• Reproductive and Developmental Biology
• /
• v.31 no.3
• /
• pp.161-167
• /
• 2007

In this study, we constructed and tested retrovirus vectors designed to express the human thrombopoietin gene under the control of the tetracycline-inducible promoters. To increase the hTPO gene expression at him-on state, WPRE sequence was also introduced into retrovirus vector at downstream region of either the hTPO gene or the sequence encoding reverse tetracycline-controlled transactivator (rtTA). Primary culture cells (PFF, porcine fetal fibroblast; CEF, chicken embryonic fibroblast) infected with the recombinant retrovirus were cultured in the medium supplemented with or without doxycycline for 48hr, and induction efficiency was measured by comparing the hTPO gene expression level using RT-PCR, western blot and ELISA. Higher hPTO expression and tighter expression control were observed from the vector in which the WPRE sequence was placed at downstream of the hTPO (in CEF) or rtTA(in PFF) gene. This resulting tetracycline inducible vector system may be helpful in solving serious physiological disturbance problems which have been a major obstacle in successful production of transgenic animals.

• Journal of the Korean Society of Fisheries and Ocean Technology
• /
• v.21 no.2
• /
• pp.131-136
• /
• 1985

The calculation of the resulting fluid motion is an important problem of ship hydrodynamics. For a partially immersed body the condition of constant pressure at the free surface can be linearized. The resulting linear boundary-value problem for the velocity potential is the Neumann-Kelvin problem. The two-dimensional Neumann-Kelvin problem is studied for the half-immersed circular cylinder by Ursell. Maruo introduced a slender body approach to simplify the Neumann-Kelvin problem in such a way that the integral equation which determines the singularity distribution over the hull surface can be solved by a marching procedure of step by step integration starting at bow. In the present pater for the two-dimensional Neumann-Kelvin problem, it has been suggested that any solution of the problem must have singularities in the corners between the body surface and free surface. There can be infinitely many solutions depending on the singularities in the coroners.

• Journal of Intelligence and Information Systems
• /
• v.16 no.4
• /
• pp.21-41
• /
• 2010

This paper proposes a system that can serve users with appropriate search results through real time filtering, and implemented adaptive user profiling based personalized information retrieval system(PIRS) using users' implicit feedbacks in order to deal with the problem of existing search systems such as Google or MSN that does not satisfy various user' personal search needs. One of the reasons that existing search systems hard to satisfy various user' personal needs is that it is not easy to recognize users' search intentions because of the uncertainty of search intentions. The uncertainty of search intentions means that users may want to different search results using the same query. For example, when a user inputs "java" query, the user may want to be retrieved "java" results as a computer programming language, a coffee of java, or a island of Indonesia. In other words, this uncertainty is due to ambiguity of search queries. Moreover, if the number of the used words for a query is fewer, this uncertainty will be more increased. Real-time filtering for search results returns only those results that belong to user-selected domain for a given query. Although it looks similar to a general directory search, it is different in that the search is executed for all web documents rather than sites, and each document in the search results is classified into the given domain in real time. By applying information filtering using real time directory classifying technology for search results to personalization, the number of delivering results to users is effectively decreased, and the satisfaction for the results is improved. In this paper, a user preference profile has a hierarchical structure, and consists of domains, used queries, and selected documents. Because the hierarchy structure of user preference profile can apply the context when users perfomed search, the structure is able to deal with the uncertainty of user intentions, when search is carried out, the intention may differ according to the context such as time or place for the same query. Furthermore, this structure is able to more effectively track web documents search behaviors of a user for each domain, and timely recognize the changes of user intentions. An IP address of each device was used to identify each user, and the user preference profile is continuously updated based on the observed user behaviors for search results. Also, we measured user satisfaction for search results by observing the user behaviors for the selected search result. Our proposed system automatically recognizes user preferences by using implicit feedbacks from users such as staying time on the selected search result and the exit condition from the page, and dynamically updates their preferences. Whenever search is performed by a user, our system finds the user preference profile for the given IP address, and if the file is not exist then a new user preference profile is created in the server, otherwise the file is updated with the transmitted information. If the file is not exist in the server, the system provides Google' results to users, and the reflection value is increased/decreased whenever user search. We carried out some experiments to evaluate the performance of adaptive user preference profile technique and real time filtering, and the results are satisfactory. According to our experimental results, participants are satisfied with average 4.7 documents in the top 10 search list by using adaptive user preference profile technique with real time filtering, and this result shows that our method outperforms Google's by 23.2%.

