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Liao, T. Warren, Guogang Hua, J. Qu, and P. J. Blau. "GRINDING WHEEL CONDITION MONITORING WITH HIDDEN MARKOV MODEL-BASED CLUSTERING METHODS." Machining Science and Technology 10, no. 4 (December 2006): 511–38. http://dx.doi.org/10.1080/10910340600996175.
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Zang, Xiaohui, and Chengming Bai. "Markov Model-Based Learning Aid for Students’ Civics Course." Mobile Information Systems 2022 (August 29, 2022): 1–10. http://dx.doi.org/10.1155/2022/6026875.
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Since the 19th National Congress, students’ ideological education has become more and more one of the national priorities, so the Civics course has become one of the essential compulsory courses for students at all stages of school and university, and the learning methods of Civics course have also become a hot issue of concern to students, which makes the learning process of supplementary learning methods very important. In this paper, a Markov model was developed to calculate the probability transfer matrix and predict the supplementary learning methods used by students. This paper also establishes a Markov model to predict the frequency of students’ online classroom learning at different stages, and it is found that, in the future, more and more students will use the Internet for their Civics course assisted learning; therefore, it is very important to establish a perfect Civics course online assisted learning platform, and this paper also puts forward some suggestions for establishing a Civics course online assisted learning system, which provides some methods for subsequent students’ Civics course learning. This paper also proposes some suggestions for establishing a web-assisted learning system for Civics courses and provides some methods for subsequent student learning in Civics courses.
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Zhang, Huifang, Wangsen Lan, and Desheng Zhang. "Anomaly Intrusion Detection of Wireless Communication Network-Based on Markov Chain Model." Security and Communication Networks 2022 (July 5, 2022): 1–11. http://dx.doi.org/10.1155/2022/3255006.
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In order to solve the increasingly serious security problems of wireless networks, research on abnormal intrusion detection methods of wireless communication networks based on Markov chain model is proposed. What is usually observed is not the known intrusion behavior but the abnormal phenomenon in the communication process studied, which is completed by detecting the change of system behavior or usage. In this paper, the Markov chain model is used to detect the abnormal intrusion of wireless communication networks. Through the analysis and selection of parameters, the experimental results are ideal, and a variety of judgment methods are compared and analyzed. First, this method can easily distinguish between normal and abnormal data, which reduces the time by about 50% compared with the previous method; Second, the detection result of analysis method 2 is better than that of analysis method 1, and the accuracy is about 20%. The new method proposed in this paper has the characteristics of simple calculation, low algorithm complexity, and easy online detection. This method overcomes the disadvantage that the single-step Markov chain analysis and detection method cannot be strictly established in the nature of the Markov chain, has lower algorithm complexity than the multistep Markov chain analysis and detection method, and is simpler than the parameter calculation of hidden Markov chain model.
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Lin, Bingjie, Jie Cheng, Jiahui Wei, and Ang Xia. "A Sensing Method of Network Security Situation Based on Markov Game Model." International Journal of Circuits, Systems and Signal Processing 16 (January 14, 2022): 531–36. http://dx.doi.org/10.46300/9106.2022.16.66.
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The sensing of network security situation (NSS) has become a hot issue. This paper first describes the basic principle of Markov model and then the necessary and sufficient conditions for the application of Markov game model. And finally, taking fuzzy comprehensive evaluation model as the theoretical basis, this paper analyzes the application fields of the sensing method of NSS with Markov game model from the aspects of network randomness, non-cooperative and dynamic evolution. Evaluation results show that the sensing method of NSS with Markov game model is best for financial field, followed by educational field. In addition, the model can also be used in the applicability evaluation of the sensing methods of different industries’ network security situation. Certainly, in different categories, and under the premise of different sensing methods of network security situation, the proportions of various influencing factors are different, and once the proportion is unreasonable, it will cause false calculation process and thus affect the results.
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Bouton, Maxime, Jana Tumova, and Mykel J. Kochenderfer. "Point-Based Methods for Model Checking in Partially Observable Markov Decision Processes." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 06 (April 3, 2020): 10061–68. http://dx.doi.org/10.1609/aaai.v34i06.6563.
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Autonomous systems are often required to operate in partially observable environments. They must reliably execute a specified objective even with incomplete information about the state of the environment. We propose a methodology to synthesize policies that satisfy a linear temporal logic formula in a partially observable Markov decision process (POMDP). By formulating a planning problem, we show how to use point-based value iteration methods to efficiently approximate the maximum probability of satisfying a desired logical formula and compute the associated belief state policy. We demonstrate that our method scales to large POMDP domains and provides strong bounds on the performance of the resulting policy.
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Bertolami, Roman, and Horst Bunke. "Hidden Markov model-based ensemble methods for offline handwritten text line recognition." Pattern Recognition 41, no. 11 (November 2008): 3452–60. http://dx.doi.org/10.1016/j.patcog.2008.04.003.
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Dong, Lei, Wei-min Li, Ching-Hsin Wang, and Kuo-Ping Lin. "Gyro motor fault classification model based on a coupled hidden Markov model with a minimum intra-class distance algorithm." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 234, no. 5 (August 3, 2019): 646–61. http://dx.doi.org/10.1177/0959651819866281.
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In this study, we developed a fault classification model that combines a coupled hidden Markov model based on multi-channel information fusion with a minimum intra-class distance algorithm. This model relies on statistical features in the current time domain, which are the easiest features to extract for clustering. First, an algorithm is used to select and sequence the statistical features with the minimum intra-class distance in order to form feature vectors, which in turn enhance inter-class discrimination and feature reduction. Following reduction, the coupled hidden Markov model is used to perform classification. The coupled hidden Markov model was shown to reflect the coupling relationships between and among channels. We evaluated the efficacy of the proposed scheme by applying it to the diagnosis of faults in a gyro motor in three groups of experiments. Our results were compared with those obtained using a single-chain hidden Markov model and other intelligent fault diagnosis methods. The proposed scheme outperformed the other methods in terms of correct diagnosis rate, fluctuations in correct diagnosis rate, and excellent robustness against the effects of interference.
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Zhang, Wei, Zhaoxiang Qin, and Jun Tang. "Economic Benefit Analysis of Medical Tourism Industry Based on Markov Model." Journal of Mathematics 2022 (March 14, 2022): 1–9. http://dx.doi.org/10.1155/2022/6401796.
