Design of stochastic fault tolerant control for H2 performance

In this paper, the controller synthesis problem for fault tolerant control systems (FTCS) with stochastic stability and H₂ performance is studied. System faults of random nature are modelled by a Markov chain. Because the real system fault modes are not directly accessible in the context of FTCS, the controller is reconfigured based on the output of a fault detection and identification (FDI) process, which is modelled by another Markov chain. Then state feedback and output feedback control are developed to achieve the mean square stability (MSS) and the H₂ performance for both continuous‐time and discrete‐time systems with model uncertainties. Copyright © 2006 John Wiley & Sons, Ltd.     

Bead-based polymerase chain reaction on a microchip

We present a bead-based approach to microfluidic polymerase chain reaction (PCR), enabling fluorescent detection and sample conditioning in a single microchamber. Bead-based PCR, while not extensively investigated in microchip format, has been used in a variety of bioanalytical applications in recent years. We leverage the ability of bead-based PCR to accumulate fluorescent labels following DNA amplification to explore a novel DNA detection scheme on a microchip. The microchip uses an integrated microheater and temperature sensor for rapid control of thermal cycling temperatures, while the sample is held in a microchamber fabricated from (poly)dimethylsiloxane and coated with Parylene. The effects of key bead-based PCR parameters, including annealing temperature and concentration of microbeads in the reaction mixture, are studied to achieve optimized device sensitivity and detection time. The device is capable of detecting a synthetically prepared section of the Bordetella pertussis genome in as few as 10 temperature cycles with times as short as 15?min. We then demonstrate the use of the procedure in an integrated device; capturing, amplifying, detecting, and purifying template DNA in a single microfluidic chamber. These results show that this method is an effective method of DNA detection which is easily integrated in a microfluidic device to perform additional steps such as sample pre-conditioning.     

Compact optical diagnostic device for isothermal nucleic acids amplification

We recently reported the successful use of the loop-mediated isothermal amplification (LAMP) reaction for hepatitis B virus (HBV) DNA amplification and its optimal primer design method. In this study, we report the development of an integrated isothermal device for both amplification and detection of targeted HBV DNA. It has two major components, a disposable polymethyl methacrylate (PMMA) micro-reactor and a temperature-regulated optical detection unit (base apparatus) for real-time monitoring of the turbidity changes due to the precipitation of DNA amplification by-product, magnesium pyrophosphate. We have established a correlation curve (R² = 0.99) between the concentration of pyrophosphate ions and the level of turbidity by using a simulated chemical reaction to evaluate the characteristics of our device. For the applications of rapid pathogens detection, we also have established a standard curve (R² = 0.96) by using LAMP reaction with a standard template in our device. Moreover, we also have successfully used the device on seven clinical serum specimens where HBV DNA levels have been confirmed by real-time PCR. The result indicates that different amounts of HBV DNA can be successfully detected by using this device within 1 h.     

A fault detection scheme for linear discrete-time systems with an integrated online performance evaluation

This paper is concerned with the design of the fault detection systems, into which a residual generation, evaluation and threshold are integrated, for linear discrete time-varying processes over a finite horizon. In the proposed design scheme, the residual generation is realised in the context of H_∞ fault estimation with a prescribed attenuation level. This attenuation level is minimised by using the Krein-space linear estimation theory and, subsequently, an H_∞ fault estimator with the minimum attenuation level is designed in terms of the solution to a set of Riccati-like recursions. For the residual evaluation and decision making purpose, the false alarm rate and fault detection rate indicators are introduced in the norm-based framework, which is integrated into the decision making procedure. For the online computations of the false alarm rate and fault detection rate indicators, further estimates delivered by the H_∞ fault estimator are applied without additional (online) computations. By means of checking the change in the false alarm rate and fault detection rate indicators, a decision is then made. In this way, the fault detection performance can be significantly improved. Finally, one application example is exploited to demonstrate the application of the proposed integrated fault detection and performance evaluation schemes.     

