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Urban areas are subject to multiple and very different changes, in a two- and three-dimensional sense, mostly as a consequence of human activities, such as urbanization, but also because of catastrophic and sudden events, such as earthquakes, landslides, or floods. This paper aims at designing a procedure able to cope with both types of changes by combining interferometric coherence and backscatter amplitude, and provide a semantically meaningful analysis of the changes detected in both city inner cores and suburban areas. Specifically, this paper focuses on detecting multi-dimensional changes in urban areas using a stack of repeat-pass SAR data sets from Sentinel-1A/B satellites. The proposed procedure jointly exploits amplitude and coherence time series to perform this task. SAR amplitude is used to extract changes about the urban extents, i.e. in 2D, while interferometric coherence is sensitive to the presence of buildings and to their size, i. e. to 3D changes. The proposed algorithm is tested using a time-series of two years of Sentinel-1 data, from May 2016 to October 2018, and in two different Chinese cities, Changsha and Hangzhou, with the aim to understand both the temporal evolution of the urban extents, and the changes within what is constantly classified as "urban" throughout the considered time period. [ABSTRACT FROM AUTHOR]

Total thrust and torque are two key indicators of shield movement performance. Most existing data-driven machine learning studies focus on developing more accurate models for predicting total thrust and torque but overlook the interpretability of the models. To address this black-box issue, this study proposes an interpretable probabilistic prediction algorithm for the shield movement performance. The algorithm uses the natural gradient boosting (NGBoost) model to iteratively update the parametric probability distributions (e.g., mean and variance) and achieve probabilistic predictions of the total thrust and torque. The impact of each feature on the prediction values and uncertainty is quantified by extending the importance analysis of a single deterministic predictive value to both the mean and variance. The feature interactions are analyzed and their predictive contributions are quantified by the shapley additive explanations (SHAP) method. The transparency of the NGBoost model is improved through the visualization of the decision-making process. A shield tunneling project in Hangzhou is used to validate the effectiveness of the proposed algorithm. The results indicate that the NGboost model outperforms other five models in terms of accuracy. The prediction results are interpretable, and the interpretable probabilistic model provides decision-makers with a more intuitive and reliable reference. [ABSTRACT FROM AUTHOR]

In order to realize the visual positioning for Hangzhou white chrysanthemums harvesting robot in natural environment, a color image segmentation method for Hangzhou white chrysanthemum based on least squares support vector machine (LS-SVM) was proposed. Firstly, bilateral filter was used to filter the RGB channels image respectively to eliminate noise. Then the pixel-level color feature and texture feature of the image, which was used as input of LS-SVM model (classifier) and SVM model (classifier), were extracted via RGB value of image and gray level co-occurrence matrix. Finally, the color image was segmented with the trained LS-SVM model (classifier) and SVM model (classifier) separately. The experimental results showed that the trained LS-SVM model and SVM model could effectively segment the images of the Hangzhou white chrysanthemums from complicated background taken under three illumination conditions such as front-lighting, back-lighting and overshadow, with the accuracy of above 90%. When segmenting an image, the SVM algorithm required 1.3 s, while the LS-SVM algorithm proposed in this paper just needed 0.7 s, which was better than the SVM algorithm obviously. The picking experiment was carried out and the results showed that the implementation of the proposed segmentation algorithm on the picking robot could achieve 81% picking success rate. [ABSTRACT FROM AUTHOR]

