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The lack of data due to the high cost of experiments, limited knowledge of system structure, and coupling failure mechanism make it necessary to consider uncertainties when analyzing the reliability of complex systems. This paper provides a comprehensive work for system reliability analysis under consideration of mixed uncertainties. First, Bayesian networks (BNs) are used to model the structure of complex systems. Second, mixed uncertainties are described via a probability box and accurately quantified by the Bhattacharyya distance, which can synthetically represent the mean and variance of two data sets. Then, the importance of components with mixed uncertainties is defined and analyzed. Finally, a comprehensive sensitivity index is proposed for sensitivity analysis and epistemic uncertainty tracing of data sources based on the Bhattacharyya distance. This method is used to analyze the reliability of an auxiliary power supply system of a train and verifies the feasibility and accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

• A spatial temporal graph neural network model is proposed to predict building heating load for multiple thermal zones. • The spatial block of the model captures the impact of environmental factors and heat transfer between thermal zones. • The temporal block of the model captures the change in heating load with time. • The effectiveness of feature selection, model architecture and hyperparameters are discussed. Machine learning models have seen widespread application in predicting building thermal loads. Yet, these existing models generally predict the thermal load for a single thermal zone or an entire building, overlooking buildings with multiple thermal zones. Moreover, interactions between thermal zones are seldom accounted for these methods. Addressing this gap, our study introduces a graph-based approach that employs a Temporal Graph Attention Network (TGAT) model for building thermal load prediction. This model accommodates both the spatial and temporal dependence across different thermal zones. Specifically, the TGAT model initially utilizes graph attention neural networks (GATs) to identify the spatial relationships between thermal zones. The spatial representation thus obtained is subsequently processed via Gated Recurrent Units (GRUs) to ascertain temporal dependencies. Lastly, a fully connected layer is engaged to decode the representation for the final output. Our model incorporates 11 features as inputs to depict the characteristics of outdoor environment and thermal zones. Testing the proposed TGAT model in a small office virtual environment for heating load prediction revealed that a configuration of four GAT attention heads achieved optimal result for predicting the ensuing hour's heating load with a six-hour lookback. Additionally, we scrutinized and discussed the efficacy of feature selection and the GAT module. This research thus establishes a novel groundwork for forthcoming studies on zone-based building energy management optimization. [ABSTRACT FROM AUTHOR]

Graph Neural Networks (GNNs) have emerged as a promising solution for effectively handling non-Euclidean data in construction, including building information models (BIM) and scanned point clouds. However, despite their potential, there is a lack of comprehensive scholarly work providing a holistic understanding of the application of GNNs in the construction domain. This paper addresses this gap by conducting a thorough review of 34 publications on GNNs in construction, presenting a comprehensive overview of the current research landscape. By analyzing the existing literature, this paper aims to identify opportunities and challenges for further advancing the application of GNNs in construction. The findings from this review shed light on diverse approaches for constructing graph data from common construction data types and demonstrate the significant potential of GNNs for the industry. Moreover, this paper contributes to the existing body of knowledge by increasing awareness of the current state of GNNs in the construction industry and offering practical recommendations to overcome challenges in real-world practice. [Display omitted] • A systematic review of GNNs research and applications in the construction industry • Identification of GNNs potential in handling the non-Euclidean data commonly found in construction activities. • Various approaches to developing graph data from common data types in construction • Discussion of current applications and potential of GNNs in construction, as well as its challenges and opportunities. [ABSTRACT FROM AUTHOR]

Electrostatic micromirrors can be widely used in LiDAR, displays, AR/VR and medical imaging. Electrically insulated silicon enabled by trenches filled with thermal oxide and polysilicon is commonly used for 2-axis electrostatic comb-drive scanning mirrors made on SOI (silicon-on-insulator) wafers. These trenches provide both electrical isolation and mechanical stability. However, this method has drawbacks such as high deposition temperature, voids, and high residual stress. In this work, a polymer filling method is proposed to form electrical insulation trenches in the silicon device layer of SOI wafers. Only low-temperature processes are used in this method and the silicon trenches can be fully filled up reliably. Furthermore, the filling polymer not only relieves residual stresses but also significantly increases the shock resistance of the micromirrors made with this method. In particular, Benzocyclobutene (BCB) is employed and a process has been developed to effectively fill deep silicon trenches. Using this method, two-axis electrostatic micromirrors have been successfully fabricated and tested. Experimental results show that the BCB-filled trenches demonstrate excellent electrical insulation performance and mechanical stability and the fabricated 2-axis mirrors can achieve a large field of view of 60°×40° and stand high impact over 2500 g. [Display omitted] • A polymer filling method is proposed to form electrical insulation trenches for electrostatic micromirrors. • Only low-temperature processes are used in this method and the silicon trenches can be fully filled up reliably. • Polymer-filled trench can meet the mechanical stability and electrical insulation requirements. • The fabricated 2-axis mirrors can achieve a large field of view of 60°×40° and stand high impact over 2500 g. [ABSTRACT FROM AUTHOR]

In this work, a real-time capacitive sensing scheme based on electromechanical amplitude modulation is developed to detect the scanning angle and phase of a uniaxial electrostatic comb-drive micromirror for close loop control. In this scheme, a sine-wave voltage signal superimposed with a high frequency carrier signal is applied to the shared comb drives of the micromirror for both sensing and driving. The driving/sensing circuit is fully analyzed in both frequency and time domain for feedthrough elimination and signal distortion minimization. Experimental results have shown that using a carrier signal with 2.5 V pp and 1 MHz, the measurement accuracy of the scanning angle of the micromirror reaches 0.15° and the time delay can be controlled within 0.47 μs. The effect of the temperature change on the phase response of the micromirror is also investigated for a better understanding of the micromirror's scanning stability. When the temperature changes from 25 °C to 35 °C, the measured time delay of the micromirror actuated at 3840 Hz changes from 0 to 2.4 μs. The proposed capacitive sensing scheme can be used to effectively measure the angular position and phase of electrostatic comb-drive MEMS mirrors simultaneously without the need of adding any external components. [Display omitted] • A capacitive angular sensing circuit developed for electrostatic micromirrors. • Simultaneous real-time detection of the angle and phase of the micromirror has been achieved. • The phase change of the scanning micromirror on the temperature variation has been investigated experimentally. • The relation between the scan angle and the circuit output voltage can be accurately modelled through piecewise fitting. [ABSTRACT FROM AUTHOR]