Deep learning approaches that jointly learn feature extraction have achieved remarkable progress in image matching. However, current methods often treat central and neighboring pixels homogeneously and rely on static feature selection strategies, which fail to account for environmental variations. This results in limited robustness of descriptors and keypoints, thereby affecting matching accuracy. To address these limitations, we propose a robust joint optimization network for feature detection and description in optical and SAR image matching. A Center-Weighted Module (CWM) is designed to enhance local feature representation by emphasizing the hierarchical relationship between central and surrounding features. Furthermore, a Multi-Scale Gated Aggregation (MSGA) module is introduced to suppress redundant responses and improve keypoint discriminability through a gating mechanism. To address the inconsistency of score maps across heterogeneous modalities, we design a position-constrained repeatability loss to guide the network in learning stable and consistent keypoint correspondences. Experimental results across various scenarios demonstrate that the proposed method outperforms state-of-the-art techniques in terms of both matching accuracy and the number of correct matches, highlighting its robustness and effectiveness.

Wastewater treatment yields significant societal benefits in resource recycling, economic development, and public health. Dissolved oxygen (DO) concentration during the wastewater treatment process serves as a critical indicator for assessing effluent quality. Therefore, maintaining DO within an appropriate range is essential. This study proposes a data-driven tracking controller based on an action-dependent heuristic dynamic programming (ADHDP) approach incorporating lifelong learning (LL) to achieve precise DO concentration tracking. First, the LL-ADHDP controller, acting as an auxiliary controller, is integrated with a prior-knowledge-based controller to achieve model-free tracking control. The online LL-ADHDP approach enhances the approximation accuracy of both the critic and action networks. Second, integrating the LL mechanism into these networks mitigates catastrophic forgetting and improves overall robustness. Third, the method is applied to the benchmark simulation model no. 1. Experimental results demonstrate its superior tracking performance. Finally, simulations with diverse reference trajectories confirm the good dynamic performance and effectively reduce the tracking error of the proposed LL-ADHDP method.

Wastewater treatment plays a crucial role in urban society, requiring efficient control strategies to optimize its performance. In this paper, we propose an enhanced offline reinforcement learning (RL) approach for wastewater treatment. Our algorithm improves the learning process. It uses a transition filter to sort out low-performance transitions and employs prioritized approximation loss to achieve prioritized experience replay with uniformly sampled loss. Additionally, the variational autoencoder is introduced to address the problem of distribution shift in offline RL. The proposed approach is evaluated on a nonlinear system and wastewater treatment simulation platform, demonstrating its effectiveness in achieving optimal control. The contributions of this paper include the development of an improved offline RL algorithm for wastewater treatment and the integration of transition filtering and prioritized approximation loss. Evaluation results demonstrate that the proposed algorithm achieves lower tracking error and cost.

In this paper, based on the adaptive critic control method, an improved event-based trajectory tracking mechanism of continuous-time (CT) nonlinear multiplayer zero-sum games (MZSGs) is established. It is worthy of note that previous papers studying the trajectory tracking issue of nonlinear CT MZSGs only apply to the case where the reference trajectory eventually converges to zero. Consequently, this paper develops an improved mechanism to overcome this weakness. Later, an event-triggered framework is brought in to reduce the amount of computation and improve control efficiency. In this process, an innovative triggering condition is provided. At the same time, the infamous Zeno behavior is ruled out through theoretical analysis. Furthermore, the event-based near-optimal controls and event-based near-worst disturbances for tracking error dynamics are gained by building and adjusting a single critic neural network. Immediately after, by utilizing the Lyapunov method, the uniform ultimate boundedness stability of the tracking error and the weight estimation error is ensured. Lastly, an example containing two case studies is offered to validate the validity of the established mechanism. Note to Practitioners—Complex industrial processes often involve multiple control inputs and may also be affected by multiple disturbances at the same time, which can be referred to as a MZSG. Since many industrial processes can be viewed as a tracking question of nonlinear systems and the event-triggered mechanism can decrease the computational cost, the tracking problem for event-based nonlinear MZSGs is studied in this paper, which is significant for control practitioners. Moreover, the Hamilton-Jacobi-Isaacs equation is often difficult to solve when dealing with the game problem. Hence, an adaptive critic technique is presented to acquire the near-optimal controls and the near-worst disturbances, which replaces the traditional actor-critic framework and thus simplifies the theoretical analysis. Note that this paper proposes an innovative triggering condition to relax the restriction on the choice of disturbance rejection level. Compared to previous works dealing with the tracking problem of nonlinear MZSGs, the method presented in this paper makes the choice of the reference trajectory more flexible and thus enhances the applicability in general industrial processes. Finally, stability analysis and simulation results are given. Note that for different practical situations, practitioners can adjust the related parameters to achieve the tracking control of MZSGs and minimize the computational cost.

