Person re-identification (re-ID) is one of the essential tasks for modern visual intelligent systems to identify a person from images or videos captured at different times, viewpoints, and spatial positions. In fact, it is easy to make an incorrect estimate for person re-ID in the presence of illumination change, low resolution, and pose differences. To provide a robust and accurate prediction, machine learning techniques are extensively used nowadays.
View Article and Find Full Text PDFRobotic arms have been widely used in various industries and have the advantages of cost savings, high productivity, and efficiency. Although robotic arms are good at increasing efficiency in repetitive tasks, they still need to be re-programmed and optimized when new tasks are to be deployed, resulting in detrimental downtime and high cost. It is therefore the objective of this paper to present a learning from demonstration (LfD) robotic system to provide a more intuitive way for robots to efficiently perform tasks through learning from human demonstration on the basis of two major components: understanding through human demonstration and reproduction by robot arm.
View Article and Find Full Text PDFSensors (Basel)
November 2020
Survey-grade Lidar brands have commercialized Lidar-based mobile mapping systems (MMSs) for several years now. With this high-end equipment, the high-level accuracy quality of point clouds can be ensured, but unfortunately, their high cost has prevented practical implementation in autonomous driving from being affordable. As an attempt to solve this problem, we present a cost-effective MMS to generate an accurate 3D color point cloud for autonomous vehicles.
View Article and Find Full Text PDFAction recognition has gained great attention in automatic video analysis, greatly reducing the cost of human resources for smart surveillance. Most methods, however, focus on the detection of only one action event for a single person in a well-segmented video, rather than the recognition of multiple actions performed by more than one person at the same time for an untrimmed video. In this paper, we propose a deep learning-based multiple-person action recognition system for use in various real-time smart surveillance applications.
View Article and Find Full Text PDFAccurate estimation of 3D object pose is highly desirable in a wide range of applications, such as robotics and augmented reality. Although significant advancement has been made for pose estimation, there is room for further improvement. Recent pose estimation systems utilize an iterative refinement process to revise the predicted pose to obtain a better final output.
View Article and Find Full Text PDFThis paper reports a new approach to realize direct selective electroless deposition (ELD) without the requirement of photolithography. This method involves sequential silane-compound modifications in which the first modification creates a hydrophobic surface on the TiO-coated glass using a fluorine-rich alkoxysilane compound, followed by a laser ablation to create the pattern. Then, the entire substrate is immersed into an aqueous solution containing amino-silane equipped Pd nanoparticles for the second modification.
View Article and Find Full Text PDFAmino-terminated silane compound modification was wet-processed on a silicon wafer using four different solvents to investigate the property of the self-assembled monolayer (SAM) and its influence on the adhesion of electroless deposited nickel-phosphorus (Ni-P) films. Analyzed by various tools including dynamic light scattering, the atomic force microscope, X-ray photoelectron spectroscopy, inductively coupled plasma with mass spectroscopy, a proper link between the processing solvent and SAM quality is established. It is found that at least the chemical compatibility, the polarity, and the acidity of solvents can affect the final morphology of the resultant SAM.
View Article and Find Full Text PDFIn this study, the effect of 3-2-(2-aminoethylamino) ethylamino propyl trimethoxysilane (ETAS) modification and post rapid thermal annealing (RTA) treatment on the adhesion of electroless plated nickel-phosphorus (ELP Ni-P) film on polyvinyl alcohol-capped palladium nanoclusters (PVA-Pd) catalyzed silicon wafers is systematically investigated. Characterized by pull-off adhesion, atomic force microscopy, X-ray spectroscopy and water contact angle, a time-dependent, three-staged ETAS grafting mechanism including islandish grafting, a self-assembly monolayer (SAM) and multi-layer grafting is proposed and this mechanism is well correlated to the pull-off adhesion of ELP Ni-P film. In the absence of RTA, the highest ELP Ni-P film adhesion occurs when ETAS modification approaches SAM, where insufficient or multi-layer ETAS grafting fails to provide satisfactory results.
View Article and Find Full Text PDFA single-webcam distance measurement technique for indoor robot localization is proposed in this paper. The proposed localization technique uses webcams that are available in an existing surveillance environment. The developed image-based distance measurement system (IBDMS) and parallel lines distance measurement system (PLDMS) have two merits.
