• Journal of the Korean Society of Clothing and Textiles
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• v.31 no.12
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• pp.1784-1792
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• 2007

This study set out to identify the problems with hat labels and to search for improvement measures by examining and analyzing consumers' practice of managing their hats. It also intended to provide accurate and enough information about how to keep and wash hats and thus help consumers use their hats for a long period. In an attempt to investigate how consumers wash and manage their hats, a survey was carried out to 395 individuals in their twenties and over who owned hats living in urban areas including Seoul, and were quota sampled according to age and gender. The survey period is March to April 2007. The collected data were statistically treated with the SPSS 12.0 program in terms of frequency, percentage, mean, standard error, cross tabulation, t-test, and one-way ANOVA. The findings were as followed. First, the respondents were in the average level of perceiving and practicing the washing methods of their hats. The female respondents who had more experiences with laundering than the males knew and practiced the washing methods for hats better than males. Second, compared to other clothing items, hat wearers were more likely to pay careful attention to their hats by putting their hats in a laundry net and applying a laundry detergent for wool fabrics when using a washing machine or washing their hats with their own hands. And third, most of the hat wearers were aware of the importance of hat labels and showed a lower level of trust in them than other clothing items. The suppliers need to offer accurate and practical labels in order to regain the consumers' trust. Many consumers had some difficulties figuring out the size system of hats. In particular, the male consumers had a low level of perception of labels, which implies that there should be specific efforts to educate them about general labels.

• Journal of Food Hygiene and Safety
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• v.29 no.3
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• pp.181-188
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• 2014

The objective of this study was to analyze the microbiological hazards of Astragalus membranaceus Bunge on the post-harvest processing. Samples from processing equipments (cleaner, water, cart, table, tray and packaging machine), personal hygiene (hand) and harvested crops (before washing, after washing, after sorting, and after drying) were collected from four farms (A, B, C, and D) located in Chungchengbuk-do, Korea. The samples were analyzed for sanitary indication bacteria and pathogenic bacteria. First, total aerobic bacteria and coliform in processing facilities were detected at the levels of 0.93~4.86 and 0.33~2.28 log CFU/$100cm^2$ and/mL respectively. In particular, microbial contamination in hand (5.43~6.11 and 2.52~4.12 log CFU/Hand) showed higher than processing equipments. Among the pathogenic bacteria, Bacillus cereus was detected at the levels of 0.33~2.41 log CFU/$100cm^2$, 1.48~3.27 log CFU/Hand and 0.67~3.65 log CFU/g in equipments, hands, and plants and Staphylococcus aureus were detected in cleaner, table, hand and harvested crops (before washing and after sorting) by qualitative test. Escherichia coli O157:H7, Listeria monocytogenes, and Salmonella spp. were not detected. These results indicated that personal hygiene and processing equipments should be managed to reduce the microbial contamination of A. membranaceus Bunge. Therefore, management system such as good agricultural practices (GAP) criteria is needed for hygienic agricultural products.

• Radiation Oncology Journal
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• v.25 no.1
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• pp.54-61
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• 2007

[ $\underline{Purpose}$ ]: The purpose of this study is to develop a practical method for determining accurate marker positions for prostate cancer radiotherapy using CT images and kV x-ray images obtained from the use of the on- board imager (OBI). $\underline{Materials\;and\;Methods}$: Three gold seed markers were implanted into the reference position inside a prostate gland by a urologist. Multiple digital image processing techniques were used to determine seed marker position and the center-of-mass (COM) technique was employed to determine a representative reference seed marker position. A setup discrepancy can be estimated by comparing a computed $COM_{OBI}$ with the reference $COM_{CT}$. A proposed algorithm was applied to a seed phantom and to four prostate cancer patients with seed implants treated in our clinic. $\underline{Results}$: In the phantom study, the calculated $COM_{CT}$ and $COM_{OBI}$ agreed with $COM_{actual}$ within a millimeter. The algorithm also could localize each seed marker correctly and calculated $COM_{CT}$ and $COM_{OBI}$ for all CT and kV x-ray image sets, respectively. Discrepancies of setup errors between 2D-2D matching results using the OBI application and results using the proposed algorithm were less than one millimeter for each axis. The setup error of each patient was in the range of $0.1{\pm}2.7{\sim}1.8{\pm}6.6\;mm$ in the AP direction, $0.8{\pm}1.6{\sim}2.0{\pm}2.7\;mm$ in the SI direction and $-0.9{\pm}1.5{\sim}2.8{\pm}3.0\;mm$ in the lateral direction, even though the setup error was quite patient dependent. $\underline{Conclusion}$: As it took less than 10 seconds to evaluate a setup discrepancy, it can be helpful to reduce the setup correction time while minimizing subjective factors that may be user dependent. However, the on-line correction process should be integrated into the treatment machine control system for a more reliable procedure.

