Optimal Strictly Stealthy Attack Design on Cyber-Physical Systems: A Data-Driven Approach.

In this article, an issue of data-driven optimal strictly stealthy attack design for the stochastic linear invariant systems is investigated, with the aim of maximizing the system performance degradation under an energy bounded constraint and bypassing the parity-space-based attack detector. Importantly, the proposed attack policy refrains from the assumption that the system knowledge is known to attackers. A novel strictly stealthy attack sequence (SSAS), coordinating the sensor and actuator signals simultaneously, is proposed with a sufficient and necessary condition for the existence of such an attack presented. Specifically, the SSAS is parameterized as a vector in the null space of a specific matrix which is constructed by a parity matrix and the system Markov parameters. For the purpose of data-driven attack realization, modified subspace identification methods are utilized to achieve an unbiased estimation of the required parameters via the closed-loop data. On this basis, the attack design is formulated as a constrained optimization problem, an explicit solution to which is given to characterize the optimal strictly stealthy attack. Finally, the vulnerability of the cyber-physical systems is analysed from the perspective of the parameter selection for the parity space-based detector. A case study on a three-tank model verifies the efficiency of the proposed approach.

Bayesian Hierarchical Graph Neural Networks With Uncertainty Feedback for Trustworthy Fault Diagnosis of Industrial Processes.

Deep learning (DL) methods have been widely applied to intelligent fault diagnosis of industrial processes and achieved state-of-the-art performance. However, fault diagnosis with point estimate may provide untrustworthy decisions. Recently, Bayesian inference shows to be a promising approach to trustworthy fault diagnosis by quantifying the uncertainty of the decisions with a DL model. The uncertainty information is not involved in the training process, which does not help the learning of highly uncertain samples and has little effect on improving the fault diagnosis performance. To address this challenge, we propose a Bayesian hierarchical graph neural network (BHGNN) with an uncertainty feedback mechanism, which formulates a trustworthy fault diagnosis on the Bayesian DL (BDL) framework. Specifically, BHGNN captures the epistemic uncertainty and aleatoric uncertainty via a variational dropout approach and utilizes the uncertainty information of each sample to adjust the strength of the temporal consistency (TC) constraint for robust feature learning. Meanwhile, the BHGNN method models the process data as a hierarchical graph (HG) by leveraging the interaction-aware module and physical topology knowledge of the industrial process, which integrates data with domain knowledge to learn fault representation. Moreover, the experiments on a three-phase flow facility (TFF) and secure water treatment (SWaT) show superior and competitive performance in fault diagnosis and verify the trustworthiness of the proposed method.

Gradient Monitored Reinforcement Learning.

This article presents a novel neural network training approach for faster convergence and better generalization abilities in deep reinforcement learning (RL). Particularly, we focus on the enhancement of training and evaluation performance in RL algorithms by systematically reducing gradient's variance and, thereby, providing a more targeted learning process. The proposed method, which we term gradient monitoring (GM), is a method to steer the learning in the weight parameters of a neural network based on the dynamic development and feedback from the training process itself. We propose different variants of the GM method that we prove to increase the underlying performance of the model. One of the proposed variants, momentum with GM (M-WGM), allows for a continuous adjustment of the quantum of backpropagated gradients in the network based on certain learning parameters. We further enhance the method with the adaptive M-WGM (AM-WGM) method, which allows for automatic adjustment between focused learning of certain weights versus more dispersed learning depending on the feedback from the rewards collected. As a by-product, it also allows for automatic derivation of the required deep network sizes during training as the method automatically freezes trained weights. The method is applied to two discrete (real-world multirobot coordination problems and Atari games) and one continuous control task (MuJoCo) using advantage actor-critic (A2C) and proximal policy optimization (PPO), respectively. The results obtained particularly underline the applicability and performance improvements of the methods in terms of generalization capability.

Interaction-Aware Graph Neural Networks for Fault Diagnosis of Complex Industrial Processes.

