• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.37 no.1
• /
• pp.67-76
• /
• 2024

In this paper, we present a DIP-MLS testing method that combines digital image processing with a rigid body-based MLS differencing approach to measure mechanical variables and analyze the impact of target location and image resolution. This method assesses the displacement of the target attached to the sample through digital image processing and allocates this displacement to the node displacement of the MLS differencing method, which solely employs nodes to calculate mechanical variables such as stress and strain of the studied object. We propose an effective method to measure the displacement of the target's center of gravity using digital image processing. The calculation of mechanical variables through the MLS differencing method, incorporating image-based target displacement, facilitates easy computation of mechanical variables at arbitrary positions without constraints from meshes or grids. This is achieved by acquiring the accurate displacement history of the test specimen and utilizing the displacement of tracking points with low rigidity. The developed testing method was validated by comparing the measurement results of the sensor with those of the DIP-MLS testing method in a three-point bending test of a rubber beam. Additionally, numerical analysis results simulated only by the MLS differencing method were compared, confirming that the developed method accurately reproduces the actual test and shows good agreement with numerical analysis results before significant deformation. Furthermore, we analyzed the effects of boundary points by applying 46 tracking points, including corner points, to the DIP-MLS testing method. This was compared with using only the internal points of the target, determining the optimal image resolution for this testing method. Through this, we demonstrated that the developed method efficiently addresses the limitations of direct experiments or existing mesh-based simulations. It also suggests that digitalization of the experimental-simulation process is achievable to a considerable extent.

• Journal of Food Hygiene and Safety
• /
• v.39 no.1
• /
• pp.54-60
• /
• 2024

This study investigated the effectiveness of using pathogens and aqueous acids to reduce the Aspergillus ochraceus and Rhodotorula mucilaginosa contamination in livestock production environments. For this study, 1 mL of each bacterial suspension (10⁷-10⁸ spores/mL) was inoculated on a knife surface, dried at 37℃, and used under each treatment condition. First, to investigate the effect of organic acids, acetic, lactic, and citric acids were used. Subsequently, to select the appropriate concentration, they were prepared at concentrations of 0.5, 1, 2, 3, 4, and 5%, respectively. Accordingly, to further maximize the effect of organic acid treatment, we combined the treatment with ultraviolet light. The two strains showed a significant difference (P<0.05) compared to the initial strain, with a greater than 90% decrease in the concentrations of all organic acids. Consequently, acetic and lactic acids decreased by approximately 5 and 2 log colony forming unit (CFU)/cm², respectively, when treated with ultraviolet light (360 mJ/cm²); however, citric acid decreased by less than 1 log CFU/cm². However, when manufactured with 4% acetic acid, a severe malodor was emitted, making it difficult for workers to use it in a production environment. Accordingly, the optimal treatment conditions for organic acid and ultraviolet light for application were selected as follows: immersion in a 4% lactic acid solution for 1 minute and then, sterilization with ultraviolet light at 360 mJ/cm². Finally, when a pork meat sample was cut with a knife that was finally washed with lactic acid and treated with ultraviolet light, the low level of inoculum transferred from the cleaned knife to the surface of the sample was not detected. In conclusion, using this established method can prevent cross-contamination of the surface of the meat during processing.

