基于改进强跟踪滤波的广义系统传感器故障诊断及隔离

在广义系统故障诊断过程中,若系统动态模型中存在不确定性,传统的无迹卡尔曼滤波算法将失去其传感器故障估计精度。为解决该问题,提出一种改进的强跟踪卡尔曼滤波算法以实现广义连续-离散系统的传感器故障诊断及隔离。首先,提出基于多重渐消因子的强跟踪滤波算法以实现动态模型存在不确定性广义连续-离散系统的故障诊断;然后提出一种结合多模型自适应估计的强跟踪卡尔曼滤波(STUKFMMAE)算法以实现传感器故障的有效隔离。最后,针对基于广义连续-离散系统的惯性传感器故障模型提出仿真算例。仿真数据表明,传统无迹卡尔曼滤波对于传感器故障估计误差为0.002左右,而提出的基于多重渐消因子的强跟踪滤波算法对于传感器故障估计误差最大值为未超过4×10^(-4),且STUKFMMAE相较于UKFMMAE算法具有更好的隔离效果。仿真结果验证了设计方案的有效性。

一类具有执行器故障的马尔科夫跳跃系统容错控制

研究一类转移概率部分已知马尔科夫跳跃线性系统的自适应容错控制问题.所考虑的转移概率情况更具一般性,即转移概率包括完全已知,未知但已知转移概率的上下界和完全未知三种情况.针对执行器失效故障,首先设计自适应故障估计器;而后,基于故障参数的估计值设计鲁棒补偿控制器,保证系统在发生执行器故障时的鲁棒稳定性.在处理未知转移概率时,采用自由权重的方法,以保证所得线性矩阵不等式条件具有更小的保守性.最后,数值仿真算例验证了所提方法的有效性.

Automated postoperative blood pressure control

It is very important to maintain the level of mean arterial pressure （MAP）. The MAP control is applied in many clinical situations, including limiting bleeding during cardiac surgery and promoting healing for patient＇ s post-surgery. This paper presents a fuzzy controller-based multiple-model adaptive control system for postoperative blood pressure management. Multiple-model adaptive control （MMAC） algorithm is used to identify the patient model, and it is a feasible system identification method even in the presence of large noise. Fuzzy control （FC） method is used to design controller bank. Each fuzzy controller in the controller bank is in fact a nonlinear proportional-integral （PI） controller,whose proportional gain and integral gain are adjusted continuously according to error and rate of change of error of the plant output, resulting in better dynamic and stable control performance than the regular PI controller, especially when a nonlinear process is involved. For demonstration, a nonlinear, pulsatile-flow patient model is used for simulation, and the results show that the adaptive control system can effectively handle the changes in patient＇s dynamics and provide satisfactory performance in regulation of blood pressure of hypertension patients.

Quick identification of guidance law for an incoming missile using multiple-model mechanism

A guidance law parameter identification model based on Gated Recurrent Unit(GRU)neural network is established. The scenario of the model is that an incoming missile(called missile)attacks a target aircraft(called aircraft) using Proportional Navigation(PN) guidance law. The parameter identification is viewed as a regression problem in this paper rather than a classification problem, which means the assumption that the parameter is in a finite set of possible results is discarded. To increase the training speed of the neural network and obtain the nonlinear mapping relationship between kinematic information and the guidance law parameter of the incoming missile, an output processing method called Multiple-Model Mechanism(MMM) is proposed. Compared with a conventional GRU neural network, the model established in this paper can deal with data of any length through an encoding layer in front of the input layer. The effectiveness of the proposed Multiple-Model Mechanism and the performance of the guidance law parameter identification model are demonstrated using numerical simulation.

Geometrical entropy approach for variable structure multiple-model estimation

The variable structure multiple-model（VSMM） estimation approach, one of the multiple-model（MM） estimation approaches, is popular in handling state estimation problems with mode uncertainties.In the VSMM algorithms, the model sequence set adaptation（MSA） plays a key role.The MSA methods are challenged in both theory and practice for the target modes and the real observation error distributions are usually uncertain in practice.In this paper, a geometrical entropy（GE） measure is proposed so that the MSA is achieved on the minimum geometrical entropy（MGE） principle.Consequently, the minimum geometrical entropy multiple-model（MGEMM） framework is proposed, and two suboptimal algorithms, the particle filter k-means minimum geometrical entropy multiple-model algorithm（PF-KMGEMM） as well as the particle filter adaptive minimum geometrical entropy multiple-model algorithm（PF-AMGEMM）, are established for practical applications.The proposed algorithms are tested in three groups of maneuvering target tracking scenarios with mode and observation error distribution uncertainties.Numerical simulations have demonstrated that compared to several existing algorithms, the MGE-based algorithms can achieve more robust and accurate estimation results when the real observation error is inconsistent with a priori.

Feedback structure based entropy approach for multiple-model estimation

The variable-structure multiple-model（VSMM）approach,one of the multiple-model（MM）methods,is a popular and effective approach in handling problems with mode uncertainties.The model sequence set adaptation（MSA）is the key to design a better VSMM.However,MSA methods in the literature have big room to improve both theoretically and practically.To this end,we propose a feedback structure based entropy approach that could fnd the model sequence sets with the smallest size under certain conditions.The fltered data are fed back in real time and can be used by the minimum entropy（ME）based VSMM algorithms,i.e.,MEVSMM.Firstly,the full Markov chains are used to achieve optimal solutions.Secondly,the myopic method together with particle flter（PF）and the challenge match algorithm are also used to achieve sub-optimal solutions,a trade-off between practicability and optimality.The numerical results show that the proposed algorithm provides not only refned model sets but also a good robustness margin and very high accuracy.

