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This paper is concerned with the recursive state estimation (RSE) problem under minimum mean-square error sense for a class of nonlinear complex networks (CNs) with uncertain inner coupling, random link failures and packet disorders. Firstly, a set of random variables obeying the Bernoulli distribution is adopted to characterize whether there are connections between different network units, i.e., there is no random link failure when the random variable is equal to 1, otherwise the random link failure occurs. In addition, the inner coupling strength is assumed to be varying within a given interval and the phenomenon of packet disorders caused by the random transmission delay (RTD) is also taken into account. In our study, the nonlinearity satisfies the continuously differentiable condition, which can be linearized by resorting to the Taylor expansion. The focus of the addressed RSE problem is on the design of an RSE approach in the mean-square error sense such that, for all uncertain inner coupling, random link failures and packet disorders, a suboptimal upper bound of the state estimation error covariance is obtained and minimized by parameterizing the state estimator gain with explicit expression form. Furthermore, a sufficient condition with respect to the uniform boundedness of state estimation error in mean-square sense is elaborated. Finally, a numerical experiment is introduced to demonstrate the validity of the presented RSE approach.

This paper deals with the problem of finite-time stabilization for the multi-rate networked control system with network-induced time delays. In order to guarantee the finite-time stability and overcome the adverse impact of network-induced time delays in both sensor-to-controller channel and controller-to-actuator channel, a networked predictive control strategy is proposed based on multi-rate sampling mechanism. By utilizing the techniques of lifting and augmenting, the augmented closed-loop system is derived with a uniform sampling rate. By applying the Lyapunov stability theorem, sufficient conditions are established such that the multi-rate networked control system can be finite-time stabilized. Furthermore, the gain matrices are determined for the observer and the controller by solving bilinear matrix inequality or linear matrix inequality. Finally, a numerical example is presented to show the validity of the obtained results.

This paper is concerned with the partial-nodes-based state estimation (PNBSE) problem for a class of time-varying complex networks with randomly occurring sensor delay (ROSD) and stochastic coupling strength (SCS). We assume that only partial outputs of nodes can be measured and utilized in the estimation algorithm design. The ROSD is expressed by a set of Bernoulli distributed random variables and the occurrence probabilities are certain. Moreover, the SCS is represented by a set of random variables which obeys the uniform distribution. The aim of this paper is to design a recursive state estimator based on the framework of the Kalman filter, where the optimization problem of the upper bound of the estimation error covariance (EEC) is discussed by employing the variance-constrained method. It can be seen that the gain matrix of each node can be obtained by solving two Riccati-like difference equations. In addition, the performance of the PNBSE method is characterized by discussing the relationship between the occurrence probabilities of ROSD and the upper bound of the EEC, where the related mathematical proof is provided. Finally, some simulations are given to show the validity and correctness of the presented PNBSE approach.

This paper is concerned with the non-fragile protocol-based set-membership estimation problem for a class of discrete memristive neural networks (MNNs) with mixed time-delays, quantization and unknown but bounded noises. The nonlinear neural activation function satisfies the sector-bounded condition and the logarithmic quantization error is transformed to the norm-bounded uncertainty. In order to save the networks resources, the maximum-error-first (MEF) protocol is introduced to allocate the utilization order of the network channel. The focus is on the design of non-fragile state estimator to ensure such that, in the simultaneous presence of the mixed time-delays, quantization errors and estimator gain perturbations, real state is confined to the ellipsoid. In particular, a minimization problem is given to determine the radius of the designed ellipsoid and the estimator gain matrix by testifying the feasibility of some recursive matrix inequalities. Finally, some simulations are used to show the feasibility of the developed non-fragile suboptimal state estimation strategy.

