This article is concerned with a recursive remote estimation problem for a class of nonlinear complex networks subject to the token bucket protocol (TBP) and amplify-and-forward (AF) relays. The influence of the TBP is considered, for the first time, in the context of networked state estimation, where the token consumptions are modeled in a stochastic manner, so as to describe the potential size variability of transmitted measurement signals. Once processed by the TBP, the signals, with stochastic channel coefficients, are transmitted to the remote estimator via AF relays, where a failure in transmission under the TBP may occur due to insufficient tokens in the bucket. An extended-Kalman-filter-based novel recursive estimator is proposed, and by solving Riccati-like difference equations, an upper bound of prediction/estimation error covariance is determined and further minimized through the design of an appropriate estimator gain. The impact of the TBP on estimation performance is also investigated. Some numerical simulations are presented to demonstrate the effectiveness of the proposed estimator and the effects of the TBP.

In this article, the remote estimation problem is addressed for a class of discrete-time complex networks under the influence of probabilistic quantization and amplify-and-forward (AF) relays. The underlying complex network model, which is inherently nonlinear and stochastic, is affected by additive process and measurement noises. Owing to the limited bandwidth of the transmission channel, the measurement outputs are quantized by a probabilistic quantizer prior to transmission. To enhance the signal quality over long-distance transmissions, the quantized measurements are sent to AF relays and subsequently forwarded to the estimator. Utilizing the unscented Kalman filter approach, a novel state estimator is designed to minimize an upper bound on the estimation error covariance. Moreover, sufficient conditions are derived to ensure that the estimation error is exponentially bounded in the mean-square sense. Lastly, the efficacy of the proposed scheme is illustrated through numerical simulations.

Surgical phase segmentation plays an important role in computer-assisted surgery systems, aiming to recognize what step or what action is operating in the video frame. Existing methods focus on improving the accuracy and precision of video segmentation, but ignore semantic consistency and temporal continuity of video frames in the intra-phase, which is necessary to apply in realistic computer-assisted equipment. Meanwhile, recent works almost extract long-term dependencies by Temporal Convolutional Network, but we heed high layers in TCN lose fine-grained information for detecting surgical steps and further affect phase segmentation task. To address these problems, we propose a Surgical Phase Segmentation Framework (SPHASE) which contains a multimodal feature fusion process and follows a multi-branch predictor. Moreover, we design a multimodal feature fusion mechanism when aggregate optical flow feature and I3D feature. The extensive experiments on AutoLaparo, Cholec80, and M2CAI2016 datasets demonstrate our method outperforms the state-of-the-art method by a large margin, especially in the JACC metric, which means SPHASE is more applicable in the surgical operating room.

This article addresses the particle filtering problem for a class of nonlinear/non-Gaussian systems with quantized measurements and multiple degrading sensors. A degradation variable described by the Wiener process is proposed to describe the phenomenon of sensor degradation that is often encountered in engineering practice. The measurement output of each sensor is quantized by a uniform quantizer before being sent to the remote filter. An augmented system is constructed, which aggregates the original system state and the degradation variables. In the presence of the sensor degradation and the quantization errors, a new likelihood function at the remote filter is calculated by resorting to all the transmitted measurements. According to the mathematical characterization of the likelihood function, a novel particle filtering algorithm is developed, where the parameters of both the degradation processes and the quantization functions are exploited to obtain the modified importance weights. Finally, the effectiveness of the proposed method is shown via a target tracking example with bearing measurements.