• Applied Biological Chemistry
• /
• v.29 no.1
• /
• pp.62-72
• /
• 1986

At present, the definition and chemical analysis method of available soil phosphorus for plants have not been standardized because of the complexity of crop and soil characteristics in Korea and many analysis methods have been suggested with different extraction conditions. Suitable analytical method of available soil P should be established by the trial of various methods based on crop nutrition and soil conditions. To establish the most suitable analysis method of available soiIP, a pot experiment with young maize was conducted over 44 different upland soils collected over the land of Korea. The amount of uptaken P by the plant was determined by ten different chemical methods for the available soil P. The results obtained were as follows: 1. Total phosphorus content in the sample soils ranged ranged $533{\sim}4917\;ppm$, and showed significant positive correlation with the content of organic matter. 2. The P content was relatively low in the acid sulfate soil and very high in the volcanic ash soil although both types of soil contained high level of orgic matter. 3. The amount of extractable P determined by ten different methods were varied more or less, and the ratios of the extractable P to the total soil P were in the range of $1{\sim}48%$. 4. The relative values to the amount of extractable soil P by different methods were in the order of $H_2O(5\;min.)\;1.0\;<\;H_2O(60min.)\;2.27\;<\;NH_4HCO_3\;5.57\;<\;NaHCO_3\;7.42\;<\;Double\;lactate\;9.71\;<\;Bray\;No.1\;12.53\;<\;Lancaster\;17.63\;<\;Nelson\;25.96\;<\;AcOH\;27.6\;<\;CAL-method\;50.27$ 5. The amount of extractable P determined by all of applied methods was very low in acid sulfate soil, volcanic ash soil and coarse textured soil. 6. Soil pH and total soil P generally showed significant positive correlation with the chemically extracted P, and soil organic matter was negatively correlated with the determined by Nelson-and CAL-method. Olsen method which showed significant correlation with exchangeable calcium seemed to be recommendable for calcareous soils. 7. Total amount of uptaken P by Young maize through continuos twice cropping was 4.05% of total soil P in average, and the uptake in the second cropping was twice as much as that of the first cropping. 8. Three determination methods, i.e. Soltanpour-, Double lactate and Bray No. 1-method seemed to be more suitable than Lancaster method which is widely practiced at present in Korea. However, further study should be carried out with other crops and soils to most adequate chemical method for determination of available soil P.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
• /
• v.18 no.1
• /
• pp.40-46
• /
• 2013

The isotope ratios of $^{13}C/^{12}C$ and $^{18}O/^{16}O$ for a sample in a mass spectrometer are measured relative to those of a pure $CO_2$ reference gas (i.e., laboratory working standard). Thus, the calibration of a laboratory working standard gas to the international isotope scales (Pee Dee Belemnite (PDB) for ${\delta}^{13}C$ and Vienna Standard Mean Ocean Water (V-SMOW) for ${\delta}^{18}O$) is essential for comparisons between data sets obtained by other groups on other mass spectrometers. However, one often finds difficulties in getting well-calibrated standard gases, because of their production time and high price. Additional difficulty is that fractionation processes can occur inside the gas cylinder most likely due to pressure drop in long-term use. Therefore, studies on laboratory production of pure $CO_2$ isotope standard gas from stable solid calcium carbonate standard materials, have been performed. For this study, we propose a method to extract pure $CO_2$ gas without isotope fractionation from a solid calcium carbonate material. The method is similar to that suggested by Coplen et al., (1983), but is better optimized particularly to make a large amount of pure $CO_2$ gas from calcium carbonate material. The $CaCO_3$ releases $CO_2$ in reaction with 100% pure phosphoric acid at $25^{\circ}C$ in a custom designed, evacuated reaction vessel. Here we introduce optimal procedure, reaction conditions, and samples/reactants size for calcium carbonate-phosphoric acid reaction and also provide the details for extracting, purifying and collecting $CO_2$ gas out of the reaction vessel. The measurements for ${\delta}^{18}O$ and ${\delta}^{13}C$ of $CO_2$ were performed at Seoul National University using a stable isotope ratio mass spectrometer (VG Isotech, SIRA Series II) operated in dual-inlet mode. The entire analysis precisions for ${\delta}^{18}O$ and ${\delta}^{13}C$ were evaluated based on the standard deviations of multiple measurements on 15 separate samples of purified $CO_2$. The pure $CO_2$ samples were taken from 100-mg aliquots of a solid calcium carbonate (Solenhofen-ori $CaCO_3$) during 8-day experimental period. The multiple measurements yielded the $1{\sigma}$ precisions of ${\pm}0.01$‰ for ${\delta}^{13}C$ and ${\pm}0.05$‰ for ${\delta}^{18}O$, comparable to the internal instrumental precisions of SIRA. Therefore, we conclude the method proposed in this study can serve as a way to produce an accurate secondary and/or laboratory $CO_2$ standard gas. We hope this study helps resolve difficulties in placing a laboratory working standard onto the international isotope scales and does make accurate comparisons with other data sets from other groups.

