Markov Algorithm Research Articles

Smart plant project scheduling optimization based on hidden Markov algorithm

ABSTRACT In order to enhance the reasonableness of project scheduling optimization process, this paper puts forward a smart plant project scheduling optimization method based on hidden Markov algorithm. First, it introduces a kind of new method to cope with conflict. It refers to that once the conflict appears in the scheduling scheme, conduct status transfer aiming at the time schedule of the conflict instead of re-refining the starting time of the project to get another feasible time schedule, and enhance the calculation efficiency of the algorithm. Second, it takes the active and passive project scheduling problems as single and comprehensive problems for formulation, wherein, the target function includes a benchmark schedule cost and project response cost, and it finds out an optimal active and passive comprehensive project scheduling strategy; finally, it conducts modeling on the above project scheduling optimization problem by aid of Markov decision-making process, designs a kind of solution based on the dynamic planning of random graph. The experiment result shows that the method is effective in the convergence precision and calculation efficiency, and it demonstrates the reasonableness of the scheduling scheme by the means of Gantt chart.

A Novel Selection and Matching of Patterns Model for Traffic Prediction in Wireless Network

Data mining techniques are becoming increasing popular and important in the big data era. Among them, frequent patterns mining technique has matured, with this technique, we can obtain the association rules to predict the data in cellular network. Existing research mainly focus on the frequent item sets mining, rarely involves in the time series and FPM algorithm usually used user-defined minimum support in various training datasets, which is rigorous, may influence the number of the frequent patterns and fail to select the reasonable patterns. Moreover, traditional association rules mining only relies on single evaluation criterion such as confidence or support, which leads to select unsound rules. This paper introduces an optimized frequent pattern mining algorithm, improving the setting of support in the process of mining frequent sequences and presenting a new evaluation criterion for the candidate patterns. Experiments were conducted to select appropriate parameters, support and evaluation criterion. Furthermore, we apply the above conclusion to the cellular flow data prediction, and compare the runtime, matching rate, RMSE and MAPE of proposed algorithms with those of improved Markov algorithm to examine the effectiveness of algorithm.

Generating Intrinsically Disordered Protein Conformational Ensembles from a Database of Ramachandran Space Pair Residue Probabilities Using a Markov Chain

Intrinsically disordered proteins (IDPs), involved in regulatory pathways and cell signaling, sample a range of conformations. Constructing structural ensembles of IDPs is a difficult task for both experiment and simulation. In this work, we produce potential IDP ensembles using an existing database of pair residue φ and ψ angle probabilities chosen from turn, coil, and bend parts of sequences from the Protein Data Bank. For all residue pair types, a k-means-based discretization is used to create a set of rotamers and their probabilities in this pair Ramachandran space. For a given sequence, a Markov-based probabilistic algorithm is used to create Ramachandran space database-Markov ensembles that are converted to Cartesian coordinates of the backbone atoms. From these Cartesian coordinates and φ and ψ dihedral angles of a sequence, various observables: the radius of gyration and shape parameters, the distance probability distribution that is related to the small-angle X-ray scattering intensity, atom-atom contact percentages, local structural information, NMR three-bond J couplings, CA chemical shifts, and residual dipolar couplings are evaluated. A benchmark set of ensembles for 16 residue, regular sequences is constructed and used to validate the method and to explore the implications of the database for some of the above-mentioned observables. Then, we examine a set of nonapeptides of the form EGAAXAASS where X denotes residues of different characters. These peptides were studied by NMR, and subsequent molecular dynamics (MD) simulations were carried out using various force fields to find which one best agrees with the NMR data. Our analysis of these peptides shows that the combination of the database and the Markov algorithm yields ensembles that agree very well with the NMR and MD results for the above-listed observables. Thus, this database-Markov method is a promising method to generate IDP conformational ensembles.