• Journal of Biosystems Engineering
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• v.5 no.1
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• pp.1-14
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• 1980

Threshing operation may be one of the most important processes in the paddy post-production system as far as the grain loss and labor requirement are concerned . head-feeding type threshers commercially available now in Korea originally were developed for threshing dry paddy in the range of 15 to 17 % in wet basis. However, threshing wet-paddy with the grain moisture content above 20 % has been strongly recommended, especially for new high-yielding Indica -type varieties ; (1) to reduce high grain loss incurred due to the handling operations, and (2) to prevent the quantitative and qualitative loss of milled -rice when unthreshed grains are rewetted due to the rainfall. The objective of this study were to investigate the adaptability of both a head-feeding type thresher and a throw-in type thresher to wet-paddy , and to find out the possiblilities of improving the components of these threshers threshing. Four varieties, Suweon 264 and Milyang 24 as Tongil sister line varieties, minehikari and Jinhueng as Japonica-type varieties, were used at the different levels of the moisture content of grains. Both the feed rate and the cylinder speed were varied for each material and each machine. The thresher output quality , composition of tailing return, and separating loss were analyzed from the sampels taken at each treatment. A separate experiment for measurement opf the power requirement of the head-feeding type thresher was also performed. The results are summarized as follows : 1. There was a difference in the thresher output quality between rice varieties. In case of wet-paddy threshing at 550 rpm , grains with branchlet and torn heads for the Suweon 264 were 12 % and 7 % of the total output in weight, respectively, and for the Minehikari 4.5 % and 2 % respectively. In case of dry paddy threshing , those for the Suweon 264 were 8 % and 5% , and for the Minehikari 4% and 1% respectively. However, those for the Milyang 23 , which is highly susceptable to shattering, were much lower with 1 % and 0.5% respectively, regardless of the moisture content of the paddy. Therefore, it is desirable to breed rice varieties of the same physical properties as well as to improve a thresher adaptable to all the varieties. Torn heads, which increased with the moisture content of rall the varieties except the Milyang 23 , decreased as the cylinder speed increased, but grains with branchlet didnt decrease. The damaged kernels increased with the cylinder speed. 3. The thresher output quality was not affected much by the feed rate. But grains with branchlet and torn heads increased slightly with the feed rate for the head-feeding type thresher since higher resistance lowered at the cylinder speed. 4. In order to reduce grains with branchlet and torn heads in wet-paddy threshing , it is desirable to improve the head-feeding type thresher by developing a new type of cylinder which to not give excess impact on kernels or a concave which has differenct sizes of holes at different locations along the cylinder. 5. For the head-feeding type thresher, there was a difference in separating loss between the varieties. At the cylinder speed of 600 rpm the separating losses for the Minehikari and the Suweon 264 were 1.2% and 0.6% respectively. The separating loss of the head-feeding type thresher was not affected by the moisture content of paddy while that of the Mini-aged thresher increased with the moisture content. 6. From the analysis of the tailings return , to appeared that the tailings return mechanism didn't function properly because lots of single grains and rubbishes were unnecessarily returned. 7. Adding a vibrating sieve to the head-feeding type thresher could increase the efficiency of separation. Consequently , the tailing return mechanism would function properly since unnecessary return could be educed greatly. 8. The power required for the head-feeding type thresher was not affected by the moisture content of paddy, but the average power increased linearly with the feed rate. The power also increased with the cylinder speed.

• International Journal of Ocean System Engineering
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• v.2 no.3
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• pp.185-194
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• 2012

