Introduction to Mathematical Statistics. By R.V. Hogg and A. T. Craig. Pp. ix, 245. 47s. 1959. (The Macmillan Company, New York)

A boundary control approach is used to control a two-link rigid-flexible wing in this article. Its design is based on the principle of bionics to improve the mobility and the flexibility of aircraft. First, a series of partial differential equations (PDEs) and ordinary differential equations (ODEs) are derived through the Hamilton's principle. These PDEs and ODEs describe the governing equations and the boundary conditions of the system, respectively. Then, a control strategy is developed to achieve the objectives including restraining the vibrations in bending and twisting deflections of the flexible link of the wing and achieving the desired angular position of the wing. By using Lyapunov's direct method, the wing system is proven to be stable. The numerical simulations are carried out with the finite difference method to prove the effectiveness of designed boundary controllers.

Dynamical Modeling and Boundary Vibration Control of a Rigid-Flexible Wing System

In this article, the event-triggered security consensus problem is studied for time-varying multiagent systems (MASs) against false data-injection attacks (FDIAs) and parameter uncertainties over a given finite horizon. In the process of information transmission, the malicious attacker tries to inject false signals to destroy consensus by compromising the integrity of measurements and control signals. The randomly occurring stealthy FDIAs on sensors and actuators are modeled by the Bernoulli processes. In order to reduce the unnecessary utilization of communication resources, an event-triggered control mechanism with state-dependent threshold is adopted to update the control input signal. The main objective of this article is to design a controller such that, under randomly occurring FDIAs and admissible parameter uncertainties, the MASs achieve consensus. By utilizing stochastic analysis method, two sufficient criteria are derived to ensure that the prescribed $H_{\infty }$ consensus performance can be achieved. Then, the desired controller gains are derived by solving recursive linear matrix inequalities. Simulation results are presented to illustrate the effectiveness and applicability of the proposed control method.

Event-Triggered Consensus Control for Multi-Agent Systems Against False Data-Injection Attacks

Applications of Bayesian networks and Petri nets in safety, reliability, and risk assessments: A review

An adaptive neural network (NN) event-triggered control scheme is proposed for nonlinear nonstrict-feedback multiagent systems (MASs) against input saturation, unknown disturbance, and sensor faults. Mean-value theorem and Nussbaum-type function are invoked to transform the structure of the input saturation and overcome the difficulty of unknown control directions, respectively. On the basis of the universal approximation property of NNs, a nonlinear disturbance observer is designed to estimate the unknown compounded disturbance composed of external disturbance and the residual term of input saturation. According to the measurement error defined by control signal, an event-triggered mechanism is developed to save network transmission resource and reduce the number of controller update. Then, an adaptive NN compensation control approach is proposed to tackle the problem of sensor faults via the dynamic surface control (DSC) technique. It is proved that all signals in the closed-loop system are semi-globally uniformly ultimately bounded. Finally, simulation results demonstrate the effectiveness of the presented control strategy.

Event-Triggered Control for Multiagent Systems With Sensor Faults and Input Saturation

Accurate monitoring the surrounding environment is an important research direction in the field of unmanned systems such as bio-robotics, and has attracted much research attention in recent years. The trajectories of surrounding vehicles should be predicted accurately in space and time to realize active defense and running safety of an unmanned system. However, there is uncertainty and uncontrollability in the process of trajectory prediction of surrounding obstacles. In this study, we propose a trajectory prediction method based on a sequential model, that fuses two neural networks of a convolutional neural network (CNN) and a long short-term memory network (LSTM). First, a box plot is used to detect and eliminate abnormal values of vehicle trajectories, and valid trajectory data are obtained. Second, the trajectories of surrounding vehicles are predicted by merging the characteristics of CNN space expansion and LSTM time expansion; the hyper-parameters of the model are optimized according to a grid search algorithm, which satisfies the double-precision prediction requirement in space and time. Finally, data from next generation simulation (NGSIM) and Creteil roundabout in France are taken as test cases; the correctness and rationality of the method are verified by prediction error indicators. Experimental results demonstrate that the proposed CNN-LSTM method is more accurate and features a shorter time cost, which meets the prediction requirements and provides an effective method for the safe operation of unmanned systems.