In this paper, an improved numerical scheme is developed to estimate the initial conditions (ICs) based on a three-dimensional suspended sediment transport model (3D SSTM) with adjoint data assimilation, and the method is then applied to Hangzhou Bay as an example. Specifically, the ICs are estimated by assimilating artificial observations in twin experiments and suspended sediment concentrations (SSCs) retrieved from the Geostationary Ocean Color Imager (GOCI) in practical experiments. In the twin experiments, the sensitivity of the estimated ICs to several influential factors is discussed. The results demonstrate that the conjugate descent algorithm of Fletcher is proven to be better than the steepest descent, finite memory BFGS, and five other conjugate gradient algorithms in estimating the ICs; the estimated ICs are sensitive to initial guess values, and appropriate initial values are necessary for improving the efficiency of convergence and obtaining good results. Additionally, the errors of observations can significantly influence the estimated results. In contrast, the estimated results are not very sensitive to cloud coverage, errors in the background flow field, and length of the assimilation time window. In practical experiments, according to the conclusions of twin experiments, an improved 3D SSTM with the adjoint method is developed for Hangzhou Bay, and the surface GOCI-retrieved SSCs during typical neap and spring tidal cycles are assimilated to estimate the practical ICs. The experimental results imply that with the present estimation method, more accurate ICs can be obtained, which indicates that the adjoint method is effective in the estimation of the ICs in SSTMs. Furthermore, this study verifies that accurate ICs are critical for the numerical modeling of SSCs on the tidal cycle scale. This study is not only useful for further improving the accuracy of ICs in SSTMs but also suggestive for the initialization schemes of other matter transport models. • Initial conditions are critical for simulating suspended sediment concentrations over tidal cycles. • The adjoint method is effective in estimating initial conditions. • Data assimilation can compensate for information in areas of missing satellite data. [ABSTRACT FROM AUTHOR]

The inverse dynamic analysis of vehicle-bridge (VB) systems provides a superior scheme for track irregularity identification using the onboard measurement data of vehicle dynamic responses. However, available algorithms face a great challenge when applied to high-speed railway bridges because of the increase in the number of unknown inputs, resulting in a degradation of identification accuracy. Here, we propose a condensation procedure to reduce the unknown inputs of the VB system, and then propose a novel algorithm for track irregularity identification using the augmented Kalman filter. A numerical simulation of a real continuous three-span simply supported railway bridge was conducted to validate the estimation accuracy of the proposed algorithm. Numerical results illustrate that the proposed algorithm can accurately estimate the track irregularities by using the vertical acceleration responses of the vehicle body only, thanks to the unknown input condensation. A comparative study demonstrates that the proposed algorithm outperforms the conventional track irregularity identification methods in terms of estimation accuracy. To evaluate the actual performance of the proposed algorithm, a set of onboard measurement data recorded by a typical high-speed train when running on the Hangzhou–Changsha high-speed railway in China was employed to identify the track irregularities of the railway line, and the results suggest that the proposed algorithm is highly efficient and low in cost. This study may help develop highly efficient real-time track irregularity monitoring in the years ahead. [ABSTRACT FROM AUTHOR]

Aiming at the problem of intersection signal control, a method of traffic phase combination and signal timing optimization based on the improved K-medoids algorithm is proposed. Firstly, the improvement of the traditional K-medoids algorithm embodies in two aspects, namely, the selection of the initial medoids and the parameter k , which will be applied to the cluster analysis of historical saturation data. The algorithm determines the initial medoids based on a set of probabilities calculated from the distance and determines the number of clusters k based on an exponential function, weight adjustment, and elbow ideas. Secondly, a phase combination model is established based on the saturation and green split data, and the signal timing is optimized through a bilevel programming model. Finally, the algorithm is evaluated over a certain intersection in Hangzhou, and results show that this algorithm can reduce the average vehicle delay and queue length and improve the traffic capacity of the intersection in the peak hour. [ABSTRACT FROM AUTHOR]

The true principal factors for the diurnal water-demand pattern of urban water are often difficult to identify using traditional rough-set algorithms because the demand pattern is usually affected by many factors that are uncertain and hard to quantify. An improved attribute-reduction algorithm based on the cumulative weighting coefficient was proposed to solve this problem. The weighting coefficient was determined by the result of the variable precision rough-set algorithm. To discuss the consecutive curves with mathematical tools, an improved fuzzy c-mean (FCM) algorithm was proposed to discretize the diurnal water-demand pattern spatially. The proposed algorithms were then used to analyze the principal factors of the diurnal water-demand pattern in the city of Hangzhou, China. The results show that the improved attribute-reduction algorithm is capable of distinguishing the false attribute from the dynamic reduction sets, and the fuzzy c-mean algorithm is an effective and feasible method of solving the cluster problem for the consecutive curves. The principal factors affecting the diurnal water-demand pattern in Hangzhou are maximum air temperature, minimum air temperature, and weekday or weekend. [ABSTRACT FROM AUTHOR]