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The design and construction of highly efficient electrocatalysts for overall water splitting and urea electrolysis are significantly important for promoting energy conversion and realizing green hydrogen production. In this work, we constructed a multi‐phase heterojunction through a simple hydrothermal and phosphorization process. The P‐doped NiFe2O4 (P‐NiFe2O4) nanoparticles were uniformly anchored on the bamboo‐like N‐doped carbon nanotubes (NCNTs) grown via a NiFe‐alloy autocatalysis. The electronic structure and coordination environment of active species were optimized by the synergistic action of P doping, well‐dispersed ultrafine NiFe2O4, and NCNTs matrix with good conductivity, enhancing their quantity and activity for electrocatalysis. Consequently, the P‐NiFe2O4/NCNTs/NiFe exhibits excellent HER and OER activities with an overpotential of 111 and 266 mV at 10 mA cm−2 in 1 M KOH, respectively. The symmetrical overall water‐splitting cell using P‐NiFe2O4/NCNTs/NiFe as both anode and cathode delivers 10 mA cm−2 at a voltage of 1.604 V in 1 M KOH. Notably, the two‐electrode cell requires a low voltage of 1.467 V to achieve a current density of 10 mA cm−2 in 1 M KOH solution with 0.6 M urea. This designed catalysts display outstanding reaction kinetics and catalytic stability. This work provides useful guidance for applying transition metal‐based catalysts for hydrogen production. [ABSTRACT FROM AUTHOR]

Positioning based on wireless communication networks has great application potential. In this paper, we propose a positioning method for the 5G-Advanced (5GA) or 6G network. Firstly, we establish the communication link and generate the map-based hybrid channel model based on 3GPP standards and open-source maps, where each multipath channel is expanded into a cluster that contains 20 rays. Then, we improve the Orthogonal-Matching-Pursuit (OMP) algorithm, which can estimate Angle-Of-Arrival (AOA) through only one OFDM symbol and does not require the signal to have a very narrow bandwidth, a high Signal-to-Noise-Ratio (SNR) or multiple snapshots like the classical OMP algorithm. Finally, we propose a positioning algorithm, which locates the target through the estimated AOA and the open-source map. The proposed method can locate the target with a single Base Station and has the advantages of lower delay, lower cost, and higher accuracy. The simulation results show that the positioning error of the proposed algorithm is submeter in 63% of the cases and less than 2.2 m in 80% of the cases. [ABSTRACT FROM AUTHOR]

Conventional sandwich structure photoelectrochemical UV detectors cannot detect UV light below 300 nm due to UV filtering problems. In this work, we propose to place the electron collector inside the active material, thus avoiding the effect of electrodes on light absorption. We obtained a TiO2-nanotubes@Ti@quartz photoanode structure by precise treatment of a commercial Ti mesh by anodic oxidation. The structure can absorb any light in the near-UV band and has superior stability to other metal electrodes. The final encapsulated photoelectrochemical UV detectors exhibit good switching characteristics with a response time below 100 ms. The mechanism of the oxidation conditions on the photovoltaic performance of the device was investigated by the electrochemical impedance method, and we obtained the optimal synthesis conditions. Response tests under continuous spectroscopy confirm that the response range of the device is extended from 300–400 nm to 240–400 nm. This idea of a built-in collector is an effective way to extend the response range of a photoelectrochemical detector. [ABSTRACT FROM AUTHOR]

Implementation of tunable digital and analog resistive switching (RS) behaviors for memristors is crucial toward their development in data storage and energy-efficient computations. Herein, the CsPbBr3 film-based RS memristors with different switching film thickness and adaptable electrodes are constructed, which are implemented by the solution processed method. All the constructed memristors demonstrate stable bipolar switching behaviors, and in special, the gradual analog RS behavior with multi-level conductance is observed for the thin memristor, making it as the promising artificial synapse. As a comparison, the thick memristor demonstrates digital abrupt RS behavior, accompanying with a high memory window (>103), which can be applied in data storage field. Furthermore, the evolution of conductive filaments, stemming from synergetic bromine vacancies and aluminum atoms, is proposed to clarify the distinguished dynamic changes in the RS process. This work provides an optimization strategy to tune RS behavior for CsPbBr3-based RS memristors and paves the way for both data storage and artificial synapse applications. [ABSTRACT FROM AUTHOR]

This study presents a novel fuzzy observer synthesis method for a class of nonlinear systems characterized by discrete-time interval type-2 Takagi–Sugeno fuzzy models, which involves measurable/unmeasurable premise variables and bounded disturbances within ellipsoids. A nonfragile observer design strategy is employed to ensure resilience against potentially stochastic variations in the observer gain. A simplified adaptive event-triggered communication mechanism with a threshold that can be dynamically adjusted online is proposed to mitigate the network transmission pressure and reduce the energy consumption. A new definition of the stochastic quadratic boundedness employing multiple Lyapunov functions is proposed to concurrently determine the observer and event generator parameters. Resorting to this definition guarantees the stochastically quadratic boundedness and the stability of the estimation error systems. The codesign of the desired observer and event generator parameters is accomplished using slack-variable and convex optimization techniques. Further, the corresponding upper bounds on the estimation errors are explicitly provided. The feasibility and the superiority of the proposed method are validated through a continuously stirred tank reactor system and a tunnel diode circuit model. [ABSTRACT FROM AUTHOR]

