• Korean Journal of Construction Engineering and Management
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• v.23 no.1
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• pp.83-88
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• 2022

Companies make decisions with risks such as choosing an investment plan in order to pursue profits. This study explained the decision making of the management of construction companies in South Korea using the tendency to avoid losses in the Prospect Theory. To this end, 20-year financial data of 2,881 companies engaged in real estate development, which have to bear the greatest risk among the construction industry, were collected. The collected companies were roughly classified based on the reference point, and the causal relationship between average return on equity and risk preference by group was empirically analyzed through regression analysis. As a result, it was confirmed that if the average return on equity of a company decreases for the group above the reference point, it tends to select an investment plan with low uncertainty in order not to lose additional money. In addition, it was confirmed that if the average return on equity of a company decreases for the group below the reference point, it tends to select an investment plan with high uncertainty to move to the profit area. This result is exactly consistent with the loss aversion tendency of the Prospect Theory.

• Journal of the Korea Convergence Society
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• v.12 no.11
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• pp.45-51
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• 2021

With the development of deep learning, semantic segmentation methods are being studied in various fields. There is a problem that segmenation accuracy drops in fields that require accuracy such as medical image analysis. In this paper, we improved PSPNet, which is a deep learning based segmentation method to minimized the loss of features during semantic segmentation. Conventional deep learning based segmentation methods result in lower resolution and loss of object features during feature extraction and compression. Due to these losses, the edge and the internal information of the object are lost, and there is a problem that the accuracy at the time of object segmentation is lowered. To solve these problems, we improved PSPNet, which is a semantic segmentation model. The multi-scale attention proposed to the conventional PSPNet was added to prevent feature loss of objects. The feature purification process was performed by applying the attention method to the conventional PPM module. By suppressing unnecessary feature information, eadg and texture information was improved. The proposed method trained on the Cityscapes dataset and use the segmentation index MIoU for quantitative evaluation. As a result of the experiment, the segmentation accuracy was improved by about 1.5% compared to the conventional PSPNet.

• Korean small business review
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• v.42 no.4
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• pp.123-146
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• 2020

This study, targeting KOSDAQ-listed companies, examined the relationship between variability of accruals and corporate characteristics. First, the analysis results show that executives of companies with high debt ratios are more likely to violate debt contracts, so there is a strong temptation to use discretionary accrual items. Second, for companies with large volatility in operating cash flows, Executives of these companies are strongly inclined to utilize accruals for the purpose of abuse of discretion. Third, the larger the company, the more sensitive it is to political costs, so it is less tempted to use the accruals item than a smaller company. Fourth, the corporate age is thought to be the maturity of the company, Executives of such companies have little room to use accruals to abuse their discretion. Fifth, in the case of profit dummy variables, the companies reporting losses have more temporary accrual items than those reporting profits, so this increases the uncertainty in their accounting information than the latter. Sixth, for those companies that are indicated as inappropriate as a result of audit, the more likely their executives are to use the accrual items, and the lower the quality of their accounting profits is. Lastly, Companies audited by 4 Big domestic accounting firms have less discretionary accrual fluctuations than companies audited by non-big 4 accounting firms. Thus, it was found that the accrual amount allows the discretion of corporate executives differently according to the characteristics of the company.

• Journal of Animal Science and Technology
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• v.63 no.6
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• pp.1376-1385
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• 2021

Foot-and-mouth disease, one of the most contagious diseases in cloven-hoofed animals, causes significant economic losses. The pathogenesis of foot-and-mouth disease virus (FMDV) infection is known to differ with age of the animals. In this study, we aimed to reveal the difference in immunological response in the initial stage of FMDV infection between piglets and adult pigs. Peripheral blood mononuclear cells (PBMCs) were isolated from 3 piglets (8 weeks old) and 3 pigs (35 weeks old) that were not vaccinated against FMDV. O-type FMDV (2 × 10² median tissue culture infectious dose) was inoculated into porcine PBMCs and the cells were incubated at 37.0℃ under 5% CO₂ for various time periods (0, 1, 3, 6, 12, 24, and 48 h). The total RNA was obtained from the FMDV-inoculated PBMCs after each time point, and the virus titer was investigated in these RNA samples. Furthermore, dynamics of mRNA expression of the six tested cytokines (interferon [IFN]-α, IFN-γ, interleukin [IL]-6, IL-8, IL-10, and tumor necrosis factor [TNF]-α) in FMDV-inoculated porcine PBMCs were evaluated by time-series analysis to determine the differences, if any, based on the age of the pigs. The PBMCs of piglets contained the highest quantity of FMDV mRNA at 6 hours post-inoculation (hpi), and the PBMCs of pigs had the highest quantity of FMDV mRNA at 3 hpi. The mean cycle threshold-value in the PBMCs steadily decreased after the peak time point in the piglets and pigs (6 and 3 hpi, respectively). The dynamics of mRNA expression of all cytokines except TNF-α showed age-dependent differences in FMDV-inoculated PBMCs. The mRNA expression of most cytokines was more pronounced in the piglets than in the pigs, implying that the immune response against FMDV showed an age-dependent difference in pigs. In conclusion, within 48 hpi, the 8-week-old piglets responded more rapidly and were more sensitive to FMDV infection than the 35-week-old pigs, which could be associated with the difference in the pathogenesis of FMDV infection among the pigs. These results provide valuable insights into the mechanisms underlying the age-dependent differences in immune response in pigs against FMDV infection.

