• Journal of the Korean GEO-environmental Society
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• v.18 no.7
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• pp.37-47
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• 2017

In this study, a series of full-scale field tests on prebored and precast steel pipe piles and the corresponding numerical analysis have been conducted in order to study the characteristics of pile load-settlement relations and shear stress transfer at the pile-soil interface. Dynamic pile load tests (EOID and restrike) have been performed on the piles and the estimated design pile loads from EOID and restrike tests were analysed. Class-A type numerical analyses conducted prior to the pile loading tests were 56~105%, 65~121% and 38~142% respectively of those obtained from static load tests. In addition, design loads estimated from the restrike tests indicate increases of 12~60% compared to those estimated in the EOID tests. The EOID tests show large end bearing capacity while the restrike tests demonstrate increased skin friction. When impact energy is insufficient during the restrike tests, the end bearing capacity may be underestimated. It has been found that total pile capacity would be reasonably estimated if skin friction from the restrike tests and end bearing capacity from the EOID are combined. The load-settlement relation measured from the static pile load tests and estimated from the numerical modelling is in general agreement until yielding occurs, after which results from the numerical analyses substantially deviated away from those obtained from the static load tests. The measured pile behaviour from the static load tests shows somewhat similar behaviour of perfectly-elastic plastic materials after yielding with a small increase in the pile load, while the numerical analyses demonstrates a gradual increase in the pile load associated with strain hardening approaching ultimate pile load. It has been discussed that the load-settlement relation mainly depends upon the stiffness of the ground, whilst the shear transfer mechanism depends on shear strength parameters.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.107-120
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• 2014

This study proposes a novel recommender system using the structural hole analysis to reflect qualitative and emotional information in recommendation process. Although collaborative filtering (CF) is known as the most popular recommendation algorithm, it has some limitations including scalability and sparsity problems. The scalability problem arises when the volume of users and items become quite large. It means that CF cannot scale up due to large computation time for finding neighbors from the user-item matrix as the number of users and items increases in real-world e-commerce sites. Sparsity is a common problem of most recommender systems due to the fact that users generally evaluate only a small portion of the whole items. In addition, the cold-start problem is the special case of the sparsity problem when users or items newly added to the system with no ratings at all. When the user's preference evaluation data is sparse, two users or items are unlikely to have common ratings, and finally, CF will predict ratings using a very limited number of similar users. Moreover, it may produces biased recommendations because similarity weights may be estimated using only a small portion of rating data. In this study, we suggest a novel limitation of the conventional CF. The limitation is that CF does not consider qualitative and emotional information about users in the recommendation process because it only utilizes user's preference scores of the user-item matrix. To address this novel limitation, this study proposes cluster-indexing CF model with the structural hole analysis for recommendations. In general, the structural hole means a location which connects two separate actors without any redundant connections in the network. The actor who occupies the structural hole can easily access to non-redundant, various and fresh information. Therefore, the actor who occupies the structural hole may be a important person in the focal network and he or she may be the representative person in the focal subgroup in the network. Thus, his or her characteristics may represent the general characteristics of the users in the focal subgroup. In this sense, we can distinguish friends and strangers of the focal user utilizing the structural hole analysis. This study uses the structural hole analysis to select structural holes in subgroups as an initial seeds for a cluster analysis. First, we gather data about users' preference ratings for items and their social network information. For gathering research data, we develop a data collection system. Then, we perform structural hole analysis and find structural holes of social network. Next, we use these structural holes as cluster centroids for the clustering algorithm. Finally, this study makes recommendations using CF within user's cluster, and compare the recommendation performances of comparative models. For implementing experiments of the proposed model, we composite the experimental results from two experiments. The first experiment is the structural hole analysis. For the first one, this study employs a software package for the analysis of social network data - UCINET version 6. The second one is for performing modified clustering, and CF using the result of the cluster analysis. We develop an experimental system using VBA (Visual Basic for Application) of Microsoft Excel 2007 for the second one. This study designs to analyzing clustering based on a novel similarity measure - Pearson correlation between user preference rating vectors for the modified clustering experiment. In addition, this study uses 'all-but-one' approach for the CF experiment. In order to validate the effectiveness of our proposed model, we apply three comparative types of CF models to the same dataset. The experimental results show that the proposed model outperforms the other comparative models. In especial, the proposed model significantly performs better than two comparative modes with the cluster analysis from the statistical significance test. However, the difference between the proposed model and the naive model does not have statistical significance.

