Probability Prediction in Multistate Survival Models for Patients with Chronic Myeloid Leukaemia

In order to find an appropriate model suitable for a multistate survival experiment, 634 patients with chronic myeloid leukaemia (CML) were selected to illustrate the method of analysis. After transplantation, there were 4 possible situations for a patient: disease free, relapse but still alive, death before relapse, and death after relapse. The last 3 events were considered as treatment failure. The results showed that the risk of death before relapse was higher than that of the relapse, especially in the first year after transplantation with competing-risk method. The result of patients with relapse time less than 12 months was much poor by the Kaplan-Meier method. And the multistate survival models were developed, which were detailed and informative based on the analysis of competing risks and Kaplan-Meier analysis. With the multistate survival models, a further analysis on conditional probability was made for patients who were disease free and still alive at month 12 after transplantation. It was concluded that it was possible for an individual patient to predict the 4 possible probabilities at any time. Also the prognoses for relapse either death or not and death either before or after relapse may be given. Furthermore, the conditional probabilities for patients who were disease free and still alive in a given time after transplantation can be predicted.

Determination of Scheduled Travel Time for a Fixed Transit Route Based on Multistate Model

In order to provide the guideline for bus drivers to adjust speed to minimize scheduled deviation,the method for setting bus scheduled travel time is proposed. Firstly,multistate model is introduced to fit historical travel time data and identify different service states. Based on the calibrated travel time distribution parameters,an optimization model is proposed,followed by a Monte Carlo( MC) simulation based genetic algorithm( GA)procedure to obtain the optimal scheduled time. A case study from a fixed bus route from Shenzhen is used to demonstrate the model applicability. The sensitivity analysis is conducted to study the effects of parameters setting on optimal slack time for each segment. The results show that multistate model fits travel time under peak hours better than Lognormal distribution,and the length of scheduled travel time basically reflects travel time reliability.

Locating Error Considering Dimensional Errors Modeling for Multistation Manufacturing System

Multistation machining process is widely applied in contemporary manufacturing environment. Modeling of variation propagation in multistation machining process is one of the most important research scenarios. Due to the existence of multiple variation streams, it is challenging to model and analyze variation propagation in a multi-station system. Current approaches to error modeling for multistation machining process are not explicit enough for error control and ensuring final product quality. In this paper, a mathematic model to depict the part dimensional variation of the complex multistation manufacturing process is formulated. A linear state space dimensional error propagation equation is established through kinematics analysis of the influence of locating parameter variations and locating datum variations on dimensional errors, so the dimensional error accumulation and transformation within the multistation process are quantitatively described. A systematic procedure to build the model is presented, which enhances the way to determine the variation sources in complex machining systems. A simple two-dimensional example is used to illustrate the proposed procedures. Finally, an industrial case of multistation machining part in a manufacturing shop is given to testify the validation and practicability of the method. The proposed analytical model is essential to quality control and improvement for multistation systems in machining quality forecasting and design optimization.

RUIN PROBABILITY IN A SEMI-MARKOV RISK MODEL WITH CONSTANT INTEREST FORCE AND HEAVY-TAILED CLAIMS

In the present paper, we consider a kind of semi-Markov risk model （SMRM） with constant interest force and heavy-tailed claims~ in which the claim rates and sizes are conditionally independent, both fluctuating according to the state of the risk business. First, we derive a matrix integro-differential equation satisfied by the survival probabilities. Second, we analyze the asymptotic behaviors of ruin probabilities in a two-state SMRM with special claim amounts. It is shown that the asymptotic behaviors of ruin probabilities depend only on the state 2 with heavy-tailed claim amounts, not on the state 1 with exponential claim sizes.