• Food Science and Preservation
• /
• v.14 no.4
• /
• pp.414-418
• /
• 2007

The physicochemical characteristics and sensory properties of Deodeok wine, formed by leaching of Deodeok at room temperature for 180 days, were investigated over the following range of Deodeok levels: 10, 15 and 20% (all w/v). The higher the level of Deodeok, the greater were the final values of total sugars, reducing sugars, total polyphenols, and crude saponins. The Hunter's b-value (yellowness) of Deodeok wine varied markedly with Deodeok levels, and yellowness was highest in Deodeok wine containing 20% (w/v) Deodeok. Non-volatile compounds, that form the basis of the liquor tax law, were 0.64, 1.38 and 2.11% (all w/v), respectively, at day 160. Of these values, that of 2.11% (w/v), the level of non-volatile compounds in Deodeok wine containing 20% (w/v) Deodeok, was in accord with the liquor tax law (that requires this figure to be 2.0%). Sensory evaluation showed that Deodeok wine containing 20% (w/v) Deodeok was superior to the other wines tested.

• The Journal of Small Business Innovation
• /
• v.20 no.1
• /
• pp.1-18
• /
• 2017

This study examined whether divided and spun-off companies design and execute spin-offs to minimize tax burdens by analyzing multiple spin-off transactions in the Dongbu Group, when the Korean tax law regarding corporate restructuring was amended in July 2010. Before the July 2010 tax amendment, taxes on the capital gains arising from the qualifying spin-off were deferred to the earlier of the shares in or assets acquired by the spun-off company are disposed. This tax treatment relieves the divided company's tax burden by deferring taxes on capital gains, compared with non-qualifying spin-offs. However, if shares in or assets acquired by the spun-off company are disposed after the July 2010 tax amendment, the capital gain incurred at the time of the qualifying spin-off would be taxed again at the spun-off company, in addition to a taxation on the divided company's capital gains. This creates double taxation implications for the parties involved in the spin-off. As a result, the double taxation may outweigh the benefit from the tax deferral on the qualifying spin-off, which may make a qualifying spin-off tax unfavorable. Among the four spin-off cases in the Dongbu Group addressed in this study, a spin-off occurred before the tax amendment, whereas three spin-offs occurred after the tax amendment. Initially, we expected that the spin-off before the tax amendment would be a qualifying spin-off, and the other three spin-offs would be non-qualifying spin-offs, considering the taxation rules before and after the July 2010 tax amendment. However, based on the review of summarized balance sheets disclosed in the spin-offs' corporate filings, no capital gains arose during the four spin-offs that occurred in the Dongbu Group. Therefore, we concluded that the Dongbu Group considered non-tax factors more than tax factors while designing and executing the spin-offs. The local media posited during this period that these spin-offs may intend to resolve financial issues in the Dongbu Group, and this analysis was supported by the fact that some shares in the new spun-off companies were sold by the Dongbu Group. Our case studies provide evidence that all costs, including both tax and non-tax costs, must be considered in the course of spin-offs, in addition to the tax burdens on all parties involved in the corporate restructuring, which parallels the work of Scholes et al. (2008). This study provides implications that various aspects should be considered and reviewed in advance when the management makes decisions for effective tax planning.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.24 no.3
• /
• pp.247-262
• /
• 2022

