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Gas-phase polyethylene (PE) processes are among the most important methods for PE production. A deeper understanding of the process characteristics and dynamic behavior, such as properties of PE and reactor stability, holds substantial interest for both academic researchers and industries. In this study, both steady-state and dynamic models for a gas-phase polyethylene process are established as a simulation platform, which can be used to study a variety of operation tasks for commercial solution polyethylene processes, such as new product development, process control and real-time optimization. The copolymerization kinetic parameters are fitted by industrial data. Additionally, a multi-reactor series model is developed to characterize the temperature distribution within the fluidized bed reactor. The accuracy in predicting melt index and density of the polymer, and the dynamic behavior of the developed models are verified by real plant data. Moreover, the dynamic simulation platform is applied to compare four practical control schemes for reactor temperature by a series of simulation experiments, since temperature control is important in industrial production. The results reveal that all four schemes effectively track the setpoint temperature. However, only the demineralized water temperature cascade control demonstrates excellent performance in handling disturbances from both the recycle gas subsystem and the heat exchange subsystem.

The causality between different variables can reveal the flows of material, energy, and information in the process system. It is beneficial to reflect the relationship between quality variables and process variables. In this study, a concurrent quality and process monitoring method is proposed with intrinsic causality analytics. The proposed method explores the causality between different variables using transfer entropy. Then, the directly related variables and their corresponding time lags are combined to extract convolutional features, which are used to generate feature matrices for process and quality variables. In this way, the quality related information is extracted from the process variables which are directly related to the quality variables. After that, monitoring models are established for each pair of feature matrices, and the monitoring results are integrated to provide a final monitoring result. Since the process disturbances usually smear to directly related variables, the fault signature can be amplified to improve the detection sensitivity when the directly related variables are combined. Finally, the operation status of the process system is identified through the designed monitoring policy, which combines the decisions of different statistics. It is noted that the proposed strategy can be readily generalized to many other existing quality related monitoring methods. Experiments on a real industrial condenser show that the proposed method can distinguish the quality related faults from the process related faults in the condenser. Besides, it has better detection sensitivity than some commonly used quality related monitoring methods.

Although reinforcement learning (RL) is effective in stabilizing systems, it faces many challenges in solving the tracking problem of unknown continuous-time systems. One of the major challenges is that RL-based control can hardly satisfy both the transient and steady-state performance requirements for the tracking problem simultaneously. In this study, instead of implementing an RL controller, the RL agent acts as a planner in the closed-loop reference model. The RL-based planner concentrates on tracking performance optimization by the constrained integral RL algorithm. Meanwhile, the system is controlled by the proposed library-based adaptive controller, which contains a library of candidate functions for modeling the unknown system dynamics. A natural gradient-like adaptive law is developed to update the controller, ensuring asymptotic tracking and promoting sparsity in the controller parameter. Compared with the conventional RL-based control, the proposed framework can eliminate the tracking error while avoiding the high-frequency oscillation and peaking phenomenon. Furthermore, we theoretically demonstrate that our approach can improve the transient performance in terms of the ${\mathcal{ L}}_{2}$ norm of the tracking error and explicitly limit the ${\mathcal{ L}}_{\infty }$ norm of the peaking value through the Lyapunov analysis. Simulations are presented to support the theoretical findings at the end of the paper. Note to Practitioners—Practical control design is often interested in tracking non-zero reference trajectories. However, the non-optimal transient response, such as oscillation and overshoot, is the major obstacle to the development of a high-performance tracking control system. The proposed method addresses this issue by designing a constrained RL-based CRM to ensure the optimal transient performance of the system. The primary advantage is that the maximum peaking value can be conveniently tuned as a hyperparameter by users, which is extremely useful in practice. Furthermore, the proposed library-based adaptive controller can handle unknown system dynamics, where the governing equations of the dynamics can be determined through engineering experience.

Process monitoring provides a guarantee for the efficient and safe operation of industrial equipment. However, traditional methods rarely consider the dependency relationship between inputs and outputs in industrial control systems, which can lead to misalignment of process state identification. This study solves the problem based on the following recognitions: 1) the inputs of control systems can drive the changes in the output states, termed input–output directed dependency; 2) both the inputs and the process inertia are related to the dependency, showing the diversity of dependency relationship; and 3) the dependency can be further disentangled into different subspaces, revealing different types of process variations for fine-grained monitoring. Thereupon, a driving-disentangled dynamic mode decomposition with control method is proposed for control systems input–output directed dependency representation learning and monitoring. The process variables are divided into inputs and outputs. The input–output directed dependency is identified, revealing the response of outputs to inputs during adjustment of the process states, which is quite different from common multivariate correlations. A linearly independent vector SelecTion strategy is designed to construct a transformation matrix, which can further disentangle the input–output dependency into external-driving and self-driving subspaces. Three monitoring statistics are developed for process monitoring. The effectiveness is verified through a gas turbine thermal power experimental rig.

Here, we describe a strategy for the copper-catalyzed asymmetric heteroarylation of yne-thiophene carbonates with indoles viaremote substitution. The key to the success of this strategy lies in the design of the alkynyl group at the ortho-position of the heterocycle thiophene, enabling the formation of a triarylmethane moiety viavery remote substitution. Thus, the concept of remote copper-catalyzed asymmetric transformation extends not only to yne-allylic esters but also to yne-aryl esters. The reaction readily provides a diverse array of chiral triarylmethanes with high efficiency, enantioselectivity, and excellent functional group compatibility. Moreover, facile follow-up transformations underscore their potential utility in the synthesis of various enantioenriched building blocks. Preliminary mechanistic studies support the plausibility of the remote substitution pathway.

Existing cross-domain few-shot learning (FSL) methods for hyperspectral image (HSI) classification have garnered widespread attention due to their excellent performance in recognizing novel classes. To mitigate domain shift, researchers focus on designing sophisticated domain adaptation (DA) modules to directly apply biased metaknowledge in the target domain (TD). However, this paradigm proves somewhat inadequate in the face of significant differences in distribution. To cope with this dilemma, we adopted a new mindset of treating metaknowledge extraction and debiasing from the source domain (SD) as a synergistic process and proposed a cross-domain FSL framework based on decoupled knowledge distillation for HSI classification (HSIC). In general, to efficiently acquire and utilize unbiased metaknowledge, this framework centralizes on a knowledge distillation (KD) strategy. Through the effective information transfer process, the extraction and debiasing of metaknowledge were integrated into a comprehensive and productive process. Simultaneously, to release the constraints imposed by the coupled logits in the KD process on the knowledge interaction, the decoupled logit interaction (DLI) module is employed in the framework. This module decouples the traditional KD into two controllable components, making a more balanced and comprehensive interaction of task-related knowledge and data-intrinsic knowledge between models. Moreover, to facilitate the extraction of critical discriminative metaknowledge from the abundant redundant information in HSI, the discriminative information refinement (DIR) module is designed to develop distinctive features for similar bands. Extensive experiments on three public HSI datasets exhibited the superior performance of the proposed cross-domain few-shot learning method based on decoupled knowledge distillation for HSIC (DKD-FSL) method in comparison with seven state-of-the-art approaches.

