• Journal of Service Research and Studies
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• v.12 no.3
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• pp.26-44
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• 2022

Job stress experienced during work has a positive effect on the organization, such as performance improvement, but if not properly managed, it can cause physical diseases such as digestive diseases and mental diseases such as depression and neurological diseases. If job stress persists for a long time, it causes emotional exhaustion and depression, which has a significant adverse effect on individuals and organizations, so proper management is essential. Therefore, in this study, a descriptive survey study was conducted using a self-report questionnaire method to find out the relationship between job stress, emotional exhaustion and depression of medical institution workers. As a result of the analysis, it was found that job stress of medical institution workers had a significant (+) effect on emotional exhaustion and depression, and emotional exhaustion of medical institution workers had a significant (+) effect on depression. Through this study, it was found that there was a significant relationship between job stress, emotional exhaustion, and depression of hospital employees, and that emotional exhaustion acts as a parameter in the relationship between job stress and depression. Considering that job stress of hospital employees causes adverse organizational effects, such as threatening workers' mental and physical health and causing deterioration in the quality of medical services, organizational efforts will be needed to relieve and properly manage job stress of hospital employees.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.2A
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• pp.171-179
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• 2009

The porosity in porous media like concrete can be considered as a durability index since it may be a routine for the intrusion of harmful ions and room for the keeping moisture. Recently, modeling and analysis techniques for deterioration are provided based on the pore structure with the significance of durability and the relationship between porosity and durability characteristics is an important issue. In this paper, a series of mortar samples with five water to cement ratios are prepared and tests for durability performance are carried out including porosity measurement. The durability test covers those for compressive strength, air permeability, chloride diffusion coefficient, absorption, and moisture diffusion coefficient. They are compared with water to cement ratios and porosity. From the normalized data, when porosity increases to 1.45 times, air permeability, chloride diffusion coefficient, absorption, and moisture diffusion coefficient decrease to 2.3 times, 2.1 times, 5.5 times and 3.7 times, respectively, while compressive strength decreases to 0.6 times. It was evaluated that these are linearly changed with porosity showing high corelation factors. Additionally, intended durability performances are established from the test results and literature studies and a porosity for durable concrete is proposed based on them.

• Journal of the Korean Geotechnical Society
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• v.39 no.7
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• pp.31-37
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• 2023

Since tunnels are built underground, it is impossible to check visually the location and degree of deterioration of steel ribs. Therefore, in tunnel maintenance, GPR images are generally used to detect steel ribs. While research on GPR image analysis employing artificial neural networks has primarily focused on detecting underground pipes and road damage, there have been limited applications for analyzing tunnel GPR data, specifically for steel rib detection, both internationally and domestically. In this study, a one-step object detection algorithm called YOLO, based on a convolutional neural network, was utilized to automate the localization of steel ribs using GPR data. The performance of the algorithm is then analyzed. Two datasets were employed for the analysis. A dataset comprising 512 original images and another dataset consisting of 2,048 augmented images. The omission rate, which represents the ratio of undetected steel ribs to the total number of steel ribs, was 0.38% for the model using the augmented data, whereas the omission rate for the model using only the original data was 7.18%. Thus, from an automation standpoint, it is more practical to employ an augmented dataset.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5D
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• pp.831-838
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• 2006

An experimental/analytic study has been conducted to understand the adverse effects of low vehicle speed, high axle load and high tire pressure on the performance of asphalt pavements. Of 33 asphalt sections at KHC test road, two sections having different base layer thickness (180 mm versus 280 mm) are adopted for rollover tests. During the test, a standard three-axle dump truck maintains a steady state condition as moving along the wheel path of a passing lane, and lateral offsets and real travel speed are measured with a laser-based wandering system. Test results suggest that vehicle speed affects both longitudinal and transverse strains at the bottom of asphalt layer (290 mm and 390 mm below the surface), and even slightly influences the measured vertical stresses at the top of subbase and subgrade due to the dynamic effect of rolling vehicle. Since the anisotropic nature of asphalt-aggregate mixtures, the difference between longitudinal and transverse strains appears prominent throughout the measurements. As the thickness of asphalt pavement increases, the measured lateral strains become larger than its corresponding longitudinal strains. Over the limited testing conditions, it is concluded that higher axle weight and higher tire pressures induce more strains and vertical stresses, leading to a premature deterioration of pavements. Finally, a layered elastic analysis overestimates the maximum strains measured under the 1st axle load, while underestimating the maximum vertical stress in both pavement sections.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.3
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• pp.10-18
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• 2024