Degradation-model-based remaining useful life (RUL) estimation is essential for effective prognostic and health management; this method can provide information for enabling effective maintenance decisions pertaining to degrading systems to avoid or mitigate loss due to impending failures. According to existing studies that estimate degradation-model-based RUL, stochastic-process-based methods are widely advocated by many researchers because they can capture stochastic dynamics within the degradation processes of systems. However, most existing studies primarily utilize the first passage time (FPT) to define the lifetime/RUL. The definition is generally conservative; thus, the lifetime/RUL estimation may be underestimated. This is particularly true for nonmonotonic stochastic degradation processes. In some extreme cases, with strong fluctuations within the degradation processes, the estimated lifetime/RUL under the FPT can be significantly less than the actual lifetime/RUL. To address this limitation, this study investigates prognostic issues, based on the stochastic degradation process, from the last exit time (LET) perspective. In contrast to the FPT, the lifetime/RUL of the degrading system is defined as the LET of its degradation process, i.e., the instant at which the degradation process passes the failure threshold for the last time. Given the new definition, we consider the most widely used degradation process model (i.e., Wiener-process-based model) as an example to demonstrate how the lifetime/RUL is estimated. Two general methods of lifetime estimation for the Wiener-process-based model are given, and some examples with associated exact and closed-form solutions are also provided for better illustration. Finally, numerical examples and a practical case study are presented to substantiate the theoretical results and illustrate the superiority of the proposed method. The results imply that the proposed method exhibits the potential to prevent premature maintenance and resource wastage because the lifetime/RUL estimation from the LET perspective can help avoid conservative results from being obtained.

This article is concerned with the distributed filtering problem for a class of discrete complex networks over time-varying topology described by a sequence of variables. In the developed scalable filtering algorithm, only the local information and the information from the neighboring nodes are used. As such, the proposed filter can be implemented in a truly distributed manner at each node, and it is no longer necessary to have a certain center node collecting information from all the nodes. The aim of the addressed filtering problem is to design a time-varying filter for each node such that an upper bound of the filtering error covariance is ensured and the desired filter gain is then calculated by minimizing the obtained upper bound. The filter is established by solving two sets of recursive matrix equations, and thus, the algorithm is suitable for online application. Sufficient conditions are provided under which the filtering error is exponentially bounded in mean square. The monotonicity of the filtering error with respect to the coupling strength is discussed as well. Finally, an illustrative example is presented to demonstrate the feasibility and effectiveness of our distributed filtering strategy.

Due to the inner deteriorating mechanism or the mutant environmental stress, the degradation systems with multi-phase features have frequently been encountered in engineering practice. The key issue for prognostics of such systems is to account for the impact of the changing-point variability and the associated degradation state at this point on the progression of the degradation process. However, current studies usually treat the degradation state at the change point as a fixed value rather a random variable. Thus, it is still challenging to predict the lifetime of such multi-phase degrading systems. To this end, we first formulate a general degradation modeling framework based on a two-phase Wiener process. In prognostics, we take into full account the uncertainty of the degradation state at the changing point and then derive the analytical expressions of the lifetime and remaining useful life under the concept of the first passage time. The derived results are distinguished from existing results limited to the fixed state at the changing point. Furthermore, we extend our approach and results to cases with unit-to-unit variability and multiple phases. To facilitate the model implementation, we propose both offline and online methods for parameter identification, which make full use of the historical data and the in-service data. Finally, a numerical simulation and a practical case study are provided for illustration.

In this paper, the problems of fault detection, isolation, and estimation are considered for a class of discrete time-varying networked sensing systems with incomplete measurements. A unified measurement model is utilized to simultaneously characterize both the phenomena of multiple communication delays and data missing. A least-squares filter that minimizes the estimation variance is first designed for the addressed time-varying networked sensing systems, and then a novel residual matching (RM) approach is developed to isolate and estimate the fault once it is detected. The RM strategy is implemented via a series of Kalman filters, where each filter is designed to estimate the augmented signal composed of the system state and a specific fault signal. The design scheme for each filter is proposed in a recursive way. The main idea for the fault detection and estimation is that the Kalman filter with least residual value is regarded as corresponding to the right fault signal, and its estimation is utilized to represent the actual occurred fault. The effectiveness of our proposed method is demonstrated via simulation experiments on a real Internet-based three-tank system.