Y2O3 has attracted attention as the representative emerging candidate of a resistive switching (RS) medium in memristors due to its excellent electrical properties and good thermal stability. However, many challenges for Y2O3 film-based memristors remain to be resolved, particularly for the small switching window. Here, the doping engineering strategy is proposed, and in particular, the Mg doped amorphous Y2O3 film is adopted as the RS layer to construct memristors. The prepared Pt/Mg:Y2O3/Pt memristor exhibits a typical reproducible bipolar switching behavior with ultra-high HRS resistance and excellent switching window (>105), compared with the undoped counterparts (∼50). In addition, the multilevel storage capability is also achieved by controlling compliance current. Furthermore, the mechanisms and corresponding physical models for the striking RS characteristics for Pt/Mg:Y2O3/Pt memristors, stemming from the Mg dopant, are discussed and illustrated in detail. This work affords a deep understanding of RS mechanisms for Mg-doped Y2O3 film-based memristors and provides an effective strategy to enlarge the switching window for other transition metal oxide memristors. [ABSTRACT FROM AUTHOR]

UV transparent conductive electrodes based on transferable ITO nanowire networks were prepared to solve the problem of low UV light utilization in conventional photoelectrochemical UV detectors. The mutually cross-linked ITO nanowire network achieved good electrical conductivity and light transmission, and the novel electrode had a transmission rate of more than 80% throughout the near-UV and visible regions. Compared to Ag nanowire electrodes with similar functionality, the chemical stability of the ITO nanowire transparent conductive electrode ensured that the device worked stably in iodine-based electrolytes. More importantly, ITO electrodes composed of oxides could withstand temperatures above 800 °C, which is extremely critical for photoelectrochemical devices. After the deposition of a TiO2 active layer using the high-temperature method, the response range of the photoelectrochemical UV detector was extended from a peak-like response between 300–400 nm to a plateau-like response between 200–400 nm. The responsivity was significantly increased to 56.1 mA/W. The relationship between ITO nanowire properties and device performance, as well as the reasons for device performance enhancement, were intensively investigated. [ABSTRACT FROM AUTHOR]

The integration of communication and perception is one of the developing trends of the communication system. The severe multipath effect is one of the biggest challenges of indoor positioning. Most existing studies are based on 2D models, which avoid multipath effects and require multiple Base Stations (BSs) to coordinate for positioning. In this paper, we build a 3D indoor multipath signal model and achieve 3D indoor positioning with a single BS by making full use of multipath channels. The Angles of Arrival (AoAs) of the multipath signals are estimated by two mutually perpendicular Uniform Linear Array (ULA) antennas. And the Time of Arrival (ToAs) are estimated by the pilot subcarriers of the OFDM signal. Then, we propose an improved matching algorithm, which matches the AoAs and ToA of each signal accurately. We get the linear equations of multipath channels with ray tracing modeling to achieve 3D indoor positioning. An angle compensation algorithm is proposed by evaluating the probability weight of each multipath AoA and compensating the AoA according to the weights. The simulation results show that the positioning accuracy of the proposed algorithm is within 0.64 m in 90% of the cases, and within 0.25 m in 50% of the cases. [ABSTRACT FROM AUTHOR]

The traditional Ag nanowire preparation means that it cannot meet the demanding requirements of photoelectrochemical devices due to the undesirable conductivity, difficulty in compounding, and poor heat resistance. Here, we prepared an Ag nanonetwork with superior properties using a special template method based on electrospinning technology. The transparent conductive films based on Ag nanonetworks have good transmittance in a wide range from ultraviolet to visible. It is important that the films have high operability and are easy to be compounded with other materials. After compounding with high-melting-point W metal, the heat-resistance temperature of the W/Ag composite transparent conductive films is increased by 100 °C to 460 °C, and the light transmission and electrical conductivity of the films are not significantly affected. All experimental phenomena in the study are analyzed theoretically. This research can provide an important idea for the metal nanowire electrode, which is difficult to be applied to the photoelectrochemical devices. [ABSTRACT FROM AUTHOR]

Halide perovskites have attracted surge of interest in the memristor field due to their superior electrical property and corresponding remarkable device performances. However, the issues of toxicity and unstable properties still severely restrict their potential applications. Here, the lead-free all-inorganic perovskite Cs3Cu2Br5 films are adopted as the switching layer to fabricate memristors with Al/Cs3Cu2Br5/ITO structure. The prepared Al/Cs3Cu2Br5/ITO memristors exhibit typical reproducible bipolar resistive switching (RS) behavior with striking characteristics, including ultralow operating voltages (0.45, −0.39 V), moderate high resistance state/low resistance state ratio (≈102), and remarkable retention time (>104 s). In addition, the multilevel storage capability can be achieved by controlling compliance current. The RS effect, stemming from the formation/rupture of both localized conductive Br vacancy/Al atom filaments, is proposed to illustrate the memristors. More importantly, the RS behavior of Al/Cs3Cu2Br5/ITO memristors maintains robustness in harsh environments with humidity up to 80%, enabling secure hardware applicable in extreme environments. This work demonstrates the opportunity for exploring the next-generation nonvolatile memories based on lead-free all-inorganic halide perovskites in future environmental-friendly and humidity robust electronics. [ABSTRACT FROM AUTHOR]

