• Journal of Korea Society of Industrial Information Systems
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• v.28 no.4
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• pp.45-58
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• 2023

In a world of uncertainty and complexity, leadership is essential to lead collaborative and positive interactions among employees. In other words, if members share opinions and work through voluntary leadership, they will respond more effectively to uncertain challenges and get closer to the targeted management performance. Therefore, in this study, we would like to elucidate the importance of shared leadership, which has recently become an issue. We will examine the impact of shared leadership on team efficacy, team organizational citizenship behavior, and turnover intention. A survey was conducted among members working in a team organization in Busan, and the results were as follows. First, the effects of shared leadership on team efficacy were found to have significant positive(+) effects, such as the hypotheses set at planning and organizing 0.202(C.R.=2.853), problem solving 0.463(C.R.=5.620), support and caring 0.237(C.R.=3.326), and development and mentoring 0.366(C.R.=5.132), respectively. Second, the effects of team efficacy on team organizational citizenship behavior and turnover intention were 0.545(C.R.=5.895) and -0.143(C.R.=-0.817), respectively, and team efficacy was found to have a positive(+)positive(+) effect on team organizational citizenship behavior, but team efficacy did not have a significant effect on turnover intention.

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.127-135
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• 2023

This research paper explores the application of FinBERT, a variational BERT-based model pre-trained on financial domain, for sentiment analysis in the financial domain while focusing on the process of identifying suitable training data and hyperparameters. Our goal is to offer a comprehensive guide on effectively utilizing the FinBERT model for accurate sentiment analysis by employing various datasets and fine-tuning hyperparameters. We outline the architecture and workflow of the proposed approach for fine-tuning the FinBERT model in this study, emphasizing the performance of various datasets and hyperparameters for sentiment analysis tasks. Additionally, we verify the reliability of GPT-3 as a suitable annotator by using it for sentiment labeling tasks. Our results show that the fine-tuned FinBERT model excels across a range of datasets and that the optimal combination is a learning rate of 5e-5 and a batch size of 64, which perform consistently well across all datasets. Furthermore, based on the significant performance improvement of the FinBERT model with our Twitter data in general domain compared to our news data in general domain, we also express uncertainty about the model being further pre-trained only on financial news data. We simplify the complex process of determining the optimal approach to the FinBERT model and provide guidelines for selecting additional training datasets and hyperparameters within the fine-tuning process of financial sentiment analysis models.

• Computers and Concrete
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• v.32 no.3
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• pp.247-261
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• 2023

Due to the non-uniform distribution of meso-structure, the diffusion of chloride ions in concrete show the characteristics of characteristics of randomness and fuzziness, which leads to the non-uniform distribution of chloride ions and the non-uniform corrosion of steel rebar in concrete. This phenomenon is supposed as the main reason causing the uncertainty of the bearing capacity deterioration of reinforced concrete structures. In order to analyze and predict the durability of reinforced concrete structures under chloride environment, the random features of chloride ions transport in concrete were studied in this research from in situ meso-structure of concrete. Based on X-ray CT technology, the spatial distribution of coarse aggregates and pores were recognized and extracted from a cylinder concrete specimen. In considering the influence of ITZ, the in situ mesostructure of concrete specimen was reconstructed to conduct a numerical simulation on the diffusion of chloride ions in concrete, which was verified through electronic microprobe technology. Then a stochastic study was performed to investigate the distribution of chloride ions concentration in space and time. The research indicates that the influence of coarse aggregate on chloride ions diffusion is the synthetic action of tortuosity and ITZ effect. The spatial distribution of coarse aggregates and pores is the main reason leading to the non-uniform distribution of chloride ions both in spatial and time scale. The chloride ions concentration under a certain time and the time under a certain concentration both satisfy the Lognormal distribution, which are accepted by Kolmogorov-Smirnov test and Chi-square test. This research provides an efficient method for obtain mass stochastic data from limited but representative samples, which lays a solid foundation for the investigation on the service properties of reinforced concrete structures.