Generalized zero-shot diagnosis (GZSD) for industrial processes has gained increasing attention as it aims to diagnose both seen and unseen faults based on fault attributes. Nevertheless, the lack of unseen fault data for training poses a critical domain shift problem (DSP), where a biased model prediction toward seen faults is caused by overfitting available seen fault data so that unseen faults tend to be falsely identified as seen faults. Therefore, we propose a Fault Attribute-driven bIas calibRation (FAIR) model to address the model bias toward seen faults, which consists of a bias calibration module and a generation module based on attribute feature recombination. The proposed bias calibration module is the first gating mechanism designed in the attribute space to differentiate between seen and unseen faults. Different from existing methods that construct the boundary for both faults in the feature space, we first propose to employ attributes of unseen faults as substitutes for unavailable training samples to construct the classification boundary in the attribute space, thereby mitigating the model bias caused by the lack of unseen fault features. To further alleviate the DSP, we propose a generation module to generate samples for unseen faults. In particular, to improve the quality of generated samples, the generation module leverages fault attribute features extracted from seen faults and recombines them under the guidance of fault attributes. We conduct GZSD experiments on a real hydraulic system and the Tennessee-Eastman process (TEP) benchmark dataset. In comparison with the SOTA methods, the proposed method achieves an average improvement of 11.84% in terms of the harmonic mean of the accuracy of seen and unseen faults on the TEP, and 6.22% on the real hydraulic system.

Three-dimensional reconstruction utilizing few-view satellite images can effectively reduce the cost of resources. However, methods that rely on dense stereo matching suffer severe performance degradation when confronted with differences of intersection angles and illumination in such non-standard stereo images, which are limited by the matching mechanism. Neural radiance fields (NeRFs) get rid of the matching restriction by utilizing the rendering pipeline, which has made significant progress in the synthesizing novel views and 3-D reconstruction. Nevertheless, when the available images are few-view and multidate, insufficient geometric constraints and illumination variations present great challenges in generating accurate 3-D models. In this article, we proposed a 3-D reconstruction framework called few-view multidate (FVMD)-implicit surface representation (ISRe), which is based on the ISRe of NeRF, for generating complete watertight mesh and digital surface model (DSM) from FVMD satellite images. We introduce the positional encoding with adaptive frequency module to extract finer geometric information from few-view images. Then, the solar information is incorporated into the rendering process, allowing the network to distinguish color changes arising from illumination differences in multidate images. Additionally, we employ some tricks like coordinate system optimization and network restructuring for enhancing network training. Advantages of FVMD-ISRe are demonstrated through both qualitative and quantitative experiments conducted on the US3D dataset. The results highlight our framework’s capacity to reconstruct accurate 3-D geometry, overcome the performance of traditional methods based on stereo matching and other NeRFs in various evaluation metrics. Code and data are available at https://github.com/HEU-super-generalized-remote-sensing/FVMD-ISRe.

The generalized zero-shot diagnosis (GZSD) has attracted rising attention from researchers, but faces a major bottleneck: the domain shift problem (DSP), which causes most unseen faults to be misclassified as seen. In this study, we propose a novel method termed semisupervised hybrid triplet network (SSHTN) with high resistance toward DSP to achieve GZSD by first introducing auxiliary set. To leverage the unlabeled data of auxiliary set, which is different from known faults (both seen and unseen), SSHTN defines relative boundaries of seen and unseen faults in the similarity space and comprises two branches: the data–data branch (DDB) and the data–semantic branch (DSB). For DDB, it constructs homogeneous and heterogeneous data pairs to learn data–data similarity. With the support of auxiliary set, the semisupervised similarity learning mechanism is constructed. Specifically, DDB analyzes differences between data pairs mixed by seen and auxiliary fault data to narrow down the decision boundaries of seen faults. Meanwhile, similarities between data pairs formed by disturbing auxiliary set data are learned to improve the generalization of DDB. For DSB, it learns data–semantic similarity by matching fault data with corresponding descriptions and extends SSHTN discriminative ability toward unseen faults. The SSHTN obtained from the comprehensive learning of similarity by the two branches could determine the boundaries of seen and unseen faults more accurately, thereby alleviating the effect of DSP. Experiments are implemented on the Tennessee Eastman process and a real thermal power plant process, and results show the superior performance of SSHTN over state-of-the-art ZSD methods.

Control systems regulate industrial processes by processing input signals to generate corresponding output signals. The monitoring of control systems ensures process safety. For control systems, we have the following recognition: the future outputs are determined by the output states and input excitations at the past moments, which have different interpretability capacities. It is a significant challenge to decouple the intricate relationship between inputs and outputs to provide comprehensive and interpretable monitoring results for control systems. In this paper, we propose a synergetic decomposition variational information bottleneck (SDVIB) approach to solve this problem from the perspective of information theory. Firstly, a compressed representation strategy is proposed to extract interpretable features from the output and input separately by employing the information bottleneck principle, which can provide initial state information and excitation information, respectively. Secondly, a reconstruction model is established within the framework of variational autoencoder (VAE), which can decouple the remaining interpretable-unrelated features to describe the cross-correlation separately among the input and output variables. In this way, the relationships between input and output variables are decoupled into multiple subspaces with specific physical meanings, which can be described by the constructed monitoring statistics. The effectiveness of the proposed method is validated through a well-known benchmark process.

Alarm flood classification and alarm prediction are significant ways to assist the on-site operators to manage alarm floods and maintain process safety. The two tasks are interdependent considering the decisive role of different alarm floods on the arising alarms. To give comprehensive consideration of both, this work proposed a hierarchical strategy for alarm flood classification and alarm prediction leveraging the structure feature of alarm floods. The structure feature aims at revealing the sparse causal dependencies among alarm variables. It is achieved by a deep learning model under the guidance of a designed objective function that probabilistically parametrizes causal dependencies with sparsity constraint. Due to its interpretable physical meaning, desirable robustness and discriminate properties are achieved, allowing a win-win situation for both tasks. Based on the structure features, the hierarchical strategy is given, where an overall classifier is built while prediction models are trained for each category. The classifier trained by structure features is predisposed to generate satisfactory early classification results, enabling timely prediction. For the prediction, the structure features are also used to incorporate temporal features to achieve better performance. Experimental results illustrate the interpretability of structure features and show the feasibility of the proposed hierarchical strategy. Note to Practitioners—During alarm floods, the alarm patterns are different than usual and the generic prediction model may fail. The focus of this study is to achieve a win-win situation for both alarm flood classification and prediction, thereby providing comprehensive information required for handling alarm floods. Considering the arising alarm is strongly affected by the type of current alarm flood, a hierarchical alarm flood classification and alarm prediction strategy is given. It exploits the essential characteristic of alarm interactions in alarm floods to generate robust and early classification results, allowing timely predictions by category. In this way, the performance of both tasks can be guaranteed. The proposed method requires labeled historical alarm flood data.

