Multiple extended target tracking algorithm based on Gaussian surface matrix

In this paper, we consider the problem of irregular shapes tracking for multiple extended targets by introducing the Gaussian surface matrix（GSM） into the framework of the random finite set（RFS） theory. The Gaussian surface function is constructed first by the measurements, and it is used to define the GSM via a mapping function. We then integrate the GSM with the probability hypothesis density（PHD） filter, the Bayesian recursion formulas of GSM-PHD are derived and the Gaussian mixture implementation is employed to obtain the closed-form solutions. Moreover, the estimated shapes are designed to guide the measurement set sub-partition, which can cope with the problem of the spatially close target tracking. Simulation results show that the proposed algorithm can effectively estimate irregular target shapes and exhibit good robustness in cross extended target tracking.

A Novel Collaborative Filtering Algorithm and its Application for Recommendations in E-Commerce

With the rapid development of the Internet,the amount of data recorded on the Internet has increased dramatically.It is becoming more and more urgent to effectively obtain the specific information we need from the vast ocean of data.In this study,we propose a novel collaborative filtering algorithm for generating recommendations in e-commerce.This study has two main innovations.First,we propose a mechanismthat embeds temporal behavior information to find a neighbor set in which each neighbor has a very significant impact on the current user or item.Second,we propose a novel collaborative filtering algorithm by injecting the neighbor set into probability matrix factorization.We compared the proposed method with several state-of-the-art alternatives on real datasets.The experimental results show that our proposed method outperforms the prevailing approaches.

A new fast algorithm for multitarget tracking in dense clutter

A fast joint probabilistic data association （FJPDA） algorithm is proposed in tiffs paper. Cluster probability matrix is approximately calculated by a new method, whose elements βi^t（K） can be taken as evaluation functions. According to values of βi^t（K）, N events with larger joint probabilities can be searched out as the events with guiding joint probabilities, tiros, the number of searching nodes will be greatly reduced. As a result, this method effectively reduces the calculation load and nnkes it possible to be realized on real-thne, Theoretical ,analysis and Monte Carlo simulation results show that this method is efficient.

State and Mode Feedback Control for Discrete-time Markovian Jump Linear Systems With Controllable MTPM

In this note, the state and mode feedback control problems for a class of discrete-time Markovian jump linear systems(MJLSs) with controllable mode transition probability matrix(MTPM) are investigated. In most achievements, controller design of MJLSs pays more attention to state/output feedback control for stability, while the system cost in practice is out of consideration. In this paper, we propose a control mechanism consisting of two parts: finite-path-dependent state feedback controller design with which uniform stability of MJLSs can be ensured, and mode feedback control which aims to decrease system cost. Differing from the traditional state/output feedback controller design, the main novelty is that the proposed control mechanism not only guarantees system stability, but also decreases system cost effectively by adjusting the occurrence probability of system modes. The effectiveness of the proposed mechanism is illustrated via numerical examples.

Analysis on Stochastic Passage Characteristics for Gun Barrel in Shooting Domain

Aiming at some weapon systems with shooting domain,the stochastic passage characteristics of the barrel were studied.On the basis of the exact definition of the stochastic passage characteristics,its opportunity-awaiting time,residence time and stochastic passage period were given by using the transition probability matrix,and they all obeyed the geometry distributions.Their means and variances were also derived,and the relations between the time indexes and the structure and parameters of weapon control system were established.Finally,the creditability of the conclusions was verified by the test data of weapon system in proving ground.

Using Markov chains to predict the natural progression of diabetic retinopathy

AIM: To study the natural progression of diabetic retinopathy in patients with type 2 diabetes.METHODS: This was an observational study of 153 cases with type 2 diabetes from 2010 to 2013. The state of patient was noted at end of each year and transition matrices were developed to model movement between years. Patients who progressed to severe non-proliferative diabetic retinopathy(NPDR) were treated.Markov Chains and Chi-square test were used for statistical analysis.RESULTS: We modelled the transition of 153 patients from NPDR to blindness on an annual basis. At the end of year 3, we compared results from the Markov model versus actual data. The results from Chi-square test confirmed that there was statistically no significant difference(P =0.70) which provided assurance that the model was robust to estimate mean sojourn times. The key finding was that a patient entering the system in mild NPDR state is expected to stay in that state for 5y followed by 1.07 y in moderate NPDR, be in the severe NPDR state for 1.33 y before moving into PDR for roughly8 y. It is therefore expected that such a patient entering the model in a state of mild NPDR will enter blindness after 15.29 y.CONCLUSION: Patients stay for long time periods in mild NPDR before transitioning into moderate NPDR.However, they move rapidly from moderate NPDR to proliferative diabetic retinopathy(PDR) and stay in that state for long periods before transitioning into blindness.

Prediction of End-Use Energy Consumption in a Region of Northwest China

End-use energy consumption can reflect the industrial development of a country and the living standards of its residents. The study of end-use energy consumption can provide a solid basis for industrial restructuring, energy saving, and emission reduction. In this paper, we analyzed the end-use energy consumption of a region in Northwestern China, and applied the Markov prediction method to forecast the future demand of different types of end-use energy. This provides a reference for the energy structure optimization in the Northwestern China.

Estimating Digital Inter-Symbol Interference Channel Blindly Based on the One-Step Branch Transition Rules in Trellises

A novel discrete-time digital inter-symbol interference (ISI) channel blind estimation sub-optimal algorithm is proposed. This algorithm reduces the complexity of the optimal maximum likelihood sequence estimation (MLSE) considerably based on the one-step branch transition rules in trellises, and is suitable for the estimation of the channels with small lengths of ISI.

Research on pedestrian escape route without visibility based on Markov chain model

It is a pioneering work to use a Markov chain model to study the pedestrian escape route without visibility.In this paper,based on the Markov chain probability transition matrix,the algorithms with random numbers and the spatial-grid,an escape route in a limited invisible space is obtained.Six pace states(standing,crawling,walking,leaping,jogging,and running)are applied to describe the characteristics of pedestrian behaviors.Besides,eight main direction changes are used to describe the transition characteristic of a pedestrian.At the same time,this paper analyzes the escape route from two views,i.e.,pedestrian pace states and directions.The research results show that the Markov chain model is more realistic as a means of studying pedestrian escape routes.

A Markov chain model of mixing kinetics for ternary mixture of dissimilar particulate solids

This paper presents a simple but informative mathematical model to describe the mixing of three dissimilar components of particulate solids that have the tendency to segregate within one another. A nonlinear Markov chain model is proposed to describe the process. At each time step, the exchange of particulate solids between the cells of the chain is divided into two virtual stages. The first is pure stochastic mixing accompanied by downward segregation. Upon the completion of this stage, some of the cells appear to be overfilled with the mixture, while others appear to have a void space. The second stage is related to upward segregation. Components from the overfilled cells fill the upper cells （those with the void space） according to the proposed algorithm. The degree of non-homogeneity in the mixture （the standard deviation） is calculated at each time step, which allows the mixing kinetics to be described. The optimum mixing time is found to provide the maximum homogeneity in the ternary mixture. However, this “common” time differs from the optimum mixing times for individual components. The model is verified using a lab-scale vibration vessel, and a reasonable correlation between the calculated and experimental data is obtained