scholarly journals Understanding the Complexity of Regional Innovation Capacity Dynamics in China: From the Perspective of Hidden Markov Model

2021 ◽  
Vol 13 (4) ◽  
pp. 1658
Author(s):  
Shuai Liu ◽  
Xiao-Yu Xu ◽  
Kai Zhao ◽  
Li-Ming Xiao ◽  
Qi Li
Keyword(s):  
Intellectual Property ◽  
Markov Model ◽  
Hidden Markov Model ◽  
State Transition ◽  
Hidden Markov ◽  
High State ◽  
Regional Innovation ◽  
Heterogeneous Effects ◽  
Innovation Capacity ◽  
Positive Impacts

This study aimed to explore the state transition of regional innovation capacity (RIC) and analyze the heterogeneous effects of determinants in an innovative subject and environment of RIC state transition based on the data collected from 30 provinces in China during 2000–2017. By applying a hidden Markov model (HMM), this study identified three RIC states: low, medium, and high. The results suggested that (1) the overall state of RIC rapidly improved but with a significant disparity across regions in China; (2) the lock-in effect of RIC is most significant in regions with a medium state, while the enterprise-dominated mode of regional innovation helps RIC transition from a medium state to a high state or to remain in a high state; and (3) the interaction and collaboration between universities and enterprises in a region can stimulate RIC to higher states for all regions. Intellectual property administrative protection exerts positive impacts on RIC transitions to higher states. Intellectual property judicial protection only exerts positive impacts on an RIC’s transition from a medium state to a high state or remaining in a high state, while these positive impacts are not significant when RIC is in a low state. Highlighting the dynamic nature of RIC evolution and the heterogeneity of determinants affecting RIC state transition, the findings provide policymakers a roadmap to identify RIC states and make precise policies based on the current RIC state.

A model based on hidden graphic evaluation and review technique network to evaluate reliability and lifetime of multi-state systems

2018 ◽  
Vol 233 (3) ◽  
pp. 369-378 ◽  
Author(s):  
Wenjie Dong ◽  
Sifeng Liu ◽  
Zhigeng Fang ◽  
Yingsai Cao ◽  
Ye Ding
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Network Model ◽  
State Transition ◽  
Likelihood Function ◽  
Hidden Markov ◽  
Reliability Assessment ◽  
Assessment Method ◽  
Performance Degradation ◽  
State System

The essence of multi-state system performance degradation is a process of deteriorating state transition. On the basis of hidden Markov model and graphic evaluation and review technique network, this article proposes a new reliability assessment method called hidden graphic evaluation and review technique network model for multi-state system. Specifically, nodes in graphic evaluation and review technique network represent hidden states of a system at different deteriorating times, and they can be expanded through a series of observable sequences. Baum–Welch algorithm in hidden Markov model is introduced to train parameters and when logarithmic likelihood function of the output reaches convergent, we can estimate the most probable output state and obtain the state transition probability eventually. Suppose performance degradation amount between different nodes follows gamma distribution, a method of improved maximum likelihood function is introduced to estimate parameters. According to signal flow graph theory and Mason formula, equivalent transfer function from the initial node to any other nodes can be obtained, then expectation and variance of performance degradation amount can be presented. In the real case study, we compare the reliability assessment method proposed in this article with other two traditional methods, which show the rationality of hidden graphic evaluation and review technique network model.

scholarly journals Network Security Situation Assessment Model Based on Extended Hidden Markov

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Yiwei Liao ◽  
Guosheng Zhao ◽  
Jian Wang ◽  
Shu Li
Keyword(s):  
Network Security ◽  
Probability Distribution ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Transition Matrix ◽  
State Transition ◽  
Hidden Markov ◽  
State Transition Matrix ◽  
State Probability ◽  
Risk Loss

A network security situation assessment system based on the extended hidden Markov model is designed in this paper. Firstly, the standard hidden Markov model is expanded from five-tuple to seven-tuple, and two parameters of network defense efficiency and risk loss vector are added so that the model can describe network security situation more completely. Then, an initial algorithm of state transition matrix was defined, observation vectors were extracted from the fusion of various system security detection data, the network state transition matrix was created and modified by the observation vectors, and a solution procedure of the hidden state probability distribution sequence based on extended hidden Markov model was derived. Finally, a method of calculating risk loss vector according to the international definition was designed and the current network risk value was calculated by the hidden state probability distribution; then the global security situation was assessed. The experiment showed that the model satisfied practical applications and the assessment result is accurate and effective.

Auscultating Diagnosis for Hemodialysis Shunt Stenosis using a Self-Organizing Map and Hidden Markov Model

2012 ◽  
Vol 132 (10) ◽  
pp. 1589-1594 ◽  
Author(s):  
Hayato Waki ◽  
Yutaka Suzuki ◽  
Osamu Sakata ◽  
Mizuya Fukasawa ◽  
Hatsuhiro Kato
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Self Organizing Map ◽  
Self Organizing

Engaging Voluntary Contributions in Online Communities: A Hidden Markov Model

MIS Quarterly ◽  
2018 ◽  
Vol 42 (1) ◽  
pp. 83-100 ◽  
Author(s):  
Wei Chen ◽  
◽  
Xiahua Wei ◽  
Kevin Xiaoguo Zhu ◽  
◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Online Communities ◽  
Hidden Markov ◽  
Voluntary Contributions
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