Fault diagnosis of complex industrial processes becomes a challenging task due to various fault patterns in sensor signals and complex interactions between different units. However, how to explore the interactions and integrate with sensor signals remains an open question. Considering that the sensor signals and their interactions in an industrial process with the form of nodes and edges can be represented as a graph, this article proposes a novel interaction-aware and data fusion method for fault diagnosis of complex industrial processes, named interaction-aware graph neural networks (IAGNNs). First, to describe the complex interactions in an industrial process, the sensor signals are transformed into a heterogeneous graph with multiple edge types, and the edge weights are learned by the attention mechanism, adaptively. Then, multiple independent graph neural network (GNN) blocks are employed to extract the fault feature for each subgraph with one edge type. Finally, each subgraph feature is concatenated or fused by a weighted summation function to generate the final graph embedding. Therefore, the proposed method can learn multiple interactions between sensor signals and extract the fault feature from each subgraph by message passing operation of GNNs. The final fault feature contains the information from raw data and implicit interactions between sensor signals. The experimental results on the three-phase flow facility and power system (PS) demonstrate the reliable and superior performance of the proposed method for fault diagnosis of complex industrial processes.

Distributed Adaptive Leader-Following Consensus for Nonlinear Multiagent Systems With Actuator Failures Under Directed Switching Graphs.

This article studies the distributed adaptive failures compensation output-feedback consensus for a class of nonlinear multiagent systems (MASs) with multiactuator failures allowing unmatched redundancy under directed switching graphs. With estimated information of neighbors, a novel distributed reference generator is designed. To compensate the unmeasured state variables of each agent, a reduced-order dynamic gain filter is constructed. Based on the generator and filter, and using the recursive design method, a distributed adaptive protocol is designed, where the adaptive technique is used to compensate the actuator failures. The proposed scheme can significantly relax conditions on the communication graph, which allows the graph to be disconnected at any time instant. The number of introduced variables in the filter and its dimension is greatly reduced and, thus, reduces the numerical challenge. The output-feedback consensus for nonlinear MASs with actuator failures and possible unmatched actuator redundancy is addressed for the first time. The consensus error can converge to an arbitrarily small set not affected by actuator failures, and the resulting closed-loop system is semiglobally stable. Finally, simulation results are given to illustrate the effectiveness of the proposed method.

PLC-Informed Distributed Game Theoretic Learning of Energy-Optimal Production Policies.

This article describes a novel concept to optimize manufacturing systems distributively through data-based learning. We propose a game-theoretic (GT) learning set-up that is incorporated with accessible control code of the programmable logic controller (PLC) to accelerate the optimal policies learning procedures, instead of learning everything from scratch. Therefore, we offer to process the accessible and available control code into a GT-based learning framework which is subsequently optimized in a fully distributed manner. To this end, we employ the recently developed framework of state-based potential games (PGs) and prove that under mild conditions PLC-informed (PLCi) learning forms a state-based PG framework. We conduct the experiment on a laboratory scale testbed in numerous production scenarios. The experiment's results highlight the major potential of using the PLCi GT-learning, which is the reduction of energy consumption of the production timescales and improvement of production efficiency while nearly halven the learning times.

Curriculum-Based Deep Reinforcement Learning for Quantum Control.

Deep reinforcement learning (DRL) has been recognized as an efficient technique to design optimal strategies for different complex systems without prior knowledge of the control landscape. To achieve a fast and precise control for quantum systems, we propose a novel DRL approach by constructing a curriculum consisting of a set of intermediate tasks defined by fidelity thresholds, where the tasks among a curriculum can be statically determined before the learning process or dynamically generated during the learning process. By transferring knowledge between two successive tasks and sequencing tasks according to their difficulties, the proposed curriculum-based DRL (CDRL) method enables the agent to focus on easy tasks in the early stage, then move onto difficult tasks, and eventually approaches the final task. Numerical comparison with the traditional methods [gradient method (GD), genetic algorithm (GA), and several other DRL methods] demonstrates that CDRL exhibits improved control performance for quantum systems and also provides an efficient way to identify optimal strategies with few control pulses.

The first Aotearoa New Zealand case of NUDT15-variant-related thiopurine-induced myelotoxicity.