Although the personality traits (PT), patient-reported outcome (PRO), and chronic prostatitis (CP) symptoms in premature ejaculation (PE) have been evaluated, there was no study to assess their correlations in men with different PE syndromes. The purpose of this study was to assess the correlations between the PT, PRO, and CP symptoms in men with different PE syndromes. Between January 2019 and January 2021, a cross-sectional field study was conducted in our andrology clinic. Men with the complaints of PE were divided into lifelong PE (LPE), acquired PE (APE), variable PE (VPE), and subjective PE (SPE). All subjects were required to complete a verbal questionnaire with the PRO, National Institutes of Health Chronic Prostatitis Symptom Index (NIH-CPSI), and Temperament and Character Inventory (TCI-R). Finally, 479 men with the complaints of PE and 365 without the complaints of PE were enrolled. The incidence of PE syndromes in PE complaint group was as follows: LPE 16.70%, APE 48.85%, VPE 11.27%, and SPE 23.17%. Mean ages in PE complaint group were 42.53 ± 12.25 years. In the PE complaint group, the novelty seeking (NS) scores were strongest correlated with the personal distress and quality of life (QOL). The harm avoidance (HA) scores were strongest correlated with the severity of PE and pain syndromes. The self-transcendence (ST) scores were strongest correlated with the satisfaction with sexual intercourse and QOL. In addition, strongest association between the total scores of NIH-CPSI and the NS or ST scores was also found in the APE group. The HA scores were also strongest correlated with the total scores of NIH-CPSI in SPE. Strongest association between the total scores of NIH-CPSI and the NS/TI or ST/CI scores was also found in the APE group. The HA/TI scores were also strongest correlated with the total scores of NIH-CPSI in SPE.

This article gives primarily attention to the resilient set-membership state estimation problem for a class of discrete time-varying complex networks with distributed delays, uncertain inner coupling and sensor saturation under the round-robin (RR) protocol. The process and measurement noises are unknown but bounded, which are confined to the ellipsoidal sets. The RR protocol is utilized to assign the priority of accessing to the communication network to nodes in terms of a fixed circular order. By means of the recursive linear matrix inequality (RLMI) technique, a sufficient criterion is established to guarantee that the one-step ahead estimation error is confined to the ellipsoidal set. In term of the convex optimization approach and the recursive algorithm, the desired estimator gains can be derived by utilizing the sufficient condition and optimizing the corresponding constraint matrices. Finally, a simulation example is used to verify the effectiveness of the designed resilient set-membership state estimation scheme.

The dispersion and attenuation of first and second sound waves under four models of G-N generalized thermo-elasticity are studied in the present work. Among the various generalized thermo-elastic t...

This paper is concerned with the robust fault detection (FD) problem for discrete uncertain systems with data drift, randomly occurring faults (ROFs) and randomly occurring nonlinearities (RONs) under weighted try-once-discard (WTOD) protocol. The phenomena of the RONs are characterized by the Bernoulli distributed variable. The Markov chain with two states is introduced to describe the ROFs in the system. The data drift is depicted by an array of mutually independent stochastic variables with individual probabilistic distribution functions. Moreover, the WTOD protocol is utilized to lessen the incidence of data collisions and enhance the communication efficiency. An FD filter is established, and sufficient conditions are proposed under which the resultant filtering error system is stochastically stable and achieves a prescribed $$H_{\infty }$$ performance irrespective of the simultaneous presence of the WTOD protocol mechanism, ROFs, RONs and data drift. Besides, the form of the desired filter parameters is presented in terms of the solutions to certain matrix inequalities. Finally, two examples are exploited to demonstrate the feasibility of the obtained robust FD algorithm.

This paper is concerned with the distributed variance-constrained robust filtering problem for a class of time-varying stochastic systems subject to both randomly occurring nonlinearities and missing measurements. The target plant is disturbed by the multiplicative noises, randomly occurring nonlinearities as well as additive noises. The phenomena of the randomly occurring nonlinearities and missing measurements are modeled by the Bernoulli distributed random variables with known occurrence probabilities. The available measurements of each sensor node and its neighbor nodes can be communicated based on the network topology structure. Attention is focused on the design of a new distributed variance-constrained robust filtering algorithm such that, in the simultaneous presence of the missing measurements, multiplicative noises and randomly occurring nonlinearities, an upper bound of the filtering error covariance is obtained via the solutions to two recursive matrix equations. Subsequently, the filter parameters are designed to minimize the obtained upper bound of the filtering error covariance. Furthermore, by utilizing the mathematical induction method, a sufficient condition is provided to guarantee the boundedness of the upper bound of the filtering error covariance. At last, we provide a numerical simulation to illustrate the effectiveness of distributed variance-constrained robust filtering method.

