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This paper aims to provide a precise tracking control scheme for multi-input multi-output “MIMO” nonlinear systems with unknown input time-delay in industrial process.

The predictive control scheme based on multi-dimensional Taylor network (MTN) model is proposed. First, for the unknown input time-delay, the cross-correlation function is used to identify the input time-delay through just the input and output data. And then, the scheme of predictive control is designed based on the MTN model. It goes as follows: a recursive d-step-ahead MTN predictive model is developed to compensate the influence of time-delay, and the extended Kalman filter (EKF) algorithm is applied for its learning; the multistep predictive objective function is designed, and the optimal controlled output is determined by iterative refinement; and the convergence of MTN predictive model and the stability of closed-loop system are proved.

Simulation results show that the proposed scheme is of desirable generality and capable of performing the tracking control for MIMO nonlinear systems with unknown input time-delay in industrial process effectively, such as the continuous stirred tank reactor (CSTR) process, which provides a considerably improved performance and effectiveness. The proposed scheme promises strong robustness, low complexity and easy implementation.

For the limitations of proposed scheme, the time-invariant time-delay is only considered in time-delay identification and control schemes. And the CSTR process is only introduced to prove that the proposed scheme can adapt to practical industrial scenario.

The originality of the paper is that the proposed MTN control scheme has good tracking performance, which solves the influence of time-delay, coupling and nonlinearity and the real-time performance for MIMO nonlinear systems with unknown input time-delay.

This paper aims to present a multi-source information fusion algorithm based on factor graph for autonomous underwater vehicles (AUVs) navigation and positioning to address the asynchronous and heterogeneous problem of multiple sensors.

The factor graph is formulated by joint probability distribution function (pdf) random variables. All available measurements are processed into an optimal navigation solution by the message passing algorithm in the factor graph model. To further aid high-rate navigation solutions, the equivalent inertial measurement unit (IMU) factor is introduced to replace several consecutive IMU measurements in the factor graph model.

The proposed factor graph was demonstrated both in a simulated and vehicle environment using IMU, Doppler Velocity Log, terrain-aided navigation, magnetic compass pilot and depth meter sensors. Simulation results showed that the proposed factor graph processes all available measurements into the considerably improved navigation performance, computational efficiency and complexity compared with the un-simplified factor graph and the federal Kalman filtering methods. Semi-physical experiment results also verified the robustness and effectiveness.

The proposed factor graph scheme supported a plug and play capability to easily fuse asynchronous heterogeneous measurements information in AUV navigation systems.

This paper aims to provide a novel integrated fault detection method for industrial process monitoring.

A novel integrated fault detection method based on the combination of Mallat (MA) algorithm, weight-elimination (WE) algorithm, conjugate gradient (CG) algorithm and multi-dimensional Taylor network (MTN) dynamic model, namely, MA-WE-CG-MTN, is proposed in this paper. First, MA algorithm is taken as data pre-processing. Second, in virtue of approximation ability and low computation complexity owing to the simple structure of MTN, MTN dynamic models are constructed for each frequency band. Furthermore, the CG algorithm is used to discipline the model parameters and the outputs of MTN model of each frequency band are gained. Third, the authors introduce the WE algorithm to cut down the number of middle layer nodes of MTN, reducing the complexity of the network. Finally, the outputs of MTN model for each frequency band are superimposed to achieve outputs of MTN model, and fault detection is proceeded by the residual error generator based on the difference between the output of MTN model and the actual output.

The novel proposed method is used to perform fault detection for industrial process monitoring effectively, such as the Benchmark Simulation Model 1 wastewater treatment process.

The novel proposed method has generality and provides considerably improved performance and effectiveness, which is used to perform fault detection for industrial process monitoring. The proposed method has good robustness, low complexity and easy implementation.

The fabric self-lubricating liners are the key factors impacting the performances of self-lubricating spherical plain bearings. The purpose of this paper is to improve the friction and wear properties of self-lubricating radial spherical plain bearings by modification of the liners.