Improving inventory effectiveness in RFID-enabled global supply chain with Grey forecasting model

A RFID-enabled global TFT–LCD supply chain associated with Grey forecasting model (GM) of Company A has been simulated and analyzed in this research. Three key performance indicates (KPI) including total inventory cost, inventory turnover and bullwhip effect are analyzed in the simulation experiments in order to compare the effectiveness of five different supply chain inventory models. The effectiveness of integrated system which is composed of supply chain operation, Grey short-term forecasting model and RFID system has been examined by aforementioned three KPIs. According to the result of Taguchi experiments, RFID-enabled R-SCI_GM supply chain model which integrates the GM(1,1) forecasting model based on (s, Q) pull-based replenishment policy reduces 43.36% of the total inventory cost compared with that of the non-RFID SCI_GM model. It apparently shows that a great improving effectiveness of supply chain inventory cost can be conducted while RFID system is incorporated with the GM(1,1) forecasting model.     

An integrated microfluidic system using mannose-binding lectin for bacteria isolation and biofilm-related gene detection

Molecular diagnosis of biofilm-related genes (BRGs) in common bacteria that cause periprosthetic joint infections may provide crucial information for clinicians. In this study, several BRGs, including ica, fnbA, and fnbB, were rapidly detected (within 1 h) with a new integrated microfluidic system. Mannose-binding lectin (MBL)-coated magnetic beads were used to isolate these bacteria, and on-chip nucleic acid amplification (polymerase chain reaction, PCR) was then performed to detect BRGs. Both eukaryotic and prokaryotic MBLs were able to isolate common bacterial strains, regardless of their antibiotic resistance, and limits of detection were as low as 3 and 9 CFU for methicillin-resistant Staphylococcus aureus and Escherichia coli, respectively, when using a universal 16S rRNA PCR assay for bacterial identification. It is worth noting that the entire process including bacteria isolation by using MBL-coated beads for sample pre-treatment, on-chip PCR, and fluorescent signal detection could be completed on an integrated microfluidic system within 1 h. This is the first time that an integrated microfluidic system capable of detecting BRGs by using MBL as a universal capturing probe was reported. This integrated microfluidic system might therefore prove useful for monitoring profiles of BRGs and give clinicians more clues for their clinical judgments in the near future.     

An integrated microfluidic system for live bacteria detection from human joint fluid samples by using ethidium monoazide and loop-mediated isothermal amplification

Periprosthetic joint infection (PJI) is one of the severe complications of prosthetic joint replacement. Delayed PJI diagnosis may anchor bacteria in periprosthetic tissues, and removal of the prosthesis might be inevitable. The diagnosis of PJI depends on the identification of microorganisms by standard microbiological cultures or more advanced molecular diagnostic methods for detection of bacterial genes. However, these methods are relatively time-consuming, labor-intensive and not human error-free. Moreover, it is challenging to distinguish live from dead bacteria by using DNA-based molecular diagnostics since bacterial DNA will be remained in the tissue even after the death of the bacteria. In this work, an integrated microfluidic system has been developed to perform the entire molecular diagnostic process for the PJI diagnosis in a single chip. We combined the loop-mediated isothermal amplification (LAMP) with ethidium monoazide (EMA) in an integrated microfluidic system to identify live bacteria with reasonable sensitivity and high specificity. All the diagnostic processes including bacteria isolation, cell lysis, DNA amplification and optical detection can be automatically performed on the integrated microfluidic system by using a compact custom-made control system. The integrated system can accommodate four primers complementary to six regions of the target genes and improve the detection limit by using LAMP. The limit of detection in this multiple EMA-LAMP assay could be as low as 5 fg/reaction (~1 CFU/reaction) when choosing an optimized primer set as we demonstrated in mecA gene detection. Thus, the developed system for PJI diagnosis has great potential to become a point-of-care device.     

Highly sensitive identification of foodborne pathogenic Listeria monocytogenes using single-phase continuous-flow nested PCR microfluidics with on-line fluorescence detection

