• Journal of Internet Computing and Services
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• v.22 no.6
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• pp.129-140
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• 2021

The next generation mBcN should be built to cooperate with the wireless network to support hyper-speed and hyper-connectivity. In this paper, we propose a network architecture for the cooperation mBcN and 5G commercial network and architecture of the cooperative security gateway required for the cooperation. The proposed cooperative security gateway is between gNB and UPF to support LBO, SFC, and security. Our analysis shows that the proposed architecture has several advantages. First of all, user equipment connected with the mBcN can be easily connected through the 5G commercial radio network to the mBcN. Second, the military application traffic can be transmitted to mBcN without going through the 5G core network, reducing the end-to-end transmission delay without causing the traffic load on the 5G core network. In addition, the security level of the military application can effectively be maintained because the user equipment can be connected to the cooperative security gateway, and the traffic generated by the user equipment is transmitted to the mBcN without going through the 5G core network. Finally, we demonstrate that LBO, SFC, and security modules are essential functions of the proposed gateway in the 5G test-bed environment.

• Journal of the Korea Concrete Institute
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• v.20 no.1
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• pp.3-11
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• 2008

An experimental investigation was conducted with half-scale representations of the reinforced concrete shear walls with the opening subjected to cyclic loads. Specimens were half scale representations of a one-story wall in the apartment built in 1980. The area ratio of the opening section, as well as the size and critical section of coupling slabs, were decided based on results from a previous researches. The test result of WS-0.23 specimen, which has artificial damages to install the opening, the strength of the wall decreased due to the opening. It is apparent that influence of cutting reinforcing bars and decrease of effective section area lead to early first yield of the reinforcing bars before the allowable limit of drift ratio of the shear walls was reached. Therefore, proper reinforcing method is needed to prevent this. The decrease of strength of the shear walls by installation of openings shows a great deal of difference compared to previous researches. This is because flexural capacity of the slabs is working as coupling elements for the shear walls. The critical section of coupling slabs that works as coupling elements for shear walls was a little different from the results of previous researches.

• Journal of the Korean Recycled Construction Resources Institute
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• v.6 no.2
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• pp.100-107
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• 2018

The material and geometrical nonlinear finite elment analysis of UHPFRC 50M composite box girder was carried out. Constitute law in tension and compressive region of UHPFRC and HPC were modeled based on specimen test. The accuracy of nonlinear FEM analysis was verified by the experimental result of UHPFRC 50M composite girder. The UHPFRC 50M segmental composite box girder which has 1.5% steel fiber of volume fraction, 135MPa compressive strength and 18MPa tensile strength was tested. The post-tensioned UHPFRC composite girder consisted of three segment UHPFRC U-girder and High Strength Concrete reinforced slab. The parts of UHPFRC girder were modeled by 8nodes hexahedron elements and reinforcement bars and tendons were built by 2nodes linear elements by Midas FEA software. The constitutive laws of concrete materials were selected Multi-linear model both of tension and compression function under total strain crack model, which was included in classifying of smeared crack model. The nonlinearity of reinforcement elements and tendon was simulated by Von Mises criteria. The nonlinear static analysis was applied by incremental-iteration method with convergence criteria of Newton-Raphson. The validation of numerical analysis was verified by comparison with experimental result and numerical analysis result of load-deflection response, neutral axis coordinate change, and cracking pattern of girder. The load-deflection response was fitted very well with comparison to the experimental result. The finite element analysis is seen to satisfactorily predict flexural behavioral responses of post-tensioned, reinforced UHPFRC composite box girder.

• Korean Journal of Optics and Photonics
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• v.22 no.1
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• pp.46-52
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• 2011

A confocal microscope was developed utilizing a scanning sample stage based on a home-built double-compound flexure guide. A scanning sample stage with nano-scale resolution consisted of a double leaf spring based flexure, a displacement amplifying lever, a Piezo-electric Transducer(PZT) actuator and capacitance sensors. The performance of the two-axis stage was analyzed using a commercial finite element method program prior to the implementation. A single line laser was employed as the light source along with the Photo Multiplier Tube(PMT) that served as the detector. The performance of the developed confocal microscope was evaluated with a mouse ear skin imaging test. The designed scanning stage enabled us to build the confocal microscope without the two optical scanning mirror modules that are essential in the conventional laser scanning confocal microscope. The elimination of the scanning mirror modules makes the optical design of the confocal microscope simpler and more compact than the conventional system.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.30 no.2
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• pp.169-177
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• 2017

