• Journal of the Institute of Convergence Signal Processing
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• v.25 no.2
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• pp.100-112
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• 2024

This study investigates the ethical and legal implications of utilizing artificial intelligence (AI) in human resource management, with a particular focus on AI interviews in the recruitment process. AI, defined as the capability of computer programs to perform tasks associated with human intelligence such as reasoning, learning, and adapting, is increasingly being integrated into HR practices. The deployment of AI in recruitment, specifically through AI-driven interviews, promises efficiency and objectivity but also raises significant ethical and legal concerns. These concerns include potential biases in AI algorithms, transparency in AI decision-making processes, data privacy issues, and compliance with existing labor laws and regulations. By analyzing case studies and reviewing relevant literature, this paper aims to provide a comprehensive understanding of these challenges and propose recommendations for ensuring ethical and legal compliance in AI-based HR practices. The findings suggest that while AI can enhance recruitment efficiency, it is imperative to establish robust ethical guidelines and legal frameworks to mitigate risks and ensure fair and transparent hiring practices.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.4
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• pp.141-160
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• 2024

In recent years, turnover intention among employees in China's banking industry has surged due to internal and external environmental pressures. This surge surpasses those occurring in other sectors. This rising trend in employee turnover has resulted in reducing employee turnover becoming a critical priority for banks in their ongoing development. Traditional leadership styles have proven inadequate for addressing this issue, requiring a shift towards more effective management approaches. This study explores the potential of servant leadership, a management style that prioritizes employees' needs, supports their development, and motivates them to realize their potential, as a means to mitigate rising turnover intentions. The research focuses on employees in the Chinese banking sector so as to examine the impact of the servant leadership management approach on their turnover intentions. Job crafting is investigated as a mediating variable, while stress mindset is assessed as a moderating variable. Data were collected from 401 valid questionnaires across 62 teams, comprising 62 leaders and 339 employees. Hypotheses were tested using SPSS 26.0 and HLM 6.0. The findings reveal that: (1) servant leadership significantly reduces turnover intentions; (2) job crafting mediates the relationship between servant leadership and turnover intentions; and (3) stress mindset positively moderates the influence of job crafting on turnover intentions. This study provides empirical evidence supporting the relationship between servant leadership and turnover intentions. The study's findings offer valuable insights into how to enhance employee management practices. It also discusses the theoretical and practical implications of integrating servant leadership into the banking sector, highlights the study's limitations, and proposes directions for future research.

• Journal of the Korean Society of Radiology
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• v.18 no.5
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• pp.557-566
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• 2024

Ultrasound-guided regional anesthesia is one of the most common techniques used in peripheral nerve blockade by enhancing pain control and recovery time. However, accurate Brachial Plexus (BP) nerve detection and identification remains a challenging task due to the difficulty in data acquisition such as speckle and Doppler artifacts even for experienced anesthesiologists. To mitigate the issue, we introduce a BP nerve small target segmentation network by incorporating BP object detection and U-Net based semantic segmentation into a single deep learning framework based on the multi-scale approach. To this end, the current BP detection and identification was estimated: 1) A RetinaNet model was used to roughly locate the BP nerve region using multi-scale based feature representations, and 2) U-Net was then used by feeding plural BP nerve features for each scale. The experimental results demonstrate that our proposed model produces high quality BP segmentation by increasing the accuracies of the BP nerve identification with the assistance of roughly locating the BP nerve area compared to competing methods such as segmentation-only models.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.5
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• pp.47-53
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• 2024

This study investigates the noise reduction effects of various road pavement methods to mitigate traffic noise caused by the increasing proximity between roads and residential areas in urban environments. The noise characteristics of four types of road pavement-Dense Asphalt Concrete (DAC), Double Layer Porous Asphalt Concrete (DLPAC), Transverse Tining Concrete (TTC), and Exposed Aggregate Concrete (EAC)-were evaluated using CPX close-proximity noise and pass-by noise measurements. The CPX measurements showed that noise levels increased logarithmically with vehicle speed for all pavements. Specifically, DLPAC demonstrated higher noise levels in the low-frequency range below 800 Hz and lower noise levels in the high-frequency range, which is attributed to resonance effects within the internal pores of the pavement and the reduction of compression and expansion noise. In pass-by noise measurements, DLPAC exhibited higher low-frequency noise compared to DAC, likely due to pavement durability deterioration and the influence of external environmental noise. The results indicate that the CPX measurement method is more effective in evaluating road noise performance as it better reflects the impact of vehicle speed. However, since the study was conducted under limited site conditions, further research across various sites and conditions is necessary to enhance reliability.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.5
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• pp.1037-1046
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• 2024

