korea. The results seemed to explain why the vineyards in the warmer part of Hwaseong County have been hit by more freeBe damage than those in the cooler part of the county. A dormancy depth-minimum temperature dual engine freeze warning system was designed for vineyards in major production counties in Korea by combining the site-specific dormancy depth and minimum temperature forecasts with the freeze risk model. In this system, daily accumulation of thermal time since last fall leads to the dormancy state (depth) for today. The regional minimum temperature forecast for tomorrow by the Korea Meteorological Administration is converted to the site specific forecast at a 30m resolution. These data are input to the freeze risk model and the percent damage probability is calculated for each grid cell and mapped for the entire county. Similar approaches may be used to develop freeze warning systems for other deciduous fruit trees.