Zero-shot fault diagnosis can identify unseen faults by predicting attributes. However, existing methods ignore the multigrained characteristics of attributes, namely, the varying levels of detail in describing fault categories. We recognize the following considerations for the first time: attributes show typical multigrained characteristics, which could be expressed in a coarse-to-fine-grained hierarchical structure; multigrained attributes play different roles in fault diagnosis, where coarse-grained attributes indicate the rough range of faults, while fine-grained attributes facilitate the precise identification of fault types. In this article, a fuzzy hierarchical zero-shot learning method is proposed to solve these issues. First, the attributes are divided into different layers according to the coarse-to-fine granularity via expert knowledge rather than being treated equally. Then, a knowledge transfer strategy is designed to transfer the knowledge from coarse-grained attributes to fine-grained ones, which can improve attribute prediction accuracy. Finally, a fuzzy inference strategy is developed to distinguish the effect of attributes with different granularity on fault inference. This strategy can identify the faults stepwise in a coarse-to-fine-grained order. The effectiveness of the proposed method is verified by a real thermal power plant process.

Hyperspectral remote sensing images exhibit fine spectral curves, but they are also susceptible to spectral variations caused by factors such as cloud and haze. It is evident that these issues become more pronounced when there is a limited number of labeled samples available. Thus, a full range feature extraction network (FRFENet) based on quality–quantity-balance sample enhancement is proposed for hyperspectral image classification (HSIC). First, the full-range feature extraction method combines local-range, short-range, and long-range spatial–spectral features to address spectral variability and ensure accurate feature extraction, particularly in scenarios with limited labeled samples. Furthermore, the approach of balancing quality and quantity for pseudolabeled samples allows for an increased number of pseudolabels while maintaining their quality, effectively leveraging unlabeled samples. In addition, the utilization of superpixel region homogeneity directly contributes to an expanded training sample set, resulting in improved classification performance of the algorithm. Experiments on three hyperspectral image (HSI) encompass datasets indicate that the FRFENet can obtain better classification performance when compared with the other ten state-of-the-art methods.

Existing hyperspectral cross-domain few-shot learning (FSL) methods focus mainly on elaborating on training strategies or domain alignment algorithms, while paying less attention to the biased metaknowledge introduced by a large amount of source data and the implicit encouragement of learning target domain-specific attributes. In this article, from the perspective of disentangled representation learning, a novel cross-domain FSL method based on feature disentanglement (FDFSL) is proposed for hyperspectral image classification (HSIC). Specifically, to suppress the representation biased toward the source data and enable the model to implicitly focus on the inherent knowledge of the target domain, an orthogonal low-rank feature disentanglement method is employed to acquire desired features of source and target pipelines. Furthermore, to preserve more shared and discriminative information from the heterogeneous data space (i.e., the spectral dimensions of the source and target scenes are typically different), a multiorder spectral interaction block based on central position encoding (MICD) is proposed to fully integrate the respective features into the spectral domain, which allows the model to emphasize informative spectral dimensions in a data-driven manner. Finally, to diversify the feature representation space while preventing the model overfitting to domain alignment task, a self-distillation scheme is developed to facilitate the acquisition of task-relevant feature components. Extensive experiments and analysis on three public HSI datasets suggest the superiority of the proposed method. The code will be available on the website at https://github.com/Qba-heu/FDFSL.

Unsupervised fault detection and diagnosis methods generally have the following shortcomings in their projection vectors: 1) they may not be specially designed to differentiate between normal and abnormal samples; 2) they may remain unchanged for different abnormal conditions; and 3) the key variables for the process anomalies may have not been effectively selected. In this study, a fault detection and diagnosis scheme [sparse distribution dissimilarity analytics (SDDA)] is proposed with a lasso penalty and distribution dissimilarity to solve these issues. The proposed method is formulated through a nonconvex optimization problem with a lasso penalty, which aims to maximize the distribution dissimilarity between different data sets. Then, the nonconvex optimization problem is recast to an iterative convex optimization problem using the minorization–maximization algorithm. After that, the constraint conditions are removed using the Karush–Kuhn–Tucker conditions for further simplification. Finally, the unconstraint optimization problem is solved through the proposed feasible gradient direction method. Based on the obtained sparse projection vectors, a fault detection model with both static deviation and dynamic fluctuation is developed. Since the statistics are designed using distribution dissimilarity, some abnormal conditions with small fault magnitudes can also be accurately detected. Besides, a reconstruction-based contribution (RBC) method is proposed for the statistics, and its diagnosability has been strictly demonstrated in theory. The detection and diagnosis performance of the proposed SDDA method is validated using a simulated process and a real industrial process. Experimental results illustrate the superiority of the proposed method to some commonly used methods.

Change detection is a prominent research direction in the field of remote sensing image processing. However, most current change detection methods focus solely on detecting changes without being able to differentiate the types of changes, such as “appear” or “disappear” of objects. Accurate detection of change types is of great significance in guiding decision-making processes. To address this issue, this article introduces the object fine-grained change detection (OFCD) task and proposes a method based on frequency decoupling interaction (FDINet). Specifically, in order to enhance the model’s ability to detect change types and improve its robustness to temporal information, a temporal exchange framework is designed. Additionally, to better capture spatial–temporal correlation in bi-temporal features, a wavelet interaction module (WIM) is proposed. This module utilizes wavelet transform for frequency decoupling, separating features into different components based on their frequency magnitudes. Then the module applies different interaction methods according to the characteristics of these frequency components. Finally, to aggregate complementary information from different-scale feature maps and enhance the representational capabilities of the extracted features, a feature aggregation and upsampling module (FAUM) is adopted. A series of experiments show the superiority of FDINet over most state-of-the-art methods, achieving good results on three different datasets.

The classification of land cover material based on hyperspectral image (HSI) has important research significance. Owing to the high cost of obtaining labeled samples and insufficient training samples, the research of cross-scene HSI classification (CS-HSIC) is receiving more and more attention. At present, the performance of the feature extraction module of CS-HSIC is relatively poor, and the number of training parameters is usually large. To fill the shortcomings of domain generalization (DG) methods and reduce the number of parameters, we propose a lightweight DG network with an attention-assisted cascaded bottleneck (ACB), and it adopts a lightweight bottleneck and multiattention design. This model is adept at extracting domain invariant information contained in the source domain (SD), and it may be flexibly embedded into other models. The experimental results show that our network has good classification accuracy and DG ability when the number of training samples is a little small. As a feature extraction subnetwork, it can improve the performance of the original model or reduce the required resources. The code will be available at https://github.com/zhulongyu1234/ACB.

