• 한국산업보건학회지
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• 제31권1호
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• pp.50-59
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• 2021

Objectives: The aim of this study was to examine factors influencing depressive symptoms among married working women using the dataset of the Korean Longitudinal Survey of Women and Family (KLoWF 7th). There were 1,030 subjects. Methods: A multiple regression model was used to study the factors influencing depressive symptoms among married working women. Results: The authors found a negative relationship between satisfaction with spouse household-labor (b=-0.606, p=0.022) and depressive symptoms among married working women, a negative relationship with spouse (b=-0.237, p<0.001) and a negative spousal perception of working (b=-0.709, p=0.045), a positive relationship with excessive working hours (b=0.397, p=0.027), a positive relationship with temporal oppression on workload (b=0.422, p=0.002), and a positive relationship between workplace discrimination (b=0.053, p=0.046) and depressive symptoms among married working women. Conclusions: This study suggests that family life and working environments are important factors for depressive symptoms in married women workers. The findings of this study will be helpful to policymakers to design plans to decrease depressive symptoms among married working women.

• 공학교육연구
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• 제26권3호
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• pp.35-41
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• 2023

The study was conducted to develop two parallel short forms for the Convergent thinking and Problem solving questionnaires which are part of H University's core competency diagnostic tools, based on Multi-Item Response Theory. Item responses of 2,580 students were analyzed using Graded Response Model(GRM) to determine item difficulty and discrimination of each item. The research results are as follows. Two parrallel short tests were developed for the Convergent thinking questionnaire consisting of 12 items which were originally 17 items. Likewise, the Problem solving questionnaire, which originally consisted of 15 questions, was divided into two parallel short forms, each consisting of 9 items. The reliability of the shortened parallel tests was confirmed through internal consistency analysis, and their similarity to the original tests was established through correlation analysis. This study contributed to quality management of competency-based education and programs at H University by developing shortened tests. Based on the results, implications were presented as well as limitations and discussions.

• Journal of Information Processing Systems
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• 제19권4호
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• pp.527-539
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• 2023

In the positioning algorithm of two-dimensional planar sensor array, the estimation error of time difference-ofarrival (TDOA) algorithm is difficult to avoid. Thus, how to achieve accurate positioning is a key problem of the positioning technology based on planar array. In this paper, a method of sensor reliability discrimination is proposed, which is the foundation for selecting positioning sensors with small error and excellent performance, simplifying algorithm, and improving positioning accuracy. Then, a positioning model is established. The estimation characteristics of the least square method are fully utilized to calculate and fuse the positioning results, and the non-uniform weighting method is used to correct the weighting factors. It effectively handles the decreased positioning accuracy due to measurement errors, and ensures that the algorithm performance is improved significantly. Finally, the characteristics of the improved algorithm are compared with those of other algorithms. The experiment data demonstrate that the algorithm is better than the standard least square method and can improve the positioning accuracy effectively, which is suitable for vibration detection with large noise interference.

• 한국보육지원학회지
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• 제19권6호
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• pp.1-19
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• 2023

Objective: The aim of the present study was to develop and validate a recognition scale for childcare teachers' rights. Methods: Statistical methods for data analysis involved the use of SPSS 20.0 and AMOS 20.0. To confirm the reliability and validity of the developed scale, various analyses, including item quality assessment, item discrimination, exploratory factor analysis, confirmatory factor analysis, and Pearson correlation analysis, were conducted. The maximum likelihood estimation method was employed for model fitting. Goodness of fit was assessed using SRMR, RMSEA and its 90% confidence interval, CFI, and TLI. Through these analyses, the scale's reliability and validity exceeded the standard. Consequently, 5 factors and 30 questions were ultimately selected as the recognition scale for childcare teachers' rights. Results: First, a recognition scale for childcare teachers' rights was developed to reflect changes in childcare settings. Second, an objective measurement was incorporated into the recognition scale of childcare teachers' rights. Third, the analysis using the proposed scale revealed a correlation between the recognition of childcare teachers' rights and life satisfaction. Conclusion/Implications: The study developed a scale capable of objectively measuring the recognition of childcare teachers' rights.