In domestic tunnels, it is mandatory to install CCTVs in tunnels longer than 200 m which are also recommended by installation of a CCTV-based automatic accident detection system. In general, the CCTVs in the tunnel are installed at a low height as well as near by the moving vehicles due to the spatial limitation of tunnel structure, so a severe perspective effect takes place in the distance of installed CCTV and moving vehicles. Because of this effect, conventional CCTV-based accident detection systems in tunnel are known in general to be very hard to achieve the performance in detection of unexpected accidents such as stop or reversely moving vehicles, person on the road and fires, especially far from 100 m. Therefore, in this study, the region of interest is set up and a new concept of inverse perspective transformation technique is introduced. Since moving vehicles in the transformed image is enlarged proportionally to the distance from CCTV, it is possible to achieve consistency in object detection and identification of actual speed of moving vehicles in distance. To show this aspect, two datasets in the same conditions are composed with the original and the transformed images of CCTV in tunnel, respectively. A comparison of variation of appearance speed and size of moving vehicles in distance are made. Then, the performances of the object detection in distance are compared with respect to the both trained deep-learning models. As a result, the model case with the transformed images are able to achieve consistent performance in object and accident detections in distance even by 200 m.

• Economic Analysis
• /
• v.26 no.1
• /
• pp.84-132
• /
• 2020

A central bank infers market expectations of future yields based on yield curves. The central bank needs to precisely understand the changes in market expectations of future yields in order to have a more effective monetary policy. This need explains why a range of models have attempted to produce yield curves and market expectations that are as accurate as possible. Alongside the development of bond markets, the interconnectedness between them and macroeconomic factors has deepened, and this has rendered understanding of what macroeconomic variables affect yield curves even more important. However, the existence of various theories about determinants of yields inevitably means that previous studies have applied different macroeconomics variables when estimating yield curves. This indicates model uncertainties and naturally poses a question: Which model better estimates yield curves? Put differently, which variables should be applied to better estimate yield curves? This study employs the Dynamic Nelson-Siegel Model and takes the Bayesian approach to variable selection in order to ensure precision in estimating yield curves and market expectations of future yields. Bayesian variable selection may be an effective estimation method because it is expected to alleviate problems arising from a priori selection of the key variables comprising a model, and because it is a comprehensive approach that efficiently reflects model uncertainties in estimations. A comparison of Bayesian variable selection with the models of previous studies finds that the question of which macroeconomic variables are applied to a model has considerable impact on market expectations of future yields. This shows that model uncertainties exert great influence on the resultant estimates, and that it is reasonable to reflect model uncertainties in the estimation. Those implications are underscored by the superior forecasting performance of Bayesian variable selection models over those models used in previous studies. Therefore, the use of a Bayesian variable selection model is advisable in estimating yield curves and market expectations of yield curves with greater exactitude in consideration of the impact of model uncertainties on the estimation.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.18 no.5
• /
• pp.185-196
• /
• 2023

This paper investigates the effects of entrepreneurs' cognitive biases on business opportunity evaluation, given their strong entrepreneurial spirit, which is characterized by innovation, proactivity, and risk-taking. When making decisions related to business activities, entrepreneurs typically make rational judgments based on their knowledge, experience, and the advice of external experts. However, in situations of extreme stress or when quick decisions are required, they often rely on heuristics based on their cognitive biases. In particular, we often see cases where entrepreneurs fail because they make decisions based on heuristics in the process of evaluating and selecting new business opportunities that are planned to guarantee the growth and sustainability of their companies. This study was conducted in response to the need for research to clarify the effects of entrepreneurs' cognitive biases on new business opportunity evaluation, given that the cognitive biases of entrepreneurs, which are formed by repeated successful experiences, can sometimes lead to business failure. Although there have been many studies on the effects of cognitive biases on entrepreneurship and opportunity evaluation among university students and general people who aspire to start a business, there have been few studies that have clarified the relationship between cognitive biases and social networks among entrepreneurs. In contrast to previous studies, this study conducted empirical surveys of entrepreneurs only, and also conducted research on the relationship with social networks. For the study, a survey was conducted using a parallel survey method using online mobile surveys and self-report questionnaires from 150 entrepreneurs of small and medium-sized enterprises. The results of the study showed that 'overconfidence' and 'illusion of control', among the independent variables of entrepreneurs' cognitive biases, had a statistically significant positive(+) effect on business opportunity evaluation. In addition, it was confirmed that the moderating variable, social network, moderates the effect of overconfidence on business opportunity evaluation. This study showed that entrepreneurs' cognitive biases play a role in the process of evaluating and selecting new business opportunities, and that social networks play a role in moderating the structural relationship between entrepreneurs' cognitive biases and business opportunity evaluation. This study is expected to be of great help not only to entrepreneurs, but also to entrepreneur education and policy making, by showing how entrepreneurs can use cognitive biases in a positive way and the influence of social networks.

