Handover algorithm for multiple networks based on Bayesian decision

An improved vertical handover algorithm for multiple networks based on Bayesian decision is proposed. Firstly, the handover probability distribution is established considering multiple conditions including signal strength, bit error rate, blocking probability and user demands, and accordingly the prior handover probability is calculated. Secondly, the posterior probability based on Bayesian decision algorithm is got. Finally, the optimal access network is selected according to the decision strategy based on posterior probability. Simulation results indicate that the proposed algorithm not only effectively achieves vertical handover among WLAN, WiMAX and LTE with the least number of handovers, but also keeps high average network load, which can provide the users with good service quality.

Bayesian decision-based pattern recognition on spectrum signal of metal transfer modes

A software system of pattern recognition on spectrum signal of metal transfer mode has been developed using Visual Basic under Windows environment. On the basis of the coincidence relation between the spectrum signal and metal transfer mode, according to the geometrical pattern feature of the spectrum signal, several key characteristic parameters are extracted. The correspondent recognition function and a minimum distance classifier have been constructed based on Bayesian decision theory. The results show that using this system, the metal transfer mode of MIG, MAG, CO2 welding can be recognized automatically which provides the basis for automatically controlling of the metal gas arc welding metal transfer.

A Receiver-Forwarding Decision Scheme Based on Bayesian for NDN-VANET

Named Data Network(NDN) has caused wide concerns in VANET community because NDN uses a content-centric mechanism that naming content rather than the host. However, integrating NDN into VANET(NDN-VANET) also faces several challenges including consumer/provider mobile, broadcast storm problem and so on. In this paper, we propose a Bayesian-based Receiver Forwarding Decision(BRFD) scheme to mitigate the broadcast storm problem incurred by interest packets in NDN-VANET. In the BRFD, vehicles received an interest packet are required to make forwarding decisions based on Bayesian decision theory according to current network conditions obtained by neighbor interaction. However, the receiver-forwarding decision in BRFD can also cause a conflict issue because multiple vehicles forward copies of the same packet at the same time. So a back-off mechanism is introduced in BRFD. Experimental results show that the BRFD algorithm has better performance in several aspects in contrast to probability-based forwarding scheme and "bread crumb" routing.

Visual detection of rice rows based on Bayesian decision theory and robust regression least squares method

Paddy field management is complicated and labor intensive.Correct row detection is important to automatically track rice rows.In this study,a novel method was proposed for accurate rice row recognition in paddy field transplanted by machine before the disappearance of row information.Firstly,Bayesian decision theory based on the minimum error was used to classify the period of collected images into three periods(T1:0-7 d;T2:7-28 d;T3:28-45 d),and resulting in the correct recognition rate was 97.03%.Moreover,secondary clustering of feature points was proposed,which can solve some problems such as row breaking and tilting.Then,the robust regression least squares method(RRLSM)for linear fitting was proposed to fit rice rows to effectively eliminate interference by outliers.Finally,a credibility analysis of connected region markers was proposed to evaluate the accuracy of fitting lines.When the threshold of credibility was set at 40%,the correct recognition rate of fitting lines was 96.32%.The result showed that the method can effectively solve the problems caused by the presence of duckweed,high-density inter-row weeds,broken rows,tilting(±60°),wind and overlap.

A Bayesian Theory of Games： An Analysis of Strategic Interactions with Statistical Decision Theoretic Foundation

This paper introduces a new game theoretic equilibrium which is based upon the Bayesian subjective view of probability, BEIC （Bayesian equilibrium iterative conjectures）. It requires players to make predictions, starting from first order uninformative predictive distribution functions （or conjectures） and keep updating with statistical decision theoretic and game theoretic reasoning until a convergence of conjectures is achieved. Information known by the players such as the reaction functions are thereby incorporated into their higher order conjectures and help to determine the convergent conjectures and the equilibrium. In a BEIC, conjectures are consistent with the equilibrium or equilibriums they supported and so rationality is achieved for actions, strategies and conjectures. The BEIC approach is capable of analyzing a larger set of games than current Nash Equilibrium based games theory, including games with inaccurate observations, games with unstable equilibrium and games with double or multiple sided incomplete information games. On the other hand, for the set of games analyzed by the current games theory, it generates far lesser equilibriums and normally generates only a unique equilibrium. It treats games with complete and perfect information as special cases of games with incomplete information and noisy observation whereby the variance of the prior distribution function on type and the variance of the observation noise term tend to zero. Consequently, there is the issue of indeterminacy in statistical inference and decision making in these games as the equilibrium solution depends on which variances tends to zero first. It therefore identifies equilibriums in these games that have so far eluded the classical theory of games. Finally, it also resolves inconsistencies in equilibrium results by different solution concepts in current games theory such as that between Nash Equilibrium and iterative elimination of dominated strategies and that between Perfect Bayesian Equilibrium and backward induction （Subgame Perfect Equilibrium）.

Bayesian Equilibrium by Iterative Conjectures as a Refinement of Nash Equilibrium

BEIC （Bayesian equilibrium by iterative conjectures） analyzes games with players forming their conjectures about what other players will do through iterative reasoning starting with first order uninformative conjectures and keep updating their conjectures iteratively with game theoretic reasoning until a convergence of conjectures is achieved. In a BEIC, beliefs about the other players＇ strategies are specified and they are consistent with the equilibrium strategies they supported. A BEIC is therefore a perfect Bayesian equilibrium and hence a refinement of Nash equilibrium. Through six examples, the BE1C solutions are compared with those obtained by the other refining criteria of payoff-dominance, risk-dominance, iterated admissibility, subgame perfect equilibrium, Bayesian Nash equilibrium, perfect Bayesian equilibrium and the intuitive criterion. The outstanding results from the comparisons are that the BEIC approach is able to pick the natural focal point of a game when the iterated admissibility criterion fails to, the BEIC approach rules out equilibrium depending upon non credible threat, and that in simultaneous and sequential games of incomplete information, the BEIC approach not only normally narrows down the equilibriums to one but it also picks the most compelling equilibrium compare with Bayesian Nash equilibrium or perfect Bayesian equilibrium or intuitive criterion.