In this paper, an adaptive critic design with performance guarantee is established based on the discounted value iteration algorithm to settle with the optimal regulation problem for discrete-time zero-sum games. Value iteration is implemented to obtain the approximate optimal solutions to the Hamilton-Jacobi-Isaacs equation for nonlinear systems and the game algebraic Riccati equation for linear systems. Then, we focus on system stability affected by the introduction of the discount factor and the admissibility of the policy pairs in the value iteration process. The appropriate selection range of the discount factor and the criteria for ensuring system stability are established to assist in obtaining the stabilized optimal policy pair, which not only makes the cost function converge to the optimal value, but also guarantees the asymptotic stability of the closed-loop system. Finally, practical examples for the power system and the ball-beam system are conducted to demonstrate the effectiveness of the presented method. Note to Practitioners—Since there exist a multitude of dynamic systems with uncertainty and interference, the zero-sum game problems are ubiquitous, especially when dealing with dynamic systems featuring antagonistic properties. As an important research direction in the field of optimal control, zero-sum games usually involve designing policy pairs that can optimize the system performance in the presence of adversarial disturbances. Due to the excellent adaptability, value iteration in adaptive dynamic programming is employed to deal with this kind of issues. In addition to focusing on the optimality of policies, the system stability during the control process is equally significance, where the stability is the premise of all operations. Therefore, we are dedicated to providing guidance on the optimal regulation of discrete-time zero-sum games with performance guarantee, which contributes to obtain the stable optimal policy pair. Theoretical analysis of the stability is provided and the asymptotic stability of the system is ensured, which improves the performance of the designed controller. Furthermore, simulation experiments for practical applications are conducted, which verify the feasibility and effectiveness of the proposed control design.

Land surface temperature (LST) is a key factor in monitoring and improving thermal environments. However, conventional LST retrieval algorithms have not sufficiently accounted for the cavity and adjacency effects caused by the three-dimensional (3D) structures in urban settings. In this study, we propose an urban multiple scattering radiative transfer model (UMS-RTM). Based on this model, we develop an urban nonlinear split-window (UNSW) algorithm to retrieve urban land surface temperature (ULST) from Landsat-9 satellite data. The UMS-RTM optimizes the thermal radiation transfer process by correcting the cavity and adjacency effects. Analysis shows that land surface emissivity (LSE) and sky view factor (SVF) are the primary factors influencing these effects. The cavity effect increases the effective LSE by 0.01 to 0.08, while the adjacency effect raises the ground-leaving brightness temperature (BT) by 0.82 K to 3.51 K. The UNSW algorithm’s coefficients were calibrated across various LST, water vapor content (WVC), and SVF groupings to eliminate atmospheric effects and correct for cavity and adjacency effects. Sensitivity analyses of instrument noise, WVC, effective LSE, and SVF uncertainties demonstrated the reliability of the UNSW algorithm. Validation using simulated data showed that the ULST retrieved by the UNSW algorithm had a root-mean-square error (RMSE) of 0.35 K. When applied to Landsat-9 satellite data, the UNSW algorithm revealed that conventional algorithms and LST products overestimate ULST by 0 K to 2 K, with overestimations exceeding 1 K in areas with low SVF. The UNSW algorithm provides more accurate ULST retrieval and finer spatial distribution details.

The wastewater treatment process (WWTP) is beneficial for maintaining sufficient water resources and recycling wastewater. A crucial link of WWTP is to ensure that the dissolved oxygen (DO) concentration is continuously maintained at the predetermined value, which can actually be considered as a tracking problem. In this article, an experience replay-based action-dependent heuristic dynamic programming (ER-ADHDP) method is developed to design the model-free tracking controller to accomplish the tracking goal of the DO concentration. First, the online ER-ADHDP controller is regarded as a supplementary controller to conduct the model-free tracking control alongside a stabilizing controller with a priori knowledge. The online ER-ADHDP method can adaptively adjust weight parameters of critic and action networks, thereby continuously ameliorating the tracking result over time. Second, the ER technique is integrated into the critic and action networks to promote the data utilization efficiency and accelerate the learning process. Third, a rational stability result is provided to theoretically ensure the usefulness of the ER-ADHDP tracking design. Finally, simulation experiments including different reference trajectories are conducted to show the superb tracking performance and excellent adaptability of the proposed ER-ADHDP method.