In modern industrial production, rotating machinery plays an important role. The gears in this machinery adjust the speed and transmission of torque. Therefore, when the gear fails, it is very important to be able to diagnose the fault quickly and accurately. Gear vibration signals are often used in gear fault diagnosis, but the fault signal is often overwhelmed by noises. To enable the scientific and efficient detection of faults, this study proposes a gear fault diagnosis method based on variational modal decomposition (VMD) and wide+narrow visual field neural networks (WNVNNs), namely VMD-WNVNN. VMD-WNVNN consists of two stages. In the feature extraction stage, VMD and Pearson correlation coefficients are used to decompose and reconstruct the original data to obtain the features of these data in the frequency domain. In the classification stage, WNVNN is used to classify the data based on features. The final results of the gear fault diagnosis experiments show that this method not only has higher classification accuracy but also has higher classification stability than other recently proposed methods. Note to Practitioners—The gearbox is composed of many mechanical parts, such as gears, shafts, and bearings. Therefore, the vibration signal collected by the vibration sensor on the gearbox housing will contain the vibration signal of each part and the noise caused by processing errors. Therefore, if some methods can be used to efficiently extract the characteristic signals required for diagnosis in the data processing stage, the efficiency of fault diagnosis will be greatly improved. This article takes the health of gears as the research object and proposes a method that combines adaptive signal decomposition and deep learning technology. Experimental results show that this method has higher classification accuracy and classification stability than other methods proposed recently. [ABSTRACT FROM AUTHOR]

The FTO/ITO transparent conductive films currently used in photoelectrochemical devices limit performance improvement due to their low conductivity, poor flexibility, and inability to transmit UV light. Ag nanowire-based films are a very promising alternative to address these problems, and are considered to be the next generation in transparent conductive film. Here, we prepared a cross-linked nano-network composed of ultra-long Ag nanowires by a special physical template method. The obtained Ag nanowire transparent conductive film has a transmittance of over 80% in a wide range of 200 nm–900 nm, a sheet resistance as small as 5.2 Ω/sq, and can be easily transferred to various substrates without damage. These results have obvious advantages over Ag nanowire films obtained by traditional chemical methods. Considering the special requirements of photoelectrochemical devices, we have multifunctionally enhanced the film by a TiO2 layer. The heat-resistant temperature of transparent conductive film was increased from 375 °C to 485 °C, and the mechanical stability was also significantly improved. The presence of the multifunctional layer is expected to suppress the carrier recombination in self-powered photoelectrochemical devices and improve the electron diffusion in the longitudinal direction of the electrode, while serving as a seed layer to grow active materials. The high-quality Ag nanowire network and functional layer synergize to obtain a UV–Visible transparent conductive film with good light transmittance, conductivity, and stability. We believe that it can play an important role in improving the performance of photoelectrochemical devices, especially the UV devices. [ABSTRACT FROM AUTHOR]

Recursive statistical process monitoring (RSPM) methods have superior monitoring performance for industrial processes, especially those with time‐varying characteristics, and have recently been studied by many researchers. However, there is no survey paper yet that summarizes and analyzes the existing RSPM methods. In this survey, approximately 60 papers related to RSPM methods are reviewed and categorized from different aspects. Existing RSPM methods are categorized using the classification criteria proposed in this study based on the ways of recursively collecting data and the stages of recursive updating. Three core algorithms that are most commonly used in RSPM methods are uniformly summarized and analyzed specifically for studying recursive computation problems in RSPM methods. Furthermore, different types of multivariate statistical process monitoring (MSPM) methods that combine recursive computation are reviewed. [ABSTRACT FROM AUTHOR]

Background and Aim: Though one‐size‐fits‐all age‐based screening for colorectal cancer (CRC) is effective in reducing the incidence and mortality, the evidence regarding on personized screening based on individual risk factors has been growing. The study aimed to perform a systematic review to synthesize economic evidence of risk‐tailored CRC screening strategies. Methods: This systematic review was conducted in EMBASE, Web of Science, PubMed, Cochrane Library, Econlit, and National Institute for Health Research Economic Evaluation Database from inception to June 30, 2021. We calculated the incremental cost‐effectiveness ratio (ICER) of cost per life year or quality‐adjusted life year gained for the risk‐tailored screening compared with no screening or uniform screening. A strategy was cost‐effective with less cost and equal or more effectiveness than the comparator along with lower ICER than the willingness‐to‐pay threshold. Results: Our review finally comprised seven studies. Five studies reported the results of comparisons of risk‐tailored CRC screening with no screening, and supported that risk‐tailored screening was cost‐effective. All of seven studies reported the ICERs of risk‐tailored screening and age‐based screening. Disparities in the discrimination of risk‐prediction tool, accuracy of adopted techniques, uptake rate of screening and cost estimation impacted the cost‐effectiveness. Conclusions: Studies on the economic evaluation of risk‐tailored CRC screening are limited, and current evidence is not sufficient to support the replacement of risk‐tailored screening for traditional age‐based screening. [ABSTRACT FROM AUTHOR]

One-dimensional nanowires can provide a direct electron transmission channel, as a result, the photogenerated carriers can be more easily and quickly transferred to the current collector. In a photoelectrochemical (PEC) ultraviolet (UV) detector, photoanode assemble using nanowires also possesses a large specific surface area to be beneficial for contacting between the electrolyte and photoanode. In this work, the SrTiO3 nanowires are fabricated by electrospinning and then assembled PEC UV detectors. Under UV illumination, the detector based on the SrTiO3 nanowires presents a superior detection performance with a response time of 8 ms. In particular, the device exhibits an efficient detection ability for weak UV light. Under the UV illumination intensity of 5 μW cm− 2, the current density Jsc reaches 11.3 nA cm− 2. The device also presents visible-blind, outstanding linear response and excellent repeatability under periodic UV irradiation. [ABSTRACT FROM AUTHOR]

Secure state estimation (SSE) is a problem to defense false-data injection attacks. This study designs an online distributed SSE of heterogeneous multiagent systems under homologous attack. A triple-loop observer is proposed to estimate the state and attack signal simultaneously. The inner loop keeps the estimations of the attack signal the same using average consensus. The middle loop adjusts the estimations via residual information. The outer loop runs when system measurements change. A sufficient condition that estimations asymptotically converge to the real value is obtained and proved. Finally, the proposed observer has been tested on the global positioning system. [ABSTRACT FROM AUTHOR]