• Korean Journal of Medicinal Crop Science
• /
• v.13 no.4
• /
• pp.161-170
• /
• 2005

This study was performed to examine immuno-regulatory activities of Ehpedrae Sinica STAPF, Rubus Coreanus Miq. and Angelica gigas Nakai extracts in conbination with ultrasonification. The extract yields of plants were the highest in the extraction system of $60^{\circ}C$ and 40 kHz of ultrasonification. The immune cell growth ratio of human immune B and T cells was increased compared to other fractions by the water fraction of the plants at $60^{\circ}C$ and 40 kHz. The water fractions of the plants at $60^{\circ}C$ and 40 kHz increased the specific secretion of IL-6 and $TNF-{\alpha}$ of human immune B and T cells compared to other fractions of the plants. The water fraction of Ehpedrae Sinica STAPF among the plants was observed to show the highest specific secretion of IL-6 and $TNF-{\alpha}$. Also, NK-92 MI cells growth was increased in adding the water fractions of the plants at $60^{\circ}C$ and 40 kHz. The water fraction of Ehpedrae Sinica STAPF among the plants showed the highest in NK-92 MI cell growth ratio. The differentiation activity of the HL-60 cells significantly increased in adding the water fraction of Ehpedrae Sinica STAPF compared to other fractions of the plants. These results suggest that the water fractions of the plants in extraction system of temperature $60^{\circ}C$ and ultrasonification 40 kHz have marked useful immuno-stimulatory activities.

• Radiation Oncology Journal
• /
• v.20 no.4
• /
• pp.323-327
• /
• 2002

Purpose : To evaluate the outcome of early stage non-small cell lung cancer patients who were treated with radiation therapy alone and define the optimal radiotherapeutic regimen for these patients. Materials and Methods : A retrospective review was peformed on patients with sage I or II non-small cell carcinoma of the lung that were treated at our institution between June, 1987 and May, 2000. A total of 21 patients treated definitively with radiation therapy alone were included in this study. The age of the patients ranged from 53 to 81 years with a median of 66 years. All the patients were male. The medical reasons for inoperability were lack of pulmonary reserve, cardiovascular disease, poor performance status, old age, and patient refusal in the decreasing order. Pathological evidence was not adequate to characterize the non-small cell subtype in two patients. Of the remaining 19 patients, 16 had squamous cell carcinoma and 3 had adenocarcinoma. Treatment was given with conventional fractionation, once a day, five times a week. The doses to the primary site ranged from 56 Gy to 59 Gy. No patients were lost to follow-up. Results : The overall survival rates for the entire group at 2, 3 and 5 years were 41, 30 and $21\%$, respectively. The cause specific survivals at 2, 3 and 5 years were 55, 36 and $25\%$, respectively. An intercurrent disease was the cause of death in two patients. The cumulative local failure rate at 5 years was $43\%$. Nine of the 21 patients had treatment failures after the curative radiotherapy was attempted. Local recurrences as the first site of failure were documented in 7 patients. Therefore, local failure alone represented $78\%$ of the total failures. Those patients whose tumor sizes were less than 4 cm had a significantly better 5 year disease free survival than those with tumors greater than 4 cm $(0\%\;vs\;36\%)$. Those patients with a Karnofsky performance status less than 70 did not differ significantly with respect to actuarial survival when compared to those with a status greater than 70 $(25\%\;vs\;26\%,\;p>0.05)$. Conclusion : Radiation therapy 리one is an effective and safe treatment for early stage non-small ceil lung cancer patients who are medically inoperable or refuse surgery. Also we believe that a higher radiation dose to the primary site could improve the local control rate, and ultimately the overall survival rate.

