• Tunnel and Underground Space
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• v.20 no.5
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• pp.352-359
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• 2010

Stability of twin tunnels depends on the pillar width and the ground condition. In this study, large scale model tests were conducted for investigating the influence of the pillar width of twin tunnels on their behavior in the regular horizontal jointed rock mass. Jointed rocks was composed of concrete blocks. Pillar width of twin tunnels varied in 0.29D, 0.59D, 0.88D and 1.18D, where D is the tunnel width. During the test, pillar stress, lining stress, tunnel distortion, and ground displacement were measured. Lateral earth pressure coefficient was kept in a constant value 1.0. As a result, it was found that the pillar stress and the displacement of the ground and tunnel were increased by decreasing pillar width. The maximum displacement rate was measured just after the upper excavation in each construction sequence. And the maximum influence position was the right shoulder of the preceeding tunnel at the pillar side. It was also found that for the stability assessment the inner displacement was more critical than the crown displacement. The influence zone was formed at the pillar width 0.59D~0.88D that was smaller than 0.8D~2.0D, which was proposed by experience for a good ground condition. And it would be concluded that horizontal joints could also influence on the stability of the twin tunnels.

• The Journal of Art Theory & Practice
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• no.3
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• pp.51-66
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• 2005

With the advent of the time when the competitiveness of a nation or a city can be evaluated by the extent of cultural reception, the art exhibitions, like other genres of art, are actively held in various ways by different institutions. The spaces of exhibition offered not only by public museums and galleries but also by private galleries, alternative spaces and open-air exhibition spots are being increased. Likewise, the number of exhibitions organized on a large scale by Biennales and local governments is on the increase. In accordance with actual tendency, the Ministry of Culture and Tourism has planned to increase the number of museums and galleries up to 500 by the year of 2011. Under these circumstances, the actual situation shows that the central and local governments are adopting beneficial measures to support the private galleries such as permission of construction on green belt areas and tax deduction. That is, our society has come to realize that the necessity and importance of public as well as private galleries to play their roles as cultural and educational institution. Now, the number of galleries is growing. Exhibitions are held everywhere. Therefore, spectators have more chance to visit them than before. With these conditions and the growing number of amateurs, social interest in curator organizing exhibitions is getting popular. Because of the rise of curatorial popularity on the social level, the influence of curators which is exercised within the gallery seems to become gradually enlarged. In reality, it is true that the curatorial influence cannot be ignored more than ever for appear in various fields their new features made from their professional cometence. The function of gallery is more strengthened and its position is more heightened than those in 80s. For these reasons, the curators whose playground is the gallery, they may be regarded as invisible power to the artists. Futhermore, their influence as such seems to be more likely because our world of art does not still have any reliable system of art criticism. Nevertheless, considering the actual situation in which national and public galleries as cultural institution would rather exist on the local sentiments or political dynamics than on autonomy, there must be some restrictions for the galleries and the curators to obtain the power. In this context, the actual moment is looked upon as transitional period to make the curatorship settled down. Before discussing the power relevant to curatorship, we are going to examine first the role and function of curator and then the new curatorial role and function at the age of information. Finally, we will see in sequence the development and problems of curatorship, the power and dilemmas of curator.

• Korean Journal of Breeding Science
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• v.50 no.4
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• pp.434-441
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• 2018

High-density genetic linkage mapping is critical for undertaking marker-assisted selection and confirming quantitative trait loci, as well as helping to build pseudomolecules of genomes. We constructed a genetic map using 94 $F_1$ populations generated from the interspecific cross between Korean cultivar "Wonwhang" (Pyrus pyrifolia, NCBI BioSample SAMN05196235) and European cultivar "Bartlett" (Pyrus communis). We designed a total of 24,267 SSR markers based on the genome sequences of "Wonwhang" for this. To select the markers that are linked to the traits important in pear breeding programs, SSR-containing genomic sequences were subjected to nucleotide sequence homology searches, which resulted in 510 SSR markers with high similarity to genes encoding proteins with putative functions such as transcription factors, resistance proteins, flowering time, and regulatory genes. Of these, 70 markers showed polymorphisms in parents and segregating populations and were used to construct a genetic linkage map, together with the unpublished 579 SNPs obtained from genotyping by sequencing analysis. The genetic linkage map covered 3,784.2 cM and the average distance between adjacent markers was 5.8 cM. Seventy SSR markers were distributed across 17 chromosomes with more than one locus.

• 한국사회정책
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• v.24 no.4
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• pp.3-29
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• 2017

The study checked whether the trade-off relationship between the home ownerhip and welfare by Kemeny and Castles was still valid in four welfare regimes, and analysed the factors of change and stability on rate of home ownership between social democratic states and southern european states. Based on the results of the study, the relationship between housing ownership and public expenditure was confirmed only in the liberal welfare regime and in the conservative welfare regime, as evidenced by the 2014 data collection. Second, social democratic states have dramatically increased housing mortgage loans and showed signs of housing commodification but southern european states have showed pre commodification of housing, maintained comparatively whole home ownership and low mortgage loans. Third, social democratic states has resulted in a rise in housing demand and housing prices, through reduced new housing and social rented housing construction, home owenership friendly taxation and generous lending policies, but southern european states have maintained a stable housing demand and housing prices thanks to the rich housing stocks, extended family, self provision and self promotion by close relatives, and intergenerational inheritance and transfers of housing. Although sequence of the equity ownership and welfare are still unclear, it is not a rational alternatives to induce housing ownership through large mortgage loans.