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To study the impact of the medical Tourism Industry on regional economic performance, a Markov prediction method was proposed. An improved Markov chain combination forecasting method was established by analyzing the economy of healthcare tourism industry through Markov chain forecasting method and various processing methods for economic results of different years. The research results show that healthcare tourism industry service is a new and highly potential tourism product service. It can generate significant economic and social benefits. The value and market size of healthcare tourism industry is analyzed and studied by using Markov model to explore the complementary roles of Medical and Tourism, which helps to predict the development of market size and benefits. The model results are also analyzed and calculated. The benefits and scale of the development of the healthcare tourism industry are evaluated by combining the actual data situation and development conditions in each year.
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Wei, An Shi. "The Construction of Piano Teaching Innovation Model Based on Full-depth Learning." International Journal of Emerging Technologies in Learning (iJET) 13, no. 03 (March 5, 2018): 32. http://dx.doi.org/10.3991/ijet.v13i03.8369.
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This paper presents a new method of building piano teaching innovation model based on full depth learning. The model includes the following main steps: (1) The normal behavior samples of piano teaching are obtained by the method of spectral clustering based on dynamic time homing (DTW), and the hidden Markov model; (2) to further train the hidden Markov model parameters in a large sample by means of iterative learning; (3) to use the maximum a posteriori (MAP) adaptive method to estimate the Hidden Markov Model (HMM) of the piano teaching behavior in a supervised manner; (4) The behavioral hidden Markov topology model is established for model estimation. The main features of this method are: it can automatically select the kinds and samples of the normal behavior patterns of piano teaching to establish an innovative model of piano teaching; the problem of under-learning of Hidden Markov Model (HMM) can be avoided in the case of fewer samples. The experimental results show that this model is more reliable than other methods.
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Huan, Hongyan, and Qing-mei Tan. "The forecast of cultivate land quantity based on Grey-Markov model." Grey Systems: Theory and Application 5, no. 1 (February 2, 2015): 127–36. http://dx.doi.org/10.1108/gs-03-2012-0016.
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Purpose – The purpose of this paper is to employ the Grey-Markov Chain Model for the scale prediction of cultivated land and took an empirical research with the case of Jiangsu province. Design/methodology/approach – Along with China’s industrialization and urbanization accelerated, a large number of cultivated land converse into construction land. The change of utilization of cultivated land concerns national food security and sustainable development of economy and society. Due to the fact that the different investigation methods of arable land usually cause a uncertain. The Grey-Markov model combines the Grey GM(1,1) and Markov chain, with two advantages of dealing with poor information and long-term and volatile series. A numeric example of scale prediction of cultivated land in Jiangsu province is also computed in the third part of the paper. Findings – The results show that the Grey-Markov Chain Model has a higher prediction accuracy compared with GM (1,1), which is a reliable guarantee for the change of cultivated land resources. Practical implications – The forecast of cultivated land can provide useful information for the general land use planning. Originality/value – The paper confirmed the feasibility of the Grey-Markov model in scale prediction of cultivated land.
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Abdolhossein Harisi, Rashin, and Hamid Reza Kobravi. "A Hidden Markov Model Based Detecting Solution for Detecting the Situation of Balance During Unsupported Standing Using the Electromyography of Ankle Muscles." International Clinical Neuroscience Journal 9, no. 1 (January 17, 2022): e3-e3. http://dx.doi.org/10.34172/icnj.2022.03.
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Background: In this study, three detecting approaches have been proposed and evaluated for online detection of balance situations during quiet standing. The applied methods were based on electromyography of the gastrocnemius muscles adopting the hidden Markov models. Methods: The levels of postural stability during quiet standing were regarded as the hidden states of the Markov models while the zones in which the center of pressure lies within determines the level of stability. The Markov models were trained by using the well-known Baum-Welch algorithm. The performance of a single hidden Markov model, the multiple hidden Markov model, and the multiple hidden Markov model alongside an adaptive neuro-fuzzy inference system (ANFIS), were compared as three different detecting methods. Results: The obtained results show the better and more promising performance of the method designed based on a combination of the hidden Markov models and optimized neuro-fuzzy system. Conclusion: According to the results, using the combined detecting method yielded promising results.
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Wang, Bing, Ping Yan, Qiang Zhou, and Libing Feng. "State recognition method for machining process of a large spot welder based on improved genetic algorithm and hidden Markov model." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 231, no. 11 (January 27, 2016): 2135–46. http://dx.doi.org/10.1177/0954406215626942.
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Large spot welder is an important equipment in rail transit equipment manufacturing industry, but having the problem of low utilization rate and low effectlvely machining rate. State monitoring can master its operating states real time and comprehensively, and providing data support for state recognition. Hidden Markov model is a state classification method, but it is sensitive to the initial model parameters and easy to trap into a local optima. Genetic algorithm is a global searching method; however, it is quite poor at hill climbing and also has the problem of premature convergence. In this paper, proposing the improved genetic algorithm, and combining improved genetic algorithm and hidden Markov model, a new method of state recognition method named improved genetic algorithm–hidden Markov model is proposed. In the proposed method, improved genetic algorithm is used for optimizing the initial parameters, and hidden Markov model as a classifier to recognize the operating states for machining process. This method is also compared with the other two recognition methods named adaptive genetic algorithm–hidden Markov model and hidden Markov model, in which adaptive genetic algorithm is similarly used for optimizing the initial parameters, however hidden Markov model (in both methods) as a classifier. Experimental results show that the proposed method is very effective, and the improved genetic algorithm–hidden Markov model recognition method is superior to the adaptive genetic algorithm–hidden Markov model and hidden Markov model recognition method.
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Waluyo, Dhita Septiani Yogo Sri, and Mariana Marselina. "Optimization Model of Cirata Reservoir Management Using Discrete Markov and ARIMA Discharge Forecasting Methods." IOP Conference Series: Earth and Environmental Science 1111, no. 1 (December 1, 2022): 012059. http://dx.doi.org/10.1088/1755-1315/1111/1/012059.
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Abstract Even though the management operation guidelines have been determined, the optimal management of the Citarum Cascade Reservoir has not yet been achieved. Optimal management of cascade reservoirs in an integrated manner is difficult to be carried out due to complex operating procedures and high uncertainty of hydrological components. So, research on the optimal management of each reservoir considering the discharge forecasting model as input discharge (Qin) was proposed. The objective of this study was to optimize Cirata Reservoir’s operational management. Hence all raw water demands are met without any water wasted through the spillway, as indicated by the correlation between inflow discharge (historical and model) and the correlation between trajectory (guideline and actual). In this case, discharge forecasting methods used were Discrete Markov and ARIMA. The continuous with five years return period (R5 continuous), continuous with ten years return period (R10 continuous), 3-classes Discrete Markov, and 5-classes Discrete Markov methods were applied for guideline trajectories. Based on the results, the correlation coefficients between inflow discharge were 0.63 (3-Classes Discrete Markov); 0.78 (5-Classes Discrete Markov); and 0.68 (ARIMA (1,0,0)(1,0,1) & ARIMA (0,0,2)(1,0,1)). Optimization simulations were carried out for 16 scenarios with combinations of four discharge models and four guideline trajectories during the 2016-2020 period. Based on the research, the scenario by the 5-Classes Discrete Markov discharge model and the R10 continuous guideline trajectory is the most optimal management of the Cirata Reservoir, with a correlation coefficient between the trajectory was 0.91.