This study addresses the problem of the estimation of state when heterogeneous multiagent systems are affected by homologous unknown inputs (UIs). Homologous UIs refer to identical UIs affecting different agents. An improved semidistributed filter based on previous research is proposed. The improved filter uses neighbors’ information for UI estimation but not state estimation. A necessary and sufficient condition for the proposed filter to achieve minimum-variance unbiased estimation is presented and proven. Moreover, the asymptotic stability of the filter is analyzed. A sufficient condition of the asymptotic stability is presented and proven. The theoretical and numerical analyses indicate that the proposed filter has less communication pressure, fewer calculation requirements, and better estimation performance compared with the existing solutions. Note to Practitioners—In the industry, homologous unknown inputs (UIs) exist in many different systems. For example, the same ambient temperature affects the performance of every battery in a battery pack. Similarly, the same wind power can affect different aircrafts flying in the same region. Temperature and wind power can be considered the homologous UIs of a multiagent system. Estimation of homologous UIs is important because of the latter’s massive impact on the system. In this study, data transmission delay and packet loss are ignored. Hence, the study is limited to low-rate systems. Moreover, nonlinear filters must be studied further in future work. [ABSTRACT FROM AUTHOR]

This article proposes a new combination of a correlative statistical analysis and the sliding window technique to detect incipient faults. Compared with the existing monitoring methods based on principal component and transformed component analyses, the combination fully uses the information from the process and quality variables. The sliding window, however, inevitably increases the computational burden due to the repeated window calculations. Therefore, a recursive algorithm is proposed in this article, which has been shown to have less calculation complexity. Furthermore, a randomized algorithm is proposed to determine the width of the sliding window. A numerical example and the thermal power plant process are presented to show the effectiveness and advantages of the proposed method. [ABSTRACT FROM AUTHOR]

The photoelectrochemical (PEC) UV detector based on the traditional transparent conductive electrode (TCE) has a narrow response range due to the UV filtering effect. Here we prepared an Ag nanowire network by electrospinning and magnetron sputtering, and then transferred it to a quartz substrate to obtain a UV-visible TCE, which exhibits high transmittance of 80% in the UV region, and a very small sheet resistance of about 2 Ω sq−1. An ITO functional layer is deposited to further improve the vertical electron conductivity and inhibit electronic recombination. After the photoactive layer was prepared by a sintering-free method, the PEC UV detector based on the new electrode system showed excellent self-powered response performance, including fast response, linear response and high sensitivity, and the response range of the device was greatly extended to 220–400 nm. Our work provides an effective strategy for solving the key problems of PEC UV detectors. [ABSTRACT FROM AUTHOR]

The problem of sensor fault estimation has been studied for Unmanned Surface Vehicle (USV) systems in networked environment. Assuming that the process noise is related to the measurement noise and packet loss will occur during the measurement transmission. We use an event-trigger mechanism (ETM) to save communication resources. Accordingly, an observer is proposed to estimate the state and sensor fault, and the trigger threshold is associated with the correlation coefficient. By adjusting the threshold, the observer performance and energy consumption can be balanced in an ideal situation. The observer designed in this paper reduces the effects of correlation noises effectively and good handling network packet losses, and it has been proved that the expected covariance of the estimation error is bounded. Finally, the simulation experiment on the identified model of USV in the laboratory is performed to validate the relevant conclusions. [ABSTRACT FROM AUTHOR]

In this article, a novel fault detection and process monitoring method referred to as artificial neural correlation analysis (ANCA) is proposed. Because nonlinear characteristics are common in complex industrial processes, the classic canonical correlation analysis (CCA) always perform poorly. Many scholars have noticed the nonlinear problem of the process and have also proposed some improved schemes, such as the kernel method. However, the selection of suitable parameters in the kernel method is extremely difficult, so most of the kernel learning methods are slightly unsatisfactory. Considering that the artificial neural network (ANN) can well extract the required feature components from the nonlinear data, we combined ANN and CCA from their respective principles, and proposed a new nonlinear monitoring method and the detailed gradient descent method derivation for the ANCA network is presented. In addition, we have designed two indices to monitor the changes of process variables and performance indicators. Finally, a numerical example, the Tennessee Eastman benchmark, and the Zhoushan thermal power plant process illustrate the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Traditional multivariate statistical-based process monitoring (MSPM) methods are effective data-driven approaches for monitoring large-scale industrial processes, but have a shortcoming in handling the redundant correlations between process variables. To address this shortcoming, this study proposes a new MSPM method called minimalist module analysis (MMA). MMA divides process data into several different minimalist modules and one more independent module. All variables in the minimalist module are strongly correlated, and no redundant variables exist; therefore, the extracted feature components in one minimalist module will not be disturbed by noise from the other modules. This study also proposes new monitoring indices and a fault localization strategy for MMA, and simulation tests demonstrate that MMA achieves superior performance in fault detection and localization. [ABSTRACT FROM AUTHOR]

In order to solve the 'ultraviolet (UV) filtering problem' caused by traditional sandwich-type structure in photoelectrochemical (PEC) UV detector, we design a special electrode based on stainless steel mesh, which integrates the light absorption layer and the electron collection electrode in a simple way. In combination with an UV-transparent quartz substrate, UV light can directly reach the active material. The improved detector shows good visible-blind, self-powered, and linear response characteristics. The serious recombination caused by metal electrode is suppressed by depositing a barrier layer. The optimized device exhibits a high photoresponse of 0.103 A W−1 at 296 nm, a short recovery time of 250 ms, and very sensitive switching ability. Furthermore, the response range of the detector is expanded from 300 to 400 nm to the full near-UV region. Our work provides an efficient strategy to solve the key problem of the PEC UV detector. [ABSTRACT FROM AUTHOR]