In this paper, classification of spall and crack faults of gear teeth is studied by applying the ensemble empirical mode decomposition(EEMD) for the gear transmission error(TE). Finite element models of the gears with the two faults are built, and TE is obtained by simulation of the gears under loaded contact. EEMD is applied to the residuals of the TE which are the difference between the normal and faulty signal. From the result, the difference of spall and crack faults are clearly identified by the intrinsic mode functions(IMF). A simple test bed is installed to illustrate the approach, which consists of motor, brake and a pair of spur gears. Two gears are employed to obtain the TE for the normal, spalled, and cracked gears, and the type of the faults are separated by the same EEMD application process. In order to quantify the results, crest factors are applied to each IMF. Characteristics of spall and crack are well represented by the crest factors of the first and the third IMF, which are used as the feature signals. The classification is carried out using the Bayes decision theory using the feature signals acquired through the experiments.

• The Journal of Korean Academy of Prosthodontics
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• v.47 no.2
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• pp.199-205
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• 2009

Purpose: The purpose of this research was to evaluate the shear bond strength between zirconia core and veneer ceramic after surface treatment. Material and methods: Zirconia cores(N=40, n=10, $10mm{\times}10mm{\times}3mm$) were fabricated according to the manufacturers' instructions and ultrasonically cleaned. The veneering ceramics(thickness 3 mm) were built and fired onto the zirconia core materials. Four groups of specimens with different surface treatment were prepared. Group I: without any pre-treatment, Group II: treated with sandblasting, Group III: treated with liner, Group IV: treated with sandblasting and liner. The shear bond strength was tested in a universal testing machine. Data were compared with an ANOVA and $Scheff{\acute{e}}$ post hoc test(P=.05). Results: The shear bond strength of group VI was significantly higher than the other groups. Conclusion: Both mechanically and chemically treated simultaneously on zirconia core surface influenced the shear bond strength between the core and veneering ceramic in all-ceramic systems.

• The Korean Journal of Applied Statistics
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• v.37 no.2
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• pp.209-224
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• 2024

Speaker verification is becoming popular as a method of non-face-to-face identity authentication. It involves determining whether two voice data belong to the same speaker. In cases where the criminal's voice remains at the crime scene, it is vital to establish a speaker verification system that can accurately compare the two voice evidence. In this study, to achieve this, a new speaker verification system was built using a deep learning model for Korean language. High-dimensional voice data with a high variability like background noise made it necessary to use deep learning-based methods for speaker matching. To construct the matching algorithm, the ECAPA-TDNN model, known as the most famous deep learning system for speaker verification, was selected. A large dataset of the voice data, Voxceleb, collected from people of various nationalities without Korean. To study the appropriate form of datasets necessary for learning the Korean language, experiments were carried out to find out how Korean voice data affects the matching performance. The results showed that when comparing models learned only with Voxceleb and models learned with datasets combining Voxceleb and Korean datasets to maximize language and speaker diversity, the performance of learning data, including Korean, is improved for all test sets.

• Asia pacific journal of information systems
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• v.19 no.2
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• pp.139-155
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• 2009

