• Journal of the Korean Society for Marine Environment & Energy
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• v.9 no.1
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• pp.45-54
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• 2006

The study was to review methods by which a ship can unfold and tow an oil boom by attaching the opening apparatus to an oil boom through experiments. The shape and dimension of the opening apparatus were designed with the measurement value of the towing tension load of the oil boom and the dimension of winch drum of the oil boom installed in the ship considered. For the field experiment to identify the performance of the opening apparatus, opening apparatuses were prepared to have the dimension of $3.0m^2$ and $6.0m^2$ which is 91% and 75% of the calculation value for type B and C respectively. As a result, T(kg), the value of tension in type B oil boom according to the towing speed(v) change when two ships are towed together were proved to be $T=920v^{1.1}\;and\;T=500v^{0.9}$ in case the distance is 100 m and 50 m. Based on the result, the dimension of the opening apparatus for type B and C oil boom was calculated as $3.3m^2$ and $8.0m^2$ respectively. When unfolding and towing by attaching the opening apparatus and 200 m of towing line at both ends of type B and type C oil boom, the maximum width of the opening apparatus was shown as 114 m and 95 m in average(width of opening/total length of oil boom: 33% and 57%) in the towing speed of 1.5 kt. It was evaluated that the opening apparatus could concentrate the spilled oil in a good performance. However as far as the increase rate of oil boom opening width according to the length of the towing line is debatable, the increase rate is remarkably reduced when it is lengthened from 100 m to 150 m and to 200 m although it showed extreme increase of 31% and 40% when the length of the towing line was changed from 50 m to 100 m. Therefore, it is inferred that the towing line should be maintained more or less 100 m to get good spread efficiency of the opening apparatus. Additionally, if the towing speed is faster than 1.5 kt, the opening width was narrowed because of the reduced spread efficiency and the shape of the oil boom can be unstable because of the partial sinking of the oil boom, run over waves, or flap of skirt. Thus the reasonable towing speed can be within 1.5 kt for the operation of the opening apparatus.

• Korean Journal of Agricultural Science
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• v.3 no.1
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• pp.77-84
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• 1976

The preservation efficiency, quality and utilization of silage from 3 species of pumpkins (Mammoth pumpkin, Queensland blue pumpkin, Korean pumpkin) without and with 10, and 20% wheat bran additive were studied in this experiment. Silages were analysised and tested the chemical composition, pH and quality of silages between at 40-60 days and egg performance were carried out with mammoth pumpkin silage without additive. The results were summaried as follows. 1. The losses of all silage ware lower and similar as about 15% at 6 monthes following after silage-making but all raw pumpkins were spoilaged during the winter storaging. 2. The moisture content of silages were higher as about 97% in mammoth pumpkin silage, 94% in Queensland pumpkin silage and 91% in Korean pumpkin silage without additive and all nutrient content of silage without and with additive were depended on its content of raw silage material of pumpkins and wheat bran. The contents of moisture and N-free extract were slightly decrease but not significantly difference during the silaging and other contents were not so much changed. 3. Good quality of silage were made from all pumpkins with and without additive. Organic acid contents were 2.09-2.93% of lactic acid, 0.68-1.71% of acetic acid and 0% of butyric acid and it was pH 3.8-4.0 in silages. 4. Feed intakes, egg production and quality of egg were showed good result in 5.0 and 7.5% silage feeding group as D.M. base for egg performance. (P<0.01) 5. It was concluded that good quality of silage were made from pumpkins with and without wheat bran additive and it was suggested that poor quality feedstuff may be improved it feeding value by extended palatability with pumpkin additive silage.

• Journal of Life Science
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• v.19 no.11
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• pp.1547-1552
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• 2009

This study was carried out to evaluate the supplementation effect of dietary natural plant extracts (NP: $Coxynil^{(R)}$, $Growell^{(R)}$ and $Respowell^{(R)}$) on broiler chickens. Forty thousand male broilers with 7 days adaptation after hatching were fed experiment diets for 34 days. The supplementation effects of NP on growth performance, blood parameters and biopsy were examined with twenty thousand broilers as the treatment group. Twenty thousand broilers for the control group (CON) were fed the diet with salinomycin-6, clopidol-25, enramycin-1, and BMD-2.5. In the diet of the treatment group, the antibiotics were replaced with 0.03%, 0.035% and 0.03% of $Coxynil^{(R)}$, $Growell^{(R)}$ and $Respowell^{(R)}$, respectively. The weight gain of the treatment group was increased but the feed intake was decreased, indicating that feed efficiency was increased compared to the CON. The mortality of the NP group was also lower compared to the CON group (1,008 birds to 1,693 birds), showing positive dietary effects from natural plant extracts. In the activity of infectious bursal disease virus (IBDV) and new cattle disease virus (NDV) antibodies, the NP showed lower antibody titer levels for both of IBDV and NDV compared to the CON. The levels of total cholesterol, HDL-cholesterol, globulin, and IgG in blood did not show significant differences between the groups. In the microscopic tissue analysis, no significant differences were detected. These results may suggest that a complex of three natural plant extracts can be used as alternative antibiotics in broilers.