• Korean Journal of Soil Science and Fertilizer
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• v.11 no.4
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• pp.283-295
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• 1979

A new Farmland Expansion and Development Promotion Law was enacted in 1975. This law authorizes the Government to undertake development within a declared "reclamation area", wherever the land owners are unable to do so. In order to give additional impetus to conversion of waste hilly land into productive farmland, these hilly land development projects were conducted as large scale scheme which include soil fertility improvements such as the application of lime and phosphate. Farmland Expansion and Development Promotion Corps has attempted to undertake annual farm surveys in order to obtain some information about hilly land agriculture and farming operations within the reclamation project areas since 1976. As survey data accumulates, more and more clear picture of hilly land farming come to appear and enable us to conduct in-depth study. Effects of such upland reclamation include converting of previously unproductive slopeland into cultivable farmland for lucrative and commercial farming or food production. Furthermore, idle or marginal resources such as farm labor, equipment and compost would be fully employed. Socio-economic effects would include increases in land value and attitude change of farmers. On the other hand the preservation of natural environments might be damaged to the some extend by the projects. As shown in Table 7, the average farm size increased from 3,156 pyeong($3.3m^2$) to 5,562 pyeong, a 76.2% increase. The proportion of small farms with less than I ha dropped from 59.8% to 34.4%, but that of the large farms over 2 ha rose from 13.1% to 32.0% (See Table 8). The survey results indicate that as the farming on reclaimed uplands become time-honored, the acreage devoted for food crop production decreases against the economic crop growing acreage (see Table 6). For example, in the case of uplands reclaimed in 1972, the ratio of food crop acreages decreased from 99.7% in 1972 to 62.5% in 1977, whereas that of economic crop acreages increased from 0.3% in 1972 to 37.5% in 1977. The government used to actively encourage the farmers to carry out food crop production in the reclaimed upland targting toward the realization of self-sufficiency in food grains. It is, however, apparent that the farmers did hardly take the government advises as far as their economic interest were concerned. Yield per 10a. of various crops from the reclaimed uplands by year were surveyed as seen in Table 12. On the average, barley production in the reclaimed areas achieved 83.3% of the average unit yield from the existing upland in its 5 th year. Soybean yields showed a modest increase from 64% in the first year to 95%, in the 5 th year. In contrast, economic crops such as red pepper, totacco and radish achieved their maximum target yields in 3 years from starting to cultivate on the reclaimed farms. In order to test the post economic viability, an economic analysis was performed for each of selected subprojects on the basis of the data obtained through survey. The average actual internal economic rate of return on upland reclamation investments was found to be 20.3% which exceeded other types of projects of land and water development such as tidal land reclamation, irrigation or paddy rearrangement. The actual IRRs of subcategories of upland reclamation projects varied from 17.9% to 21.4% depending upon the kinds of cropping system adopted in each reclaimed areas such as food, economic, fruit or forage crops.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.1-23
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• 2018