By utilizing a neural-network-based adaptive critic mechanism, the optimal tracking control problem is investigated for nonlinear continuous-time (CT) multiplayer zero-sum games (ZSGs) with asymmetric constraints. Initially, we build an augmented system with the tracking error system and the reference system. Moreover, a novel nonquadratic function is introduced to address asymmetric constraints. Then, we derive the tracking Hamilton-Jacobi-Isaacs (HJI) equation of the constrained nonlinear multiplayer ZSG. However, it is extremely hard to get the analytical solution to the HJI equation. Hence, an adaptive critic mechanism based on neural networks is established to estimate the optimal cost function, so as to obtain the near-optimal control policy set and the near worst disturbance policy set. In the process of neural critic learning, we only utilize one critic neural network and develop a new weight updating rule. After that, by using the Lyapunov approach, the uniform ultimate boundedness stability of the tracking error in the augmented system and the weight estimation error of the critic network is verified. Finally, two simulation examples are provided to demonstrate the efficacy of the established mechanism.

In this article, to solve the optimal tracking control problem (OTCP) for discrete-time (DT) nonlinear systems, general value iteration (GVI) scheme and online value iteration (VI) algorithms with novel value function are discussed. First, the disadvantage of the traditional value function for the OTCP is presented and the novel value function is introduced. Second, we analyze the monotonicity and convergence of GVI and establish the admissibility condition of GVI to evaluate the admissibility of the current iterative control. Note that a novel approach is introduced to analyze the admissibility. Third, based on the attraction domain, improved control policies with online VI can be obtained by judging the location of the current tracking error and reference point. Finally, the stability of the online VI-based control system is guaranteed. Besides, we provide two simulation examples to show the performance of the proposed methods.

In this article, a decentralized event-triggered control mechanism is established to solve the interconnected issue of continuous-time nonlinear systems with asymmetric input constraints and matched interconnections based on the adaptive critic technology. First, by inserting the discount factor, a novel nonquadratic cost function is constructed for the constrained subsystem with nonzero equilibrium point. Meanwhile, the decentralized event-triggered control issue is transformed into a set of optimal control issues. Then, the execution of nominal subsystem is based on the event-triggered mechanism (ETM) with an event-triggering condition which increases the algorithm efficiency. Moreover, we derive the associated event-triggered Hamilton–Jacobi–Bellman (HJB) equation which arising in the discounted-cost optimal event-triggered control issues of nominal subsystems. In the implementation, an adaptive critic framework is employed to approximate the optimal cost function. Later, the experience replay (ER) approach is introduced into a novel weight tuning mechanism, which converts the traditional persistence of excitation (PE) condition into an easy-checked rank condition. Theoretically, the stability of the system and the exclusion of Zeno behavior are demonstrated. Finally, one representative example is simulated to validate the efficacy of the constructed framework.

In this paper, multi-player non-zero-sum games with control constraints are studied by utilizing a novel model-free approach based on adaptive dynamic programming framework. First, the model-based policy iteration (PI) method is provided, which requires the system dynamics, and the convergence is demonstrated. Then, aiming to eliminate the need for the system dynamics, a model-free iterative method is obtained by using the off-policy integral reinforcement learning (IRL) scheme based on the PI approach. Moreover, the system data is collected in order to construct the model-free approach. Besides, we analyze the convergence of the off-policy IRL approach by proving the equivalence between the model-free iterative approach and the model-based iterative approach. Remarkably, in the implementation of the scheme, the control policy and cost function are approximated by utilizing the actor-critic networks. The least square algorithm is utilized to learn the actor-critic networks weights depended on the collected data sets. Finally, two cases are provided to demonstrate the effectiveness of the established framework.

Fall is one of great threats affecting people with old age. Aimed at the falling issue of aged, the paper explored a pre-impact fall early warning and protection system. This system consists of an early fall alarm, protection airbags, a remote monitoring platform and a guardian's cellphone app. The early fall alarm and airbags are integrated in a belt, convenient for wearing and hip-protection. The inner of early fall alarm has a MEMS sensor, which collects 3-axis accelerated velocity and 3-axis angular velocity. A fall detection algorithm is applied to recognize falls from activities of daily living (ADL). When there is a dangerous movement approaching fall, the early fall alarm will warn the aged to stop the movement. When the fall happens, the early fall alarm will trigger the airbag system, then the airbags in the belt will inflate as soon as possible to reduce the damage to the aged. In addition, the early fall alarm will ring and send message to the guardian's cellphone for help. Meanwhile, the kinematics of the human body during falling time will be stored in TF card and sent to remote monitoring platform for storage. Then the monitoring platform can show the fall location where the fall incident happens in the electronic map. In order to test the reliability of this early fall alarm and protection system, a series of experiments have been designed. The results show that this system can be relatively accurate to detect falls, accomplishing functions including early fall warning and alarming, airbag inflation, statics transferring and storage, real-time location, which has significant benefit for reducing the direct damage and shortening the aiding time.