A modeling study on alleviating uneven defrosting for a vertical three-circuit outdoor coil in an air source heat pump unit during reverse cycle defrosting

Reverse cycle defrosting is the most widely used standard defrosting method for air source heat pump (ASHP) units. It was suggested in previous experimental studies that downwards flowing of the melted frost over a vertical multi-circuit outdoor coil in an ASHP unit has negative effects on reverse cycle defrosting performance. To quantitatively study the negative effects, an experimental study and a modeling study on draining away locally the melted frost for an experimental ASHP unit with a three-circuit outdoor coil were carried out and separately reported. However, for exiting ASHP units, it is hardly possible to install water collecting trays between circuits. To alleviate uneven defrosting for a vertical multi-circuit outdoor coil in an existing ASHP unit, an effective alternative is to vary the heat supply to each refrigerant circuit by varying the opening values of modulating valves installed at an inlet pipe to each circuit. In this paper, a modeling study on varying heat (via refrigerant) supply to each refrigerant circuit in a three-circuit outdoor coil to alleviate uneven defrosting is reported. Finally, in the designed three study cases, defrosting energy use could be decreased to 94.6%, as well as a reduction of 7 s in defrosting duration by fully closing the modulating valve on the top circuit when its defrosting terminated

UV R-CNN: Stable and Efficient Dense Human Pose Estimation

Dense pose estimation is a dense 3D prediction task for instance-level human analysis, aiming to map human pixels from an RGB image to a 3D surface of the human body. Due to a large amount of surface point regression, the training process appears to be easy to collapse compared to other region-based human instance analyzing tasks. By analyzing the loss formulation of the existing dense pose estimation model, we introduce a novel point regression loss function, named Dense Points} loss to stable the training progress, and a new balanced loss weighting strategy to handle the multi-task losses. With the above novelties, we propose a brand new architecture, named UV R-CNN. Without auxiliary supervision and external knowledge from other tasks, UV R-CNN can handle many complicated issues in dense pose model training progress, achieving 65.0% $AP_{gps}$ and 66.1% $AP_{gpsm}$ on the DensePose-COCO validation subset with ResNet-50-FPN feature extractor, competitive among the state-of-the-art dense human pose estimation methods.Comment: 9pages, 4 figure

Differentiable Genetic Programming for High-dimensional Symbolic Regression

Symbolic regression (SR) is the process of discovering hidden relationships from data with mathematical expressions, which is considered an effective way to reach interpretable machine learning (ML). Genetic programming (GP) has been the dominator in solving SR problems. However, as the scale of SR problems increases, GP often poorly demonstrates and cannot effectively address the real-world high-dimensional problems. This limitation is mainly caused by the stochastic evolutionary nature of traditional GP in constructing the trees. In this paper, we propose a differentiable approach named DGP to construct GP trees towards high-dimensional SR for the first time. Specifically, a new data structure called differentiable symbolic tree is proposed to relax the discrete structure to be continuous, thus a gradient-based optimizer can be presented for the efficient optimization. In addition, a sampling method is proposed to eliminate the discrepancy caused by the above relaxation for valid symbolic expressions. Furthermore, a diversification mechanism is introduced to promote the optimizer escaping from local optima for globally better solutions. With these designs, the proposed DGP method can efficiently search for the GP trees with higher performance, thus being capable of dealing with high-dimensional SR. To demonstrate the effectiveness of DGP, we conducted various experiments against the state of the arts based on both GP and deep neural networks. The experiment results reveal that DGP can outperform these chosen peer competitors on high-dimensional regression benchmarks with dimensions varying from tens to thousands. In addition, on the synthetic SR problems, the proposed DGP method can also achieve the best recovery rate even with different noisy levels. It is believed this work can facilitate SR being a powerful alternative to interpretable ML for a broader range of real-world problems

Evolution of modes in a metal-coated nano-fiber.

We report on the evolution of modes in cylindrical metal/dielectric systems. The transition between surface plasmon polaritons and localized modes is documented in terms of the real and imaginary parts of the effective refractive index as a function of geometric and optical parameters. We show the evolution process of SPP and localized modes. New phenomena of coupling between SPP and core-like modes, and of mode gap and super-long surface plasmon polaritons are found and discussed. We conclude that both superluminal light and slow light can be solutions of metallically coated dielectric fibers

Further experimental results on modelling and algebraic control of a delayed looped heating-cooling process under uncertainties