Optimal sensor scheduling for hybrid estimation

A sensor scheduling problem was considered for a class of hybrid systems named as the stochastic linear hybrid system （SLHS）. An algorithm was proposed to select one （or a group of） sensor at each time from a set of sensors. Then, a hybrid estimation algorithm was designed to compute the estimates of the continuous and discrete states of the SLHS based on the observations from the selected sensors. As the sensor scheduling algorithm is designed such that the Bayesian decision risk is minimized, the true discrete state can be better identified. Moreover, the continuous state estimation performance of the proposed algorithm is better than that of hybrid estimation algorithms using only predetermined sensors. Finallyo the algorithms are validated through an illustrative target tracking example.

An Analysis of the Value of Additional Information Provided by Water Quality Measurement Network

European Community policy concerning water is placing increasing demands on the acquisition of information about the quality of aquatic environments. The cost of this information has led to a reflection on the rationalization of monitoring networks and, therefore, on the economic value of information produced by these networks. The aim of this article is to contribute to this reflection. To do so, we used the Bayesian framework to define the value of additional information in relation to the following three parameters: initial assumptions (prior probabilities) on the states of nature, costs linked to a poor decision (error costs) and accuracy of additional information. We then analyzed the impact of these parameters on this value, particularly the combined role of prior probabilities and error costs that increased or decreased the value of information depending on the initial uncertainty level. We then illustrated the results using a case study of a stream in the Bas-Rhin department in France.

An Information Fusion Model of Innovation Alliances Based on the Bayesian Network

To solve the problem of information fusion from multiple sources in innovation alliances, an information fusion model based on the Bayesian network is presented. The multi-source information fusion process of innovation alliances was classified into three layers, namely, the information perception layer, the feature clustering layer,and the decision fusion layer. The agencies in the alliance were defined as sensors through which information is perceived and obtained, and the features were clustered. Finally, various types of information were fused by the innovation alliance based on the fusion algorithm to achieve complete and comprehensive information. The model was applied to a study on economic information prediction, where the accuracy of the fusion results was higher than that from a single source and the errors obtained were also smaller with the MPE less than 3%, which demonstrates the proposed fusion method is more effective and reasonable. This study provides a reasonable basis for decision-making of innovation alliances.

Transit smart card data mining for passenger origin information extraction

The automated fare collection(AFC) system,also known as the transit smart card(SC) system,has gained more and more popularity among transit agencies worldwide.Compared with the conventional manual fare collection system,an AFC system has its inherent advantages in low labor cost and high efficiency for fare collection and transaction data archival.Although it is possible to collect highly valuable data from transit SC transactions,substantial efforts and methodologies are needed for extracting such data because most AFC systems are not initially designed for data collection.This is true especially for the Beijing AFC system,where a passenger's boarding stop(origin) on a flat-rate bus is not recorded on the check-in scan.To extract passengers' origin data from recorded SC transaction information,a Markov chain based Bayesian decision tree algorithm is developed in this study.Using the time invariance property of the Markov chain,the algorithm is further optimized and simplified to have a linear computational complexity.This algorithm is verified with transit vehicles equipped with global positioning system(GPS) data loggers.Our verification results demonstrated that the proposed algorithm is effective in extracting transit passengers' origin information from SC transactions with a relatively high accuracy.Such transit origin data are highly valuable for transit system planning and route optimization.

Optimal procurement strategy of fresh produce retailer under stochastic product qualification and market demand

Consider an optimal procurement strategy for fresh produce,which is a type of perishable product.Due to the different quality provided by each manufacturer,the fresh produce qualification rates are dissimilar.Simultaneously,consumers demand is influenced by the freshness and price of products,as a result,the demand in the market is not fixed.In this scenario,how a particular retailer should develop an optimal procurement strategy will be a core issue in supply chain management.In order to address the above questions,the Bayesian approach is adopted to explore retailer optimal procurement strategies with uncertainty about product supply and market demand.Finally,simulation data are used to analyse the results of the proposed model and expected non-random model to illustrate the validity and feasibility of the proposed model.

A Novel Fusion Forecast Model for Hail Weather in Plateau Areas Based on Machine Learning

In order to improve the accuracy of hail forecasting for mountainous and plateau areas in China,this study presents a novel fusion forecast model based on machine learning techniques.Specifically,known mechanisms of hail formation and two newly proposed elevation features calculated from radar data,sounding data,automatic station data,and terrain data,are firstly combined,from which a hail/short-duration heavy rainfall(SDHR)classification model based on the random forest(RF)algorithm is built up.Then,we construct a hail/SDHR probability identification(PI)model based on the Bayesian minimum error decision and principal component analysis methods.Finally,an"and"fusion strategy for coupling the RF and PI models is proposed.In addition to the mechanism features,the new elevation features improve the models’performance significantly.Experimental results show that the fusion strategy is particularly notable for reducing the number of false alarms on the premise of ensuring the hit rate.A comparison with two classical hail indexes shows that our proposed algorithm has a higher forecasting accuracy for hail in mountainous and plateau areas.All 19 hail cases used for testing could be identified,and our algorithm is able to provide an early warning for 89.5%(17 cases)of hail cases,among which 52.6%(10 cases)receive an early warning of more than 42 minutes in advance.The PI model sheds new light on using Bayesian classification approaches for highdimensional solutions.