A Markov Decision Process for managing a Hybrid Energy Storage System

Abstract The high penetration of photovoltaic installations in the domestic sector and the clear reduction of the feed-in-tariffs have made the need for storage systems more and more imminent when greater amounts of energy ought to be daily allocated and assigned. Hybrid Energy Storage Systems (HESS) are a nascent technology which can contribute towards maximization of self-energy consumption and grid stability by smoothing out peaks. In the frame of this paper it is attempted to delimitate the capability of a HESS so as to support the domestic load demand of a single family house in a central north area in Germany, accumulated from the load demand of an E-vehicle which is used for commute reasons of the family and always charges at home. The designed HESS is composed of two storage devices, namely a lead acid battery system (LAB) and a Vanadium Redox Flow Battery (VRB), and supported also from a photovoltaic installation. So as to succeed an optimized utilization of the dual storage system a novel algorithm is developed based on the Markov Decision Process, according to which the priority for charging and discharging process is assigned to the storage facility which depicts a favorable performance under the respective given conditions. The performance of this algorithm is compared to a naive policy—a control strategy based on the constant prioritization of the LAB over the VRB due to LAB’s efficiency overperformance. Results demonstrate that with the proposed markov algorithm the system consumes annually almost 5% more from the on-site renewable production whereas less load peaks are noticed during grid exchange, compared to those extracted from the naive method. Furthermore, it is shown that during sunnier months results are more distinct in comparison to the naive policy (up to 9% increase in self-energy consumption and 3% reduction in grid fluctuations), due to higher power rating setting thus the designed algorithm as more suitable to coordinate the operation of the two storage devices.

A resource management method based on organizational behavior theory and hidden Markov algorithm

In order to improve the level of human resource management in modern enterprises, a human resource management method in modern enterprises based on organizational behavior theory and hidden Markov algorithm is proposed. First of all, according to characteristics of human resources training, human resources are analyzed from the aspects of psychological capital and incentive theory in the organizational behavior theory to maximize the advantages of human resources, which is of great significance. Secondly, targeted at data partitioning and unknown model quantities in discovery process of the hidden model, the hidden Markov algorithm using Dirichlet process and non-parametric bayesian factors are analyzed for data flow partitioning of human resource management in modern enterprises and model discovery; with psychological capital and incentive theory in organizational behavior theory as the starting point, role of organizational behavior theory in human resources training is explored in this Thesis.

Land use change assessment in coastal mangrove forests of Iran utilizing satellite imagery and CA–Markov algorithms to monitor and predict future change

Mangrove forest stores large organic carbon stocks in a setting that is highly vulnerable to climate change and direct anthropogenic influences. As such there is a need to elucidate the causes and consequences of land use change on these ecosystems that have high value in terms of ecosystem services. We examine the areal pattern of land types in a coastal region located in southern Iran over a period of 14 years to predict future loss and gain in land types to the year 2025. We applied a CA–Markov model to simulate and predict mangrove forest change. Landsat satellite images from 2000 to 2014 were used to analyze the land cover changes between soil, open water and mangroves. Major changes during this period were observed in soil and water which could be attributed to rising sea level. Furthermore, the mangrove area in the more seaward position was converted to open water due to sea-level rise. A cellular automata model was then used to predict the land cover changes that would occur by the year 2025. Results demonstrated that approximately 21 ha of mangrove area will be converted to open water, while mangroves are projected to expand by approximately 28 ha in landward direction. These changes need to be delineated to better inform precise mitigation and adaptation measures.

Fluctuations in the heterogeneous multiscale methods for fast\u2013slow systems

How heterogeneous multiscale methods (HMM) handle fluctuations acting on the slow variables in fast–slow systems is investigated. In particular, it is shown via analysis of central limit theorem (CLT) and large deviation principle (LDP) that the standard version of HMM artificially amplifies these fluctuations. A simple modification of HMM, termed parallel HMM, is introduced and is shown to remedy this problem, capturing fluctuations correctly both at the level of the CLT and the LDP. All results in this article assume the HMM speedup factor lambda to be constant and in particular independent of the scale parameter varepsilon . Similar type of arguments can also be used to justify that the tau -leaping method used in the context of Gillespie’s stochastic simulation algorithm for Markov jump processes also captures the right CLT and LDP for these processes.

