• 운동영양학회지
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• 제25권1호
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• pp.35-41
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• 2021

[Purpose] This pilot study aimed to develop a regression model to estimate the excess post-exercise oxygen consumption (EPOC) of Korean adults using various easy-to-measure dependent variables. [Methods] The EPOC and dependent variables for its estimation (e.g., sex, age, height, weight, body mass index, fat-free mass [FFM], fat mass, % body fat, and heart rate_sum [HR_sum]) were measured in 75 healthy adults (31 males, 44 females). Statistical analysis was performed to develop an EPOC estimation regression model using the stepwise regression method. [Results] We confirmed that FFM and HR_sum were important variables in the EPOC regression models of various exercise types. The explanatory power and standard errors of estimates (SEE) for EPOC of each exercise type were as follows: the continuous exercise (CEx) regression model was 86.3% (R²) and 85.9% (adjusted R²), and the mean SEE was 11.73 kcal, interval exercise (IEx) regression model was 83.1% (R²) and 82.6% (adjusted R²), while the mean SEE was 13.68 kcal, and the accumulation of short-duration exercise (AEx) regression models was 91.3% (R²) and 91.0% (adjusted R²), while the mean SEE was 27.71 kcal. There was no significant difference between the measured EPOC using a metabolic gas analyzer and the predicted EPOC for each exercise type. [Conclusion] This pilot study developed a regression model to estimate EPOC in healthy Korean adults. The regression model was as follows: CEx = -37.128 + 1.003 × (FFM) + 0.016 × (HR_sum), IEx = -49.265 + 1.442 × (FFM) + 0.013 × (HR_sum), and AEx = -100.942 + 2.209 × (FFM) + 0.020 × (HR_sum).

• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제7권8호
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• pp.211-218
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• 2018

본 연구에서는 6축 센서를 이용하여 식사 행동을 검출하고 식사 시간을 추론하는 알고리즘을 제안한다. 식사 행동을 음식을 집는 동작, 음식을 먹는 동작, 팔을 내려놓는 동작으로 분류하고, 각 동작 별로 자이로 신호의 특징점을 선정하고 특징점이 순서대로 나타날 경우 식사 행동을 검출한다. 제안한 알고리즘은 정확도 94.3%와 정밀도 84.1%를 달성하였다.

• 한국경영과학회지
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• 제30권3호
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• pp.137-149
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• 2005

This study proposes a framework enhancing the accuracy of estimation for project duration by combining linear Bayesian updating scheme with the learning curve effect. Activities in a particular project might share resources in various forms and might be affected by risk factors such as weather Statistical dependence stemming from such resource or risk sharing might help us learn about the duration of upcoming activities in the Bayesian model. We illustrate, using a Monte Carlo simulation, that for partially repetitive projects a higher degree of statistical dependence among activity duration results in more variation in estimating the project duration in total, although more accurate forecasting Is achievable for the duration of an individual activity.

• 지식경영연구
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• 제6권2호
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• pp.23-47
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• 2005

This study is performed the multinomial logistic regression with the officials needs level about a component of knowledge administration for drawing a demand estimation model in the knowledge administration activities. This study is not that an activity and domain of knowledge administration is to apply and to operate uniformly it in public sector, one is suggested an application with a demand diagnose of knowledge administration in order to saw a course of the knowledge administration programs to suit a function and role of public administration. A result of this study is that an activity and domain of the knowledge administration is different from a component of it namely, knowledge creating, knowledge organizing, knowledge sharing and distribution, knowledge utility, and knowledge store. And the officials individual characteristics, administration agency, a kind of business, and a function and role of work are different from demand of knowledge administration. Also, the practical use of KMS (knowledge management system) is not so high in public sector. Accordingly, the tools of knowledge administration will deliberate on a consolidation with the existing system in the device.

• 한국방사성폐기물학회:학술대회논문집
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• 한국방사성폐기물학회 2003년도 가을 학술논문집
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• pp.322-327
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• 2003

It is important to know the accurate radionuclide inventory of radioactive waste for the reliable management. However, estimation of radionuclide concentrations in drummed radioactive waste is difficult and unreliable because of difficulties of direct detection, high cost, and radiation exposure of sampling personnel. In order to overcome these difficulties, scaling factors (SFs) have been used to assess the activities of radionuclides that could not be directly analyzed. A radionuclide assay system has been operated at KORI site since 1996 and consolidated scaling factor method has played a dominant role in determination of radionuclides concentrations. However, some problems are still remained such as uncertainty of estimated scaling factor values, inaccuracy of analyzed sample values, and disparity between the actual and ideal correlation pairs and the others. Therefore, it needs to improve the accuracy of scaling factor values. The scope of this paper is focused on the improvement of accuracy and representativeness of calculated scaling factor values based on statistical techniques. For the selection of reliable activity determination method, the accuracy of estimated SF values for each activity determination method is compared. From the comparison of each activity determination methods, it is recommended that SF determination method should be changed from the arithmetic mean to the geometrical mean for more reliable estimation of radionuclide activity. Arithmetic mean method and geometric mean method are compared based on the data set in KORI system.