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Ji, Ming, Fei Wang, Jia Ning Wan, and Yuan Liu. "Literature Review on Hidden Markov Model-Based Sequential Data Clustering." Applied Mechanics and Materials 713-715 (January 2015): 1750–56. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.1750.
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The purpose of this report is to investigate current existing algorithm to cluster sequential data based on hidden Markov model (HMM). Clustering is a classic technique that divides a set of objects into groups (called clusters) so that objects in the same cluster are similar in some sense. The clustering of sequential or time series data, however, draws lately more and more attention from researchers. Hidden Markov model (HMM)-based clustering of sequences is probabilistic model-based approach to clustering sequences. Generally, there are two kinds of methodologies: parametric and semi-parametric. The parametric methods make strict assumptions that each cluster is represented by a corresponding HMM, while the semi-parametric approaches relax this assumption and transform the problem to a similarity-based issue. Generally, the semi-parametric methods perform better than parametric approaches as reported by some researchers. Future research can be done in exploring new distance measures between sequences and extending current HMM-based methodologies by using other models.
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Xu, Jiawei, and Qian Luo. "Human action recognition based on mixed gaussian hidden markov model." MATEC Web of Conferences 336 (2021): 06004. http://dx.doi.org/10.1051/matecconf/202133606004.
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Human action recognition is a challenging field in recent years. Many traditional signal processing and machine learning methods are gradually trying to be applied in this field. This paper uses a hidden Markov model based on mixed Gaussian to solve the problem of human action recognition. The model treats the observed human actions as samples which conform to the Gaussian mixture model, and each Gaussian mixture model is determined by a state variable. The training of the model is the process that obtain the model parameters through the expectation maximization algorithm. The simulation results show that the Hidden Markov Model based on the mixed Gaussian distribution can perform well in human action recognition.
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Jiang, Pei, and Dongchen Wang. "Background Speech Synchronous Recognition Method of E-commerce Platform Based on Hidden Markov Model." International Journal of Circuits, Systems and Signal Processing 16 (January 12, 2022): 344–51. http://dx.doi.org/10.46300/9106.2022.16.42.
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In order to improve the effect of e-commerce platform background speech synchronous recognition and solve the problem that traditional methods are vulnerable to sudden noise, resulting in poor recognition effect, this paper proposes a background speech synchronous recognition method based on Hidden Markov model. Combined with the principle of speech recognition, the speech feature is collected. Hidden Markov model is used to input and recognize high fidelity speech filter to ensure the effectiveness of signal processing results. Through the de-noising of e-commerce platform background voice, and the language signal cache and storage recognition, using vector graph buffer audio, through the Ethernet interface transplant related speech recognition sequence, thus realizing background speech synchronization, so as to realize the language recognition, improve the recognition accuracy. Finally, the experimental results show that the background speech synchronous recognition method based on Hidden Markov model is better than the traditional methods.
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Peyravi, Farzad, Alimohammad Latif, and Seyed Mohammad Moshtaghioun. "Protein tertiary structure prediction using hidden Markov model based on lattice." Journal of Bioinformatics and Computational Biology 17, no. 02 (April 2019): 1950007. http://dx.doi.org/10.1142/s0219720019500070.
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The prediction of protein structure from its amino acid sequence is one of the most prominent problems in computational biology. The biological function of a protein depends on its tertiary structure which is determined by its amino acid sequence via the process of protein folding. We propose a novel fold recognition method for protein tertiary structure prediction based on a hidden Markov model and 3D coordinates of amino acid residues. The method introduces states based on the basis vectors in Bravais cubic lattices to learn the path of amino acids of the proteins of each fold. Three hidden Markov models are considered based on simple cubic, body-centered cubic (BCC) and face-centered cubic (FCC) lattices. A 10-fold cross validation was performed on a set of 42 fold SCOP dataset. The proposed composite methodology is compared to fold recognition methods which have HMM as base of their algorithms having approaches on only amino acid sequence or secondary structure. The accuracy of proposed model based on face-centered cubic lattices is quite better in comparison with SAM, 3-HMM optimized and Markov chain optimized in overall experiment. The huge data of 3D space help the model to have greater performance in comparison to methods which use only primary structures or only secondary structures.
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Janssens, Eva F., and Sean McCrary. "Finite-State Markov-Chain Approximations: A Hidden Markov Approach." Finance and Economics Discussion Series, no. 2023-040 (June 2023): 1–62. http://dx.doi.org/10.17016/feds.2023.040.
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This paper proposes a novel finite-state Markov chain approximation method for Markov processes with continuous support, providing both an optimal grid and transition probability matrix. The method can be used for multivariate processes, as well as non-stationary processes such as those with a life-cycle component. The method is based on minimizing the information loss between a Hidden Markov Model and the true data-generating process. We provide sufficient conditions under which this information loss can be made arbitrarily small if enough grid points are used. We compare our method to existing methods through the lens of an asset-pricing model, and a life-cycle consumption-savings model. We find our method leads to more parsimonious discretizations and more accurate solutions, and the discretization matters for the welfare costs of risk, the marginal propensities to consume, and the amount of wealth inequality a life-cycle model can generate.
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Al-Farrel, Rendhy, Donni Richasdy, and Mahendra Dwifebri Purbolaksono. "Analysis of Telkom University News Subjects on Popular Indonesian News Portals Using a Combination of Hidden Markov Model (HMM) and Rule Based Methods." JURNAL MEDIA INFORMATIKA BUDIDARMA 6, no. 4 (October 25, 2022): 2187. http://dx.doi.org/10.30865/mib.v6i4.4566.
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News media are often found in everyday life as a means of information for the public about something that is happening. In news articles, it is common to see several sentences that support the object to increase its popularity by being promoted by the subject. Part of Speech Tagging can determine the class of words in the sentence according to Tagsets provided by the corpus. That way, the search for the subject in the news article can be found from the word class obtained from a corpus. This research was focused on finding the subject "who" repeatedly spreading the news about Telkom University by using Part of Speech Tagging with the Hidden Markov Model and Rule Based on a news dataset from popular news portals about Telkom University. The process is taking all news about Telkom University on popular news portals and classifying it using the Hidden Markov Model and Rule-Based. We conducted to enhance the research results by changing the probability estimator on Hidden Markov Model. After running some scenarios, the best results obtained by the Hidden Markov Model and Rule-Based are the Accuracy of 94.96%, the Precision of 94.99%, the Recall of 94.96%, and the F1-Score of 94.95%.
