In this paper, a velocity controller based on H∞ loop shaping for an unmanned helicopter is presented. H∞ loop shaping controller guaranteed the stability margin of the closed-loop system above desired level. The pe...
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This paper presents the framework, modeling, controller design, simulation and experiment results of a bi-brachiate inspection robot, which is designed to inspect the high voltage power transmission lines. A bionic st...
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This paper presents the framework, modeling, controller design, simulation and experiment results of a bi-brachiate inspection robot, which is designed to inspect the high voltage power transmission lines. A bionic structure is adopted for the mechanical design of the inspection robot. It has two multi-joint arms with a claw and a wheel on the top of each arm. A counterweight box is designed to adjust its center of mass (COM). With the wheels it can ride along the line when there are no obstacles. Once it encounters obstacles, the robot can negotiate the obstacle with a turning around way. In order to design a feedback control law, the simplified model of the robot body and its dynamic equation have been developed. Based on the models, an adaptive control law using fuzzy compensator is described in detail. Simulation and experiment results show the effectiveness of the control law and the designed robot prototype.
In this paper, a velocity controller based on H ∞ loop shaping for an unmanned helicopter is presented. H ∞ loop shaping controller guaranteed the stability margin of the closed-loop system above desired level. Th...
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In this paper, a velocity controller based on H ∞ loop shaping for an unmanned helicopter is presented. H ∞ loop shaping controller guaranteed the stability margin of the closed-loop system above desired level. The performance of the controller is improved using gain-scheduling of key parameters. The simulation results show that the controller is robust and has good performance.
Electroencephalography (EEG) is considered a reliable indicator of a person's vigilance level. In this paper, we use EEG recordings to discriminate three vigilance states of a person, namely alert, drowsy, and sle...
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ISBN:
(纸本)9781424453153
Electroencephalography (EEG) is considered a reliable indicator of a person's vigilance level. In this paper, we use EEG recordings to discriminate three vigilance states of a person, namely alert, drowsy, and sleep, while driving a car in a simulation environment. The proposed framework explores the use of continuous wavelet transform in EEG signal processing. A large set of features is extracted from the wavelet coefficients, which are computed from EEG signals with multiple wavelet functions. We use random forest to rank the plenty of features and select the most important ones for later classification. Samples of EEG data are then trained and classified by SVM (Support Vector Machine). On datasets acquired from 5 subjects, our method reveals high classification accuracy (over 96%).
This paper describes a statistical machine translation system for our participation for the WMT10 shared task. Based on MOSES, our system is capable of translating German, French and Spanish into English. Our main con...
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The paper presents a target tracking algorithm which is based on the mean shift algorithm and the normalized moment of inertia (NMI) feature, because the result of the moving target tracking in the air is not satisfac...
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The news filtering and summarization (NFAS) system can automatically recognize Web news pages, retrieve each news page's title and news content, and extract key phrases. This extraction method substantially outper...
A visual control system is designed for a table tennis robot with five degrees of Freedom (DOFs). It consists of four parts such as ball sensing, trajectory predicting, motion planning, and motion control. A high-velo...
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ISBN:
(纸本)9781424478149
A visual control system is designed for a table tennis robot with five degrees of Freedom (DOFs). It consists of four parts such as ball sensing, trajectory predicting, motion planning, and motion control. A high-velocity stereo vision system with parallel architecture is developed to sense the motions of table tennis ball. The striking parameters including position, velocity, and time are predicted according to the predicted trajectory of the ball based on several measured positions. The motion computer receives the predicted striking parameters and performs motion planning for the robot. A motion control card embedded in the motion computer receives the planning results and controls the motions of the robot via the servo drivers for X and Y axes. A microprocessor is designed to produce pulses to control the motions of the rest three axes via the drivers. Experiments are well conducted to verify the effectiveness of the developed robot and control system.
Many SoCs adopt multicore architectures. As a result, embedded programmers are also facing the challenge of parallel programming. We propose a parallel skeleton library that can be used on embedded multicores. Our lib...
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Many SoCs adopt multicore architectures. As a result, embedded programmers are also facing the challenge of parallel programming. We propose a parallel skeleton library that can be used on embedded multicores. Our library is implemented in standard C++ using template features. We propose two parallel skeletons to support common program patterns on multicores. In our skeleton library, programmers can easily choose underlying parallel implementations with no code changes. Experimental results show that many applications can take advantage of these two skeletons for performance improvement, sometimes better than hand-parallelized code.
Hierarchies are very popular in organizing documents and web pages, hence automated hierarchical classification techniques are desired. However, the current dominant hierarchical approach of top-down method suffers ac...
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Hierarchies are very popular in organizing documents and web pages, hence automated hierarchical classification techniques are desired. However, the current dominant hierarchical approach of top-down method suffers accuracy decrease compared with flat classification approaches, because of error propagation and bottom nodes' data sparsity. In this paper we flatten hierarchies to relieve such accuracy decrease in top-down method, which aims to make hierarchies both effective enough to make large-scale classification tasks feasible, and simple enough to ensure high classification accuracy. We propose two flattening strategies based on these two causes of the accuracy decrease, and experimental results show that the flattening strategy designed for error propagation is more effective, which suggests that hierarchies with lots of branches at top layers can provide high classification accuracy. Besides, we analyze the computational complexity before and after flattening, which approximately agree with the experimental results.
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