Outlier detection is an important aspect in the field of data mining. In order to solve the problem of outlier detection in high-dimensional datasets, an outlier detection algorithm based on Gaussian mixture model is ...
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ISBN:
(纸本)9781728137216
Outlier detection is an important aspect in the field of data mining. In order to solve the problem of outlier detection in high-dimensional datasets, an outlier detection algorithm based on Gaussian mixture model is proposed. First of all, for the data set to be tested, the global optimization expectation maximization algorithm is used to fit a Gaussian mixture model, and then the three-time standard deviation principle is introduced on each Gaussian component, the outlier is the data point outside the range of the mean deviation of the mean value of three times the standard deviation. Through the experiments on the simulation dataset and the real data set, the effectiveness of the algorithm on the outlier detection of high-dimensional data sets is verified.
The identification of AutoRegressive eXogenous(ARX) model by outliers is addressed in this paper. Shifted(noncentralized) asymmetric Laplace(SAL) distribution and expectationmaximization(EM) algorithm are employed to...
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The identification of AutoRegressive eXogenous(ARX) model by outliers is addressed in this paper. Shifted(noncentralized) asymmetric Laplace(SAL) distribution and expectationmaximization(EM) algorithm are employed to estimate the unknown model parameters. Outliers are common in the signal acquisition process and have a serious impact on data-driven modeling method. In this paper, the probability method is used to solve the problem of outliers. When the noise parameter is regarded as a prior exponential distribution, the model output obeys the SAL distribution which is robust to outliers. The known statistical properties of SAL distribution are applied to calculate the M-step in the EM algorithm and get the iterative parametric formula. The accuracy of the proposed algorithm is verified by a numerical simulation example.
We study estimation of large Dynamic Factor models implemented through the expectationmaximization (EM) algorithm, jointly with the Kalman smoother. We prove that as both the cross-sectional dimension, n, and the sam...
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In this paper, we aim to enhance the first-person indoor navigation and scene understanding experience by fusing inertial data collected from a smartphone carried by the user with the vision information obtained throu...
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ISBN:
(纸本)9781538616475
In this paper, we aim to enhance the first-person indoor navigation and scene understanding experience by fusing inertial data collected from a smartphone carried by the user with the vision information obtained through the phone's camera. We employed the concept of vanishing directions together with the orthogonality constraints of the man-made environments in an expectationmaximization framework to estimate person's orientation with respect to the known indoor coordinates from video frames. This framework allows to include prior information about camera rotation axis for better estimations as well as to select candidate edge-lines for estimation of hallways' depth and width from monocular video frames, and 31) modeling of the scene. Our proposed algorithm concurrently combines the vision-based estimated orientation with the inertial data using a Kalman filter in order to reline estimations and remove substantial measurement drift from inertial sensors. We evaluated the performance of our vision-inertial data fusion method on an IMU-augmented video recorded from a rotary hallway in which a participant completed a full lap. We demonstrated that this fusion provides virtually drift-free instantaneous information about the person's relative orientation. We were able to estimate hallways' depth and width, and generate a closed-path map from the rotary hallway over a roughly 60-meter lap.
This paper proposes a direct learning controller for wireless sensor and robot network to coverage an environment by utilizing expectation maximization algorithm. In addition to sensors, including high-level and low-l...
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ISBN:
(纸本)9781509021826
This paper proposes a direct learning controller for wireless sensor and robot network to coverage an environment by utilizing expectation maximization algorithm. In addition to sensors, including high-level and low-level sensors, mounted on mobile robots, low-level stationary sensors are considered to provide information for enhance the performance of coverage control. The main objective is to maximize the information quantity of high-level sensors from the sensing density generated by a group of low-level sensors. This direct method uses the parameter of basis function to design controller based on expectationmaximization (EM) algorithm. Moreover, the proposed estimation and learning law are also used in indirect method for other coverage controller. Subsequently, this paper proposes a transformation method based on EM algorithm for coverage problem with complicated sensing function such as Gaussian function. Numerical examples are introduced to demonstrate the performance of the proposed direct coverage control in wireless sensor and robot network.
This paper presents a time-domain stochastic system identification method based on Maximum Likelihood Estimation and the expectation maximization algorithm. The effectiveness of this structural identification method i...
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ISBN:
(纸本)9789076019314
This paper presents a time-domain stochastic system identification method based on Maximum Likelihood Estimation and the expectation maximization algorithm. The effectiveness of this structural identification method is evaluated through numerical simulation in the context of the ASCE benchmark problem on structural health monitoring. Modal parameters (eigenfrequencies, damping ratios and mode shapes) of the benchmark structure have been estimated applying the proposed identification method to a set of 100 simulated cases. The numerical results show that the proposed method estimates all the modal parameters reasonably well in the presence of 30% measurement noise even. Finally, advantages and disadvantages of the method have been discussed.
Parameter estimation is one of the most important research areas in wireless sensor networks. In this study, we consider the problem of estimating a deterministic parameter over fading channels with unknown noise vari...