The liners of hybrid woven PTFE/Kevlar fabrics were treated respectively by the LaCl₃ and CeO₂ solutions. The tribological properties of self-lubricating spherical plain bearings with treated or untreated liners under continuous swaying conditions were investigated with the bearing tester at the swaying frequency of 2.5 Hz and the swaying angle of ±10°. The film formation and wear mechanisms were analyzed based on the observation of worn surfaces with a scanning electron microscope (SEM) and an energy dispersive spectrometer (EDS).

Results show that the tribological properties of the bearings treated by the LaCl₃ or CeO₂ solution were improved compared with those of the untreated bearings. In particular, the wear resistance of bearings treated by the CeO₂ solution was remarkably improved under higher swaying cycles, but the anti-friction properties and cooling effects of bearings treated by the LaCl₃ solution were better under lower swaying cycles. Through SEM analysis, the reasons were analyzed. The bearings with treated liners only produced slight adhesive and abrasive wear, but the bearings with untreated liners produced more serious adhesive and abrasive wear under higher swaying cycles.

The paper proposed a new pretreatment process for the self-lubricating liners. The investigation on the friction and wear behaviors of the bearings is beneficial for prolonging the service lives of the radial spherical plain bearings.

Digital technologies provide an opportunity for the rail sector to achieve social, economic and environmental sustainability, if implemented correctly. Unlocking the full potential of technology, however, will require significant changes beyond the technological. Physical assets will need to link with digital assets, making best use of data, simulation and modelling. Transformational leadership informed by systems engineering will be necessary to deliver the change process required to innovate across the whole railway life cycle. Each of these digital railway elements – technology, data, simulation, transformational leadership and systems engineering – presents challenges to be overcome. The authors believe that by instilling core values alongside technical expertise, by being open, resilient, responsive, customer-centric and valuing people, the digital railway has the power to transform the sector. It will enable improved railway processes; safer, faster and more reliable trains; better customer experience; cost-effectiveness; and reduced carbon emissions and more. The digital railway will not just realise the current vision but form the foundation for a sustainable railway to meet changing mobility needs well beyond 2050.

Intelligent diagnosis of equipment faults can effectively avoid the shutdown caused by equipment faults and improve the safety of the equipment. At present, the diagnosis of various kinds of bearing fault information, such as the occurrence, location and degree of fault, can be carried out by machine learning and deep learning and realized through the multiclassification method. However, the multiclassification method is not perfect in distinguishing similar fault categories and visual representation of fault information. To improve the above shortcomings, an end-to-end fault multilabel classification model is proposed for bearing fault diagnosis.

In this model, the labels of each bearing are binarized by using the binary relevance method. Then, the integrated convolutional neural network and gated recurrent unit (CNN-GRU) is employed to classify faults. Different from the general CNN networks, the CNN-GRU network adds multiple GRU layers after the convolutional layers and the pool layers.

The Paderborn University bearing dataset is utilized to demonstrate the practicability of the model. The experimental results show that the average accuracy in test set is 99.7%, and the proposed network is better than multilayer perceptron and CNN in fault diagnosis of bearing, and the multilabel classification method is superior to the multiclassification method. Consequently, the model can intuitively classify faults with higher accuracy.

The fault labels of each bearing are labeled according to the failure or not, the fault location, the damage mode and the damage degree, and then the binary value is obtained. The multilabel problem is transformed into a binary classification problem of each fault label by the binary relevance method, and the predicted probability value of each fault label is directly output in the output layer, which visually distinguishes different fault conditions.

The Internet of Things (IoT) has attracted a lot of attention in both industrial and academic fields for recent years. Artificial intelligence (AI) has developed rapidly in recent years as well. AI naturally combines with the Internet of Things in various ways, enabling big data applications, machine learning algorithms, deep learning, knowledge discovery, neural networks and other technologies. The purpose of this paper is to provide state of the art in AI powered IoT and study smart public services in China.

This paper reviewed the articles published on AI powered IoT from 2009 to 2018. Case study as a research method has been chosen.

The AI powered IoT has been found in the areas of smart cities, healthcare, intelligent manufacturing and so on. First, this study summarizes recent research on AI powered IoT systematically; and second, this study identifies key research topics related to the field and real-world applications.

