Oxygen consumption prediction models for individual and combination materials handling tasks

Ergonomics ◽  
2008 ◽  
Vol 51 (11) ◽  
pp. 1776-1789 ◽  
Author(s):  
Patrick G. Dempsey ◽  
Vincent M. Ciriello ◽  
Rammohan V. Maikala ◽  
Niall V. O'Brien
2016 ◽  
Vol 37 (10) ◽  
pp. 831-837
Author(s):  
R. Mays ◽  
F. Goss ◽  
E. Nagle ◽  
M. Gallagher ◽  
L. Haile ◽  
...  

2021 ◽  
Author(s):  
Sedef Akinli Koçak

In recent years, a significant amount of energy consumption of ICT products has resulted in environmental concerns. Growing demand for mobile devices, personal computers, and the widespread adaptation of cloud computing and data centers are the main drivers for the energy consumption of the ICT systems. Finding solutions for improving the energy efficiency of the systems has become an important objective for both industry and academia. In order to address the increase in ICT energy consumption, hardware technology, such as production of energy efficient processors, has been substantially improved. However, demand for energy is growing faster than improvements are being made on these energy-aware technologies. Therefore, in addition to hardware, software technologies must also be a focus of research attention. Although software does not consume energy by itself, its characteristics determine which hardware resources are made available and how much electrical energy is used. Current literature on the energy efficiency of software, highlights, in particular, a lack of measurements and models. In this dissertation, first, the relationship between software code properties and energy consumption is explored. Second, using static code metrics regression based energy consumption prediction models are investigated. Finally, the models performance are assessed using within product and cross-product energy consumption prediction approaches. For this purpose, a quantitative based retrospective cohort study was employed. As research methods, observational data collection, mining software repositories, and regression analysis were utilized. This research results show inconsistent relationships between energy consumption and code size and complexity attributes considering different types of software products. Such results provide a foundation of knowledge that static code attributes may give some insights but would not be the sole predictors of energy consumption of software products.


2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Dong Xiao ◽  
Jichun Wang

Piercing manufacture of seamless tubes is the process that pierces solid blank into tube hollow. Piercing efficiency and energy consumption are the important indexes in the production of seamless tubes. Piercing process has the multivariate, nonlinear, cross-coupling characteristics. The complex factors that affect efficiency and consumption make it difficult to establish the mechanism models for optimization. Based on the production process, this paper divides the piercing process into three parts and proposes the piercing efficiency and energy consumption prediction models based on mean value staged KELM-PLS method. On the basis of mean value staged KELM-PLS prediction model, the minimum piercing energy consumption and maximum piercing efficiency are calculated by genetic optimization algorithm. Simulation and experiment prove that the optimization method based on the piercing efficiency and energy consumption prediction model can obtain the optimal process parameters effectively and also provide reliable evidences for practical production.


2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Tomoaki Matsuo ◽  
Rina So ◽  
Masaya Takahashi

Abstract Background Sedentary behavior (SB) and cardiorespiratory fitness (CRF) are important issues in occupational health. Developing a questionnaire to concurrently assess workers’ SB and CRF could fundamentally improve epidemiological research. The Worker’s Living Activity-time Questionnaire (WLAQ) was developed previously to assess workers’ sitting time. WLAQ can be modified to evaluate workers’ CRF if additional physical activity (PA) data such as PA frequency, duration, and intensity are collected. Methods A total of 198 working adults (93 women and 105 men; age, 30–60 years) completed anthropometric measurements, a treadmill exercise test for measuring maximal oxygen consumption (VO2max), and modified WLAQ (m-WLAQ, which included questions about PA data additional to the original questions). Multiple regression analyses were performed to develop prediction equations for VO2max. The generated models were cross-validated using the predicted residual error sum of squares method. Among the participants, the data of 97 participants who completed m-WLAQ twice after a 1-week interval were used to calculate intraclass correlation coefficient (ICC) for the test–retest reliability analyses. Results Age (r = − 0.29), sex (r = 0.48), body mass index (BMI, r = − 0.20), total sitting time (r = − 0.15), and PA score (total points for PA data, r = 0.47) were significantly correlated with VO2max. The models that included age, sex, and BMI accounted for 43% of the variance in measured VO2max [standard error of the estimate (SEE) = 5.04 ml·kg− 1·min− 1]. These percentages increased to 59% when the PA score was included in the models (SEE = 4.29 ml·kg− 1·min− 1). Cross-validation analyses demonstrated good stability of the VO2max prediction models, while systematic underestimation and overestimation of VO2max were observed in individuals with high and low fitness, respectively. The ICC of the PA score was 0.87 (0.82–0.91), indicating excellent reliability. Conclusions The PA score obtained using m-WLAQ, rather than sitting time, correlated well with measured VO2max. The equation model that included the PA score as well as age, sex, and BMI had a favorable validity for estimating VO2max. Thus, m-WLAQ can be a useful questionnaire to concurrently assess workers’ SB and CRF, which makes it a reasonable resource for future epidemiological surveys on occupational health.


2019 ◽  
Vol 40 (8) ◽  
pp. 789-796
Author(s):  
Marwah Almakhaita ◽  
Lubna Al Asoom ◽  
Nazish Rafique ◽  
Rabia Latif ◽  
Anas Alduhishy

Author(s):  
Sakshi Tyagi ◽  
Pratima Singh

Background: Electricity is considered as the basic essential unit in today’s high-tech world. The electricity demand has been increased very rapidly due to increased urbanization, smart buildings, and usage of smart devices to a large extent. Building a reliable and accurate electricity consumption prediction model becomes necessary with the increase in building energy. From recent studies, prediction models such as support vector regression (SVR), gradient boosting decision tree (GBDT), artificial neural network (ANN), random forest (RF), and extreme gradient boosting (XGBoost) have been compared for the prediction of electricity consumption and XGBoost is found to be most efficient thus leading to the motivation for the proposed research work. Objective: The objective of this research is to propose a model that performs future electricity consumption prediction for different time horizons: short term prediction and long term prediction using extreme gradient boosting method and reduce the prediction errors. In addition to this based on the prediction, best and worst predicted days are also recognized. Methods: The method used in this research is the extreme gradient boosting for future building electricity consumption prediction. The extreme gradient boosting method performs prediction for the short term and long term for different seasons. The model is trained on a household building in Paris. Results: The model is trained and tested on the dataset and it predicts accurately with the lowest errors compared to other machine learning techniques. The model predicts accurately with RMSE of 140.45 and MAE of 28 which is the least errors when compared to the baseline prediction models. Conclusion: A model that is robust to all the conditions should be built by enhancing the prediction mechanism such that the model should be dependent on less factors to make electricity consumption prediction.


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