• Geomechanics and Engineering
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• v.35 no.5
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• pp.525-538
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• 2023

The development of a city is closely linked to the construction and operation of its subway system. However, constructing a new subway tunnel under an existing station is an extremely complex task, and the deformation characteristics and mechanical behavior of the new subway tunnel during the excavation process can greatly impact the normal operation of the existing station. Although the previous studies about the case of underpass engineering have been carried out, there is limited research on the condition of a newly-built subway tunnel that closely undercrossing an existing station with zero distance between them. Therefore, this study analyzes the deformation law and mechanical behavior characteristics of the preliminary lining of the underpass tunnel during the excavation process based on the real engineering case of Chengdu Metro Line 8. This study also makes an in-depth comparison of the influence of different excavation methods on this issue. Finally, the accuracy of numerical simulation is verified by comparing it with on-site result. The results indicate that the maximum bending moment mainly occurs at the floor slab of the preliminary lining, while that of the ceiling is small. The stress state at the ceiling position is less affected by the construction process of the pilot tunnel. Compared to the all-in-one excavation method, although the process of partial excavation method is more complicated, the deformation of preliminary lining caused by it is basically less than the upper limit value of the standard, while that of the all-in-one excavation method is beyond standard requirements.

• ETRI Journal
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• v.42 no.1
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• pp.67-77
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• 2020

Image captioning has received significant interest in recent years, and notable results have been achieved. Most previous approaches have focused on generating visual descriptions from images, whereas a few approaches have exploited visual descriptions for image classification. This study demonstrates that a good performance can be achieved for both description generation and image classification through an end-to-end joint learning approach with a loss function, which encourages each task to reach a consensus. When given images and visual descriptions, the proposed model learns a multimodal intermediate embedding, which can represent both the textual and visual characteristics of an object. The performance can be improved for both tasks by sharing the multimodal embedding. Through a novel loss function based on class activation mapping, which localizes the discriminative image region of a model, we achieve a higher score when the captioning and classification model reaches a consensus on the key parts of the object. Using the proposed model, we established a substantially improved performance for each task on the UCSD Birds and Oxford Flowers datasets.

• Knowledge Management Research
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• v.12 no.4
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• pp.1-15
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• 2011

The purpose of this study is to determine abusive supervision and its effect on the resistance to such behavior by workers, and also to determine the moderating roles of subordinate's personalities that can strengthen or weaken the relationship between the abusive supervision and employee behaviors. Because the key factors underlying the choice of individual OCB(Organizational Citizenship Behavior) or organizational OCB have to do with subordinates' concern for the task and relational consequences of their behavior, the analysis focused on Neuroticism, the Big Five domains that represent one's orientations toward task and relational matters, respectively(Costa & McCrae, 1992). For empirical study, survey was performed for the analysis, and a total of 233 was used. The following is a summary of the verification results. First, in the relationships between the use of abusive supervision and employee's OCB, the relationship is negatively correlated to the abusive supervision. Second, moderating effects of subordinates' personalities(neuroticism) between abusive supervision and subordinates' were not verified. Finally, future research will explore the effects of situational variables that affect the extent to which supervisors engage in abusive behavior and how subordinates respond to abusive supervision.

• Convergence Security Journal
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• v.16 no.3_1
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• pp.87-98
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• 2016

As the informative society becomes intensified, the rise of the convergence security offers an alternative strategic correspondence to the technology leaks that are becoming more advanced, complex, and intelligent. In order to the convergence security to provide its efficacy, training convergence security specialists is essential. However, research on the subject has yet to be considered sufficient. Thus this research focuses on defense industry security specialists to define the duty and analze critical task as well as drawn and therefore the required academic level of the critical task was examined. These research work contributes to the competence development of convergence security specialists and further enhancement on convergence security training process of academic institutions and job training institutions.