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Obzherin, Yuriy, Mikhail Nikitin, and Stanislav Sidorov. "Hidden Markov Model of System Elements Technical Maintenance by Age." E3S Web of Conferences 216 (2020): 01030. http://dx.doi.org/10.1051/e3sconf/202021601030.
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Technical maintenance is between the methods of operation reliability and effectiveness increasing for systems of different purposes including power systems. In the paper the hidden semi-Markov model of technical maintenance is built basing on the semi-Markov model of two-component system elements technical maintenance by age. The hidden Markov model is used to solve the problems of dynamics analyzing, predicting the states of a system modelled based on the vector of signals obtained during its operation.
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Saito, Erin, Beau K. Nakamoto, Mario F. Mendez, Bijal Mehta, and Aaron McMurtray. "Cost Effective Community Based Dementia Screening: A Markov Model Simulation." International Journal of Alzheimer's Disease 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/103138.
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Background. Given the dementia epidemic and the increasing cost of healthcare, there is a need to assess the economic benefit of community based dementia screening programs.Materials and Methods. Markov model simulations were generated using data obtained from a community based dementia screening program over a one-year period. The models simulated yearly costs of caring for patients based on clinical transitions beginning in pre dementia and extending for 10 years.Results. A total of 93 individuals (74 female, 19 male) were screened for dementia and 12 meeting clinical criteria for either mild cognitive impairment(n=7)or dementia(n=5)were identified. Assuming early therapeutic intervention beginning during the year of dementia detection, Markov model simulations demonstrated 9.8% reduction in cost of dementia care over a ten-year simulation period, primarily through increased duration in mild stages and reduced time in more costly moderate and severe stages.Discussion. Community based dementia screening can reduce healthcare costs associated with caring for demented individuals through earlier detection and treatment, resulting in proportionately reduced time in more costly advanced stages.
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Xie, Zhen, and Zhao Wei Zhong. "Unmanned Vehicle Path Optimization Based on Markov Chain Monte Carlo Methods." Applied Mechanics and Materials 829 (March 2016): 133–36. http://dx.doi.org/10.4028/www.scientific.net/amm.829.133.
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Recently, unmanned vehicle (UV) research has increased its popularity around the globe not only for military applications but also for civilian uses. For military fields, UVs can enhance homeland defense, carry out coast and air surveillance, counter terrorists and most importantly, reduce harm to the manned force when certain mission may contain threat. As a consequence, UVs become an inevitable part of the Navy Force and extend the Navy mission handling capabilities. When it comes to research, UVs can be used to observe the climate, deliver goods, perform undersea testing, etc. But the open environment is dynamic, unforeseen and fast changing. Thus, a UV which has the ability to choose the optimal path autonomously based on the current situation not only can increase the efficiency of the UV, but also can save costs and time for the users. As a result, increasing the autonomy of the UV has attracted the attention of many researchersin recent years. Our research is based on the Markov chain Monte Carlo simulation model. We develop a simulation model architecture so as to realize collision free path planning and path optimization of an unmanned vehicle.
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Chen, Deng, Rubing Huang, Binbin Qu, Sheng Jiang, and Jianping Ju. "Mining Class Temporal Specification Dynamically Based on Extended Markov Model." International Journal of Software Engineering and Knowledge Engineering 25, no. 03 (April 2015): 573–604. http://dx.doi.org/10.1142/s0218194015500047.
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Class temporal specification is a kind of important program specifications especially for object-oriented programs, which specifies that interface methods of a class should be called in a particular sequence. Currently, most existing approaches mine this kind of specifications based on finite state automaton. Observed that finite state automaton is a kind of deterministic models with inability to tolerate noise. In this paper, we propose to mine class temporal specifications relying on a probabilistic model extending from Markov chain. To the best of our knowledge, this is the first work of learning specifications from object-oriented programs dynamically based on probabilistic models. Different from similar works, our technique does not require annotating programs. Additionally, it learns specifications in an online mode, which can refine existing models continuously. Above all, we talk about problems regarding noise and connectivity of mined models and a strategy of computing thresholds is proposed to resolve them. To investigate our technique's feasibility and effectiveness, we implemented our technique in a prototype tool ISpecMiner and used it to conduct several experiments. Results of the experiments show that our technique can deal with noise effectively and useful specifications can be learned. Furthermore, our method of computing thresholds provides a strong assurance for mined models to be connected.
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Yan, Ming, Shuijing Li, Chien Aun Chan, Yinghua Shen, and Ying Yu. "Mobility Prediction Using a Weighted Markov Model Based on Mobile User Classification." Sensors 21, no. 5 (March 3, 2021): 1740. http://dx.doi.org/10.3390/s21051740.
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The vast amounts of mobile communication data collected by mobile operators can provide important insights regarding epidemic transmission or traffic patterns. By analyzing historical data and extracting user location information, various methods can be used to predict the mobility of mobile users. However, existing prediction algorithms are mainly based on the historical data of all users at an aggregated level and ignore the heterogeneity of individual behavior patterns. To improve prediction accuracy, this paper proposes a weighted Markov prediction model based on mobile user classification. The trajectory information of a user is extracted first by analyzing real mobile communication data, where the complexity of a user’s trajectory is measured using the mobile trajectory entropy. Second, classification criteria are proposed based on different user behavior patterns, and all users are classified with machine learning algorithms. Finally, according to the characteristics of each user classification, the step threshold and the weighting coefficients of the weighted Markov prediction model are optimized, and mobility prediction is performed for each user classification. Our results show that the optimized weighting coefficients can improve the performance of the weighted Markov prediction model.
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Mohagheghi, Mohammadsadegh, and Khayyam Salehi. "Improving Graph-based methods for computing qualitative properties of markov decision processes." Indonesian Journal of Electrical Engineering and Computer Science 17, no. 3 (March 1, 2020): 1571. http://dx.doi.org/10.11591/ijeecs.v17.i3.pp1571-1577.
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<span>Probabilistic model checking is a formal verification method, which is used to guarantee the correctness of the computer systems with stochastic behaviors. Reachability probabilities are the main class of properties that are proposed in probabilistic model checking. Some graph-based pre-computation can determine those states for which the reachability probability is exactly zero or one. Iterative numerical methods are used to compute the reachability probabilities for the remaining states. In this paper, we focus on the graph-based pre-computations and propose a heuristic to improve the performance of these pre-computations. The proposed heuristic approximates the set of states that are computed in the standard pre-computation methods. The experiments show that the proposed heuristic can compute a main part of the expected states, while reduces the running time by several orders of magnitude.</span>
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Yi, Siqi, Yong Zhou, and Qing Li. "A New Perspective for Urban Development Boundary Delineation Based on the MCR Model and CA-Markov Model." Land 11, no. 3 (March 9, 2022): 401. http://dx.doi.org/10.3390/land11030401.
