In this paper, a sliding mode controller with a generalized H 2 performance is proposed for a unicycle-like mobile robot to implement the trajectory tracking mission. First, a kinematic controller is introduced for t...
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In this paper, a sliding mode controller with a generalized H 2 performance is proposed for a unicycle-like mobile robot to implement the trajectory tracking mission. First, a kinematic controller is introduced for the mobile robot and generating the desired values of the linear and angular velocity for the dynamic controller. Secondly, a dynamic controller based on the sliding mode control with a generalized H 2 performance is proposed to make the real velocity of the mobile robot and reach the desired velocity command. The stability properties of the controllers are proved by the Lyapunov method. This control law providing smaller errors and better performance to deal with the slipping of the wheels and parameter uncertainty. Simulation is carried out for a mobile robot to verify the performance of the proposed control scheme.
This paper presents a snake robot able to pass different and difficult paths because of special physical form and movement joints mechanism. These snake robots have no passive wheels. The robot moves by friction betwe...
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This paper presents a snake robot able to pass different and difficult paths because of special physical form and movement joints mechanism. These snake robots have no passive wheels. The robot moves by friction between the robot body and the surface on which it is. The joints have been designed and fabricated in a way that each joint has two freedom grades and it may move 228 degrees in every direction. Each joint has two DC servo motors and the power is transferred from the motors output to the joint shaft through bevel gear. The flexibility of the robot makes possible to move forward, back and laterally by imitating real snake's moves. In this paper different measures have been presented in order to design and assemble the joints, motors driver, different ways to guide the robot and its vision.
Hill climbing algorithm is one of the famous optimization algorithms which has been applied to solve the problem of pruning an ensemble of classifiers. In this study, we propose an ensemble pruning method using Hill C...
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Hill climbing algorithm is one of the famous optimization algorithms which has been applied to solve the problem of pruning an ensemble of classifiers. In this study, we propose an ensemble pruning method using Hill Climbing algorithm whose evaluation measure is “Human-Like Foresight” (HLF). To invent this novel measure, we are inspired by human foresight in facing different situations in his life. Experimental comparisons on 10 datasets indicate that pruning a hetrogeneous ensemble of classifiers using the proposed measure achieves higher accuracy compared with the state-of-the-art measures.
The UMTS and LTE/LTE-Advanced specifications have been proposed to offer high data rate for the forwarding link under high-mobility wireless communications. The keys include supporting multi-modes of various coding sc...
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Geometric dilution of precision (GDOP) is a powerful, simple and widely used measure for assessing the effectiveness of potential measurements to specify the precision and accuracy of the data received from global pos...
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Geometric dilution of precision (GDOP) is a powerful, simple and widely used measure for assessing the effectiveness of potential measurements to specify the precision and accuracy of the data received from global positioning system (GPS) satellites. The most correct method to classify or approximate the GPS GDOP is to use inverse matrix on all the combinations and choosing the lowest one, but inversing a matrix puts a lot of computational burden on the navigator's processor. This approach however is a time-consuming task. To overcome the problem, basic back propagation neural network (BPNN) was used. Since the BPNN is too slow for practical problems, including the GPS GDOP classification, in this paper several methods, namely, resilient back propagation (RBP) to train a NN, naive Bayes classifier, Fisher's linear discriminant (FLD) and k-nearest neighbor (KNN) for classification of the GPS GDOP are proposed. Simulation results show that these methods are much more efficient to classify the GPS GDOP data than previous methods.
In this paper, a mathematical modeling of pneumatic actuator system is developed by using RLS algorithm. An ARX model is chosen for the model structure. In order to cater the time-varying parameter of pneumatic system...
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In this paper, a mathematical modeling of pneumatic actuator system is developed by using RLS algorithm. An ARX model is chosen for the model structure. In order to cater the time-varying parameter of pneumatic system, a self-tuning controller is implemented based on the pole-assignment controller. An online RLS algorithm update the parameter estimation at every sample interval. The pole-assignment control parameter is then updated accordingly to the changes of the system parameters. Result of the system performance is compared with the conventional PID controller optimized by PSO algorithm. It is observed that the self-tuning controller performed well with almost zero error at steady state condition and overshoot less than 1%.
In this paper, Persian handwritten digits reorganization by using zoning features and projection histogram for extracting feature vectors with 69-dimensions is presented. In classification stage, support vector machin...
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In this paper, Persian handwritten digits reorganization by using zoning features and projection histogram for extracting feature vectors with 69-dimensions is presented. In classification stage, support vector machines (SVM) with three linear kernels, polynomial kernel and Gaussian kernel have been used as classifier. We tested our algorithm on the dataset that contained 8600 samples of Persian handwritten digits for performance analysis. Using 8000 samples in learning stage and another 600 samples in testing stage. The results got with use of every three kernels of support vector machine and achieved maximum accuracy by using Gaussian kernel with gamma equal to 0.16. In pre-processing stage only image binarization is used and all the images of this dataset had been normalized at center with size 40 × 40. The recognition rate of this method, on the test dataset 97.83 % and on all samples of dataset 100% was earned.
Designing an efficient search algorithm is an important issue in unstructured peer-to-peer networks when there is no central control or information on the locations of objects. There are various search strategies with...
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Designing an efficient search algorithm is an important issue in unstructured peer-to-peer networks when there is no central control or information on the locations of objects. There are various search strategies with different effects on network performance. In k-random walks as a search strategy, having an adaptive value of k instead of a random value can affect performance of the network. Therefore in this paper, a distributed novel self-adaptive search algorithm has been developed by application of learning automata to overcome this challenge. This method does not aim to determine the value of k for k-random walks algorithm and each peer can issue walkers in a self adaptive manner. Simulation results show that the proposed search algorithm improves some features such as average number of walkers per query, average number of produced messages, number of hits per query and also success rate efficiently in comparison with the k-random walks algorithm.
Magnetic code is widely used in check, securities, tax invoice, etc. However, the traditional recognizing and reading method of magnetic code is mostly based on correlation coefficient and it takes significant time an...
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Occlusion of objects during spatio-temporal phenomena such as navigation is common for intelligent autonomous systems in a domain with real world objects. Enabling to fill gaps in observation as human do, can enhance ...
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ISBN:
(纸本)9781467345279
Occlusion of objects during spatio-temporal phenomena such as navigation is common for intelligent autonomous systems in a domain with real world objects. Enabling to fill gaps in observation as human do, can enhance the cognition and adaptability of autonomous system multifold. Most cognitive process are contextual. The essence of context in our everyday commonsense reasoning cannot be ignored. Further, everyday spatial and temporal reasoning is qualitative rather than quantitative. Building on work done in the area of qualitative spatial and temporal reasoning, we present an approach for completion of qualitative spatio-temporal explanations using context.
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