The widespread adoption of the Internet of Things (IoT) has enhanced efficiency and convenience across various fields, but it has also led to a surge in security threats. Among these, IoT botnets are particularly concerning as they can rapidly infect a large number of devices and launch various types of attacks, making them a significant security threat. In IoT environments where implementing security measures on individual devices is challenging, establishing a security monitoring system for real-time detection and response is essential to mitigate the risks posed by botnets. In the field of security monitoring, it is crucial not only to detect botnets but also to analyze their detailed behaviors to devise effective countermeasures. Security experts devote considerable effort to analyzing the payloads of detected threats to understand botnet behavior and develop appropriate responses. However, analyzing all threats manually is time-consuming and costly. To address this, our study proposes an XAI-based network feature extraction methodology to enhance the effectiveness of IoT botnet behavior analysis. This study proposes a practical security monitoring methodology for IoT botnet behavior analysis and response, consisting of three steps: 1) BPE and TF-IDF based payload feature extraction, 2) XAI-based feature importance analysis, and 3) visualization of decision rationale based on feature importance. This approach provides security experts with intuitive visual evidence of IoT attacks and reduces analysis time, contributing to faster decision-making and response strategy development in security monitoring.

• Journal of Korea Port Economic Association
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• v.40 no.3
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• pp.187-210
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• 2024

This study examines the factors influencing container shipping carrier selection among coastal shippers in China amid the pressing external challenges posed by the U.S.-China trade conflict and the COVID-19 pandemic, during a period when China is implementing policies to open its maritime market. Using Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze survey data, the research identifies multi-modal transportation services, carrier operational capabilities, and logistics risk management as key determinants. Notably, salesperson professionalism, cargo safety, and fleet stability during peak periods emerged as critical factors for shippers. The disruptions caused by the trade conflict and the pandemic have heightened shippers' focus on risk management and transportation reliability, further emphasizing the importance of carrier service quality and operational capabilities. This study underscores the need for carriers to strengthen their risk management capacities and enhance service quality in response to shifting global conditions. Additionally, the findings suggest that policymakers should consider regulatory adjustments to mitigate uncertainties in the shipping industry. Future research should delve deeper into the long-term impacts of external shocks on carrier selection decisions.

• Journal of Korea Water Resources Association
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• v.57 no.10
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• pp.797-808
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• 2024

In this study, a comprehensive review of hydraulics and water quality was conducted to analyze the cause of algal bloom in the Sueo Dam, which is currently operating by connecting a waterway tunnel from another water system(Seomjin River→Sueo Dam) in order to resolve the problem of water supply shortage, after the 2021 algae warning level was issued. It was analyzed that the period when the number of blue-green algae cells increased rapidly in the Sueo Dam was when the water temperature was above 26℃, and it was closely related to the increase in the concentration of T-P in Sueo Dam when the inflow through the waterway tunnel increased rather than the inflow into the watershed itself. The analysis using the EFDC+ lake model identified water temperature as the primary growth-limiting factor for blue-green algae from November to April(when temperatures are below 20℃), while phosphorus was determined to be the limiting factor during the months of June to October, when algal cell counts increase. In addition, as a result of a comparative analysis of the period of increase in the number of blue-green algae cells in 2021 and 2022, it was analyzed that blue-green algae can grow rapidly when the water age is more than 30 days and the T-P concentration is more than 0.025 mg/L. The application of the EFDC+ model confirmed that reducing surface water age through changes in the dam's water intake levels contributes to decreased blue-green algae growth. Based on these findings, it is expected that developing operational strategies tailored to the specific characteristics and purposes of the dam will help mitigate algae occurrences and improve water quality management.

