In this paper, the implementation in the e-puck robot of an algorithm to keep going in a trajectory while evading fixed obstacles is presented. A review of some existing algorithms for trajectory tracking with obstacl...
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ISBN:
(纸本)9781424478279
In this paper, the implementation in the e-puck robot of an algorithm to keep going in a trajectory while evading fixed obstacles is presented. A review of some existing algorithms for trajectory tracking with obstacles avoidance is done. Also the basic characteristics of the mobile robot e-puck and the programming environment used to implement the control algorithm and prove the performance of the robot are summarized. By modeling the kinematics of the robot and simulating the implementation of the algorithm, the good performance of the control in both the simulated environment and the real robot in different surroundings are shown. The effects of different environmental factors in the performance of the robot are analyzed. This leads to suggest some algorithm improvements as a matter of future works.
Control of three phase voltage source pulse width modulation (PWM) LCL filtered rectifier based on input - output linearization nonlinear control is realized. This control algorithm needs to transformation between a -...
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ISBN:
(纸本)9781424470198
Control of three phase voltage source pulse width modulation (PWM) LCL filtered rectifier based on input - output linearization nonlinear control is realized. This control algorithm needs to transformation between a - b - c and d - q coordinates. In this paper, grid connection of rectifier which consists of six IGBT power switches, is provided through LCL filter and sinusoidal pulse width modulation (SPWM) technique which has 9 kHz switching frequency, is used for switching. All simulations are carried out with Matlab/Simulink. By the simulation results, line currents' harmonic distortions, and dc - link voltage and the obtaining of unity power factor are shown under load and reference changes.
This paper presents a sensorless speed observer method of an induction motor using an artificial intelligent technique in field-oriented control system. Speed and rotor flux are estimated from only measurable variable...
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ISBN:
(纸本)9781424478279
This paper presents a sensorless speed observer method of an induction motor using an artificial intelligent technique in field-oriented control system. Speed and rotor flux are estimated from only measurable variables, the stator voltages and currents. The proposed estimation algorithm uses a deterministic state observer combined with an intelligent adaptive mechanism based on fuzzy logic, and using a knowledge base on human expertise. The introducing of fuzzy logic is applied to achieve high-performance, a low sensibility to parameter variations and external influences. The developed algorithm is robust and efficient, it also assures precise speed estimation, a good trajectory tracking with the different prescribed dynamics. In order to verify its performances test the behavior of the control algorithm, numerical simulations are achieved. The performances of the induction motor drive have been analyzed under steady state and transient conditions. The simulation results have shown a good estimate speed and flux and an excellent tracking performance of the proposed control system, and have demonstrated convincingly the usefulness of adaptive observer based on fuzzy logic in variable speed drives with high performance.
The purpose of this synthesis is to create an overall picture about the control algorithms based on the evolutionary model. These methods offer a new and modern approach of solving complex problems seen in terms of au...
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ISBN:
(纸本)9781424467259
The purpose of this synthesis is to create an overall picture about the control algorithms based on the evolutionary model. These methods offer a new and modern approach of solving complex problems seen in terms of automation and electrical drives controlled by computer algorithms. The modular approach also allows easy automation procedure: the genetic algorithm receives some parameters, which are processed and then will be selected the best results based on information provided by the evaluation function. This method allows a better response of the system by making changes to the evaluation function. In the case of using on-line genetic algorithms, by changing the evaluation function, results are generated in real time, the only condition is to ensure sufficient computing capacity in order to execute the genetic algorithm (in fact the execution of genetic algorithm and the response from the evaluation function must be in the interval that allows the system to react in real time). Because it takes a large computing capacity for running the program in a very short time, genetic algorithms are not suitable in solving small problems with simple solutions because there will be always solutions that are cheaper and easier to implement. But if are considered complex problems, which don't have a clear solving algorithm, or there are some problems with few known data, the genetic algorithms and evolutionary methods in general are more suitable because they can offer solutions, which are otherwise difficult to obtain.
