Maximum margin clustering (MMC) is a typical clustering method which aims to maximize the margin between different clusters. However, in practical applications, a data object may be represented by multiple feature sets (views), with each feature set representing different information of the underlying data. The traditional MMC methods can handle only the data from a single view and are unable to utilize the multi-view data to enhance the clustering model. In multi-view clustering, there are two basic principles: the consensus principle and complementarity principle. Most multi-view clustering methods implement mainly the consensus principle, while the complementarity principle has not been sufficiently taken into account. Distinguished from the existing methods,$\text {M}^{3}\text {CP}$introduces the idea of privileged information learning into multi-view clustering and implements both of the consensus principle and complementarity principle. Based on privileged information learning,$\text {M}^{3}\text {CP}$embodies the complementarity principle by considering one view as the main learning information and the other views as the privileged information, so that multiple views can provide information to complement each other. The derived learning problem is then solved by applying the constrained concave–convex procedure and cutting plane techniques. By employing these techniques, the computational time of$\text {M}^{3}\text {CP}$is able to scale linearly with respect to the dataset size. Numerical experiments on real-life multi-view datasets demonstrate that$\text {M}^{3}\text {CP}$is able to achieve better clustering accuracy and meanwhile needs less computational time, compared to state-of-the-art multi-view clustering methods.

Ordinal regression (OR) aims to solve multiclass classification problems with ordinal classes. Support vector OR (SVOR) is a typical OR algorithm and has been extensively used in OR problems. In this article, based on the characteristics of OR problems, we propose a novel pinball loss function and present an SVOR method with pinball loss (pin-SVOR). Pin-SVOR is fundamentally different from traditional SVOR with hinge loss. Traditional SVOR employs the hinge loss function, and the classifier is determined by only a few data points near the class boundary, called support vectors, which may lead to a noise sensitive and re-sampling unstable classifier. Distinctively, pin-SVOR employs the pinball loss function. It attaches an extra penalty to correctly classified data that lies inside the class, such that all the training data is involved in deciding the classifier. The data near the middle of each class has a small penalty, and that near the class boundary has a large penalty. Thus, the training data tend to lie near the middle of each class instead of on the class boundary, which leads to scatter minimization in the middle of each class and noise insensitivity. The experimental results show that pin-SVOR has better classification performance than state-of-the-art OR methods.

In this article, to exclusively suppress unuseful underwater noise feature and effectively avoid overenhancement, simultaneously, an underwater attentional generative adversarial network (UAGAN) is innovatively established. Main contributions are as follows: combining dense concatenation with global maximum and average pooling techniques, a cascade dense-channel attention (CDCA) module is devised to adaptively distinguish noise feature and recalibrate channel weight, simultaneously, such that low-contribution feature map can be effectively suppressed; to sufficiently capture long-range dependence between any two nonlocal spatial patches, the position attention (PA) module is created such that the deviation among independent patches can be sufficiently eliminated, thereby avoiding overenhancement; and in conjunction with CDCA and PA modules, the entire UAGAN framework is eventually developed in an end-to-end manner. Comprehensive experiments conducted on underwater image enhancement benchmark (UIEB) and underwater robot professional contest (URPC) datasets demonstrate remarkable effectiveness and superiority of the proposed UAGAN scheme by comparing with typical underwater image enhancement approaches including unsupervised color correction method, image blurriness and light absorption, underwater dark channel prior, underwater generative adversarial network, underwater convolutional neural network, and WaterNet in terms of peak signal-to-noise ratio, underwater color image quality evaluation, underwater image quality measures, etc.

Trusted requirements affect the trusted attributes of software, and have an important impact on whether trusted software can meet the trusted requirements. However, despite the continuous development of society and technology, the work of obtaining and analyzing trusted requirements has not become easy, has become more and more difficult. In the process of obtaining trusted requirements, a series of problems, such as low efficiency and inaccurate acquisition of requirements, serious software quality problems, budget overruns and delivery delays, have become increasingly prominent. In view of the above problems, this paper integrates the concept of meta-requirement into trusted requirement. Based on the respective characteristics of meta-requirement and trusted requirement, this paper puts forward the concept of trusted meta-requirement, and introduces the basic elements and characteristics. Then the trusted meta-requirements and some rules involved in it are formalized by the combination of first-order logic and set theory in order to improve the accuracy of the description and analysis of trusted requirements.

Increasing complexity and decreasing time-to-market require changes in the traditional way of building automation systems. The paper describes a novel approach to automatically generate the Human Machine Interface (HMI) screens for component-based manufacturing cells based on their corresponding virtual models. Manufacturing cells are first prototyped and commissioned within a virtual engineering environment to validate and optimise the control behaviour. A framework for reusing the embedded control information in the virtual models to automatically generate the HMI screens is proposed. Finally, for proof of concept, the proposed solution is implemented and tested on a test rig.

Modular approaches and virtual commissioning are regarded as two key enablers to reduce the effort, cost and time of automation system engineering. This contribution reviews existing researches on the virtual commissioning of modular automation systems. The research work carried out by the authors, which provides a new engineering toolset for the virtual commissioning component-based modular automation system engineering, is reported. Combing the industrial needs related to our research works and the limitations of existing virtual commissioning approaches, an approach to the direct deployment of control systems of component-based automation systems based on virtual commissioning is presented. A use case of the proposed solution using related engineering tools developed by the authors is also provided.