• Economic and Environmental Geology
• /
• v.45 no.3
• /
• pp.317-333
• /
• 2012

The Yeongweol Group is a Lower Paleozoic mixed carbonate-siliciclastic sequence in the Taebaeksan Basin of Korea, and consists of five lithologic formations: Sambangsan, Machari, Wagok, Mungok, and Yeongheung in ascending order. Sequence stratigraphic interpretation of the group indicates that initial flooding in the Yeongweol area of the Taebaeksan Basin resulted in basal siliciclastic-dominated sequences of the Sambangsan Formation during the Middle Cambrian. The accelerated sea-level rise in the late Middle to early Late Cambrian generated a mixed carbonate-siliciclastic slope or deep ramp sequence of shale, grainstone and breccia intercalations, representing the lower part of the Machari Formation. The continued rise of sea level in the Late Cambrian made substantial accommodation space and activated subtidal carbonate factory, forming carbonate-dominated subtidal platform sequence in the middle and upper parts of the Machari Formation. The overlying Wagok Formation might originally be a ramp carbonate sequence of subtidal ribbon carbonates and marls with conglomerates, deposited during the normal rise of relative sea level in the late Late Cambrian. The formation was affected by unstable dolomitization shortly after the deposition during the relative sea-level fall in the latest Cambrian or earliest Ordovician. Subsequently, it was extensively dolomitized under the deep burial diagenetic condition. During the Early Ordovician (Tremadocian), global transgression (viz. Sauk) was continued, and subtidal ramp deposition was sustained in the Yeongweol platform, forming the Mungok Formation. The formation is overlain by the peritidal carbonates of the Yeongheung Formation, and is stacked by cyclic sedimentation during the Early to Middle Ordovician (Arenigian to Caradocian). The lithologic change from subtidal ramp to peritidal facies is preserved at the uppermost part of the Mungok Formation. The transition between Sauk and Tippecanoe sequences is recognized within the middle part of the Yeongheung Formation as a minimum accommodation zone. The global eustatic fall in the earliest Middle Ordovician and the ensuing rise of relative sea level during the Darrwillian to Caradocian produced broadly-prograding peritidal carbonates of shallowing-upward cyclic successions within the Yeongheung Formation. The reconstructed relative sea-level curve of the Yeongweol platform is very similar to that of the Taebaek platform. This reveals that the Yeongweol platform experienced same tectonic movements with the Taebaek platform, and consequently that both platform sequences might be located in a body or somewhere separately in the margin of the North China platform. The significant differences in lithologic and stratigraphic successions imply that the Yeongweol platform was much far from the Taebaek platform and not associated with the Taebaek platform as a single depositional system. The Yeongweol platform was probably located in relatively open shallow marine environments, whereas the Taebaek platform was a part of the restricted embayments. During the late Paleozoic to early Mesozoic amalgamations of the Korean massifs, the Yeongweol platform was probably pushed against the Taebaek platform by the complex movement, forming fragmented platform sequences of the Taebaeksan Basin.

• Journal of Intelligence and Information Systems
• /
• v.27 no.3
• /
• pp.57-73
• /
• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.