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Methods based on order statistics are often used in finance, quality control, data and signal processing, especially when signals of interest are immersed in impulsive noise. These allow to include rank information by increasing the dimension of the problem. In large dimension problems, we are usually required to know only the second order statistics. In this article we use a rank-one quadratic measurement...
Consider a problem of estimating an unknown high dimensional density whose support lies on unknown low-dimensional data manifold. This problem arises in many data mining tasks, and the paper proposes a new geometrically motivated solution for the problem in manifold learning framework, including an estimation of an unknown support of the density. Firstly, tangent bundle manifold learning problem is...
Sparse Discriminant Analysis (SDA) has been widely used to improve the performance of classical Fisher's Linear Discriminant Analysis in supervised metric learning, feature selection and classification. With the increasing needs of distributed data collection, storage and processing, enabling the Sparse Discriminant Learning to embrace the Multi-Party distributed computing environments becomes an...
Model-based software estimation uses algorithms and past project data to make predictions for new projects. This paper presents a comparative assessment of four modeling approaches, including the original COCOMO, COCOMO calibration, k-Nearest Neighbors, and a combination of COCOMO calibration and k-Nearest Neighbors. Our results indicate that using kNN to select the nearest projects and calibrating...
Randomized experiments have been critical tools of decision making for decades. However, subjects can show significant heterogeneity in response to treatments in many important applications. Therefore it is not enough to simply know which treatment is optimal for the entire population. What we need is a model that correctly customize treatment assignment base on subject characteristics. The problem...
Traffic retention is an important factor of intelligent transportation system, which can vividly reflect current state of the road. Accurate estimation of traffic retention can provide support for the dynamic allocation of the traffic signal, thus alleviating the problem of urban traffic congestion. The traditional method can only estimate the long-term or red light traffic retention according to...
High-throughput methylation detection approaches are epigenetic exploration strategies that map sites of DNA methylation to the genome and thus provide insight into the regulatory program of specific cells. Using methyl-binding domain affinity proteins, MBD2-based Methyl-Seq has been established to identify short regions in which a minimum of 5 CpG residues are methylated within a 200bp window requiring...
In this paper, we consider an optimal parameter and state estimation problem arising in an one-dimensional (1D) magnetohydrodynamic (MHD) flow system, whose dynamics can be modeled by a coupled partial differential equations (PDEs). In this model, the coefficients of the Reynolds number and initial conditions as well as state variables are supposed to be unknown and need to be estimated. An adjoint-based...
State-of-charge (SOC) estimation methods based on battery model rely heavily on the accuracy of model parameters. And these parameters could vary with environment and the types of batteries. Online battery modeling methods can improve the robustness of SOC estimation algorithms through updating model constantly with real-time data. These methods have far more profound significance on algorithm adaptability...
Total number of failures of a software system can help practitioners to have a better understanding of the software quality. In this paper, we propose a model to predict the total number of software failures in a software system by analyzing the failure data from testing using models based on Zipf's law together with the information on code coverage. Failure data and code coverage are combined in...
The objective of this paper is to estimate the compressor discharge temperature measurements on an industrial gas turbine that is undergoing commissioning at site, using a data-driven model which is built using the test bed measurements of the engine. This paper proposes a Bayesian neuro-fuzzy modelling (BNFM) approach, which combines the adaptive neuro-fuzzy inference system (ANFIS) and variational...
There are quite a few high dimensional time-series data co-ocurring each other such as lip motions, voices, and face appearances and so on. When capturing the correspondent relationships among those time-series data with different dimensionality, we need to make the dimensionality all the same size so that they can be compared each other. To achieve this, Gaussian Process Latent Variable Models (GPLVM)...
The article considers the problem of complex estimation of the product state, associated with the need to help decision-makers in managing the life cycle of space facilities. A system analysis of the subject area was conducted, which showed the presence of limitations in the existing information system of the technical state and reliability of space facilities. The article presents a new intellectual...
A new linear weighting method and a calibration technique tested comparatively to the proposed linear method and to inverse distance weighting method are analyzed. The proposed methods are tested for temperature estimation in inter weather stations regions, with cumulative minimum deviations from the real values. The analysis reveals that for some weather stations configurations, with certain statistical...
This paper proposes an estimation method for the latent variable Rasch model based on the method of least squares which allows a continuous data set using. The research suggests the application of original approaches within the method for the solution of some applied problems. The authors explain how to use it for task assignment and work organization, decision-making under certainty and the securities...
Fault detection method using k nearest neighbor rule has shown its advantages in dealing with nonlinear, multi-mode, and nonGaussian distributed data. Once a fault is detected in industrial processes, recognizing fault variables becomes the crucial task subsequently. Recently, the method of fault variables recognition using k nearest neighbor reconstruction (FVR-kNN) has been reported. However, the...
Body Condition Scoring (BCS) is a method of evaluating fatness or thinness in cows, and it is important to manage productivity of the cows. However, it is not easy to measure BCS by observing animals because it consumes much time and costs, especially in the large-scale farming. Therefore, almost farmers are not conducting regular evaluation of BCS. In this paper, we propose the noninvasive method...
Development of precise active traffic control strategies urgently requires real-time estimation for operational metrics in transportation systems satisfying the level of smaller spatial granularity simultaneously. This paper proposed a probability approach to estimate real-time lane-based queue length using license plate recognition (LPR) data. The method first developed a nested logit model to depict...
Markov Random Fields are widely used to model lightfield stereo matching problems. However, most previous approaches used fixed parameters and did not adapt to lightfield statistics. Instead, they explored explicit vision cues to provide local adaptability and thus enhanced depth quality. But such additional assumptions could end up confining their applicability, e.g. algorithms designed for dense...
The paper deals with the problem of synthesis and analysis for procedures of reliability parameters estimation in case of radioelectronic devices technical state deterioration. The synthesis problem was solved on the basis of the maximum likelihood method. The analysis problem was solved using analytical calculations and based on statistical simulation.
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