Objective To evaluate the role of cyclic protein arginine methyltransferase 5 (circPRMT5) in the occurrence and development of breast cancer (BC). Methods A total of 90 BC patients who underwent radical mastectomy and 40 age-matched healthy female controls were recruited in the Second People's Hospital of China Three Gorges University, Yichang Second People's Hospital from 2017 to 2020. Quantitative reverse transcription polymerase chain reaction (RT-qPCR) was used to detect the expression levels of circPRMT5 in BC tissues, serum, normal breast cell line (MCF-10A) and BC cell line (T47D, MCF-7, BT549, Hs-578T and MDA-MB-231, MDA-MB-468). The associations between circPRMT5 expression level and age, tumor size, degree of differentiation, TNM stage, distant metastasis, estrogen receptor (ER) or progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status were analyzed. BC cell lines with circPRMT5 knockdown or overexpression were subject to CCK-8 cell proliferation assay, and transwell cell invasion/migration assay. Results CircPRMT5 expression in BC tissue was higher than that in adjacent normal breast tissue. Consistently, the expression level of circPRMT5 was also elevated in serum samples collected from BC patients when compared with healthy controls. And in multiple breast cancer cell lines, circPRMT5 was upregulated as compared to normal breast epithelial MCF-10A cells. CircPRMT5 expression level was correlated with tumor size, TNM stage, lymph node metastasis distant metastasis, but no correlation was observed with ER, PR, HER2 status. Overexpression of circPRMT5 promoted the proliferation, invasion and migration of MCF7 cells; while the knockdown of circPRMT5 inhibited cell proliferation, invasion and migration. Conclusion CircPRMT5 seems to act as an oncogene in the progression of BC. Our data suggest that CircPRMT5 may be used as a biomarker for the diagnosis, prognosis evaluation and targeted therapy of breast cancer.

Study Design: Technique note.Objectives: To describe our modified oblique lumbar interbody fusion (OLIF) technique in the reconstruction of the L5-S1 segment.Summary of Background Data: Recently, OLIF has been generally recognized as an effective procedure in the treatment of various spinal pathologies at L2-L5 segments. However, the usage of OLIF at the L5-S1 segment doesn’t have gained widespread acceptance in spine community. Some authors still concern about the feasibility of OLIF used in lumbosacral fusion Methods: Ten consecutive patients underwent L5-S1 interbody fusion using the OLIF technique in our institution. The L5–S1 disc space is approached via one retroperitoneal oblique corridor between the psoas muscle and the great vessels. The discectomy and endplate preparation are performed through a surgical window developed on the anterolateral side of L5-S1 disc. A secondary cage insertion technique is used for safe placement of interbody fusion cages.Results: Of the 10 patients, 6 were males and 4 were females, with an average age of 55.4±6.8 years. There were 8 single-level and 2 two-level procedures, including 2 at L4–L5 and 10 at L5–S1. Preoperative axial MR images confirmed 1 patient with type I LCIV (left common iliac vein), 6 with type II LCIV and 3 with type III LCIV. The average blood loss was 133.4±88.5 ml, and the average operative times were 153.6±38.3 minutes. Postoperative radiographs examination confirmed all patients obtained a better reconstruction at the lumbosacral junction. Two patients with type III LCIV sustained iliolumbar vein laceration during the exposure, and no other perioperative complications were encountered.Conclusion: Our novel OLIF L5-S1 technique is a more feasible procedure of lumbosacral fusion, which shared the common surgical plane with OLIF L2-5, allowing for L2 to S1 reproducible multi-levels interbody fusions via a retroperitoneal oblique corridor between the psoas muscle and the great vessels. Detailed preoperative plan and meticulous intraoperative manipulation are prerequisite for the success of OLIF L5-S1 procedure.

Background Understanding the position of the normal ankle is the key before every successfully surgical planning. The aims of our study were to quantify the normal ankle angles, measures and reference points systematically on ankle radiographs. Methods A total of 900 angles and 300 basic information were measured on 100 healthy ankles. The radiographic measurements were performed on standard weight-bearing anteroposterior (AP) and lateral view of the ankle. A total of 6 angles were made from the AP view, 3 angles from the lateral view. All angles were measured by 2 foot and ankle surgeons, independent of each other. For intraobserver reliability, after six weeks, the same angles were measured by each observer. Results A total of 900 angles and 300 basic information were measured on 100 healthy ankles. The mean age of the subjects was 33.0 ± 9.1years, with 57 females(57%) and 43 males(43%), with 44 left feet(44%) and 56 right feet(56%). Intraobserver is reliability greater than 0.8 and the interobserver reliability greater than 0.8. The following angles were measured from the AP of ankle: TAS’s angle, TT’s angle, TC’s angle, TMM’s angle, TLM’s angle, MA’s angle. The following angles were measured from the lateral view: TLS’s angle, Kite angle, TTA’s angle. Conclusions Our study will provide a set of standard angle measures. The range of angles can be used as a reference for preoperative design and intraoperative and postoperative evaluations.

