• The Korean Journal of Applied Statistics
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• v.37 no.3
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• pp.339-353
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• 2024

In cases where the response variable in proportional data is confined to a limited interval, a regression model based on the assumption of normality can yield inaccurate results due to issues such as asymmetry and heteroscedasticity. In such cases, the beta regression model can be considered as an alternative. This model reparametrizes the beta distribution in terms of mean and precision parameters, assuming that the response variable follows a beta distribution. This allows for easy consideration of heteroscedasticity in the data. In this paper, we therefore aim to analyze proportional data using the beta regression model in two empirical analyses. Specifically, we investigate the relationship between smoking rates and coffee consumption using data from the 6th National Health Survey, and examine the association between regional characteristics in the U.S. and cumulative mortality rates based on COVID-19 data. In each analysis, we apply the ordinary least squares regression model, the beta regression model, and the extended beta regression model to analyze the data and interpret the results with the selected optimal model. The results demonstrate the appropriateness of applying the beta regression model and its extended version in proportional data.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.687-692
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• 2024

The energy crisis is emerging as a serious problem around the world. In the case of Korea, there is great interest in energy efficiency research related to industrial complexes, which use more than 53% of total energy and account for more than 45% of greenhouse gas emissions in Korea. One of the studies is a study on saving energy through sharing facilities between factories using the same utility in an industrial complex called a virtual energy network plant and through transactions between energy producing and demand factories. In such energy-saving research, data collection is very important because there are various uses for data, such as analysis and prediction. However, existing systems had several shortcomings in reliably collecting time series data. In this study, we propose an intelligent IIoT platform to improve it. The intelligent IIoT platform includes a preprocessing system to identify abnormal data and process it in a timely manner, classifies abnormal and missing data, and presents interpolation techniques to maintain stable time series data. Additionally, time series data collection is streamlined through database optimization. This paper contributes to increasing data usability in the industrial environment through stable data collection and rapid problem response, and contributes to reducing the burden of data collection and optimizing monitoring load by introducing a variety of chatbot notification systems.

• Journal of Platform Technology
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• v.12 no.1
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• pp.55-66
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• 2024

The K-POP market has become influential not only in culture but also in society as a whole, including diplomacy and environmental movements. As a result, various papers have been conducted based on machine learning to identify the success factors of idols by utilizing traditional data such as music and recordings. However, there is a limitation that previous studies have not reflected the influence of new media platforms such as Instagram releases, YouTube shorts, TikTok, Twitter, etc. on the popularity of idols. Therefore, it is difficult to clarify the causal relationship of recent idol success factors because the existing studies do not consider the daily changing media trends. To solve these problems, this paper proposes a data collection system and analysis methodology for idol-related data. By developing a container-based real-time data collection automation system that reflects the specificity of idol data, we secure the stability and scalability of idol data collection and compare and analyze the clusters of successful idols through a K-Means clustering-based outlier detection model. As a result, we were able to identify commonalities among successful idols such as gender, time of success after album release, and association with new media. Through this, it is expected that we can finally plan optimal comeback promotions for each idol, album type, and comeback period to improve the chances of idol success.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.4
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• pp.577-586
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• 2024

As new technologies are developed, new security threats such as the emergence of AI technologies that create ransomware are also increasing. New security equipment such as XDR has been developed to cope with these security threats, but when using various security equipment together rather than a single security equipment environment, there is a difficulty in creating numerous regular expressions for identifying and classifying essential data. To solve this problem, this paper proposes a method of identifying essential information for identifying threat information by introducing artificial intelligence-based entity name recognition technology in various security equipment usage environments. After analyzing the security equipment log data to select essential information, the storage format of information and the tag list for utilizing artificial intelligence were defined, and the method of identifying and extracting essential data is proposed through entity name recognition technology using artificial intelligence. As a result of various security equipment log data and 23 tag-based entity name recognition tests, the weight average of f1-score for each tag is 0.44 for Bi-LSTM-CRF and 0.99 for BERT-CRF. In the future, we plan to study the process of integrating the regular expression-based threat information identification and extraction method and artificial intelligence-based threat information and apply the process based on new data.

• Journal of Platform Technology
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• v.12 no.2
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• pp.34-44
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• 2024

This paper proposes a dynamic clock supply control algorithm and a system load power stabilization algorithm that minimizes the power consumption of the sensing system, which accounts for the largest percentage of power consumption in mobile devices, to extend the usage time of the rechargeable battery mounted on the mobile device. The dynamic clock supply control algorithm can reduce the power consumed by the sensing system by configuring a circuit to cut off the power of the sensing system and by recognizing the state of low sensor change and adjusting the measurement cycle. The system load power stabilization algorithm is an algorithm that controls the power of the surrounding module according to the power consumption state, and when it requires a lot of power, it controls the clock supply to stabilize the operation. The experimental results confirmed that applying only the dynamic clock supply control algorithm reduces the power consumed by the sensing system by 17%, and applying only the system load power stabilization algorithm reduces power consumption by 9.3%, enabling it to operate stably in situations that require a lot of power such as image processing. When both algorithms were applied, the power consumption of the battery was reduced by 67% compared to before applying the algorithm. Through this, the reliability of the proposed method was confirmed.