Continuous non-autonomous memristive Rulkov model with extreme multistability

Based on the two-dimensional(2D)discrete Rulkov model that is used to describe neuron dynamics,this paper presents a continuous non-autonomous memristive Rulkov model.The effects of electromagnetic induction and external stimulus are simultaneously considered herein.The electromagnetic induction flow is imitated by the generated current from a flux-controlled memristor and the external stimulus is injected using a sinusoidal current.Thus,the presented model possesses a line equilibrium set evolving over the time.The equilibrium set and their stability distributions are numerically simulated and qualitatively analyzed.Afterwards,numerical simulations are executed to explore the dynamical behaviors associated to the electromagnetic induction,external stimulus,and initial conditions.Interestingly,the initial conditions dependent extreme multistability is elaborately disclosed in the continuous non-autonomous memristive Rulkov model.Furthermore,an analog circuit of the proposed model is implemented,upon which the hardware experiment is executed to verify the numerically simulated extreme multistability.The extreme multistability is numerically revealed and experimentally confirmed in this paper,which can widen the future engineering employment of the Rulkov model.

A Study of Aviation Weapon Equipment Maintenance Based on the Semi-Markov Model

Based on the Semi-Markov mathematical description, the multiple states of maintenance processes for aviation weapon equipment are studied. Six kinds of maintenance states are determined and the Semi-Markov model of the maintenance process is given. According to maintenance characteristic, the multiple states maintenance processes are divided into the wait, use and alternate stages. Through using the mathematical model for the different stages, the probability in different states and effective index on different stages are obtained. These results are available to the maintenance practice.

Risk Identification based on Hidden Semi-Markov Model in Smart Distribution Network

The smart distribution system is the critical part of the smart grid, which also plays an important role in the safe and reliable operation of the power grid. The self-healing function of smart distribution network will effectively improve the security, reliability and efficiency, reduce the system losses, and promote the development of sustainable energy of the power grid. The risk identification process is the most fundamental and crucial part of risk analysis in the smart distribution network. The risk control strategies will carry out on fully recognizing and understanding of the risk events and the causes. On condition that the risk incidents and their reason are identified, the corresponding qualitative / quantitative risk assessment will be performed based on the influences and ultimately to develop effective control measures. This paper presents the concept and methodology on the risk identification by means of Hidden Semi-Markov Model (HSMM) based on the research of the relationship between the operating characteristics/indexes and the risk state, which provides the theoretical and practical support for the risk assessment and risk control technology.

Evaluating Traffic Congestion Using the Traffic Occupancy and Speed Distribution Relationship: An Application of Bayesian Dirichlet Process Mixtures of Generalized Linear Model

Accurate classification and prediction of future traffic conditions are essential for developing effective strategies for congestion mitigation on the highway systems. Speed distribution is one of the traffic stream parameters, which has been used to quantify the traffic conditions. Previous studies have shown that multi-modal probability distribution of speeds gives excellent results when simultaneously evaluating congested and free-flow traffic conditions. However, most of these previous analytical studies do not incorporate the influencing factors in characterizing these conditions. This study evaluates the impact of traffic occupancy on the multi-state speed distribution using the Bayesian Dirichlet Process Mixtures of Generalized Linear Models (DPM-GLM). Further, the study estimates the speed cut-point values of traffic states, which separate them into homogeneous groups using Bayesian change-point detection (BCD) technique. The study used 2015 archived one-year traffic data collected on Florida’s Interstate 295 freeway corridor. Information criteria results revealed three traffic states, which were identified as free-flow, transitional flow condition (congestion onset/offset), and the congested condition. The findings of the DPM-GLM indicated that in all estimated states, the traffic speed decreases when traffic occupancy increases. Comparison of the influence of traffic occupancy between traffic states showed that traffic occupancy has more impact on the free-flow and the congested state than on the transitional flow condition. With respect to estimating the threshold speed value, the results of the BCD model revealed promising findings in characterizing levels of traffic congestion.

Multiyear Discrete Stochastic Programming with a Fuzzy Semi-Markov Process

Drought conditions at a given location evolve randomly through time and are typically characterized by severity and duration. Researchers interested in modeling the economic effects of drought on agriculture or other water users often capture the stochastic nature of drought and its conditions via multiyear, stochastic economic models. Three major sources of uncertainty in application of a multiyear discrete stochastic model to evaluate user preparedness and response to drought are: (1) the assumption of independence of yearly weather conditions, (2) linguistic vagueness in the definition of drought itself, and (3) the duration of drought. One means of addressing these uncertainties is to re-cast drought as a stochastic, multiyear process using a “fuzzy” semi-Markov process. In this paper, we review “crisp” versus “fuzzy” representations of drought and show how fuzzy semi-Markov processes can aid researchers in developing more robust multiyear, discrete stochastic models.