The scientific study of networks, including computer networks, social networks, and biological networks, has received an enormous amount of interest in the last few years. The rise of the Internet and the wide availab...
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ISBN:
(数字)9780191594175
ISBN:
(纸本)9780199206650
The scientific study of networks, including computer networks, social networks, and biological networks, has received an enormous amount of interest in the last few years. The rise of the Internet and the wide availability of inexpensive computers have made it possible to gather and analyze network data on a large scale, and the development of a variety of new theoretical tools has allowed us to extract new knowledge from many different kinds of networks. The study of networks is broadly interdisciplinary and important developments have occurred in many fields, including mathematics, physics, computer and information sciences, biology, and the social sciences. This book brings together the most important breakthroughs in each of these fields and presents them in a coherent fashion, highlighting the strong interconnections between work in different areas. Subjects covered include the measurement and structure of networks in many branches of science, methods for analyzing network data, including methods developed in physics, statistics, and sociology, the fundamentals of graph theory, computer algorithms, and spectral methods, mathematical models of networks, including random graph models and generative models, and theories of dynamical processes taking place on networks.
This paper presents a method to develop an intelligent master-slave system between agricultural vehicles, which will enable a semi-autonomous agricultural vehicle (slave) to follow a leading tractor (master) with a gi...
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ISBN:
(纸本)9781424478668
This paper presents a method to develop an intelligent master-slave system between agricultural vehicles, which will enable a semi-autonomous agricultural vehicle (slave) to follow a leading tractor (master) with a given lateral and longitudinal offset. In our study not only the follow-up motions but also the site-specific control of the apparatus such as rear and front power lift was considered. In the first part of this paper the recent research works in the area autonomous farming were discussed and the restrictions of these research works were illustrated. In the second part an approach to construct a master-slave system between two agricultural vehicles was demonstrated. In the next part the mathematic modeling of this master-slave system and the simulation results about the control algorithm were demonstrated. Afterwards the result of a real field test was presented and the safety considerations about such an intelligent vehicle system were made.
We investigate several basic problems in the distributed streaming model. In the this model, we have k sites, each receiving a stream of elements over time. There is a designated coordinator who would like to track, t...
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We investigate several basic problems in the distributed streaming model. In the this model, we have k sites, each receiving a stream of elements over time. There is a designated coordinator who would like to track, that is, maintain continuously at all times, some function ƒ of all the elements received from the k sites. There is a two-way communication channel between each site and the coordinator, and the goal is to track ƒ with minimum communication. This model is motivated by applications in distributed databases, network monitoring and sensor networks. In this thesis, we design algorithms to track some fundamental and useful functions, including random sampling, heavy hitters and quantiles. We also show that our algorithms have optimal communication costs in the worst case (up to some polylogarithmic factors in a few cases). In addition, we observed that for some problems considering the worst-case communication cost is meaningless, so we propose to use competitive analysis and give online tracking algorithms whose performance is competitive against the optimal offline algorithm that knows the entire stream in advance. Although this thesis is primarily concerned with the theory of distributed streaming, we expect that our study will also have a significant impact on real-world distributed streaming systems.
Modern computer systems often involve multiple processes or threads of control that communicatethrough shared memory. However, the implementation of correct and efficient datastructures that can be shared by several p...