From the 21st century, various high-quality services have come up with the growth of the internet or 'Information and Communication Technologies'. Especially, the scale of E-commerce industry in which Amazon and E-bay are standing out is exploding in a large way. As E-commerce grows, Customers could get what they want to buy easily while comparing various products because more products have been registered at online shopping malls. However, a problem has arisen with the growth of E-commerce. As too many products have been registered, it has become difficult for customers to search what they really need in the flood of products. When customers search for desired products with a generalized keyword, too many products have come out as a result. On the contrary, few products have been searched if customers type in details of products because concrete product-attributes have been registered rarely. In this situation, recognizing texts in images automatically with a machine can be a solution. Because bulk of product details are written in catalogs as image format, most of product information are not searched with text inputs in the current text-based searching system. It means if information in images can be converted to text format, customers can search products with product-details, which make them shop more conveniently. There are various existing OCR(Optical Character Recognition) programs which can recognize texts in images. But existing OCR programs are hard to be applied to catalog because they have problems in recognizing texts in certain circumstances, like texts are not big enough or fonts are not consistent. Therefore, this research suggests the way to recognize keywords in catalog with the Deep Learning algorithm which is state of the art in image-recognition area from 2010s. Single Shot Multibox Detector(SSD), which is a credited model for object-detection performance, can be used with structures re-designed to take into account the difference of text from object. But there is an issue that SSD model needs a lot of labeled-train data to be trained, because of the characteristic of deep learning algorithms, that it should be trained by supervised-learning. To collect data, we can try labelling location and classification information to texts in catalog manually. But if data are collected manually, many problems would come up. Some keywords would be missed because human can make mistakes while labelling train data. And it becomes too time-consuming to collect train data considering the scale of data needed or costly if a lot of workers are hired to shorten the time. Furthermore, if some specific keywords are needed to be trained, searching images that have the words would be difficult, as well. To solve the data issue, this research developed a program which create train data automatically. This program can make images which have various keywords and pictures like catalog and save location-information of keywords at the same time. With this program, not only data can be collected efficiently, but also the performance of SSD model becomes better. The SSD model recorded 81.99% of recognition rate with 20,000 data created by the program. Moreover, this research had an efficiency test of SSD model according to data differences to analyze what feature of data exert influence upon the performance of recognizing texts in images. As a result, it is figured out that the number of labeled keywords, the addition of overlapped keyword label, the existence of keywords that is not labeled, the spaces among keywords and the differences of background images are related to the performance of SSD model. This test can lead performance improvement of SSD model or other text-recognizing machine based on deep learning algorithm with high-quality data. SSD model which is re-designed to recognize texts in images and the program developed for creating train data are expected to contribute to improvement of searching system in E-commerce. Suppliers can put less time to register keywords for products and customers can search products with product-details which is written on the catalog.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.53-69
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• 2011

Vision and voice-based technologies are commonly utilized for human-robot interaction. But it is widely recognized that the performance of vision and voice-based interaction systems is deteriorated by a large margin in the real-world situations due to environmental and user variances. Human users need to be very cooperative to get reasonable performance, which significantly limits the usability of the vision and voice-based human-robot interaction technologies. As a result, touch screens are still the major medium of human-robot interaction for the real-world applications. To empower the usability of robots for various services, alternative interaction technologies should be developed to complement the problems of vision and voice-based technologies. In this paper, we propose the use of accelerometer-based gesture interface as one of the alternative technologies, because accelerometers are effective in detecting the movements of human body, while their performance is not limited by environmental contexts such as lighting conditions or camera's field-of-view. Moreover, accelerometers are widely available nowadays in many mobile devices. We tackle the problem of classifying acceleration signal patterns of 26 English alphabets, which is one of the essential repertoires for the realization of education services based on robots. Recognizing 26 English handwriting patterns based on accelerometers is a very difficult task to take over because of its large scale of pattern classes and the complexity of each pattern. The most difficult problem that has been undertaken which is similar to our problem was recognizing acceleration signal patterns of 10 handwritten digits. Most previous studies dealt with pattern sets of 8~10 simple and easily distinguishable gestures that are useful for controlling home appliances, computer applications, robots etc. Good features are essential for the success of pattern recognition. To promote the discriminative power upon complex English alphabet patterns, we extracted 'motion trajectories' out of input acceleration signal and used them as the main feature. Investigative experiments showed that classifiers based on trajectory performed 3%~5% better than those with raw features e.g. acceleration signal itself or statistical figures. To minimize the distortion of trajectories, we applied a simple but effective set of smoothing filters and band-pass filters. It is well known that acceleration patterns for the same gesture is very different among different performers. To tackle the problem, online incremental learning is applied for our system to make it adaptive to the users' distinctive motion properties. Our system is based on instance-based learning (IBL) where each training sample is memorized as a reference pattern. Brute-force incremental learning in IBL continuously accumulates reference patterns, which is a problem because it not only slows down the classification but also downgrades the recall performance. Regarding the latter phenomenon, we observed a tendency that as the number of reference patterns grows, some reference patterns contribute more to the false positive classification. Thus, we devised an algorithm for optimizing the reference pattern set based on the positive and negative contribution of each reference pattern. The algorithm is performed periodically to remove reference patterns that have a very low positive contribution or a high negative contribution. Experiments were performed on 6500 gesture patterns collected from 50 adults of 30~50 years old. Each alphabet was performed 5 times per participant using $Nintendo{(R)}$ $Wii^{TM}$ remote. Acceleration signal was sampled in 100hz on 3 axes. Mean recall rate for all the alphabets was 95.48%. Some alphabets recorded very low recall rate and exhibited very high pairwise confusion rate. Major confusion pairs are D(88%) and P(74%), I(81%) and U(75%), N(88%) and W(100%). Though W was recalled perfectly, it contributed much to the false positive classification of N. By comparison with major previous results from VTT (96% for 8 control gestures), CMU (97% for 10 control gestures) and Samsung Electronics(97% for 10 digits and a control gesture), we could find that the performance of our system is superior regarding the number of pattern classes and the complexity of patterns. Using our gesture interaction system, we conducted 2 case studies of robot-based edutainment services. The services were implemented on various robot platforms and mobile devices including $iPhone^{TM}$. The participating children exhibited improved concentration and active reaction on the service with our gesture interface. To prove the effectiveness of our gesture interface, a test was taken by the children after experiencing an English teaching service. The test result showed that those who played with the gesture interface-based robot content marked 10% better score than those with conventional teaching. We conclude that the accelerometer-based gesture interface is a promising technology for flourishing real-world robot-based services and content by complementing the limits of today's conventional interfaces e.g. touch screen, vision and voice.

