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Nonstationary time series are ubiquitous in almost all natural and engineering systems. Capturing the time-varying signatures from nonstationary time series is still a challenging problem for data mining. Quadratic Time-Frequency Distribution (TFD) provides a powerful tool to analyze these data. However, they suffer from Cross-Term (CT) issues that impair the readability of TFDs. Therefore, to achieve high-resolution and CT-free TFDs, an end-to-end architecture termed Quadratic TF-Net (QTFN) is proposed in this paper. Guided by classic TFD theory, the design of this deep learning architecture is heuristic, which firstly generates various basis functions through data-driven. Thus, more comprehensive TF features can be extracted by these basis functions. Then, to balance the results of various basis functions adaptively, the Efficient Channel Attention (ECA) block is also embedded into QTFN. Moreover, a new structure called Muti-scale Residual Encoder-Decoder (MRED) is also proposed to improve the learning ability of the model by highly integrating the multi-scale learning and encoder-decoder architecture. Finally, although the model is only trained by synthetic signals, both synthetic and real-world signals are tested to validate the generalization capability and superiority of the proposed QTFN.

In this paper, a metal object detection (MOD) for wireless electric vehicle charger (WEVC) employing DD coils is proposed. Conventional single-layer symmetric coils exhibit reduced sensitivity near the coils and blind-zone along their symmetry axis. To address these limitations, we propose a dual-layer MOD coil configuration. And in this configuration, the second coil layer features rectangular coils in the less sensitive regions, and an optimal concave-convex coil design is given. By using the configuration, the proposed design can enhance the sensitivity and overcome the blind-zone challenges. Finally, simulation and experimental results also show the effectiveness and robustness of the proposed design, which can also be used to improve the detection capability in wireless power transmission applications.

ABSTRACT Biodiversity and ecosystem multifunctionality are currently hot topics in ecological research. However, little is known about the role of multitrophic diversity in regulating various ecosystem functions, which limits our ability to predict the impact of biodiversity loss on human well‐being and ecosystem multifunctionality. In this study, multitrophic diversity was divided into three categories: plant, animal, and microbial communities (i.e., plant diversity, rodent diversity, and bacterial and fungal diversity). Also, 15 ecosystem functions were divided into four categories—water conservation, soil fertility, nutrient cycling and transformation, and community production—to evaluate the significance of biotic and abiotic variables in maintaining ecosystem multifunctionality. Results indicated that species diversity at multiple trophic levels had a greater positive impact on ecosystem multifunctionality than species diversity at a single trophic level. Notably, the specific nature of this relationship depended on the niche breadths of plants, indicating that plants played a key role in linking above and belowground trophic levels. Abiotic factors such as altitude and pH directly acted on ecosystem multifunctionality and could explain changes in ecosystem functions. Overall, our study offers valuable insights into the critical role of multitrophic species diversity in preserving ecosystem multifunctionality within alpine grassland communities, as well as strong support for the importance of biodiversity protection.

The rapid loss of global biodiversity affects the creation and maintenance of community biodiversity and ecosystem structure and function. Thus, it is insufficient to focus solely on the effects of biodiversity loss on community biodiversity without also considering other impacts such as community assembly, niches, interspecific relationships, community stability, and biodiversity–ecosystem function. In this study, a 3- and 10-year biodiversity manipulation experiment was conducted in an alpine meadow to examine the effects of the individual plant functional group (PFG) removal on the niches of community dominant species by removal of Gramineae, Cyperaceae, legumes, and other forbs. The results indicated that PFG removal led to variation in community niches. The long-term PFG removal led to a gradual decline in the number of Gramineae and Cyperaceae species in the community. Over time, the niche widths of dominant Gramineae and Cyperaceae species gradually narrowed, and the degree of niche overlapping decreased. The number of positively and negatively associated species tended to decrease and increase, respectively. Reduced species diversity led to significant differences in the niches of the remaining species within the community. Thus, species niche differences, resulting from variation in resource allocation, commonly determined the dynamic construction of species composition within the community.