Icing tolerance envelope protection based on variable-weighted multiple-model predictive control

Multiple-model predictive control(MMPC) is a fundamental icing tolerance envelope protection(ITEP) design method that can systematically handle nonlinear and time-varying constraints. However, few studies have addressed the envelope protection failure that results from the inaccurate prediction of multiple linear predictive models when actual conditions deviate from design conditions. In this study, weights that vary with icing conditions and flight parameters are considered to develop an effective and reliable envelope protection control strategy. First, an ITEP structure based on variable-weighted MMPC was implemented to improve the protection performance with condition departure information. Then, a variable-weighted rule was proposed to guarantee the stability of variable-weighted MMPC. A design approach involving a variable-weighted function that uses icing conditions and flight parameters as arguments was also developed with the proposed rules. Finally, a systematic ITEP design method on variable-weighted MMPC was constructed with additional design criteria for other normal control parameters.Simulations were conducted, and the results show that the proposed method can effectively enhance ITEP performance.

Advancements in adoptive CAR immune cell immunotherapy synergistically combined with multimodal approaches for tumor treatment

Adoptive immunotherapy,notably involving chimeric antigen receptor(CAR)-T cells,has obtained Food and Drug Administration(FDA)approval as a treatment for various hematological malignancies,demonstrating promising preclinical efficacy against cancers.However,the intricate and resource-intensive autologous cell processing,encompassing collection,expansion,engineering,isolation,and administration,hamper the efficacy of this therapeutic modality.Furthermore,conventional CAR T therapy is presently confined to addressing solid tumors due to impediments posed by physical barriers,the potential for cytokine release syndrome,and cellular exhaustion induced by the immunosuppressive and heterogeneous tumor microenvironment.Consequently,a strategic integration of adoptive immunotherapy with synergistic multimodal treatments,such as chemotherapy,radiotherapy,and vaccine therapy etc.,emerges as a pivotal approach to surmount these inherent challenges.This collaborative strategy holds the key to addressing the limitations delineated above,thereby facilitating the realization of more precise personalized therapies characterized by heightened therapeutic efficacy.Such synergistic strategy not only serves to mitigate the constraints associated with adoptive immunotherapy but also fosters enhanced clinical applicability,thereby advancing the frontiers of therapeutic precision and effectiveness.

基于STC-IMM结构的自适应多模型跟踪算法

针对机动目标跟踪问题,基于转换时间条件交互多模型(STC-IMM)结构,提出一种转换概率自适应的STC-AIMM算法.该算法根据滤波器收敛时间预设了模型转换时间条件,保证了滤波器对目标后验状态的合理逼近,同时通过模型转换概率的自适应算法实现了模型与目标运动模式的实时最优匹配.理论和仿真分析结果表明:相比交互多模型(IMM)算法和STC-IMM算法,该算法能够发挥滤波器最优性能,实现模型概率的优化分配,对目标不同强度的机动具有良好的适应性、跟踪稳定性和更高的跟踪精度.

Dataset of temperature and precipitation over the major Belt and Road Initiative regions under different temperature rise scenarios

Changes in temperature and precipitation have a profound effect on the ecological environment and socioeconomic systems.In this study,we focus on the major Belt and Road Initiative(BRI)regions and develop a dataset of temperature and precipitation at global temperature rise targets of 1.5°C,2°C,and 3°C above pre-industrial levels under the Representative Concentration Pathway(RCP)8.5 emission scenario using 4 downscaled global model datasets data at a fine spatial resolution of 0.0449147848°(~5 km)globally from EnviDat.The temperature variables include the daily maximum(Tmax),minimum(Tmin)and average(Tmp)surface air temperatures,and the diurnal temperature range(DTR).We first evaluate the performance of the downscaled model data using CRU-observed gridded data for the historical period 1986-2005.The results indicate that the downscaled model data can generally reproduce the pattern characteristics of temperature and precipitation variations well over the major BRI regions for 1986-2005.Furthermore,we project temperature and precipitation variations over the major BRI regions at global temperature rise targets of 1.5°C,2°C,and 3°C under the RCP8.5 emission scenario based on the dataset by adopting the multiple-model ensemble mean.Our dataset contributes to understanding detailed the characteristics of climate change over the major BRI regions,and provides data fundamental for adopting appropriate strategies and options to reduce or avoid disadvantaged consequences associated with climate change over the major BRI regions.The dataset is available at https://doi.org/10.57760/sciencedb.01850.

Center-Distance Continuous Probability Models and the Distance Measure

In this paper, a new statistic model named Center-Distance Continuous Probability Model (CDCPM) for speech recognition is described, which is based on Center-Distance Normal (CDN) distribution. In a CDCPM, the probability transition matrix is omitted, and the observation probability density function (PDF) in each state is in the form of embedded multiple-model (EMM) based on the Nearest Neighbour rule. The experimental results on two giant real-world Chinese speech databases and a real-world continuous-manner 2000 phrase system show that this model is a powerful one. Also,a distance measure for CDCPMs is proposed which is based on the Bayesian minimum classification error (MCE) discrimination.