• Smart Structures and Systems
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• v.32 no.4
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• pp.267-279
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• 2023

Data modelling and interpretation for structural health monitoring (SHM) field data are critical for evaluating structural performance and quantifying the vulnerability of infrastructure systems. In order to improve the data modelling accuracy, and extend the application range from data regression analysis to out-of-sample forecasting analysis, an improved most likely heteroscedastic Gaussian process (iMLHGP) methodology is proposed in this study by the incorporation of the outof-sample forecasting algorithm. The proposed iMLHGP method overcomes this limitation of constant variance of Gaussian process (GP), and can be used for estimating non-stationary typhoon-induced response statistics with high volatility. The first attempt at performing data regression and forecasting analysis on structural responses using the proposed iMLHGP method has been presented by applying it to real-world filed SHM data from an instrumented cable-stay bridge during typhoon events. Uncertainty quantification and correlation analysis were also carried out to investigate the influence of typhoons on bridge strain data. Results show that the iMLHGP method has high accuracy in both regression and out-of-sample forecasting. The iMLHGP framework takes both data heteroscedasticity and accurate analytical processing of noise variance (replace with a point estimation on the most likely value) into account to avoid the intensive computational effort. According to uncertainty quantification and correlation analysis results, the uncertainties of strain measurements are affected by both traffic and wind speed. The overall change of bridge strain is affected by temperature, and the local fluctuation is greatly affected by wind speed in typhoon conditions.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.685-694
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• 2023

This study is a descriptive research study to analyze factors affecting patient safety management activities by identifying shared leadership, organizational communication, and nursing service perceived by nurses to prepare basic data for theoretical and practical information and intervention measures. This study collected data from 155 clinical nurses in C region in G-do from July 17 to July 28, 2023, and a total of 154 copies were finally analyzed. Using the SPSS Win. 25.0 program, technical statistics, t-test, one-way ANOVA, Pearson correlation coefficient, and hierarchical multiple regression were analyzed. As a result of analyzing variables affecting the subject's patient safety management activities with multiple regression using hierarchical selection, the higher the shared leadership, the higher the patient safety management. In order to efficiently perform nursing for nurses' patient safety management activities, research should be continuously conducted to develop specific intervention programs that can support patient safety nursing activities and verify their effectiveness

• Phonetics and Speech Sciences
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• v.15 no.3
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• pp.17-25
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• 2023

This study examines the fundamental frequency(f0) distribution of 2,740 Korean speakers in a dialogue speech corpus. Praat and R were used for the collection and analysis of acoustical f0 data after removing extreme values considering the interquartile f0 range of the intonational phrases produced by each individual speaker. Results showed that the average f0 value of all speakers was 185 Hz and the median value was 187 Hz. The f0 data showed a positively skewed distribution of 0.11, and the kurtosis was -0.09, which is close to the normal distribution. The pitch values of daily conversations varied in the range of 238 Hz. Further examination of the male and female groups showed distinct median f0 values: 114 Hz for males and 199 Hz for females. A t-test between the two groups yielded a significant difference. The skewness representing the distribution shape was 1.24 for the male group and 0.58 for the female group. The kurtosis was 5.21 and 3.88 for the male and female groups, and the male group values appeared leptokurtic. A regression analysis between the median f0 and age yielded a slope of 0.15 for the male group and -0.586 for the female group, which indicated a divergent relationship. In conclusion, a normative f0 distribution of different Korean age and sex groups can be examined in the conversational speech corpus recorded by a massive number of participants. However, more rigorous data might be required to define a relation between age and f0 values.