Driving range estimation for electric vehicles based on driving condition identification and forecast

With the impact of serious environmental pollution in our cities combined with the ongoing depletion of oil resources, electric vehicles are becoming highly favored as means of transport. Not only for the advantage of low noise, but for their high energy efficiency and zero pollution. The Power battery is used as the energy source of electric vehicles. However, it does currently still have a few shortcomings, noticeably the low energy density, with high costs and short cycle life results in limited mileage compared with conventional passenger vehicles. There is great difference in vehicle energy consumption rate under different environment and driving conditions. Estimation error of current driving range is relatively large due to without considering the effects of environmental temperature and driving conditions. The development of a driving range estimation method will have a great impact on the electric vehicles. A new driving range estimation model based on the combination of driving cycle identification and prediction is proposed and investigated. This model can effectively eliminate mileage errors and has good convergence with added robustness. Initially the identification of the driving cycle is based on Kernel Principal Component feature parameters and fuzzy C referring to clustering algorithm. Secondly, a fuzzy rule between the characteristic parameters and energy consumption is established under MATLAB/Simulink environment. Furthermore the Markov algorithm and BP(Back Propagation) neural network method is utilized to predict the future driving conditions to improve the accuracy of the remaining range estimation. Finally, driving range estimation method is carried out under the ECE 15 condition by using the rotary drum test bench, and the experimental results are compared with the estimation results. Results now show that the proposed driving range estimation method can not only estimate the remaining mileage, but also eliminate the fluctuation of the residual range under different driving conditions.

A prediction method based on wavelet transform and multiple models fusion for chaotic time series

In order to improve the prediction accuracy of chaotic time series, a prediction method based on wavelet transform and multiple models fusion is proposed. The chaotic time series is decomposed and reconstructed by wavelet transform, and approximate components and detail components are obtained. According to different characteristics of each component, least squares support vector machine (LSSVM) is used as predictive model for approximation components. At the same time, an improved free search algorithm is utilized for predictive model parameters optimization. Auto regressive integrated moving average model (ARIMA) is used as predictive model for detail components. The multiple prediction model predictive values are fusion by Gauss–Markov algorithm, the error variance of predicted results after fusion is less than the single model, the prediction accuracy is improved. The simulation results are compared through two typical chaotic time series include Lorenz time series and Mackey–Glass time series. The simulation results show that the prediction method in this paper has a better prediction.

Geometric ergodicity of Rao and Teh’s algorithm for Markov jump processes and CTBNs

Rao and Teh (2012, 2013) introduced an efficient MCMC algorithm for sampling from the posterior distribution of a hidden Markov jump process. The algorithm is based on the idea of sampling virtual jumps. In the present paper we show that the Markov chain generated by Rao and Teh’s algorithm is geometrically ergodic. To this end we establish a geometric drift condition towards a small set. A similar result is also proved for a special version of the algorithm, used for probabilistic inference in Continuous Time Bayesian Networks.

Geometric ergodicity of Rao and Teh’s algorithm for homogeneous Markov jump processes

Rao and Teh (2013) introduced an efficient MCMC algorithm for sampling from the posterior distribution of a hidden Markov jump process. The algorithm is based on the idea of sampling virtual jumps. In the present paper we show that the Markov chain generated by Rao and Teh’s algorithm is geometrically ergodic. To this end we establish a geometric drift condition towards a small set. We work under the assumption that the parameters of the hidden process are known and the goal is to restore its trajectory.

Maximum likelihood state estimation for Markov jump systems with uncertain mode-dependent delays

This paper presents a recursive maximum likelihood state estimator based on the expectation maximization algorithm for Markov jump linear systems with uncertain mode-dependent delays. To calculate the posterior probability of each possible candidate time delay, a recursive algorithm is derived within the Bayesian framework conditioned on the likelihood density function of state with respect to the candidate time delay related to the reference mode. By combining the optimal principle of expectation maximization and the interacting multiple mode approximation, we propose a double-reweight interacting multiple model (D-RIMM) algorithm to obtain the maximum a posterior estimator of state that is of low computational complexity. An example is given to demonstrate the effectiveness and potential of the proposed D-RIMM algorithm.

