The building-integrated PV/T technologies have garnered significant attention due to their considerable potential in low-carbon and zero-energy buildings. To gain deeper insights into the practical ...application of this technology, a comprehensive study was conducted on a rural building integrated with PV/T walls for demonstration purposes. A novel simulation approach, coupling the numerical models of the PV/T walls with the building energy software, was proposed and experimentally validated. Based on the validated simulation approach, this study investigated the comprehensive performance of the demo building and explored the optimal capacity of the energy storage system (ESS). The main findings are as follows: (1) The proposed simulation approach could accurately predict the energy behaviors of the multi-room building integrated PV/T wall. (2) The demo building with the optimized EES yielded 1355.32 kWh of clean electricity and 455.3 kWh of heat gain for water heating, while reducing cooling and heating loads by 5% and 12%, respectively. (3) The optimal azimuth for the building in the studied region was 45°, and the total energy benefits of the building decreased by 28.5% with an increase in dust density from 0 g/m2 to 10 g/m2.
•A rural multi-room building was used to study the performance of the PV/T wall application.•A novel simulation approach was proposed for modeling the building integrated PV/T walls.•The energy storage configuration was analyzed and optimized in terms of 3E performance.•The sensitivity of the energy benefits to building orientation and dust deposition was analyzed.
Automatic 3D object detection using monocular cameras presents significant challenges in the context of autonomous driving. Precise labeling of 3D object scales requires accurate spatial information, ...which is difficult to obtain from a single image due to the inherent lack of depth information in monocular images, compared to LiDAR data. In this paper, we propose a novel approach to address this issue by enhancing deep neural networks with depth information for monocular 3D object detection. The proposed method comprises three key components: 1)Feature Enhancement Pyramid Module: We extend the conventional Feature Pyramid Networks (FPN) by introducing a feature enhancement pyramid network. This module fuses feature maps from the original pyramid and captures contextual correlations across multiple scales. To increase the connectivity between low-level and high-level features, additional pathways are incorporated. 2)Auxiliary Dense Depth Estimator: We introduce an auxiliary dense depth estimator that generates dense depth maps to enhance the spatial perception capabilities of the deep network model without adding computational burden. 3)Augmented Center Depth Regression: To aid center depth estimation, we employ additional bounding box vertex depth regression based on geometry. Our experimental results demonstrate the superiority of the proposed technique over existing competitive methods reported in the literature. The approach showcases remarkable performance improvements in monocular 3D object detection, making it a promising solution for autonomous driving applications.
As percentages of elderly people rise in many societies, age‐related diseases have become more prevalent than ever. Research interests have been shifting to delaying age‐related diseases by delaying ...or reversing aging itself. We use metformin as an entry point to talk about the important molecular and genetic longevity‐regulating mechanisms that have been extensively studied with it. Then we review a number of observational studies, animal studies, and clinical trials to reflect the clinical potentials of the mechanisms in lifespan extension, cardiovascular diseases, tumors, and neurodegeneration. Finally, we highlight remaining concerns that are related to metformin and future anti‐aging research.
In this paper, we review mechanisms and clinical studies of metformin. Papers reviewed include studies in longevity, cardiovascular diseases, cancer, and neurodegeneration. In the end, we provide insights to guide future research.
Employing the demand flexibility strategy in PV powered buildings can effectively balance solar energy supply and building energy demand, thereby increasing the self-consumption ratio of PV ...electricity. Despite this, its solar energy utilization is still low due to the limit of the PV efficiency. On the other hand, PV/T modules not only generate electricity but also produce domestic hot water, thus providing higher solar efficiency. In this study, the demand flexibility of various shiftable appliances in a PV/T powered building was investigated. An optimization-based demand flexibility strategy was proposed to reduce electricity cost and maximum grid power. The case study showed that the proposed strategy could reduce the electricity cost by 23 % and smooth grid power fluctuation. Moreover, compared with the PV powered building, the PV/T powered building could reduce the electricity cost by 10 % and significantly improve utility grid friendliness. Furthermore, the forecast error of boundary conditions negatively affected the electricity cost and grid power fluctuations. The sensitivity analysis revealed that the ambient temperature and solar irradiation on the PV/T modules had a greater impact on the optimization objective. Overall, this work aims to provide guidance for planning the flexibility operation of PV/T powered buildings.
•A MILP model for optimizing operation of PV/T powered building was proposed.•The optimization strategy can significantly improve economy and grid friendless.•The performance of PV/T powered building was compared to that of PV powered building.•The effect of the forecast error of boundary conditions was analyzed.
Building-integrated PV technologies have drawn increasing attention to PV double-skin façades (DSFs) due to their flexible structure and excellent energy performance. However, the traditional PV DSF ...often faces the challenges related to low solar utilization and limited seasonal adaptability. This paper explored a novel PV/T DSF aimed at addressing these issues by harnessing heat from PV modules for air heating in winter and water heating in other seasons. The PV/T DSF was designed and fabricated using a self-developed manufacturing process, and its thermal and electrical performance was tested. The experimental results revealed the air heating efficiency of approximately 30∼50 %, the water heating efficiency of 35∼45 %, and the PV efficiency ranging from 5.0 % to 6.0 %. Subsequently, the mathematical model of the PV/T DSF was validated against dynamic experimental data. Leveraging this model, the study investigated the energy-saving potential of the PV/T DSF in regions characterized by cold winters and hot summers. Compared to conventional PV DSF systems, the PV/T DSF demonstrated higher solar energy utilization, achieving a tenfold increase. Moreover, it reduced summer heat gain by 7 % and increased winter heat gain by 70 %. Furthermore, the system significantly mitigated winter heat loss by 42 % and summer heat gain by 65 % compared to double clear glazing. Overall, this study offers valuable insights into the concept and performance of this innovative solar building envelope.