The prognostic and health management (PHM) of rolling bearings has been a popular research area. Since bearing fault is inevitable during degradation, how to improve the PHM performance based on both degradation states and fault types is still an open problem. In this study, two multilabel learning algorithms are proposed for PHM of rolling bearings, named personalized binary relevance (PBR) and hierarchical multilabel K-nearest neighbor (HML-KNN), respectively. Degradation states and fault types are used as the labels of the bearing data so that each sample has a corresponding label sequence, that is to say, the PHM problem is converted to a multilabel learning problem. Both algorithms have a personalized search process, which can not only help samples build a personalized model to improve classification accuracy but also solve the problem of data imbalance between labels. At the same time, the two algorithms also have their own characteristics and focus on different application situations. The PBR algorithm has faster modeling speed, more flexible use, and replaceable subclassifiers. HML-KNN is a high-order algorithm with global information analysis capabilities through the hierarchical processing of data and the conversion of label information. Both methods have achieved good enough results in the XJTU-SY bearing dataset. In order to illustrate the practicality of the algorithm, the experimental part further increases the difficulty. Using only a single faulty sample as the training set to determine the type of compound fault, the algorithms also show high performance. In the era of industrial big data, the depth of data mining and the design of algorithm models will help us better manage equipment health. [ABSTRACT FROM AUTHOR]

In this article, we present an active fault tolerant control scheme based on fault detection and isolation (FDI) for a class of uncertain systems subject to sensor faults. The FDI unit consists of a fault detection (FD) module and a fault isolation (FI) module, to determine when and where the sensor fault happens. It is noted that the FD module is based on a proposed adaptive interval observer where adaptive parameters instead of conservative uncertainty bounds are used, as a result, the sensitivity to faults is improved. When the FD module detects a fault, a compensation controller (CC) and an FI module are activated. The CC is designed to maintain some level of performance assuming all sensors are faulty. After the FI module determines the location of the fault, a reconfigured controller is activated to give improved performance with reduced conservatism. Importantly, the unmeasurable states and faulty output are approximated as a linear combination of their upper and lower bounds, and linear parameter are determined by adaptive laws. Finally, a simulation example is utilized to verify the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

We have demonstrated a photoelectrochemical solar-blind ultraviolet (UV) detector based on SrTiO3 (STO) nanocrystalline film in this work. The assembled UV detector presents a high on/off ratio of about 6433 under periodic UV irradiation, a high photocurrent density of 220.6 μA cm−2, and a fast response time of 9 ms. By introducing a block layer (BL) on fluorine-doped tin oxide substrate, the response and decay times are shortened to 6 ms and 8 ms, respectively. The BL plays a crucial part in hindering the recombination of electrons from electrolyte carriers and avoiding short circuits. Due to its excellent light capture capability, the detector based on SrTiO3 nanocrystalline exhibits high sensitivity to weak UV light (5 μW cm−2). Moreover, the detector also exhibits visible-blind characteristics and a good linear response. [ABSTRACT FROM AUTHOR]

Although the partial least squares approach is an effective fault detection method, some issues of nonlinear process monitoring related to key performance indicators (KPIs) still exist. To address the nonlinear characteristics in the industrial processes, kernel partial least squares (KPLS) method was proposed in the literature. However, the KPLS method also faces some difficulties in fault detection. None of the existing KPLS methods can accurately decompose measurements into KPI-related and KPI-unrelated parts, and these methods usually ignore the fact that the residual subspace still contains some KPI-related information. In this article, a new improved KPLS method, which considers the KPI-related information in the residual subspace, has been proposed for KPI-related process monitoring. First, the proposed method performs general singular value decomposition (GSVD) on the calculable loadings based on the kernel matrix. Next, the kernel matrix can be suitably divided into KPI-related and KPI-unrelated subspaces. Besides, we present the design of two statistics for process monitoring as well as a detailed algorithm performance analysis for kernel methods. Finally, a numerical case and Tennessee Eastman benchmark process demonstrate the efficacy and merits of the improved KPLS-based method. [ABSTRACT FROM AUTHOR]

In recent years, autoencoder has been widely used for the fault diagnosis of mechanical equipment because of its excellent performance in feature extraction and dimension reduction; however, the original autoencoder only has limited feature extraction ability due to the lack of label information. To solve this issue, this study proposes a feature distance stack autoencoder (FD-SAE) for rolling bearing fault diagnosis. Compared with the existing methods, FD-SAE has stronger feature extraction ability and faster network convergence speed. By analyzing the characteristics of original rolling bearing data, it is found that there are evident differences between normal data and faulty data. Therefore, a simple linear support vector machine (SVM) is used to classify normal data and faulty data, and then the proposed FD-SAE is used for fault classification. The novel combination of SVM and FD-SAE has simple structure and little computational complexity. Finally, the proposed method is verified on the rolling bearing data set of Case Western Reserve University (CWRU). [ABSTRACT FROM AUTHOR]

Multivariate statistical process monitoring has been widely used in industry. However, traditional algorithms often ignore the dynamic characteristics of actual industry process. This study proposes a novel algorithm called multistep dynamic slow feature analysis (MS-DSFA), which has completed the full-condition monitoring of a dynamic system and divided dynamic structures more precisely. This algorithm achieves an optimal detection rate according to multiple control limits. To enrich the experiments, we select a numerical example, Tennessee Eastman process, and XJTU-SY bearing data sets to verify the universality of the algorithm. According to the overall score for optimal detection rates and false alarm rates, MS-DSFA stands out in the comparison of existing algorithms. [ABSTRACT FROM AUTHOR]