The existing methods for hyperspectral image classification (HSIC) mainly focus on the extraction of spectral and spatial features while paying less attention to the interaction of each other. Besides, most of them directly use a parameterized classifier as the final layer of the network. While this design is convenient for end-to-end optimization with the backbone, it overlooks the utilization of the metric space. In this article, a novel hyperspherical structural-aware distillation enhanced spatial–spectral bidirectional interaction network (HSDBIN) is proposed for HSIC. HSDBIN uses a dual-branch design combining the 1-D CNN and transformer to separately learn the detailed spectral correlations and global spatial relationships in parallel. Then, by interacting and aggregating the independent information between two parallel branches, a bidirectional interaction block across branches is designed to explore complementary clues between spectral and spatial pipelines. Finally, to enhance the utilization of metric space and keep compact intraclass relationship, we propose a hyperspherical structural-aware distillation (HSD) to transfer the geometric relationship of hyperspherical space into the metric space of output logits. Extensive experiments and analysis on three public HSI datasets suggest the superiority of the proposed method and verify the effectiveness of the proposed modules.

Unmanned aerial vehicle (UAV) orthoimages and digital surface models (DSMs) can provide valuable insights for semantic segmentation methods in comprehending gully erosion (GE) from diverse perspectives. While the integration of these two modalities has the potential to improve the GE extraction performance, the extent of enhancement primarily depends on the quality of modality-specific features and the synergistic fusion manner employed for integrating features from both modalities. Toward this end, we propose a novel multimodal segmentation method, which is called foreground-driven fusion network (FFNet). Guided by the prototypes of foreground objects (i.e., gullies), the network effectively tackles the challenges from the modality itself and between different modalities, ultimately achieving high-quality GE extraction results. Specifically, a foreground prototype sampling (FPS) module is first devised for precisely sampling foreground prototypes related to gullies from two modalities. Then, a local-global hybrid purification (LHP) module is proposed to effectively mitigate the erroneous activation within each modality at multiple dimensions by leveraging foreground prototypes. Finally, a multimodal foreground synergy (MFS) module is introduced to further activate foreground features and facilitate full complementarity between multimodal foreground features. To validate our network, a comprehensive multimodal dataset for GE extraction is constructed based on UAV orthoimages and DSMs from northeastern China. Furthermore, a public road extraction dataset is employed to evaluate the generalizability of this network. In the experiments conducted on these two datasets, the proposed FFNet exhibits obvious superiority, outperforming the second-best method with an average improvement of 2.55% in terms of intersection over union (IoU) and 2.77% in terms of $F1$ -score. These experimental results not only demonstrate the practicality of FFNet in GE extraction tasks, but also highlight its significant advantage in similar road extraction tasks.

The fault detection capability of traditional data-driven process monitoring methods is highly dependent on the quality of process data. However, affected by measurement noise, harsh operation scenarios and other factors, the process data are inevitably contaminated by uncertainty in real processes. In this article, a vertices packaging-based interval independent component analysis (VP-I2CA) method is proposed to monitor the uncertain non-Gaussian processes. First, a variable bandwidth-kernel density estimation-based measurement error estimation method is developed to describe the uncertainty-contaminated process data in interval form using limited reliable data samples. Then, VP-I2CA is developed to estimate the demixing matrix based on hypermatrices constructed by vertices encoding, which includes all possible combinations between the bounds of interval data by explicitly considering the existence of uncertainty. In order to reduce computational complexity, the idea of data packaging is introduced to represent the hyper-independent components in interval form with a series of values by packaging all possible feature information hidden in uncertain process data. Afterwards, four monitoring statistics are constructed to monitor the systematic and nonsystematic parts of process operation variation. The proposed algorithm is verified in both a six-variable numerical simulation system and a continuous stirred tank reactor system.

Real industrial processes often present coupled static and dynamic characteristics, leading to significant challenges for fault detection and isolation. However, traditional dynamic modeling methods may lead to the coupling problem of statics and dynamics, which provide ambiguous process status descriptions and incorrect fault isolation results. In this work, a novel full decoupling high-order dynamic mode decomposition (FDHODMD) method is developed for fault detection and isolation of dynamic processes. Different from the existing dynamic methods, the proposed FDHODMD can separate the high-order dynamic information from static information. First, the static characteristics are separated by discarding the decomposed features corresponding to smaller singular values. Then, a high-order dynamic model is established to present the temporal relationships between variables. In this way, the effect of static characteristics on dynamics analysis can be eliminated. Accordingly, from both static and dynamic perspectives, multiple statistics are designed to comprehensively detect the anomalies and provide an explicit status identification. In addition to the static fault isolation strategy, a dynamic fault isolation strategy is designed to recognize the fault variables after detecting the deviation, which divides the complex dynamic system into several individual dynamic modes to avoid the interaction of dynamic characteristics. Thereupon, the contributions of different variables to each dynamic mode can be clearly presented to recognize the fault variables. Finally, the validity of the proposed method is illustrated through both a numerical case and a real industrial process. Note to Practitioners—In industrial processes, the static and dynamic characteristics of data are often coupled. This work presents a FDHODMD method, which can be considered the high-order version of DMD, to achieve the decoupling of dynamics and statics while extracting high-order dynamic characteristics. Then the proposed fault detection strategy designs multiple statistics for monitoring the process from both dynamic and static perspectives, which ensures the safe operation of industrial processes. After detecting the anomaly, the proposed fault isolation strategy can recognize the fault variables separately dominating the dynamic and static deviation, which may help engineers locate the fault source.

Frequent switching of operating conditions in industrial processes tends to make the data distribution time varying and variable correlations nonuniform, which brings considerable challenges for explicit representation and monitoring of nonstationary processes. This study addresses the abovementioned problem based on the following recognitions: 1) despite changes in operating conditions, there exist time-invariant process mechanisms, which are nonlinearly coupled with nonstationarity; 2) such a coupling relationship is not uniform but may show diverse modes changing with time, defined as multimodal coupling here; 3) diverse coupling relations can be derived from the superposition of nonstationarity induced by definite driving forces, i.e., changeable operating condition settings, reflecting their intrinsic association under complex changes. Thereupon, a cascaded deep information separation (CDIS) architecture with a compatibility learning algorithm is proposed to extract multimodal decoupled representations. We design an information refinement module (IRM) to capture the basic coupling source (BCS) under the influence of driving forces, where a shortcut connection is incorporated into the nonlinear autoencoding structure. Multiple IRMs can be flexibly cascaded to achieve the superposition of BCSs, thus, portraying diverse multimodal couplings. Furthermore, by balancing the stationarity requirement and reconstruction constraint, the designed compatibility learning algorithm induces the cascaded IRMs to capture and filter out nonstationarity, thus, obtaining stationary refined data. In this way, stationarity and nonstationarity with multimodal couplings can be fully separated. The validity of CDIS is illustrated through a simulated example and a real condensing system experimental rig.