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Modern computer systems often involve multiple processes or threads of control that communicatethrough shared memory. However, the implementation of correct and efficient datastructures that can be shared by several processes is frequently challenging. This thesis is concerned with the design and verification of a class of shared memory algorithms known as nonblocking algorithms, which are implementations of shared data structures that provide strong progress guarantees. Nonblocking algorithms offer an appealing alternative to traditional techniques for the implementation of shared memory data structures, but they are difficult to design, and extant algorithms can often be applied in only a limited range of systems. Furthermore, because of their subtlety, it is notoriously difficult to determine whether a given nonblocking algorithm is correct. This thesis addresses these difficulties in two ways. First, we present techniques for theverification of nonblocking algorithms that dynamically allocate memory. These techniquesallow the construction of formal and complete proofs of correctness, so that each proof maybe checked by a mechanical proof assistant. Applying techniques first developed for theverification of distributed algorithms, we use labelled-transition systems to model algorithmsand their specifications, and simulation relations to prove that an implementation meets itsspecification. Nonblocking algorithms often require a particular notion of simulation, calledbackward simulation, that is rarely necessary in other contexts. This thesis contributes to therelatively limited collective experience in the use of backward simulation. The second set of contributions addresses the limitations of many extant nonblocking algorithms. While many nonblocking algorithms allocate memory dynamically, it is difficult to determine in a nonblocking context when it is safe to free memory. We present techniques to accomplish this. Furthermore, many nonblocking algorithms depend
Clustering and classification are two basic tasks in data mining. As the complexity of data increases, the existing techniques for classification face a lot of challenges, for instance, classifying large high dimensio...
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Clustering and classification are two basic tasks in data mining. As the complexity of data increases, the existing techniques for classification face a lot of challenges, for instance, classifying large high dimensional data with multiple classes. Therefore, new techniques need to be innovated to deal with data in large volume and high dimensions. In this thesis, we aim to propose a possible way to solve this problem by integrating clustering algorithm into classification work. We propose a new classification framework. This framework consists of three phases: (i) a clustering algorithm is called recursively to build a decision cluster tree, (ii) a classification model is built from this decision cluster tree, (iii) new samples are classified by this classification model. There are many research problems existing in this framework. In this thesis, we describe our methodology for those problems. In this framework, we propose a new classification method ADCC (Automatic Decision Cluster Classifier) that is designed to use a variable weighting k-means algorithm W-k-means to build a decision cluster tree so that the variable weights of each dimension can be obtained from the training data and used in classification. In partitioning the training data, W-k-means automatically computes the variable weights according to the data distributions so that important variables can get more weights and the noisy variables get less weight. In clustering a data set (i.e., a node), the class variable is removed from the data, so the class variable has no impact on the clustering results. The class variable is used in determining the dominant class for each cluster. To build a better cluster tree, effective methods for selection of the number of clusters and the initial cluster centers at each node are introduced. Furthermore, we use various tests including Anderson-Darling test to determine whether a node can be further partitioned or not. In this way, distribution of the training sam
Due to the myriad of geometric topologies that modern computational fluid dynamicistsdesire to mesh and run solutions on, the need for a robust Cartesian Mesh Generation algorithm is paramount...
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Due to the myriad of geometric topologies that modern computational fluid dynamicistsdesire to mesh and run solutions on, the need for a robust Cartesian Mesh Generation algorithm is paramount. Not only do Cartesian meshes require less elements and often help resolve flow features but they also allow the grid generator to have a great deal ofcontrol in so far as element aspect ratio, size, and gradation. Fully Anisotropic Split-TreeAdaptive Refinement (FASTAR) is a code that allows the user to exert a great deal of control and ultimately generate a valid, geometry conforming mesh. Due to the split-tree nature and the use of volumetric pixels (voxels), non-unit aspect ratio meshing is easily achieved. Nodes are not generated until the end which mitigates tolerance issues. The tree is retained coherently, and viscous layers may be inserted in the space between thegeometry and the Cartesian mesh before it is tetrahedralized. FASTAR uses tree traversalto determine neighbors robustly, and with the tetrahedralization of only a small amountof space around the geometry, sliver cells and inverted elements are avoided. The code uses Riemannian Metric Tensors to generate geometry-appropriate spacing and is capable of adaptive meshing from a spacing field generated either by the user or from solution data. FASTAR is a robust, general mesh generator that allows maximum flexibility with minimal post-processing
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