• Journal of the Korean Society of Radiology
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• v.18 no.1
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• pp.53-63
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• 2024

The purpose of this study was to analyze radiological technologists' (RT) task perceptions of intravenous injection behavior of contrast medium and use it as basic data for future workforce response plans. We surveyed a total of 172 RT using questionnaire terms consisting of demographic characteristics, job priorities, and RT' task perceptions of intravenous injection behavior. Statistical analysis was performed using descriptive statistics, frequency analysis, independent samples T-test, and ANOVA analysis. As a result, first, current clinical RT were highly aware of the need for intravenous injection behavior as a response to the future workforce of them, and the workload burden resulting from this was evaluated as low. Second, the fear of intravenous injection behavior was found to be significant, so it is judged to be useful to perform them as selective job actions rather than all RT' task. Third, the need for training courses and certification for RT' intravenous injection behavior is being raised, and additional specific research on this is required. Last, RT' positive perception of intravenous injection behavior could be expected as a foundation for improving national medical services, strengthening RT expertise, and expanding tasks.

• Journal of Food Hygiene and Safety
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• v.39 no.2
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• pp.171-179
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• 2024

In previous Cost-of-Illness (COI) studies, the economic impact of lost leisure time of patients has been mostly underexplored. Furthermore, few studies have focused on chronic or severe diseases, thereby inadequately addressing the segment of self-care patients who do not fall into the categories of inpatients or outpatients. In the present study, we used a comprehensive approach to calculate the annual cost of leisure loss, incorporating factors such as employment status, self-care options, and total period of leisure activity disruption. This required analyzing data from various sources, including health and labor statistics, and applying methods to accurately assess the leisure time lost due to food poisoning. The findings showed that the annual cost of leisure loss for South Korean patients with food poisoning is significant, amounting to approximately 784.5 billion KRW (702.8 million USD, USD/KRW : 1128.34). This study revealed that overlooking self-care patients and not accounting for the affected time in addition to treatment time and employment status significantly underestimated these costs. This study highlights the importance of considering a wider range of factors, including self-care, employment status, as well as the entire affected period, in assessing the societal impact of diseases such as food poisoning. These findings provide valuable insights for policymakers and healthcare professionals to understand the broader economic implications of illness and allocate healthcare resources more effectively.

• Knowledge Management Research
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• v.25 no.2
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• pp.69-94
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• 2024

With the development of cyberspace and its increasing interaction with our daily lives, cybercrime has been steadily increasing in recent years and has become more prominent as a serious social problem. Notably, the "four major malicious cybercrimes" - cyber fraud, cyber financial crime, cyber sexual violence, and cyber gambling - have drawn significant attention. In order to minimize the damage of cybercrime, it's crucial to delve into the specifics of each crime and develop targeted prevention and intervention strategies. Yet, most existing research relies on indirect data sources like statistics, victim testimonials, and public opinion. This study seeks to uncover the characteristics and factors of cybercrime by directly interviewing suspects involved in 'copyright infringement', 'gambling' related to illicit online content, and 'pornography crime'. Through coding analysis and text mining, the study aims to offer a more in-depth understanding of cybercrime dynamics. Furthermore, by suggesting preventative and remedial measures, the research aims to equip policymakers with vital information to reduce the repercussions of this escalating digital threat.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.196-207
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• 2023

As part of measures to address the climate crisis, buses are also being converted to electric and hydrogen buses. Local authorities need to prioritize carbon emissions when allocating newly introduced and converted electric and hydrogen buses, and as a method, consider the introduction of Low Emission Bus Zones (LEBZ) to propose the reduction of pollution from specific links. To introduce LEBZ, it is necessary to compare the carbon emissions before and after its implementation, yet there is a shortage of studies that focus solely on buses or analyze the effects of introducing LEBZ to specific links. In this paper, we utilized bus information system data to calculate and compare the effects of introducing LEBZ to bus priority lanes in Jeju. We categorized scenarios into five groups, with scenarios 1 through 4 involving the introduction of LEBZ, and scenario 5 designating cases where LEBZ was not introduced. Comparative results confirmed that in scenarios with LEBZ introduction, the reduction per km reached a maximum of 0.097t per km, whereas in cases without LEBZ, it amounted to 0.022t per km, demonstrating higher efficiency. It underscores the significance of conducting carbon emission calculations and comparing the effects of LEBZ introduction using bus information system data, which can be directly applied by local authorities to make informed and rational decisions.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.373-382
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• 2024