Vehicle loading is one of the main causes of bridge deterioration. Although WiM (Weigh in Motion) can be used to measure vehicle loading on a bridge, it has disadvantage of high installation and maintenance cost due to its contactness. In this study, a non-contact method is proposed to estimate the vehicle loading history of bridges using deep learning and CCTV images. The proposed method recognizes the vehicle type using an object detection deep learning model and estimates the vehicle loading based on the load-based vehicle type classification table developed using the weights of empty vehicles of major domestic vehicle models. Faster R-CNN, an object detection deep learning model, was trained using vehicle images classified by the classification table. The performance of the model is verified using images of CCTVs on actual bridges. Finally, the vehicle loading history of an actual bridge was obtained for a specific time by continuously estimating the vehicle loadings on the bridge using the proposed method.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.6
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• pp.71-86
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• 2023

Although starting a business plays a key role in strengthening national competitiveness and creating jobs, it is recognized as a risky choice. Failure to start a business can result in a wide range of negative effects, such as loss of personal wealth as well as deterioration of national competitiveness. This study considers startups that have reached a level of sustainable growth by achieving performance above the minimum profitability and sales standards for KOSDAQ listing, or achieved EXIT through sale or listing, as successful startups. based on the practical experiences of 23 successful entrepreneurs and Based on perception, the importance and priorities of startup success factors were derived through stratification analysis (Analytic Hierarchy Process, AHP), and interviews were conducted. In particular, using the ERIS model, we comprehensively analyze various variables of a start-up by considering the four elements of the entrepreneur, resources, industry, and strategy, and examine the changes and importance of success factors according to the characteristics of each growth stage of the start-up. As a goal, we specifically identified the challenges and opportunities faced by entrepreneurs at each stage. As a result of the study, the order of importance of the top factors of success factors in the start-up period was found to be the entrepreneur, resources, industry, and strategy. In particular, the importance of the entrepreneur's entrepreneurship spirit, special capabilities, general capabilities, and human resources was emphasized. The order of importance of the top factors of success factors during the growth period was found in the following order: entrepreneur, resources, industry, and strategy. In particular, the importance of general capabilities, entrepreneurship, and human and organizational resources was emphasized. This study is significant in that it analyzes startup success factors from the perspective of successful entrepreneurs and provides useful insights and directions to entrepreneurs and policy makers.

• Journal of the Korean Society of Radiology
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• v.18 no.2
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• pp.65-71
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• 2024

To achieve high resolution and sensitivity of positron emission tomography (PET) for small animals, the detector is constructed using very thin and long scintillation pixels. Due to the structure of these scintillation pixels, spatial resolution deterioration occurs outside the system's field of view. To solve this problem, we designed a detector that could improve spatial resolution by measuring the interaction depth and improve sensitivity by using a quasi-block scintillator. A quasi-block scintillator size of 12.6 mm x 12.6 mm x 3 mm was arranged in four layers, and optical sensors were placed on all sides to collect light generated by the interaction between gamma rays and the scintillator. DETECT2000 simulation was performed to evaluate the performance of the designed detector. Flood images were acquired by generating gamma-ray events at 1 mm intervals from 1.3 mm to 11.3 mm within the scintillator of each layer. The spatial resolution and peak-to-peak distance for each location were measured in an 11 x 11 array of flood images. The average measured spatial resolution was 0.25 mm, and the average distance between peaks was 1.0 mm. Through this, it was confirmed that all locations were separated from each other. In addition, because the light signals of all layers were measured separately from each other, the layer of the scintillator that interacted with the gamma rays could be completely separated. When the designed detector is used as a detector in a PET system for small animals, it is considered that excellent spatial resolution and sensitivity can be achieved and image quality can be improved.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.1
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• pp.12-23
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• 2024

