• Journal of the Korean Society of Marine Environment & Safety
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• v.15 no.3
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• pp.217-222
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• 2009

Safe navigation is closely related to the fatigue of marine officers. Also, the fatigue of duty officer can cause serious marine accidents. In this study, the documentary survey about the marine officers' working environments, fatigue factors and symptoms was conducted. And the questionnaire survey which is related to the fatigue analysis factors such as sleepiness, mental physical workload and alcohol for apprentice officers was carried out, and the results of questionnaire survey were analyzed. Lastly, on the basis of this study, the fatigue analysis model was suggested in order to assess the marine officers' performance in the future.

• Proceedings of KOSOMES biannual meeting
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• 2006.05a
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• pp.1-6
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• 2006

해상에서 선박을 운항하는 항해사의 피로도 (Fatigue) 는 안전항해와 매우 밀접한 관계를 가지고 있으며 당직근무 중에 피로가 누적되어 업무수행능력이 저하되면 선박의 충돌 및 좌초 둥과 같은 매우 위험하고 중대한 해양사고를 유발할 가능성이 높아지게 된다. 따라서 본 연구에서는 항해사의 근무환경, 피로유발요인 및 피로증세 등에 대하여 조사하였고, 항해경험자를 대상으로 정신적 육체적인 작업부하도에 대한 피로도 평가관련 설문조사를 실시하고 그 결과를 분석하였다. 그리고 이와 같은 피로도 조사 및 설문조사 분석결과를 바탕으로 항해사의 피로도 평가항목을 선정하고 이를 토대로 항해사의 업무수행능력을 평가하기 위한 피로도 평가모델을 제시하였다.

• Journal of the Korea Society of Computer and Information
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• v.27 no.6
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• pp.43-52
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• 2022

We can sense somebody's feeling fatigue, which means that fatigue can be detected through sensing human biometric signals. Numerous researches for assessing fatigue are mostly focused on diagnosing the edge of disease-level fatigue. In this study, we adapt quantitative analysis approaches for estimating qualitative data, and propose video analysis models for measuring fatigue state. Proposed three deep-learning based classification models selectively include stages of video analysis: object detection, feature extraction and time-series frame analysis algorithms to evaluate each stage's effect toward dividing the state of fatigue. Using frontal face videos collected from various fatigue situations, our CNN model shows 0.67 accuracy, which means that we empirically show the video analysis models can meaningfully detect fatigue state. Also we suggest the way of model adaptation when training and validating video data for classifying fatigue.

• Proceedings of the ESK Conference
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• 1997.10a
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• pp.406-413
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• 1997

근전도(EMG:Electromyogram)를 사용하여 국부 근육 피로(Localized Muscle Fatigue)를 정량화으로 분석 하기 위해 널리 이용되고 있는 AR(Autoregressive)모델의 1차 계수, RMS(Root Mean Square), ZCR(Zero Crossing Rate), MPF(Mean Power Frequency), MF(Median Frequency)를 선택하여, 근육이 발휘하는 힘과 시간의 흐름에 따라 근육 피로의 정도를 민감하게 나타내는 매개변수를 규명하였다. 피실험자 10명의 좌우 척추세움근(Erector Spinae Muscle)을 대상으로 등장수축(Sustained Isometric Contraction)조건에서 허리의 신전(Extension)운동을 실시하였다. 이때 발휘해야 하는 힘의 수준은 15%, 30%, 45%, 60%, 75% MVC 로 정하였고 각 수준마다 20초 동안 근전도를 측정하 였다. 데이터 분석은 총20초 구간의 근전도를 0.5초 간격으로 나누어 매개변수들을 각각 구하고 분석을 실시하였다. 시간의 흐름에 대한 피로도 분석 결과, AR 모델의 1차 계수와 MPF가 유의한 차이를 보였으며, 낮은 수준의 %MVC에서는 AR 계수가, 높은 수준에서는 MPF가 민감한 반응 결과를 나타냈다. 그리고 근육이 발휘하는 힘의 정도를 분석하기 위해 주로 사용되고 있는 RMS 보다는 더 AR 계수가 모든 수준에서 뚜렷하게 차이를 보인 것이 확인되었다. 따라서 AR 모델의 1차 계수가 근육의 피로 정도와 힘의 수준을 다른 매개변수에 비해 더욱 민감하게 구별함이 입증되었다. 이러한 결과는 다른 분야에서도 근육 피로를 정량적으로 측정하는데 사용될 수 있을 것으로 생각되며, 개인적 변이도를 고려한 확률 기법을 사용한다면 보다 정확한 근전도 분석이 이루어질 것으로 기대된다.있음을 알 수 있었다. 사료된다.의 결과는 자전거 에르고노미터의 결과가 트레드밀의 결과에 87.60%정도 나타났다.음을 관찰하였다. 특히 vitamin C와 E의 병용투여는 상승적으로 적용하여 간세포손상을 더욱 억제시킴을 알 수 있었다.mance and on TFP(Total Factor Productivity) growth which is a pure measure of firm performance. To utilize the advantage of panel data, FEM(Fixed Effect Model) and REM(Random Effect Model) were used. The empirical result shows that the entropy index as a measurement of inter-business relatedness is not significant but technological relatedness index is significant. OLS estimates on pooled data were considerably different from FEM or REM estimates on panel data. By introducing interaction effect among the three variables for business portfolio properties, we obtained three findings. First, only VI (Vertical integration) has a significant positive correlation with ROS. Second, when using TFP growth as an dependent variable, both TR(Technological Relatedness) and f[ are signif

• Journal of the Society of Disaster Information
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• v.8 no.3
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• pp.234-241
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• 2012

The main objective of this research is to calibrate the performance prediction models for the growth of fatigue cracking in multi-layered asphalt concrete pavement systems. However, the calibration factors are dependent upon the prediction model, testing method, and the laboratory loading history. A detailed study on the field data has revealed that the performance of flexible pavements is affected by both the traffic loading and the environmental cycling which is related to the age of the pavements. Thus, a composite indicator was developed in this study which utilizes both the traffic and the age information with appropriate weighting factors. Using the proposed fatigue performance model the calibration factors were also estimated through the comparisons between the field performances on fatigue cracking and the laboratory-based fatigue life.