• The Korean Journal of Nuclear Medicine Technology
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• v.14 no.2
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• pp.197-202
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• 2010

Purpose: Recently, many hospitals have been tried to increase the satisfaction of the outpatients through blood-gathering, exam, result notice and process in a day. Each laboratory has been used the automatic equipment for the rapid requests of the result notice and the increase of the reliability and efficiency. Current automatic equipments that have been limited short TAT(Turn-Around Time)because of the restricted batch lists and 1 tip-5 detectors. The Gamma Pro which is made in Korea to improve the shortcomings of existing automation equipment, complemented with capacity to perform a wide range of domestic automation equipment. In this study, we evaluated the usefulness and reliability of short TAT by comparing Gamma Pro with current automatic equipment. Materials and Methods: We studied the correlation between Gamma Pro and RIA-mat 280 using the respective 100 specimens of low or high density to the patients who were requested the thyroid hormone test (Total T3, TSH and Free T4) in Samsung Medical Center Sep. 2009. To evaluate the split-level Gamma Pro, First, we measured accuracy and carry over on the tips. Second, the condition of optimal incubation was measured by the RPM (Revolution Per Minute) and revolution axis diameter on the incubator. For the analysis for the speed of the specimen-processing, TAT was investigated with the results in a certain time. Result: The correlation coefficients (R2) between the Gamma Pro and RIA-mat 280 showed a good correlation as T3 (0.98), TSH (0.99), FT4 (0.92). The coefficient of variation (C.V) and accuracy was 0.38 % and 98.3 % at tip 1 and 0.39 % and 98.6 % at tip 2. Carry over showed 0.80 % and 1.04% at tip 1 and tip 2, respectively. These results indicate that tips had no effect on carry over contamination. At the incubator condition, we found that the optimal condition was 1.0mm of diameter at 600RPM in 1.0mm and 1.5mm of at 500RPM or 1.0mm and 1.5 mm of diameter at 600 RPM. the Gamma Pro showed that the number of exam times were increased as maximum 20 times/day comparing to 6 times/day by current automatic equipment. These results also led to the short TAT from 4.20 hour to 2.19 hours in whole processing. Conclusion: The correlation of between the Gamma Pro and RIA-mat 280 was good and has not carry over contamination in tips. The domestic automation equipment (Gamma Pro) decreases the TAT in whole test comparing to RIA-280. These results demonstrate that Gamma Pro has a good efficiency, reliability and practical usefulness, which may contribute to the excellent skill to process the large scale specimens.