Efficient monitoring of production performance is crucial for ensuring safe operations and enhancing the economic benefits of the Iron and Steel Corporation. Although basic modeling algorithms and visualization diagrams are available in many scientific platforms and industrial applications, there is still a lack of customized research in production performance monitoring. Therefore, this article proposes an interactive visual analytics approach for monitoring the heavy-plate production process (iHPPPVis). Specifically, a multicategory aggregated monitoring framework is proposed to facilitate production performance monitoring under varying working conditions. In addition, A set of visualizations and interactions are designed to enhance analysts’ analysis, identification, and perception of the abnormal production performance in heavy-plate production data. Ultimately, the efficacy and practicality of iHPPPVis are demonstrated through multiple evaluations.

Weakly supervised temporal action localization (WSTAL) aims to localize actions in untrimmed videos using video-level labels. Despite recent advances, existing approaches mainly follow a localization-by-classification pipeline, generally processing each segment individually, thereby exploiting only limited contextual information. As a result, the model will lack a comprehensive understanding (e.g. appearance and temporal structure) of various action patterns, leading to ambiguity in classification learning and temporal localization. Our work addresses this from a novel perspective, by exploring and exploiting the cross-video contextual knowledge within the dataset to recover the dataset-level semantic structure of action instances via weak labels only, thereby indirectly improving the holistic understanding of fine-grained action patterns and alleviating the aforementioned ambiguities. Specifically, an end-to-end framework is proposed, including a Robust Memory-Guided Contrastive Learning (RMGCL) module and a Global Knowledge Summarization and Aggregation (GKSA) module. First, the RMGCL module explores the contrast and consistency of cross-video action features, assisting in learning more structured and compact embedding space, thus reducing ambiguity in classification learning. Further, the GKSA module is used to efficiently summarize and propagate the cross-video representative action knowledge in a learnable manner to promote holistic action patterns understanding, which in turn allows the generation of high-confidence pseudo-labels for self-learning, thus alleviating ambiguity in temporal localization. Extensive experiments on THUMOS14, ActivityNet1.3, and FineAction demonstrate that our method outperforms the state-of-the-art methods, and can be easily plugged into other WSTAL methods.

Due to record errors, transmission interruptions, etc., low-quality process data, including outliers and missing data, commonly exist in real industrial processes, challenging the accurate modeling and reliable monitoring of the operating statuses. In this study, a novel variational Bayesian Student’s-t mixture model (VBSMM) with a closed-form missing value imputation method is proposed to develop a robust process monitoring scheme for low-quality data. First, a new paradigm for the variational inference of Student’s-t mixture model is proposed to develop a robust VBSMM model, which optimizes the variational posteriors in an extended feasible region. Second, conditioned on the complete and partially missing data information, a closed-form missing value imputation method is derived to address the challenges of outliers and multimodality in accurate data recovery. Then, a robust online monitoring scheme that can maintain its fault detection performance in the presence of poor data quality is developed, where a novel monitoring statistic called the expected variational distance (EVD) is first proposed to quantify the changes in operating conditions and can be easily extended to other variational mixture models. Case studies on a numerical simulation and a real-world three-phase flow facility illustrate the superiority of the proposed method in missing value imputation and fault detection of low-quality data.

Temporal data contain a wealth of valuable information, playing an essential role in various machine-learning tasks. Slow feature analysis (SFA), one of the most classic temporal feature extraction models, has been deeply explored in two decades of development. SFA extracts slowly varying features as high-level representations of temporal data. Its core idea of “slow” has been proven to be consistent with the nature of biological vision and beneficial in capturing significant temporal information for various tasks. So far, SFA has evolved into numerous improved versions and is widely applied in many fields such as computer vision, industrial control, remote sensing, signal processing, and computational biology. However, there currently lacks an insightful review of SFA. In this article, a comprehensive overview of SFA and its extensions is provided for the first time. The formulation and optimization of SFA are introduced. Two mainstream solutions, geometric interpretation, and a gradient-based training method of SFA are presented and discussed. Following that, a taxonomy of the current progress of SFA is proposed. We classify improved versions of SFA into six categories, including dual-input SFA (DISFA), online slow feature analysis (OSFA), probabilistic SFA (PSFA), multimode SFA, nonlinear SFA, and discrete labeled SFA. For each category, we illustrate its main ideas, mathematical principles, and applicable scenarios. In addition, the practical applications of SFA are summarized and presented. Finally, we bring new insights into SFA according to its research status and provide potential research directions, which may serve as a good reference for promoting future work.

Typically, industrial processes possess both temporal and spatial dependencies due to intravariable dynamics and intervariable couplings. The two dependencies have different manifestations, indicating diverse process characteristics. However, the existing methods fail to separate temporal and spatial information well, leading to inappropriate representation and inaccurate fault detection and isolation results. This study proposes an explicit representation and customized fault isolation framework to tackle temporal and spatial characteristics, so as to identify and locate anomalies affecting different dependencies. First, we design a double-level separation method for temporal and spatial information. In the first level, we construct two independent auto-encoding modules to extract temporal correlation and spatial graph structure in parallel. In the second level, we propose an information aliasing loss function to guild the two modules to distinguish between temporal and spatial characteristics, further facilitating information separation. By monitoring the explicit temporal and spatial statistics obtained by the two modules, spatiotemporal dependencies of anomalies can be determined for subsequent isolation. Furthermore, we propose a customized isolation strategy for anomalies in temporal and spatial characteristics. By quantifying changes in intravariable temporal dynamics and intervariable spatial graph structure individually, temporal impact and spatial propagation of faults can be finely characterized and isolated. Three examples are adopted to verify the performance of the proposed framework, including a numerical example, a real condensing system of the thermal power plant process, and the Tennessee Eastman benchmark process.

Tracing the cause of control performance degradation (CPD) as early as possible is important to reducing the loss and risks for industrial processes. The existing tracing algorithms for CPD generally require massive amounts of data for model training. However, in practical industries, many CPD types may not be supported by historical data, which are denoted as unseen CPD types here but require online identification. This common but challenging scenario is termed the zero-sample tracing of CPD. In this work, a semantic knowledge integrated graph convolutional network is proposed to solve this problem. While data corresponding to some CPD types are not available during the training phase, the CPD types themselves are known to the users. We describe all of the CPD types in both training and testing sets by a series of attributes, including degradation locations, characteristics, causal reasons, and so forth, which contain semantic knowledge. Unlike existing methods that directly predict class labels of CPD types, we predict the semantic attributes of the unseen CPD types to further infer the class labels. In this way, semantic attributes act as a bridge connecting degraded data and labels, which can transfer the knowledge of seen CPD types to unseen ones to realize the zero-sample tracing of CPD. Besides, the sparse graph convolutional network is designed to remove redundant information while concurrently analyzing the correlated and temporal relationships hidden in the loop data. The practical utility and feasibility of the proposed method are verified with the Tennessee–Eastman process.