A Macroscopic combination of two or more distinct materials is commonly referred to as a "Composite Material", having been designed mechanically and chemically superior in function and characteristic than its individual constituent materials. Composite materials are used not only for aerospace and military, but also heavily used in boat/ship building and general composite industries which we are seeing increasingly more. Regardless of the various applications for composite materials, the industry is still limited and requires better fabrication technology and methodology in order to expand and grow. An example of this is that the majority of fabrication facilities nearby still use an antiquated wet lay-up process where fabrication still requires manual hand labor in a 3D environment impeding productivity of composite product design advancement. As an expert in the advanced composites field, I have developed fabrication skills with the use of machinery based on my past composite experience. In autumn 2011, the Korea government confirmed to fund my project. It is the development of a composite sanding machine. I began development of this semi-robotic prototype beginning in 2009. It has possibilities of replacing or augmenting the exhaustive and difficult jobs performed by human hands, such as sanding, grinding, blasting, and polishing in most often, very awkward conditions, and is also will boost productivity, improve surface quality, cut abrasive costs, eliminate vibration injuries, and protect workers from exposure to dust and airborne contamination. Ease of control and operation of the equipment in or outside of the sanding room is a key benefit to end-users. It will prove to be much more economical than normal robotics and minimize errors that commonly occur in factories. The key components and their technologies are a 360 degree rotational shoulder and a wrist that is controlled under PLC controller and joystick manual mode. Development on both of the key modules is complete and are now operational. The Korean government fund boosted my development and I expect to complete full scale development no later than 3rd quarter 2012. Even with the advantages of composite materials, there is still the need to repair or to maintain composite products with a higher level of technology. I have learned many composite repair skills on composite airframe since many composite fabrication skills including repair, requires training for non aerospace applications. The wind energy market is now requiring much larger blades in order to generate more electrical energy for wind farms. One single blade is commonly 50 meters or longer now. When a wind blade becomes damaged from external forces, on-site repair is required on the columns even under strong wind and freezing temperature conditions. In order to correctly obtain polymerization, the repair must be performed on the damaged area within a very limited time. The use of pre-impregnated glass fabric and heating silicone pad and a hot bonder acting precise heating control are surely required.

• Journal of the Korean Society of Radiology
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• v.7 no.2
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• pp.145-149
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• 2013

Helical Tomotherapy is an innovative means of delivering intensity modulated radiation therapy (IMRT) using a device that merges features of a linear accelerator and helical computed tomography (CT) scanner. Hereat, during helical tomotherapy process, megavoltage computed tomography (MVCT) image are usually used for guiding the precise set-up of patient before/after treatment delivery. But which would certainly increase the total dose for patients, this study was to investigate the imaging dose of MVCT using the cylindrical "Cheese" phantom on a tomotherapy machine. A set of cylindrical "Cheese" phantom was adopted for scanning with respectively pitch value (1, 2, 3 mm) with same number slice (10 slice), same length (approximately 9 cm) and phantom set-ups on the couch of tomotherapy system. The average MVCT imaging dose were measured using A1SL ion chamber inserted in the phantom with preset geometry. The MVCT scanning average dose for the cylindrical "Cheese" phantom was 2.24 cGy, 1.02 cGy, 0.81 cGy during respectively pitch value (pitch 1, 2, 3 mm) with same number slice (10 slice), and same length's average dose was 2.47 cGy, 1.28 cGy, 0.88 cGy respectively (pitch 1, 2, 3 mm). Two major parameters, the assigned pitch numbers and scanning length, where the most important impacts to the dose variation. The MVCT dose was inversely proportional to the CT pitch value. The results may provide a reliable guidance for proper planning design of the scanning region, which is valuable to help minimize the extra dose to patient. Questionnaires were distributed to Radiology departments at hospitals with 300 sickbeds throughout the Pohang region of North Gyeongsang Province concerning awareness and performance levels of infection control. The investigation included measurements of the pollution levels of imaging equipment and assistive apparatuses in order to prepare a plan for the activation of prevention and management of hospital infections. The survey was designed to question respondents in regards to personal data, infection management prevention education, and infection management guidelines.

• Clinics in Shoulder and Elbow
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• v.15 no.2
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• pp.79-85
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• 2012

Objective: It has been expected that patient with posttraumatic recurrent anterior shoulder dislocation might have limited daily life activity because of pain and apprehension of dislocation. But there have been only a small number of investigations regarding the rotator strength in this patient. The aim of this study is to find the characteristics about rotator strength of patient with posttraumatic recurrent anterior shoulder dislocation using an isokinetic testing. Method: We enrolled thirteen patients with posttraumatic recurrent anterior shoulder dislocation and fifteen sex, age-matched healthy nonathletic subjects in this controlled study. All participants were male and there were no significant differences between the two groups in age, height, weight, BMI. Isokinetic internal rotator and external rotator strength was evaluated with a Biodex Isokinetic Testing machine (Biodex Medical Systems, Shirley, NY, USA), tests were performed at 60 deg/sec and 180 deg/sec for both sides. Peak torque normalized to body weight, external rotator to internal rotator ratio, total work and fatigue were calculated for each angular velocity. The association between internal rotator and external rotator strength and shoulder instability was analyzed by comparisons with a control group. Results: Any notable differences could not be found between the two groups given all data from no symptomatic left shoulder. There were no significant differences between the two groups statistically in internal rotation strength of right shoulder. However, there has been a tendency that at all angular velocities, external rotator peak torque to body weight, total work and external rotator to internal rotator ratio were significantly lower in the anterior instability group than the control group at all angular velocities. There was no substantial difference between those groups with respect to the fatigue of external rotator and internal rotator in our study. Conclusion: The prominent characteristics of posttraumatic recurrent anterior shoulder dislocation are external rotator weakness and loss of balance with external rotator and internal rotator. Therefore selective training using this information rotator might be helpful in conservative treatment and rehabilitation.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.241-254
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• 2011