考虑阶段备份的多态多阶段任务系统可靠性建模方法

近些年来,由于在航空航天、分布式计算以及核电系统等系统中广泛的应用,多阶段任务系统(phased mission system, PMS)可靠性理论与方法得到了广泛的关注与研究。在现代航天系统,尤其是载人航天系统,为了使整个系统的任务可靠性更高,经常会采用任务备份的方法来提高系统的任务可靠性。针对该类型的问题,提出一种基于多态多值决策图(multistate multi-valued decision diagram, MMDD)的方法。首先,将阶段故障树模型转化为阶段模型,其次,根据阶段代数方法将阶段模型融合为系统模型。在阶段模型融合过程中,通过增加随机变量的方法对单元经历的阶段进行统计,从而实现降低所建立的系统模型大小,提高建模效率,并以某型航天器推进子系统为例对建模方法进行说明。

Multistate Models for the Recovery Process in the Covid-19 Context:An Empirical Study of Chinese Enterprises

The Covid-19 pandemic has severely affected enterprises worldwide.It is thus of practical significance to study the process of enterprise recovery from Covid-19.However,the research on the effects of relevant determinants of business recovery is limited.This article presents a multistate modeling framework that considers the determinants,recovery time,and transition likelihood of Chinese enterprises by the state of those enterprises as a result of the pandemic(recovery state),with the help of an accelerated failure time model.Empirical data from 750 enterprises were used to evaluate the recovery process.The results indicate that the main problems facing non-manufacturing industries are supply shortages and order cancellations.With the increase of supplies and orders,the probability of transition between different recovery states gradually increases,and the recovery time of enterprises becomes shorter.For manufacturing industries,the factors that hinder recovery are more complex.The main problems are employee panic and order cancellations in the initial stage,employee shortages in the middle stage,and raw material shortages in the full recovery stage.This study can provide a reference for enterprise recovery in the current pandemic context and help policymakers and business managers take necessary measures to accelerate recovery.

基于多状态建模的独立型微网优化配置

针对风速、光照强度和负荷不确定性对独立型微网优化配置的影响,应用三者的概率密度函数对每个月份构造典型日,利用多状态系统理论对典型日各个时刻风光出力和负荷进行了多状态建模。以经济性和环保性为目标,建立了基于多状态建模的独立型微网多目标优化配置模型,并采用改进的非劣排序遗传算法(NAGA-Ⅱ)对模型进行了求解。最后,针对某一算例分别使用所提出的优化配置方法和已有的基于确定历史场景的优化配置方法进行了仿真计算,通过对仿真结果在若干随机场景下的比较分析,验证了所提优化配置模型和方法的合理性和有效性。

多工位薄板装配偏差流传递的状态空间模型

借鉴控制理论中的状态空间方法,研究多工位薄板装配过程中偏差流传递、变换和累积关系的状态空间模型。以零件偏差为状态矢量,夹具偏差为控制矢量,研究夹具偏差和零件偏差之间的对应关系以及零件在工位间转换过程中的重定位偏差,建立多工位装配过程偏差流传递的状态方程;以零件测量偏差为输出矢量,建立表征测点位置和数量的输出方程。最后通过实际零件的装配过程说明该模型的应用方法。

多状态Markov模型及其在慢性病流行病学研究中的应用

目的 促进多状态Markov模型在慢性病流行病学研究中的推广应用。方法 应用多状态Markov模型研究2型糖尿病不同发展阶段的影响因素。结果 应用多状态Markov模型分析具体案例取得了较为满意的效果。结论 多状态Markov模型是探讨慢性病不同发展阶段影响因素的一种有效工具 ,在慢性病流行病学研究中具有广阔的应用前景。

