Using cellular floating vehicle data is a crucial technique for measuring and forecasting real-time traffic information based on anonymously sampling mobile phone positions for intelligent transportation systems (ITSs...
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Using cellular floating vehicle data is a crucial technique for measuring and forecasting real-time traffic information based on anonymously sampling mobile phone positions for intelligent transportation systems (ITSs). However, a high sampling frequency generates a substantial load for ITS servers, and traffic information cannot be provided instantly when the sampling period is long. In this paper, two analytical models are proposed to analyze the optimal sampling period based on communication behaviors, traffic conditions, and two consecutive fingerprint positioning locations from the same call and estimate vehicle speed. The experimental results show that the optimal sampling period is 41.589 seconds when the average call holding time was 60 s, and the average speed error rate was only 2.87%. ITSs can provide accurate and real-time speed information under lighter loads and within the optimal sampling period. Therefore, the optimal sampling period of a fingerprint positioning algorithm is suitable for estimating speed information immediately for ITSs.
An efficient pattern recognition system based on soft computing concepts has been developed. A new reliable genetic stereo vision algorithm is used in order to estimate depth of objects without using any point-to-poin...
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An efficient pattern recognition system based on soft computing concepts has been developed. A new reliable genetic stereo vision algorithm is used in order to estimate depth of objects without using any point-to-point correspondence. Instead, correspondence of the contours as a whole is required. Invariant breakpoints are located on a shape contour using the colinearity principle. Thus, a localized representation of a shape contour including 3-D moments as well as a chain code can be obtained. This representation is invariant to rotation, translation, scale, and starting point. The system is provided with a neural network classifier and a dynamic alignment procedure at its output. Combining the robustness of neural network classifier with the genetic algorithm capability results in a reliable pattern recognition system which can tolerate high degrees of noise and occlusion levels. The performance of the system has been demonstrated using five different types of aircraft and the experimental results are reported. (C) 2000 Elsevier Science Inc. All rights reserved.
Finite element analysis (FEA) refers to simulating real system by using limited number of unknowns to appraisal an approximate real system with infinite number. To overcome the shortcomings of FM in stand-alone Ansys,...
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Finite element analysis (FEA) refers to simulating real system by using limited number of unknowns to appraisal an approximate real system with infinite number. To overcome the shortcomings of FM in stand-alone Ansys, a collaborative computing system over multiple virtual machines (CCSMVM) is proposed to convert stand-alone Ansys into multi-machines. We present the architecture and workflow of CCSMVM, and then focus on key technologies of task construction and scheduling. A FEA application related to FPBS is developed to verify the prototype of CCSMVM integrated Ansys. The experiments show that: (1) the feasibility of key technologies has been verified since load balancing among virtual machines is achieved. (2) CCSMVM can complete the tasks in short completion time with high resource unitization. (3) Our task scheduling algorithm can complete the tasks in shorter time than other algorithms. It is concluded that CCSMVM can accelerate the execution of tasks by exchanging the running speed with resources. (C) 2013 Elsevier Ltd. All rights reserved.
Based on the correlation and ranging methods, we developed an algorithm determining the coordinates of moving sources of wideband noise. The main advantage of this algorithm is the ability to eliminate the ambiguities...
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Based on the correlation and ranging methods, we developed an algorithm determining the coordinates of moving sources of wideband noise. The main advantage of this algorithm is the ability to eliminate the ambiguities of determining the target coordinates arising in the multistatic systems. To test the proposed algorithm, a laboratory passive multistatic radar system was designed and assembled. This paper describes the parameters of the experiment conducted using this system. The obtained results are discussed and analyzed.
The random neural network is a biologically inspired neural model where neurons interact by probabilistically exchanging positive and negative unit-amplitude signals that has superior learning capabilities compared to...
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The random neural network is a biologically inspired neural model where neurons interact by probabilistically exchanging positive and negative unit-amplitude signals that has superior learning capabilities compared to other artificial neural networks. This paper considers non-negative least squares supervised learning in this context, and develops an approach that achieves fast execution and excellent learning capacity. This speedup is a result of significant enhancements in the solution of the non-negative least-squares problem which regard (a) the development of analytical expressions for the evaluation of the gradient and objective functions and (b) a novel limited-memory quasi-Newton solution algorithm. Simulation results in the context of optimizing the performance of a disaster management problem using supervised learning verify the efficiency of the approach, achieving two orders of magnitude execution speedup and improved solution quality compared to state-of-the-art algorithms.