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In order to control the development of urban space, it is important to explore scientific methods to provide a reference for regional territorial space planning. On the basis of the minimum cumulative resistance (MCR) model and the cellular automaton (CA)-Markov model, we constructed a new technical method for delineating urban development boundaries, exploring the temporal and spatial distribution characteristic of land use in Wuhan from 2010 to 2020 through nighttime and remote sensing images, and simulating the urban development boundaries of Wuhan from 2025 to 2035. The results show that: (1) the scales of Wuhan City’s built-up areas in 2010, 2015, and 2020 were 500 km2, 566.13 km2, and 885.11 km2, respectively, and the trends of expansion run to the east and southeast, and (2) on the basis of the MCR model, the urban development boundary scale of Wuhan City in 2025, 2030, and 2035 from the perspective of actual supply will be 903.52 km2, 937.48 km2, and 1021.44 km2, respectively, and based on the CA-Markov model, the urban development boundary scales of Wuhan City in 2025, 2030, and 2035 from the perspective of ideal land demand will be 912.75 km2, 946.40 km2, and 1041.91 km2, respectively. By combining the results of the two methods, we determined areas of 901.62 km2, 944.39 km2, and 1015.36 km2 as the urban development boundaries of Wuhan City in 2025, 2030, and 2035, respectively. According to the principle of supply–demand balance, the urban development boundary delineated by the integration of the MCR model and CA-Markov model, which is in line with the spatial expansion trend of growing cities, could optimize the urban development pattern; solve the contradiction between urban development, farmland protection, and ecological protection; and provide a methodological reference and decision-making basis for planning practice.
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Müller, Christian, Fabian Weysser, Thomas Mrziglod, and Andreas Schuppert. "Markov-Chain Monte-Carlo methods and non-identifiabilities." Monte Carlo Methods and Applications 24, no. 3 (September 1, 2018): 203–14. http://dx.doi.org/10.1515/mcma-2018-0018.
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Abstract We consider the problem of sampling from high-dimensional likelihood functions with large amounts of non-identifiabilities via Markov-Chain Monte-Carlo algorithms. Non-identifiabilities are problematic for commonly used proposal densities, leading to a low effective sample size. To address this problem, we introduce a regularization method using an artificial prior, which restricts non-identifiable parts of the likelihood function. This enables us to sample the posterior using common MCMC methods more efficiently. We demonstrate this with three MCMC methods on a likelihood based on a complex, high-dimensional blood coagulation model and a single series of measurements. By using the approximation of the artificial prior for the non-identifiable directions, we obtain a sample quality criterion. Unlike other sample quality criteria, it is valid even for short chain lengths. We use the criterion to compare the following three MCMC variants: The Random Walk Metropolis Hastings, the Adaptive Metropolis Hastings and the Metropolis adjusted Langevin algorithm.
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Umezaki, Taizo, Hideyo Takeuchi, and Hironobu Fujiyoshi. "Comparison of the Fingerprint Verification Methods Based on the Discrete and Continuous Hidden Markov Model." IEEJ Transactions on Electronics, Information and Systems 118, no. 6 (1998): 955–60. http://dx.doi.org/10.1541/ieejeiss1987.118.6_955.
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Matsuda, Yu, Itsuo Hanasaki, Ryo Iwao, Hiroki Yamaguchi, and Tomohide Niimi. "Estimation of diffusive states from single-particle trajectory in heterogeneous medium using machine-learning methods." Physical Chemistry Chemical Physics 20, no. 37 (2018): 24099–108. http://dx.doi.org/10.1039/c8cp02566e.
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Zhu, Wei Hua, and Jing Yang. "Study on the Prediction of Shanghai Composite Index Based on a Fusion Model of RBF Neural Network, Markov Chain and GA." Advanced Materials Research 1049-1050 (October 2014): 1413–16. http://dx.doi.org/10.4028/www.scientific.net/amr.1049-1050.1413.
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From numerous approaches studying the prediction of stock price, this paper proposed a new approach which was the combination of RBF neural network and Markov chain to forecast the stock closing price of the Shanghai composite index. Markov chain was aimed at making the error between the actual price and predicted price obtained by RBF neural network correct. Besides, for higher prediction accuracy, genetic algorithm was used to optimize the state division of Markov chain. The experimental result confirmed its effectiveness and superiority in comparison with the other two methods in some time interval.
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BREUNIG, ROBERT, and ALISON STEGMAN. "TESTING FOR REGIME SWITCHING IN SINGAPOREAN BUSINESS CYCLES." Singapore Economic Review 50, no. 01 (April 2005): 25–34. http://dx.doi.org/10.1142/s0217590805001834.
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We examine a Markov-Switching model of Singaporean GDP using a combination of formal moment-based tests and informal graphical tests. The tests confirm that the Markov-Switching model fits the data better than a linear, autoregressive alternative. The methods are extended to allow us to identify precisely which features of the data are better captured by the nonlinear model. The methods described here allow model selection to be related to the intended use of the model.
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Dai, Falcon Z., and Matthew R. Walter. "Loop Estimator for Discounted Values in Markov Reward Processes." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 8 (May 18, 2021): 7169–75. http://dx.doi.org/10.1609/aaai.v35i8.16881.
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At the working heart of policy iteration algorithms commonly used and studied in the discounted setting of reinforcement learning, the policy evaluation step estimates the value of states with samples from a Markov reward process induced by following a Markov policy in a Markov decision process. We propose a simple and efficient estimator called loop estimator that exploits the regenerative structure of Markov reward processes without explicitly estimating a full model. Our method enjoys a space complexity of O(1) when estimating the value of a single positive recurrent state s unlike TD with O(S) or model-based methods with O(S^2). Moreover, the regenerative structure enables us to show, without relying on the generative model approach, that the estimator has an instance-dependent convergence rate of O~(\sqrt{\tau_s/T}) over steps T on a single sample path, where \tau_s is the maximal expected hitting time to state s. In preliminary numerical experiments, the loop estimator outperforms model-free methods, such as TD(k), and is competitive with the model-based estimator.
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Seiser, Eric L., and Federico Innocenti. "Hidden Markov Model-Based CNV Detection Algorithms for Illumina Genotyping Microarrays." Cancer Informatics 13s7 (January 2014): CIN.S16345. http://dx.doi.org/10.4137/cin.s16345.