•Designing and fabricating a novel PV/T double skin facade.•Experiment testing and simulation analysis of thermal and electrical behaviors.•The solar efficiency of PV/T DSF is ten times that of the conventional PV DSF.
Abstract
In this work, a deep reinforcement learning (DRL) method is proposed to address the problem of real-time object tracking. The adopted framework in this paper is based on the ‘Actor-Critic’ ...tracker (ACT), since ACT only considers the scale change instead of regression object boundary, which cannot adapt the object size variation. To this end, the ACT method is improved by using a more reasonable action space, which contains a left-top and right-bottom corner coordinates. Precise shape estimation is given by regressing the variation of width and height, respectively. Furthermore, to speed up the whole training and tracking process, the Advantage Function (AF) is adopted, and its performance is compared with ACT, ACT with improved action space (IAS), and ACT with IAS and AF. This method is tested on the OTB100 dataset to validate its effectiveness.
Previous building-integrated solar thermal fenestrations were limited to the single function, either air heating or water heating. Thus, they failed to meet the seasonal thermal needs of buildings. ...To address this limitation, this paper proposed a novel building fenestration-integrated solar collector. This system is designed to harness solar irradiation on the fenestrations for air heating in winter and recover irradiation for water heating in summer, effectively addressing seasonal thermal demands and improving annual solar utilization efficiency. Firstly, the prototype of the proposed system was fabricated, and its thermal performance was tested. The experimental results indicated the thermal efficiency of 40∼50% for air heating and 39% for water heating. Subsequently, a mathematical model of the proposed system was developed and experimentally validated. Based on the validated model, a comparative performance analysis was conducted between the proposed system and double clear glazing. Additionally, the energy-saving potential and economic viability of the proposed system were predicted. Compared to double clear glazing, the proposed system exhibited a higher solar heat gain coefficient (SHGC) in air heating mode, lower SHGC in water heating mode, and lower U value at night. The prediction outcomes underscored the substantial energy-saving advantages conferred by the proposed system across diverse building types. The projected payback periods in subtropical and tropical regions were estimated to be around 2–4 years.
•A novel building fenestration-integrated solar collector with seasonal adaptability was proposed.•The proposed system was experimentally tested and mathematically modeled.•The impacts of structure parameters on thermal performance were analyzed.•The energy saving potential and economics were predicted.
Deep Reinforcement Learning (DRL) has made promising progress in autonomous driving planning and guidance within dynamic urban scenarios. As the potentials for real-world applications increase, so ...does the demand for a safe and robust driving system. For example, an end-to-end deep reinforcement learning agent makes guidance decisions based on the observation states extracted from perception sensor inputs, which can be interfered by unpredictable adversarial attacks. The latter causes adversarial observation states, which easily leads autonomous driving agent to incorrect decisions and ultimately to unintended accidents. In this work, we propose both attack and defence approaches for robust learning-based self-driving agent. The optimal observation perturbation is realized using an efficient augmented gradient-based method. An attack detection deep network with saliency map based explainability is then proposed to flag up to the users the existing danger of the attacks on the sensor perception. Furthermore, to ensure safe driving under these perceptional perturbations, we propose a deep adversarial reinforcement learning based approach for robust autonomous driving in a roundabout passing scene. We adopt PPO (Policy Proximal Optimization) 1 as our baseline guidance algorithm and develop a theoretical supported constraint, multi-object objective function optimization to efficiently mitigate the effect on the deep guidance autonomous driving policy from strong adversarial attacks. We conduct extensive experiments and evaluate our robust model under various adversarial attack configurations in traffic scenarios. Our proposed method shows significant improvements coping with optimal adversary in dynamic environments.
Building integrated photovoltaic (BIPV) glazing is currently regarded as a promising building material with a wide range of benefits. Photovoltaic combined vacuum glazing is a relatively new ...innovative concept in BIPV glazing. On the other hand, photovoltaic combined hybrid vacuum glazing (PVCHVG) is a rarely studied topic in which an air gap exists between vacuum glazing and photovoltaic glazing to form an insulated glazing unit. This paper investigates the overall energy-saving performance of a CdTe-based semi-transparent PVCHVG. A dynamic simulation model was developed and validated with an outdoor experiment to explore the energy-saving performance of the PVCHVG under five different climate conditions in China, and the results were compared with commonly used window systems. The results indicated good insulation properties against both heat loss and heat gain due to the combined action of vacuum glazing and semi-transparent photovoltaic glazing. Compared to clear single-glazing and double-glazing window systems, PVCHVG can save overall energy consumption up to 59.39% and 39.97% in heating-dominated region, and 76.33% and 73.766% in cooling-dominated region, respectively. Furthermore, the PVCHVG window system generated electricity with a good performance ratio and total system efficiency ranging from 85.7% to 85.78% and 7.45%–7.55%, respectively, considering five climate conditions.
•A CdTe-based STPV glazing combined hybrid vacuum glazing is investigated.•A dynamic simulation model is developed and validated with an outdoor experiment.•It poses inferior heat loss and heat gain in 5 distinct climate conditions in China.•The energy-saving potential is better in cooling than heating-dominated regions.•Key performance parameters for the window PV system are discussed.
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