• Journal of Intelligence and Information Systems
• /
• v.19 no.2
• /
• pp.73-85
• /
• 2013

Nowadays, in today's information society, the importance of the knowledge service using the information to creative value is getting higher day by day. In addition, depending on the development of IT technology, it is ease to collect and use information. Also, many companies actively use customer information to marketing in a variety of industries. Into the 21st century, companies have been actively using the culture arts to manage corporate image and marketing closely linked to their commercial interests. But, it is difficult that companies attract or maintain consumer's interest through their technology. For that reason, it is trend to perform cultural activities for tool of differentiation over many firms. Many firms used the customer's experience to new marketing strategy in order to effectively respond to competitive market. Accordingly, it is emerging rapidly that the necessity of personalized service to provide a new experience for people based on the personal profile information that contains the characteristics of the individual. Like this, personalized service using customer's individual profile information such as language, symbols, behavior, and emotions is very important today. Through this, we will be able to judge interaction between people and content and to maximize customer's experience and satisfaction. There are various relative works provide customer-centered service. Specially, emotion recognition research is emerging recently. Existing researches experienced emotion recognition using mostly bio-signal. Most of researches are voice and face studies that have great emotional changes. However, there are several difficulties to predict people's emotion caused by limitation of equipment and service environments. So, in this paper, we develop emotion prediction model based on vision-based interface to overcome existing limitations. Emotion recognition research based on people's gesture and posture has been processed by several researchers. This paper developed a model that recognizes people's emotional states through body gesture and posture using difference image method. And we found optimization validation model for four kinds of emotions' prediction. A proposed model purposed to automatically determine and predict 4 human emotions (Sadness, Surprise, Joy, and Disgust). To build up the model, event booth was installed in the KOCCA's lobby and we provided some proper stimulative movie to collect their body gesture and posture as the change of emotions. And then, we extracted body movements using difference image method. And we revised people data to build proposed model through neural network. The proposed model for emotion prediction used 3 type time-frame sets (20 frames, 30 frames, and 40 frames). And then, we adopted the model which has best performance compared with other models.' Before build three kinds of models, the entire 97 data set were divided into three data sets of learning, test, and validation set. The proposed model for emotion prediction was constructed using artificial neural network. In this paper, we used the back-propagation algorithm as a learning method, and set learning rate to 10%, momentum rate to 10%. The sigmoid function was used as the transform function. And we designed a three-layer perceptron neural network with one hidden layer and four output nodes. Based on the test data set, the learning for this research model was stopped when it reaches 50000 after reaching the minimum error in order to explore the point of learning. We finally processed each model's accuracy and found best model to predict each emotions. The result showed prediction accuracy 100% from sadness, and 96% from joy prediction in 20 frames set model. And 88% from surprise, and 98% from disgust in 30 frames set model. The findings of our research are expected to be useful to provide effective algorithm for personalized service in various industries such as advertisement, exhibition, performance, etc.

• Journal of The Korean Society of Grassland and Forage Science
• /
• v.18 no.2
• /
• pp.79-88
• /
• 1998

This experiment was conducted from May to August in 1997 to selected the wrn hybrids being suitable for silage at farm in the Kongju National University through the comparison of growth characteristics, forage yield and growth analysis about native and imported corn hybrids for silage production. In this experiment, trial design was a randomized block design with three replication, testing varieties were 4 hybrids (Suwon 19, Kwanganok, Whengsungok, Suwonok ) of native corn hybrids and 13 hybrids (P 3156, P 3352, P 3144w, DK 501, DK 689, DK 713, DK 729, H 643.99, H 545.64, H 645.12, HC 7466, H 644.18, H ALISEO) of imported corn hybrids. The results obtained are summarized as follows; 1. The emergence rate of H643.99 was the highest with 97.0%. In rice black streaked dwarf virus(RBSOV), the hybrid of HC 7466 was lower infected with 1.6% than other hybrids. The plant hight of P 3144w was the highest with 339 cm and the stem length of P 3156 was the highest with 261 cm. In native com hybrids, the plant height and stem length of Kwanganok were recorded with 306 cm and 235 cm, respectively. 2. Leaf number and leaf area of Kwanganok were the greatest with 16 sheet per plant and $5,180\;{\textrm{m}^2}/l0a$, respectively. H 645.12 and H 545.64 had the greatest in ear to total dry matter ratio with 49.5% and 49.4%, respectively. 3. The fresh matter yield was significantly difference between growth stage, So Suwon 19 had the most level at 15 days before silking, P 3352 had the most level at silking date, Kwananok had the most level at 35 days a after silking. The fresh matter yield of native com hybrids such as Suwon 19 and Kwanganok was not apparent diffreences as compared with imported corn hybrids. 4. As the results of survey with dry weight, the quantity of dry matter accumulation were increase after silking. The varieties of P 3352, P 3156, Kwanganok, OK 713 were more quantity of dry matter production than DK 501, HC 7466. The Kwanganok of native com hybrid and Pioneer strain with high percentage of dry matter were higher dry weight than Limagrain strain. 5. HC 7466 had the largest LAR with $6.53\;{\textrm{cm}^2}/g$, H545.12 had the lowest LAR with $3.30\;{\textrm{cm}^2/g}$. P 3144 had the largest LAI, DeKalb strain including DK 713 were larger apparently than Limagrain strain including HC 7466 with 3.15. 6. The RGR of testing varieties was little difference of statistical significantly, but DK 501, and HC 7466 were lower than other corn hybrids. The CGR of native and American varieties was no apparent differences, but that of Limagrain strains were a large variation. According to the results obtained by this experiment, the eary growth such as emergence rate and RBSDV infection rate of Limagrain strains was more excellent than other strains. P 3156, P 3352, P 3144w, DK 713 and HC 7466 were suitable for silage condition such as dry matter yield, percentage of dry matter and % ear to total dry matter. The fresh and dry matter yield of native corn hybrids such as Suwon 19 and Kwanganok were not apparent differences as compared with imported corn hybrids, but percentage of dry matter was lower than other imported corn hybrids.