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Somatic alterations in DNA copy number have been well studied in numerous malignancies, yet the role of germline DNA copy number variation in cancer is still emerging. Genotyping microarrays generate allele-specific signal intensities to determine genotype, but may also be used to infer DNA copy number using additional computational approaches. Numerous tools have been developed to analyze Illumina genotype microarray data for copy number variant (CNV) discovery, although commonly utilized algorithms freely available to the public employ approaches based upon the use of hidden Markov models (HMMs). QuantiSNP, PennCNV, and GenoCN utilize HMMs with six copy number states but vary in how transition and emission probabilities are calculated. Performance of these CNV detection algorithms has been shown to be variable between both genotyping platforms and data sets, although HMM approaches generally outperform other current methods. Low sensitivity is prevalent with HMM-based algorithms, suggesting the need for continued improvement in CNV detection methodologies.
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Wang, Chia-Hung, Qigen Zhao, and Rong Tian. "Short-Term Wind Power Prediction Based on a Hybrid Markov-Based PSO-BP Neural Network." Energies 16, no. 11 (May 23, 2023): 4282. http://dx.doi.org/10.3390/en16114282.
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Wind power prediction is an important research topic in the wind power industry and many prediction algorithms have recently been studied for the sake of achieving the goal of improving the accuracy of short-term forecasting in an effective way. To tackle the issue of generating a huge transition matrix in the traditional Markov model, this paper introduces a real-time forecasting method that reduces the required calculation time and memory space without compromising the prediction accuracy of the original model. This method is capable of obtaining the state probability interval distribution for the next moment through real-time calculation while preserving the accuracy of the original model. Furthermore, the proposed Markov-based Back Propagation (BP) neural network was optimized using the Particle Swarm Optimization (PSO) algorithm in order to effectively improve the prediction approach with an improved PSO-BP neural network. Compared with traditional methods, the computing time of our improved algorithm increases linearly, instead of growing exponentially. Additionally, the optimized Markov-based PSO-BP neural network produced a better predictive effect. We observed that the Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE) of the prediction model were 12.7% and 179.26, respectively; compared with the existing methods, this model generates more accurate prediction results.
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Ahmadian, Yashar, Jonathan W. Pillow, and Liam Paninski. "Efficient Markov Chain Monte Carlo Methods for Decoding Neural Spike Trains." Neural Computation 23, no. 1 (January 2011): 46–96. http://dx.doi.org/10.1162/neco_a_00059.
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Stimulus reconstruction or decoding methods provide an important tool for understanding how sensory and motor information is represented in neural activity. We discuss Bayesian decoding methods based on an encoding generalized linear model (GLM) that accurately describes how stimuli are transformed into the spike trains of a group of neurons. The form of the GLM likelihood ensures that the posterior distribution over the stimuli that caused an observed set of spike trains is log concave so long as the prior is. This allows the maximum a posteriori (MAP) stimulus estimate to be obtained using efficient optimization algorithms. Unfortunately, the MAP estimate can have a relatively large average error when the posterior is highly nongaussian. Here we compare several Markov chain Monte Carlo (MCMC) algorithms that allow for the calculation of general Bayesian estimators involving posterior expectations (conditional on model parameters). An efficient version of the hybrid Monte Carlo (HMC) algorithm was significantly superior to other MCMC methods for gaussian priors. When the prior distribution has sharp edges and corners, on the other hand, the “hit-and-run” algorithm performed better than other MCMC methods. Using these algorithms, we show that for this latter class of priors, the posterior mean estimate can have a considerably lower average error than MAP, whereas for gaussian priors, the two estimators have roughly equal efficiency. We also address the application of MCMC methods for extracting nonmarginal properties of the posterior distribution. For example, by using MCMC to calculate the mutual information between the stimulus and response, we verify the validity of a computationally efficient Laplace approximation to this quantity for gaussian priors in a wide range of model parameters; this makes direct model-based computation of the mutual information tractable even in the case of large observed neural populations, where methods based on binning the spike train fail. Finally, we consider the effect of uncertainty in the GLM parameters on the posterior estimators.
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Brandejsky, Adrien, Benoîte De Saporta, and François Dufour. "Numerical Methods for the Exit Time of a Piecewise-Deterministic Markov Process." Advances in Applied Probability 44, no. 1 (March 2012): 196–225. http://dx.doi.org/10.1239/aap/1331216650.
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We present a numerical method to compute the survival function and the moments of the exit time for a piecewise-deterministic Markov process (PDMP). Our approach is based on the quantization of an underlying discrete-time Markov chain related to the PDMP. The approximation we propose is easily computable and is even flexible with respect to the exit time we consider. We prove the convergence of the algorithm and obtain bounds for the rate of convergence in the case of the moments. We give an academic example and a model from the reliability field to illustrate the results of the paper.
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Brandejsky, Adrien, Benoîte De Saporta, and François Dufour. "Numerical Methods for the Exit Time of a Piecewise-Deterministic Markov Process." Advances in Applied Probability 44, no. 01 (March 2012): 196–225. http://dx.doi.org/10.1017/s0001867800005504.
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We present a numerical method to compute the survival function and the moments of the exit time for a piecewise-deterministic Markov process (PDMP). Our approach is based on the quantization of an underlying discrete-time Markov chain related to the PDMP. The approximation we propose is easily computable and is even flexible with respect to the exit time we consider. We prove the convergence of the algorithm and obtain bounds for the rate of convergence in the case of the moments. We give an academic example and a model from the reliability field to illustrate the results of the paper.
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Wellmann, J. "Markov Models for Repeated Ordinal Data." Methods of Information in Medicine 45, no. 04 (2006): 414–18. http://dx.doi.org/10.1055/s-0038-1634097.
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Summary Objectives: To demonstrate the application of Markov models, especially for ordinal outcomes, within the context of regression models for correlated data. Methods: A brief review of regression methods for correlated data is given. A proportional odds model and a continuation ratio model is applied to repeated measurements of macular pigment density, obtained in an intervention study on the supplementation of macular carotenoids. The correlation between repeated assessments is assumed to follow a first-order Markov model. The models are implemented with standard statistical software. Results: Both models, though not directly comparable, provide a similar conclusion. The application of these models with standard statistical software is straightforward. Conclusions: Markov models can be valuable alternatives to random effects modes or procedures based on generalized estimation equations.
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Ormonova, E. "Analysis of Assessing the Reliability Quality of the Software Product Based on the Graphs Theory and Markov Chains." Bulletin of Science and Practice 6, no. 4 (April 15, 2020): 12–17. http://dx.doi.org/10.33619/2414-2948/53/01.