Financial time-series forecasting is one of the most important issues because it is essential for the risk management of financial institutions. Therefore, researchers have tried to forecast financial time-series using various data mining techniques such as regression, artificial neural networks, decision trees, k-nearest neighbor etc. Recently, support vector machines (SVMs) are popularly applied to this research area because they have advantages that they don't require huge training data and have low possibility of overfitting. However, a user must determine several design factors by heuristics in order to use SVM. For example, the selection of appropriate kernel function and its parameters and proper feature subset selection are major design factors of SVM. Other than these factors, the proper selection of instance subset may also improve the forecasting performance of SVM by eliminating irrelevant and distorting training instances. Nonetheless, there have been few studies that have applied instance selection to SVM, especially in the domain of stock market prediction. Instance selection tries to choose proper instance subsets from original training data. It may be considered as a method of knowledge refinement and it maintains the instance-base. This study proposes the novel instance selection algorithm for SVMs. The proposed technique in this study uses genetic algorithm (GA) to optimize instance selection process with parameter optimization simultaneously. We call the model as ISVM (SVM with Instance selection) in this study. Experiments on stock market data are implemented using ISVM. In this study, the GA searches for optimal or near-optimal values of kernel parameters and relevant instances for SVMs. This study needs two sets of parameters in chromosomes in GA setting : The codes for kernel parameters and for instance selection. For the controlling parameters of the GA search, the population size is set at 50 organisms and the value of the crossover rate is set at 0.7 while the mutation rate is 0.1. As the stopping condition, 50 generations are permitted. The application data used in this study consists of technical indicators and the direction of change in the daily Korea stock price index (KOSPI). The total number of samples is 2218 trading days. We separate the whole data into three subsets as training, test, hold-out data set. The number of data in each subset is 1056, 581, 581 respectively. This study compares ISVM to several comparative models including logistic regression (logit), backpropagation neural networks (ANN), nearest neighbor (1-NN), conventional SVM (SVM) and SVM with the optimized parameters (PSVM). In especial, PSVM uses optimized kernel parameters by the genetic algorithm. The experimental results show that ISVM outperforms 1-NN by 15.32%, ANN by 6.89%, Logit and SVM by 5.34%, and PSVM by 4.82% for the holdout data. For ISVM, only 556 data from 1056 original training data are used to produce the result. In addition, the two-sample test for proportions is used to examine whether ISVM significantly outperforms other comparative models. The results indicate that ISVM outperforms ANN and 1-NN at the 1% statistical significance level. In addition, ISVM performs better than Logit, SVM and PSVM at the 5% statistical significance level.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.95-108
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• 2011

