Over the past twenty years, numerous research outcomes have been published, related to the design and implementation of soft sensors. In modern industrial processes, various types of soft sensors are used, which play essential roles in process monitoring, control and optimization. Emerging new theories, advanced techniques and the information infrastructure have enabled the elevation of the performance of soft sensing. However, novel opportunities are accompanied by novel challenges. This work is motivated by these observations and aims to present a comprehensive review of the developments since the start of the millennium. While a few books and review articles are published on the related topics, more focus on the most up-to-the-date advancement is put in this work, from the perspective of systems and control.

Retinal vessel image is an important biological information that can be used for personal identification in the social security domain, and for disease diagnosis in the medical domain. While automatic vessel image segmentation is essential, it is also a challenging task because the retinal vessels have complex topological structures, and the retinal vessels vary in size and shape. In recent years, image segmentation based on the deep learning technique has become a mainstream method. Unfortunately, the existing methods cannot make the best use of the global information, and the model complexity is high. In this paper, a convolutional neural network integrated with the attention mechanism is proposed. The overall network structure consists of a basic U-Net and an attention module, and the latter is used to capture global information and to enhance features by placing it in the process of feature fusion. Experiment results on five public datasets show that the proposed scheme outperforms other existing mainstream approaches, and most of the performance indicators are in the leading positions. More importantly, the proposed method has a significant reduction in the number of parameters.

Industrial cyber-physical systems (ICPSs) are the backbones of Industry 4.0 and as such, have become a core transdisciplinary area of research, both in industry and academia. New challenges brought about by the growing scale and complexity of systems, insufficient information exchange, and the exploitation of knowledge available have started threatening the overall system safety and stability. This work is motivated by these challenges and the strategic and practical demands of developing ICPSs for safety critical systems such as the intelligent factory and the smart grid. It investigates the current status of research in ICPS monitoring and control, and reviews the recent advances in monitoring, fault diagnosis, and control approaches based on data-driven realization, which can take full advantage of the abundant data available from past observations and those collected online in realtime. The practical requirements in the typical ICPS applications are summarized as the major issues to be addressed for the monitoring and the safety control tasks. The key challenges and the research directions are proposed as references to the future work.

Industrial cyberphysical systems (ICPSs) are the cornerstone research subject in the era of Industry 4.0 [1]. The study of ICPSs has, therefore, become a worldwide research focus [2]-[4]. ICPSs integrate physical entities with cyber networks to build systems that can work more harmoniously, benefiting from integrated design and system-wide optimization [5]. The safety and performance of industrial systems can be improved by developing specific information infrastructure, monitoring, and control approaches aimed at maintaining controllability under external disturbances and unexpected faults [6]. Based on these observations, the design and deployment of ICPSs have both theoretical and practical significance.

Process safety, system reliability, and product quality are becoming increasingly essential in the modern industry. As a result, prognosis and fault diagnosis of the complex systems have gained a substantial amount of research attention. In order to evaluate the influence of the detected faults to systems' behavior, there is a pressing need to design prognosis and diagnosis systems oriented to the key-performance-indicators (KPIs). Dedicated to this requirement, we have recently developed a MATLAB toolbox data based key-performance-indicator oriented fault detection toolbox (DB-KIT), which realizes a series of effective algorithms, to provide a systematic and illustrative material to the peer researchers. This paper investigates the recent advances in the multivariate statistical analysis based approaches. Formulations based on the optimization problems are proposed to better clarify the ideas behind different solutions and to study them in a unified data-driven framework. Theoretical fundamentals of some selected algorithms in the DB-KIT are elaborated. Moreover, new evaluation results on dataset defects are presented, which compare the algorithms' robustness and demonstrate the power of DB-KIT. The open-source code and the demonstrative simulations can be regarded as baseline and resources for innovation research, comparative studies, and educational purposes.