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In this paper, we use graph theory and Markov chain methods to determine the quality of a software product based on a mathematical model. Since modern programming, developing a data structure and creating a mathematical model of a software product, we cannot do without graph theory. As an object of research, the PascalABC programming language is used. We have created a mathematical model of a software product using graph theory. The principles of software quality assessment by methods of graph theory, as well as Markov chains, are developed. Models of probabilistic assessment of the quality of computer programs using the Kolmogorov equations, based on graph theory.
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Jian Wu, Jian Wu, Honghui Deng Jian Wu, Fei Cheng Honghui Deng, and Hongjun Wang Fei Cheng. "Camera Tripod Removal Model in Panoramic Images Based on Generative Adversarial Networks." 電腦學刊 34, no. 3 (June 2023): 019–29. http://dx.doi.org/10.53106/199115992023063403002.
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<p>There are often residual images of the camera tripod in panoramic images, which may reduce the image quality and deteriorate the post-processing speed. To address this problem, a camera tripod removal network (TRNet) based on generative adversarial network is proposed. As an end-to-end model, the generator is designed to include recognition and reconstruction branches, which reduce the number of parameters and improve the training efficiency by sharing the encoder and correspond to scaffold recognition and texture reconstruction respectively. The recognition branch based on the U-Net structure can effectively identify the tripod area, while the reconstruction branch can brilliantly reconstruct the texture details through an intermediate layer formed by stacking dilated convolution residual blocks. Furthermore, spectral normalized Markov discriminator and multiple combined loss function are adopted to promote global texture consistency and thus result in a better texture filling effect. Finally, a data set of 400 panoramic images is constructed and experimental results on this data set demonstrate the better repair ability of TRNet against other state-of-the-art methods.</p> <p> </p>
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Kazhan, V. Ye. "STATISTICAL SEMI-MARKOV MODEL FOR RELIABILITY EVALUATIONOF ELECTROMECHANICAL SYSTEMS." Science and Transport Progress, no. 4 (August 25, 2004): 34–38. http://dx.doi.org/10.15802/stp2004/20630.
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BORGES, JOSÉ, and MARK LEVENE. "A COMPARISON OF SCORING METRICS FOR PREDICTING THE NEXT NAVIGATION STEP WITH MARKOV MODEL-BASED SYSTEMS." International Journal of Information Technology & Decision Making 09, no. 04 (July 2010): 547–73. http://dx.doi.org/10.1142/s0219622010003956.
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The problem of predicting the next request during a user's navigation session has been extensively studied. In this context, higher-order Markov models have been widely used to model navigation sessions and to predict the next navigation step, while prediction accuracy has been mainly evaluated with the hit and miss score. We claim that this score, although useful, is not sufficient for evaluating next link prediction models with the aim of finding a sufficient order of the model, the size of a recommendation set, and assessing the impact of unexpected events on the prediction accuracy. Herein, we make use of a variable length Markov model to compare the usefulness of three alternatives to the hit and miss score: the Mean Absolute Error, the Ignorance Score, and the Brier score. We present an extensive evaluation of the methods on real data sets and a comprehensive comparison of the scoring methods.
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Zhang, Xu, Ting Wu, Qiuhua Zheng, Liang Zhai, Haizhong Hu, Weihao Yin, Yingpei Zeng, and Chuanhui Cheng. "Multi-Step Attack Detection Based on Pre-Trained Hidden Markov Models." Sensors 22, no. 8 (April 8, 2022): 2874. http://dx.doi.org/10.3390/s22082874.
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Currently, hidden Markov-based multi-step attack detection models are mainly trained using the unsupervised Baum–Welch algorithm. The Baum–Welch algorithm is sensitive to the initial values of model parameters. However, its training uses random or average parameter initialization methods, which frequently results in the model training into a local optimum, thus, making the model unable to fit the alert logs well and thereby reducing the detection effectiveness of the model. To solve this issue, we propose a pre-training method for multi-step attack detection models based on the high semantic similarity of alerts in the same attack phase. The method first clusters the alerts based on their semantic information and pre-classifies the attack phase to which each alert belongs. Then, the distance of the alert vector to each attack stage is converted into the probability of generating alerts in each attack stage, replacing the initial value of Baum–Welch. The effectiveness of the proposed method is evaluated using the DARPA 2000 dataset, DEFCON21 CTF dataset, and ISCXIDS 2012 dataset. The experimental results show that the hidden Markov multi-step attack detection method based on pre-training of the proposed model parameters had higher detection accuracy than the Baum–Welch-based, K-means-based, and transfer learning differential evolution-based hidden Markov multi-step attack detection methods.
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Zhang, Huidi, and Yimao Chen. "Analysis and Application of Grey-Markov Chain Model in Tax Forecasting." Journal of Mathematics 2021 (December 18, 2021): 1–11. http://dx.doi.org/10.1155/2021/9918411.
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Tax data is a typical time series data, which is subject to the interaction and influence of economic and political factors and has dynamic and highly nonlinear characteristics. The key to correct tax forecasting is the choice of forecasting algorithm. Traditional tax forecasting methods, such as factor scoring method, factor regression method, and system adjustment method, have a certain guiding role in actual work, but there are still many shortcomings, such as the limitation from the distribution and size of sample data and difficulty of grasping the nonlinear phenomena in economic system. Grey-Markov chain model formed by the combination of grey forecasting and Markov chain forecasting can not only reveal the general developmental trend of time series data, but also predict their state change patterns. Based on the summary and analysis of previous research works, this paper expounds the current research status and significance of tax forecasting, elaborates the development background, current status, and future challenges of the Grey-Markov chain model, introduces the basic principles of grey forecasting model and Markov chain model, constructs the Grey-Markov chain model, analyzes the model’s residual error and posteriori error tests, conducts the analysis of Grey-Markov chain model, performs grey forecasting model construction and its state division, implements the calculation of transition probability matrix and the determination of tax forecasting value, discusses the application of the Grey-Markov chain model in tax forecasting, and finally carries out a simulation experiment and its result analysis. The study results show that, compared with separate grey forecasting, Markov chain forecasting, and other commonly used time series forecasting methods, the Grey-Markov chain model increases the accuracy of tax forecasts by an average of 2.3–13.1%. This indicates that the combinative forecasting of Grey-Markov chain model can make full use of the information provided by time series data for tax analysis and forecasting. It can not only avoid the influence of economic, political, and human subjective factors, but also have simple calculations, higher accuracy, and stronger practicality. The study results of this paper provide a reference for further researches on the analysis and application of Grey-Markov chain model in tax forecasting.