In this study, a structural concrete square beam was developed using the centrifugal molding technique. In order to secure the bending stiffness of the cross section, the hollow rate of the cross section was set to 10% or less. Instead of using the current poor mixture of concrete and a concrete mixing ratio with a high slump (150-200) and a design strength of 100 MPa or more was developed and applied. In order to investigate the durability of centrifugally formed PSC square beams to be used as the superstructure of the avalanch tunnel or ramen bridge, the durability performance of ultra-high-strength centrifugally formed concrete with a compressive strength of 100 MPa was evaluated in terms of deterioration and chemical resistance properties.Concrete durability tests, including chloride penetration resistance, accelerated carbonation, sulfate erosion resistance, freeze-thaw resistance, and scaling resistance, were performed on centrifugally formed square beam test specimens produced in 2022 and 2023. Considering the information verified in this study, the durability of centrifugally molded concrete, which has increased watertightness in the later manufacturing stage, was found to be superior to that of general concrete.

• Journal of Korea Water Resources Association
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• v.57 no.3
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• pp.209-223
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• 2024

In the context of the fourth industrial revolution, data-driven decision-making has increasingly become pivotal. However, the integrity of data analysis is compromised if data quality is not adequately ensured, potentially leading to biased interpretations. This is particularly critical for water level data, essential for water resource management, which often encounters quality issues such as missing values, spikes, and noise. This study addresses the challenge of noise-induced data quality deterioration, which complicates trend analysis and may produce anomalous outliers. To mitigate this issue, we propose a noise removal strategy employing Wavelet Transform, a technique renowned for its efficacy in signal processing and noise elimination. The advantage of Wavelet Transform lies in its operational efficiency - it reduces both time and costs as it obviates the need for acquiring the true values of collected data. This study conducted a comparative performance evaluation between our Wavelet Transform-based approach and the Denoising Autoencoder, a prominent machine learning method for noise reduction.. The findings demonstrate that the Coiflets wavelet function outperforms the Denoising Autoencoder across various metrics, including Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Mean Squared Error (MSE). The superiority of the Coiflets function suggests that selecting an appropriate wavelet function tailored to the specific application environment can effectively address data quality issues caused by noise. This study underscores the potential of Wavelet Transform as a robust tool for enhancing the quality of water level data, thereby contributing to the reliability of water resource management decisions.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.3
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• pp.82-98
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• 2019

The importance of spatial information is rapidly rising. In particular, 3D spatial information construction and modeling for Real World Objects, such as smart cities and digital twins, has become an important core technology. The constructed 3D spatial information is used in various fields such as land management, landscape analysis, environment and welfare service. Three-dimensional modeling with image has the hig visibility and reality of objects by generating texturing. However, some texturing might have occlusion area inevitably generated due to physical deposits such as roadside trees, adjacent objects, vehicles, banners, etc. at the time of acquiring image Such occlusion area is a major cause of the deterioration of reality and accuracy of the constructed 3D modeling. Various studies have been conducted to solve the occlusion area. Recently the researches of deep learning algorithm have been conducted for detecting and resolving the occlusion area. For deep learning algorithm, sufficient training data is required, and the collected training data quality directly affects the performance and the result of the deep learning. Therefore, this study analyzed the ability of detecting the occlusion area of the image using various image quality to verify the performance and the result of deep learning according to the quality of the learning data. An image containing an object that causes occlusion is generated for each artificial and quantified image quality and applied to the implemented deep learning algorithm. The study found that the image quality for adjusting brightness was lower at 0.56 detection ratio for brighter images and that the image quality for pixel size and artificial noise control decreased rapidly from images adjusted from the main image to the middle level. In the F-measure performance evaluation method, the change in noise-controlled image resolution was the highest at 0.53 points. The ability to detect occlusion zones by image quality will be used as a valuable criterion for actual application of deep learning in the future. In the acquiring image, it is expected to contribute a lot to the practical application of deep learning by providing a certain level of image acquisition.