An observer is presented for the simultaneous estimation of the system state and actuator and sensor faults of a discrete recurrent neural network (RNN) system. The presented approach enables disturbance attenuation and guarantees observer convergence. First, the discrete RNN is converted to a discrete linear parameter varying (LPV) model. Then, the LPV model is further transformed into a descriptor system by extending the system state and sensor fault. Next, an H∞ observer is presented for the simultaneous estimation of the extended state and actuator fault of the descriptor system. Finally, the problem of observer design is translated into solving a linear matrix inequality. Experimental tests on a three‐tank system have validated the effectiveness and correctness of the presented method. [ABSTRACT FROM AUTHOR]

An improved algorithm with feature selection and neural network classification is proposed in this paper to investigate the fault diagnosis problem of rotating machinery. The feature vectors are constructed by extracting the time- and frequency-domain characteristics of the overall machine under multiple operating conditions. To strengthen the fault diagnostic ability, an improved sensitive feature selection algorithm is proposed by improving the distance evaluation (DE) method and reconstructing a low-dimensional sensitive feature sample with selectively chosen parameters from multidimensional feature vectors. The recursive least square backpropagation (RLS-BP) neural network algorithm is used for fault diagnosis by classifying the feature vectors of normal signal and faulty signals. The effectiveness of the proposed method is verified via hardware experiments using wind turbine drivetrain diagnostics simulator (WTDDS) by comparing with conventional feature vector construction methods and neural network algorithm. [ABSTRACT FROM AUTHOR]

Background: We aimed to obtain a set of health state utility scores of patients with esophageal cancer (EC) and precancerous lesions in China, and to explore the influencing factors of health‐related quality of life (HRQoL). Methods: A hospital‐based multicenter cross‐sectional study was conducted. From 2013 to 2014, patients with EC or precancerous lesions were enrolled. HRQoL was assessed using a European quality of life‐5 dimension (EQ‐5D‐3L) instrument. Multivariable linear regression analysis was performed to explore the influencing factors of the EQ‐5D utility scores. Results: A total of 2090 EC patients and 156 precancer patients were included in the study. The dimension of pain/discomfort had the highest rate of self‐reported problems, 60.5% in EC and 51.3% in precancer patients. The mean visual analog scale (VAS) score for EC and precancer patients were 68.4 ± 0.7 and 64.5 ± 3.1, respectively. The EQ‐5D utility scores for EC and precancer patients were estimated as 0.748 ± 0.009 and 0.852 ± 0.022, and the scores of EC at stage I, stage II, stage III, and stage IV were 0.693 ± 0.031, 0.747 ± 0.014, 0.762 ± 0.015, and 0.750 ± 0.023, respectively. According to the multivariable analyses, the factors of region, occupation, household income in 2012, health care insurance type, pathological type, type of therapy, and time points of the survey were statistically associated with the EQ‐5D utility scores of EC patients. Conclusions: There were remarkable decrements of utility scores among esophageal cancer patients, compared with precancer patients. The specific utility scores of EC would support further cost‐utility analysis in populations in China. [ABSTRACT FROM AUTHOR]

The iterative learning control (ILC) combining with model predictive control (ILC–MPC) is an effective control method for constrained batch processes. However, in real applications, model uncertainty usually makes it slow for the controlled process to converge to the reference trajectory. To eliminate the restrictions in previous works, a data-driven approach is proposed, which directly describes the relationship between inputs and outputs according to the past data. Based on this method, a novel data-driven ILC–MPC controller is proposed, where the two-mode framework and the invariant updating strategy are employed to guarantee the convergence. Since the outputs caused by model uncertainty are partly known from the past data, better performance can be achieved by the proposed design which is verified by experimental studies on a manipulator. [ABSTRACT FROM AUTHOR]

One-dimensional nanostructured materials, which have a high aspect ratio and large interface area, provide a promising scheme to fabricate fast-response and super-sensitive electrochemical ultraviolet (UV) sensors. In this work, we demonstrate a UV sensor based on vertically aligned TiO2 nanowire arrays interconnected with ZnO nanosheets. The UV light sensing performance was investigated by integrating the nanostructure into a photoelectrochemical (PEC) cell. Benefiting from the fast transport paths of photoelectrons provided by the ZnO nanosheets, the PEC cell exhibits a super sensitivity and a fast responsivity. The on/off ratio is about 997 under a periodic UV irradiation of 25 mW cm−2, and the short-circuit photocurrent density reaches 44.63 μA cm−2, which is 70% and 59% higher than that of the bare TiO2 nanowire arrays and TiO2 nanoparticle films. The response and recovery times are also improved from 32.5 and 8.0 ms to 13.5 and 4.0 ms, respectively. This work provides an efficient strategy to improve the performance of electrochemical devices by building external electron transport paths in the working electrode. [ABSTRACT FROM AUTHOR]