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Xia, Tian, and Xuemin Chen. "A Discrete Hidden Markov Model for SMS Spam Detection." Applied Sciences 10, no. 14 (July 21, 2020): 5011. http://dx.doi.org/10.3390/app10145011.
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Many machine learning methods have been applied for short messaging service (SMS) spam detection, including traditional methods such as naïve Bayes (NB), vector space model (VSM), and support vector machine (SVM), and novel methods such as long short-term memory (LSTM) and the convolutional neural network (CNN). These methods are based on the well-known bag of words (BoW) model, which assumes documents are unordered collection of words. This assumption overlooks an important piece of information, i.e., word order. Moreover, the term frequency, which counts the number of occurrences of each word in SMS, is unable to distinguish the importance of words, due to the length limitation of SMS. This paper proposes a new method based on the discrete hidden Markov model (HMM) to use the word order information and to solve the low term frequency issue in SMS spam detection. The popularly adopted SMS spam dataset from the UCI machine learning repository is used for performance analysis of the proposed HMM method. The overall performance is compatible with deep learning by employing CNN and LSTM models. A Chinese SMS spam dataset with 2000 messages is used for further performance evaluation. Experiments show that the proposed HMM method is not language-sensitive and can identify spam with high accuracy on both datasets.
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Berchtold, André. "Confidence Intervals for the Mixture Transition Distribution (MTD) Model and Other Markovian Models." Symmetry 12, no. 3 (March 1, 2020): 351. http://dx.doi.org/10.3390/sym12030351.
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The Mixture Transition Distribution (MTD) model used for the approximation of high-order Markov chains does not allow a simple calculation of confidence intervals, and computationnally intensive methods based on bootstrap are generally used. We show here how standard methods can be extended to the MTD model as well as other models such as the Hidden Markov Model. Starting from existing methods used for multinomial distributions, we describe how the quantities required for their application can be obtained directly from the data or from one run of the E-step of an EM algorithm. Simulation results indicate that when the MTD model is estimated reliably, the resulting confidence intervals are comparable to those obtained from more demanding methods.
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Kulinich, Max, Yanan Fan, Spiridon Penev, Jason P. Evans, and Roman Olson. "A Markov chain method for weighting climate model ensembles." Geoscientific Model Development 14, no. 6 (June 11, 2021): 3539–51. http://dx.doi.org/10.5194/gmd-14-3539-2021.
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Abstract. Climate change is typically modeled using sophisticated mathematical models (climate models) of physical processes that range in temporal and spatial scales. Multi-model ensemble means of climate models show better correlation with the observations than any of the models separately. Currently, an open research question is how climate models can be combined to create an ensemble mean in an optimal way. We present a novel stochastic approach based on Markov chains to estimate model weights in order to obtain ensemble means. The method was compared to existing alternatives by measuring its performance on training and validation data, as well as model-as-truth experiments. The Markov chain method showed improved performance over those methods when measured by the root mean squared error in validation and comparable performance in model-as-truth experiments. The results of this comparative analysis should serve to motivate further studies in applications of Markov chain and other nonlinear methods that address the issues of finding optimal model weight for constructing ensemble means.
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Dong, Shaojiang, Shirong Yin, Baoping Tang, Lili Chen, and Tianhong Luo. "Bearing Degradation Process Prediction Based on the Support Vector Machine and Markov Model." Shock and Vibration 2014 (2014): 1–15. http://dx.doi.org/10.1155/2014/717465.
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Predicting the degradation process of bearings before they reach the failure threshold is extremely important in industry. This paper proposed a novel method based on the support vector machine (SVM) and the Markov model to achieve this goal. Firstly, the features are extracted by time and time-frequency domain methods. However, the extracted original features are still with high dimensional and include superfluous information, and the nonlinear multifeatures fusion technique LTSA is used to merge the features and reduces the dimension. Then, based on the extracted features, the SVM model is used to predict the bearings degradation process, and the CAO method is used to determine the embedding dimension of the SVM model. After the bearing degradation process is predicted by SVM model, the Markov model is used to improve the prediction accuracy. The proposed method was validated by two bearing run-to-failure experiments, and the results proved the effectiveness of the methodology.
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Liu, Qinming, Ming Dong, and Ying Peng. "A dynamic predictive maintenance model considering spare parts inventory based on hidden semi-Markov model." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 227, no. 9 (December 12, 2012): 2090–103. http://dx.doi.org/10.1177/0954406212469773.
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The maintenance strategies optimization can play a key role in the industrial systems, in particular to reduce the related risks and the maintenance costs, improve the availability, and the reliability. Spare part demands are usually generated by the need of maintenance. It is often dependent on the maintenance strategies, and a better practice is to deal with these problems simultaneously. This article presents a stochastic dynamic programming maintenance model considering multi-failure states and spare part inventory. First, a probabilistic maintenance model called hidden semi-Markov model with aging factor is used to classify the multi-failure states and obtain transition probabilities among multi-failure states. Then, spare parts inventory cost is integrated into the maintenance model for different failure states. Finally, a double-layer dynamic programming maintenance model is proposed to obtain the optimal spare parts inventory and the optimal maintenance strategy through which the minimum total cost can be achieved. A case study is used to demonstrate the implementation and potential applications of the proposed methods.
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Snowsill, Tristan. "A New Method for Model-Based Health Economic Evaluation Utilizing and Extending Moment-Generating Functions." Medical Decision Making 39, no. 5 (July 2019): 523–39. http://dx.doi.org/10.1177/0272989x19860119.
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Background. Health economic evaluations frequently include projections for lifetime costs and health effects using modeling frameworks such as Markov modeling or discrete event simulation (DES). Markov models typically cannot represent events whose risk is determined by the length of time spent in state (sojourn time) without the use of tunnel states. DES is very flexible but introduces Monte Carlo variation, which can significantly limit the complexity of model analyses. Methods. We present a new methodological framework for health economic modeling that is based on, and extends, the concept of moment-generating functions (MGFs) for time-to-event random variables. When future costs and health effects are discounted, MGFs can be used to very efficiently calculate the total discounted life-years spent in a series of health states. Competing risks are incorporated into the method. This method can also be used to calculate discounted costs and health effects when these payoffs are constant per unit time, one-off, or exponential with regard to time. MGFs are extended to additionally support costs and health effects which are polynomial with regard to time (as in a commonly used model of population norms for EQ-5D utility). Worked Example. A worked example is used to demonstrate the application of the new method in practice and to compare it with Markov modeling and DES. Results are compared in terms of convergence and accuracy, and computation times are compared. R code and an Excel workbook are provided. Conclusions. The MGF method can be applied to health economic evaluations in the place of Markov modeling or DES and has certain advantages over both.