• Journal of Distribution Research
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• v.17 no.5
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• pp.85-111
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• 2012

Introduction : The purpose of this study was to examine the relationship between perceptual characteristics of consumers and intent of relational continuance for PB(Private Brand) products in discount stores. This study was conducted as an empirical study based on survey. For the empirical study, factors of PB products as characteristics perceived by consumers such as perceived quality, store image, brand image and perceived value were deduced from preceding studies. The effect of such factors on intent of relational continuance mediated by brand trust and brand equity of PB products was structurally examined. Research Model : Based on theory analysis and hypotheses, constructed a Structural Equation Model(SEM). The research model is shown in Figure 1. Research Method : This paper is based on s qualitative study of selected literature and empirical data. The survey for empirical study was carried out on consumers in Gyeonggi and Busan between January 2012 and May 2012. 300 surveys were distributed and 253 (84.3%) of them were returned. After excluding omissions and insincere responses, 245 surveys (81.6%) were used for final analysis as effective samples. Result : First of all, the Reliability was carried out for instrument used. The lower limit of 0.7 for Cronbach's Alpha as suggested by Hair et al. (1998). And Construct validity was established by carrying out exploratory factor analysis by Varimax rotation for all. Four factor result for the consumer's perceptual characteristics of PB Products, two mediating factors and one dependent factor. All constructs included in research framework have acceptable validity and reliability. Table 1 shows the factor loading, eigen value, explained variance and Cronbach's alpha for each factor. In order to assure validity of constructs, I implemented Confirmatory Factor Analysis (CFA), using AMOS 20.0. In confirmatory factor analysis, researcher can take control over the specification of indicators for each factor by hypothesizing that a specific factor is loaded with the relevant indicators. Moreover, CFA is particularly useful in the validation of scale for the measurement of specific construct. CFA result summarized Table 2 shows that the fit measures of all constructs fulfill the recommended level and loadings are significant. To test causal relationship between constructs in the research model, used AMOS 20.0 that provides a graphic module as method for analysing Structural Equation Modeling. The result of hypothesis test is shown in Table 3. As a result of empirical study, perceived quality, brand image and perceived value as selected attributes for PB products showed significantly positive (+) effect on brand trust and brand equity. Furthermore, brand trust and brand equity showed significantly positive (+) effect on intent of relational continuance. However, store image of discount stores selling the PB products was analyzed to have positive (+) effect on brand trust and no significant effect on brand equity. Discussion : Based on the results of this study, the relationship between overall quality, store image, brand image and value perceived by consumers about PB products and intent of relational continuance was structurally verified as being mediated by brand trust and brand equity. Looking at the results, a strategic approach that maximizes brand trust and equity value for PB products by large discount stores is required on top of basic efforts to improve quality, brand image and value of PB products in order to maximize consumer's intent of relational continuance and to continuously attract repeated purchase of products.

• Korean Journal of Organic Agriculture
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• v.25 no.2
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• pp.345-355
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• 2017

This study was conducted to evaluate rice seed disinfection efficacy of loess-sulfur for the suppression of Bakanae disease caused by Fusarium fujikuroi. Rice seeds were treated at different concentrations of loess-sulfur, soaking time and temperature, and combination of hot-water treatment. Rice cultivar, Shindongjin harvested from Bakanae disease-infested area in 2015, was used. Loess-sulfur was treated as follows; concentration of undiluted solution, 2%, 1% and 0.5%; soaking time of 24 and 48 hours; treatment temperature of $20^{\circ}C$ and $30^{\circ}C$; hot water treatment or not. Optimal conditions of rice seed disinfection were selected soaking time of 48 hours and the suspension of 0.5% and 1% loess-sulfur by investigating seed germination and isolation frequency of Fusarium spp. on Komada agar medium in vitro, and were established 3 disinfection conditions as hot water ($60^{\circ}C$, 10 min.) + 1% loess-sulfur ($20^{\circ}C$, 48 hours), 1% loess-sulfur only ($30^{\circ}C$, 48 hours) and 1% loess-sulfur only ($20^{\circ}C$, 48 hours) through additional test in greenhouse. Above 3 conditions were verified by rice seedling box and paddy field test in the way of investigating Bakanae diseased plants (%) and healthy plants (%). Consequently, most effective rice seed disinfection conditions on Bakanae disease were combination of hot water and 1% loess-sulfur and loess-sulfur only at $30^{\circ}C$. Furthermore, treatments with these conditions showed control value of 100% were maintained from seedling to the heading stage in the field. However, treatment of 1% loess-sulfur only at $20^{\circ}C$ showed low control value of 78.2% in paddy field. Hot water only treatment turned out to be an effective disinfection method when conducted thoroughly with $60^{\circ}C$, 10 min. However, it was thought additional soaking process with loess-sulfur after hot water treatment served more high control effect against Bakanae disease when rice seeds were disinfected on a large scale. This results expected rice seed disinfection with loess-sulfur were effectively and easily usable method if farmers had only one of either hot water-disinfector or seed-disinfector. In addition, loess-sulfur is well-known to farmers, simple to manufacture method and cheap.

