fault-tolerant control (FTC), due to its characteristic of fault prevention and mitigation, is an increasingly popular topic in wastewater treatment process (WWTP) for safety purpose. However, the presence of uncertainties and external disturbances inevitably leads to unknown faults in WWTP, making it challenging for FTC strategies using existing fault data to ensure continuous safe and stable operation. To address this issue, a knowledge compensation-based active fault-tolerant control (KC-AFTC) is designed in this article. First, a knowledge-based prescribed performance function (KPPF) is introduced to constrain the transient and steady-state performance of WWTP. Then, the proposed KPPF can assist in KC-AFTC to ensure the operation of WWTP with the desirable performance in the event of faults. Second, a knowledge compensation mechanism (KCM), which is extracted from KPPF and the fault data, is employed to reconstruct the control law for different fault conditions in active FTC (AFTC). Then, KC-AFTC can maintain the continuously safe and stable operation of WWTP. Third, the stability of knowledge compensation-based AFTC (KC-AFTC) is demonstrated through Lyapunov theory. Then, the stability analysis can provide theoretical basis for further application of KC-AFTC in practical experiments. Finally, the proposed control method is validated through both a simulation case and a real WWTP. The results demonstrate that KC-AFTC can achieve outstanding performance in terms of stability and fault tolerance.

Fault-tolerant control (FTC) has been developed as a remedial strategy, which can take action with fault information to inhibit the occurrence of abnormal condition, such as sludge bulking, further to ensure the safety and stability of wastewater treatment process (WWTP). However, since the coupling relationship among fault variables results in the complexity and obscurity of fault characteristic, especially in the early stage, it is difficult to extract the available fault information to design an efficient FTC. Therefore, to tackle this issue, a knowledge-guided fault-tolerant compensation control (KG-FTCC) with fault diagnosis is presented in this article. First, a knowledge fault diagnosis strategy is employed to analyze the variable causality to extract the root fault cause. Then, the proposed strategy can identify the fault characteristic with coupling relationship to assist in KG-FTCC. Second, a knowledge fault compensation mechanism, which can leverage the fault knowledge and data, is introduced in KG-FTCC to achieve the control law reconstruction. Then, the proposed KG-FTCC can catch the desired set-point to suppress the sludge bulking. Third, the stability of KG-FTCC is proved by using the Lyapunov theory. Then, the effective implementation of KG-FTCC can be guaranteed. Finally, the merit of KG-FTCC is verified in a real WWTP and a simulation application. The experimental results indicate that KG-FTCC can enhance its diagnosis and control performance to ensure the safe and stable operation of WWTP.

Most existing visual Simultaneous Localization and Mapping (SLAM) frameworks generally presume the environment to be unchanging. Nevertheless, this assumption limits their application in real-world environments, as unavoidable dynamic objects can significantly affect the pose estimation accuracy. We designed a visual SLAM algorithm with a semantic segmentation network to detect and reject the dynamic targets. By innovatively introducing a pixel-level semantic segmentation network, Asymmetric Non-local Neural Network (ANN), into the frontend of ORB_SLAM2, feature points found within areas containing dynamic objects are segmented and discarded, and the accuracy of pose estimation is improved notably. The results of the experiments running on TUM dataset indicate that the proposed system can accurately estimate pose with higher precision in dynamic environments, demonstrating a certain level of stability and robustness.

The increasing complexity and scale of the wastewater treatment process (WWTP) demand more and more safety and stability. However, due to the unavoidable existence of external disturbance, sludge bulking is commonly encountered, which can result in risks for the efficient and stable operation of WWTP. To address this problem, a knowledge-data-driven robust fault-tolerant control (KDD-RFTC) is proposed in this article. First, a robustness evaluation strategy (RES) is constructed to extract the response and fluctuation characteristics of KDD-RFTC. Then, the antijamming ability of KDD-RFTC can be obtained in the presence of sludge bulking. Second, an adaptive knowledge transfer strategy (AKTS), based on RFTC, is designed to suppress the sludge bulking with the knowledge from the results of RES and the process data. Then, the proposed KDD-RFTC can readjust the manipulated variable to ensure a safe and stable operation. Third, the stability proof of KDD-RFTC is verified by the Lyapunov theory. Then, the successful application of KDD-RFTC can be guaranteed. Finally, KDD-RFTC is employed in the benchmark simulation model no. 1 (BSM1) to verify its merits. The experimental results illustrate that the proposed KDD-RFTC method can obtain excellent control performance and inhibit sludge bulking.

Cloud storage systems have commonly used erasure coding that encodes data in stripes of blocks as a low-cost redundancy method for data reliability. Relative to traditional erasure coding, wide-stripe erasure coding that increases the stripe size has been recently proposed and explored to achieve lower redundancy. We observe that wide-stripe erasure coding makes multi-block failures occur much more frequently than traditional erasure coding in cloud storage systems.However, how to efficiently repair multiple blocks in wide-stripe erasure-coded storage systems remains unexplored. The conventional multi-block repair method sends available blocks from surviving nodes to one single new node to repair all failed blocks in a centralized way, which may cause the new node to be the bottleneck; recent multi-block repair methods follow pipelined single-block repair methods and the former are simply built on the latter in an independent way, which may cause the surviving nodes with limited bandwidth to be bottlenecks.In this paper, we first analyze the effects of both centralized and independent ways on the multi-block repair and then propose HMBR, a hybrid multi-block repair mechanism that combines centralized and independent multi-block repairs to tradeoff the bandwidth bottlenecks caused by the new and surviving nodes, thus optimizing the multi-block repair performance. We further extend HMBR for hierarchical network topology and multi-node failures. We prototype HMBR and show via Amazon EC2 that the repair time of a multi-block failure can be reduced by up to 64.8% over state-of-the-art schemes.

Octave Convolution (OctConv) is a generic convolutional unit that has already achieved good performances in many computer vision tasks. Recent studies also have shown the potential of applying the OctConv in end-to-end image compression. However, considering the characteristic of image compression task, current works of OctConv may limit the performance of the image compression network due to the loss of spatial information caused by the sampling operations of inter-frequency communication. Besides, the correlation between multi-frequency latents produced by OctConv is not utilized in current architectures. In this paper, to address these problems, we propose a novel Two-stage Octave Residual (ToRes) block which strips the sampling operation from OctConv to strengthen the capability of preserving useful information. Moreover, to capture the redundancy between the multi-frequency latents, a context transfer module is designed. The results show that both ToRes block and the incorporation of context transfer module help to improve the Rate-Distortion performance, and the combination of these two strategies makes our model achieve the state-of-the-art performance and outperform the latest compression standard Versatile Video Coding (VVC) in terms of both PSNR and MS-SSIM.