1,721,171 research outputs found
Capturing human motion using body-fixed sensors: Outdoor measurement and clinical applications
No full textMotion capture is mainly based on standard systems using optic, magnetic or sonic technologies. In this paper, the possibility to detect useful human motion based on new techniques using different types of body-fixed sensors is shown. In particular, a combination of accelerometers and angular rate sensors (gyroscopes) showed a promising design for a hybrid kinematic sensor measuring the 2D kinematics of a body segment. These sensors together with a portable datalogger, and using simple biomechanical models, allow capture of outdoor and long-term movements and overcome some limitations of the standard motion capture systems. Significant parameters of body motion, such as nature of motion (postural transitions, trunk rotation, sitting, standing, lying, walking, jumping) and its spatio-temporal features (velocity, displacement, angular rotation, cadence and duration) have been evaluated and compared to the camera-based system. Based on these parameters, the paper outlines the possibility to monitor physical activity and to perform gait analysis in the daily environment, and reviews several clinical investigations related to fall risk in the elderly, quality of life, orthopaedic outcome and sport performance. Taking advantage of all the potential of these body-fixed sensors should be promising for motion capture and particularly in environments not suitable for standard technology such as in any field activity. Copyright © 2004 John Wiley & Sons, Ltd.LMAMLaboratory of Movement Analysis and Measurement, School of Engineering, Swiss Federal Institute of Technology, CH-1015 Lausanne, SwitzerlandCited By: 3; Export Date: 14 August 2006; Source: ScopusLanguage of Original Document: EnglishCorrespondence Address: Aminian, K.; Laboratory of Movement Analysis and Measurement; School of Engineering; Swiss Federal Institute of Technology CH-1015 Lausanne, Switzerland; email: [email protected]: Sparks, D.R., Huang, X., Higdon, W., Johnson, J.D., Angular rate sensor and accelerometer combined on the same micromachined CMOS chip (1998) Microsystem Technologies, 4, pp. 139-142; Komura, T., Shinagawa, Y., Kunii, T.L., Calculation and visualization of the dynamic ability of the human body (1999) Journal of Visualization and Computer Animation, 10 (2), pp. 57-78; Bodenheimer, B., Rose, C., Rosenthal, S., Pella, J., The process of motion capture: Dealing with the data (1997) Computer Animation and Simulation'97, pp. 3-18, Thalmann D, van de Panne M (eds). 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Building characterization through smart meter data analytics: Determination of the most influential temporal and importance-in-prediction based features
Full text linkThe present paper aims at determining the most influential features to be extracted from smart meter data to facilitate machine learning-based classification of non-residential buildings. Smart meter-driven remote estimation of the chosen characteristics (the buildings’ performance class, use type, and operation group) is significantly helpful in buildings’ commissioning, benchmarking, and diagnostics applications. As the first step, state-of-the-art feature selection methods and a proposed customized approach are utilized for determining the most influential parameters in the pool of temporal features, proposed in a previous study. Next, importance-in-prediction based features, generated from an hour-ahead load prediction pipeline, that can improve the classification accuracy are proposed and added as additional input parameters. Finally, interpretations about some of the most influential features for different classification targets are provided. The obtained results demonstrate that, while aiming at estimating the buildings’ use type, through performing feature selection and adding importance-in-prediction based features, the number of utilized features is reduced from 290 (initial pool of features proposed in a previous study) to 29, while also increasing the accuracy from 71% to 74%. Similarly, number of employed features for estimating the performance class is decreased from 224 to 17 and the achieved accuracy is improved from 56% to 62%. Finally, using only 6 selected features, compared to 287 features in the initial set, the obtained accuracy for the classification of operation group is increased from 98% to 100%. It is thus demonstrated that the proposed methodology, through selecting and utilizing notably fewer features, results in a notable simplification of the feature extraction procedures, improves the achieved accuracy, and facilitates providing interpretations about the reason behind the influence of some of the most important features
Sustainable and Energy-Efficient Industrial Systems: Modelling the Environmental Impact of Logistics Facilities
Full text linkIn the last decade industrial systems have been affected by increased challenges, and among those the need for more sustainable production and logistics has been called into question by both practitioners and academia. Within industrial networks, logistics facilities traditionally represent key nodes as they have a direct impact on both companies’ service levels and logistics costs. Recently, their complexity has dramatically increased due to their ever-demanding requirements and pressures from stakeholders and society. In this context, companies have started to search for solutions for greener warehousing processes and energy efficiency improvements. Still, on the academic side, a limited number of studies have been found addressing the quantification of logistics facilities’ environmental performance, the impact of the green warehousing practices in place, and the related effects on warehouse consumption and emission reduction. This paper aims to address this research gap by proposing a simulation-based approach where multiple scenarios of a real logistics facility are discussed, grounded on a conceptual framework that offers a roadmap towards sustainable and energy-efficient warehousing. Different scenarios are outlined, and the related performances are examined in terms of energy consumption and CO2eq emissions. Implications of the results are discussed and streams for future investigation are identified