Incipient faults with small magnitudes are usually masked by the data outliers and ambient noise, and thus the robustness should be taken into consideration when developing monitoring models for them. In this study, a robust dissimilarity distribution analytics (RDDA) method is proposed for incipient industrial fault detection. The probabilistic model of the RDDA method is formulated with Laplace distribution, and thus it is more robust to the disturbance when compared with the Gaussian distribution based monitoring models. Using the variational inference, the maximum likelihood estimations of the latent variables and model parameters in the RDDA method can be derived. After that, a monitoring strategy is established based on the obtained results with both static and dynamic statistics, which are designed using the dissimilarity between the distributions of different datasets. Since the missing data problem is also considered, the proposed RDDA method is more suitable for practical industrial applications. The proposed method is applied to identify the operation status of a deaerator. Experimental results illustrate that the proposed method can be established using the historical data with missing values, and it can accurately detect the incipient faults with small magnitude.

Due to unexpected data transition or equipment failures, irregular data with missing values, which have both irregular sampling intervals and missing values, become very common in industrial processes and bring significant challenges for existing dynamic monitoring methods to explore temporal correlations. Therefore, this article develops an interval-aware probabilistic slow feature analysis (IA-PSFA) method along with the corresponding monitoring strategy to address the above problems for industrial processes. The IA-PSFA method incorporates functions of sampling intervals to adjust the influences of previous samples on the current one when inferring state variables. Specifically, different functions are designed such that the changing temporal correlations between adjacent samples caused by irregular sampling intervals can be tracked effectively. Parameters of the IA-PSFA model are estimated through the expectation-maximization (EM) algorithm with an interval-aware Kalman filter, which addresses the missing variable issue along with irregular sampling intervals. After that, three statistics are constructed based on the state variables, transition and emission errors, and the varying speed of the state variables, to establish comprehensive evaluations of processes. Finally, cases from the Tennessee Eastman (TE) process are provided to validate the effectiveness of the proposed method confronted with different degrees of data irregularity and missing values.

Operational data from industrial processes typically consist of continuous and categorical variables. They reveal different aspects of the operational conditions, and both are useful for comprehensive fault detection and diagnosis (FDD). However, due to multiple production modes, the variables usually do not follow Gaussian or Bernoulli distributions. Furthermore, their correlations can be different across normal and various faulty classes. Thus, the main challenge is how to fuse the complementary information in the two types of variables and accurately characterize the complicated distribution for each class. This article proposes a flexible probabilistic framework that can concurrently analyze continuous and categorical variables for FDD. Our framework specifies a finite mixture model for each class. Thus, it can handle non-Gaussian and non-Bernoulli variables and capture their correlations under the conditional independence assumption. We then introduce the variational inference for parameter estimation, which makes our framework adaptive to the different distributions of various classes. Furthermore, a unified statistical index is designed, which gives our method extra capability to distinguish unknown faults. Finally, the effectiveness of our method is validated on the Tennessee Eastman (TE) process and a practical industrial plant process. The average F1 score of our method is improved by 3.4/3.9 percentage points in the single-mode/multimode situation on the TE process compared with traditional mixture discriminant analysis. In the industrial plant process, when unknown faults are added, the average F1 score of our method is 91.1% and only 1.2 percentage points lower than that on the test set without unknown faults.

The accurate online soft sensor in complex industrial processes remains challenging because underlying spatial coupling relations among process variables have not been effectively mined and exploited. Recently, some deep learning based studies construct static graphs to explicitly represent underlying spatial coupling relations among process variables, but they neglect the fact that spatial coupling relations have mutable characteristics, leading to poor performance in the online soft sensor. Therefore, in this article, we propose a novel deep learning model to address this issue to achieve accurate soft sensors. Specifically, a dynamic graph is proposed to realize adaptive learning and automatic inference for mutable spatial coupling relations so that the proposed model is endowed with the ability to real-timely sense spatial coupling relations in the online industrial soft sensor. Then, based on the dynamic graph, a new multihop attention graph convolutional network is proposed to systematically aggregate various crucial node feature representations during graph convolution processes to capture fine-grained spatial dependence features, thereby achieving effective modeling for variation patterns of process variables. Finally, a new multivariate incremental training algorithm is designed for deep learning models to further improve the prediction performance. The verification study on a coal mill rig demonstrates the feasibility and effectiveness of the proposed model.

Monocular 3D vehicle localization is an important task for vehicle behaviour analysis, traffic flow parameter estimation and autonomous driving in Intelligent Transportation System (ITS) and Cooperative Vehicle Infrastructure System (CVIS), which is usually achieved by monocular 3D vehicle detection. However, monocular cameras cannot obtain depth information directly due to the inherent imaging mechanism, resulting in more challenging monocular 3D tasks. Currently, most of the monocular 3D vehicle detection methods still rely on 2D detectors and additional geometric constraint modules to recover 3D vehicle information, which reduces the efficiency. At the same time, most of the research is based on datasets of onboard scenes, instead of roadside perspective, which is limited in large-scale 3D perception. Therefore, we focus on 3D vehicle detection without 2D detectors in roadside scenes. We propose a 3D vehicle localization network CenterLoc3D for roadside monocular cameras, which directly predicts centroid and eight vertexes in image space, and the dimension of 3D bounding boxes without 2D detectors. To improve the precision of 3D vehicle localization, we propose a multi-scale weighted-fusion module and a loss with spatial constraints embedded in CenterLoc3D. Firstly, the transformation matrix between 2D image space and 3D world space is solved by camera calibration. Secondly, vehicle type, centroid, eight vertexes, and the dimension of 3D vehicle bounding boxes are obtained by CenterLoc3D. Finally, centroid in 3D world space can be obtained by camera calibration and CenterLoc3D for 3D vehicle localization. To the best of our knowledge, this is the first application of 3D vehicle localization for roadside monocular cameras. Hence, we also propose a benchmark for this application including a dataset (SVLD-3D), an annotation tool (LabelImg-3D), and evaluation metrics. Through experimental validation, the proposed method achieves high accuracy with AP3Dof 51.30%, average 3D localization precision of 98%, average 3D dimension precision of 85% and real-time performance with FPS of 41.18.

Due to the frequent changes in operating conditions, time-varying behaviors, including slow-varying dynamics and switching modes, commonly exist in industrial processes, resulting in different degrees of shifting in the process data distribution. When the data distribution shifts in a relatively wide range, conventional adaptive methods become ineffective since they are unable to distinguish normal shifts from real faults, leading to false alarms. In this study, an incremental variational Bayesian Gaussian mixture model (IncVBGMM) is proposed for developing a fine-scale adaptive monitoring scheme to efficiently accommodate the shifting data distribution caused by different degrees of time-varying behaviors. First, IncVBGMM with decremental optimization is proposed to adapt to the changing data distribution via the automatic complement of local models while reducing redundancy to optimize the mixture model. Then, a fine-scale adaptive monitoring scheme is built with physical interpretations to discern between normal shifts and real faults by joint analysis of the static and dynamic information. In addition, a novel monitoring statistic called the expectation of variational Bayesian inference distance (EVBID) is proposed, which can quantify the distance from samples to the variational monitoring model and indicate the fault effects. Case studies involving a real-world three-phase flow facility reveal that the proposed method can accurately differentiate various types of faults from normal shifts and effectively adapt to the time-varying dynamics.

