• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.3
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• pp.329-337
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• 2017

This paper proposes a search method that explores parameters C and σ values of support vector machines (SVM) to improve performance while maintaining search accuracy. A traditional grid search method requires tremendous computational times because it searches all available combinations of C and σ values to find optimal combinations which provide the best performance of SVM. To address this issue, this paper proposes a deep search method that reduces computational time. In the first stage, it divides C-σ- accurate metrics into four regions, searches a median value of each region, and then selects a point of the highest accurate value as a start point. In the second stage, the selected start points are re-divided into four regions, and then the highest accurate point is assigned as a new search point. In the third stage, after eight points near the search point. are explored and the highest accurate value is assigned as a new search point, corresponding points are divided into four parts and it calculates an accurate value. In the last stage, it is continued until an accurate metric value is the highest compared to the neighborhood point values. If it is not satisfied, it is repeated from the second stage with the input level value. Experimental results using normal and defect bearings show that the proposed deep search algorithm outperforms the conventional algorithms in terms of performance and search time.

• The Journal of Engineering Geology
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• v.33 no.2
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• pp.257-273
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• 2023

Hydrochemical characteristics and microbial communities of spring (Samtong), surface water, and groundwater in Cheorwon, Korea, were analyzed. Field surveys and water quality analyses were undertaken at 10 sampling points for five spring, two surface, and three groundwater samples on 15 December 2022. Hydrochemical analysis revealed that most water samples were Ca-HCO₃ type and that water-rock interactions were the predominant mineral source. Radon concentrations were <1 kBq m^-3 for surface water, 1~10 kBq m^-3 for spring water, and 1~1,000 kq m^-3 for groundwater. Microbial cluster analysis showed that the main phyla were Proteobacteria, Planctomyceta, Verrucomicrobia, Acidobacteria, and Actinomycetota.Non-metric multidimensional scaling (NMDS) analysis indicated that water temperature, pH, and Si content were closely related to microorganism content. NMDS and canonical correspondence analysis results revealed that environmental factors affecting spring water were temperature, and Mg and Si concentrations, particularly for Acidobacteria and Proteobacteria, and Pseudomonas brenneri. Both hydrochemical and microbial community analyses yielded similar results at some spring and groundwater sampling points, likely due to the effects of a basalt aquifer.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.143-156
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• 2023

To precisely and reliably analyze the contents of the satellite imagery, recognizing the clouds which are the obstacle to gathering the useful information is essential. In recent times, deep learning yielded satisfactory results in various tasks, so many studies using deep neural networks have been conducted to improve the performance of cloud detection. However, existing methods for cloud detection have the limitation on increasing the performance due to the adopting the network models for semantic image segmentation without modification. To tackle this problem, we introduced the multi-branch neural architecture search to find optimal network structure for cloud detection. Additionally, the proposed method adopts the soft intersection over union (IoU) as loss function to mitigate the disagreement between the loss function and the evaluation metric and uses the various data augmentation methods. The experiments are conducted using the cloud detection dataset acquired by Arirang-3/3A satellite imagery. The experimental results showed that the proposed network which are searched network architecture using cloud dataset is 4% higher than the existing network model which are searched network structure using urban street scenes with regard to the IoU. Also, the experimental results showed that the soft IoU exhibits the best performance on cloud detection among the various loss functions. When comparing the proposed method with the state-of-the-art (SOTA) models in the field of semantic segmentation, the proposed method showed better performance than the SOTA models with regard to the mean IoU and overall accuracy.

• Korean Journal of Social Welfare Studies
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• v.42 no.3
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• pp.381-407
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• 2011

This study set out to construct an social capital scale that could be commonly used by social welfare organizations and to apply it to examine any differences in social capital among social welfare organizations. For those purposes, the study distributed a questionnaire by mail to social welfare organizations in 15 cities and provinces across the nation and conducted latent means analysis. The major research findings were as follows. First, as a result of exploratory factor analysis and confirmatory factor analysis, the validity and reliability in measurement indicators of social capital were proved to be satisfactory level. Secondly, social capital were found to be interpreted at the organizational level through the review of levels of analysis. Meanwhile, the configural, metric, and scalar invariance of social capital scale were confirmed, which indicates that the social capital scale can be commonly applied to social welfare organizations. Finally, latent means analysis was carried out to examine differences among social welfare organizations in the subindexes of social capital including network connectivity, setting and sharing of vision, reciprocal norm, trust and cooperation, and group participation. As a result, there were significant differences among social welfare organizations in network connectivity, reciprocal norm, trust and cooperation and group participation but no statistically significant differences among them in sharing of vision, goal and core value. Those findings led to implications needed to manage of social welfare organizations.

