• Journal of Animal Science and Technology
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• 제64권3호
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• pp.531-538
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• 2022

In Italy, buffalo mozzarella is a largely sold and consumed dairy product. The fraudulent adulteration of buffalo milk with cheaper and more available milk of other species is very frequent. In the present study, Fourier transform infrared spectroscopy (FTIR), in combination with multivariate analysis by partial least square (PLS) regression, was applied to quantitatively detect the adulteration of buffalo milk with cow milk by using a fully automatic equipment dedicated to the routine analysis of the milk composition. To enhance the heterogeneity, cow and buffalo bulk milk was collected for a period of over three years from different dairy farms. A total of 119 samples were used for the analysis to generate 17 different concentrations of buffalo-cow milk mixtures. This procedure was used to enhance variability and to properly randomize the trials. The obtained calibration model showed an R² ≥ 0.99 (R² cal. = 0.99861; root mean square error of cross-validation [RMSEC] = 2.04; R² val. = 0.99803; root mean square error of prediction [RMSEP] = 2.84; root mean square error of cross-validation [RMSECV] = 2.44) suggesting that this method could be successfully applied in the routine analysis of buffalo milk composition, providing rapid screening for possible adulteration with cow's milk at no additional cost.

• 한국원자력학회:학술대회논문집
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• 한국원자력학회 1997년도 춘계학술발표회논문집(1)
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• pp.611-616
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• 1997

The Level 1 probabilistic safety assessment(PSA) for Wolsong(WS) 2/3/4 nuclear power plant(NPPs) in design stage is performed using the methodologies being equivalent to PWR PSA. Accident sequence evaluation program(ASEP) human reliability analysis(HRA) procedure and technique for human error rate prediction(THERP) are used in HRA of WS 2/3/4 NPPs PSA. The purpose of this paper is to introduce the procedure and methodology of HRA in WS 2/3/4 NPPs PSA. Also, this paper describes the interim results of importance analysis for human actions modeled in WS 2/3/4 PSA and the findings and recommendations of administrative control of secondary control area from the view of human factors.

• Safety and Health at Work
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• 제8권3호
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• pp.267-275
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• 2017

Background: The mining industry is known worldwide for its highly risky and hazardous working environment. Technological advancement in ore extraction techniques for proliferation of production levels has caused further concern for safety in this industry. Research so far in the area of safety has revealed that the majority of incidents in hazardous industry take place because of human error, the control of which would enhance safety levels in working sites to a considerable extent. Methods: The present work focuses upon the analysis of human factors such as unsafe acts, preconditions for unsafe acts, unsafe leadership, and organizational influences. A modified human factor analysis and classification system (HFACS) was adopted and an accident predictive fuzzy reasoning approach (FRA)-based system was developed to predict the likelihood of accidents for manganese mines in India, using analysis of factors such as age, experience of worker, shift of work, etc. Results: The outcome of the analysis indicated that skill-based errors are most critical and require immediate attention for mitigation. The FRA-based accident prediction system developed gives an outcome as an indicative risk score associated with the identified accident-prone situation, based upon which a suitable plan for mitigation can be developed. Conclusion: Unsafe acts of the worker are the most critical human factors identified to be controlled on priority basis. A significant association of factors (namely age, experience of the worker, and shift of work) with unsafe acts performed by the operator is identified based upon which the FRA-based accident prediction model is proposed.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제5권8호
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• pp.393-400
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• 2016