For the last few decades, many studies have tried to explore and unveil venture companies' success factors and unique features in order to identify the sources of such companies' competitive advantages over their rivals. Such venture companies have shown tendency to give high returns for investors generally making the best use of information technology. For this reason, many venture companies are keen on attracting avid investors' attention. Investors generally make their investment decisions by carefully examining the evaluation criteria of the alternatives. To them, credit rating information provided by international rating agencies, such as Standard and Poor's, Moody's and Fitch is crucial source as to such pivotal concerns as companies stability, growth, and risk status. But these types of information are generated only for the companies issuing corporate bonds, not venture companies. Therefore, this study proposes a method for evaluating venture businesses by presenting our recent empirical results using financial data of Korean venture companies listed on KOSDAQ in Korea exchange. In addition, this paper used multi-class SVM for the prediction of DEA-based efficiency rating for venture businesses, which was derived from our proposed method. Our approach sheds light on ways to locate efficient companies generating high level of profits. Above all, in determining effective ways to evaluate a venture firm's efficiency, it is important to understand the major contributing factors of such efficiency. Therefore, this paper is constructed on the basis of following two ideas to classify which companies are more efficient venture companies: i) making DEA based multi-class rating for sample companies and ii) developing multi-class SVM-based efficiency prediction model for classifying all companies. First, the Data Envelopment Analysis(DEA) is a non-parametric multiple input-output efficiency technique that measures the relative efficiency of decision making units(DMUs) using a linear programming based model. It is non-parametric because it requires no assumption on the shape or parameters of the underlying production function. DEA has been already widely applied for evaluating the relative efficiency of DMUs. Recently, a number of DEA based studies have evaluated the efficiency of various types of companies, such as internet companies and venture companies. It has been also applied to corporate credit ratings. In this study we utilized DEA for sorting venture companies by efficiency based ratings. The Support Vector Machine(SVM), on the other hand, is a popular technique for solving data classification problems. In this paper, we employed SVM to classify the efficiency ratings in IT venture companies according to the results of DEA. The SVM method was first developed by Vapnik (1995). As one of many machine learning techniques, SVM is based on a statistical theory. Thus far, the method has shown good performances especially in generalizing capacity in classification tasks, resulting in numerous applications in many areas of business, SVM is basically the algorithm that finds the maximum margin hyperplane, which is the maximum separation between classes. According to this method, support vectors are the closest to the maximum margin hyperplane. If it is impossible to classify, we can use the kernel function. In the case of nonlinear class boundaries, we can transform the inputs into a high-dimensional feature space, This is the original input space and is mapped into a high-dimensional dot-product space. Many studies applied SVM to the prediction of bankruptcy, the forecast a financial time series, and the problem of estimating credit rating, In this study we employed SVM for developing data mining-based efficiency prediction model. We used the Gaussian radial function as a kernel function of SVM. In multi-class SVM, we adopted one-against-one approach between binary classification method and two all-together methods, proposed by Weston and Watkins(1999) and Crammer and Singer(2000), respectively. In this research, we used corporate information of 154 companies listed on KOSDAQ market in Korea exchange. We obtained companies' financial information of 2005 from the KIS(Korea Information Service, Inc.). Using this data, we made multi-class rating with DEA efficiency and built multi-class prediction model based data mining. Among three manners of multi-classification, the hit ratio of the Weston and Watkins method is the best in the test data set. In multi classification problems as efficiency ratings of venture business, it is very useful for investors to know the class with errors, one class difference, when it is difficult to find out the accurate class in the actual market. So we presented accuracy results within 1-class errors, and the Weston and Watkins method showed 85.7% accuracy in our test samples. We conclude that the DEA based multi-class approach in venture business generates more information than the binary classification problem, notwithstanding its efficiency level. We believe this model can help investors in decision making as it provides a reliably tool to evaluate venture companies in the financial domain. For the future research, we perceive the need to enhance such areas as the variable selection process, the parameter selection of kernel function, the generalization, and the sample size of multi-class.

• Asia pacific journal of information systems
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• v.22 no.1
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• pp.29-52
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• 2012