Climate warming and human disturbance are supposed to have significantly impacted the alpine grasslands. However, it is still unclear how human activity affects the community composition and niche characteristics in response to warming. We conducted a two-factorial experiment in an alpine meadow, and set up four treatments: warming, mowing, warming with mowing, and control. Based on the investigation of community composition and niche characteristics, we evaluated the impacts of soil carbon, nitrogen, and phosphorus on species niche overlap. The results showed that mowing significantly increased species richness of Grass, Sedge, Forbs and the importance value of Sedge compared to warming (P < 0.05). The niche breadth of species (>50 %) was reduced by warming, but increased under mowing. The niche overlap mainly occurred between Grass and Forbs in warming, while it was evenly distributed among species after mowing, which alleviated the negative effects of warming on interspecific competitiveness. Warming increased the number of species pairs with a niche overlap value >0.9 by 24.15 %, while warming with mowing decreased it by 2.7 %. The number of species pairs with niche overlap was significantly correlated with soil total nitrogen and soil available nitrogen (P < 0.05). In particular, the species pairs with highly competitive showed a greater dependency on soil nitrogen. Our work highlights that moderate utilization and soil nitrogen are two crucial factors influencing the response of community structure in alpine meadows to future climate change. The study provides an important reference for predicting and addressing the impact of global climate change on adaptive management and grassland protection.

Abstract The platoon control of connected and automated vehicles is an important topic in transportation research. The characteristics of non‐linearities, external disturbances, and strong coupling are non‐negligible in two‐dimensional motion control. An integrated longitudinal and lateral vehicle dynamics is required. A Gaussian Process‐based Distributed Stochastic Model Predictive Control (GP‐DSMPC) for two‐dimensional motion is proposed. It achieves global longitudinal stability and lateral error suppression. Gaussian process (GP) regression is employed to approximate the unknown model error. For the two‐norm chance constraints, over‐approximating the confidence ellipse to an outer polyhedron is an effective way to reduce the conservativeness and coupling effect in longitudinal and lateral motion. A neighbour‐average target trajectory is designed with an upper‐level optimization for adjustable target coefficients. The sum of the local cost functions is used as a Lyapunov candidate to achieve the global stability of the longitudinal motion. Some conditions on penalty weights and target coefficients among subsystems, and terminal outputs are derived. Simulation results reveal that the proposed method is effective for disturbance attenuation and performs better than distributed non‐linear model predictive control without GP estimation under low‐friction road conditions.

Abstract The rapid loss of global biodiversity can greatly affect the normal functioning of ecosystems. However, how biodiversity losses affect plant community structure and soil nutrients is unclear. We conducted a field experiment to examine the short‐ and long‐term effects of removing plant functional groups (Gramineae, Cyperaceae, legumes, and forbs) on the interrelationships among the species diversity, productivity, community structure, and soil nutrients in an alpine meadow ecosystem at Menyuan County, Qinghai Province. The variations in the species richness, above‐ and belowground biomass of the community gradually decreased over time. Species richness and productivity were positively correlated, and this correlation tended to be increasingly significant over time. Removal of the Cyperaceae, legumes, and other forbs resulted in fewer Gramineae species in the community. Soil total nitrogen, phosphorus, organic matter, and moisture contents increased significantly in the legume removal treatment. The removal of other forbs led to the lowest negative cohesion values, suggesting that this community may have difficulty recovering its previous equilibrium state within a short time. The effects of species removal on the ecosystem were likely influenced by the species structure and composition within the community. Changes in the number of Gramineae species indicated that they were more sensitive and less resistant to plant functional group removal. Legume removal may also indirectly cause distinct community responses through starvation and compensation effects. In summary, species loss at the community level led to extensive species niche shifts, which caused community resource redistribution and significant changes in community structure.

To ensure the reliability of the Wireless Power Transfer (WPT), the simultaneous wireless power and data transmission technology has gained more attention to realize real-time information exchange between the transmitter and receiver sides. In this paper, the shared channel technology and 2FSK modulation method are proposed to accomplish data and power transmission simultaneously. The theoretical analysis of the series-series compensation network is established, and the data modulation method through frequency-shifted modulation is analyzed. In order to reduce the bit error rate of transmitted data, a communication data format containing start and stop bits is proposed in this proposed system. Finally, an experimental prototype is constructed to validate the correctness and effectiveness of the proposed design and approach.