최근, 컴퓨터 비전과 기계 학습 기술의 도움을 받아 효율적이고 자동적인 도시 환경에 대한 분석 방법의 개발에 대한 연구가 이루어지고 있다. 많은 분석들 중에서도 도시의 안전도 분석은 지역 사회의 많은 관심을 받고 있다. 더욱 정확한 안전도 점수 예측과 인간의 시각적 인지를 반영하기 위해서, 인간의 시각적 인지에서 가장 중요한 전역 정보와 지역 정보의 고려가 필요하다. 이를 위해 우리는 전역 칼럼과 지역 칼럼으로 구성된 Double-column Convolutional Neural Network를 사용한다. 전역 칼럼과 지역 칼럼 각각은 입력은 크기가 변환된 원 영상과 원 영상에서 무작위로 크로핑을 사용한다. 또한, 학습 과정에서 특정 칼럼에 오버피팅되는 문제를 해결하기 위한 새로운 학습방법을 제안한다. 우리의 DCNN 모델의 성능 비교를 위해 2개의 SVR 모델과 3개의 CNN 모델의 평균 제곱근 오차와 상관관계 분석을 측정하였다. 성능 비교 실험 결과 우리의 모델이 0.7432의 평균 제곱근 오차와 0.853/0.840 피어슨/스피어맨 상관 계수로 가장 좋은 성능을 보여주었다.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2008년도 추계학술대회A
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• pp.1588-1593
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• 2008

This paper describes a real-time isometric pinch force prediction algorithm from surface electromyogram (sEMG) using multilayer perceptron (MLP) for human robot interactive applications. The activities of seven muscles which are observable from surface electrodes and also related to the movements of the thumb and index finger joints were recorded during pinch force experiments. For the successful implementation of the real-time prediction algorithm, an off-line analysis was performed using the recorded activities. Four muscles were selected for the force prediction by using the Fisher linear discriminant analysis among seven muscles, and the four muscle activities provided effective information for mapping sEMG to the pinch force. The MLP structure was designed to make training efficient and to avoid both under- and over-fitting problems. The pinch force prediction algorithm was tested on five volunteers and the results were evaluated using two criteria: normalized root mean squared error (NRMSE) and correlation (CORR). The training time for the subjects was only 2 min 29 sec, but the prediction results were successful with NRMSE = 0.112 ${\pm}$ 0.082 and CORR = 0.932 ${\pm}$ 0.058. These results imply that the proposed algorithm is useful to measure the produced pinch force without force sensors in real-time. The possible applications include controlling bionic finger robot systems to overcome finger paralysis or amputation.

• International Journal of Computer Science & Network Security
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• 제24권2호
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• pp.31-42
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• 2024

Different industries mostly rely on quality certification for promoting their products or brands. Although getting quality certification, specifically by human experts is a tough job to do. But the field of machine learning play a vital role in every aspect of life, if we talk about quality certification, machine learning is having a lot of applications concerning, assigning and assessing quality certifications to different products on a macro level. Like other brands, wine is also having different brands. In order to ensure the quality of wine, machine learning plays an important role. In this research, we use two datasets that are publicly available on the "UC Irvine machine learning repository", for predicting the wine quality. Datasets that we have opted for our experimental research study were comprised of white wine and red wine datasets, there are 1599 records for red wine and 4898 records for white wine datasets. The research study was twofold. First, we have used a technique called backward elimination in order to find out the dependency of the dependent variable on the independent variable and predict the dependent variable, the technique is useful for predicting which independent variable has maximum probability for improving the wine quality. Second, we used a robust machine learning algorithm known as "XGBoost" for efficient prediction of wine quality. We evaluate our model on the basis of error measures, root mean square error, mean absolute error, R2 error and mean square error. We have compared the results generated by "XGBoost" with the other state-of-the-art machine learning techniques, experimental results have showed, "XGBoost" outperform as compared to other state of the art machine learning techniques.