This study attempted to identify the relationship between nursing competency, clinical reasoning competence, and empathy ability according to the center of enneagram power for nursing students. The subjects of the study were 218 students enrolled in the department of nursing at two universities located in one region, data collection was conducted from 16 October to 27 October 2023. Data analysis was performed using SPSS/WIN version 26.0 program, descriptive statistics, and difference verification were analyzed by t-test, ANOVA, pearson's correlation coefficient, Results, The enneagram personality type of the subjects of this study was the most common type 9. And in the enneagram center of power, the instinct-centered type had the highest nursing competence, the thought-centered type had the highest clinical reasoning competence, and the emotion-centered type had the highest empathy ability. In addition, nursing competence and clinical reasoning competence showed a significant positive correlation, and clinical reasoning competence and empathy ability were also found to be positively correlated. Therefore, it is important to continue to develop and apply individualized competency building programs that reflect personality type tests to nursing students. In addition, the higher the empathy ability, the higher the clinical reasoning competence, so it is thought that it is necessary to develop a standardized curriculum that can improve nursing competence and clinical reasoning competence and verify its effectiveness.

• Journal of the Korean Applied Science and Technology
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• v.40 no.6
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• pp.1506-1520
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• 2023

The purpose of this study was to analyze the mediating effect of self-management in the relationship between the grit of skin care workers and its impact on self-efficacy and job performance. Research participants were 344 workers at skin care shop and hospitals in Seoul and Gyeonggi province, and data were collected through a structured questionnaire. The collected data were analyzed through descriptive statistics, confirmatory factor analysis(CFA), correlation analysis, structural equation model, and mediation effect analysis using bootstrapping method using SPSS, AMOS 26.0 Statistical programs. The conclusions drawn through a series of research procedures are as follows. First, the grit of skin care workers showed a significant positive(+) influence on self-management, self-efficacy, and job performance. Second, Self-management of skin care workers showed a significant positive(+) relationship with self-efficacy and job performance. Third, self-management of skin care workers was found to have a mediating effect in the relationship between grit and job performance. Therefore, it is judged that there is an urgent need to apply human resources management and education programs that can increase self-management, self-efficacy, and job performance through cultivating the grit of beauty industry workers.

• Journal of Radiation Industry
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• v.17 no.1
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• pp.19-32
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• 2023

The amount of radioactive waste is expected to dramatically increase with decommissioning of nuclear power plants such as Kori-1, the first nuclear power plant in South Korea. Accurate nuclide analysis is necessary to manage the radioactive wastes safely, but research on verification of radionuclide analysis has yet to be well established. This study aimed to develop the technology that can verify the results of radionuclide analysis based on artificial intelligence. In this study, we propose an anomaly detection algorithm for inspecting the analysis error of radionuclide. We used the data from 'Updated Scaling Factors in Low-Level Radwaste' (NP-5077) published by EPRI (Electric Power Research Institute), and resampling was performed using SMOTE (Synthetic Minority Oversampling Technique) algorithm to augment data. 149,676 augmented data with SMOTE algorithm was used to train the artificial neural networks (classification and anomaly detection networks). 324 NP-5077 report data verified the performance of networks. The anomaly detection algorithm of radionuclide analysis was divided into two modules that detect a case where radioactive waste was incorrectly classified or discriminate an abnormal data such as loss of data or incorrectly written data. The classification network was constructed using the fully connected layer, and the anomaly detection network was composed of the encoder and decoder. The latter was operated by loading the latent vector from the end layer of the classification network. This study conducted exploratory data analysis (i.e., statistics, histogram, correlation, covariance, PCA, k-mean clustering, DBSCAN). As a result of analyzing the data, it is complicated to distinguish the type of radioactive waste because data distribution overlapped each other. In spite of these complexities, our algorithm based on deep learning can distinguish abnormal data from normal data. Radionuclide analysis was verified using our anomaly detection algorithm, and meaningful results were obtained.