This paper deals with the problem of overcomplete transform learning. An alternating minimization based procedure is proposed for solving the formulated sparsifying transform learning problem. A closed-form solution i...
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This paper deals with the problem of overcomplete transform learning. An alternating minimization based procedure is proposed for solving the formulated sparsifying transform learning problem. A closed-form solution is derived for the minimization involved in transform update stage. Compared with existing ones, our proposed algorithm significantly reduces the computation complexity. Experiments and simulations are carried out with synthetic data and real images to demonstrate the superiority of the proposed approach in terms of the averaged representation and denoising errors, the percentage of successful and meaningful recovery of the analysis dictionary, and, more significantly, the computation efficiency.
This article presents a new approach for the hybrid position/force control of a manipulator by using self-tuning regulators (STR). For this purpose, the discrete-time stochastic multi-input multi-output (MIMO) and sin...
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This article presents a new approach for the hybrid position/force control of a manipulator by using self-tuning regulators (STR). For this purpose, the discrete-time stochastic multi-input multi-output (MIMO) and single-input single-output (SISO) models are introduced. The MIMO model's output vector has the positions and velocities of the gripper expressed in the world (xyz) coordinate system as the components. The SISO model outputs are the hybrid errors consisting of the derivatives of the position and force errors at the joints. The inputs of both models are the joint torques. The unknown parameters of those models can be calculated recursively on-line by the square-root estimation algorithm (SQR). An adaptive MIMO and SISO self-tuning type controllers are then designed by minimizing the expected value of a quadratic criterion. This performance index penalizes the deviations of the actual position and force path of the gripper from the desired values expressed in the Cartesian coordinate system. An integrating effect is also included in the performance index to remove the steady-state errors. Digital simulation results using the parameter estimation and the control algorithms are presented and the performances of those two controllers are discussed. (C) 1996 John Wiley & Sons, Inc.
Here, we consider the solution of constrained global optimization problems, such as those arising from the fields of chemical and biosystems engineering. These problems are frequently formulated (or can be transformed...
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Here, we consider the solution of constrained global optimization problems, such as those arising from the fields of chemical and biosystems engineering. These problems are frequently formulated (or can be transformed to) nonlinear programming problems (NLPs) subject to differential-algebraic equations (DAEs). In this work, we extend a popular multistart clustering algorithm for solving these problems, incorporating new key features including an efficient mechanism for handling constraints and a robust derivative-free local solver. The performance of this new method is evaluated by solving a collection of test problems, including several challenging case studies from the (bio)process engineering area.
In this paper we present an algorithm for medical image retrieval system examines in particular image processing algorithms used for image retrieval based on transformation domain, segmentation and fuzzy logic. This i...
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In this paper we present an algorithm for medical image retrieval system examines in particular image processing algorithms used for image retrieval based on transformation domain, segmentation and fuzzy logic. This is to implement image retrieval algorithm to achieve better classification accuracy for retrieving medical images. It is proposed to implement image segmentation using fuzzy logic with certain improvements for feature extraction with global optimisation using fast Hartley transform (FHT) to reduce the dimensionality of feature space. A new image segmentation algorithm, fuzzy edge detection and segmentation (FEDS) and a bell fuzzy multilayer perceptron (BF-MLP) for classifying images are used. Then BF-MLP is optimised using genetic algorithm (GA) for improved performance.
Identifying communication signals under low SNR environment has become more difficult due to the increasingly complex communication environment. Most relevant literatures revolve around signal recognition under stable...
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Identifying communication signals under low SNR environment has become more difficult due to the increasingly complex communication environment. Most relevant literatures revolve around signal recognition under stable SNR, but not applicable under time-varying SNR environment. To solve this problem, we propose a new feature extraction method based on entropy cloud characteristics of communication modulation signals. The proposed algorithm extracts the Shannon entropy and index entropy characteristics of the signals first and then effectively combines the entropy theory and cloudmodel theory together. Compared with traditional feature extraction methods, instability distribution characteristics of the signals' entropy characteristics can be further extracted from cloud model's digital characteristics under low SNR environment by the proposed algorithm, which improves the signals' recognition effects significantly. The results from the numerical simulations show that entropy cloud feature extraction algorithm can achieve better signal recognition effects, and even when the SNR is -11 dB, the signal recognition rate can still reach 100%.
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