• International Journal of Computer Science & Network Security
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• 제23권12호
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• pp.213-219
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• 2023

In recent years, popularity of deep learning (DL) is increased due to its ability to extract features from Hyperspectral images. A lack of discrimination power in the features produced by traditional machine learning algorithms has resulted in poor classification results. It's also a study topic to find out how to get excellent classification results with limited samples without getting overfitting issues in hyperspectral images (HSIs). These issues can be addressed by utilising a new learning network structure developed in this study.EfficientNet-B4-Based Convolutional network (EN-B4), which is why it is critical to maintain a constant ratio between the dimensions of network resolution, width, and depth in order to achieve a balance. The weight of the proposed model is optimized by Search and Rescue Operations (SRO), which is inspired by the explorations carried out by humans during search and rescue processes. Tests were conducted on two datasets to verify the efficacy of EN-B4, with Indian Pines (IP) and the University of Pavia (UP) dataset. Experiments show that EN-B4 outperforms other state-of-the-art approaches in terms of classification accuracy.

• 한국군사과학기술학회지
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• 제27권2호
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• pp.189-196
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• 2024

Classification of drones and birds is challenging due to diverse flight patterns and limited data availability. Previous research has focused on identifying the flight patterns of unmanned aerial vehicles by emphasizing dynamic features such as speed and heading. However, this approach tends to neglect crucial spatial information, making accurate discrimination of unmanned aerial vehicle characteristics challenging. Furthermore, training methods for situations with imbalanced data among classes have not been proposed by traditional machine learning techniques. In this paper, we propose a data processing method that preserves angle information while maintaining positional details, enabling the deep learning model to better comprehend positional information of drones. Additionally, we introduce a training technique to address the issue of data imbalance.

• Ocean and Polar Research
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• 제41권1호
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• pp.35-46
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• 2019

The purpose of this study is to identify factors influencing fish production of shallow-sea aquaculture in South Korea. This study employed the two-way fixed effect and random effect models based on the panel models and also the difference between GMM and system GMM models based on the dynamic panel models using the amount of fish farming production, the number of stocked fry, the number of cultured fish, the amount of inputted feed, the farming area, the number of workers, and the sales price data from 2010 to 2017. First, the two-way fixed effect model of the panel models was selected by panel characteristics, time characteristics and Hausman tests and also the model was statistically significant. As a result of the two-way fixed effect model, the number of stocked fry, the amount of inputted feed, and the number of workers were identified as factors that increase the fish production of shallow-sea aquaculture. However, the number of cultured fish and the sales price were analyzed as factors that reduce the fish production of shallow-sea aquaculture. Second, the system GMM model of the dynamic panel models was selected by Hansen test and Arellano-Bond test in order to identify whether or not the over-discrimination condition is appropriate. Based on the system GMM model, the number of stocked fry, the amount of inputted feed, the number of workers in this year and 1 year ago, the number of cultured fish 2 years ago, and the sale price 3 years ago were analyzed as factors that increase the fish production of shallow-sea aquaculture. However, the amount of fish farming production 1, 2, 3 years ago, the farming area in this year, and the number of cultured fish in this year and 1 year ago were identified as factors that reduce the fish production of shallow-sea aquaculture. In conclusion, this study suggests that it is desirable to control the amount of stocked fry rather than to expand the farming area for fish farming in shallow-sea aquaculture, so as to keep the sale price at a certain level by maintaining the appropriate amount of fish production.

• 한국체육학회지인문사회과학편
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• 제58권1호
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• pp.295-308
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• 2019

본 연구는 청소년의 비만율이 증가하는 현 상황에서 건강신념모형을 적용하여 청소년 비만예방척도를 개발하고 선행연구에서 구축된 통합적 타당도의 틀로 분석하고 근거를 제시하고자 하였다. 이 연구에서는 Messick(1995)의 통합적 타당도, Benson(1998)의 강력한 구인타당도 프로그램 틀, Wolfe와 Smith(2007a, 2007b)가 제안한 Rasch 모형의 통합적 타당도 구현방안을 종합적으로 반영할 수 있도록 개발된 통합적 타당도 분석 틀(서은철, 2015)을 수정하여, 실제영역의 3단계를 분석하여 건강신념모형을 적용한 청소년 비만예방척도의 타당도 증거를 제시하였다. 연구대상은 서울-경기지역 12개 고등학교에서 1801명을 표집하였으며, 통합적 타당도 분석 틀의 적용을 위해 표본1에 902명(남:464명, 여:438명)과 표본2에 899명(남:464명, 여:435명)으로 무선 분류하였다. 자료처리는 SPSS 23.0과 WINSTEPS 4.30(Linacre, 2018)을 사용하였다. 연구결과 실제영역 1단계에서 개발된 청소년 비만예방척도는 5영역(민감성, 심각성, 유익성, 장애성, 건강동기) 33문항이었다. 2단계에서는 자료의 적합성(일차원성 검증, global fit statistic)을 확인, Rasch 모형의 문항적합도 검증에서 2개 문항이 삭제되었다. 3단계에서는 Rasch 부분점수모형을 적용하여 단계 조정값에 문제가 있는 3개 문항이 삭제되었다.