• Korean Journal of Construction Engineering and Management
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• v.24 no.5
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• pp.52-62
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• 2023

The objective of this study is to propose an automated algorithm for precise cutting length of slab rebar complying with regulations such as anchorage length, standard hooks, and lapping length. This algorithm aims to improve the traditional manual quantity take-off process typically outsourced by external contractors. By providing accurate rebar quantity data at BBS(Bar Bending Schedule) level from the bidding phase, uncertainty in quantity take-off can be eliminated and reliance on out-sourcing reduced. In addition, the algorithm allows for early determination of precise quantities, enabling construction firms to preapre competitive and optimized bids, leading to increased profit margins during contract negotiations. The proposed algorithm not only streamlines redundant tasks across various processes, including estimating, budgeting, and BBS generation but also offers flexibility in handling post-contract structural drawing changes. In particular, the proposed algorithm, when combined with BIM, can solve the technical problems of using BIM in the early phases of construction, and the algorithm's formulas and shape codes that built as REVIT-based family files, can help saving time and manpower.

• Korean Journal of Forensic Psychology
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• v.11 no.3
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• pp.329-347
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• 2020

When a person suggests an estimate under uncertainty, (s)he tend to rely on the information and number provided in advance. As a result, their final estimate would be assimilated to the initial value. This phenomenon is called "anchoring effect". The present research examined anchoring effects observed in law courts. Sentencing decision of jurors can be influenced by the sentence demanded by the prosecutor. Specifically, this study demonstrated the condition in which anchoring effect would be stronger and practical solutions for lowering anchoring effect. Study 1 demonstrated whether gravity of criminal cases and levels of anchor influenced anchoring effects. As expected, anchoring effect was stronger in a heavier criminal case than in a lighter one. When a low anchor was provided in a lighter case, anchoring effect was stronger compared to when a high anchor was provided. Study 2 examined how emotion affects anchoring effects. The results showed that anchoring effect appeared to be significantly stronger with feelings of anger than of sadness. Study 3 examined the solution for reducing anchoring effects in a court. When activation of selective-accessibility model was prevented, anchoring effects significantly decreased. These results can help solve the problems about juror judgmental bias and contribute to the development of Korean jury trial.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.1
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• pp.192-207
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• 2023

Commercialization of level-4 (Lv.4) autonomous driving applications requires the definition of a safe road environment under which autonomous vehicles can operate safely. Thus, a risk assessment model is required to determine whether the operation of autonomous vehicles can provide safety to is sufficiently prepared for future real-life traffic problems. Although the risk factors of autonomous vehicles were selected and graded, the decision-making method was applied as qualitative data using a survey of experts in the field of autonomous driving due to the cause of the accident and difficulty in obtaining autonomous driving data. The fuzzy linguistic representation of decision-makers and the fuzzy analytic hierarchy process (AHP), which converts uncertainty into quantitative figures, were implemented to compensate for the AHP shortcomings of the multi-standard decision-making technique. Through the process of deriving the weights of the upper and lower attributes, the road alignment, which is a physical infrastructure, was analyzed as the most important risk factor in the operation risk of autonomous vehicles. In addition, the operation risk of autonomous vehicles was derived through the example of the risk of operating autonomous vehicles for the 5 areas to be evaluated.

• Journal of the Korean Geotechnical Society
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• v.25 no.10
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• pp.87-97
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• 2009

The seismic site coefficients are often used with the seismic hazard maps to develop the design response spectrum at the surface. The site coefficients are most commonly developed deterministically, while the seismic hazarde maps are derived probabilistically. There is, hence, an inherent incompatibility between the two approaches. However, they are used together in the seismic design codes without a clear rational basis. To resolve the fundamental imcompatibility between the site coefficients and hazard maps, this study uses a novel probabilistic seismic hazard analysis (PSHA) technique that simulates the results of a standard PSHA at a rock outcrop, but integrates the site response analysis function to capture the site amplification effects within the PSHA platform. Another important advantage of the method is its ability to model the uncertainty, variability, and randomness of the soil properties. The new PSHA was used to develop fully probabilistic site coefficients for site classes of the seismic design code and another sets of site classes proposed in Korea. Comparisons highlight the pronounced discrepancy between the site coefficients of the seismic design code and the proposed coefficients, while another set of site coefficients show differences only at selected site classes.