Motion trajectory prediction based on a CNN-LSTM sequential model

Conventionally, the transition probabilities in the interacting multiple model (IMM) are often fixed based on the prior information. However, this conservative setting may result in inaccurate state estimations. To solve this problem, a Bayesian-based online correction function is proposed in this paper, which can adaptively adjust the transition probabilities. To deal with the response lag and the short-term peak estimation error problem during the respond to model jump, a model jumping threshold is defined, so that the current information of the models can be fully utilized by the IMM algorithm and the correction function of the transition probabilities can be further improved. Subsequently, an adaptive transition probability-based parallel IMM algorithm is proposed in this paper. Finally, three maneuvering target tracking simulations are conducted to verify the performance of the proposed algorithm, the results show that the proposed algorithm can improve the response speed of the system model jump and the state estimation accuracy. The effectiveness and feasibility of the algorithm are proven.

Adaptive Transition Probability Matrix-Based Parallel IMM Algorithm

piRNAs are a class of small non-coding RNA molecules, which interact with the PIWI family and have many important and diverse biological functions. The present review is aimed to provide guidelines and contribute to piRNA research. We focused on the four types of identification models on piRNA-related molecules, including piRNA, piRNA cluster, piRNA target, and disease-related piRNA. We evaluated the types of tools for the identification of piRNAs based on five aspects: datasets, features, classifiers, performance, and usability. We found the precision of 2lpiRNApred was the highest in datasets of model organisms, piRNN had a better performance of datasets of non-model organisms, and 2L-piRNA had the fastest recognition speed of all tools. In addition, we presented an overview of piRNA databases. The databases were divided into six categories: basic annotation, comprehensive annotation, isoform, cluster, target, and disease. We found that piRNA data of non-model organisms, piRNA target data, and piRNA-disease-associated data should be strengthened. Our review might assist researchers in selecting appropriate tools or datasets for their studies, reveal potential problems and shed light on future bioinformatics studies.

Computational Methods and Online Resources for Identification of piRNA-Related Molecules.

As a powerful and object-oriented graphical modeling language, Unified Modeling Language (UML) has been introduced into the design and development of safety-critical computer systems. However, UML model is difficult to reflect the concurrency and consistency of constraint rules between objects and impossible to completely demonstrate the dynamic behavior characteristics of a system. Further, it cannot be directly expressed and analyzed by mathematical tools. Petri net modeling technology uses graphical element place and transition to clearly describe the internal interactions of a system. Meanwhile, Petri net has mature mathematical analysis methods. We have proposed to combine these two tools for designing and analyzing a system with a set of transformation rules from UML statecharts to Petri net. In this paper, an automatic conversion tool is introduced. The tool performs the transformation with three steps as follows: (1) Transforming UML model into XML document, which makes information extraction of the UML model become easier. (2) Analyzing semantics of UML model and Petri net model, then designing data structure for transformation, and completing the transformation based on the transformation rules. (3) Analyzing the transformed Petri net model to verify the characteristics of the UML model. The transformation rules and the tools are developed based on a new railway interlocking system.

Automatic transformation from UML statechart to Petri nets for safety analysis and verification

The effective prediction of remaining useful life is essential to realize system failure diagnosis and health management. The existing researches often assume that the degradation model is constant or the degradation process is measurable. The accurate degradation model, however, usually can not be established, and the parametric variation and measurement error of the degradation process are unavoidable, which makes it hard to obtain the exact value for predicting the remaining useful life. Regarding this problem, on basis of the concept of first failure time, a real-time probability density function is derived for the Wiener degradation process with the uncertainty of parameters, the stochasticity of degradation process and the randomness of measurement error. The main steps are as follows: firstly, the degradation model with three kinds of uncertainties is established, and then the stochastic degradation state and the parameters of the uncertainty model are estimated by fusion Kalman/UFIR filter; then, the analytical expression of the probability density function of remaining useful life is deduced. Finally, the correctness and effectiveness of the proposed method are verified by a group of comparison experiments and Monte Carlo simulations.

Guo Xie

Papers

Motion trajectory prediction based on a CNN-LSTM sequential model

Adaptive Transition Probability Matrix-Based Parallel IMM Algorithm

Computational Methods and Online Resources for Identification of piRNA-Related Molecules.

Automatic transformation from UML statechart to Petri nets for safety analysis and verification

Estimating the Probability Density Function of Remaining Useful Life for Wiener Degradation Process with Uncertain Parameters