• KEPCO Journal on Electric Power and Energy
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• 제7권1호
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• pp.171-177
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• 2021

In terms of distribution planning, accurate electric load prediction is one of the most important factors. The future load prediction has manually been performed by calculating the maximum electric load considering loads transfer/switching and multiplying it with the load increase rate. In here, the risk of human error is inherent and thus an automated maximum electric load forecasting system is required. Although there are many existing methods and techniques to predict future electric loads, such as regression analysis, many of them have limitations in reflecting the nonlinear characteristics of the electric load and the complexity due to Photovoltaics (PVs), Electric Vehicles (EVs), and etc. This study, therefore, proposes a method of predicting future electric loads on distribution lines by using Machine Learning (ML) method that can reflect the characteristics of these nonlinearities. In addition, predictive models were developed based on actual data collected at KEPCO's existing distribution lines and the adequacy of developed models was verified as well. Also, as the distribution planning has a direct bearing on the investment, and amount of investment has a direct bearing on the maximum electric load, various baseline such as maximum, lowest, median value that can assesses the adequacy and accuracy of proposed ML based electric load prediction methods were suggested.

• 대한인간공학회지
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• 제17권3호
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• pp.13-22
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• 1998

Aircraft and the three-dimensional environment in which they operate are not user-friendly for human beings. As a result, maintaining the proficiencies necessary to safely and efficiently fly an airplane are difficult, and costly. The physiological and emotional status of the human element remains crucial in maintaining safe performance by all crew members. In the study of Hagiwara et al.(1993). they called the physiological and emotional status of the human element into the human alertness or physiological activity and stress, fatigue, circadian rhythm, alcohol. smoking, and self-medication are known the major factors that deteriorate the human alertness. Accordingly. this paper deals with the quantitative and objective performance test based on tracking error and reaction time by means of the new computer test program into which the perception-motion system of human beings is applied. Throughout this experiment using performance test, the results suggest that performance capability in state of sleep deprivation 2 hours and alcoholic 0.05~0.06% in blood were more impaired than one in a normal state, and they further showed statistically significant differences between them, which were influenced by impairment factors of body regulation and pilot's grade. We also obtained the prediction value and the 95% confidence interval of tracking error and reaction time at the normal state for the purpose of distinguishing performance capability between the normal state and the abnormal state. And it is expected that the evaluation of human alertness using performance test will be applied to the quantitative assessment of an each pilot's realistic consciousness/attention, and will lead a flight commander to the accurate decision of mission approval prior to a flight.

• 한국원자력학회:학술대회논문집
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• 한국원자력학회 1996년도 추계학술발표회논문집(1)
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• pp.381-388
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• 1996

THERP (Technique for Human Error Rate Prediction) 방법론은 원전의 확률론적 위험성 평가 (PSA)시 운전원과 작업자의 인간오류평가에 가장 널리 사용되고 있는 방법이다. HRA Handbook 이라고도 불리는 이 모델은 운전원 행위를 시스템 부품의 한 요소로 가정하고 인간오류를 평가한다. 본 논문은 이 방법론을 이용하여 원전 등과 같이 위험시설물 중의 하나인 개스밸브기지에서의 작업자 보수시 인적오류를 평가하고 기계적 오류와 함께 인적오류의 기여도를 계산하였다. 본 방법론은 원전, 개스밸브 기지 뿐만아니라 석유화학 플랜트와 같은 위험시설물의 인적오류평가에도 유연하게 사용될 수 있음을 보여주었다.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2002년도 가을 학술발표회 논문집
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• pp.513-520
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• 2002

The stability analysis of rock slope can be predicted using a suitable field data but it cannot be predicted unless suitable field data was taken. In this study, artificial neural networks theory is applied to predict plane failure that has a few data. It is well known that human brain has the advantage of handling disperse and parallel distributed data efficiently. On the basis of this fact, artificial neural networks theory was developed and has been applied to various fields of science successfully In this study, error back-propagation algorithm that is one of the teaching techniques of artificial neural networks is applied to predict plane failure. In order to verify the applicability of this model, a total of 30 field data results are used. These data are used for training the artificial neural network model and compared between the predicted and the measured. The simulation results show the potentiality of utilizing the neural networks for effective safety factor prediction of plane failure. In conclusion, the well-trained artificial neural network model could be applied to predict the plane failure stability of rock slope.