This study proposes a virtual-metrology-based control system for conjecturing machining states and suggesting controller operating modes. The two machining state conjecture models were integrated to validate the virtu...
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This study proposes a virtual-metrology-based control system for conjecturing machining states and suggesting controller operating modes. The two machining state conjecture models were integrated to validate the virtual metrology results using a dual-stage featuring scheme and a two-phase modeling procedure. The featuring scheme is to extract significant features from collecting data for modeling and to minimize the modeling features by using a genetic algorithm. The modeling procedure adopts two quality indicators to evaluate model effectiveness. Two case studies indicate that the system achieves a mean MAPE of precision estimation less than 8.5% and suggests the controller with operating modes in 1 s.
The present work reports a sufficient condition for the consensus of a network of nonidentical Euler-Lagrange (EL) systems with variable time-delays in the communications. The EL-systems are controlled by simple Propo...
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ISBN:
(纸本)9781467320658
The present work reports a sufficient condition for the consensus of a network of nonidentical Euler-Lagrange (EL) systems with variable time-delays in the communications. The EL-systems are controlled by simple Proportional plus damping (P+d) schemes and the interconnection network is modeled as an undirected weighted graph. Additionally, for the case without delays, the paper reports a new Strict Lyapunov Function (SLF) for the closed-loop system. Experimental evidence, using three 3-Degrees-of-Freedom manipulators interconnected through the Internet, support the theoretical results of this paper.
Multi-player games are important for analyzing complex real-world applications that involve both cooperative and adversarial agents, but computational complexity complicates solving such games. We study a modified pur...
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Multi-player games are important for analyzing complex real-world applications that involve both cooperative and adversarial agents, but computational complexity complicates solving such games. We study a modified pursuit-evasion game with multiple pursuers and a single evader, played in a convex domain with an exit through which the evader may escape. We present a strategy whereby one pursuer acts as a defender, utilizing a multi-mode switching strategy to prevent the evader from escaping while the other pursuers subsequently capture the evader. The strategy requires each pursuer to have knowledge only of its Voronoi neighbors and the evader, and runs in real time. The existence and uniqueness of the players' trajectories are proved using non-smooth analysis, and it is also shown that the evader can never reach the exit regardless of its control inputs, resulting in eventual capture. Simulation results are presented demonstrating the algorithm.
Inverted pendulum system is a nonlinear multivariable system which is inherently unstable. It requires designing of a hybrid controller which can adapt in different disturbance conditions and work appreciably well whe...
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Inverted pendulum system is a nonlinear multivariable system which is inherently unstable. It requires designing of a hybrid controller which can adapt in different disturbance conditions and work appreciably well when compared to conventional controllers. In this paper, a hybrid controller for inverted pendulum is designed. Initially Fuzzy and LQR controllers for inverted pendulum are designed, then data are collected from these controllers, which then are used to train Adaptive neuro-fuzzy inference system (ANFIS). This hybrid controller has advantages of Fuzzy, LQR controllers and of neural networks; so it gives better performance.
To enhance classification performance by making use of easily available unlabelled data to overcome the scarcity of labelled data, this paper proposes an Embedded Co-Adaboost algorithm that integrates multi-view learn...
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To enhance classification performance by making use of easily available unlabelled data to overcome the scarcity of labelled data, this paper proposes an Embedded Co-Adaboost algorithm that integrates multi-view learning into the Adaboost learning framework and at the same time leverages the advantages of Co-training algorithm for performance enhancement. Experimental results demonstrate the effectiveness of the proposed algorithm in terms of the convergence rate, the accuracy, and the steady performance as compared to the original AdaBoost algorithm, without relying on redundant and sufficient feature sets. As a algorithm application in software engineering, the Embedded Co-AdaBoost has been applied to the classification of software document relations to improve the quality of the architecture design documents and the reusability of design knowledge.
We present the hardware design, software architecture, and core algorithms of Herb 2.0, a bimanual mobile manipulator developed at the Personal Robotics Lab at Carnegie Mellon University, Pittsburgh, PA. We have devel...
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In this letter, we propose an improved bilateral filter of high capability of removing impulse noise with maintaining smoothness of signal and edge preservation. Our improved bilateral filter incorporates an idea of t...
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In this paper,a robust distributed order PI controller design method is derived,which tolerates certain system *** theory and implementation of the distributed order operators are discussed in both time and frequency
In this paper,a robust distributed order PI controller design method is derived,which tolerates certain system *** theory and implementation of the distributed order operators are discussed in both time and frequency
Pixel clustering is a basic process in image segmentation for classifying various regions in an image. However, this clustering becomes complex for multispectral and hyperspectral images due to the high dimensionality...
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Pixel clustering is a basic process in image segmentation for classifying various regions in an image. However, this clustering becomes complex for multispectral and hyperspectral images due to the high dimensionality of these images. For such cases, dimensionality reduction is usually used to remove component images unsuitable for pixel clustering, in order to reduce computaional cost and improves accuracy. We propose a simple dimensionality reduction method in which a subset of component images is selected from multiple images using the joint singular value decomposition. Results of experiments for LandsatTM multispectral images demonstrate the effectiveness of the proposed method. Segmentation using a subimage chosen by the proposed enables us to avoid mixture of inappropriate component images and improve the performance of the segmentation.
This paper presents a combination of existing advanced methods to solve the partial volume segmentation problem. It uses region-based active surface modelling in a hierarchical scheme to eliminate segmentation errors,...
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This paper presents a combination of existing advanced methods to solve the partial volume segmentation problem. It uses region-based active surface modelling in a hierarchical scheme to eliminate segmentation errors, followed by an alpha matting step to further refine the segmentation. This method can have an interest in several applications in medical imaging. We have validated our method on real PET images of head-and-neck cancer patients as well as custom designed phantom PET images. Experiments show that our method can generate more accurate segmentation than existing approaches.
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