In the research of robot systems, path planning and obstacle avoidance are important research directions, especially in unknown dynamic environments where flexibility and rapid decision makings are required. In this paper, a state attention network (SAN) was developed to extract features to represent the interaction between an intelligent robot and its obstacles. An auxiliary actor discriminator (AAD) was developed to calculate the probability of a collision. Goal-directed and gap-based navigation strategies were proposed to guide robotic exploration. The proposed policy was trained through simulated scenarios and updated by the Soft Actor-Critic (SAC) algorithm. The robot executed the action depending on the AAD output. Heuristic knowledge (HK) was developed to prevent blind exploration of the robot. Compared to other methods, adopting our approach in robot systems can help robots converge towards an optimal action strategy. Furthermore, it enables them to explore paths in unknown environments with fewer moving steps (showing a decrease of 33.9%) and achieve higher average rewards (showning an increase of 29.15%).

Abstract Rock mass condition assessment during tunnel excavation is a critical step for the intelligent control of tunnel boring machine (TBM). To address this and achieve automatic detection, a visual assessment system is installed to the TBM and a lager in-situ rock mass image dataset is collected from the water conveyance channel project. The rock mass condition assessment task is transformed into a fine-grain classification task. To fulfill the task, a self-convolution based attention fusion network (SAFN) is designed in this paper. The core of our method is the discovery and fusion of the object attention map within a deep neural network. The network consists of two novel modules, the self-convolution based attention extractor (SAE) module and the self-convolution based attention pooling algorithm (SAP) module. The former is designed to detect the intact rock regions generating the attention map, and the latter is designed to improve the performance of classifier by fusing the attention map that focuses on the intact rock regions. The results of SAFN are evaluated from aspects of interpretability, ablation, accuracy and cross-validation, and it outperforms state-of-the-art models in the rock mass assessment dataset. Furthermore, the dynamic filed test show that our assessment system based on the SAFN model is accurate and efficient for automated classification of rock mass.

Climate warming are supposed to have irreversible effects on the biodiversity and ecosystem functions of grassland. Alpine meadow is the predominant ecosystem in Qinghai-Tibet Plateau, which has attracted much attention because of its sensitivity to global changes. However, there is still no unified understanding of the impact of warming on the relationship between diversity and productivity of alpine meadows. In this study, we conducted multi-gradient warming, used fiberglass open-top chambers (OTCs) to raise the temperature, based on the investigation of community structure and productivity, we evaluated the effect of field simulated warming on the relationship of species diversity-primary productivity. Our result showed that gradient warming reduced species richness, diversity and dominance, inhibited the accumulation of above-ground biomass (AGB) and below-ground biomass (BGB), which was more significant under higher warming (P

The wireless power transmission (WPT) system for electric vehicle charging has the advantages of being safe and reliable, without plugging and unplugging, but the random misalignment between the energy transmitting coil and the energy receiving coil will cause uncertain disturbance to the transmission power and system performance. Therefore, it is necessary to improve the robustness of the wireless power transmission system. This paper designs a TUBE model predictive control (TMPC) to make the system converge to the desired value along the reference trajectory. To reduce the difficulty of solving the reference trajectory, the idea of segmented optimization is adopted. State feedback is presented to further enhance the system’s anti-disturbance ability to increase the system transmission power. Moreover, the compensation for the sampling delay of the controller is proposed to ensure the stability of the control system. Experimental results demonstrate that, compared with traditional predictive control, the proposed TUBE MPC based on state feedback control strategy can increase the transmission power of the system by about 10% under uncertain misalignment.

Abstract Planning a feasible, and safe trajectory is a crucial procedure for autonomous parking which remains to be fully solved. Generally, a trajectory is depicted by a sequence of configurations which include positions, and orientations. However, existing approaches usually design parking trajectories without considering the evolution from one configuration to the next. Hence, these trajectories are practically infeasible in some scenarios, and safety risks may exist. In this paper, a guaranteed collision‐free trajectory planning method is proposed for autonomous parking. A dynamic optimisation problem is built, which includes vehicle kinematics, collision avoidance constraints, and physical restraints. Then the dynamic optimisation problem is discretised into a finite‐dimensional nonlinear programming problem. To describe collision avoidance constraints, the concepts of the virtual protection frame, and the magnification parameter are introduced. It is highlighted that a novel collision detection criterion is raised from the view of geometry. Tests in three common scenarios, and a complex scenario illustrate the effectiveness of the proposed approach. Moreover, the stability of the algorithm is discussed in this paper. Via the proposed approach, an integrated design of planning, and control is possible.