The aim of this research is to revise and substantially extend experimental modelling and control of a looped heating-cooling laboratory process with long input-output and internal delays under uncertainties. This research follows and extends the authors' recent results. As several significant improvements regarding robust modelling and control have been reached, the obtained results are provided with a link and comparison to the previous findings. First, an infinite-dimensional model based on mathematical-physical heat and mass transfer principles is developed. All important heat-fluid transport and control-signal delays are considered when assembling the model structure and relations of quantities. Model parameter values optimization based on the measurement data follows. When determining static model parameter values, all variations in steady-state measured data are taken into account simultaneously, which enhances previously obtained models. Values of dynamic model parameters and delays are further obtained by least mean square optimization. This innovative model is compared to two recently developed process models and to the best-fit model that ignores the measured variations. Controller structures are designed using algebraic tools for all four models. The designed controllers are robust in the sense of robust stability and performance. Both concepts are rigorously assessed, and the obtained conditions serve for controller parameter tuning. Two different control systems are assumed: the standard closed-loop feedback loop and the two-feedback-controllers control system. Numerous experimental measurements for nominal conditions and selected perturbations are performed. Obtained results are further analyzed via several criteria on manipulated input and controlled temperature. The designed controllers are compared to the Smith predictor structure that is wellestablished for time-delay systems control. An essential drawback of the predictor regarding disturbance rejection is highlighted.College of Polytechnics Jihlava; National Foreign Expert Project, (G2022178023L); Tomas Bata University in Zlin, TBU; Grantová Agentura České Republiky, GA ČR, (GAČR 21–45465L)Czech Science Foundation [GAC?R 21-45465L]; National Foreign Expert Project [G2022178023L

An experimental study on defrosting performance for an air source heat pump unit with a horizontally installed multi-circuit outdoor coil

When frost forms and accumulates over the outdoor coil’s surface in an air source heat pump (ASHP) unit, system operating performance will be dramatically deteriorated. Reverse cycle defrosting is the most widely used standard defrosting method. A previous related study reported that downwards flowing of melted frost due to gravity over a vertical multi-circuit outdoor coil would decrease the reverse cycle defrosting performance. If the outdoor coil can be changed to horizontally installed, the flow path of melted frost over coil surface can be shortened, and the flow directions of refrigerant and melted frost changed from opposite to orthogonal. Consequently, a better defrosting performance is expected. In this paper, therefore, an experimental study on defrosting performance for an ASHP unit with a horizontally installed multi-circuit outdoor coil was conducted. Experimental results show that, when a vertical outdoor coil was changed to horizontally installed, the defrosting efficiency increased 9.8%, however, with the same defrosting duration at 186 s. Furthermore, when the outdoor air fan was reversed to blowing the melted frost during defrosting, the total mass of the retained water collected decreased 222 g. However, the defrosting efficiency was not increased, but decreased 6.6% because of the heat transfer enhanced between hot coil and cold ambient air.The Hong Kong Polytechnic University, and the Guangdong University of Technology.http://www.elsevier.com/locate/apenergy2017-03-31hb2016Electrical, Electronic and Computer Engineerin

A study on influential factors of occupant window-opening behavior in an office building in China

Occupants often perform many types of behavior in buildings to adjust the indoor thermal environment. In these types, opening/closing the windows, often regarded as window-opening behavior, is more commonly observed because of its convenience. It not only improves indoor air quality to satisfy occupants' requirement for indoor thermal comfort but also influences building energy consumption. To learn more about potential factors having effects on occupants' window-opening behavior, a field study was carried out in an office building within a university in Beijing. Window state (open/closed) for a total of 5 windows in 5 offices on the second floor in 285 days (9.5 months) were recorded daily. Potential factors, categorized as environmental and non-environmental ones, were subsequently identified with their impact on window-opening behavior through logistic regression and Pearson correlation approaches. The analytical results show that occupants' window-opening behavior is more strongly correlated to environmental factors, such as indoor and outdoor air temperatures, wind speed, relative humidity, outdoor PM2.5 concentrations, solar radiation, sunshine hours, in which air temperatures dominate the influence. While the non-environmental factors, i.e. seasonal change, time of day and personal preference, also affects the patterns of window-opening probability. This paper provides solid field data on occupant window opening behavior in China, with high resolutions and demonstrates the way in analyzing and predicting the probability of window-opening behavior. Its discussion into the potential impact factors shall be useful for further investigation of the relationship between building energy consumption and window-opening behavior

Purified dietary red and white meat proteins show beneficial effects on growth and metabolism of young rats compared to casein and soy protein

This study compared the effects of casein, soy protein (SP), red (RMP) and white meat (WMP) proteins on growth and metabolism of young rats. Compared to casein, the ratio of daily feed intake to daily body weight gain of rats was not changed by meat protein but reduced by SP by 93.3% (P<0.05). Feeding RMP and WMP reduced the liver total cholesterol (TC) contents by 24.3% and 17.8% respectively (P<0.05). Only RMP increased plasma HDL-cholesterol concentrations (by 12.7%, P<0.05), whereas SP increased plasma triacylglycerol, TC and LDL-cholesterol concentrations by 23.7%, 19.5% and 61.5% respectively (P<0.05). Plasma essential and total amino acid concentrations were increased by WMP (by 18.8% and 12.4%, P<0.05) but reduced by SP (by 28.3 and 37.7%, P<0.05). Twenty five liver proteins were differentially expressed in response to different protein sources. Therefore, meat proteins were beneficial for growth and metabolism of young rats compared to casein and SP