Highly sensitive detection of foodborne pathogens such as Listeria monocytogenes (L. monocytogenes) is crucial to the prevention and recognition of problems related to public health and legal repercussions, due to “zero tolerance” standard adopted for food safety in many countries. Here we first propose a single-phase continuous-flow nested polymerase chain reaction (SP-CF-NPCR) strategy for identification of the low level of L. monocytogenes on an integrated microfluidic platform. The PCR reactor is constructed by a disposable capillary embedded in the grooved heating column, coupled with a fluorescence microscopy for on-line semi-quantitative end-point fluorescence detection. As a proof-of-concept microfluidic system, the nested PCR is performed in a continuous-flow format without the need of any non-aqueous oil or solvent. On this device, the performance of nested PCR amplification has been evaluated by investigating the effect of reaction parameters, including polymerase concentration, flow rates, and template DNA concentration. In addition, the types of samples the presented system can accept, such as the unpurified DNA samples and artificially contaminated clinical stool samples were also evaluated. With the optimized reaction parameters, 0.2 copies/μL of genomic DNA from L. monocytogenes can be detected on the presented device. To our knowledge, this is the highest detection sensitivity in single-phase continuous-flow PCR microsystems reported so far. The high sensitivity of the analysis method, combined with the flexibility of reaction volumes and convenience of continuous operation, renders it to be further developed for potential analytical and diagnostic applications.     

Asymptotic properties of consensus-type algorithms for networked systems with regime-switching topologies

This paper is concerned with asymptotic properties of consensus-type algorithms for networked systems whose topologies switch randomly. The regime-switching process is modeled as a discrete-time Markov chain with a finite state space. The consensus control is achieved by using stochastic approximation methods. In the setup, the regime-switching process (the Markov chain) contains a rate parameter ε>0 in the transition probability matrix that characterizes how frequently the topology switches. On the other hand, the consensus control algorithm uses a stepsize μ that defines how fast the network states are updated. Depending on their relative values, three distinct scenarios emerge. Under suitable conditions, we show that when 0<ε=O(μ), a continuous-time interpolation of the iterates converges weakly to a system of randomly switching ordinary differential equations modulated by a continuous-time Markov chain. In this case a scaled sequence of tracking errors converges to a system of switching diffusion. When 0<ε?μ, the network topology is almost non-switching during consensus control transient intervals, and hence the limit dynamic system is simply an autonomous differential equation. When μ?ε, the Markov chain acts as a fast varying noise, and only its averaged network matrices are relevant, resulting in a limit differential equation that is an average with respect to the stationary measure of the Markov chain. Simulation results are presented to demonstrate these findings.     

A single dynamic observer-based module for design of simultaneous fault detection,isolation and tracking control scheme

The problem of simultaneous fault detection, isolation and tracking (SFDIT) control design for linear systems subject to both bounded energy and bounded peak disturbances is considered in this work. A dynamic observer is proposed and implemented by using the H_∞/H_?/L₁ formulation of the SFDIT problem. A single dynamic observer module is designed that generates the residuals as well as the control signals. The objective of the SFDIT module is to ensure that simultaneously the effects of disturbances and control signals on the residual signals are minimised (in order to accomplish the fault detection goal) subject to the constraint that the transfer matrix from the faults to the residuals is equal to a pre-assigned diagonal transfer matrix (in order to accomplish the fault isolation goal), while the effects of disturbances, reference inputs and faults on the specified control outputs are minimised (in order to accomplish the fault-tolerant and tracking control goals). A set of linear matrix inequality (LMI) feasibility conditions are derived to ensure solvability of the problem. In order to illustrate and demonstrate the effectiveness of our proposed design methodology, the developed and proposed schemes are applied to an autonomous unmanned underwater vehicle (AUV).     

面向维修过程的多态混联系统综合重要度计算方法

在对综合重要度（Integrated importance measure,IIM）计算方法研究的基础上,面向维修过程,分别给出了多态并-串联和串-并联系统综合重要度具体化计算公式及其推论. 在假设组（部）件的状态转移过程为不可约连续时间马氏链的条件下,推论给出了多态并-串联和串-并联系统组（部）件综合重要度的等价计算公式,等价计算公式将重要度计算时间复杂度从平方阶降为线性阶和常数阶. 数值仿真演绎了多态混联系统重要度计算过程,验证了综合重要度计算公式及其等价计算公式的一致性.     