Batch processes are commonly involved by a succession of working phases with implicit non-Gaussian behaviors. Besides, in most cases, batch-to-batch processes also show similar but yet not identical running trajectory variations. To deal with these issues, this paper introduces a systematic analysis flowchart based on local outlier factor (LOF) for monitoring multiphase batch processes. First, a step-wise phase dividing algorithm is proposed with LOF to conduct phase dividing for a better understanding of batch process. Afterward, we develop the multiphase LOF for similar batch data modeling and then fault detection. The fault isolation method is proposed, where variable contributions with LOFs are induced, also with the analysis of isolability. The developed method is validated on a numerical example and the fed-batch fermentation benchmark process, both of which are compared with the multiphase principal component analysis method. Results demonstrate the feasibility and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Integrated states/faults observers for two-dimensional (2-D) linear systems, which can simultaneously estimate system states and faults, are studied in this study. Multiple channel faults, occurring in both measurement equation and state equation, are considered. On the basis of the singular system observer method and stability theory, asymptotically stable observers and uniformly ultimately bounded observers are proposed for the 2-D systems. For these two cases, constructive methods for observer design and parameter tuning are further provided. Under different system conditions, the necessary and sufficient conditions for the existence of integrated observers are derived and proved through matrix rank analysis. Finally, two examples are given to demonstrate the performance of the proposed methods. [ABSTRACT FROM AUTHOR]

A sensitivity-enhanced strain sensor constructed by a 7-mm-long thin-core fiber (TCF) splicing with a 10-mm-long tilted fiber Bragg grating (TFBG) was proposed. The modal interferometer formed by the TCF will interact with the spectrum of the TFBG to form an overlapped spectrum, which will extremely increase the axial strain sensitivity of the TFBG. Experimental results showed that the wavelength shifts of the resonant dip in the overlapped spectra for the applied axial strain have a high sensitivity of 0.67 pm/ $\mu \varepsilon $. Meanwhile, the proposed sensor has the temperature self-compensation function for the variation of the external temperature simply by measuring the relative wavelength shifts of the Bragg mode of the TFBG. [ABSTRACT FROM AUTHOR]

This paper addresses the finite horizon ${H_\infty }$ fault estimation problem for 2-D linear discrete time-varying systems with bounded unknown input and measurement noise. The main contribution of this paper is the ${H_\infty }$ fault estimator for 2-D systems with a necessary and sufficient existence condition. By introducing a partially equivalent stochastic dynamic system in Krein space, the necessary and sufficient condition for the existence of the ${H_\infty }$ fault estimator is derived based on innovation analysis and projection formula in Krein space. Then, the solution of the estimator is achieved by means of a Riccati-like difference equation for 2-D systems. Finally, a thermal process example is given to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract: Multivariate statistical process monitoring (MSPM) methods are significant for improving production efficiency and enhancing safety. However, to the authors’ best knowledge, there is no survey paper providing statistics of published papers over the past decade. In this paper, several issues related to MSPM methods are reviewed and studied. First, the annual publication numbers of journal articles concerning MSPM are provided to show the active development of this important research field and to point out several promising directions in the future. Second, the annual numbers of patents are also shown to demonstrate the practicality of different MSPM methods. Particularly, this paper also lists and analyzes the number of MSPM‐related publications in China. The statistics indicate that Chinese researchers and engineers may have different viewpoints from those of other countries, which results in different development trends of MSPM in China. [ABSTRACT FROM AUTHOR]

The performance of a soil-pile system can be significantly influenced by ground motion parameters. However, few research efforts have been performed to provide a complete description of the influence of key ground motion parameters on the pile’s behavior in liquefiable soil. In this study, a three-dimensional finite element (FE) model, incorporating a multisurface plasticity solid-fluid fully coupled formulation soil constitutive model, is developed and calibrated based on centrifuge test data. Seventy-two near-fault non-pulse-like (NF-NP) and seventy-two near-fault pulse-like (NF-P) ground motion records are studied with the calibrated FE model to distinguish the effects of several common ground motion parameters soon afterwards. Base on the parametric study results, a simple index, RPGV/PGA (i.e., the ratio of peak ground velocity (PGV) to peak ground acceleration (PGA)), shows its capability on characterizing the pile behavior under both NF-NP and NF-P ground motions. Furthermore, two equations are developed to characterize the relationships between the RPGV/PGA as well as the maximum pile’s moments and displacements. In general, this study can be helpful to gain new insights on the influence of typical index parameters for near-field ground motions on the response of the pile foundation in liquefiable soil. [ABSTRACT FROM AUTHOR]

In this paper, control performance assessment (CPA) is studied for batch processes controlled by iterative learning control (ILC). A 2-D linear quadratic Gaussian (LQG) benchmark is proposed to assess the performance of ILC in a 2-D framework. Based on the 2-D theory, an ILC-controlled batch process is first converted into a 2-D Roesser model. Subsequently, in order to assess the control performance of the converted 2-D system, the conventional LQG tradeoff curve is upgraded to the LQG performance assessment tradeoff surface. However, the complete knowledge of the system model is required to obtain the LQG tradeoff surface. For system without accurate model knowledge, a novel data-driven CPA method is further proposed. In this case, a novel 2-D closed-loop subspace identification method is proposed to identify the converted 2-D Roesser system. Based on the identified model, the LQG tradeoff surface can be obtained and utilized to assess the control performance. Overall, several simulation examples verified the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

It has been proven that combining open-loop subspace identification with prior information can promote the accuracy of obtaining state-space models. In this study, prior information is exploited to improve the accuracy of closed-loop subspace identification. The proposed approach initially removes the correlation between future input and past innovation, a significant obstacle in closed-loop subspace identification method. Then, each row of the extended subspace matrix equation is considered an optimal multi-step ahead predictor and prior information is expressed in the form of equality constraints. The constrained least squares method is used to obtain improved results, so that the accuracy of the closed-loop subspace can be enhanced. Simulation examples are provided to demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This study investigates a new fault-tolerant control method for uncertain nonlinear systems with multiple intermittent faults and time-varying delays. The considered intermittent faults appear in sensors and actuators simultaneously. A Markov chain is used to describe the random occurrence and disappearance of intermittent faults. The uncertain nonlinear system with intermittent faults is augmented as a Markovian jump system. By using H-infinity control theory and linear matrix inequality (LMI), we design fault tolerant controllers to make augmented Markovian jump system work steadily. Several sufficient conditions for stochastic stability with given H-infinity performance index and the existence of output-feedback controllers are derived. The effectiveness of the proposed fault-tolerant method is validated by a continuously stirred tank reactor (CSTR). [ABSTRACT FROM AUTHOR]

