In the practical application of rotating machinery , the change of working conditions can meet different manufacturing requirements. When fault diagnosis is performed on monitoring data with different working conditions, the change of data distribution will bring interference information which is highly related to working conditions and inconsistent matching problems in the process of multi-target domain transfer. In order to solve these problems, a working condition decoupling adversarial network (WCDAN) is proposed for multi-target domain fault diagnosis. Specifically, the prototype discrepancy alignment module is constructed following a weight-shared wavelet convolution feature extractor to ensure a clear prototype representation boundary. Then, the adaptive domain discriminator weight, along with the acquired multi-domain discrepancy, are utilized to decouple the working conditions. This process filters out interference information that highly associated with the source domain working conditions while preserving the inherent fault characteristics. Furthermore, the strategy of multi-domain hybrid alignment aims to minimize the disparity between different domains and solve the inconsistent matching issue. Based on two gearbox fault datasets under stable and unstable conditions, the comparative experimental results show that the WCDAN can be generalized from a single source domain to multiple target domains at the same time and achieve excellent fault diagnosis performance.

Electrocardiogram (ECG) signals frequently encounter diverse types of noise, such as baseline wander (BW), electrode motion (EM) artifacts, muscle artifact (MA), and others. These noises often occur in combination during the actual data acquisition process, resulting in erroneous or perplexing interpretations for cardiologists. To suppress random mixed noise (RMN) in ECG with less distortion, we propose a Transformer-based Convolutional Denoising AutoEncoder model (TCDAE) in this study. The encoder of TCDAE is composed of three stacked gated convolutional layers and a Transformer encoder block with a point-wise multi-head self-attention module. To obtain minimal distortion in both time and frequency domains, we also propose a frequency weighted Huber loss function in training phase to better approximate the original signals. The TCDAE model is trained and tested on the QT Database (QTDB) and MIT-BIH Noise Stress Test Database (NSTDB), with the training data and testing data coming from different records. All the metrics perform the most robust in overall noise and separate noise intervals for RMN removal compared with the baseline methods. We also conduct generalization tests on the Icentia11k database where the TCDAE outperforms the state-of-the-art models, with a 55% reduction of the false positives in R peak detection after denoising. The TCDAE model approximates the short-term and long-term characteristics of ECG signals and has higher stability even under extreme RMN corruption. The memory consumption and inference speed of TCDAE are also feasible for its deployment in clinical applications.

As matrix computation becomes increasingly prevalent in large-scale data analysis, distributed matrix computation solutions have emerged. These solutions support query interfaces of linear algebra expressions, which often contain redundant subexpressions, i.e., common and loop-constant subexpressions. Hence, existing compilers rewrite queries to eliminate such redundancy. However, due to the large search space, they fail to find all redundant subexpressions, especially for matrix multiplication chains. Furthermore, redundancy elimination may change the original execution order of operators, and have negative impacts. To reduce the large search space and avoid the negative impacts, we propose automatic elimination and adaptive elimination, respectively. In particular, automatic elimination adopts a block-wise search that exploits the properties of matrix computation for speed-up. Adaptive elimination employs a cost model and a dynamic programming-based method to generate efficient plans for redundancy elimination. Finally, we implement ReMac atop SystemDS, eliminating redundancy in distributed matrix computation. In our experiments, ReMac is able to generate efficient execution plans at affordable overhead costs, and outperforms state-of-the-art solutions by an order of magnitude.

Distributed matrix computation solutions support query interfaces of linear algebra expressions, which often contain redundancy, i.e., common and loop-constant subexpressions. However, existing solutions fail to find all redundant subexpressions. Moreover, eliminating the found redundancy leads to new execution order of operators, which may have side effect. To exploit the benefits of redundancy elimination, we propose a new system called ReMac , which performs automatic and adaptive elimination. In particular, automatic elimination adopts a block-wise search that exploits the properties of matrix computation for speed-up. Adaptive elimination employs a cost model and a dynamic programming-based method to generate efficient plans with redundancy elimination. In this demonstration, attendees will have an opportunity to experience the effect that automatic and adaptive elimination have on distributed matrix computation.

There is uncertainty in the system, and we consider that uncertainty is (possibly fast) time varying, but with definite bound. Fuzzy set theory is used to describe the inexact boundary and then the problem of robust control of uncertain dynamical systems is studied. Based on two adjustable design parameters, a robust control method for general mechanical systems is proposed. The control is deterministic, not the conventional IF-THEN rule based. By using the Lyapunov minimax approach, it is proved that the proposed control can guarantee system performance to be uniformly bounded and uniformly ultimately bounded. In order to find the optimal solution in the prescribed range, a two-player cooperative game is used. To reduce costs while ensuring control performance, two performance indices are developed, each of which is controlled by an adjustable parameter (i.e., player). Both necessary and sufficient conditions for Pareto-optimality are established. Using these conditions, the Pareto-optimal solution can be obtained. The effectiveness of the control design is demonstrated by the simulation of the two-body pendulum.

Control design and performance enhancement for uncertain mechanical systems are pursued in this article. Uncertainty in a physical system is often inevitable in practice, which is best characterized by its possible bound. Mechanical systems with uncertain nonlinearity are considered. Furthermore, even the knowledge of the coefficients in the bound is unknown, which can only be described by its fuzzy association to a set. In controlling the system, there is a hierarchical performance requirement. The first level is deterministic, including uniform boundedness and uniform ultimate boundedness. This is the part the system must meet regardless of the actual value of the uncertainty. The second level is optimality, in terms of minimizing a fuzzy-theoretic performance index. We propose a novel control design with a tunable design parameter. The control guarantees the first-level performance when the design parameter falls in a range. We, then, take the advantage of this range flexibility to address the second-level requirement. The optimal choice of the design parameter can be made by solving an optimization problem. This problem is completely solved. Both the analytic (i.e., closed form) expressions of the design parameter and the resulting minimum cost are given. As a result, we accomplish a two-level control design task.