• Journal of Intelligence and Information Systems
• /
• v.25 no.2
• /
• pp.167-194
• /
• 2019

This research starts from the four basic concepts of incentive incompatibility, limited information, myopia and decision variable which are confronted when making decisions in keyword bidding. In order to make these concept concrete, four framework approaches are designed as follows; Strategic approach for the incentive incompatibility, Statistical approach for the limited information, Alternative optimization for myopia, and New model approach for decision variable. The purpose of this research is to propose the statistical optimization model in constructing the portfolio of Sponsored Search Advertising (SSA) in the Sponsor's perspective through empirical tests which can be used in portfolio decision making. Previous research up to date formulates the CTR estimation model using CPC, Rank, Impression, CVR, etc., individually or collectively as the independent variables. However, many of the variables are not controllable in keyword bidding. Only CPC and Rank can be used as decision variables in the bidding system. Classical SSA model is designed on the basic assumption that the CPC is the decision variable and CTR is the response variable. However, this classical model has so many huddles in the estimation of CTR. The main problem is the uncertainty between CPC and Rank. In keyword bid, CPC is continuously fluctuating even at the same Rank. This uncertainty usually raises questions about the credibility of CTR, along with the practical management problems. Sponsors make decisions in keyword bids under the limited information, and the strategic portfolio approach based on statistical models is necessary. In order to solve the problem in Classical SSA model, the New SSA model frame is designed on the basic assumption that Rank is the decision variable. Rank is proposed as the best decision variable in predicting the CTR in many papers. Further, most of the search engine platforms provide the options and algorithms to make it possible to bid with Rank. Sponsors can participate in the keyword bidding with Rank. Therefore, this paper tries to test the validity of this new SSA model and the applicability to construct the optimal portfolio in keyword bidding. Research process is as follows; In order to perform the optimization analysis in constructing the keyword portfolio under the New SSA model, this study proposes the criteria for categorizing the keywords, selects the representing keywords for each category, shows the non-linearity relationship, screens the scenarios for CTR and CPC estimation, selects the best fit model through Goodness-of-Fit (GOF) test, formulates the optimization models, confirms the Spillover effects, and suggests the modified optimization model reflecting Spillover and some strategic recommendations. Tests of Optimization models using these CTR/CPC estimation models are empirically performed with the objective functions of (1) maximizing CTR (CTR optimization model) and of (2) maximizing expected profit reflecting CVR (namely, CVR optimization model). Both of the CTR and CVR optimization test result show that the suggested SSA model confirms the significant improvements and this model is valid in constructing the keyword portfolio using the CTR/CPC estimation models suggested in this study. However, one critical problem is found in the CVR optimization model. Important keywords are excluded from the keyword portfolio due to the myopia of the immediate low profit at present. In order to solve this problem, Markov Chain analysis is carried out and the concept of Core Transit Keyword (CTK) and Expected Opportunity Profit (EOP) are introduced. The Revised CVR Optimization model is proposed and is tested and shows validity in constructing the portfolio. Strategic guidelines and insights are as follows; Brand keywords are usually dominant in almost every aspects of CTR, CVR, the expected profit, etc. Now, it is found that the Generic keywords are the CTK and have the spillover potentials which might increase consumers awareness and lead them to Brand keyword. That's why the Generic keyword should be focused in the keyword bidding. The contribution of the thesis is to propose the novel SSA model based on Rank as decision variable, to propose to manage the keyword portfolio by categories according to the characteristics of keywords, to propose the statistical modelling and managing based on the Rank in constructing the keyword portfolio, and to perform empirical tests and propose a new strategic guidelines to focus on the CTK and to propose the modified CVR optimization objective function reflecting the spillover effect in stead of the previous expected profit models.