• Journal of Digital Convergence
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• v.19 no.10
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• pp.295-301
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• 2021

Individual mental fatigue not only reduces cognitive ability and work performance, but also becomes a major factor in large and small accidents occurring in daily life. In this paper, a CNN model for EEG-based mental fatigue discrimination was proposed. To this end, EEG in the resting state and task state were collected and applied to the proposed CNN model, and then the model performance was analyzed. All subjects who participated in the experiment were right-handed male students attending university, with and average age of 25.5 years. Spectral analysis was performed on the measured EEG in each state, and the performance of the CNN model was compared and analyzed using the raw EEG, absolute power, and relative power as input data of the CNN model. As a result, the relative power of the occipital lobe position in the alpha band showed the best performance. The model accuracy is 85.6% for training data, 78.5% for validation, and 95.7% for test data. The proposed model can be applied to the development of an automated system for mental fatigue detection.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.741-747
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• 2022

Fatigue lowers an individual's ability and makes it difficult to perform work. As fatigue accumulates, concentration decreases and thus the possibility of causing a safety accident increases. Awareness of fatigue is subjective, but it is necessary to quantitatively measure the level of fatigue in the actual field. In previous studies, it was proposed to measure the level of fatigue by expert judgment by adding objective indicators such as bio-signal analysis to subjective evaluations such as multidisciplinary fatigue scales. However this method is difficult to evaluate fatigue in real time in daily life. This paper is a study on the fatigue classification model that determines the fatigue level of workers in real time using speech data recorded in the field. Machine learning models such as logistic classification, support vector machine, and random forest are trained using speech data collected in the field. The performance evaluation showed good performance with accuracy of 0.677 to 0.758, of which logistic classification showed the best performance. From the experimental results, it can be seen that it is possible to classify the fatigue level using speech signals.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.33 no.2
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• pp.81-93
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• 2020

The fatigue characteristics of glass fiber reinforced plastic (GFRP) composites were studied under repeated loads using the finite element method (FEM). To realize the material characteristics of GFRP composites, Digimat, a mean-field homogenization tool, was employed. Additionally, the micro-structures and material models of GFRP composites were defined with it to predict the fatigue behavior of composites more realistically. Specifically, the fatigue characteristics of polybutylene terephthalate with short fiber fractions of 30wt% were investigated with respect to fiber orientation, stress ratio, and thickness. The injection analysis was conducted using Moldflow software to obtain the information on fiber orientations. It was mapped over FEM concerned with fatigue specimens. LS-DYNA, a typical finite element commercial software, was used in the coupled analysis of Digimat to calculate the stress amplitude of composites. FEMFAT software consisting of various numerical material models was used to predict the fatigue life. The results of coupled analysis of linear and nonlinear material models of Digimat were analyzed to identify the fatigue characteristics of GFRP composites using FEMFAT. Neuber's rule was applied to the linear material model to analyze the fatigue behavior in LCF regimen. Additionally, to evaluate the morphological and mechanical structure of GFRP composites, the coupled and fatigue analysis were conducted in terms of thickness.

• Proceedings of the KAIS Fall Conference
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• 2012.05b
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• pp.785-788
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• 2012

본 논문에서는 접착제로 접합된 DCB 구조체의 피로 해석을 수행하였다. 두께가 25및 40 mm인 두 모델들의 피로수명과 피로손상의 해석 결과를 비교해보면, 두께 25 mm인 모델이 두께 40 mm인 모델에 비하여 수명과 손상이 불리한 것으로 나타났으며 불규칙 피로하중에서는 'SAE transmission'에서 가장 불리한 값을 나타냈다. 또한 'SAE transmission'에서 가장 안정한 경향을 보이고 있고 상대적인 손상으로서 약 1.1에서 1.8 % 정도로 가장 작은 것으로 나타났다. 본 연구에서 얻어진 해석 결과를 접착제로 접합된 실제 복합재 구조물에 적용시켜 피로거동을 분석하고 그 기계적인 특성을 파악할 수 있다.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.1
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• pp.35-40
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• 2019

The objective of this paper is to identify the fatigue properties of carbon-fiber composite which is widely applied for the development of aircraft structures and obtain data for full-scale fatigue test. The durability and damage tolerance evaluation of composite structures is achieved by fatigue tests and parameters such as fatigue life factor and load enhancement factor. The specimens are made with carbon-fiber/epoxy UD tape and fabric prepreg. Fatigue tests are performed with several stress ratios and lay-up patterns. The Weibull shape parameters are analyzed by Sendeckyj model and individual fatigue lives with Weibull distribution. And the fatigue life factor and load enhancement factor considering reliability are evaluated.