Highly energy-efficient buildings have generated remarkable interest over the last few years.There is a need for simulation based effective control systems for efficient usage of electrical and fossil fuel driven devi...Highly energy-efficient buildings have generated remarkable interest over the last few years.There is a need for simulation based effective control systems for efficient usage of electrical and fossil fuel driven devices,as they contribute to energy-efficient buildings and assist in gaining flexibility for the human occupancy-based energy loads.In this context,the integrated energy profile of a building can be ascertained by effective research approaches,as this knowledge would be beneficial to understand the demographics with respect to human occupancy and activities,as well as estimate varying energy consumption over time.Utility data from Smart Meter(SM)readings can reveal detailed information that could be mapped to predict resident occupancy and the usage patterns of specific types of appliances over desired time intervals.This research develops a user-driven simulation tool with realistic data acquisition options and assumptions of potential human behavior to determine energy usage patterns over time without the utility billing information.In this work,factors such as level of human occupancy,the possibility of space being occupied,thermostat settings,building envelope infrastructural aspects,types of appliances used in households,appliance energy related capacities,and the probability of using each appliance is considered,along with variance in weather,and heating-cooling systems specifications.For five specific benchmarked scenarios,the range of the random numbers is specified based on assumed potential human behavior for occupancy and energy-consuming appliances usage probabilities,with respect to the time of the day,weekday,and weekends.The simulation is developed using the Visual Basic Application(VBA)^(R)in Microsoft Excel^(R),based on the discrete-event Monte Carlo Simulation(MCS).The simulated energy usage and the cost are reflected in the sensitivity analysis by comparing factors such as the level of human occupancy,appliance type,and time intervals.展开更多
Elastography is a non-invasive medical imaging technique to map the spatial variation of elastic properties of soft tissues.The quality of reconstruction results in elastography is highly sensitive to the noise induce...Elastography is a non-invasive medical imaging technique to map the spatial variation of elastic properties of soft tissues.The quality of reconstruction results in elastography is highly sensitive to the noise induced by imaging measurements and processing.To address this issue,we propose a deep learning(DL)model based on conditional Generative Adversarial Networks(cGANs)to improve the quality of nonhomogeneous shear modulus reconstruction.To train this model,we generated a synthetic displacement field with finite element simulation under known nonhomogeneous shear modulus distribution.Both the simulated and experimental displacement fields are used to validate the proposed method.The reconstructed results demonstrate that the DL model with synthetic training data is able to improve the quality of the reconstruction compared with the well-established optimization method.Moreover,we emphasize that our DL model is only trained on synthetic data.This might provide a way to alleviate the challenge of obtaining clinical or experimental data in elastography.Overall,this work addresses several fatal issues in applying the DL technique into elastography,and the proposed method has shown great potential in improving the accuracy of the disease diagnosis in clinical medicine.展开更多
文摘Highly energy-efficient buildings have generated remarkable interest over the last few years.There is a need for simulation based effective control systems for efficient usage of electrical and fossil fuel driven devices,as they contribute to energy-efficient buildings and assist in gaining flexibility for the human occupancy-based energy loads.In this context,the integrated energy profile of a building can be ascertained by effective research approaches,as this knowledge would be beneficial to understand the demographics with respect to human occupancy and activities,as well as estimate varying energy consumption over time.Utility data from Smart Meter(SM)readings can reveal detailed information that could be mapped to predict resident occupancy and the usage patterns of specific types of appliances over desired time intervals.This research develops a user-driven simulation tool with realistic data acquisition options and assumptions of potential human behavior to determine energy usage patterns over time without the utility billing information.In this work,factors such as level of human occupancy,the possibility of space being occupied,thermostat settings,building envelope infrastructural aspects,types of appliances used in households,appliance energy related capacities,and the probability of using each appliance is considered,along with variance in weather,and heating-cooling systems specifications.For five specific benchmarked scenarios,the range of the random numbers is specified based on assumed potential human behavior for occupancy and energy-consuming appliances usage probabilities,with respect to the time of the day,weekday,and weekends.The simulation is developed using the Visual Basic Application(VBA)^(R)in Microsoft Excel^(R),based on the discrete-event Monte Carlo Simulation(MCS).The simulated energy usage and the cost are reflected in the sensitivity analysis by comparing factors such as the level of human occupancy,appliance type,and time intervals.
基金National Natural Science Foundation of China (12002075)National Key Research and Development Project (2021YFB3300601)Natural Science Foundation of Liaoning Province in China (2021-MS-128).
文摘Elastography is a non-invasive medical imaging technique to map the spatial variation of elastic properties of soft tissues.The quality of reconstruction results in elastography is highly sensitive to the noise induced by imaging measurements and processing.To address this issue,we propose a deep learning(DL)model based on conditional Generative Adversarial Networks(cGANs)to improve the quality of nonhomogeneous shear modulus reconstruction.To train this model,we generated a synthetic displacement field with finite element simulation under known nonhomogeneous shear modulus distribution.Both the simulated and experimental displacement fields are used to validate the proposed method.The reconstructed results demonstrate that the DL model with synthetic training data is able to improve the quality of the reconstruction compared with the well-established optimization method.Moreover,we emphasize that our DL model is only trained on synthetic data.This might provide a way to alleviate the challenge of obtaining clinical or experimental data in elastography.Overall,this work addresses several fatal issues in applying the DL technique into elastography,and the proposed method has shown great potential in improving the accuracy of the disease diagnosis in clinical medicine.