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Parameter estimation is one of the most important research areas in wireless sensor networks. In this study, we consider the problem of estimating a deterministic parameter over fading channels with unknown noise variance. Owing to the bandwidth constraints in wireless sensor networks, sensor observations are quantized and subsequently transmitted to the fusion center. Two types of communication channels are considered, namely, parallel-access channels and multiple-access channels. Based on the knowledge of channel statistics, the power of the received signals at the fusion center can be described by the mode of the exponential mixture distribution. The expectation maximization algorithm is used to determine maximum likelihood solutions for this mixture model. A new estimator based on the expectation maximization algorithm is subsequently proposed. Simulation results show that this estimator exhibits superior performance compared to the method of moments estimator in both parallel- and multiple-access schemes. In addition, we determine that the parallel-access scheme outperforms the multiple-access scheme when the noise variance is small and it loses its superiority when the noise variance is large.
The accurate evaluation of wind characteristics and wind-induced structural responses during a typhoon is of significant importance for bridge design and safety assessment. This paper presents an expectation maximizat...
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The accurate evaluation of wind characteristics and wind-induced structural responses during a typhoon is of significant importance for bridge design and safety assessment. This paper presents an expectationmaximization (EM) algorithm-based angular-linear approach for probabilistic modeling of field-measured wind characteristics. The proposed method has been applied to model the wind speed and direction data during typhoons recorded by the structural health monitoring (SHM) system instrumented on the arch Jiubao Bridge located in Hangzhou, China. In the summer of 2015, three typhoons, i.e., Typhoon Chan-hom, Typhoon Soudelor and Typhoon Goni, made landfall in the east of China and then struck the Jiubao Bridge. By analyzing the wind monitoring data such as the wind speed and direction measured by three anemometers during typhoons, the wind characteristics during typhoons are derived, including the average wind speed and direction, turbulence intensity, gust factor, turbulence integral scale, and power spectral density (PSD). An EM algorithm-based angular-linear modeling approach is proposed for modeling the joint distribution of the wind speed and direction. For the marginal distribution of the wind speed, the finite mixture of two-parameter Weibull distribution is employed, and the finite mixture of von Mises distribution is used to represent the wind direction. The parameters of each distribution model are estimated by use of the EM algorithm, and the optimal model is determined by the values of R-2 statistic and the Akaike's information criterion (AIC). The results indicate that the stochastic properties of the wind field around the bridge site during typhoons are effectively characterized by the proposed EM algorithm-based angular-linear modeling approach. The formulated joint distribution of the wind speed and direction can serve as a solid foundation for the purpose of accurately evaluating the typhoon-induced fatigue damage of long-span bridges.
作者：
Yao, LeGe, ZhiqiangZhejiang Univ
Coll Control Sci & Engn Inst Ind Proc Control State Key Lab Ind Control Technol Hangzhou 310027 Zhejiang Peoples R China
Process nonlinearity and state shifting are two of the main factors that cause poor performance of online soft sensors. Adaptive soft sensor is a common practice to ensure high predictive accuracy. In this paper, the ...
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Process nonlinearity and state shifting are two of the main factors that cause poor performance of online soft sensors. Adaptive soft sensor is a common practice to ensure high predictive accuracy. In this paper, the moving window method is introduced to the supervised latent factor analysis model to capture the state shifting feature of the process. To make the moving window strategy more efficient, the weighted form of the supervised latent factor analysis approach is applied. In this method, contributions of training samples are expressed through incorporating the similarity index into the noise variance of the process variable, which renders strong adaptability of the method for describing nonlinear relationships and abrupt changes of the process. A numerical example and a real industrial process are provided to demonstrate the effectiveness of the proposed adaptive soft sensor.
作者：
Kubota, TakuyaAritsugi, MasayoshiKumamoto Univ
Grad Sch Sci & Technol Comp Sci & Elect Engn Chuo Ku 2-39-1 Kurokami Kumamoto 8608555 Japan Kumamoto Univ
Fac Adv Sci & Technol Div Environm Sci Big Data Sci & TechnolChuo Ku 2-39-1 Kurokami Kumamoto 8608555 Japan
It is expected that ground truths can result in many good labels in the crowdsourcing of labeling tasks. However, the use of ground truths has so far not been adequately addressed. In this paper, we develop algorithms...
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It is expected that ground truths can result in many good labels in the crowdsourcing of labeling tasks. However, the use of ground truths has so far not been adequately addressed. In this paper, we develop algorithms that determine the number of ground truths that are necessary. We determine this number by iteratively calculating the expected quality of labels for tasks with various sets of ground truths, and then comparing the quality with the limit of the estimated label quality expected to be obtained by crowd sourcing. We assume that each worker has a different unknown labeling ability and performs a different number of tasks. Under this assumption, we develop assignment strategies for ground truths based on the estimated confidence intervals of the workers. Our algorithms can utilize different approaches based on the expectationmaximization to estimate good-quality consensus labels. An experimental evaluation demonstrates that our algorithms work well in various situations. (C) 2016 Elsevier Inc. All rights reserved.
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