Abstract Although current attention‐based speech enhancement methods have been proven to be capable of significantly improving the noise reduction performance, a bottleneck has arisen in juggling both detailed features and high‐level features: The more attention paid to such performance indicators as speech intelligibility index and articulation index will lead to the loss of subtle features such as syllable continuity and timbre distortion. To tackle such a problem, we divide the speech enhancement model into multiple stages, with a special attention mechanism introduced in each stage so that the detailed speech information is retained and the features of high levels are captured. In this research, a Multi‐Stage Attention Network (MSANet) is implemented in cascade by using three different attention modules: Self Attention, Channel Attention and Spatial Attention. The attention module in each stage needs only to focus on the features of its corresponding stage, thus giving full play to their respective advantages without affecting or damaging the features of other stages, helping decouple the feature extraction process and obtaining the features capable of complementing one another. The comparisons and ablation experiments show that the performance of MSANet is superior to those of current mainstream time domain or time‐frequency domain state‐of‐the‐art methods. Compared to the baseline, PESQ score of their model (3.11) has increased by 5%, CSIG (4.44) has increased by 2.5%, CBAK (3.63) has increased by 2.8% and COVL (3.81) has increased by 3.8%, which demonstrates the potential of MSANet as speech feature extraction backbones. An implementation of the PyTorch version is available here: http://www.msp‐lab.cn:1436/msp/MSANet .

During the process of cloud manufacturing, service composition is an essential technology to ensure the smooth execution of the task. An important challenge for cloud manufacturing is how to implement effective and accurate service composition strategy, which are to execute the task effectively while also satisfy the requirements of user by maximizing the overall quality of service (QoS). However, the current cloud manufacturing service composition methods generally have the problem of poor solution quality in the large scale environment. To solve this problem, we propose a hybrid optimization algorithm, named hybrid-TC, which is a hybrid of the teaching-learning-based optimization algorithm (TLBO) and the crisscross optimization algorithm (CSO). First, we added Skyline query in the initialization phase to improve the convergence speed and the quality of the solution. Second, the horizontal crossover of CSO is used in the teaching phase of original TLBO, thereby some dimensions in the population that trapped in the local optimum have the chance to jump out of the iteration. Finally, the offspring individuals learned in the teaching-phase will continue to learn to improve the quality of the solution. Experiments show that our proposed method is effective for improving the solution quality of large-scale environmental service composition.

Abstract Object detection is one of the main tasks in computer vision and has made great progress in recent years. However, the performance of target detectors is significantly dropped by the differences between existing datasets and application scenarios, leading to the so‐called domain shift problem. To address such an issue, a novel co‐teaching based pseudo label refinery framework for cross‐domain object detection is developed, which cooperates with two models to select data from target domain for each other. This strategy can effectively purify the predicted pseudo labels and resist noisy labels. Specifically, the framework consists of two encoders (i.e. structure encoder and global encoder), two classifiers and one discriminator, in which structure encoder is used to extract structural features that are not disturbed by colour, and the global encoder is used to extract the complete discriminant features. The two encoders are each followed by a classifier. In training, the structure and global encoder with labelled source samples are first trained, so that it has the initial recognition ability. Then the samples assigned are used with pseudo labels by the classifier following the structure encoder to fine‐tune the global encoder which pre‐trained on the labelled source domain and obtain the refined labels for the target data. With the refined labels, the structure encoder is further optimised on the target domain. During this process, the proposal is to cross use the two classifiers to promote the mutual transfer of complementary capabilities of the two encoders. Moreover, a novel residual channel attention block (RCA) embedded with salient features is designed to pay more attention to the target regions. Extensive experiments demonstrate that the developed framework can generate clean labels for unlabelled target data and boost the performance of cross domain object detection. The code is available at http://www.msp‐lab.cn:1436/msp/cbplr‐master .

Service discovery aims to address the problem of service information explosion and find and locate services that meet the needs of service requesters. Because service description information is mostly composed of short text with noise and has the characteristics of semantic sparseness, it is difficult to extract the implied context information of service description. This paper proposes a service discovery framework based on Knowledge graphs and neural Similarity Network (KSN). Which uses knowledge graphs to connect entities to obtain rich external information to enhance the semantic information of service descriptions. convolutional neural network and similarity network is utilized to extract context information. Through extensive experiments on a real service data set show that KSN is superior to existing web service discovery methods in terms of multiple evaluation metrics.

Delay systems may be encountered stochastic perturbation, and the stability conditions of such systems are different from delay systems and stochastic systems. This paper considers the problems of robust stabilization for 2-D state-delayed system in Roesser model (RM) with stochastic perturbation for the first time. Delay-dependent sufficient stability conditions are derived in terms of linear matrix inequalities (LMIs).A state feedback controller is designed to ensure robust mean-square stability of the closed-loop system. An example is provided to demonstrate the effectiveness of the proposed approach.

This paper is concerned with stability analysis for two-dimensional (2-D) discrete systems described by the Roesser models (RM) with varying delay in the state. By utilizing the delay partitioning idea and the Lyapunov method, a new stability criteria is proposed in terms of linear matrix inequalities (LMIs). This result is delay-dependent and also dependent on the partitioning size. A numerical example is given to demonstrate the effectiveness and the benefits of the presented methods.

The static output feedback control problem is investigated in this paper for continuous descriptor systems. New necessary and sufficient conditions are established to ensure the admissibility of the continuous descriptor systems. Then, based on the new necessary and sufficient conditions, sufficient conditions are derived for admissibility of the closed-loop systems. The desired static output feedback controllers can be derived directly via the cone complementarity linearization techniques. Finally, an illustrative example is presented to demonstrate the effectiveness of the proposed approach in this paper.

This paper is concerned with stability analysis for Two-dimensional (2-D) discrete systems with shift delays in the General Models (GM). Delay-dependent sufficient conditions for stability are derived and expressed in terms of linear matrix inequalities (LMIs). The stability theorem concludes stability theorems for 2-D discrete systems by the General Models and the Fornasini-Marchesini local state space models as special cases. A numerical example is given to demonstrate the effectiveness and the benefits of the present results.