QEFSM model and Markov Algorithm for translating Quran reciting rules into Braille code

The Holy Quran is the central religious verbal text of Islam. Muslims are expected to read, understand, and apply the teachings of the Holy Quran. The Holy Quran was translated to Braille code as a normal Arabic text without having its reciting rules included. It is obvious that the users of this transliteration will not be able to recite the Quran the right way. Through this work, Quran Braille Translator (QBT) presents a specific translator to translate Quran verses and their reciting rules into the Braille code. Quran Extended Finite State Machine (QEFSM) model is proposed through this study as it is able to detect the Quran reciting rules (QRR) from the Quran text. Basis path testing was used to evaluate the inner work for the model by checking all the test cases for the model. Markov Algorithm (MA) was used for translating the detected QRR and Quran text into the matched Braille code. The data entries for QBT are Arabic letters and diacritics. The outputs of this study are seen in the double lines of Braille symbols; the first line is the proposed Quran reciting rules and the second line is for the Quran scripts.

Multi-target FIR tracking algorithm for Markov jump linear systems based on true-target decision-making

Most existing multi-target tracking (MTT) algorithms are based on Kalman filters (KFs). However, KFs exhibit poor estimation performance or even diverge when system models have parameter uncertainties. To overcome this drawback, finite impulse response (FIR) filters have been studied; these are more robust against model uncertainty than KFs. In this paper, we propose a novel MTT algorithm based on FIR filtering for Markov jump linear systems (MJLSs). The proposed algorithm is called the multi-target FIR tracking algorithm (MTFTA). The MTFTA is based on the decision-making process to identify the true-target׳s state among candidate states. The true-target decision-making process utilizes the likelihood function and the Mahalanobis distance. We show that the proposed MTFTA exhibits better robustness against model parameter uncertainties than the conventional KF-based algorithm.

Estimating effective population size from temporally spaced samples with a novel, efficient maximum-likelihood algorithm.

The effective population size is a key parameter in population genetics and evolutionary biology, as it quantifies the expected distribution of changes in allele frequency due to genetic drift. Several methods of estimating have been described, the most direct of which uses allele frequencies measured at two or more time points. A new likelihood-based estimator for contemporary effective population size using temporal data is developed in this article. The existing likelihood methods are computationally intensive and unable to handle the case when the underlying is large. This article tries to work around this problem by using a hidden Markov algorithm and applying continuous approximations to allele frequencies and transition probabilities. Extensive simulations are run to evaluate the performance of the proposed estimator , and the results show that it is more accurate and has lower variance than previous methods. The new estimator also reduces the computational time by at least 1000-fold and relaxes the upper bound of to several million, hence allowing the estimation of larger . Finally, we demonstrate how this algorithm can cope with nonconstant scenarios and be used as a likelihood-ratio test to test for the equality of throughout the sampling horizon. An R package “NB” is now available for download to implement the method described in this article.

A new signal processing algorithm of pulsed infrared thermography

A new signal processing algorithm combining Markov and principal component analysis (PCA) algorithm, which was named as Markov–PCA algorithm, was proposed to process the pulsed infrared thermography. First, the image sequence was reconstructed using Markov algorithm, then the original complex data dimensionality was reduced using PCA algorithm, which can remove the noise and redundancy of the infrared image sequences, and thus improve the detectability of defects. Results show that both the starting frame position and size of analysis window has an obvious effect on the processing results of Markov–PCA algorithm. And the proposed Markov–PCA algorithm improves the signal to noise ratio (SNR) of feature images more significantly than the commonly used PPT algorithm.