A 37-year-old Han Chinese man, with a history of severe ulcerative colitis with incomplete response to oral glucocorticoids, was commenced on azathioprine [AZA] 200mg once a day. His pre-treatment thiopurine S-methyltransferase [TPMT] levels were in the normal range. Eleven days later he developed symptoms of stomatitis and gingivitis. Chinese herbal medications were taken in an attempt to treat these symptoms. He presented to the emergency department with this, with normal vital signs. A full blood count five days post-onset of symptoms showed pancytopenia with an absolute neutrophil count [ANC] of 0.0x10(9)/l, C-reactive protein was 120 mg/L. Initial chest radiograph, urinalysis and peripheral blood cultures were unremarkable and he was commenced on broad spectrum antibiotics and granulocyte colony stimulating factor [G-CSF]. He remained an inpatient under the gastroenterology team for 16 days and developed infectious complications of herpes simplex stomatitis, oral candidiasis, dental abscess, and scalp abscess. On day 16 his ANC recovered to 1.0x10(9)/L and was discharged from the hospital. He underwent nudix hydrolase 15 [NUDT15] genotyping and was found to have homozygosity for the variant NUDT15:c.415C>T. This case demonstrates the importance of pre-treatment testing for NUDT15 genetic variants, to predict the risk of severe leucopaenia, particularly in a patient of East Asian ethnicity.

Distributed Self-Optimization of Modular Production Units: A State-Based Potential Game Approach.

This article presents a novel approach for distributed optimization of production units based on potential game (PG) theory and machine learning. The core of our approach is split into two parts: the first part concentrates on the conceptual treatment of modular installed production units in terms of a PG scenario. The second part focuses on the development and incorporation of suitable learning algorithms to finally form an intelligent autonomous system. In this context, we model the production environment as a state-based PG where each actuator of each module has the role of an agent in the game aiming to maximize its utility value by learning the optimal process behavior. The benefit of the additional state information is visible in the performance of the algorithm making the environment dynamic and serving as a connector between the players. We propose a novel learning algorithm based on a global interpolation method that is applied to a laboratory scale modular bulk good system. The thorough analysis of the encouraging results yields to highly interesting insights into the learning dynamics and the process itself. The benefits of our distributed optimization approach are the plug-and-play functionality, the online capability, fast adaption to changing production requirements, and the possibility of an IEC 61131 conforming to PLC implementation.

Robust Asymptotic Fault Estimation of Discrete-Time Interconnected Systems With Sensor Faults.

In this article, a robust asymptotic fault estimation (RAFE) design is proposed for discrete-time interconnected systems with sensor faults. By constructing a singular augmented system, an equivalent description of the considered interconnected systems is presented. Then, a novel RAFE observer is proposed for the singular augmented system. Furthermore, gain matrices of the RAFE observer are calculated based on multiconstrained design. Simulation results are illustrated to show the feasibility of the presented approaches.

Extended Relevance Vector Machine-Based Remaining Useful Life Prediction for DC-Link Capacitor in High-Speed Train.

Remaining useful life (RUL) prediction is a reliable tool for the health management of components. The main concern of RUL prediction is how to accurately predict the RUL under uncertainties. In order to enhance the prediction accuracy under uncertain conditions, the relevance vector machine (RVM) is extended into the probability manifold to compensate for the weakness caused by evidence approximation of the RVM. First, tendency features are selected based on the batch samples. Then, a dynamic multistep regression model is built for well describing the influence of uncertainties. Furthermore, the degradation tendency is estimated to monitor degradation status continuously. As poorly estimated hyperparameters of RVM may result in low prediction accuracy, the established RVM model is extended to the probabilistic manifold for estimating the degradation tendency exactly. The RUL is then prognosticated by the first hitting time (FHT) method based on the estimated degradation tendency. The proposed schemes are illustrated by a case study, which investigated the capacitors' performance degradation in traction systems of high-speed trains.

A Comparative Study of Deep Neural Network-Aided Canonical Correlation Analysis-Based Process Monitoring and Fault Detection Methods.

