State estimation for systems with sojourn-time-dependent Markov model switching

1991 ◽  
Vol 36 (2) ◽  
pp. 238-243 ◽  
Author(s):  
L. Campo ◽  
P. Mookerjee ◽  
Y. Bar-Shalom
Keyword(s):  
Markov Model ◽  
State Estimation ◽  
Sojourn Time ◽  
Time Dependent

scholarly journals Ground Vehicle Tracking Using Context-Based Sojourn Time Dependent Markov Model and Pseudo-Measurement

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 111536-111552
Author(s):  
Zhen Tian ◽  
Ming Cen ◽  
Yinguo Li ◽  
Hao Zhu
Keyword(s):  
Markov Model ◽  
Sojourn Time ◽  
Vehicle Tracking ◽  
Time Dependent ◽  
Ground Vehicle

scholarly journals States Transitions Inference of Postpartum Depression Based on Multi-State Markov Model

2021 ◽  
Vol 18 (14) ◽  
pp. 7449
Author(s):  
Juan Xiong ◽  
Qiyu Fang ◽  
Jialing Chen ◽  
Yingxin Li ◽  
Huiyi Li ◽  
...  
Keyword(s):  
Postpartum Depression ◽  
Markov Model ◽  
Normal State ◽  
Sojourn Time ◽  
Transition Probabilities ◽  
Transition Probability ◽  
Public Health Problem ◽  
Mental States ◽  
Hazard Ratio ◽  
Multiple Time

Background: Postpartum depression (PPD) has been recognized as a severe public health problem worldwide due to its high incidence and the detrimental consequences not only for the mother but for the infant and the family. However, the pattern of natural transition trajectories of PPD has rarely been explored. Methods: In this research, a quantitative longitudinal study was conducted to explore the PPD progression process, providing information on the transition probability, hazard ratio, and the mean sojourn time in the three postnatal mental states, namely normal state, mild PPD, and severe PPD. The multi-state Markov model was built based on 912 depression status assessments in 304 Chinese primiparous women over multiple time points of six weeks postpartum, three months postpartum, and six months postpartum. Results: Among the 608 PPD status transitions from one visit to the next visit, 6.2% (38/608) showed deterioration of mental status from the level at the previous visit; while 40.0% (243/608) showed improvement at the next visit. A subject in normal state who does transition then has a probability of 49.8% of worsening to mild PPD, and 50.2% to severe PPD. A subject with mild PPD who does transition has a 20.0% chance of worsening to severe PPD. A subject with severe PPD is more likely to improve to mild PPD than developing to the normal state. On average, the sojourn time in the normal state, mild PPD, and severe PPD was 64.12, 6.29, and 9.37 weeks, respectively. Women in normal state had 6.0%, 8.5%, 8.7%, and 8.8% chances of progress to severe PPD within three months, nine months, one year, and three years, respectively. Increased all kinds of supports were associated with decreased risk of deterioration from normal state to severe PPD (hazard ratio, HR: 0.42–0.65); and increased informational supports, evaluation of support, and maternal age were associated with alleviation from severe PPD to normal state (HR: 1.46–2.27). Conclusions: The PPD state transition probabilities caused more attention and awareness about the regular PPD screening for postnatal women and the timely intervention for women with mild or severe PPD. The preventive actions on PPD should be conducted at the early stages, and three yearly; at least one yearly screening is strongly recommended. Emotional support, material support, informational support, and evaluation of support had significant positive associations with the prevention of PPD progression transitions. The derived transition probabilities and sojourn time can serve as an importance reference for health professionals to make proactive plans and target interventions for PPD.