Background: Although several treatment options described in paper, Haglund’s Syndrome (HS) is a challenging common lower extremity disorder. This study reported the clinical outcomes of Zadek osteotomy (ZO) for the treatment of HS.Methods: This retrospective study included 19 patients who underwent ZO from January 2016 to June 2018. Patients were evaluated using the American Orthopedic Foot Ankle Society ankle-hindfoot scale (AOFAS), Visual Analogue Scale (VAS), ankle range of motion (AROM), and Chauveaux-Liet (CL) angle preoperatively and at final follow-up visit (16.3±4.2) months. Postoperative complications of patients were also recorded.Results: The ZO exhibited a significant improvement (P＜0.05) in preoperative to postoperative AOFAS (from 53.2±5.4 to 86.4±6.5), VAS ( from 6.3±2.1 to 0.9±1.2 ) scores and CL angle (from 18.2±1.3 to 9.4±2.1 ) degrees, the dorsiflexion of AROM (DAROM) (from 23.4±2.3 to 29.4±1.2) degrees. The planter flexion of AROM (PAROM) (from 32.5±3.5 to 33.2±3.4) degrees (P＞0.05). One postoperative complication was observed: a case of hardware pain in healthy patient. The overall rate of satisfaction after surgery was (94.7%).Conclusions: ZO is effective and safe procedure for the treatment of HS. This method can be a good option for those patients of HS.

// Xiaohu Li 1, * , Hongmin Shu 1, * , Yifei Zhang 2 , Xiaoshu Li 1 , Jian Song 1 , Junhua Du 2 , Yinfeng Qian 1 , Bin Liu 1 and Yongqiang Yu 1 1 Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China 2 Department of Urology, The First Affiliated Hospital of Anhui Medical University, Hefei, China * These authors contributed equally to this work and co-first authors Correspondence to: Yongqiang Yu, email: yuyongqiang@hotmail.com Bin Liu, email: ayfyliubin@126.com Keywords: urinary stone; low dose; adaptive statistical iterative reconstruction; computed tomography Received: September 25, 2017 Accepted: March 24, 2018 Published: April 13, 2018 ABSTRACT Purpose: To prospectively determine the diagnostic performance of low-dose CT (LDCT) with adaptive statistical iterative reconstruction (ASIR) technique for the detection of urinary stone disease. Results: The average DLP and ED was 408.16 ± 119.04 mGy and 6.12 ± 1.79 mSv in CDCT, and 138.19 ± 76.87 mGy and 2.07 ± 1.15 mSv in LDCT, respectively. The dose reduction rate of LDCT was nearly 66.1% for both DLP and ED ( P 0.05), but higher than LDCT-FBP images (mean score = 2.77) ( P < 0.05). Materials and Methods: 70 consetutive patients with clinically suspected urolithiasis underwent non-enhanced CT. Followed by both conventional-dose CT (CDCT) and low-dose CT (LDCT) scans. Automatic tube current modulation (ATCM) scanning was used, with a noise index setting of 13 in CDCT and 25 in LDCT. Reconstructions were performed with filtered back projection (FBP) and different settings of adaptive statistical iterative reconstruction [ASIR(40%, 60%, 80%)]. Urinary calculi (size, location, number), image quality (scale 1–5), image noise (scale 1–3) and diagnostic confidence levels (scale 1–3) were evaluated and measured by two radiologists independently. Radiation dose was recorded by calculating dose length product (DLP) and effective dose (ED). Statistical analyses included Mann-Whitney U test and Paired t tests. Conclusions: LDCT with ASIR can reduce the radiation dose while maintain relatively high image quality in the diagnosis of urinary stone diseases.

This paper addresses the resilient $H_{\infty }$ state estimation problem under variance constraint for discrete uncertain time-varying recurrent neural networks with randomly varying nonlinearities and missing measurements. The phenomena of missing measurements and randomly varying nonlinearities are described by introducing some Bernoulli distributed random variables, in which the occurrence probabilities are known a priori. Besides, the multiplicative noise is employed to characterize the estimator gain perturbation. Our main purpose is to design a time-varying state estimator such that, for all missing measurements, randomly varying nonlinearities and estimator gain perturbation, both the estimation error variance constraint and the prescribed $H_{\infty }$ performance requirement are met simultaneously by providing some sufficient criteria. Finally, the feasibility of the proposed variance-constrained resilient $H_{\infty }$ state estimation method is verified by some simulations.