View Article and Find Full Text PDFIEEE Trans Syst Man Cybern B Cybern
April 2011
This paper proposes a novel method of online modeling and control via the Takagi-Sugeno (T-S) fuzzy-neural model for a class of uncertain nonlinear systems with some kinds of outputs. Although studies about adaptive T-S fuzzy-neural controllers have been made on some nonaffine nonlinear systems, little is known about the more complicated uncertain nonlinear systems. Because the nonlinear functions of the systems are uncertain, traditional T-S fuzzy control methods can model and control them only with great difficulty, if at all.
View Article and Find Full Text PDFThis paper presents a distance measurement method based on pixel number variation of CCD images by referencing to two arbitrarily designated points in the image frames. By establishing a relationship between the displacement of the camera movement along the photographing direction and the difference in pixel count between reference points in the images, the distance from an object can be calculated via the proposed method. To integrate the measuring functions into digital cameras, a circuit design implementing the proposed measuring system in selecting reference points, measuring distance, and displaying measurement results on CCD panel of the digital camera is proposed in this paper.
View Article and Find Full Text PDFIEEE Trans Syst Man Cybern B Cybern
October 2012
In this paper, a novel approach to adjust both the control points of B-spline membership functions (BMFs) and the weightings of fuzzy-neural networks using a reduced-form genetic algorithm (RGA) is proposed. Fuzzy-neural networks are traditionally trained by using gradient-based methods, which may fall into local minimum during the learning process. To overcome the problems encountered by the conventional learning methods, genetic algorithms are adopted because of their capabilities of directed random search for global optimization.
View Article and Find Full Text PDFIEEE Trans Syst Man Cybern B Cybern
October 2012
A novel adaptive fuzzy-neural sliding-mode controller with H(infinity) tracking performance for uncertain nonlinear systems is proposed to attenuate the effects caused by unmodeled dynamics, disturbances and approximate errors. Because of the advantages of fuzzy-neural systems, which can uniformly approximate nonlinear continuous functions to arbitrary accuracy, adaptive fuzzy-neural control theory is then employed to derive the update laws for approximating the uncertain nonlinear functions of the dynamical system. Furthermore, the H(infinity) tracking design technique and the sliding-mode control method are incorporated into the adaptive fuzzy-neural control scheme so that the derived controller is robust with respect to unmodeled dynamics, disturbances and approximate errors.
View Article and Find Full Text PDFIEEE Trans Neural Netw
July 2005
In this paper, an observer-based direct adaptive fuzzy-neural control scheme is presented for nonaffine nonlinear systems in the presence of unknown structure of nonlinearities. A direct adaptive fuzzy-neural controller and a class of generalized nonlinear systems, which are called nonaffine nonlinear systems, are instead of the indirect one and affine nonlinear systems given by Leu et al. By using implicit function theorem and Taylor series expansion, the observer-based control law and the weight update law of the fuzzy-neural controller are derived for the nonaffine nonlinear systems.
View Article and Find Full Text PDFIEEE Trans Syst Man Cybern B Cybern
February 2004
Sliding mode controllers for the bilinear systems with time varying uncertainties are developed in this paper. The bilinear coefficient matching condition which is similar to the traditional matching condition for linear system is defined for the homogeneous bilinear systems. It can be seen that the bilinear coefficient matching condition is very limited and is not generally applicable to the nonhomogeneous bilinear system.
View Article and Find Full Text PDFIEEE Trans Syst Man Cybern B Cybern
February 2004
In this paper, we investigate a novel robust control approach for a class of uncertain nonlinear systems with multiple inputs containing sector nonlinearities and deadzones. Sliding mode control (SMC) is suggested to design stabilizing controllers for these uncertain nonlinear systems. The controllers guarantee the global reaching condition of the sliding mode in these systems.
View Article and Find Full Text PDFIEEE Trans Syst Man Cybern B Cybern
February 2004
In this paper, we propose a novel design of a GA-based output-feedback direct adaptive fuzzy-neural controller (GODAF controller) for uncertain nonlinear dynamical systems. The weighting factors of the direct adaptive fuzzy-neural controller can successfully be tuned online via a GA approach. Because of the capability of genetic algorithms (GAs) in directed random search for global optimization, one is used to evolutionarily obtain the optimal weighting factors for the fuzzy-neural network.
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