高空电磁脉冲作用下电力系统主要效应模式分析

随着电网智能化和整体规模的提高,现代电力系统越来越容易受到高空电磁脉冲的威胁,一旦关键环节故障将有可能导致连锁反应,造成大面积停电。而针对不同的电力设备,其效应模式和威胁等级也有所不同,需要进行分类和分级研究。根据电力设备在电磁脉冲作用下的不同效应模式,将其分为SCADA系统与继电保护设备,变压器、互感器等线圈类设备,线路与设备避雷器与其他设备,并分析了其效应机理。然后考虑高空电磁脉冲威胁下电力设备存在多种效应等级,介绍了不同效应分类方法以及多等级效应评估模型。最后综合考虑易损性和重要性以及系统间的级联影响,分别梳理总结了在E1和E3作用下电力系统的故障链模式。

基于小波域多状态隐马尔科夫树模型多尺度文本图像分割

该文基于小波域多状态隐马尔科夫树(HMT)模型,引入一种新的文本分割方法。该分割方法是在H.Choi et al.(2001)工作的基础上,将文本按纹理分为背景、文字与图片3种类型,分别建立多状态HMT模型。另外,基于平滑图像将上述方法又作了进一步的改进,引入了多状态IHMT分割方法,最后通过实例阐明了方法的有效性。

多工位制造系统偏差源监测传感器布置优化

为了获得多工位制造系统足够的有效测量信息和最大化多工位制造系统的偏差源监测能力,应采用合适的测量策略并对传感器布置进行优化.在状态空间模型基础上,建立了多工位制造系统中3个偏差源与传感器测量值间的空间关系,给出了量化描述系统偏差源监测能力的方差监测敏感度系数,以及将数据挖掘算法与进化算法相结合的传感器布置的优化方法,解决了最大化多工位制造系统偏差源监测能力的传感器布置优化问题.通过箱形工件加工实例,验证了该方法的有效性.

一种全柔顺六稳态机构的设计

多稳态机构因不需输入能量就可保持多个稳定平衡状态而在生产生活中得到越来越广泛的应用。全柔顺多稳态机构免装配、无摩擦、低能耗,但因为柔顺片段大变形时容易产生应力集中、输出非线性等原因,全柔顺多稳态机构的设计难度较大。提出一种全柔顺六稳态机构的设计方法,由两个临界跳转力不同的双稳态机构和一个柔顺片段组成机构,成功实现六个稳定平衡状态。对柔顺片段用伪刚体模型法建模,并通过比较两部分结构的力-位移特性给出机构具有六个稳定平衡状态的条件,从而避免了非线性分析;考虑机构的实际运动给出柔顺片段的刚度范围计算公式,设计者可据此选择合适的刚度完成全柔顺六稳态机构的设计。用ANSYS得到的输出力特性和样机测试证实了该设计方法的正确。

面向过程的多工位薄板装配公差综合

针对多工位薄板装配过程,建立了一种新的公差综合方法.通过分析夹具定位偏差与公差的关系,利用偏差流传递的状态空间模型建立了公差综合的约束函数,以制造成本为目标函数,利用Matlab实现了优化的公差综合方法,同时研究了过程损失对产品质量的影响.结果表明,该方法在确保装配质量的前提下,显著降低了制造成本.

基于分段常数强度Markov模型的阿尔茨海默病进程研究

目的将分段常数强度Markov模型应用于轻度认知损害(mild cognitive impairment,MCI)向阿尔茨海默病(Alzheimer's disease,AD)转归过程中,深入研究影响转归过程的因素,为制定不同发展阶段的预防措施提供理论依据,为其他多状态慢性病不同发展阶段影响因素的探讨提供方法学借鉴。方法应用太原市600名社区老年人的4次随访资料,以MCI为状态1,中重度认知损害为状态2,AD为状态3,拟合分段常数强度Markov模型,分析MCI向AD转归不同发展阶段的影响因素,并根据模型计算3年转移概率矩阵。结果经假设检验,数据满足Markov性(P=0.89),不满足时齐性(P<0.001),应用分段常数强度Markov模型拟合,经多因素筛选,女性、年龄、吸烟、高血压和糖尿病是MCI向AD转归的危险因素,高文化程度和从事脑力劳动是MCI向AD转归的保护因素。由中重度认知损害向AD的转移概率随着随访时间的增加而增加。结论在数据满足Markov性,不满足时齐性时,分段常数强度Markov模型是对疾病转归过程的相关影响因素及其变化规律的有效分析方法。