• Journal of Veterinary Science
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• v.21 no.4
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• pp.65.1-65.13
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• 2020

Background: Aleutian mink disease virus (AMDV) causes major economic losses in fur-bearing animal production. The control of most AMDV outbreaks is complex due to the difficulties of establishing the source of infection based only on the available on-farm epidemiological data. In this sense, phylogenetic analysis of the strains present in a farm may help elucidate the origin of the infection and improve the control and biosecurity measures. Objectives: This study had the following aims: characterize the AMDV strains from most outbreaks produced at Spanish farms between 2012-2019 at the molecular level, and assess the utility of the combined use of molecular and epidemiological data to track the possible routes of infection. Methods: Thirty-seven strains from 17 farms were partially sequenced for the NS1 and VP2 genes and analyzed phylogenetically with other strains described worldwide. Results: Spanish AMDV strains are clustered in four major clades that generally show a good geographical correlation, confirming that most had been established in Spain a long time ago. The combined study of phylogenetic results and epidemiological information of each farm suggests that most of the AMDV outbreaks since 2012 had been produced by within-farm reservoirs, while a few of them may have been due to the introduction of the virus through international trade. Conclusions: The combination of phylogenetic inference, together with epidemiological data, helps assess the possible origin of AMDV infections in mink farms and improving the control and prevention of this disease.

• Journal of Industrial Convergence
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• v.21 no.1
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• pp.175-185
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• 2023

Recently, novel viral infections such as COVID-19 have spread and pose a serious public health problem. In particular, these diseases have a fatal effect on the elderly, threatening life and causing serious social and economic losses. Accordingly, applications such as telemedicine, healthcare, and disease prevention using the Internet of Things (IoT) and artificial intelligence (AI) have been introduced in many industries to improve disease detection, monitoring, and quarantine performance. However, since existing technologies are not applied quickly and comprehensively to the sudden emergence of infectious diseases, they have not been able to prevent large-scale infection and the nationwide spread of infectious diseases in society. Therefore, in this paper, we try to predict the spread of infection by collecting various infection information with regional limitations through a virus disease information collector and performing AI analysis and severity matching through an AI broker. Finally, through the Korea Centers for Disease Control and Prevention, danger alerts are issued to the elderly, messages are sent to block the spread, and information on evacuation from infected areas is quickly provided. A realistic elderly support system compares the location information of the elderly with the information of the infected area and provides an intuitive danger area (infected area) avoidance function with an augmented reality-based smartphone application. When the elderly visit an infected area is confirmed, quarantine management services are provided automatically. In the future, the proposed system can be used as a method of preventing a crushing accident due to sudden crowd concentration in advance by identifying the location-based user density.

• Wind and Structures
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• v.36 no.4
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• pp.237-247
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• 2023

The emergence of high-rise buildings has necessitated frequent structural health monitoring and maintenance for safety reasons. Wind causes damage and structural changes on tall structures; thus, safe structures should be designed. The pressure developed on tall buildings has been utilized in previous research studies to assess the impacts of wind on structures. The wind tunnel test is a primary research method commonly used to quantify the aerodynamic characteristics of high-rise buildings. Wind pressure is measured by placing pressure sensor taps at different locations on tall buildings, and the collected data are used for analysis. However, sensors may malfunction and produce erroneous data; these data losses make it difficult to analyze aerodynamic properties. Therefore, it is essential to generate missing data relative to the original data obtained from neighboring pressure sensor taps at various intervals. This study proposes a deep learning-based, deep convolutional generative adversarial network (DCGAN) to restore missing data associated with faulty pressure sensors installed on high-rise buildings. The performance of the proposed DCGAN is validated by using a standard imputation model known as the generative adversarial imputation network (GAIN). The average mean-square error (AMSE) and average R-squared (ARSE) are used as performance metrics. The calculated ARSE values by DCGAN on the building model's front, backside, left, and right sides are 0.970, 0.972, 0.984 and 0.978, respectively. The AMSE produced by DCGAN on four sides of the building model is 0.008, 0.010, 0.015 and 0.014. The average standard deviation of the actual measures of the pressure sensors on four sides of the model were 0.1738, 0.1758, 0.2234 and 0.2278. The average standard deviation of the pressure values generated by the proposed DCGAN imputation model was closer to that of the measured actual with values of 0.1736,0.1746,0.2191, and 0.2239 on four sides, respectively. In comparison, the standard deviation of the values predicted by GAIN are 0.1726,0.1735,0.2161, and 0.2209, which is far from actual values. The results demonstrate that DCGAN model fits better for data imputation than the GAIN model with improved accuracy and fewer error rates. Additionally, the DCGAN is utilized to estimate the wind pressure in regions of buildings where no pressure sensor taps are available; the model yielded greater prediction accuracy than GAIN.