• Journal of The Korean Society of Grassland and Forage Science
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• v.41 no.1
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• pp.47-55
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• 2021

The objective of this study was to access the effect of climate and soil factors on alfalfa dry matter yield (DMY) by the contribution through constructing the yield prediction model in a general linear model considering climate and soil physical variables. The processes of constructing the yield prediction model for alfalfa was performed in sequence of data collection of alfalfa yield, meteorological and soil, preparation, statistical analysis, and model construction. The alfalfa yield prediction model used a multiple regression analysis to select the climate variables which are quantitative data and a general linear model considering the selected climate variables and soil physical variables which are qualitative data. As a result, the growth degree days(GDD) and growing days(GD), and the clay content(CC) were selected as the climate and soil physical variables that affect alfalfa DMY, respectively. The contributions of climate and soil factors affecting alfalfa DMY were 32% (GDD, 21%, GD 11%) and 63%, respectively. Therefore, this study indicates that the soil factor more contributes to alfalfa DMY than climate factor. However, for examming the correct contribution, the factors such as other climate and soil factors, and the cultivation technology factors which were not treated in this study should be considered as a factor in the model for future study.

• Journal of the Korean Geotechnical Society
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• v.22 no.9
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• pp.45-59
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• 2006

The successful performance of any numerical geotechnical simulation depends on the accuracy and efficiency of the numerical implementation of constitutive model used to simulate the stress-strain (constitutive) response of the soil. The corner stone of the numerical implementation of constitutive models is the numerical integration of the incremental form of soil-plasticity constitutive equations over a discrete sequence of time steps. In this paper a well known two-surface soil plasticity model is implemented using a generalized implicit return mapping algorithm to arbitrary convex yield surfaces referred to as the Closest-Point-Projection method (CPPM). The two-surface model describes the nonlinear behavior of coarse-grained materials by incorporating a bounding surface concept together with isotropic and kinematic hardening as well as fabric formulation to account for the effect of fabric formation on the unloading response. In the course of investigating the performance of the CPPM integration method, it is proven that the algorithm is an accurate, robust, and efficient integration technique useful in finite element contexts. It is also shown that the algorithm produces a consistent tangent operator $\frac{d\sigma}{d\varepsilon}$ during the iterative process with quadratic convergence rate of the global iteration process.

• Design Convergence Study
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• v.15 no.5
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• pp.71-83
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• 2016

Interior panels are usually used in finishing of interior walls for not only decorative effects but also information transfer. According to designer's design placing interior panels may need repetitive tasks and the emphasis of this paper is to support an automation of these tasks. Considering the utilization characteristics of interior panels, we propose three method to present patterns by using bitmap image pixels and interior panels' shape changes, based on the theoretical consideration. In addition, in order to approve the possibility of the proposed methods, we have implemented the BIM based interior panels auto layout tool which applied one of the three methods to present patterns by using bitmap image pixel values and panel identification attributes. This tool also supports auto generation of quantity and panel arrangement sequence information that will be used in future construction phase. We expect that this approach will also be used in other decorative objects which require repetition of the basic units, such as floor tiles.

• Journal of Drive and Control
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• v.20 no.4
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• pp.115-122
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• 2023

This study utilized a discrete element method (DEM) simulation, as one of the virtual field trials, to predict the impact of tillage depth on the rotary blade shaft during rotavator tilling. The virtual field for the simulation was generated according to soil properties observed in an actual field. Following the generation of particles for the virtual field, a sequence of calibration steps followed to align the mechanical properties more closely with those of real soil. Calibration was conducted with a focus on bulk density and shear torque, resulting in calibration errors of just 0.02% for bulk density and 0.52% for shear torque. The prediction of the load on a rotary tiller's blade shaft involved a three-pronged approach, considering shaft torque, draft force, and vertical force. In terms of shaft torque, the values exhibited significant increases of 42.34% and 36.91% for every 5-centimeter increment in tillage depth. Similarly, the vertical force saw substantial growth by 40.41% and 36.08% for every 5-centimeter increment. In contrast, the variation in draft force based on tillage depth was comparatively lower at 18.49% and 0.96%, indicating that the effect of tillage depth on draft force was less pronounced than its impact on shaft torque and vertical force. From a perspective of agricultural machinery research, this study provides valuable insights into the DEM soil modeling process, accounting for changes in soil properties with varying tillage depths. These findings are expected to be instrumental in future agricultural machinery design studies.

• Information Systems Review
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• v.25 no.1
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• pp.129-143
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• 2023

In the era of big data, the field of artificial intelligence is showing remarkable growth, and in particular, the image classification learning methods by deep learning are becoming an important area. Various studies have been actively conducted to further improve the performance of CNNs, which have been widely used in image classification, among which a representative method is the Convolutional Recurrent Neural Network (CRNN) algorithm. The CRNN algorithm consists of a combination of CNN for image classification and RNNs for recognizing time series elements. However, since the inputs used in the RNN area of CRNN are the flatten values extracted by applying the convolution and pooling technique to the image, pixel values in the same phase in the image appear in different order. And this makes it difficult to properly learn the sequence of arrangements in the image intended by the RNN. Therefore, this study aims to improve image classification performance by proposing a novel hybrid method of CNN and RNN applying the concepts of encoder and decoder. In this study, the effectiveness of the new hybrid method was verified through various experiments. This study has academic implications in that it broadens the applicability of encoder and decoder concepts, and the proposed method has advantages in terms of model learning time and infrastructure construction costs as it does not significantly increase complexity compared to conventional hybrid methods. In addition, this study has practical implications in that it presents the possibility of improving the quality of services provided in various fields that require accurate image classification.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.127-148
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• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.