Image Parsing: Unifying Segmentation,Detection, and Recognition

In this paper we present a Bayesian framework for parsing images into their constituent visual patterns. The parsing algorithm optimizes the posterior probability and outputs a scene representation as a parsing graph, in a spirit similar to parsing sentences in speech and natural language. The algorithm constructs the parsing graph and re-configures it dynamically using a set of moves, which are mostly reversible Markov chain jumps. This computational framework integrates two popular inference approaches—generative (top-down) methods and discriminative (bottom-up) methods. The former formulates the posterior probability in terms of generative models for images defined by likelihood functions and priors. The latter computes discriminative probabilities based on a sequence (cascade) of bottom-up tests/filters. In our Markov chain algorithm design, the posterior probability, defined by the generative models, is the invariant (target) probability for the Markov chain, and the discriminative probabilities are used to construct proposal probabilities to drive the Markov chain. Intuitively, the bottom-up discriminative probabilities activate top-down generative models. In this paper, we focus on two types of visual patterns—generic visual patterns, such as texture and shading, and object patterns including human faces and text. These types of patterns compete and cooperate to explain the image and so image parsing unifies image segmentation, object detection, and recognition (if we use generic visual patterns only then image parsing will correspond to image segmentation (Tu and Zhu, 2002. IEEE Trans. PAMI, 24(5):657–673). We illustrate our algorithm on natural images of complex city scenes and show examples where image segmentation can be improved by allowing object specific knowledge to disambiguate low-level segmentation cues, and conversely where object detection can be improved by using generic visual patterns to explain away shadows and occlusions.     

Recursive least-squares temporal difference learning for adaptive traffic signal control at intersection

This paper presents a new method to solve the scheduling problem of adaptive traffic signal control at intersection. The method involves recursive least-squares temporal difference (RLS-TD(λ)) learning that is integrated into approximate dynamic programming. The learning mechanism of RLS-TD(λ) is to make an adaptation of linear function approximation by updating its parameters based on environmental feedback. This study investigates the method implementation after modeling a traffic dynamic system at intersection in discrete time. In the model, different traffic control schemes regarding signal phase sequence are considered, especially the defined adaptive phase sequence (APS). By simulating traffic scenarios, RLS-TD(λ) is superior to TD(λ) for updating functional parameters in the approximation, and APS outperforms other conventional control schemes on reducing traffic delay. By comparing with other traffic signal control algorithms, the proposed algorithm yields satisfying results in terms of traffic delay and computation time.

     

Intelligent process monitoring by interfacing knowledge-based systems and multivariate statistical monitoring

An intelligent process monitoring and fault diagnosis environment has been developed by interfacing multivariate statistical process monitoring (MSPM) techniques and knowledge-based systems (KBS) for monitoring multivariable process operation. The real-time KBS developed in G2 is used with multivariate SPM methods based on canonical variate state space (CVSS) process models. Fault detection is based on T ² charts of state variables. Contribution plots in G2 are used for determining the process variables that have contributed to the out-of-control signal indicated by large T ² values, and G2 Diagnostic Assistant (GDA) is used to diagnose the source causes of abnormal process behavior. The MSPM modules developed in Matlab are linked with G2. This intelligent monitoring and diagnosis system can be used to monitor multivariable processes with autocorrelated, crosscorrelated, and collinear data. The structure of the integrated system is described and its performance is illustrated by simulation studies.     

Densely connected convolutional networks-based COVID-19 screening model

The extensively utilized tool to detect novel coronavirus (COVID-19) is a real-time polymerase chain reaction (RT-PCR). However, RT-PCR kits are costly and consume critical time, around 6 to 9 hours to classify the subjects as COVID-19(+) or COVID-19(-). Due to the less sensitivity of RT-PCR, it suffers from high false-negative results. To overcome these issues, many deep learning models have been implemented in the literature for the early-stage classification of suspected subjects. To handle the sensitivity issue associated with RT-PCR, chest CT scans are utilized to classify the suspected subjects as COVID-19 (+), tuberculosis, pneumonia, or healthy subjects. The extensive study on chest CT scans of COVID-19 (+) subjects reveals that there are some bilateral changes and unique patterns. But the manual analysis from chest CT scans is a tedious task. Therefore, an automated COVID-19 screening model is implemented by ensembling the deep transfer learning models such as Densely connected convolutional networks (DCCNs), ResNet152V2, and VGG16. Experimental results reveal that the proposed ensemble model outperforms the competitive models in terms of accuracy, f-measure, area under curve, sensitivity, and specificity.