Multivariate analysis is an important kind of method in process monitoring and fault detection, in which the canonical correlation analysis (CCA) makes use of the correlation change between two groups of variables to distinguish the system status and has been greatly studied and applied. For the monitoring of nonlinear dynamic systems, the deep neural network-aided CCA (DNN-CCA) has received much attention recently, but it lacks a general definition and comparative study of different network structures. Therefore, this article first introduces four deep neural network (DNN) models that are suitable to combine with CCA, and the general form of DNN-CCA is given in detail. Then, the experimental comparison of these methods is conducted through three cases, so as to analyze the characteristics and distinctions of CCA aided by each DNN model. Finally, some suggestions on method selection are summarized, and the existed open issues in the current DNN-CCA form and future directions are discussed.

Deep Convolutional Clustering-Based Time Series Anomaly Detection.

This paper presents a novel approach for anomaly detection in industrial processes. The system solely relies on unlabeled data and employs a 1D-convolutional neural network-based deep autoencoder architecture. As a core novelty, we split the autoencoder latent space in discriminative and reconstructive latent features and introduce an auxiliary loss based on k-means clustering for the discriminatory latent variables. We employ a Top-K clustering objective for separating the latent space, selecting the most discriminative features from the latent space. We use the approach to the benchmark Tennessee Eastman data set to prove its applicability. We provide different ablation studies and analyze the method concerning various downstream tasks, including anomaly detection, binary and multi-class classification. The obtained results show the potential of the approach to improve downstream tasks compared to standard autoencoder architectures.

Performance-Based Fault Detection and Fault-Tolerant Control for Nonlinear Systems With T-S Fuzzy Implementation.

This article addresses the performance-based fault detection (FD) and fault-tolerant control (FTC) issues for nonlinear systems. For this purpose, in the first part of this article, the performance-based FD and FTC scheme is investigated with the aid of the nonlinear factorization technique. To be specific, the controller parameterization for nonlinear systems is first discussed. The so-called fault-tolerant margin is introduced as an indicator of the system fault-tolerant ability. Then, the FD scheme aiming at estimating and detecting the stability performance degradation of the closed-loop system caused by the system faults is developed. Furthermore, to recover the system performance, the performance-based FTC strategy is presented. In the second part of this article, the design approach of the performance-based FD and FTC scheme is studied by applying the Takagi-Sugeno fuzzy dynamic modeling technique. The achieved results are demonstrated in the end by a case study on the three-tank system.

Design of a Discrete-Time Fault-Tolerant Quantum Filter and Fault Detector.

This paper solves the problem of discrete-time fault-tolerant quantum filtering for a class of laser-atom open quantum systems subject to the stochastic faults. We show that by using the discrete-time quantum measurements, optimal estimates of both the atomic observables and the classical fault process can be simultaneously determined in terms of recursive quantum stochastic difference equations. A dispersive interaction quantum system example is used to demonstrate the proposed filtering approach.

Bidirectional deep recurrent neural networks for process fault classification.

In this study, a new approach for time series based condition monitoring and fault diagnosis based on bidirectional recurrent neural networks is presented. The application of bidirectional recurrent neural networks essentially provide a viewpoint change on the fault diagnosis task, which allows to handle fault relations over longer time horizons helping in avoiding critical process breakdowns and increasing the overall productivity of the system. To further enhance the capability, we propose a novel procedure of data preprocessing and restructuring which enforces the generalization and a more efficient data utilization and consequently yields more efficient network training, especially for sequential fault classification task. The proposed Bidirectional Long Short Term Memory network outperforms standard recurrent architectures including vanilla recurrent neural networks, Long Short Term Memories and Gated Recurrent Units. We apply the proposed approach to the Tennessee Eastman benchmark process to test the effectiveness of the mentioned deep architectures and provide a detailed comparative analysis. The experimental results for binary as well as multi-class classification show the superior average fault detection capability of the bidirectional Long Short Term Memory Networks compared to the other architectures and to results from other state-of-the-art architectures found in the literature.

Pyogenic liver abscess: incidence, causality, management and clinical outcomes in a New Zealand cohort.