• Asia Marketing Journal
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• v.15 no.3
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• pp.53-77
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• 2013

As advertising and promotions are categorized as operating expenses, managers tend to reduce marketing budget to improve their short term profitability. Gauging the value and accountability of marketing spending is therefore considered as a major research priority in marketing. To respond this call, recent studies have documented that financial market reacts positively to a firm's marketing activity or marketing related outcomes such as brand equity and customer satisfaction. However, prior studies focus on the relation of marketing variable and financial market variables. This study suggests a channel about how marketing activity increases firm valuation. Specifically, we propose that a firm's marketing activity increases the level of the firm's product market information and thereby the dispersion in financial analysts' earnings forecasts decreases. With less uncertainty about the firm's future prospect, the firm's managers and shareholders have less information asymmetry, which reduces the firm's cost of capital and thereby increases the valuation of the firm. To our knowledge, this is the first paper to examine how informational benefits can mediate the effect of marketing activity on firm value. To test whether marketing activity contributes to increase in firm value by mitigating information asymmetry, this study employs a longitudinal data which contains 12,824 firm-year observations with 2,337 distinct firms from 1981 to 2006. Firm value is measured by Tobin's Q and one-year-ahead buy-and-hold abnormal return (BHAR). Following prior literature, dispersion in analysts' earnings forecasts is used as a proxy for the information gap between management and shareholders. For model specification, to identify mediating effect, the three-step regression approach is adopted. All models are estimated using Markov chain Monte Carlo (MCMC) methods to test the statistical significance of the mediating effect. The analysis shows that marketing intensity has a significant negative relationship with dispersion in analysts' earnings forecasts. After including the mediator variable about analyst dispersion, the effect of marketing intensity on firm value drops from 1.199 (p < .01) to 1.130 (p < .01) in Tobin's Q model and the same effect drops from .192 (p < .01) to .188 (p < .01) in BHAR model. The results suggest that analysts' forecast dispersion partially accounts for the positive effect of marketing on firm valuation. Additionally, the same analysis was conducted with an alternative dependent variable (forecast accuracy) and a marketing metric (advertising intensity). The analysis supports the robustness of the main results. In sum, the results provide empirical evidence that marketing activity can increase shareholder value by mitigating problem of information asymmetry in the capital market. The findings have important implications for managers. First, managers should be cognizant of the role of marketing activity in providing information to the financial market as well as to the consumer market. Thus, managers should take into account investors' reaction when they design marketing communication messages for reducing the cost of capital. Second, this study shows a channel on how marketing creates shareholder value and highlights the accountability of marketing. In addition to the direct impact of marketing on firm value, an indirect channel by reducing information asymmetry should be considered. Potentially, marketing managers can justify their spending from the perspective of increasing long-term shareholder value.

• Asia Marketing Journal
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• v.11 no.1
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• pp.39-72
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• 2009

With the growing importance of accountability, it is getting necessary to test the impact of marketing expenditure on marketing performance. Including recent ROM, we can find a few researches about marketing accountability. But there are a few problems about definitions and metric of marketing expenditure and marketing performance. Therefore, by defining and analyzing the impact of marketing expenditure on marketing performance, we are going to set the classification scheme of marketing expenditure and marketing performance. Based on research findings, new definitions and metrics are proposed as follows. First, we suggest the classification scheme of marketing expenditure. Marketing expenditure is defined as expense accounts in the balance sheet for doing marketing tasks. Marketing expenditures includes many accounts, for example, marketing research, advertising, sales promotion, foreign market development, physical distribution, after services. Among these marketing investment, advertising expenses have a positive effect on marketing performance. Second, we suggest the classification scheme of marketing performance. Already, marketing performance has been defined as financial metrics, customer metrics, market metrics, and corporate social responsibility. But, in this study, we find that the process model is not relevant for explaining association between the performance metrics. The process model is a virtuous cycle: "customer metrics→market metrics→financial metrics→firm valuation metrics." But, in this study, it is not supported or a little significant association between these metrics. Based on these results, we suggest the balance model or flower model as the classification scheme of marketing performance.

