Volume 12, Number 4, 2015
For the past decades, networked control systems (NCSs), as an interdisciplinary subject, have been one of the main research highlights and many fruitful results from different aspects have been achieved. With these growing research trends, it is significant to consolidate the latest knowledge and information to keep up with the research needs. In this paper, the results of different aspects of NCSs, such as quantization, estimation, fault detection and networked predictive control, are summarized. In addition, with the development of cloud technique, cloud control systems are proposed for the further development of NCSs.
In this paper, we present a review of the current literature on distributed (or partially decentralized) control of chemical process networks. In particular, we focus on recent developments in distributed model predictive control, in the context of the specific challenges faced in the control of chemical process networks. The paper is concluded with some open problems and some possible future research directions in the area.
Over the past few years, nonlinear manifold learning has been widely exploited in data analysis and machine learning. This paper presents a novel manifold learning algorithm, named atlas compatibility transformation (ACT). It solves two problems which correspond to two key points in the manifold definition: how to chart a given manifold and how to align the patches to a global coordinate space based on compatibility. For the first problem, we divide the manifold into maximal linear patch (MLP) based on normal vector field of the manifold. For the second problem, we align patches into an optimal global system by solving a generalized eigenvalue problem. Compared with the traditional method, the ACT could deal with noise datasets and fragment datasets. Moreover, the mappings between high dimensional space and low dimensional space are given. Experiments on both synthetic data and real-world data indicate the effection of the proposed algorithm.
The most common reason for blindness among human beings is Glaucoma. The increase of fluid pressure damages the optic nerve which gradually leads to irreversible loss of vision. A technique for automated screening of Glaucoma from the fundal retinal images is presented in this paper. This paper intends to explore the significance of both the approximate and detail coefficients through wavelet packet decomposition (WPD). Decomposition is done with “db3” wavelet function and the images are decomposed up to level-3 producing 84 sub-bands. Two features, the energy and the entropy are calculated for each sub-band producing two feature matrices (158 images × 84 features). The above step is purely a statistical measure based on WPD. To enhance the diagnostic accuracy, the second phase considers the structural (biological) region of interest (ROI) in the image and then extracts the same features. It is worthy to note that direct biological features are not extracted to eliminate the drawbacks of segmentation whereas the biologically significant region is taken as biological-ROI. Interestingly, the detailed coefficient sub-bands (prominent edges) show more significance in the biological-ROI phase. Apart from enhancing the diagnostic accuracy by feature reduction, the paper intends to mark the significance indices, uniqueness and discrimination capability of the significant features (sub-bands) in both the phases. Then, the crisp inputs are fed to the classifier ANN. Finally, from the significant features of the biological-ROI feature matrices, the accuracy is raised to 85% which is notable than the accuracy of 79% achieved without considering the ROI.
Anomaly detection plays an important role in ensuring the data quality in wireless sensor networks (WSNs). The main objective of the paper is to design a light-weight and distributed algorithm to detect the data collected from WSNs effectively. This is achieved by proposing a distributed anomaly detection algorithm based on ensemble isolation principle. The new method offers distinctive advantages over the existing methods. Firstly, it does not require any distance or density measurement, which reduces computational burdens significantly. Secondly, considering the spatial correlation characteristic of node deployment in WSNs, local sub-detector is built in each sensor node, which is broadcasted simultaneously to neighbor sensor nodes. A global detector model is then constructed by using the local detector model and the neighbor detector model, which possesses a distributed nature and decreases communication burden. The experiment results on the labeled dataset confirm the effectiveness of the proposed method.
Vision localization methods have been widely used in the motion estimation of unmanned aerial vehicles (UAVs). The noise of the vision location result is usually modeled as a white Gaussian noise so that this location result could be utilized as the observation vector in the Kalman filter to estimate the motion of the vehicle. Since the noise of the vision location result is affected by external environment, the variance of the noise is uncertain. However, in previous researches, the variance is usually set as a fixed empirical value, which will lower the accuracy of the motion estimation. The main contribution of this paper is that we proposed a novel adaptive noise variance identification (ANVI) method, which utilizes the special kinematic properties of the UAV for frequency analysis and then adaptively identifies the variance of the noise. The adaptively identified variance is used in the Kalman filter for more accurate motion estimation. The performance of the proposed method is assessed by simulations and field experiments on a quadrotor system. The results illustrate the effectiveness of the method.
Although important progresses have been already made in face detection, many false faces can be found in detection results and false detection rate is influenced by some factors, such as rotation and tilt of human face, complicated background, illumination, scale, cloak and hairstyle. This paper proposes a new method called DP-Adaboost algorithm to detect multi-angle human face and improve the correct detection rate. An improved Adaboost algorithm with the fusion of frontal face classifier and a profile face classifier is used to detect the multi-angle face. An improved horizontal differential projection algorithm is put forward to remove those non-face images among the preliminary detection results from the improved Adaboost algorithm. Experiment results show that compared with the classical Adaboost algorithm with a frontal face classifier, the textual DP-Adaboost algorithm can reduce false rate significantly and improve hit rate in multi-angle face detection.
This paper considers the problem of simultaneous estimation of the system states and the strategy of commutation for a larger class of nonlinear switched systems. First, a hybrid high gain observer is considered to get the exact estimation of the continuous states where the strategy of switching is previously known. Then, an extension to a larger class of nonlinear hybrid systems with arbitrary switching is made. Stability analysis is widely discussed for the two cases to provide a finite-time convergence of the estimation errors. The effectiveness of the proposed hybrid high gain observer has been proved by applying it to a quadruple tank process.
In this paper, local stability and performance analysis of fractional-order linear systems with saturating elements are shown, which lead to less conservative information and data on the region of stability and the disturbance rejection. Then, a standard performance analysis and global stability by using Lyapunov s second method are addressed, and the introduction of Lyapunov s function candidate whose sub-level set provide stability region and performance with a restricted state space origin is also addressed. The results include both single and multiple saturation elements and can be extended to fractional-order linear systems with any nonlinear elements and nonlinear noise that satisfy Lipschitz condition. A noticeable application of these techniques is analysis of control fractional-order linear systems with saturation control inputs.
This paper is concerned with the finite-time control problem for a class of networked control systems (NCSs) with short time-varying delays and sampling jitter. Considering a state feedback controller, the closed-loop NCS is described as a discrete-time linear system model, and the uncertain parts reflect the effect of the the network-induced delays and short sampling jitter of the system dynamics. Then a robust approach is proposed to solve the finite-time stability and stabilization problems for the considered NCS. An illustrative example is provided to demonstrate the effectiveness of the proposed theoretical results.
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