The present study sets out to review the thermal and optical properties of electrochromic windows (ECWs) through an analysis of the improvement in the energy performance of a building resulting from their application. The performance analysis was based on the change in the room temperature according to the solar transmittance and the orientation of the ECWs, the energy consumptions of the building’s heating/cooling systems, and that of the building’s lighting according to the visible light transmittance (VLT). To achieve this, the Quick Energy Simulation Tool (eQUEST), a building energy interpretation program, was used. The solar heat gain coefficient (SHGC) of the ECWs was found to be significantly reduced. This had the effect of lowering the room temperature in summer, such that the effect on the summer cooling energy consumption was also remarkable. However, with a reduction in the VLT, the lighting energy consumption increased. The net result of the changes in the heating/cooling and lighting energy consumptions was a reduction of about 11,207 kWh/yr (8.89%). The ECWs were found to realize a greater reduction in a building’s energy consumption than was possible with windows glazed with low-E coated glass.

The effect of various intake compositions and intake pressure on combustion & emission characteristics has been investigated in a single cylinder direct injection diesel engine. The variation of intake composition is simulated using argon, nitrogen and carbon dioxide as intake air diluents, and a screw compressor is used to boost intake pressure up to 200KPa. All diluents are found to be effective in reducing NOx emissions when intake pressure is changed from 110KPa to 200Kpa. Smoke emissions are drastically increased by the addition of argon, moderately increased by the addition of nitrogen. However, the addition of carbon dioxide substantially reduces smoke emissions and NOx emissions simultaneously. At lower intake pressure, the effects of diluting intake air with argon, nitrogen and carbon dioxide on ignition delay are proportional to their specific heats respectively, whereas the addition of argon has almost no effect on ignition delay when intake pressure is higher than 150KPa. Further analysis reveals that the oxygen concentration of intake charge plays the most important role in reducing Nox emissions, and the increased premixed combustion due to longer ignition delay is the main reason of reduction in smoke emissions.

This study aims to propose a pose classification model using indoor occupant images. For developing the intelligent and automated model, a deep learning neural network was employed. Indoor posture images and joint coordinate data were collected and used to conduct the training and optimization of the model. The output of the trained model is the occupant pose of the sedentary activities in the indoor space. The performance of the developed model was evaluated for two different indoor environments: home and office. Using the metabolic rates corresponding to the classified poses, the model accuracy was compared with that of the conventional method, which considered the fixed activity. The result showed that the accuracy was improved by as much as 73.96% and 55.26% in home and office, respectively. Thus, the potential of the pose classification model was verified for providing a more comfortable and personalized thermal environment to the occupant.

The objective of this study is to develop (1) a pose-categorization model that classifies the poses of an occupant based on their image in an indoor space and (2) an activity-decision algorithm that identifies the activity being performed by the occupant. For developing an automated intelligent model, a deep neural network is adopted. The model considers the coordinates of the joints of the occupant in the image as input data and returns the pose of the occupant. Datasets composed of indoor images of home and office environments are used for training and testing the model. The training and testing accuracies of the optimized model were 100% for both the home and office environments. A representative activity of an occupant for a certain period has to be decided to control an indoor environment for comfort. The activity-decision algorithm employs a frequency-based method to determine the representative activity type for real-time occupant poses using the pose-categorization model. This study highlights the potential of the developed model and algorithm to determine the activity of occupants to provide an optimal thermal environment corresponding to the individual’s metabolic rate.