Machine learning based disaggregation of airconditioning loads using smart meter data
Full text linkThis study proposes a novel machine learning-based methodology to estimate the air-conditioning (AC) load from the hourly smart meter data. The commonly employed approaches for disaggregating the share of the AC load from the total consumption are either using data obtained from dedicated sensors or high-frequency data that cannot be provided by conventional smart meters. In the present work, an alternative approach is proposed, in which a machine learning-based pipeline is first optimised and trained using the data obtained from a set of households in a period, including both smart meter data and the AC load measurements along with corresponding weather conditions. The obtained optimal pipeline is then utilised to estimate the AC load in another set of buildings in the same period of the year, while providing it with only the smart meter data and weather conditions. As the first step of the pipeline implementation, several features are extracted from the smart meters and weather data. The most promising algorithm is then determined, the corresponding hyper-parameters are optimised and the most influential parameters are finally determined. The proposed method leads to a lower monitoring system's cost, lower user privacy concerns and fewer data processing complexity compared to the conventional energy disaggregation approaches, while providing an acceptable accuracy
Reduced FV modelling based on CFD database and experimental validation for the thermo-fluid dynamic simulation of flue gases in horizontal fire-tubes
Full text linkThe present work aims at proposing an optimized physical heat transfer correlation in smooth horizontal fire-tubes (applying a one-dimensional reduced Finite Volume Method - FVM), validated over a wide range of operating conditions. An experimental activity is first carried out, in which the temperature and pressure profiles of the flue gases, streaming through pipes while applying hot-water-boiler conditions, are measured. A Computational Fluid Dynamics (CFD) set-up is developed for simulating the thermo-physical behaviour of the flue gases undergoing a wide range of operating conditions (replicating the conditions that these streams experience in the most common industrial boiler applications). The acquired experimental data with a detailed uncertainty analysis are employed for validating the developed CFD results. Subsequently, the reduced one-dimensional FVM physical models for estimating the heat transfer (taking into account the convective and radiative terms) and pressure drops are implemented. The most accurate models are calibrated using the results of the fluid-dynamics simulations. In this procedure, the coefficients of the standard models are optimized, employing a Multi Objective Genetic Algorithm optimization procedure (MOGA). This work demonstrates that the CFD simulations can reproduce the experimental data accurately. Furthermore, the estimations of the FVM model based on the calibrated physical correlations are in good agreement with numerical results, but with a reduced computational
cost. Results indicate an improved estimation of tubes outlet temperature with respect to standard correlation (42.4% error reduction), while the estimated outlet pressure shows only a slight increase of the error with respect to the Fang correlation
Green warehousing practices: Assessing the impact of PV self-consumption enhancement strategies in a logistics warehouse
Full text linkLogistics facilities, while critical to industrial systems, significantly contribute to greenhouse gas emissions, necessitating improved operations, energy use, and renewable energy integration. The use of distributed renewable energy sources, with their intermittent and unpredictable generation, disrupts energy balance and leads to curtailment issues. This places a significant load on the electrical grid, increasing emissions and environmental problems, hindering the effective use of renewable energy. To address these challenges, modifications can be made to existing warehouses to increase their self-consumption. Empirical studies assessing the impact of such modifications, particularly in the logistics field, are lacking. This work contributes to the ongoing research by proposing a simulation-based approach that evaluates multiple scenarios for a real-world logistics facility to enhance the self-consumption ratio. This analysis is based on a conceptual framework providing a roadmap towards sustainable warehousing practices. The study simulates and presents multiple scenarios, including the base case, electrification of the heating system, and an opportunity charging strategy for MHE, aiming to enhance self-consumption, while examining environmental and economic performances, followed by a sensitivity analysis. Findings demonstrate a 25% increase in self-consumption and significant energy consumption reduction (-110 MWh/year) and CO2e emissions (-67.8 tons CO2e/year) for the final proposed scenario
Simplified finite volume-based dynamic modeling, experimental validation, and data-driven simulation of a fire-tube hot-water boiler
Full text linkThis paper proposes and implements a novel approach to simulate the dynamic behavior of hot-water fire-tube boilers, in which physical phenomena-based and data-driven modeling methodologies have both been employed. The first model, which includes a reduced one-dimensional finite volume method to simulate the flue-gas side's behavior, can be employed for accurate sizing of the unit considering end-users’ dynamic consumption profile. In the data-driven model instead, machine learning algorithms are used to estimate hot water's supply temperature, which makes it a suitable tool for real-time prediction and model predictive control. Utilizing the latter model in combination with an advanced management system allows reducing the plant's energy consumption and enhancing its controllability. Employing the measurements performed in an Italian industrial firm, the developed model is validated and is demonstrated to have a limited thermal efficiency estimation bias of 1.2%. Furthermore, the data-driven model achieves a mean absolute relative difference error lower than 6%, demonstrating its acceptable accuracy for various prediction horizons