• Nutrition Research and Practice
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• v.16 no.sup1
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• pp.113-125
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• 2022

In the current years, it has now become necessary to establish standards for micronutrient intake based on scientific evidence. This review discusses issues related to the development of the 2020 Dietary Reference Intakes for Koreans (KDRI) for magnesium (Mg), zinc (Zn), and copper (Cu), and future research directions. Following issues were encountered when establishing the KDRI for these minerals. First, characteristics of Korean subjects need to be applied to estimate nutrient requirements. When calculating the estimated average requirement (EAR), the KDRI used the results of balance studies for Mg absorption and factorial analysis for Zn, which is defined as the minimum amount to offset endogenous losses for Zn and Mg. For Cu, a combination of indicators, such as depletion/repletion studies, were applied, wherein all reference values were based on data obtained from other countries. Second, there was a limitation in that it was difficult to determine whether reference values of Mg, Zn, and Cu intakes in the 2020 KDRI were achievable. This might be due to the lack of representative previous studies on intakes of these nutrients, and an insufficient database for Mg, Zn, and Cu contents in foods. This lack of database for mineral content in food poses a problem when evaluating the appropriateness of intake. Third, data was insufficient to assess the adequacy of Mg, Zn, and Cu intakes from supplements when calculating reference values, considering the rise in both demand and intake of mineral supplements. Mg is more likely to be consumed as a multi-nutrient supplement in combination with other minerals than as a single supplement. Moreover, Zn-Cu interactions in the body need to be considered when determining the reference intake values of Zn and Cu. It is recommended to discuss these issues present in the 2020 KDRI development for Mg, Zn, and Cu intakes in a systematic way, and to find relevant solutions.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.6
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• pp.853-861
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• 2018

This paper proposes the application of harmonic constraints to address the problems caused by abnormal voltage increases when electric railway vehicles are running. The AC line that supplies the train with power during operation is used to provide electricity of 25kV/60 Hz, but gradually the size and frequency of harmonics involved in the line are varied with the technological evolution of the railroad vehicle electrical equipment. An increase in heat losses due to the failure of the instrument transformer (PT), the main circuit device, which is a serious problem with the recent train safety operation, or to the main displacement voltage. When high frequency components are introduced through low frequency Transformers of the main circuit device, the high intensity of the components is caused by the high intensity of the core and the current flow of the parasitic core is increased, thus generating heat. To solve this problem, the recent adjustment of the sequence has applied artificial NOTCH OFF of the power converter. However, the method of receiving and controlling the OFF signal operates by interaction between the ground and the vehicle's devices, thus it is invalid in the event of failure, and an actual accident is occurring. Therefore, the harmonic currents were required to prevent possible flow of harmonics, and conducted a study to prevent accidental occurrence of train accidents and to verify feasibility of the device through the simulations of the train's experimental analysis and the simulations of the train for safe operation.

• Journal of the Korean Geotechnical Society
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• v.39 no.7
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• pp.69-76
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• 2023

Cavity causes settlement and its remediation after an accident results in significant time and economic losses. This study aims to experimentally evaluate the prospect of using infrared camera to detect and measure underground subsidence. Emissivity is necessary to detect the energy emitted from an object and accurately assess temperature using an infrared camera. The emissivity in laboratory tests is fixed to evaluate a reasonable distance between the infrared camera and the object, and temperature values are assessed at various distances. In field experiments, the cavity of the field experiment is simulated using a PVC pipe with a diameter of 5 cm, artificially buried at depths of 5 and 25 cm from the surface. The infrared camera measurements are taken from 4 PM to 3 PM of the next day (a total of 23 h). The analysis included the time-series temperature distribution and the cooling rate index assessment, which represents the temperature change rate per unit of time. The results showed that various temperature trends are observed depending on the location of the subsidence. This study demonstrates that the infrared camera can be used to assess the condition of the subsurface.