• Journal of Marine Life Science
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• v.8 no.2
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• pp.121-127
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• 2023

The study was performed in order to understand the association between organic matter content in the sediments and the distribution of macrobenthos in the intertidal zone of the Muan bay. The sediment samples obtained from 21 sampling sites in August 2019 were analyzed for sediment composition and organic matter content (Ignition loss; IL and Total organic carbon; TOC). Further the macro benthos was sorted and enumerated. The sediments of inner area of bay has coarser composition where mouth part of bay, the sediments were predominantly silty. The highest organic matter content (both of IL and TOC) was measured at station 10 located near the Mokpo area while the lowest values was measured at station 20 adjacent to the north side of the bay. The 4 most abundant species accounted for more than 10% of all specimens were Assiminea sp., Musculista senhousia, Cerithideopsilla cingulate and Heteromastus filiformis. The maximum number of species and density were observed at station 10. Cluster analysis and non-metric multidimensional scaling (MDS) allow identification of four benthic assemblages based on species abundance. The correlation analysis revealed that there was a significant difference (p<0.05) in the density with TOC. Based on the analysis; it was found that the distribution of macrobenthos varied with the differences in sediment composition and organic matter content.

• Journal of the Korea Institute of Building Construction
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• v.23 no.5
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• pp.537-546
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• 2023

For the purpose of fire delineation within buildings, steel walls in Korea are mandated to undergo rigorous certification as fire-resistant entities, substantiated via a series of qualitative assessments. Predominantly, these evaluations comprise the fire resistance test paired with supplementary examinations; specifically for steel walls, these encompass the gas hazard and panel bending strength tests. Given the prevalence of semi-noncombustible core materials, gas hazard tests are largely rendered superfluous, pivoting the focus solely onto the panel bending strength test during the certification trajectory. This particular test is designed to gauge the flexural robustness of individual wall panels. An enhanced bending strength is postulated to fortify both the structural integrity and thermal insulation of the wall by mitigating potential deformations. In this scholarly exploration, an analytical deep dive was undertaken into extant, valid certification test datasets. The endeavor aimed to ascertain the depth of correlation between the designated fire resistance metric and the bending strength, the latter being the sole supplementary assessment for steel walls. In distilling the findings, it was discerned that temperature elevations beyond baseline values exhibited no statistically salient linkage with the panel's bending strength.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.508-518
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• 2023

This paper presents a new approach for the automatic mapping of discontinuities in a tunnel face based on its 3D digital model reconstructed by LiDAR scan or photogrammetry techniques. The main idea revolves around the identification of discontinuity areas in the 3D digital model of a tunnel face by segmenting its 2D projected images using a deep-learning semantic segmentation model called U-Net. The proposed deep learning model integrates various features including the projected RGB image, depth map image, and local surface properties-based images i.e., normal vector and curvature images to effectively segment areas of discontinuity in the images. Subsequently, the segmentation results are projected back onto the 3D model using depth maps and projection matrices to obtain an accurate representation of the location and extent of discontinuities within the 3D space. The performance of the segmentation model is evaluated by comparing the segmented results with their corresponding ground truths, which demonstrates the high accuracy of segmentation results with the intersection-over-union metric of approximately 0.8. Despite still being limited in training data, this method exhibits promising potential to address the limitations of conventional approaches, which only rely on normal vectors and unsupervised machine learning algorithms for grouping points in the 3D model into distinct sets of discontinuities.

• Phonetics and Speech Sciences
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• v.15 no.4
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• pp.109-118
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• 2023

Cardiac arrest is a critical medical emergency where immediate response is essential for patient survival. This is especially true for Out-of-Hospital Cardiac Arrest (OHCA), for which the actions of emergency medical services in the early stages significantly impact outcomes. However, in Korea, a challenge arises due to a shortage of dispatcher who handle a large volume of emergency calls. In such situations, the implementation of a machine learning-based OHCA detection program can assist responders and improve patient survival rates. In this study, we address this challenge by developing a machine learning-based OHCA detection program. This program analyzes transcripts of conversations between responders and callers to identify instances of cardiac arrest. The proposed model includes an automatic transcription module for these conversations, a text-based cardiac arrest detection model, and the necessary server and client components for program deployment. Importantly, The experimental results demonstrate the model's effectiveness, achieving a performance score of 79.49% based on the F1 metric and reducing the time needed for cardiac arrest detection by 15 seconds compared to dispatcher. Despite working with a limited dataset, this research highlights the potential of a cardiac arrest detection program as a valuable tool for responders, ultimately enhancing cardiac arrest survival rates.