• 전기학회논문지
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• 제65권7호
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• pp.1231-1235
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• 2016

In this study, it measures electromyogram to estimate scoliosis by using sensors in both sides of spinal erector muscle. A device is measured raw data to input mcu through a filter and amplifier. MCU is named "arduino" that is calculated muscle activity with algorithm by inputting data. By comparing with both sides of spinal erector muscle's activity, it studies about estimation of scoliosis

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1999년도 하계종합학술대회 논문집
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• pp.369-372
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• 1999

In this paper, we propose a new method for power estimation in nodes of multi-level combinational circuits and describe its application to the extraction of common expressions for low power design. It is assumed that each node is implemented as a complex gate and the capacitance and the switching activity of the nodes are considered in the power estimation. Extracting common expressions which is accomplished mostly by the extraction of kernels, can be transformed to the problem of rectangle covering. We describe how the newly proposed estimation method can be applied to the rectangle covering problem and show the experimental results with comparisons to the results of SIS-1.2.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제5권5호
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• pp.938-958
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• 2011

Ubiquitous web services have been expanding in various business areas with the evolution of wireless Internet technologies, accessible and usable with a variety of mobile Internet devices such as smart phones. Ubiquitous mobile-web information services can be evaluated for accessibility and usability with the mobile Internet devices interacting with mobile-web information servers. In human mobile-web activity, a web server could be a unified center for mobile-web interaction services as well as for real-time estimation and analysis of mobile-web interaction sessions. We present a real-time estimation and analysis scheme for time-based accessibility and usability in ubiquitous mobile-web services. With real-time estimation/analysis of sessions in a mobile-web server, we estimated the time-based accessibility and usability for comparison between different web services as well as for applications in mobile cloud computing services. We present empirical results based on the implementation of the real-time estimation/analysis scheme.

• 운동영양학회지
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• 제25권1호
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• pp.23-29
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• 2021

[Purpose] This preliminary study aimed to develop a regression model to estimate the non-exercise activity thermogenesis (NEAT) of Korean adults using various easy-to-measure dependent variables. [Methods] NEAT was measured in 71 healthy adults (male n = 29; female n = 42). Statistical analysis was performed to develop a NEAT estimation regression model using the stepwise regression method. [Results] We confirmed that age^A, weight^B, heart rate (HR)_average^C, weight × HR_average^D, weight × HR_sum^E, systolic blood pressure (SBP) × HR_rest^F, fat mass ÷ height^2G, gender × HR_average^H, and gender × weight × HR_sum^I were important variables in various NEAT activity regression models. There was no significant difference between the measured NEAT values obtained using a metabolic gas analyzer and the predicted NEAT. [Conclusion] This preliminary study developed a regression model to estimate the NEAT in healthy Korean adults. The regression model was as follows: sitting = 1.431 - 0.013 × (A) + 0.00014 × (D) - 0.00005 × (F) + 0.006 × (H); leg jiggling = 1.102 - 0.011 × (A) + 0.013 × (B) + 0.005 × (H); standing = 1.713 - 0.013 × (A) + 0.0000017 × (I); 4.5 km/h walking = 0.864 + 0.035 × (B) + 0.0000041 × (E); 6.0 km/h walking = 4.029 - 0.024 × (C) + 0.00071 × (D); climbing up 1 stair = 1.308 - 0.016 × (A) + 0.00035 × (D) - 0.000085 × (F) - 0.098 × (G); and climbing up 2 stairs = 1.442 - 0.023 × (A) - 0.000093 × (F) - 0.121 × (G) + 0.0000624 × (E).

• Advances in robotics research
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• 제2권2호
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• pp.129-140
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• 2018

With the increasing demand for the development of human pose estimation, such as human-computer interaction and human activity recognition, there have been numerous approaches to detect the 2D poses of people in images more efficiently. Despite many years of human pose estimation research, the estimation of human poses with images remains difficult to produce satisfactory results. In this study, we propose a robust 2D human body pose estimation method using an RGB camera sensor. Our pose estimation method is efficient and cost-effective since the use of RGB camera sensor is economically beneficial compared to more commonly used high-priced sensors. For the estimation of upper-body joint positions, semantic segmentation with a fully convolutional network was exploited. From acquired RGB images, joint heatmaps accurately estimate the coordinates of the location of each joint. The network architecture was designed to learn and detect the locations of joints via the sequential prediction processing method. Our proposed method was tested and validated for efficient estimation of the human upper-body pose. The obtained results reveal the potential of a simple RGB camera sensor for human pose estimation applications.