• Journal of Korean Institute of Industrial Engineers
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• v.32 no.3
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• pp.236-241
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• 2006

Monitor height is one of the key factors in the VDT workstation design. Most of the previous studies have focused on traditional VDT workplace where the operators performed data entry or word processing tasks using single monitor. This study aimed to suggest proper monitor height when the main task was monitoring information from different number of information sources. Twelve male students participated in three experiments: single information source (one monitor), two information sources (one monitor and one CCTV), and three information sources (one monitor, one CCTV and a window). Subjects performed monitoring tasks for 10 minutes with 3 different monitor center heights : 89.0 cm (Low), 111.3 cm (Middle), and 124.8 cm (High). Surface EMG signals of five neck muscles, subjective discomfort ratings, preference, and working postures were recorded. Results indicated that the middle height was proper for one monitor condition, but the low monitor height was recommended for more than two information sources.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.4
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• pp.87-92
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• 2007

The full automation welding has not yet been achieved partly because the mathematical model for the process parameters of a given welding task is not fully understood and quantified. Several mathematical models to control welding quality, productivity, microstructure and weld properties in arc welding processes have been studied. However, it is not an easy task to apply them to the various practical situations because the relationship between the process parameters and the bead geometry is non-linear and also they are usually dependent on the specific experimental results. Practically, it is difficult, but important to know how to establish a mathematical model that can predict the result of the actual welding process and how to select the optimum welding condition under a certain constraint. In this paper, an attempt has been made to develop an neural network model to predict the weld top-bead height as a function of key process parameters in the welding. and to compare the developed models using three different training algorithms in order to select an adequate neural network model for prediction of top-bead height.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.170-174
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• 2004

Despite the widespread use in the various manufacturing industries, the full automation of the robotic CO$_2$ welding has not yet been achieved partly because the mathematical model for the process parameters of a given welding task is not fully understood and quantified. Several mathematical models to control welding quality, productivity, microstructure and weld properties in arc welding processes have been studied. However, it is not an easy task to apply them to the various practical situations because the relationship between the process parameters and the bead geometry is non-linear and also they are usually dependent on the specific experimental results. Practically, it is difficult, but important to know how to establish a mathematical model that can predict the result of the actual welding process and how to select the optimum welding condition under a certain constraint. In this paper, an attempt has been made to develop an Radial basis function network model to predict the weld top-bead width as a function of key process parameters in the robotic CO$_2$ welding. and to compare the developed model and a simple neural network model using two different training algorithms in order to verify performance. of the developed model.

• Journal of Computing Science and Engineering
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• v.10 no.2
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• pp.58-67
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• 2016

With the large amount of complex network data that is increasingly available on the Web, link prediction has become a popular data-mining research field. The focus of this paper is on a link-prediction task that can be formulated as a binary classification problem in complex networks. To solve this link-prediction problem, a sparse-classification algorithm called "Truncated Kernel Projection Machine" that is based on empirical-feature selection is proposed. The proposed algorithm is a novel way to achieve a realization of sparse empirical-feature-based learning that is different from those of the regularized kernel-projection machines. The algorithm is more appealing than those of the previous outstanding learning machines since it can be computed efficiently, and it is also implemented easily and stably during the link-prediction task. The algorithm is applied here for link-prediction tasks in different complex networks, and an investigation of several classification algorithms was performed for comparison. The experimental results show that the proposed algorithm outperformed the compared algorithms in several key indices with a smaller number of test errors and greater stability.

• Health Policy and Management
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• v.12 no.4
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• pp.68-90
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• 2002

The purpose of this study is to investigate the determinants of intent to leave among hospital physicians. A causal model of intent to leave among hospital physicians was constructed based on the exchange theory. The sample of this study consisted of 185 physicians from 8 general hospitals located in Seoul, Taegu, Kyunggi-province, and Kyungsangnam-province in Korea. Data were collected with self-administered questionnaires and analyzed using LISREL. The results of this study indicate that the following variables, listed in order of size, have significant negative effects on intent to leave among hospital physicians; job satisfaction, organizational commitment, task variety, promotional chances, task significance, and pay. Sex (female=0, male=1) was found to have significant positive effects on the intent to leave among hospital physicians. The results imply that hospital administrators should make an effort to improve job satisfaction and organizational commitment which are the key determinants of intent to leave among hospital physicians.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2005.05a
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• pp.66-70
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• 2005

The full automation of welding has not yet been achieved partly because the mathematical model for the process parameters of a given welding task is not fully understood and quantified. Several mathematical models to control welding quality, productivity, microstructure and weld properties in arc welding processes have been studied. However, it is not an easy task to apply them to the various practical situations because the relationship between the process parameters and the bead geometry is non-linear and also they are usually dependent on the specific experimental results. Practically, it is difficult, but important to know how to establish a mathematical model that can predict the result of the actual welding process and how to select the optimum welding condition under a certain constraint. In this paper, an attempt has been made to develop an neural network model to predict the weld top-bead height as a function of key process parameters in the welding. and to compare the developed model and a simple neural network model using two different training algorithms in order to select an optimal neural network model.