• Journal of the Korean Society of Food Science and Nutrition
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• v.46 no.10
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• pp.1186-1194
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• 2017

Recently, many people have demanded reliable nutritional data even for minor-components. On the other hand, an analytical method for the analyses of vitamin $B_5$ and $B_6$ is lacking. Therefore, this study attempted to validate with accuracy and precision the analysis of vitamin $B_5$ and $B_6$ using a high-performance liquid chromatography (HPLC) method. The vitamin $B_5$ and $B_6$ contents were analyzed using an Agilent 1260 series HPLC system. YMC-Pack ODS-AM ($250{\times}4.6mm$ I.D.) and YMC-Pack Pro RS $C_{18}$ ($250{\times}4.6mm$ I.D.) columns were used for the analyses of vitamin $B_5$ and $B_6$, respectively. In the case of vitamin $B_5$, the flow rate was set to 1.0 mL/min by isocratic elution using the 50 mM $KH_2PO_4$ solution (pH 3.5)/acetonitrile (ACN) (95:5, v/v) with monitoring at 200 nm using HPLC/DAD, whereas the flow rate for vitamin $B_6$ was set to 1.0 mL/min of flow rate by isocratic elution using a 20 mM $CH_3CO_2Na$ solution (pH 3.6)/ACN (97:3, v/v) with monitoring by excitation at 290 nm and emission at 396 nm using HPLC/FLD. The column temperature was set to $30^{\circ}C$. The injection volume was $20{\mu}L$ for each experiment. The specificity of the accuracy and precision for vitamin $B_5$ and $B_6$ were also validated by HPLC. The results showed high linearity in the calibration curve for vitamin $B_5$ ($R^2=0.9998^{{\ast}{\ast}}$), the limit of detection (LOD) and limit of quantitation (LOQ) were 0.4 mg/L and 1.3 mg/L, respectively, In contrast, for the calibration curve of vitamin $B_6$, which showed high linearity ($R^2=0.9999^{{\ast}{\ast}}$), the LOD and LOQ were 0.006 mg/L and 0.02 mg/L, respectively.

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

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

• Journal of Korean Society of Environmental Engineers
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• v.32 no.8
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• pp.739-746
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• 2010

The purposes of this study are 1) to develop an advanced chamber system within ${\pm}10%$ of air velocity at the particulate matter (PM) collection area, 2) to research theoretical characteristics of PM10 and PM2.5 samplers, 3) to assess the performance characteristics of PM10 and PM2.5 samplers through chamber experiments. The total six one-hour experiments were conducted using the cornstarch with an mass median aerodynamic diameter (MMAD) of $20\;{\mu}m$ and an geometric standard deviation of 2.0 at the two different air velocity conditions of 0.67 m/s and 2.15 m/s in the chamber. The aerosol samplers used in the present study are one APM PM10 and one PM2.5 samplers accordance with the US federal reference methods and specially designed three mini-volume aerosol samplers (two for PM10 and one for PM2.5). The overall results indicate that PM10 and PM2.5 mini-volume samplers need correction factors of 0.25 and 0.39 respectively when APM PM samplers considered as reference samplers and there is significant difference between two mini-volume aerosol samplers when a two-way analysis of variance is tested using the measured PM10 mass concentrations. The PM10 and PM2.5 samplers with the cutpoints and slopes (PM10: $10{\pm}0.5\;{\mu}m$ and $1.5{\pm}0.1$, PM2.5: $2.5{\pm}0.2\;{\mu}m$ and $1.3{\pm}0.03$) theoretically collect the ranges of 86~114% and 64~152% considering the cornstarch characteristics used in this research. Furthermore, the calculated mass concentrations of PM samplers are higher than the ideal mass concentrations when the airborne MMADs for the cornstarch used are smaller than the cutpoints of PM samplers and the PM samplers collected less PM in another case. The chamber experiment also showed that PM10 and PM2.5 samplers had the bigger collection ranges of 37~158% and 55~149% than the theocratical calculated mass concentration ranges and the relatively similar mass concentration ranges were measured at the air velocity of 2.15 m/s comparing with the 0.67 m/s.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.39-57
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• 2012

