A fast point cloud registration algorithm is proposed for the problem that the traditional CPD (Coherent Point Drift) algorithm is time consuming and has poor the registration efficiency. Firstly, the voxel grid metho...
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ISBN:
(纸本)9789881563958
A fast point cloud registration algorithm is proposed for the problem that the traditional CPD (Coherent Point Drift) algorithm is time consuming and has poor the registration efficiency. Firstly, the voxel grid method is carried out on the three-dimensional bounding box of the cloud space, and the point in the whole voxel are expressed by the voxel centers, and the down-sampling operation of the cloud is completed to reduce the amount of the calculated data. Then, the Gaussian mixture model is established for the obtained point cloud to compute the values of negative log-likelihood functions. Finally, we use the em algorithm to iterate to solve the closed parameters by minimizing negative logarithmic likelihood function. The rotation matrix and translation vector are obtained to match two points clouds. The experimental results show that the proposed method can greatly improve the registration speed while maintaining the original registration accuracy.
This paper discusses mixture periodic GARCH (M-PGARCH) models that constitute very flexible class of nonlinear time series models of the conditional variance. It turns out that they are more parsimonious comparatively...
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This paper discusses mixture periodic GARCH (M-PGARCH) models that constitute very flexible class of nonlinear time series models of the conditional variance. It turns out that they are more parsimonious comparatively to MPARCH models. We first provide some probabilistic properties of this class of models. We thus propose an estimation method based on the expectation-maximization algorithm. Finally, we apply this methodology to model the spot rates of the Algerian dinar against euro and US dollar. This empirical analysis shows that M-PGARCH models yield the best performance among the competing models.
This paper proposes a novel Mixed-copula VaR (MCV) model for financial portfolio risk management and a novel investment strategy based on it. VaR (Value at Risk) is a traditional risk metric in computational finance t...
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ISBN:
(纸本)9781538691250
This paper proposes a novel Mixed-copula VaR (MCV) model for financial portfolio risk management and a novel investment strategy based on it. VaR (Value at Risk) is a traditional risk metric in computational finance to measure how much a set of investments might lose in a disadvantageous situation. Previous VaR models assume that the yield rates follow a single distribution (e.g. normal distribution) for simplicity, which is far from reality. In order to improve the adaptivity and the extendability of the VaR method, this paper constructs an MCV model with several families of distributions and designs a fast em algorithm to compute the mixing weights. It further leads to a strategy for portfolio investment. Experiments by Monte Carlo simulation verify the intention of MCV. Besides, experiments on two real-world financial data sets indicate that MCV measures portfolio risk more accurately and adaptively, and delivers superior investing performance.
This paper is concerned with a machine learning approach to cancel the interference for a cognitive radio (CR) system in the concurrent spectrum access (CSA) model, where the CR system is non-cooperative and has very ...
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ISBN:
(纸本)9781538670484
This paper is concerned with a machine learning approach to cancel the interference for a cognitive radio (CR) system in the concurrent spectrum access (CSA) model, where the CR system is non-cooperative and has very limited knowledge on the interference. Our transceiver design uses a turbo structure, which consists of a linear estimator, a demodulation and decoding module, and a clustering module. In the clustering module, we employ machine leaning algorithms such as K-means and expectation maximization (em) algorithms to estimate the interference. We show that the em based receiver significantly outperforms the K-means receiver, since the former is able to generate a soft clustering result. We further improve the performance of the iterative receiver by introducing the extrinsic information technique with the resulting receiver referred to as Ext-em. Considerable performance gain of the Ext-em receiver is demonstrated over the original em receiver.
The importance of extracting non-linguistic information has been highlighted in a growing variety of applications of speech signal processing. Among the audio features carrying such information, fundamental frequency ...
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ISBN:
(纸本)9781538646588
The importance of extracting non-linguistic information has been highlighted in a growing variety of applications of speech signal processing. Among the audio features carrying such information, fundamental frequency (F-0) contours are considered primarily important. The Fujisaki model is a physical model that describes a F-0 contour with only a small number of parameters, namely, the timings and magnitudes of the phrase and accent commands, and a stochastic formulation and estimation algorithm have recently been proposed for it. However, the use of linguistic information has so far been limited, while it is known that accent commands are strongly related to linguistic information in many languages, and linguistic information could be obtained from the input audio signals by using speech recognition techniques. Against this background, this paper introduces a novel F-0 command parameter estimation method that incorporates linguistic information with the stochastic framework. Experiments using real speech data show that when linguistic information is appropriately utilized, the estimation accuracy of accent command parameters is improved by 43% under the proposed criteria.