• Applied Chemistry for Engineering
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• v.35 no.4
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• pp.335-340
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• 2024

In this paper, we synthesized organic and inorganic hybrid materials to introduce antibody functionality to UIO-66 and incorporated them into a surface plasmon resonance (SPR) assay to enhance the sensitivity of detecting small molecules such as oxytocin. A biological marker peptide called oxytocin may help in the diagnosis of heart failure, Alzheimer's disease, and cancer. To detect oxytocin at concentrations as low as a few femtomole (fM), we developed a surface sandwich assay utilizing a pair of oxytocin-specific antibodies for enhancing selectivity and one of metal organic frameworks [e.g., UiO-66-(COOH)₂] possessing high porosity and surface-area as a signal amplifier. Initially, real-time SPR assays were used to confirm that each selected oxytocin-specific antibody binds strongly to oxytocin and to different binding sites on oxytocin. One of these antibodies (e.g., anti-OXT[OTI5G4]) was immobilized on the surface of a thin gold chip. Upon sequential injecting of oxytocin and the other antibody (e.g., anti-OXT[4G11]) conjugated to UiO-66-(COOH)₂ onto the surface to form the surface sandwich complex of anti-OXT[OTI5G4]/oxytocin/UiO-66-(COOH)₂-anti-OXT[4G11]), SPR changes, which varied with oxytocin concentration, were then measured in real time. The results demonstrated that sensitivity was amplified by over a million-fold compared to assays without UiO-66-(COOH)₂, enabling oxytocin detection down to approximately 10 fM.

• Korean Journal of Agricultural and Forest Meteorology
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• v.26 no.3
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• pp.199-207
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• 2024

Measuring and estimating methane (CH₄) emissions accurately is important in rice paddy field. For reliable estimation, diurnal and seasonal variations of methane must be tracked, and measured frequently. The closed chamber method proposed according to the IPCC guidelines is relatively cheap and easy to move, so it is widely used, but it is difficult to estimate accurate methane emissions due to spatiotemporal constraints such as sampling time and number of measuring times. In this paper, the diurnal variation pattern was analyzed by measuring methane emissions four times at two-hour intervals throughout the day during the rice growth stage. When the emissions for each time period were converted to a daily time-weighted average, the diurnal average methane flux appeared in the time periods of 8:00~12:00 and 16:00~20:00. Through our results, we hope to provide useful information about determining reasonable times of methane measurement to researchers who measure methane emissions in rice paddy fields using the closed chamber method in the future.

• Journal of the Korea Organic Resources Recycling Association
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• v.17 no.3
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• pp.91-99
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• 2009

This research were performed to evaluate co-incineration characteristics of sewage sludge and industrial waste in rotary kiln incinerator, and provide the fundamental data. Plastic portion (42.55%) in this industrial waste showed over 3 times higher than that (11.92%) of paper. Korean proximate analysis of the waste mixed with sewage sludge and industrial waste (3 : 7, volumetric basis) showed 16.3% of moisture, 70.5% of volatile solids, and 13.2% of ash, respectively. Low heating value of the mixed waste was 4,513kcal/kg. So it was thought that the mixed waste of sewage sludge and industrial waste (containing 43% of plastics and 12% papers) has enough heating value for co-incineration. The incineration of mixed waste showed the lowest SOx and NOx concentrations at $700^{\circ}C$. However, the operation at $950^{\circ}C$ was feasible in considering dioxin and the other hazardous gases. It was concluded that use of $Ca(OH)_2$ should be under investigation for the operation at $950^{\circ}C$.

• Journal of Aerospace System Engineering
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• v.18 no.4
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• pp.81-88
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• 2024

The payload BioCabinet of CAS500-3 is designed for 3D stem cell differentiation, culture, and analysis utilizing bio 3D printing techniques in space. The 3D printing technique was initially developed for orbital use; however, it lacks separate validation for extreme launch vibration environments, necessitating a design that mitigates the launch load on the payload. This paper proposes a passive vibration isolator with a low-stiffness elastic support structure and high damping characteristics to reduce the launch loads affecting the BioCabinet. We explore the high-damping characteristics through the superelastic effects of SMA (Shape Memory Alloys) and a multi-layered structure incorporating viscoelastic tape. The effectiveness of the proposed vibration isolation system was confirmed via launch vibration tests on a qualification model.

• Journal of Navigation and Port Research
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• v.48 no.4
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• pp.321-326
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• 2024

The purpose of this study is to investigate whether Big 4 accounting firms contribute to the reduction of earnings management when auditing shipping companies. Generally, it is understood that companies audited by the Big 4 accounting firms engage in minimal earnings management and maintain high audit quality. However, these factors may vary depending on industry and firm size. As a result, this study empirically analyzes the impact of audits conducted by large accounting firms on earnings management within the shipping industry. The Big 4 accounting firms, namely PwC, KPMG, Deloitte, and EY, are the focus of this research. Discretionary accruals are employed as a proxy for earnings management, with the modified J ones model and the performance matched model used to measure discretionary accruals. The analysis, which covers shipping companies listed on KOSP I from 2001 to 2023, reveals that audits conducted by the Big 4 accounting firms do not significantly influence earnings management in the shipping industry. Unlike the general case, it is evident that audits by the Big 4 accounting firms do not play a role in reducing earnings management in shipping companies. This paper is significant as it examines the role of auditors within the shipping industry and presents findings that deviate from commonly known information. Shipping companies should take into consideration that the audit quality of the Big 4 accounting firms may not always be guaranteed when selecting an auditor. Furthermore, supervisory authorities such as the Financial Supervisory Service should engage in oversight based on an accurate understanding of the audit quality offered by the Big 4 accounting firms.