• Journal of the Korean Geotechnical Society
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• v.40 no.5
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• pp.113-129
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• 2024

This study reclassifies the risk levels of cut-slopes and addresses the limitations inherent in existing evaluation methods using road slope maintenance data. Conventional risk assessment predominantly relies on subjective expert judgment, resulting in issues of consistency and reliability. To mitigate these limitations, this study applies dimensionality reduction techniques, specifically Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), followed by K-means clustering, to classify new risk levels. The clustering results using PCA demonstrated more distinct cluster separation compared to LDA, and also showed superior performance in terms of the silhouette coefficient and other clustering metrics. This suggests that the existing risk level labels may not adequately capture the underlying data structure. Furthermore, the inconsistency observed between LDA-based clustering results and current risk labels indicates potential reliability issues in the present labeling approach. To resolve this, new risk levels were assigned using PCA and K-means clustering, with cluster risk levels evaluated based on risk scores. A quantitative analysis of key risk factors was also conducted to establish criteria for risk classification and assess the impact of each variable on the different risk levels. This study proposes a data-driven, objective, and quantitative approach to risk level evaluation, aiming to improve the efficiency and reliability of road slope management.

• Journal of the Korean Geographical Society
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• v.43 no.3
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• pp.296-311
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• 2008

In order to examine the rates and factors of path widening in Mount Halla, the retreat of path sidewalls was monitored at 32 sites of Seongpanak Hiking Trail located between 875 m and 1,400 m in elevation. The mean rate of sidewall retreat for the period 2002-2008 is 50.6 mm, equivalent to 10.0 mm/yr. The retreat rate of frozen period is 19.3 mm/yr, while the rate of unfrozen period is 4.3 mm/yr. The latter is divided into the rainy and dry periods that exhibit the retreat rates of 5.9 mm/yr and 2.9 mm/yr, respectively. The retreat rate of sidewalls is also varied with seasons; winter shows the maximum rate of 42.2 mm/yr, while summer exhibits the minimum rate of 1.3 mm/yr. Spring and fall show the intermediate rates of 13.9 mm/yr and 6.4 mm/yr, respectively. Soil hardness and elevation are not closely related to the retreat rate of sidewalls, even though the retreat rate is larger at the north-faced sidewalls than the south-faced sidewalls during the frozen period. Pipkrake is likely to be the most important factor contributing to the path widening in that the retreat of winter months accounts for 76.7% of the total retreat. The hiking trail is placed under the climatic conditions which develop pipkrake in 85 days annually. In addition, it is usual to observe the path sidewall covered with pipkrake in the freezing month of December and the thawing months of March and April. On the other hand, deflation and rainsplash erosion are not important due to the weak wind speed and the forested trail. Rainwash is also insignificant in that the path has been almost paved to mitigate trampling effects. Although biological activity is not dominant, hikers cause a large retreat of sidewalls in the thawing months since they would walk on the sidewalls to avoid snow-melting pools on the path.

• The Korean Journal of Food And Nutrition
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• v.24 no.4
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• pp.753-760
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• 2011

This study was carried out to investigate the effects of various kinds of commercial salts, including Hanju salt, Deep sea water salt, $Salicornia$ $herbacea$ salt, Guwoon salt, Bamboo salt and salt replacement for the reduction of Na concentration in $kimchi$. The fermentative characteristics of these salts were determined during the fermentation at $10^{\circ}C$. $kimchi$ using a salt replacement and with $Salicornia$ $herbacea$ salt showed slow changes in their pH values. The use of salt replacement showed the lowest level(0.97%) of the retardation of $kimchi$ fermentation. For the preparation of $kimchi$ that used a low Na, chemical and microbial changes were investigated during the fermentation of process, examining preparations with both table salt and a salt replacement(CS-17). The salinity level of $kimchi$ prepared with table salt(control) and the salt replacement (CS-17) were 2.17~2.5% and 1.72~1.99% during fermentation, respectively. The Na contents of $kimchi$ with CS-17(562.5 mg%) showed a lower level than that with table salt(879.0 mg%). The growth of Leuconostoc sp. was highest ($1.5{\times}10^8$ cfu/g) in $kimchi$ with CS-17 at 6 day-fermentation, but the highest level($2.3{\times}10^7$ cfu/g) in $kimchi$ with table salt was at 7dayfermentation. The cells of $Lactobacillus$ sp. in the $kimchi$ prepared with CS-17 and table salt increased to $3.0{\times}10^8$ cfu/g and $6.0{\times}10^7$ cfu/g at 8day-fermentation, respectively. It was concluded that the use of CS-17 could reduce Na levels in $kimchi$ and mitigate over-maturation.