The synthesis of chiral triazole-fused pyrazine scaffolds from readily available substrates in a step-economical asymmetric catalytic way is highly appealing. We herein report that an efficient Cu/Ag relay catalyzed protocol employing cascade asymmetric propargylic amination, hydroazidation, and [3 + 2] cycloaddition reaction with high efficiency to access the target enantioenriched 1,2,3-triazolo[1,5-a]pyrazine has been accomplished by applying a novel N,N,P-ligand. The one-pot reaction of three components exhibits high functional group tolerance, excellent enantioselectivities, and a broad substrate scope with readily available starting materials.

Electrocardiogram (ECG) is commonly considered as the reference standard signal to measure the biopotential by the electrical signals that control the expansion and contraction of the heart chambers. However, for long-term heart action monitoring, devices used to collect continuous ECG signals under free-living conditions typically have several operational difficulties. As an alternative, photoplethysmography (PPG) signals can be collected by pulse sensors to sense the rate of blood flow as controlled by the heart’s pumping action. Consequently, ECG signals and PPG signals are intrinsically correlated to reflect the status of the heart. In this article, we study the relation between ECG and PPG by proposing a novel growing multilayer network (GMLN). The approach can design the number and topology of layers automatically. Relying on a growth mapping of input data, with the representation from the lower level to the higher level, the network is to fulfill a reconstruction implicitly to the target signal. Experimental results obtained from benchmark datasets show that the proposed method can achieve higher similarity for the waveform and more accurate heart rate (HR) detection with the target signal.

The concept of aggregate science was proposed to explain changes in materials performance that accompany the generation of aggregates, but aggregation-triggered multifunction improvements in a class of materials have rarely been reported. Herein, we present the first report of a new class of multifunctional aggregation-induced emission (AIE) luminogens (AIEgens) based on 5,10-diarylphenazine (DPZ) derivates with full-wavelength emission. Intriguingly, multiple properties, such as fluorescence intensity and free radical and type I reactive oxygen species (ROS) efficiencies, could be simultaneously activated from the unimolecular level to the aggregate state. The mechanisms of this multiple performance improvement are discussed in detail based on sufficient performance characterization, and some of the newly prepared AIEgens exhibited toxicity to cancer cells during photodynamic therapy. This work systematically demonstrates the positive effect of aggregation on improving multiple functions of materials, which is expected to promote the development of aggregate science theory for the design of multifunctional materials.

With the development of remote sensing technology, high-resolution (HR) remote sensing optical images have gradually become the main source of change detection data. Albeit, the change detection for HR remote sensing images still faces challenges: 1) in complex scenes, a region contains a large amount of semantic information, which makes it difficult to accurately locate the boundaries between different semantics in the feature maps and 2) due to the inability to maintain consistent conditions such as light, weather, and other factors when acquiring bitemporal images, confounding factors such as the style of bitemporal data that are not related to change detection can cause detection difficulties. Therefore, a change detection method based on feature interaction and multitask learning (FMCD) is proposed in this article. To improve the ability to detect changes in complex scenes, FMCD models the context information of features through a multilevel feature interaction module, so as to obtain representative features, and to improve the sensitivity of the model to changes, the interaction between two temporal features is realized through the mix attention block (MAB). In addition, to eliminate the influence of weather and other factors, FMCD adopts a multitask learning strategy, takes domain adaptation as an auxiliary task, and maps the features of bitemporal images to the same space through the feature relationship adaptation module (FRAM) and feature distribution adaptation module (FDAM). Experiments on three datasets show that the proposed method is superior to other state-of-the-art methods.

This article presents differential-map-aided LiDAR-based localization (DMLL), a localization framework that combines map-based localization and LiDAR odometry to achieve accurate and reliable robot pose estimation in partially known environments. In DMLL, measurement from map-matching are novelly modeled as differential constraints in the factor graph, rather than absolute constraints. This simplifies the fusion process and avoids initial alignment errors. We design uncertainty quantization for both the LiDAR odometry measurement and the map-matching measurement. Furthermore, we design an adaptive decision mechanism that selectively triggers the map-matching module based on the quantified uncertainty of these measurements. This mechanism achieves a balance between resource consumption and localization accuracy while also being capable of detecting and discarding erroneous data, thus improving the overall localization reliability. Our proposed method is extensively evaluated on both public datasets and our own field-collected data. The result indicates that our method can achieve highly accurate localization in practical scenarios.

To solve the problem of low target detection accuracy caused by the related quantities such as background, target, and noise contained in hyperspectral images (HSIs), considering the use of the spatial spectrum and spectral characteristics while increasing the degree of discrimination between target and background, a coarse-to-fine HSI target detection algorithm based on low-rank tensor decomposition (HTDLTD) is proposed. The HTD based on low-rank sparse decomposition mainly decomposes HSIs in spectral dimension, which does not make full use of the spatial information of HSIs, resulting in low detection accuracy. In order to solve this problem, in view of the fact that the hyperspectral third-order tensor can describe the spatial information and spectral information of HSIs equally, the HTD method based on low-rank tensor decomposition (LRTD) is proposed to extract pure background information. Then, in order to solve the problem of low detection accuracy in the case of low target and background discrimination, the rough target detection method based on max over (SMF-MAX) target detection method is proposed to perform rough detection on the original HSI to obtain rough detection results. Finally, in order to further improve the performance of target detection, the fine target detection method based on spectral distance is proposed. By calculating the spectral distance between the original HSI and the synthesized HSI, the final reconstructed target detection result is obtained. Experimental results on three datasets show that the proposed HTDLTD exceeds eight state-of-the-art target detection methods used for comparison.

Service combinatorial optimisation is one of the key issues of green resource supply in a cloud environment. During the execution process of service composition, service composition fails due to compatibility, resource competition, failure, and other reasons between sub-services. To solve the above problems, this paper focuses on the service combinatorial optimisation problem in green cloud computing resource supply. It proposes a service combinatorial optimisation model based on layered coloured Petri nets (LCPN). The service composition process meets the functional attributes of user needs and focuses on non-functional attributes such as service quality and execution time. It can dynamically adjust the service composition scheme to ensure feasibility and business logic compatibility. The experimental results show that the service combination scheme considering atomic service quality and virtual machine processing capability has the highest average comprehensive index value, 12.09% higher than the scheme considering only one factor. In handling concurrent events, the LCPN network can allocate resources reasonably, ensuring the compatibility of message interaction between various modules and the business logic of various transactions.