Short-term Prediction Method of Resources Occupancy for LBS Guidance System

Guidance system can improve the resources utilization of LBS (Location Based Service) system effectively, and the validity of guidance system depends on accurate predicting of the near-future system resources occupancy and its trend. Considering the strong randomicity of short-term characteristic of the trend, a multi-model fusion method integrating adaptive filter and ARMA (Auto Regressive Moving Average) model is proposed to predict the resources occupancy and trend accurately. With the method, observation series of recent resources occupancy is decomposed to different scales and reconstructed by wavelet transform firstly. According to the different features of series at different scale, for approximate signal, the adaptive filter algorithm is used to predict the trend of resources occupancy at coarse scale, and for detail signals in multiple fine scales, ARMA algorithms are adopted. Finally, integrating the prediction results of multi-model from different scales, the resources occupancy and trend with high prediction accuracy can be obtained. Experiment results show that the resources occupancy prediction accuracy of proposed method is higher than that of typical algorithms such as exponential smoothing and weighted Markov algorithms.

Hybrid Map-Based Navigation Method for Unmanned Ground Vehicle in Urban Scenario

To reduce the data size of metric map and map matching computational cost in unmanned ground vehicle self-driving navigation in urban scenarios, a metric-topological hybrid map navigation system is proposed in this paper. According to the different positioning accuracy requirements, urban areas are divided into strong constraint (SC) areas, such as roads with lanes, and loose constraint (LC) areas, such as intersections and open areas. As direction of the self-driving vehicle is provided by traffic lanes and global waypoints in the road network, a simple topological map is fit for the navigation in the SC areas. While in the LC areas, the navigation of the self-driving vehicle mainly relies on the positioning information. Simultaneous localization and mapping technology is used to provide a detailed metric map in the LC areas, and a window constraint Markov localization algorithm is introduced to achieve accurate position using laser scanner. Furthermore, the real-time performance of the Markov algorithm is enhanced by using a constraint window to restrict the size of the state space. By registering the metric maps into the road network, a hybrid map of the urban scenario can be constructed. Real unmanned vehicle mapping and navigation tests demonstrated the capabilities of the proposed method.

Identify on Acoustic Emission Signals of Tank Bottom Corrosion Using Hidden Markov Model

Aiming at the acoustic emission signals of oil storage tank bottom injured, hidden Markov algorithm is proposed to identify the tank bottom corrosion signal. Typical corrosion acoustic emission signal is divided into transient acoustic signal, continuous acoustic emission signal and mixed acoustic emission.Baum-Welch algorithm is used to train these typical corrosion acoustic emission signals model, then establish HMM model library. The forward-backward algorithm is used to compute each acoustic emission model’s output probability. The simulation experiments shows that the hidden Markov algorithm can correctly identified the acoustic emission signals.

Elephant Transcriptome Provides Insights into the Evolution of Eutherian Placentation

The chorioallantoic placenta connects mother and fetus in eutherian pregnancies. In order to understand the evolution of the placenta and provide further understanding of placenta biology, we sequenced the transcriptome of a term placenta of an African elephant (Loxodonta africana) and compared these data with RNA sequence and microarray data from other eutherian placentas including human, mouse, and cow. We characterized the composition of 55,910 expressed sequence tag (i.e., cDNA) contigs using our custom annotation pipeline. A Markov algorithm was used to cluster orthologs of human, mouse, cow, and elephant placenta transcripts. We found 2,963 genes are commonly expressed in the placentas of these eutherian mammals. Gene ontology categories previously suggested to be important for placenta function (e.g., estrogen receptor signaling pathway, cell motion and migration, and adherens junctions) were significantly enriched in these eutherian placenta–expressed genes. Genes duplicated in different lineages and also specifically expressed in the placenta contribute to the great diversity observed in mammalian placenta anatomy. We identified 1,365 human lineage–specific, 1,235 mouse lineage–specific, 436 cow lineage–specific, and 904 elephant-specific placenta-expressed (PE) genes. The most enriched clusters of human-specific PE genes are signal/glycoprotein and immunoglobulin, and humans possess a deeply invasive human hemochorial placenta that comes into direct contact with maternal immune cells. Inference of phylogenetically conserved and derived transcripts demonstrates the power of comparative transcriptomics to trace placenta evolution and variation across mammals and identified candidate genes that may be important in the normal function of the human placenta, and their dysfunction may be related to human pregnancy complications.