• Journal of Intelligence and Information Systems
• /
• v.26 no.4
• /
• pp.173-198
• /
• 2020

For a long time, many studies have been conducted on predicting the success of campaigns for customers in academia, and prediction models applying various techniques are still being studied. Recently, as campaign channels have been expanded in various ways due to the rapid revitalization of online, various types of campaigns are being carried out by companies at a level that cannot be compared to the past. However, customers tend to perceive it as spam as the fatigue of campaigns due to duplicate exposure increases. Also, from a corporate standpoint, there is a problem that the effectiveness of the campaign itself is decreasing, such as increasing the cost of investing in the campaign, which leads to the low actual campaign success rate. Accordingly, various studies are ongoing to improve the effectiveness of the campaign in practice. This campaign system has the ultimate purpose to increase the success rate of various campaigns by collecting and analyzing various data related to customers and using them for campaigns. In particular, recent attempts to make various predictions related to the response of campaigns using machine learning have been made. It is very important to select appropriate features due to the various features of campaign data. If all of the input data are used in the process of classifying a large amount of data, it takes a lot of learning time as the classification class expands, so the minimum input data set must be extracted and used from the entire data. In addition, when a trained model is generated by using too many features, prediction accuracy may be degraded due to overfitting or correlation between features. Therefore, in order to improve accuracy, a feature selection technique that removes features close to noise should be applied, and feature selection is a necessary process in order to analyze a high-dimensional data set. Among the greedy algorithms, SFS (Sequential Forward Selection), SBS (Sequential Backward Selection), SFFS (Sequential Floating Forward Selection), etc. are widely used as traditional feature selection techniques. It is also true that if there are many risks and many features, there is a limitation in that the performance for classification prediction is poor and it takes a lot of learning time. Therefore, in this study, we propose an improved feature selection algorithm to enhance the effectiveness of the existing campaign. The purpose of this study is to improve the existing SFFS sequential method in the process of searching for feature subsets that are the basis for improving machine learning model performance using statistical characteristics of the data to be processed in the campaign system. Through this, features that have a lot of influence on performance are first derived, features that have a negative effect are removed, and then the sequential method is applied to increase the efficiency for search performance and to apply an improved algorithm to enable generalized prediction. Through this, it was confirmed that the proposed model showed better search and prediction performance than the traditional greed algorithm. Compared with the original data set, greed algorithm, genetic algorithm (GA), and recursive feature elimination (RFE), the campaign success prediction was higher. In addition, when performing campaign success prediction, the improved feature selection algorithm was found to be helpful in analyzing and interpreting the prediction results by providing the importance of the derived features. This is important features such as age, customer rating, and sales, which were previously known statistically. Unlike the previous campaign planners, features such as the combined product name, average 3-month data consumption rate, and the last 3-month wireless data usage were unexpectedly selected as important features for the campaign response, which they rarely used to select campaign targets. It was confirmed that base attributes can also be very important features depending on the type of campaign. Through this, it is possible to analyze and understand the important characteristics of each campaign type.