• Journal of the Korean Geotechnical Society
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• v.26 no.7
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• pp.161-170
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• 2010

For the development of load and resistance factor design, reliability analysis is required to calibrate resistance factors in the framework of reliability theory. The distribution of measured-to-predicted pile resistance ratio was obrained based on only the results of load tests conducted to failure for the assessment of uncertainty regarding pile resistance and used in the conventional reliability analysis. In other words, successful pile load test (piles resisted twice their design loads without failure) results were discarded, and therefore, were not reflected in the reliability analysis. In this paper, a new systematic method based on Bayesian theory is used to update reliability indices of driven steel pipe piles by adding more proof pile load test results, even not conducted to failure, to the prior distribution of pile resistance ratio. Fifty seven static pile load tests performed to failure in Korea were compiled for the construction of prior distribution of pile resistance ratio. The empirical method proposed by Meyerhof is used to calculate the predicted pile resistance. Reliability analyses were performed using the updated distribution of pile resistance ratio. The challenge of this study is that the distribution updates of pile resistance ratio are possible using the load test results even not conducted to failure, and that Bayesian updates are most effective when limited data are available for reliability analysis.

• Journal of the Korean Society of Safety
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• v.38 no.2
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• pp.96-103
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• 2023

Generally, human stampedes and crowd collapses occur when people press against each other, causing falls that may result in death or injury. Particularly, crowd accidents have become increasingly common since the 1990s, with an average of 380 deaths annually. For instance, in Korea, a stampede occurred during the Itaewon Halloween festival on October 29, 2022, when several people crowded onto a narrow, downhill road, which was 45 meters long and between 3.2 and 4 meters wide. Precisely, this stampede was primarily due to the excessive number of people relative to the road size. Essentially, stampedes can occur anywhere and at any time, not just at events, but also in other places where large crowds gather. More specifically, the likelihood of accidents increases when the crowd density exceeds a turbulence threshold of 5-6 /m². Meanwhile, festivals and events, which have become more frequent and are promoted through social media, garner people from near and far to a specific location. Besides, as cities grow, the number of people gathering in one place increases. While stampedes are rare, their impact is significant, and the uncertainty associated with them is high. Currently, there is no scientific system to analyze the risk of stampedes due to crowd concentration. Consequently, to prevent such accidents, it is essential to prepare for crowd disasters that reflect social changes and regional characteristics. Hence, this study proposes using digital topographic maps and crowd-density risk simulations to develop a 3D model of the region. Specifically, the crowd density simulation allows for an analysis of the density of people walking along specific paths, which enables the prediction of danger areas and the risk of crowding. By using the simulation method in this study, it is anticipated that safety measures can be rationally established for specific situations, such as local festivals, and preparations may be made for crowd accidents in downtown areas.

• Journal of the Korean Geotechnical Society
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• v.38 no.3
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• pp.35-42
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• 2022

The N-value from the Standard Penetration Test (SPT), which is one of the representative in-situ test, is an important index that provides basic geological information and the depth of the bearing layer for the design of geotechnical structures. In the aspect of time and cost-effectiveness, there is a need to carry out a representative sampling test. However, the various variability and uncertainty are existing in the soil layer, so it is difficult to grasp the characteristics of the entire field from the limited test results. Thus the spatial interpolation techniques such as Kriging and IDW (inverse distance weighted) have been used for predicting unknown point from existing data. Recently, in order to increase the accuracy of interpolation results, studies that combine the geotechnics and deep learning method have been conducted. In this study, based on the SPT results of about 22,000 holes of ground survey, a comparative study was conducted to predict the depth of the bearing layer using deep learning methods and IDW. The average error among the prediction results of the bearing layer of each analysis model was 3.01 m for IDW, 3.22 m and 2.46 m for fully connected network and PointNet, respectively. The standard deviation was 3.99 for IDW, 3.95 and 3.54 for fully connected network and PointNet. As a result, the point net deep learing algorithm showed improved results compared to IDW and other deep learning method.