Building shadows may mitigate heat in historic districts, yet their thermal association in mountainous cities remains unclear because terrain reshapes shade formation and heat exposure. We developed a process-based framework to examine whether simulated peak-period building shade support is spatially associated with lower residual LST in three mountainous historic districts of Chongqing, China: Baixiangjie, Ciqikou, and Shibati. Using 3D-GloBFP building footprints and heights, DEM data, and summer 10 m LST rasters from 2019 to 2025, we simulated shadows at 20-min intervals from 08:00 to 18:00 on 21 June, 21 July, and 21 August. We characterized shade using all-day and peak-period shadow frequency and assessed thermal associations using residual-LST modelling, monotonic shadow-temperature response analysis, and spatial co-location analysis. Peak-period shade support was 81.1%, 71.2%, and 75.4% lower than all-day supply in Baixiangjie, Ciqikou, and Shibati, respectively. Shadow-low-temperature overlap covered only 22.1%-25.1% of open space, but its mean LST was 0.56-1.66 degrees C lower than that of neutral open space, indicating that shade alone is insufficient without temporal alignment with peak heat exposure and spatial co-location with lower-temperature areas. Ciqikou showed the strongest residual-LST reduction signal, Shibati high geometric overlap but a moderate reduction signal, and Baixiangjie weak peak-period support with limited valid-year evidence. These differences reflected the combined influence of terrain, street configuration, building morphology, and shade organization rather than building coverage alone. Peak-period shade continuity, rather than total shade, should therefore guide adaptation: shadow-low-temperature overlap zones warrant preservation, whereas shade-only zones require complementary surface, vegetation, or ventilation improvements.
This article delves into the exploration of state-space methods applied to the modeling and estimation of systems with time-varying parameters. While typically existing approaches rely on the assumption that the parameters satisfy the Markov evolution and require the knowledge of the transfer matrix, this article develops an explicit autoregressive (AR) model for time-varying parameters in which the invariant matrix represents the dynamic changes in the parameters. Unlike the previous work, the state-space model is constructed by stacking the invariant matrix and time-varying parameters into the unknown state vector. Then, the joint state estimation (JSE) algorithm is deduced based on the Kalman filtering principle, aiming to reduce the dependence on the prior knowledge of the invariant matrix. Through the numerical simulation and Monte Carlo test, it is indicated that the developed algorithm maintains reliability under various random white noises. In addition, the practical estimation results with the real-time series also verify the validity.
To address the challenges of low accuracy and poor robustness in industrial crayfish weight estimation caused by variable postures, this paper proposes a lightweight method that integrates pose awareness. First, a multi-task perception model, Crayfish-YOLO, is developed based on the YOLOv8s-Seg framework. By reconstructing the backbone with MobileNetV3 and integrating Coordinate Attention (CA), CARAFE upsampling, and the Wise Intersection over Union (Wise-IoU) loss function, the model is significantly compressed while enhancing its ability to output high-fidelity pixel-level masks and pose categories. Second, a pose-adaptive weight estimation strategy is proposed, which leverages perceived pose information to dynamically invoke the optimal regression model from a pre-constructed heterogeneous model library. Using seven core geometric features extracted from the segmentation masks, the system achieves precise weight estimation. Experimental results on a self-built dataset show that Crayfish-YOLO reduces parameters by 75.2% compared to YOLOv8s-Seg, while core segmentation accuracy (mAP(50 similar to 95) (Seg)) improves by 1.1%. The integrated end-to-end system achieves a Mean Absolute Error (MAE) of 2.1 g and a mean coefficient of determination (R-2) of 0.92, significantly outperforming comparative algorithms. This research provides an efficient visual perception and estimation solution for the automated grading of crayfish and similar non-rigid aquatic products.
This article investigates the identification issue of multivariable ARX systems with colored noise. To address the bias caused by colored noise, a data filtering method is applied to whiten the original multivariable system, which filters the input-output data without altering their inherent dynamics and yields a filtered identification model. Considering the computational complexity and burden in multivariable system identification, a three-stage filtered stochastic gradient algorithm is proposed based on the filtered identification model with a hierarchical strategy. In addition, the historical innovations are utilized to further improve estimation accuracy and convergence performance, resulting in a three-stage filtered multi-innovation stochastic gradient algorithm. The numerical examples verify the effectiveness of the proposed algorithms in identifying multivariable ARX systems.
This paper considers recursive parameter identification for autoregressive output-error autoregressive moving average (AR-OE-ARMA) systems from the perspective of computational efficiency. By means of the hierarchical identification principle, we propose an auxiliary model hierarchical generalized extended stochastic gradient algorithm (AM-HGESG), an auxiliary model hierarchical multi-innovation generalized extended stochastic gradient (AM-HMI-GESG) algorithm, an auxiliary model hierarchical generalized extended recursive gradient algorithm (AM-HGERG), an auxiliary model hierarchical multi-innovation generalized extended recursive gradient (AM-HMI-GERG) algorithm, an auxiliary model hierarchical generalized extended least squares algorithm (AM-HGELS), and an auxiliary model hierarchical multi-innovation generalized extended least squares (AM-HMI-GELS)algorithm by using the multi-innovation identification theory. The proposed hierarchical identification methods can be extended to other linear and nonlinear multivariable stochastic systems with colored noises.