• Food Science of Animal Resources
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• v.24 no.4
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• pp.349-354
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• 2004

Stress-susceptible pigs have been known as the porcine stress syndrome (PSS), swine PSS, also known as malignant hyperthermia (MH), is characterized as sudden death and production of poor meat quality such as PSE (pale, soft and exudative) meat after slaughtering. PSS and PSE meat cause major economic losses in the pig industry. A point mutation in the gene coding for the ryanodine receptor (RYR1) in porcine skeletal muscle, also known calcium (Ca$^{2+}$) release channel, has been associated with swine PSS and halothane sensitivity. We used the PCR-RFLP(restriction fragment length polymorphism) and PCR-SSCP (single strand conformation polymorphism) methods to detect the PSS gene mutation (C1843T) in the RYR1 gene and to estimate genotype frequencies of PSS gene in Korean pig breed populations. In PCR-RFLP and SSCP analyses, three genotypes of homozygous normal (N/M), heterozygous carrier (N/n) and homozygous recessive mutant (n/n) were detected using agarose or polyacrylamide gel electrophoresis, respectively. The proportions of normal, carrier and PSS pigs were 57.1, 35.7 and 7.1％ for Landrace, 82.5, 15.8 and 1.7％ far L. Yorkshire, 95.2, 4.8 and 0.0％ for Duroc and 72.0, 22.7 and 5.3％ for Crossbreed. Consequently, DNA-based diagnosis for the identification of stress-susceptible pigs of PSS and pigs producing PSE meat is a powerful technique. Especially, PCR-SSCP method may be useful as a rapid, sensitive and inexpensive test for the large-scale screening of PSS genotypes and pigs with PSE meat in the pork industry.y.

• Korean Journal of Agricultural Science
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• v.27 no.1
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• pp.39-47
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• 2000

A large scale field test of prefabricated vertical drains is performed to analyze the effect of parameters of the very soft clay at a test site. Compression index and the coefficient of horizontal consolidation obtained by back-analysis from the settlement data were compared with those obtained by means of laboratory tests. The Hyperbolic, Asaoka's and The Curve fitting methods are used to estimate final settlements and coefficients of consolidation. 1. Final settlement predicted with the Hyperbolic method was the largest, and the settlements predicted with the Asaoka's and the Curve fitting methods were nearly the same range, and it was concluded that smear effect has to be considered on design in the case that spacing of drains is small 2. The relationships of the measured consolidation ratio (Urn) and the designed consolidation ratio($U_t$) were showed as $U_m$ = (1.13~1.17)$U_t$, $U_m$ = (1.07~1.20)$U_t$, $U_m$ = (1.13~1.17)$U_t$ on the Hyperbolic, Asaoka's and the Curve fitting methods, respectively. The relations on the Asaoka's and the Curve fitting methods were nearly the same range. 3. The relationships of the field compression index($C_{cfield}$) and virgin compression index($V_{cclab}$) were showed as $C_{cfield}$ = (1.26~1.45)$V_{cclab}$, $C_{cfield}$ = (1.08~1.15) $V_{cclab}$, $C_{cfield}$ = (1.04~1.21)$V_{cclab}$, on the Hyperbolic, Asaoka's and the Curve fitting methods, respectively. 4. The ratio ($C_h/C_v$) of the coefficient of vertical consolidation and the coefficient of horizontal consolidation that is obtained by back-analysis from the settlement data was $C_h$=(0.7~0.9)$C_v$, $C_h$=(0.9~1.5)$C_v$, $C_h$=(2.4~3.0)$C_v$ on the Hyperbolic, Asaoka's and the Curve fitting methods, respectively. 5. It was concluded that the exact consolidation coefficient must be determined after the final settlement is predicted again when the consolidation is finished, because the field consolidation coefficient is decreased as the time allowed to be alone is increased.