Over the last years, image segmentation has evolved from a sub-discipline of computer science to a technique widely used in medical imaging, automated object recognition, and remote sensing. In this work, we present a...
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ISBN:
(纸本)9781538643969
Over the last years, image segmentation has evolved from a sub-discipline of computer science to a technique widely used in medical imaging, automated object recognition, and remote sensing. In this work, we present a recently Markovian model of image segmentation called Triplet Markov Chain with Independent Noise (TMC-IN), in this model, it assumes that its hidden process X is non-stationary. TMC-IN is used in this to segment some textured grey level and color images. To estimate the parameters, we use the iterative algorithmem (Expectation-Maximization) and we apply MPM (Marginal Posteriori Mode) algorithm to estimate the result segmented image. In addition, we compare the obtained results by this model with those obtained by the stationary Hidden Markov Chain with Independent Noise (HMC-IN) model. Experimental results show that TMC-IN outperforms HMC-IN in all experiments.
Voiceprint is an important component of creating a user portrait. Voiceprint Recognition can determine user's identification. However, speech signals in the customer service system are processed by encoded with co...
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ISBN:
(纸本)9781538685495
Voiceprint is an important component of creating a user portrait. Voiceprint Recognition can determine user's identification. However, speech signals in the customer service system are processed by encoded with compression for effective transmission and storage. The low-bit rate codec results that the performance of Voiceprint Recognition system dramatically reduces. What is more, the speech number of each customer is not adequate. In order to solve the problem, this paper proposes a model compensation method. The method uses a test utterance with expectation maximization (em) algorithm to estimate the distortion model and the UBM is adjusted to match the codec type of the test utterance. Voiceprint Recognition experiments are conducted. The results show that the proposed method is able to dramatically improve the performance of the system.
Computer simulation is useful to study movement of a crowd of evacuees in a large-scale urban disaster case. Multi-Agent System is often used in the simulation and agent behaviors must reflect the psychological and ph...
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ISBN:
(纸本)9781538663097
Computer simulation is useful to study movement of a crowd of evacuees in a large-scale urban disaster case. Multi-Agent System is often used in the simulation and agent behaviors must reflect the psychological and physical properties of human behaviors as much as possible. In this paper, we propose an evacuating agent walking model. The walking speed depends on each person and the elderly person's walking speed can be modeled based on pseudo-elderly walk experiments. Also, based on the real evacuation data at the time of the Great East Japan Earthquake, the distribution of the evacuation start time is estimated.
With the rapid development of information technology, big data plays an increasingly important role in the research and practice of education and teaching. Online education has also become a research hotspot. To solve...
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ISBN:
(纸本)9781450364966
With the rapid development of information technology, big data plays an increasingly important role in the research and practice of education and teaching. Online education has also become a research hotspot. To solve the problem of lack of personalized exercises and accurate teaching feedback in online education, a content-based recommendation model in big data and a clustering model based on em algorithm is proposed in this paper. First of all, the students' answer of questions is recorded. Then the characteristic information is extracted, so recommends of the exercises are provided by the model according to the personal characteristic information. Then, all the students' recommendation information is stored in the feature library, in which the information of students are clustered, and the teaching effect is fed back according to the characteristic parameters of each category. On the one hand, the status of students' learning is fed back;On the other hand, the level of teachers' teaching level is also fed back. Finally, the model works well through experiments, with the good performance that it can improve the efficiency of online learning.
Clustering stocks by their time series data is a significant but challenging task in computer supported financial decision systems. In this paper, we propose an effective stocks clustering method based on hybrid corre...
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ISBN:
(纸本)9781538614822
Clustering stocks by their time series data is a significant but challenging task in computer supported financial decision systems. In this paper, we propose an effective stocks clustering method based on hybrid correlation coefficient called SLU correlation coefficient which is a weighted combination of Spearman rank correlation, upper tail correlation and lower tail correlation. The upper and lower tail correlation is defined by Copula function and estimates parameters by em algorithm. The similarity matrix is defined by SLU and inputs into Affinity Propagation algorithm for clustering. The experiment shows the effectiveness of the SLU, compared to Pearson correlation and DTW distance.
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