Background Spinal cord injury (SCI) is a severe central nervous system trauma. The present study aimed to evaluate the effect of HIF-1α on inflammation in spinal cord injury (SCI) to uncover the molecular mechanisms of anti-inflammation. Results HIF-1α was reduced in SCI model rats and HIF-1α activation reduced TNF-α, IL-1β, IL-6 and IL-18 levels in SCI model rats. Meanwhile, Circ 0001723 expression was down-regulated and miR-380-3p expression was up-regulated in SCI model rats. In vitro model, down-regulation of Circ 0001723 promoted TNF-α, IL-1β, IL-6 and IL-18 levels, compared with control negative group. However, over-expression of Circ 0001723 reduced TNF-α, IL-1β, IL-6 and IL-18 levels in vitro model. Down-regulation of Circ 0001723 suppressed HIF-1α protein expressions and induced NLRP3 and Caspase-1 protein expressions in vitro model by up-regulation of miR-380-3p. Next, inactivation of HIF-1α reduced the pro-inflammation effects of Circ 0001723 in vitro model. Then, si-NLRP3 also inhibited the pro-inflammation effects of Circ 0001723 in vitro model via promotion of autophagy. Conclusions We concluded that HIF-1α reduced inflammation in spinal cord injury via miR-380-3p/ NLRP3 by Circ 0001723.

This paper is coped with the H ∞ state estimation problem under error variance constraint for discrete stochastic recurrent neural networks (RNNs) with parameter uncertainty and randomly occurring nonlinearities. The activation functions satisfy the sector-bounded condition, and the phenomena of randomly occurring nonlinearities are described by random variables obeying Bernoulli distribution. The major purpose is on the development of a time-varying H ∞ state estimation scheme such that some sufficient conditions are derived to ensure both the prescribed H ∞ performance requirement and estimation error variance constraint. Accordingly, the desired estimation algorithm can be realized based on recursive linear matrix inequality approach. Finally, an example is presented to show the effectiveness of the main constrained estimation results.

This paper addresses the event-triggered H∞ state estimation problem for a class of discrete recurrent neural networks subject to variance-constraint and fading measurements. The phenomena of fading measurements are described by introducing a set of mutually independent random variables, which reflect that each sensor has individual missing probability. In addition, for the purpose of energy saving, an event-triggered H∞ state estimation scheme is used for time-varying neural networks to determine whether the measurement output is transmitted to the estimator or not. Some sufficient conditions are obtained to guarantee that the estimation error system satisfies both estimation error variance constraint and prescribed H∞ performance requirement. Finally, the feasibility of the proposed event-triggered H∞ state estimation method is verified by a numerical example.

In this paper, we discuss the problem of optimal filter design for a class of networked stochastic systems subject to state delay and missing measurements. Both the random perturbations and the missing measurements are addressed in the system model, where the random perturbations are characterized by the multiplicative noises and the addressed phenomena of the missing measurements are modeled by a series of mutually independent Bernoulli random variables with individual occurrence probability. In view of the innovative analysis approach and the recursive projection formula, we design an optimal filter for networked systems with multiplicative noises and missing measurements such that the filtering error is minimized in mean square error sense. The main advantage of the proposed result lies in its recursive form applicable for online computations. In addition, we can see that the filter parameter can be obtained by solving some recursive equations. Finally, we give a numerical simulation example to illustrate the effectiveness of the filtering method proposed in this paper. HighlightsWe design a new filtering algorithm for time-varying networked systems with time-delay and missing measurements.We propose a filtering compensation scheme by taking the missing probabilities into account.We show that the new filtering method is optimal in minimum mean square error sense.