Standard partial least square (PLS) is a useful tool for process monitoring; however, it still encounters some problems for the diagnosis of key performance indicator (KPI) faults. One of its recent modifications, improved PLS (IPLS), decomposes the process measurements into KPI-related and KPI-unrelated parts according to the correlation matrix obtained from the standard PLS. The entire residual space of PLS is categorized as the IPLS's KPI-unrelated part. However, the residual space still involves some information related to KPI, and hence IPLS's decomposition may be inappropriate. In this study, a new modified PLS is proposed, which also decomposes the residual space according to the KPI. The loadings of input data are first decomposed to obtain a projection model. Next, the input data are more appropriately decomposed into KPI-related and KPI-unrelated parts. Correspondingly, two statistic indices can be designed for fault diagnosis. A numerical example and the Tennessee-Eastman (TE) benchmark process are utilized to demonstrate the effectiveness and advantages of the proposed approach. [ABSTRACT FROM AUTHOR]

In this study, a two-step principal component analysis (TS-PCA) is proposed to handle the dynamic characteristics of chemical industrial processes in both steady state and unsteady state. Differently from the traditional dynamic PCA (DPCA) dealing with the static cross-correlation structure and dynamic auto-correlation structure in process data simultaneously, TS-PCA handles them in two steps: it first identifies the dynamic structure by using the least squares algorithm, and then monitors the innovation component by using PCA. The innovation component is time uncorrelated and independent of the initial state of the process. As a result, TS-PCA can monitor the process in both steady state and unsteady state, whereas all other reported dynamic approaches are limited to only processes in steady state. Even tested in steady state, TS-PCA still can achieve better performance than the existing dynamic approaches. [ABSTRACT FROM AUTHOR]

A fault diagnosis and compensation problem for two-dimensional discrete time systems with time-varying state delays is studied in this paper. The concerned two-dimensional systems are described by the Fornasisi-Marchesini second model and are subject to unknown disturbances. First, a fault detection and diagnosis module is designed to obtain the information on sensor faults; a new fault detection and diagnosis integrated design, using the observer based on descriptor system approach, is proposed to detect and estimate the sensor faults. The integrated design can maximize the fault detection rate for a given false alarm rate. Sufficient conditions for the existence of the integrated fault detection and diagnosis design are derived in the context of norm evaluation and provided in terms of matrix inequalities. Second, a fault-tolerant control module is proposed upon an existing output feedback controller. When the sensor fault occurs, the faulty measurement can be identified and corrected by the proposed fault detection and diagnosis module. In this case, the feedback controller can guarantee the performance of the closed-loop system even when encountering sensor faults. Finally, the proposed method is applied to a thermal process to illustrate its effectiveness. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This study focuses on the problem of simultaneous estimation of multiple channel faults for two-dimensional linear systems, which are described by Fornasini–Marchesini second (FM-II) model, and the faults that exist in state equation and measurement equation. By transforming the fault in the measurement equation as augmented state, the FM-II model with faults in the state equation and measurement equation can be rewritten into a singular system. Hence, several observers are proposed for the singular systems, and then the estimation of the faults in the state equation and measurement equation can be obtained. Using Lyapunov stability theory, sufficient conditions for the existence of the asymptotically stable observer and uniformly ultimately bounded observer are derived in the context of time domain. For the bounded observer, the upper bound of estimation error can be provided referring to the fault bound. Numerical and practical examples are given to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Identification for closed-loop two-dimensional (2-D) causal, recursive, and separable-in-denominator (CRSD) systems in the Roesser form is discussed in this study. For closed-loop 2-D CRSD systems, under feedback control condition, there exists some correlation between the unknown disturbances and future inputs which offers the fundamental limitation for utilizing standard open-loop 2-D CRSD systems subspace identification methods. In other words, the existing open-loop subspace approaches will result in biased estimates of plant parameters from closed-loop data. In this study, based on orthogonal projection and principal component analysis, novel 2-D CRSD subspace identification methods are developed, which are applicable to both open-loop and closed-loop data. Additionally, the whiteness external excitation case is discussed and subsequently modified instrument variables are adopted to improve the proposed subspace algorithm. An illustrative example of the injection molding process and several numerical examples are used to validate consistency and efficiency of the proposed subspace approaches for 2-D CRSD systems. [ABSTRACT FROM AUTHOR]

This paperconsiders the evaluation of interval tracking error for sampled control performance and an associated sampling technique to enhance the tracking performance. The upper bounds of the tracking error profile of arbitrary sample interval for both the linear system and nonlinear system are first given. A practical sampled-data iterative learning control with varying sampling rates is proposed to ensure a prior given tolerant tracking error. In this control strategy, the inter-sample behaviour is checked to determine which intervals are not satisfactory when the given tracking performance at-sample time instants is satisfied, and then the sampling frequency for such intervals is increased. Both at-sample and inter-sample tracking performance are satisfied after enough learning iterations. Two examples are simulated to demonstrate the effectiveness of the proposed sampling strategy. [ABSTRACT FROM AUTHOR]