The brain-computer interface is an emerging technology,which can analyze the collected motor imagery signals to control the external auxiliary equipment.A new method based on the noise-assisted fast multivariate empirical mode decomposition (NA-FMEMD) algorithm was proposed for electroencephalogram signal feature extraction and classification.The method outperformed state-of-the-art methods based on noise-assisted multivariate empirical mode decomposition in not only computational efficiency but also classification accuracy.Firstly,all multivariate intrinsic mode functions and trend signals were obtained by the NA-FMEMD.Secondly,the multivariate signals with specific frequency bands were selected by computing their average frequencies.Thirdly,the common spatial pattern was applied to extract features.Finally,the feature vectors were classified using a support vector machine.Simulation data and BCI Competition IV data are used to verify the effectiveness and advantage of the new method,and the method is compared with noise-assisted multivariate empirical mode decomposition.

In order to solve the problems of large volume of battery and mileage anxiety,and accelerate application of electric vehicles,dynamic wireless charging system (DWCS) was presented and studied.In this paper,the basic framework of intelligent DWCS for electric vehicles was introduced,and a detailed review of the research results for DWCS by the researchers in the world was described,including the circuit structure and system modeling,electric vehicle positioning,control strategy of DWCS and the interaction with smart grid,etc.Then the intelligent DWCS of electric vehicle was prospected,mainly including system structure and optimization,the integration with the driverless technology,the integration of technologies and the collaborative optimization strategy with power grid and traffic network.The research direction and development trend in the future were also analyzed in this paper.

Fault diagnosis technology is crucial to ensure the long-term reliability of the industrial process control system. With the increase of industrial data availability, conventional monitoring approaches may not function well under the assumption that the training and the application data come from the same distribution. Following the intuition that industrial data exhibit time dependency and inherent complex characteristics, this paper proposes an adaptive sequential fault diagnosis method based on a tensor factorization layer merged with deep neural network model (TF-DNN). Tensor representation is firstly applied to preserve the number of the raw data entries and their sequential dependence between observations. Multilinear mapping with tensor-to-tensor projection is then to transform the input and hidden tensor to the low-dimensional tensors, which makes the learned representations sparser with the grid-like structures. With the tensor factorization layers, the proposed deep network shares efficient knowledge across the spatiotemporal features of fault data. The outstanding performance of this method is demonstrated and compared to the existing models in the benchmark Tennessee Eastman process and a real industrial methanol plant.

An event-based consensus filtering control scheme for multi-agents with multiple mixing delays is proposed in the paper. Firstly, a piece-wise sampling model with transmission delay defined from sensors to controllers is built, and the effect of time-varying delay on sampling is analyzed. Secondly, a self-triggered scheme is proposed to take into consideration of reducing redundant data and complexity. Thirdly, to fully utilize the available information, by employing an improved generalized free-weighting matrix inequality, a novel Lyapunov-Krasovskii functional approach is proposed to achieve global asymptotically synchronization. At last, an example of multiple unmanned aerial vehicles is offered to show the effectiveness of proposed method.