Recently, due to the introduction of high-tech equipment in interactive exhibits, many people's attention has been concentrated on Interactive exhibits that can double the exhibition effect through the interaction with the audience. In addition, it is also possible to measure a variety of audience reaction in the interactive exhibition. Among various audience reactions, this research uses the change of the facial features that can be collected in an interactive exhibition space. This research develops an artificial neural network-based prediction model to predict the response of the audience by measuring the change of the facial features when the audience is given stimulation from the non-excited state. To present the emotion state of the audience, this research uses a Valence-Arousal model. So, this research suggests an overall framework composed of the following six steps. The first step is a step of collecting data for modeling. The data was collected from people participated in the 2012 Seoul DMC Culture Open, and the collected data was used for the experiments. The second step extracts 64 facial features from the collected data and compensates the facial feature values. The third step generates independent and dependent variables of an artificial neural network model. The fourth step extracts the independent variable that affects the dependent variable using the statistical technique. The fifth step builds an artificial neural network model and performs a learning process using train set and test set. Finally the last sixth step is to validate the prediction performance of artificial neural network model using the validation data set. The proposed model is compared with statistical predictive model to see whether it had better performance or not. As a result, although the data set in this experiment had much noise, the proposed model showed better results when the model was compared with multiple regression analysis model. If the prediction model of audience reaction was used in the real exhibition, it will be able to provide countermeasures and services appropriate to the audience's reaction viewing the exhibits. Specifically, if the arousal of audience about Exhibits is low, Action to increase arousal of the audience will be taken. For instance, we recommend the audience another preferred contents or using a light or sound to focus on these exhibits. In other words, when planning future exhibitions, planning the exhibition to satisfy various audience preferences would be possible. And it is expected to foster a personalized environment to concentrate on the exhibits. But, the proposed model in this research still shows the low prediction accuracy. The cause is in some parts as follows : First, the data covers diverse visitors of real exhibitions, so it was difficult to control the optimized experimental environment. So, the collected data has much noise, and it would results a lower accuracy. In further research, the data collection will be conducted in a more optimized experimental environment. The further research to increase the accuracy of the predictions of the model will be conducted. Second, using changes of facial expression only is thought to be not enough to extract audience emotions. If facial expression is combined with other responses, such as the sound, audience behavior, it would result a better result.

• Korean Journal of Poultry Science
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• v.7 no.1
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• pp.31-42
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• 1980

In order to identify the best superior cross combination of breeder stocks for broiler production, combining ability test and analyses of phenotypic performances for parent stocks were examined on records of 1,440 broiler chicken which were produced from 4 parental strains and 3 maternal strains at Hanhyup Poultry Breeding Farm from September 28, 1978 to January 5, 1979. The results obtained were as follows; 1. There was not found heterosis effect in viability but it seems to be desirable to select Hubbard strain in paternal line to improve viability. 2. As the paternal and maternal lines, selection of Ross strain showed the best paternal and maternal performance and the best general combining ability in body weight at 8 weeks of age is expected to be able to improve body weight of it s crossbred And the most superior cross combinations based on the specific combining ability and performance of rack crossbred were identified as Hubbard x Ross ana Ross x Hypeco crossbreds. 3. The best paternal and maternal lines on the smallest feed consumption for 8 weeks were Hubbard and Ross strains, and Hypeco strain, respectively. Especially Hubbard x Hypeco cross combination was proved as the smallest feed consumption compared with other cross combinations. 4. In feed requirement per Kg body weight increase, Hubbard strain for paternal line, Hypeco strain for naternal line, and cross combinations of Hubbard x Hypeco, Hubbard x Ross and Ross x Hypeco were certified as the most superiors. 5. Also superior cross combinations of Hubbard x Hypeco and Hubbard x Ross earned the most profit per bird through economic analysis. According to results as shown above, this experiment seems to be able to reach a such conclusion that production of superior cross combinations Hubbard x Ross, Hubbard x Hypeco and Ross x Hypeco through selection of Ross and Hubbard strains to paternal line and Hypeco and Ross strains for maternal line may become to considerable improvement for important economic characters of broiler; viability, body weight, feed consumption and feed requirement.

• Korean Journal of Poultry Science
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• v.32 no.4
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• pp.245-254
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• 2005