AIMS: The determinants, management and outcomes of pyogenic liver abscess [PLA] are changing. We aimed to compare these in a New Zealand cohort. METHODS: We have retrospectively reviewed all PLA cases presenting to Christchurch Hospital over 12 months between 2014 and 2015. RESULTS: Twenty-five cases presented over this period. The incidence was 5/100,000. Eighty percent were Caucasian with overall 4:1 male preponderance. Commonest comorbidities were diabetes, hypertension, atrial fibrillation and immunosuppression. Underlying pancreatico-biliary disease featured in 20%, preceding Whipple's or hepatic resection in 24% and inflammatory bowel disease [IBD] in 12%. Commonest complication was septic shock with intensive care unit admission in four cases. The evident cause was recent Whipple's procedure or hepatic resection (24%), pancreatico-biliary (16%), diverticulitis (12%) and active IBD (8%). Cause remained cryptogenic in 28%. The commonest microorganism was Streptococcus intermedius. The management comprised of: antibiotics alone (n=6), needle aspiration (n=2), catheter drainage (n=14), biliary drainage (n=3), surgical drainage (n=2). These interventions were in accordance with current international recommendations. There were no deaths and the mean length of stay was 10.3 days. CONCLUSION: PLA continues to carry significant morbidity. Demographics, including ethnicity, play an important role. Our tertiary centre cohort may account for higher incidence and better clinical outcomes.

Learning Stylometric Representations for Authorship Analysis.

Authorship analysis (AA) is the study of unveiling the hidden properties of authors from textual data. It extracts an author's identity and sociolinguistic characteristics based on the reflected writing styles in the text. The process is essential for various areas, such as cybercrime investigation, psycholinguistics, political socialization, etc. However, most of the previous techniques critically depend on the manual feature engineering process. Consequently, the choice of feature set has been shown to be scenario- or dataset-dependent. In this paper, to mimic the human sentence composition process using a neural network approach, we propose to incorporate different categories of linguistic features into distributed representation of words in order to learn simultaneously the writing style representations based on unlabeled texts for AA. In particular, the proposed models allow topical, lexical, syntactical, and character-level feature vectors of each document to be extracted as stylometrics. We evaluate the performance of our approach on the problems of authorship characterization, authorship identification and authorship verification with the Twitter, blog, review, novel, and essay datasets. The experiments suggest that our proposed text representation outperforms the static stylometrics, dynamic n -grams, latent Dirichlet allocation, latent semantic analysis, distributed memory model of paragraph vectors, distributed bag of words version of paragraph vector, word2vec representations, and other baselines.

Can colonoscopy at peripheral day hospitals meet internationally accepted quality and safety standards?

AIMS: To perform an independent review of the quality and safety of colonoscopy service at the Canterbury Charity Hospital (CCH). METHODS: Demographic, endoscopy and histology data on all colonoscopies performed at CCH between 1 October 2016 and 31 September 2017 were collected. Quality indicators ascertained were caecal intubation rate, mean withdrawal time and adenoma detection rate (ADR). These were assessed using current recommendations by the Joint American College of Gastroenterology and American Society of Gastrointestinal Endoscopy task force. RESULTS: Thirty-four patients, mean age 44 years (range 21-62), underwent colonoscopy. The most common indications were rectal bleeding and/or altered bowel habit (19 patients). Eight asymptomatic patients underwent colonoscopy because of a family history of CRC or a personal history of colorectal polyps; six of these were over 50 years old. Twelve patients had haemorrhoids and seven patients had adenomatous polyps. The caecal intubation rate was 97.1%. Among asymptomatic patients over 50 years undergoing colonoscopy, mean withdrawal time was 7.5 minutes (range 5-10) and ADR was 33.3%. No complications were recorded. CONCLUSION: The colonoscopy service at CCH was safe and complied with the accepted quality indicators. Our data suggest that delivery of high-quality colonoscopy services might be possible in similar peripheral and day hospitals around New Zealand. Increasing colonoscopy services in such centres would reduce the excessive workload of larger public hospitals and reduce the level of unmet need for colonoscopy services.