Grassland resources occupy an important place in the national economy. However, grasslands in alpine regions of China are severely degraded, and the effects of land-use types on species composition, soil nutrients, and ecosystem multifunctionality of degraded alpine grasslands are less certain. To ascertain the effects of main land-use types (no-tillage reseeding and fertilization) on species diversity and ecosystem multifunctionality in alpine grasslands, we investigated the changes in these factors by subjecting specified areas. Using a standardized field survey, we measured the cover, richness, and evenness of plants. At each site, we measured microbial diversity and twelve soil variables critical for maintaining ecosystem multifunctionality in alpine grasslands. The results showed that: (1) the Margalef, Shannon–Wiener, and Simpson indices of plant community, and fungal diversity indices increased significantly in no-tillage reseeding and fertilization; (2) at the phyla level, the relative abundances of Basidiomycota, Olpidiomycota, and Proteobacteria increased significantly in no-tillage reseeding and fertilization, as well as, at the genus level, those of Coniochaeta, Solirubrobacter, Pseudonocardia, and Microvirga; (3) the soil physicochemical properties (except the C:N of soil) increased significantly in no-tillage reseeding and fertilization; (4) correlation analysis showed that species diversity was mainly correlated with soil nutrients in control check, while it was mainly correlated with soil physical properties in no-tillage reseeding and fertilization; (5) linear regression analysis showed significant positive relationships between Margalef, Shannon–Wiener, and Simpson indices of plant community and ecosystem multifunctionality. In addition, ecosystem multifunctionality was positively related to Pielou, Shannon–Wiener, and Simpson indices of the fungal community and it was positively related to Pielou and Shannon–Wiener indices of bacterial community. These observations indicated that no-tillage reseeding and fertilization of degraded alpine grasslands had the potential to improve ecosystem functions in many ways.

In this paper, a layered, undirected-network-structure, optimization approach is proposed to reduce the redundancy in multi-agent information synchronization and improve the computing rate. Based on the traversing binary tree and aperiodic sampling of the complex delayed networks theory, we proposed a network-partitioning method for pinning dynamic networks, with a more simplified, analyzable structure, and all of the traversed nodes are mathematically asymptotically synchronized at the same time. Moreover, a systematic implementable approach is proposed for application. The approach could be separated into two main steps: the first is a method of network partition that reduces the trivial interaction, which does not affect the information traversal, and the second involves applying the time-dependent functional approach of Lyapunov to give global exponential conditions, under the criteria for the synchronization of multiple agents, with a lower conservatism of the decision variables compared to some other results, so that the information available could fully benefit from the actual discrete-time communication pattern. Both mathematical proofs and numerical example evidence are presented to demonstrate the effectiveness of the implemented approach. This class contains a number of practically interesting systems, for instance, unmanned aerial vehicle (UAV) formation systems or the ground-air coordinated unmanned aerial system.

Abstract Posture adjustment of open-type hard rock tunnel boring machine (TBM) can be achieved by properly adjusting the hydraulic pressure of gripper cylinder and torque cylinders. However, the time-varying inhomogeneous load acting on tunneling face of TBM and complex stratum working condition can cause the trajectory deviation. In this paper, the position and posture rectification kinematics and dynamics models of TBM have been established in order to track the trajectory. Moreover, there are uncertain parameters and uncertain loads from complex working conditions in the dynamic model. An indirect adaptive robust control strategy is applied to achieve precise position and posture trajectory tracking control. Simulation results show when the position deviation only occurs in Y-axis and the current orientation is parallel with the designed axis, the deviation can be corrected by controlling the pressure of gripper cylinder and the actual trajectory meets the designed axis when TBM is pushed forward 0.14 m in X-axis. If the deviation only occurs in Z-axis, then the deviation can be corrected by controlling torque cylinders. If the position deviation occurs both in Y-axis and Z-axis at the same time, the pressure of gripper cylinder and torque cylinders should be controlled at the same time to rectify the deviation. Simulation results are shown to illustrate the effectiveness and robustness of the proposed controller. This research proposes an indirect adaptive robust controller that can track the planned tracking trajectory smoothly and rapidly.

A novel detector based on improved variational mode decomposition (VMD) is proposed to detect the nonlinearity-induced oscillations. Despite its high adaptivity and frequency resolution, the effectiveness of VMD highly depends on parameters, including mode number K, initial center frequency ωinit, and the penalty coefficient α. To tackle this problem, an improved VMD is proposed, which involves: 1) the spectrum of phase-rectified signal averaging (PRSA) to determine optimal K, ωinit and 2) the summation of permutation entropy (SPE) to optimize α, respectively. The presence of nonlinearity can be monitored by investigating the relationships among different frequencies of the process variable (PV) in the control loops. In addition, the oscillation detector based on the improved VMD is capable of distinguishing multiple oscillations, even when both nonlinear and linear oscillations from different sources occur. The proposed method is completely adaptive and data driven, which acts without a priori knowledge. The validity of the raised approach is verified by a set of simulations as well as industrial applications.