This experiment was conducted to investigate the effect of the supplemental alkali feldspar-ilite(feldspar) on growth performance and meat quality in broiler ducks for 43 days. One hundred eighty broiler ducks were divided into 5 groups of 12ducks. Dietary levels of feldspar 0, 0+antibiotics, 0.5, 1.0 and $1.5\%$ were added to experimental diets of each of the groups. Daily weight gain was slightly increased in 1.0 and $1.5\%$ feldspar treatments. Feed intake was slightly increased at all feldspar treatments. Glucose concentration of serum profile was decreased whereas BUN concentration was significantly increased (p<0.05) at $0.5\%$ feldspar. Cholesterol concentration was decreased at all feldspar treatments, this difference was especially observed in supplemental levels of $0.5\%$ feldspar(p<0.05). Carcass weight was increased at all feldspar treatments. Moisture and crude fat contents of proximate chemical composition in duck meat were decreased at all feldspar treatment, this difference especially was observed in supplemental levels of $1.5\%$ feldspar(p<0.05) on crude fat content. Lightness and yellowness was increased at all feldspar treatment. Cholesterol contents and TBA in meat were decreased, but this parameters were not difference by feldspar treatment. The composition of saturated fatty acids(SFA) was decreased, whereas unsaturated fatty acids(USFA) was slightly increased by feldspar treatment. The Pb content of heavy metal concentrations was increased with compared control, but not difference. The appearance of sensory evaluation was improved by supplemental feldspar, especially in supplemental feldspar, 1.0 and $1.5\%$(p<0.05). The results of this study indicate that the supplemental alkali feldspar may improve the production and meat quality of broiler ducks.

• Asia pacific journal of information systems
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• v.20 no.1
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• pp.81-99
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• 2010

Lately, in consumers' markets the number of new items is rapidly increasing at an overwhelming rate while consumers have limited access to information about those new products in making a sensible, well-informed purchase. Therefore, item providers and customers need a system which recommends right items to right customers. Also, whenever new items are released, for instance, the recommender system specializing in new items can help item providers locate and identify potential customers. Currently, new items are being added to an existing system without being specially noted to consumers, making it difficult for consumers to identify and evaluate new products introduced in the markets. Most of previous approaches for recommender systems have to rely on the usage history of customers. For new items, this content-based (CB) approach is simply not available for the system to recommend those new items to potential consumers. Although collaborative filtering (CF) approach is not directly applicable to solve the new item problem, it would be a good idea to use the basic principle of CF which identifies similar customers, i,e. neighbors, and recommend items to those customers who have liked the similar items in the past. This research aims to suggest a hybrid recommendation procedure based on the preference boundary of target customer. We suggest the hybrid recommendation procedure using the preference boundary in the feature space for recommending new items only. The basic principle is that if a new item belongs within the preference boundary of a target customer, then it is evaluated to be preferred by the customer. Customers' preferences and characteristics of items including new items are represented in a feature space, and the scope or boundary of the target customer's preference is extended to those of neighbors'. The new item recommendation procedure consists of three steps. The first step is analyzing the profile of items, which are represented as k-dimensional feature values. The second step is to determine the representative point of the target customer's preference boundary, the centroid, based on a personal information set. To determine the centroid of preference boundary of a target customer, three algorithms are developed in this research: one is using the centroid of a target customer only (TC), the other is using centroid of a (dummy) big target customer that is composed of a target customer and his/her neighbors (BC), and another is using centroids of a target customer and his/her neighbors (NC). The third step is to determine the range of the preference boundary, the radius. The suggested algorithm Is using the average distance (AD) between the centroid and all purchased items. We test whether the CF-based approach to determine the centroid of the preference boundary improves the recommendation quality or not. For this purpose, we develop two hybrid algorithms, BC and NC, which use neighbors when deciding centroid of the preference boundary. To test the validity of hybrid algorithms, BC and NC, we developed CB-algorithm, TC, which uses target customers only. We measured effectiveness scores of suggested algorithms and compared them through a series of experiments with a set of real mobile image transaction data. We spilt the period between 1st June 2004 and 31st July and the period between 1st August and 31st August 2004 as a training set and a test set, respectively. The training set Is used to make the preference boundary, and the test set is used to evaluate the performance of the suggested hybrid recommendation procedure. The main aim of this research Is to compare the hybrid recommendation algorithm with the CB algorithm. To evaluate the performance of each algorithm, we compare the purchased new item list in test period with the recommended item list which is recommended by suggested algorithms. So we employ the evaluation metric to hit the ratio for evaluating our algorithms. The hit ratio is defined as the ratio of the hit set size to the recommended set size. The hit set size means the number of success of recommendations in our experiment, and the test set size means the number of purchased items during the test period. Experimental test result shows the hit ratio of BC and NC is bigger than that of TC. This means using neighbors Is more effective to recommend new items. That is hybrid algorithm using CF is more effective when recommending to consumers new items than the algorithm using only CB. The reason of the smaller hit ratio of BC than that of NC is that BC is defined as a dummy or virtual customer who purchased all items of target customers' and neighbors'. That is centroid of BC often shifts from that of TC, so it tends to reflect skewed characters of target customer. So the recommendation algorithm using NC shows the best hit ratio, because NC has sufficient information about target customers and their neighbors without damaging the information about the target customers.