Bidirectional LSTM Recurrent Neural Network Plus Hidden Markov Model for Wearable Sensor-Based Dynamic State Estimation

2020 ◽  
Vol 1 (2) ◽  
Author(s):  
Ritika Sibal ◽  
Ding Zhang ◽  
Julie Rocho-Levine ◽  
K. Alex Shorter ◽  
Kira Barton
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
State Estimation ◽  
Short Term Memory ◽  
Hidden Markov ◽  
Training Data ◽  
Dynamic State ◽  
Support Vector ◽  
Similar Amount ◽  
Dynamic State Estimation

Abstract Behavior of animals living in the wild is often studied using visual observations made by trained experts. However, these observations tend to be used to classify behavior during discrete time periods and become more difficult when used to monitor multiple individuals for days or weeks. In this work, we present automatic tools to enable efficient behavior and dynamic state estimation/classification from data collected with animal borne bio-logging tags, without the need for statistical feature engineering. A combined framework of an long short-term memory (LSTM) network and a hidden Markov model (HMM) was developed to exploit sequential temporal information in raw motion data at two levels: within and between windows. Taking a moving window data segmentation approach, LSTM estimates the dynamic state corresponding to each window by parsing the contiguous raw data points within the window. HMM then links all of the individual window estimations and further improves the overall estimation. A case study with bottlenose dolphins was conducted to demonstrate the approach. The combined LSTM–HMM method achieved a 6% improvement over conventional methods such as K-nearest neighbor (KNN) and support vector machine (SVM), pushing the accuracy above 90%. In addition to performance improvements, the proposed method requires a similar amount of training data to traditional machine learning methods, making the method easily adaptable to new tasks.

Continuous-time stochastic models of a multigrade population

1978 ◽  
Vol 15 (1) ◽  
pp. 26-37 ◽  
Author(s):  
Sally I. McClean
Keyword(s):  
Markov Model ◽  
Exponential Distribution ◽  
Stochastic Models ◽  
Continuous Time ◽  
Time Dependent ◽  
Poisson Arrival ◽  
Mixed Exponential Distribution ◽  
Previous Assumption

The continuous-time Markov model of a multigrade organization is extended in several ways. Firstly the internal transitions and the leaving process are generalized to a semi-Markov formulation which allows for the inclusion of well-authenticated leaving distributions such as the mixed exponential distribution. The previous assumption of Poisson recruitment is then generalized to allow for a time-dependent Poisson arrival distribution in which the instantaneous probability of an arrival is a mixture of exponential terms. Finally we extend the capital-related manpower model to describe a multigrade organization.

Use of Markov Piping Reliability Models to Evaluate Time Dependent Frequencies of Loss of Coolant Accidents

2004 ◽  
Author(s):  
Karl N. Fleming ◽  
Bengt O. Y. Lydell
Keyword(s):  
Markov Model ◽  
Nuclear Power ◽  
Interesting Property ◽  
Time Dependent ◽  
Piping System ◽  
Accelerated Corrosion ◽  
Reliability Models ◽  
Age Related ◽  
Loss Of Coolant ◽  
Piping Systems

Markov model theory has been applied to develop a method to evaluate the influence of alternate strategies for in-service inspection and leak detection on the frequency of leaks and ruptures in nuclear power plant piping systems [1–4]. This approach to quantification of pipe rupture frequency was originally based on a Bayes’ uncertainty analysis approach to derive piping system failure rates from a combination of service experience data and some simple reliability models [5–7]. More recently the Markov model approach has been used in conjunction with probabilistic fracture mechanics methods in the study of flow accelerated corrosion [8]. One interesting property of the Markov model is its capability to evaluate time dependent rupture frequencies via the model hazard rate. In this paper this time dependent modeling capability is used to investigate the age related and time dependent frequencies of loss of coolant accident (LOCA) initiating event frequencies. A case is presented that plant age dependent LOCA frequencies should be used in lieu of other metrics commonly used in probabilistic risk assessments and in risk informed inservice inspection evaluations. Such more commonly used metrics include the assumed constant failure rate method and the lifetime average rupture probability. Both of these methods are shown to provide optimistic estimates of LOCA frequencies for plants in the latter part of their design lifetimes, which most operating plants are approaching.

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