Soft sensor techniques have been extensively utilized for predicting key variables in the process industry. To extract fine-grained and holistic features for efficient prediction, proper modeling strategies for multiscale spatial and temporal correlations are heavily desired. In this article, we summarize the multiscale correlations as a short and long temporal dependency, local and global spatial relevance, and spatiotemporal coupled correlations. Such correlations should be explicitly and comprehensively characterized as different key variables often require diverse or multiscale spatiotemporal features. To this end, a spatiotemporal multiscale correlation embedding (STMCE) framework is proposed for soft sensing with characterization and refining of multiscale spatial and temporal features. On the one hand, we design a reorder data patch embedding (RDPE) strategy to extract local spatiotemporal coupled correlations by reordering the permutation of process variables and embedding local correlations into high-dimensional vectors. On the other hand, we propose a global feature fusion and refining network (GFFRNet) via a self-attention mechanism and feature refining technique, which enables the model to effectively aggregate and privilege predictive features for key variable prediction. Superior to existing methods that partially focus on spatial or temporal dependencies, our approach explicitly models both local and global spatiotemporal coupled relevance under a multiscale perspective, encouraging the model to explore more fine-grained and comprehensive information from process data and further improve prediction accuracy. In comparison with ten baseline models, our STMCE empirically presents the state-of-the-art results on both benchmark and real-world industrial datasets, for example, obtaining a relative MAE reduction of 13.32% compared with the second-best method in the benchmark multiphase flow dataset.

Synthetic aperture radar (SAR) ship detection methods have achieved remarkable progress in recent years. However, unlike RGB images, the characteristics of SAR imaging will result in non-intuitive feature representations. Furthermore, due to the insufficient data of SAR images, existing methods relying on plenty of labeled SAR images may be hard to achieve promising performance. To address the aforementioned issues, a cross-modality feature transfer (CMFT) method is proposed in this article, which enhances feature representations in the SAR modality by transferring rich knowledge in the RGB modality. First, we propose a multilevel modality alignment network (MMAN), which encourages the model to effectively learn modality-invariant features and alleviate the large cross-modality discrepancies by aligning features from multilevels (scene level, local level, global level, and instance level). Second, to address the underperformance of samples with non-intuitive features in the modality alignment, we introduce a hard-sample supervision module (HSM) in the stage of feature extraction, which can thoroughly exploit the feature of hard-to-align samples by giving more optimization energy for them. Third, to enhance the discriminability of instance-level features, a feature complementary module (FCM) is customized to fully explore the potential complementary clues between instance-level features and context information for the instance-level feature alignment. Extensive experimental results demonstrate that the CMFT outperforms the state-of-the-art detectors. Compared to the baseline model, CMFT improves the accuracy by 3.1% mean average precision (mAP) on the SSDD dataset and 3.4% mAP on the HRSID dataset, demonstrating its superior SAR ship detection performance.

Soft sensors have been extensively developed and applied in the process industry. One of the main challenges of the data-driven soft sensors is the lack of labeled data and the need to absorb the knowledge from a related source operating condition to enhance the soft sensing performance on the target application. This article introduces deep transfer learning to soft sensor modeling and proposes a deep probabilistic transfer regression (DPTR) framework. In DPTR, a deep generative regression model is first developed to learn Gaussian latent feature representations and model the regression relationship under the stochastic gradient variational Bayes framework. Then, a probabilistic latent space transfer strategy is designed to reduce the discrepancy between the source and target latent features such that the knowledge from the source data can be explored and transferred to enhance the target soft sensor performance. Besides, considering the missing values in the process data in the target operating condition, the DPTR is further extended to handle the missing data problem utilizing the strong generation and reconstruction capability of the deep generative model. The effectiveness of the proposed method is validated through an industrial multiphase flow process. [ABSTRACT FROM AUTHOR]

The quadrotor system’s nonlinear behaviour, uncertain modelling parameters and the presence of exogenous disturbances in the surrounding environment make its flight control a critical and challenging job. Several linear and nonlinear control methodologies have been proposed so far for this purpose during the past few decades which still need improvements. In this paper, variable exponential sliding mode control (ESMC) is proposed for the attitude stabilization and altitude tracking of a quadrotor having parametric uncertainties and being exposed to exogenous disturbances. The multi-layer perceptron (MLP) neural network is incorporated with the ESMC to accommodate adaptively the effect of parametric uncertainties. The conventional weight update law of the neural network is replaced by the sliding mode effect, thereby enhancing the learning efficiency of the network without computational complexities. A finite-time disturbance observer (FTDO) is integrated with the control law to make the controller robust to exogenous disturbances and minimize the chattering issue as well. The stability of the proposed control scheme is checked and verified by the Lyapunov theory. Numerous simulations of the proposed control algorithm are carried out on a quadrotor model suffering from exogenous disturbances and parametric uncertainties in MATLAB SIMULINKand the results are compared with the SMC. The remarkable performance of the proposed control strategy demonstrates and substantiates its validity.

The first copper-catalyzed enantioselective [4 + 1] annulation of yne-allylic esters with 1,3-dicarbonyl compounds was realized through an elegant remote stereocontrol strategy. The very remote ε regioselective nucleophilic substitution was developed by employing a novel chiral copper-vinylvinylidene species from the new C4 synthon yne-allylic esters. Thus, greatly diverse spirocycles were obtained with ample scope and excellent levels of chemo-, regio-, and enantioselectivities. Moreover, detailed mechanistic studies suggest an yne-allylic substitution and Conia-ene cascade pathway on the remote stereochemical induction progress.

Background: Cognitive impairment is widely prevalent in maintenance hemodialysis (MHD) patients, and seriously affects their quality of life. The intestinal flora likely regulates cognitive function, but studies on cognitive impairment and intestinal flora in MHD patients are lacking. Methods: MHD patients (36) and healthy volunteers (18) were evaluated using the Montreal Cognitive Function Scale, basic clinical data, and 16S ribosome DNA (rDNA) sequencing. Twenty MHD patients and ten healthy volunteers were randomly selected for shotgun metagenomic analysis to explore potential metabolic pathways of intestinal flora. Both16S rDNA sequencing and shotgun metagenomic sequencing were conducted on fecal samples. Results: Roseburiawere significantly reduced in the MHD group based on both 16S rDNA and shotgun metagenomic sequencing analyses. Faecalibacterium, Megamonas, Bifidobacterium, Parabacteroides, Collinsella, Tyzzerella, and Phascolarctobacteriumwere positively correlated with cognitive function or cognitive domains. Enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways included oxidative phosphorylation, photosynthesis, retrograde endocannabinoid signaling, flagellar assembly, and riboflavin metabolism. Conclusion: Among the microbiota, Roseburiamay be important in MHD patients. We demonstrated a correlation between bacterial genera and cognitive function, and propose possible mechanisms.