Accurate evaluation of the performance of airport pavement can help managers make scientific and systematic preventive maintenance (PM) decisions, ensuring pavement safety and extending service life. To this end, firs...
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Accurate evaluation of the performance of airport pavement can help managers make scientific and systematic preventive maintenance (PM) decisions, ensuring pavement safety and extending service life. To this end, firstly, this paper sorted out the pavement performance evaluation index system, based on which a multi-dimensional evaluation system for PM was proposed, including control indexes, macroscopic indexes, and microscopic indexes. Then, the pavement decay model based on the pavement condition index (PCI) was developed, and the regression model between PCI and International roughness index (IRI) and friction coefficient (& mu;) was established. Finally, by converting the PM decisions for project-level pavement into a binary classification problem in machine learning, the optimal maintenance thresholds and intervals for PM of each index were determined using receiver operating characteristic (ROC) curve and Kolmogorov-Smirnov (K-S) curve. The optimal threshold represents the threshold when the index can separate the binary classification problem (PM decisions) with maximum probability. Based on confusion-regression model, the pavement performance can be evaluated more comprehensively, and the timing of PM can be accurately determined. The model helps airport managers develop accurate PM plans to address future maintenance needs.
This paper proposes a cellular automata-based solution of a two-dimensional binary classification problem. The proposed method is based on a two-dimensional, three-state cellular automaton (CA) with the von Neumann ne...
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ISBN:
(纸本)9783642333507
This paper proposes a cellular automata-based solution of a two-dimensional binary classification problem. The proposed method is based on a two-dimensional, three-state cellular automaton (CA) with the von Neumann neighborhood. Since the number of possible CA rules (potential CA-based classifiers) is huge, searching efficient rules is conducted with use of a genetic algorithm (GA). Experiments show an very good performance of discovered rules in solving the classificationproblem. The best found rules perform better than the heuristic CA rule designed by a human and also better than one of the most widely used statistical method: the k-nearest neighbors algorithm (k-NN). Experiments show that CAs rules can be successfully reused in the process of searching new rules.
This paper describes a new sensor fault detection approach for a vehicle dynamics controller. The detection problem is divided into two parts. First, a model-based observer is used to incorporate the knowledge of the ...
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ISBN:
(纸本)9781728154145
This paper describes a new sensor fault detection approach for a vehicle dynamics controller. The detection problem is divided into two parts. First, a model-based observer is used to incorporate the knowledge of the system into the fault detection. Next, a data driven classification algorithm based on kalman filter performance metrics is used. This machine learning algorithm is trained using real vehicle data and, therefore, able to handle model uncertainties and disturbances inherently. Due to the usage of a nonlinear observer, the fault detection is suitable up to the limits of handling. The presented structure offers the possibility to use the same classification algorithm for different vehicles as the vehicles' behavior is abstracted in the observer. Therefore, the need of extensive training data is reduced. This paper focuses on the development of features and gives a first proof of concept. The developed fault detection is validated with real car measurements.
A novel object tracking method is proposed that takes advantage of the fast learning capability of extreme learning machine (ELM). Specifically, object tracking is viewed as a binary classification problem, and ELM is...
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A novel object tracking method is proposed that takes advantage of the fast learning capability of extreme learning machine (ELM). Specifically, object tracking is viewed as a binary classification problem, and ELM is utilised for finding the optimal separate hyperplane between the object and backgrounds efficiently. To achieve a more robust tracking, two constraints are introduced in ELM training: (i) target visual changes across frames are smooth (i.e. smoothness) and (ii) probabilities to be true object of image samples around the tracked target trajectory are preferred than those of background ones (i.e. preference). Experiments on challenging sequences demonstrate that the proposed tracker performs favourably against the state-of-the-art methods.
Engineering design optimization often involves computationally expensive time consuming simulations. Although surrogate-based optimization has been used to alleviate the problem to some extent, surrogate models (like ...
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ISBN:
(纸本)9781479974863
Engineering design optimization often involves computationally expensive time consuming simulations. Although surrogate-based optimization has been used to alleviate the problem to some extent, surrogate models (like Kriging) struggle as the dimensionality of the problem increases to medium-scale. The enormity of the design space in higher dimensions (above ten) makes the search for optima challenging and time consuming. This paper proposes the use of probabilistic support vector machine classifiers to reduce the search space for optimization. The proposed technique transforms the optimization problem into a binary classification problem to differentiate between feasible (likely containing the optima) and infeasible (not likely containing the optima) regions. A model-driven sampling scheme selects batches of probably-feasible samples while reducing the search space. The result is a reduced subspace within which existing optimization algorithms can be used to find the optima. The technique is validated on analytical benchmark problems.
Cross-validation is a widely used model evaluation method in data mining applications. However, it usually takes a lot of effort to determine the appropriate parameter values, such as training data size and the number...
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Cross-validation is a widely used model evaluation method in data mining applications. However, it usually takes a lot of effort to determine the appropriate parameter values, such as training data size and the number of experiment runs, to implement a validated evaluation. This study develops an efficient cross-validation method called Complexity-based Efficient (CBE) cross-validation for binary classification problems. CBE cross-validation establishes a complexity index, called the CBE index, by exploring the geometric structure and noise of data. The CBE index is used to calculate the optimal training data size and the number of experiment runs to reduce model evaluation time when dealing with computationally expensive classification data sets. A simulated and three real data sets are employed to validate the performance of the proposed method in the study, while the validation methods compared are repeated random sub-sampling validation and K-fold cross-validation. The results show that CBE cross-validation, repeated random sub-sampling validation and K-fold cross-validation have similar validation performance, except that the training time required for CBE cross-validation is indeed lower than that for the other two methods. (C) 2010 Elsevier B.V. All rights reserved.
A Brain Computer Interface is a system that provides an artificial communication between the human brain and the external world. The paradigm based on event related evoked potentials is used in this work. Our main goa...
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ISBN:
(纸本)9781424441242
A Brain Computer Interface is a system that provides an artificial communication between the human brain and the external world. The paradigm based on event related evoked potentials is used in this work. Our main goal was to efficiently solve a binary classification problem: presence or absence of P300 in the registers. Genetic Algorithms and Support Vector Machines were used in a wrapper configuration for feature selection and classification. The original input patterns were provided by two channels (Oz and Fz) of resampled EEG registers and wavelet coefficients. To evaluate the performance of the system, accuracy, sensibility and specificity were calculated. The wrapped wavelet patterns show a better performance than the temporal ones. The results were similar for patterns from channel Oz and Fz, together or separated.
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