Resilient manufacturing is a vision in the Industry 5.0 blueprint for satisfying sustainable development goals under pandemics or the rising individualized product needs. A resilient manufacturing strategy based on the Industrial Internet of Things (IIoT) networks plays an essential role in facilitating production and supply chain recovery. IIoT contains confidential data and private information, and many security issues arise through vulnerabilities in the infrastructure. The traditional centralized IIoT framework is not only of high cost for system configuration but also vulnerable to cyber-attacks and single-point failure, which is not suitable for achieving the resilient manufacturing vision in Industry 5.0. Recently, researchers are seeking a secure solution of middleware based on blockchain technology integration for decentralized IIoT, which can effectively protect the consistency, integrity, and availability of IIoT data by utilizing the auditing and tamper-proof features of the blockchain. This paper presented a review of secure blockchain middleware for decentralized IIoT towards Industry 5.0. Firstly, the security issues of conventional IIoT solutions and the advantages of blockchain middleware are analyzed. Secondly, an architecture of secure blockchain middleware for decentralized IIoT is proposed. Finally, enabling technologies, challenges, and future directions are reviewed. The innovation of this paper is to study and discuss the distributed blockchain middleware, investigating its ability to eliminate the risk of a single point of failure via a distributed feature in the context of resilient manufacturing in Industry 5.0 and to solve the security issues from traditional centralized IIoT. Also, the four-layer architecture of blockchain middleware presented based on the IIoT application framework is a novel aspect of this review. It is expected that the paper lays a solid foundation for making IIoT blockchain middleware a new venue for Industry 5.0 research.

Pedicularis kansuensis is an indicator species of grassland degradation. Its population expansion dramatically impacts the production and service function of the grassland ecosystem, but the effects and mechanisms of the expansion are still unclear. In order to understand the ecological effects of P. kansuensis, three P. kansuensis patches of different densities were selected in an alpine grassland, and species diversity indexes, biomasses, soil physicochemical properties, and the mechanism among them were analyzed. The results showed that P. kansuensis expansion increased the richness index, the Shannon–Wiener index significantly, and the aboveground biomass ratio (ABR) of the Weed group (p < 0.05), but reduced the total biomass of the community and the ABR of the Gramineae and Cyperaceae decreased insignificantly (p > 0.05); soil moisture, soil AOC, and NO3−·N decreased significantly (p < 0.05), while soil pH and total soil nutrients did not change significantly, and available phosphorus (AP) decreased at first and then increased (p < 0.05). The structural equation model (SEM) showed that P. kansuensis expansion had a significant positive effect on the community richness index, and a significant negative effect followed on the soil AOC from the increase of the index; the increase of pH had a significant negative effect on the soil AOC, NO3−·N, and AP. It indicated that P. kansuensis expansion resulted in the increase of species richness, the ABR of the Weed group, and the community’s water demand, which promoted the over-utilization of soil available nutrients in turn, and finally caused the decline of soil quality. This study elucidated a possible mechanism of poisonous weeds expansion, and provided a scientific and theoretical basis for grassland management.

Biodiversity and ecosystem functions and their relationship with environmental response constitute a major topic of ecological research. However, the changes in and impact mechanisms of multi-dimensional biodiversity and ecosystem functions in continuously changing environmental gradients and anthropogenic activities remain poorly understood. Here, we analyze the effects of multi-gradient warming and grazing on relationships between the biodiversity of plant and soil microbial with productivity/community stability through a field experiment simulating multi-gradient warming and grazing in alpine grasslands on the Tibetan Plateau. We show the following results: (i) Plant biodiversity, soil microbial diversity and community productivity in alpine grasslands show fluctuating trends with temperature gradients, and a temperature increase below approximately 1 °C is beneficial to alpine grasslands; moderate grazing only increases the fungal diversity of the soil surface layer. (ii) The warming shifted plant biomass underground in alpine grasslands to obtain more water in response to the decrease in soil moisture caused by the temperature rise. Community stability was not affected by warming or grazing. (iii) Community stability was not significantly correlated with productivity, and environmental factors, rather than biodiversity, influenced community stability and productivity.