TThis is a study of the personalization method that intelligently adapts the level of clustering considering purchasing index of a customer. In the e-biz era, many companies gather customers' demographic and transactional information such as age, gender, purchasing date and product category. They use this information to predict customer's preferences or purchasing patterns so that they can provide more customized services to their customers. The previous Customer-Segmentation method provides customized services for each customer group. This method clusters a whole customer set into different groups based on their similarity and builds predictive models for the resulting groups. Thus, it can manage the number of predictive models and also provide more data for the customers who do not have enough data to build a good predictive model by using the data of other similar customers. However, this method often fails to provide highly personalized services to each customer, which is especially important to VIP customers. Furthermore, it clusters the customers who already have a considerable amount of data as well as the customers who only have small amount of data, which causes to increase computational cost unnecessarily without significant performance improvement. The other conventional method called 1-to-1 method provides more customized services than the Customer-Segmentation method for each individual customer since the predictive model are built using only the data for the individual customer. This method not only provides highly personalized services but also builds a relatively simple and less costly model that satisfies with each customer. However, the 1-to-1 method has a limitation that it does not produce a good predictive model when a customer has only a few numbers of data. In other words, if a customer has insufficient number of transactional data then the performance rate of this method deteriorate. In order to overcome the limitations of these two conventional methods, we suggested the new method called Intelligent Customer Segmentation method that provides adaptive personalized services according to the customer's purchasing index. The suggested method clusters customers according to their purchasing index, so that the prediction for the less purchasing customers are based on the data in more intensively clustered groups, and for the VIP customers, who already have a considerable amount of data, clustered to a much lesser extent or not clustered at all. The main idea of this method is that applying clustering technique when the number of transactional data of the target customer is less than the predefined criterion data size. In order to find this criterion number, we suggest the algorithm called sliding window correlation analysis in this study. The algorithm purposes to find the transactional data size that the performance of the 1-to-1 method is radically decreased due to the data sparity. After finding this criterion data size, we apply the conventional 1-to-1 method for the customers who have more data than the criterion and apply clustering technique who have less than this amount until they can use at least the predefined criterion amount of data for model building processes. We apply the two conventional methods and the newly suggested method to Neilsen's beverage purchasing data to predict the purchasing amounts of the customers and the purchasing categories. We use two data mining techniques (Support Vector Machine and Linear Regression) and two types of performance measures (MAE and RMSE) in order to predict two dependent variables as aforementioned. The results show that the suggested Intelligent Customer Segmentation method can outperform the conventional 1-to-1 method in many cases and produces the same level of performances compare with the Customer-Segmentation method spending much less computational cost.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.43-57
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• 2012

To enhance the competitive advantage in a constantly changing business environment, an enterprise management must make the right decision in many business activities based on both internal and external information. Thus, providing accurate information plays a prominent role in management's decision making. Intuitively, historical data can provide a feasible estimate through the forecasting models. Therefore, if the service department can estimate the service quantity for the next period, the service department can then effectively control the inventory of service related resources such as human, parts, and other facilities. In addition, the production department can make load map for improving its product quality. Therefore, obtaining an accurate service forecast most likely appears to be critical to manufacturing companies. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average simulation. However, these methods are only efficient for data with are seasonal or cyclical. If the data are influenced by the special characteristics of product, they are not feasible. In our research, we propose a forecasting framework that predicts service demand of manufacturing organization by combining Case-based reasoning (CBR) and leveraging an unsupervised artificial neural network based clustering analysis (i.e., Self-Organizing Maps; SOM). We believe that this is one of the first attempts at applying unsupervised artificial neural network-based machine-learning techniques in the service forecasting domain. Our proposed approach has several appealing features : (1) We applied CBR and SOM in a new forecasting domain such as service demand forecasting. (2) We proposed our combined approach between CBR and SOM in order to overcome limitations of traditional statistical forecasting methods and We have developed a service forecasting tool based on the proposed approach using an unsupervised artificial neural network and Case-based reasoning. In this research, we conducted an empirical study on a real digital TV manufacturer (i.e., Company A). In addition, we have empirically evaluated the proposed approach and tool using real sales and service related data from digital TV manufacturer. In our empirical experiments, we intend to explore the performance of our proposed service forecasting framework when compared to the performances predicted by other two service forecasting methods; one is traditional CBR based forecasting model and the other is the existing service forecasting model used by Company A. We ran each service forecasting 144 times; each time, input data were randomly sampled for each service forecasting framework. To evaluate accuracy of forecasting results, we used Mean Absolute Percentage Error (MAPE) as primary performance measure in our experiments. We conducted one-way ANOVA test with the 144 measurements of MAPE for three different service forecasting approaches. For example, the F-ratio of MAPE for three different service forecasting approaches is 67.25 and the p-value is 0.000. This means that the difference between the MAPE of the three different service forecasting approaches is significant at the level of 0.000. Since there is a significant difference among the different service forecasting approaches, we conducted Tukey's HSD post hoc test to determine exactly which means of MAPE are significantly different from which other ones. In terms of MAPE, Tukey's HSD post hoc test grouped the three different service forecasting approaches into three different subsets in the following order: our proposed approach > traditional CBR-based service forecasting approach > the existing forecasting approach used by Company A. Consequently, our empirical experiments show that our proposed approach outperformed the traditional CBR based forecasting model and the existing service forecasting model used by Company A. The rest of this paper is organized as follows. Section 2 provides some research background information such as summary of CBR and SOM. Section 3 presents a hybrid service forecasting framework based on Case-based Reasoning and Self-Organizing Maps, while the empirical evaluation results are summarized in Section 4. Conclusion and future research directions are finally discussed in Section 5.