Economic Feasibility Analysis and Optimization of Hybrid Renewable Energy Systems for Rural Electrification in Peru
Full text linkThe majority of rural communities in developing countries (such as Peru) are not connected to the electrical grid. Hybrid energy production from available renewable resources (e.g., wind and solar) and diesel engines is considered as an economically viable and environmentally friendly alternative for electrification in these areas. Motivated by the lack of a comprehensive investigation dedicated to the techno-economic analysis of hybrid systems (PV-wind-diesel) for off-grid electrification in Peru, the present work is focused on determining the optimal configuration of these systems for remote Peruvian villages. Three small communities without access to the grid (Campo serio, El potrero, and Silicucho), which are located in different climatic zones of Peru, have been accordingly selected as case studies. Seven different configurations including single component systems (solar, wind, and diesel) and hybrid ones are considered. While taking into account the meteorological data and load characteristics of the communities along with the diesel fuel's price and the cost of components, HOMER software is utilized to determine the optimal sizing of the system [resulting in the lowest net present cost (NPC)] considering different scenarios. The obtained configurations are then compared considering other state-of-the-art economic indices [initial capital cost, total annual operating cost, and the cost of energy (COE)], the generation fractions, and the resulting CO(2)emissions. The obtained results have revealed that, for all of the investigated communities, the hybrid solar-wind-diesel system is the most economically viable scenario. Considering the latter scenario, the obtained optimal configuration leads to an NPC of USD 227,335 (COE: 0.478 USD/kWh) for Campo serio, USD 183,851 (COE: 0.460 USD/kWh) for El potrero, and USD 146,583 (COE: 0.504 USD/kWh) for Silicucho. Furthermore, employing the optimal configurations a renewable fraction (with respect to the total generation) of 94% is obtained for Campo serio and Silicucho, while the achieved renewable fraction for El potrero is 97%. Moreover, for the case of Campo serio, the resulting CO(2)emission of the obtained optimal system is determined to be 6.1% of that of a diesel-only unit, while the latter ratio is determined to be 2.7% for El potrero and 9.9% for that of Silicucho. The optimal configurations that are obtained and presented in the present paper can be utilized as guideline for designing electrification systems (with a minimized cost) for the considered communities and other villages with similar characteristics (population and climatic conditions)
Thermal-economic-environmental analysis and multi-objective optimization of an internal-reforming solid oxide fuel cell-gas turbine hybrid system
No full textIn this article, an internal-reforming solid oxide fuel cell gas turbine (IRSOFCeGT) hybrid system is modeled and analyzed from thermal (energy and exergy), economic, and environmental points of view. The model is validated using available data in the literature.
Utilizing the genetic algorithm optimization technique, multi-objective optimization of modeled system is carried out and the optimal values of system design parameters are obtained. In the multi-objective optimization procedure, the exergy efficiency and the total cost rate of the system (including the capital and maintenance costs, operational cost (fuel cost), and social cost of air pollution for CO, NO x , and CO 2 ) are considered as objective
functions. A sensitivity analysis is also performed in order to study the effect of variations of the fuel unit cost on the Pareto optimal solutions and their corresponding design parameters. The optimization results indicate that the final optimum design chosen from the Pareto front results in exergy efficiency of 65.60% while it leads to total cost of 3.28 million US$ year. It is also demonstrated that the payback time of the chosen design is 6.14 years
Assessing the impact of smart lighting systems and on-site renewable generation in a distribution warehouse: a simulation-based approach
Full text linkIn the arena of logistics management, green warehousing has been achieving increasing attention from both practitioners and academia. On the one hand, practitioners – e.g., Logistics Service Providers (LSPs), manufacturers, and retailers – have been looking for solutions to decrease the environmental impact of their logistics facilities and incorporate measures towards greener warehousing processes. However, on the academic side, although a rising number of papers have been found addressing logistics sustainability, a need has emerged to focus on warehouses by analyzing the impact of the energy efficiency measures in place, and the related effects on warehouse consumption and environmental performance. This contribution aims at addressing this research gap. The paper proposes a simulation model based on DesignBuilder and EnergyPlus software and examines the impact of both interventions on the lighting systems and the introduction of on-site renewable generation in a distribution warehouse. Three different scenarios are proposed, and the related performance are examined in terms of consumption figures and CO2eq emissions. A discussion on the roadmap towards net-zero logistics facilities is offered, and streams for future investigation are highlighted
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