The optimal filtering problem is investigated for a class of discrete stochastic systems with finite-step autocorrelated process noises, random one-step sensor delay and missing measurements. The random disturbances existing in the system are characterized by the multiplicative noises and the phenomena of sensor delay and missing measurements occur in a random way. The random sensor delay and missing measurements are described by two Bernoulli distributed random variables with known conditional probabilities. By using the state augmentation approach, the original system is converted into a new discrete system where the random one-step sensor delay and missing measurements exist in the sensor output. The new process noises and observation noises consist of the original stochastic terms, and the process noises are still autocorrelated. Then, based on the minimum mean square error (MMSE) principle, a new linear optimal filter is designed such that, for the finite-step autocorrelated process noises, random one-step sensor delay and missing measurements, the estimation error is minimized. By solving the recursive matrix equation, the filter gain is designed. Finally, a simulation example is given to illustrate the feasibility and effectiveness of the proposed filtering scheme.

Methylprednisolone exhibits anti-inflammatory antioxidant properties, and rosiglitazone acts as an anti-inflammatory and antioxidant by activating peroxisome proliferator-activated receptor-γ in the spinal cord. Methylprednisolone and rosiglitazone have been clinically used during the early stages of secondary spinal cord injury. Because of the complexity and diversity of the inflammatory process after spinal cord injury, a single drug cannot completely inhibit inflammation. Therefore, we assumed that a combination of methylprednisolone and rosiglitazone might promote recovery of neurological function after secondary spinal cord injury. In this study, rats were intraperitoneally injected with methylprednisolone (30 mg/kg) and rosiglitazone (2 mg/kg) at 1 hour after injury, and methylprednisolone (15 mg/kg) at 24 and 48 hours after injury. Rosiglitazone was then administered once every 12 hours for 7 consecutive days. Our results demonstrated that a combined treatment with methylprednisolone and rosiglitazone had a more pronounced effect on attenuation of inflammation and cell apoptosis, as well as increased functional recovery, compared with either single treatment alone, indicating that a combination better promoted recovery of neurological function after injury.

In this paper, the non-fragile recursive filtering problem is investigated for a class of networked time-varying nonlinear systems with stochastic uncertainties and incomplete measurements. By employing a stochastic Kronecker delta function, the phenomena of the incomplete measurements are characterized in a unified framework which contain the randomly occurring signal quantization and the missing measurements. Based on the available probability information of the incomplete measurements, a new filtering compensation scheme is proposed to ensure that, for all stochastic uncertainties, incomplete measurements and stochastic perturbations of the filter gain, an upper bound of the filtering error covariance is guaranteed and such an upper bound is minimized by properly designing the filter gain at each sampling instant. It is shown that the desired filter gain can be obtained by solving two Riccati-like difference equations, and the proposed filtering algorithm is of a recursive form which is suitable for online applications. Finally, an illustrative example is provided to demonstrate the feasibility of the developed filtering approach.

Spinal cord injury (SCI) is a severe central nerve system damage. The actual roles of autophagy in the context of SCI remain controversial. This study was undertaken to determine the effects of enhanced autophagy on neural apoptosis in rat SCI model. Rapamycin or vehicle was given via an intraperitoneal injection after SCI. Spinal cord samples were extracted 24 hr after SCI. The level of phosphorylation of p70S6K was significantly reduced whereas expression of LC3-II/LC3-I was significantly increased after rapamycin treatment. Rapamycin treatment markedly reduced caspase-3 activity and staining density of TUNEL, but increased NeuN expression within the injured spinal cord. Moreover, rapamycin treatment reduced Bax translocation to the mitochondria and the release of cytochrome c into the cytosol. Rapamycin-treated rats showed significantly higher locomotor function in BBB scores compared with vehicletreated rats. Our results demonstrated rapamycinenhanced autophagy reduced neuronal apoptosis via inhibiting mTOR signal pathway, and improved locomotory recovery in rat SCI model. The antiapoptotic mechanism was related to the enhancement of autophagy, which ameliorated neural apoptosis through a mitochondrial pathway.

In this paper, the state estimation problem is investigated for a class of discrete nonlinear systems with randomly occurring uncertainties and distributed sensor delays. The norm-bounded uncertainties enter into the system in a randomly way, and such randomly occurring uncertainties (ROUs) obey certain Bernoulli distributed white noise sequence with known conditional probability. By constructing a new Lyapunov–Krasovskii functional, sufficient conditions are proposed to guarantee the convergence of the estimation error for all discrete time-varying delays, ROUs and distributed sensor delays. Subsequently, the explicit form of the estimator parameter is derived by solving two linear matrix inequalities (LMIs) which can be easily tested by using standard numerical software. Finally, a simulation example is given to illustrate the feasibility and effectiveness of the proposed estimation scheme.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.