Consumers differ in the way they make a purchase. An audio mania would willingly make a bold, yet serious, decision to buy a top-of-the-line home theater system, while he is not interested in replacing his two-decade-old shabby car. On the contrary, an automobile enthusiast wouldn't mind spending forty thousand dollars to buy a new Jaguar convertible, yet cares little about his junky component system. It is product involvement that helps us explain such differences among individuals in the purchase style. Product involvement refers to the extent to which a product is perceived to be important to a consumer (Zaichkowsky, 2001). Product involvement is an important factor that strongly influences consumer's purchase decision-making process, and thus has been of prime interest to consumer behavior researchers. Furthermore, researchers found that involvement is closely related to perceived risk (Dholakia, 2001). While abundant research exists addressing how product involvement relates to overall perceived risk, little attention has been paid to the relationship between involvement and different types of perceived risk in an electronic commerce setting. Given that perceived risk can be a substantial barrier to the online purchase (Jarvenpaa, 2000), research addressing such an issue will offer useful implications on what specific types of perceived risk an online firm should focus on mitigating if it is to increase sales to a fullest potential. Meanwhile, past research has focused on such consumer responses as information search and dissemination as a consequence of involvement, neglecting other behavioral responses like online merchant selection. For one example, will a consumer seriously considering the purchase of a pricey Guzzi bag perceive a great degree of risk associated with online buying and therefore choose to buy it from a digital storefront rather than from an online marketplace to mitigate risk? Will a consumer require greater trust on the part of the online merchant when the perceived risk of online buying is rather high? We intend to find answers to these research questions through an empirical study. This paper explores the impact of enduring product involvement and perceived risks on required trust level, and further on online merchant choice. For the purpose of the research, five types or components of perceived risk are taken into consideration, including financial, performance, delivery, psychological, and social risks. A research model has been built around the constructs under consideration, and 12 hypotheses have been developed based on the research model to examine the relationships between enduring involvement and five components of perceived risk, between five components of perceived risk and required trust level, between enduring involvement and required trust level, and finally between required trust level and preference toward an e-tailer. To attain our research objectives, we conducted an empirical analysis consisting of two phases of data collection: a pilot test and main survey. The pilot test was conducted using 25 college students to ensure that the questionnaire items are clear and straightforward. Then the main survey was conducted using 295 college students at a major university for nine days between December 13, 2010 and December 21, 2010. The measures employed to test the model included eight constructs: (1) enduring involvement, (2) financial risk, (3) performance risk, (4) delivery risk, (5) psychological risk, (6) social risk, (7) required trust level, (8) preference toward an e-tailer. The statistical package, SPSS 17.0, was used to test the internal consistency among the items within the individual measures. Based on the Cronbach's ${\alpha}$ coefficients of the individual measure, the reliability of all the variables is supported. Meanwhile, the Amos 18.0 package was employed to perform a confirmatory factor analysis designed to assess the unidimensionality of the measures. The goodness of fit for the measurement model was satisfied. Unidimensionality was tested using convergent, discriminant, and nomological validity. The statistical evidences proved that the three types of validity were all satisfied. Now the structured equation modeling technique was used to analyze the individual paths along the relationships among the research constructs. The results indicated that enduring involvement has significant positive relationships with all the five components of perceived risk, while only performance risk is significantly related to trust level required by consumers for purchase. It can be inferred from the findings that product performance problems are mostly likely to occur when a merchant behaves in an opportunistic manner. Positive relationships were also found between involvement and required trust level and between required trust level and online merchant choice. Enduring involvement is concerned with the pleasure a consumer derives from a product class and/or with the desire for knowledge for the product class, and thus is likely to motivate the consumer to look for ways of mitigating perceived risk by requiring a higher level of trust on the part of the online merchant. Likewise, a consumer requiring a high level of trust on the merchant will choose a digital storefront rather than an e-marketplace, since a digital storefront is believed to be trustworthier than an e-marketplace, as it fulfills orders by itself rather than acting as an intermediary. The findings of the present research provide both academic and practical implications. The first academic implication is that enduring product involvement is a strong motivator of consumer responses, especially the selection of a merchant, in the context of electronic shopping. Secondly, academicians are advised to pay attention to the finding that an individual component or type of perceived risk can be used as an important research construct, since it would allow one to pinpoint the specific types of risk that are influenced by antecedents or that influence consequents. Meanwhile, our research provides implications useful for online merchants (both online storefronts and e-marketplaces). Merchants may develop strategies to attract consumers by managing perceived performance risk involved in purchase decisions, since it was found to have significant positive relationship with the level of trust required by a consumer on the part of the merchant. One way to manage performance risk would be to thoroughly examine the product before shipping to ensure that it has no deficiencies or flaws. Secondly, digital storefronts are advised to focus on symbolic goods (e.g., cars, cell phones, fashion outfits, and handbags) in which consumers are relatively more involved than others, whereas e- marketplaces should put their emphasis on non-symbolic goods (e.g., drinks, books, MP3 players, and bike accessories).

• Journal of Cadastre & Land InformatiX
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• v.46 no.2
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• pp.5-25
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• 2016

The purpose of this study develops the methodology to extract the poor deprived districts using the data from the national spatial data infrastructure portal. Particularly this study tries to select more acute indicators and to test the operability of such indicators. Also this study is focused on the versatile methodology that can be adjusted to incorporate alternative indicators that might be appropriate according to the hierarchy of the spatial unit. The indicator sets are composed of three dimensions: the poor class, the poor old housing, and poor residential neighborhood environment. Each representative indicator is selected based on the characteristics of the poor deprived districts. As a result, at the level of administrative Dong, key indicators for extracting the poor deprived districts are number of recipients of national basic living security per thousand persons and ratio of households living at old detached house. At the level of the national based zip code district, the ratio of buildings built on parcels located at roads below 4m in width, the ratio of small parcels below $60m^2$ and the ratio of poor old buildings are very important indicators. The result of grid analysis by overlaying the coverage of multiple indicators shows that relatively more vulnerable and deprived districts can be extracted at the small sub-district level. This study suggests the possibility to create the high value-added information, using the data from the national spatial data infrastructure portal. This methodology enables policymakers to select the priority target districts of poor deprived district more effectively.