This paper proposes a fault diagnosis method based on the path information of a bounded Petri net model. It relies on the notions of two graphs called path marking graph and modified path marking graph, which characterize different sets of paths and markings consistent with an observed sequence, and a set of unobservable transitions whose firing will enable the sequence. The fault diagnosis method can be applied to a bounded Petri net model with unobservable and acyclic subnets. Moreover, the process of using these two particular graphs to calculate the possibilities of fault behaviors can be taken off-line, which is the most burdensome part for a bounded Petri net model, but also can reduce the memory cost. Finally, this method is verified through a production line example. The main contribution of this paper is to dig out the path information of a bounded Petri net, and to map it into different categories. By analyzing the inherent structure information of Petri net, the presented algorithm can diagnose faults without traversing all the system states.

The multivariate empirical mode decomposition (MEMD) has been pioneered recently for adaptively processing of multichannel data. Despite its high efficiency on time-frequency analysis of nonlinear and nonstationary signals, high computational load and over-decomposition have restricted wider applications of MEMD. To address these challenges, a fast MEMD (FMEMD) algorithm is proposed and featured by the following contributions: 1) A novel concept, pseudo direction-independent multivariate intrinsic mode function (IMIMF) which allows the interchange of sifting and projection operations, is defined for the purpose of developing FMEMD; 2) FMEMD is computationally efficient. Compared with MEMD, the number of time-consuming sifting operations reduces from K p to K for each iteration, where K and p denote the number of projection directions and signal dimension, respectively; 3) FMEMD is consistent with EMD in terms of the dyadic filter bank property; and 4) FMEMD is more effective in working at low sampling rate. Validity of the raised approach is demonstrated on a wide variety of real world applications.

This paper presents a linear mismatched model-based offset-free model-predictive control approach for nonlinear systems (also suits for mismatched linear systems) with both bounded stochastic and deterministic disturbances. By treating the objective function (using disturbance observer model) and the constraints (using mismatched predictive model) individually, offset-free tracking for piece-wise constant references in an expectation manner and constraints contentment has been achieved. A combined state and disturbance-affine feedback control law has been incorporated to achieve least conservativeness. Well-designed invariant set for tracking is used for convergence. An iterative computation procedure has been proposed to find the recursively feasible set, which ensures recursive feasibility. The final optimal control problem has been converted to a semidefinite programming problem that can be efficiently solved by existed solvers. The proposed method has been applied to a nonlinear continuously stirred tank reactor system, and the performance has been compared to several existing approaches.

Aerial image segmentation usually requires a large amount of pixel-level masks in order to achieve quality performance. Obtaining these annotations can be both costly and time-consuming, limiting the amount of data available for training. In this paper, we present an approach for learning to segment aerial building footprints in the absence of fully annotated label masks. Instead, we exploit cheap and efficient scribble annotations to supervise deep convolutional neural networks for segmentation. Our proposed model is based on an adversarial architecture that jointly trains two networks to produce building footprint segmentations that resemble synthetic label masks. We present competitive segmentation results on the Massachusetts Buildings data set by using only scribble supervision signals. Further experiments show that our method effectively alleviates building instance separation issue and displays strong robustness towards different scribble instance levels. We believe our cost-effective approach has the potential to be adapted for other aerial image interpretation tasks.

Optimal fast charging is an important factor in battery management systems (BMS). Traditional charging strategies for lithium-ion batteries, such as the constant current–constant voltage (CC–CV) pattern, do not take capacity aging mechanisms into account, which are not only disadvantageous in the life-time usage of the batteries, but also unsafe. In this paper, we employ the dynamic optimization (DP) method to achieve the optimal charging current curve for a lithium-ion battery by introducing limits on the intercalation-induced stresses and the solid–liquid interface film growth based on an electrochemical–thermal model. Furthermore, the backstepping technique is utilized to control the temperature to avoid overheating. This paper concentrates on solving the issue of minimizing charging time in a given target State of Charge (SoC), while limiting the capacity loss caused by intercalation-induced stresses and film formation. The results indicate that the proposed optimal charging method in this paper offers a good compromise between the charging time and battery aging.