• 한국재난정보학회 논문집
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• 제20권2호
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• pp.302-314
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• 2024

연구목적: 본 연구는 지하공동구 내 다수 작업자의 낙상을 자동으로 판별하기 위한 Top-down 방식의 딥러닝 자세 추정 모델 기반 낙상 검출 모델을 제안하고, 제안 모델의 성능을 평가한다. 연구방법: Top-down 방식의 자세 추정모델 중 하나인 YOLOv8-pose로부터 추론된 결과와 낙상 판별 규칙을 결합한 모델을 제시하고, 지하공동구 내 2인 이하 작업자가 출현한 기립 및 낙상 이미지에 대해 모델 성능지표를 평가하였다. 또한 동일한 방법으로 Bottom-up 방식 자세추정모델(OpenPose)을 적용한 결과를 함께 분석하였다. 두 모델의 낙상 검출 결과는 각 딥러닝 모델의 작업자 인식 성능에 의존적이므로, 작업자 쓰러짐과 함께 작업자 존재 여부에 대한 성능지표도 함께 조사하였다. 연구결과: YOLOv8-pose와 OpenPose의 모델의 작업자 인식 성능은 F1-score 기준으로 각각 0.88, 0.71로 두 모델이 유사한 수준이었으나, 낙상 규칙을 적용함에 따라 0.71, 0.23로 저하되었다. 작업자의 신체 일부만 검출되거나 작업자간 구분을 실패하여, OpenPose 기반 낙상 추론 모델의 성능 저하를 야기한 것으로 분석된다. 결론: Top-down 방식의 딥러닝 자세 추정 모델을 사용하는 것이 신체 관절점 인식 및 개별 작업자 구분 측면에서 지하공동구 내 작업자 낙상 검출에 효과적이라 판단된다.

• 한국근적외분광분석학회:학술대회논문집
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.1510-1510
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• 2001

NIRS uses reflectance signals resulting from bending and stretching vibrations in chemical bonds between carbon, nitrogen, hydrogen, sulfur and oxygen. These reflectance signals are used to measure the concentration of major chemical composition and other descriptors of homogenized and freeze-dried whole broiler carcasses. Six strains of chicken were analyzed and the NIRS model predictions compared to reference data. The results of this comparison indicate that NIRS is a rapid tool for predicting dry matter (DM), fat, crude protein (CP) and ash content in the broiler carcass. Males and females of six commercial strain crosses of broiler chicken (Gallus domesticus) were used in this study (6$\times$2 factorial design). Each strain was grown to 16 weeks of age, and duplicate serial samples were taken for body composition analysis. Each whole carcass was pressure-cooked, homogenized, and a representative sample was freeze-dried. Body composition determined as follows: DM by oven dried method at 105$^{\circ}C$ for 3 hours, fat by Mojonnier diethyl ether extraction, CP by measuring nitrogen content using an auto-analyzer with Kjeldhal digest and ash by combustion in a muffle furnace for 24 hour at 55$0^{\circ}C$. These homogenized and freeze-dried carcass samples were then scanned with a Foss NIR Systems 6500 visible-NIR spectrophotometer (400-2500nm) (Foss NIR Systems, Silver Spring, MD., US) using Infra-Soft-International, ISI, WinISl software (ISI, Port Matilda, US). The NIRS spectra were analyzed using principal component (PC) analysis. This data was corrected for scatter using standard normal “Variate” and “Detrend” technique. The accuracy of the NIRS calibration equations developed using Partial Least Squares (PLS) for predicting major chemical composition and carcass descriptors- such as body mass (BM), bird dry matter and moisture content was tested using cross validation. Discrimination analysis was also used for sex and strain identification. According to Dr John Shenk, the creator of the ISI software, the calibration equations with the correlation coefficient, $R^2$, between reference data and NIRS predicted results of above 0.90 is excellent and between 0.70 to 0.89 is a good quantifying guideline. The excellent calibration equations for DM ($R^2$= 0.99), fat (0.98) and CP (0.92) and a good quantifying guideline equation for ash (0.80) were developed in this study. The results of cross validation statistics for carcass descriptors, body composition using reference methods, inter-correlation between carcass descriptors and NIRS calibration, and the results of discrimination analysis for sex and strain identification will also be presented in the poster. The NIRS predicted daily gain and calculated daily gain from this experiment, and true daily gain (using data from another experiment with closely related broiler chicken from each of the six strains) will also be discussed in the paper.