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The proportional integral derivative (PID) method is widely used in industrial control applications. However, when applied to complex and dynamic train operation control systems, real-time parameter adjustment becomes a formidable challenge. Moreover, the multifaceted nature of train operation control, encompassing safety, parking precision, passenger comfort, and energy efficiency, exacerbates the difficulty of parameter adjustment. To address this problem, this paper formulates train operation control as a Markov decision process (MDP) and introduces an innovative adaptive control approach. This approach features a hierarchical structure comprising an upper-level deep deterministic policy gradient (DDPG) controller and a lower-level PID controller, leveraging the learning capability of the DDPG algorithm, as well as the stability and interpretability of the PID method. The upper-level controller acquires train status information and autonomously fine-tunes the PID parameters, while the lower-level controller accepts these parameters and adjusts the percentage of traction or braking to achieve train operation control. Furthermore, the reward function has been meticulously designed to reconcile the diverse objectives of train operation. Extensive experiments conducted on a subway simulation platform substantiate the effectiveness and adaptability of the proposed approach in various operational scenarios.

Impervious surface area (ISA) mapping at the global scale has entered a new era. Currently, the number of high-resolution global ISA products is gradually increasing; however, a literature review that systematically investigates these ISA products is still lacking, which limits the application of these products. Thus, we provide a comprehensive analysis of the existing high-resolution global ISA products, concentrating on the aspects of the data sources, training samples, features, and methods. Moreover, we evaluate these products at multitemporal and multispatial scales, using a series of independent test samples. The results demonstrate that the multitemporal accuracy of the ISA products presents an increasing trend, due to the increase of the available sensors. Among the continuous time-series products [e.g., the updated new global impervious surface area (GISA 2.0), the global impervious surface area (GISA), global annual urban dynamics, global human settlement layer, and global artificial impervious areas], the accuracy of the GISA 2.0 outperforms the others at global, continental, and regional scales. However, the mapping performance of these products in small towns and arid and rural regions needs to be enhanced. In particular, we focus on the spatio-temporal disagreement of the ISA products. We show that the high disagreement regions are predominantly concentrated in eastern Asia, western Europe, and eastern North America. In addition, the high disagreement regions are characterized by low ISA density, high vegetation coverage, and high albedo bare ground coverage. Additionally, this article concludes with some remarks about the future directions of global ISA mapping.

Crop classification is one of the focused topics in remote sensing study nowadays. Optical imageries, although providing much information, are often contaminated with cloud or other weather effects while SAR imageries are more resilient to those. Temporal series data are often used to improve classification accuracy, especially in crop classification. This article investigates the usage of temporal series SAR imagery on crop classification in vast rural areas of China. The selected area of interest has a complicated, heavily mixed agriculture as well as lots of nonagricultural landcovers. Total ten classes are considered, six of them being crop types. A pixel-based classifier using subspace kth nearest neighbor (KNN) algorithm is applied to open source Sentinel-1 polarized SAR data. Discussion includes analyzing temporal features of the SAR observation, time domain smoothing, feature engineering, different classification algorithms, and selection of temporal series. The study results in an overall accuracy of 98.2% for the ten classes in fivefold cross validation, indicating a propitious application for agricultural monitoring using SAR data.

The integration of Internet of things (IoT) and intelligent transportation system (ITS) is expected to improve the traffic efficiency and enhance the driving experience. However, due to the dynamic traffic environment and various types of vehicles, it is a challenge to perform vehicle classification and speed estimation with a single magnetic sensor. In this paper, based on a single low-cost magnetic sensor, a scheme is proposed to achieve vehicle classification and speed interval estimation by designing a two-dimensional convolutional neural network (CNN). Specifically, we extract the magnetic field data of each vehicle and then convert the collected data into a two-dimensional grayscale image. In this way, the images of vehicle signals with different types and driving speeds can be used as the input data to train the designed CNN model. With the designed CNN model, we classify the vehicles into 7 types and estimate the speed interval of each vehicle, where the speeds in the range of 10km/h-70km/h are divided into 6 intervals of size 10km/h. The performance of the proposed vehicle classification and speed estimation scheme is evaluated by experiments, where the experimental results show that the accuracy of vehicle classification and the accuracy of speed interval estimation are 97.83% and 96.85%, respectively.

Micro-positioning stages, which are capable to meet the emerging industrial trend for performing various kinds of micro-manipulation and micro-assembly tasks are the crucial tools to miniaturize the products under ultra-high precision. The current main practice is to specifically tailor a monolithic positioning stage design for one kind of micro-manipulation application. However, the design and fabrication of such a specific flexure-based micro-positioning stage are expensive in both the time and financial investment. In order to increase the flexibility and functionality of micro-positioning stages, this paper proposes a novel reconfigurable planar micro-positioning stages platform based on different function modules. A functional model-based approach is developed to divide one micro-positioning stage into a set of functionally independent sub-models, which allows flexible composition resulting in an adjustable new stage. The robustness of the proposed platform is demonstrated using some typical micro-positioning stages benchmarking cases. The effectiveness of assembly using flexible function modules is verified to show the benefits of the proposed design platform for reconfigurable micro-positioning stages.