This research is of importance and significance to both industrial and academic fields researchers who need to understand the current and future development of intelligence in IoT. To the best of authors’ knowledge, this is the first study to review the literature on AI powered IoT from 2009 to 2018. This is also the first literature review on AI powered IoT with a case study of smart public service in China.

As the engineering design process becomes increasingly complex, multidisciplinary teams need to work together, integrating diverse expertise across a range of disciplinary models. Where changes arise, these design teams often find it difficult to handle these design changes due to the complexity and interdependencies inherent in engineering systems. This paper aims to develop an innovative approach to clarifying system interdependencies and predicting the design change propagation at the asset level in complex engineering systems based on the digital-twin-driven design structure matrix (DSM).

The paper first defines the digital-twin-driven DSM in terms of elements and interdependencies, where the authors have defined three types of interdependency, namely, geospatial, physical and logical, at the asset level. The digital twin model was then used to generate the large-scale DSMs of complex engineering systems. The cluster analysis was further conducted based on the improved Idicula–Gutierrez–Thebeau algorithm (IGTA-Plus) to decompose such DSMs into modules for the convenience and efficiency of predicting design change propagation. Finally, a design change propagation prediction method based on the digital-twin-driven DSM has been developed by integrating the change prediction method (CPM), a load-capacity model and fuzzy linguistics. A section of an infrastructure mega-project in London was selected as a case study to illustrate and validate the developed approach.

The digital-twin-driven DSM has been formally defined by the spatial algebra and Industry Foundation Classes (IFC) schema. Based on the definitions, an innovative approach has been further developed to (1) automatically generate a digital-twin-driven DSM through the use of IFC files, (2) to decompose these large-scale DSMs into modules through the use of IGTA-Plus and (3) predict the design change propagation by integrating a digital-twin-driven DSM, CPM, a load-capacity model and fuzzy linguistics. From the case study, the results showed that the developed approach can help designers to predict and manage design changes quantitatively and conveniently.

This research contributes to a new perspective of the DSM and digital twin for design change management and can be beneficial to assist designers in making reasonable decisions when changing the designs of complex engineering systems.

The purpose of the study is to examine the crossover effect of leader's role overload on employee's negative affect. More importantly, the stuy will identify the buffering role of self-concordance goal on the relationship between leader's role overload and employee's negative affect.

The study builds the crossover impact of leader's role overload on employee's negative affect as well as the moderating effect of self-concordance goal. By a two-wave and paired data from 51 leaders and 225 employees, the study examines the hypothesis using cross-level analysis.

Results show that leader's role overload tends to reduce negative affect for employees who pursue high-level self-concordance goal and increase negative affect for employees who pursue low-level self-concordance goal.

It is important for employees to get rid of negative affect in the workplace. The study informs managers the benefits of pursuing self-concordance goals in helping employees alleviate the negative effect of leader's role overload.

Findings of the present study can enrich the literature of the crossover process from leader to employee and offer management strategy for enterprises about how to buffer the damaging effect of leader's role overload on employees.

The accounting literature has traditionally focused on firm-level studies to examine the capital market implications of earnings and other accounting variables. We first develop the arguments for studying capital market implications at the aggregate level as well. A central issue is that diversification makes equity investors at least partially and potentially almost completely immune to several firm-level properties of earnings by holding diversified portfolios. Diversification is particularly important when assessing the welfare consequences of random errors in accounting measurement (imperfect accruals) and, to the extent it is independent across firms, of deliberate manipulation (earnings management). Consequently, some firm-level metrics of association, timeliness, value relevance, conservatism and other earnings properties do not map easily into investor welfare. Similarly, earnings-related risk manifests itself to equity investors largely through systematic earnings risk (covariation with aggregate earnings and/or other macroeconomic indicators). We conclude that the design and evaluation of financial reporting must adopt at least in part an aggregate perspective. We then summarize the literature in accounting, economics and finance on aggregate earnings and stock prices. Our review highlights the importance of studying earnings at the aggregate level.