Complexity of analysis of landslide hazard is due to uncertainty. In this study, a novel approach multi-gene genetic programming based on separable functional network (MGGPSFN) is presented for predicting landslide di...
Complexity of analysis of landslide hazard is due to uncertainty. In this study, a novel approach multi-gene genetic programming based on separable functional network (MGGPSFN) is presented for predicting landslide displacement. Moreover, Pearson's cross-correlation coefficients and mutual information are adopted to look for the potential input variables for a forecast model in the paper. The performance of new model is verified through one case study in Baishuihe landslide in the Three Gorges Reservoir in China. In addition, we compared it with two methods, back-propagation neural network and radial basis function, and MGGPSFN got the best results in the same measurements.
This paper investigates the problem of global exponential anti-synchronization of a class of switched neural networks with time-varying delays and lag signals. Considering the packed circuits, the controller is depend...
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This paper investigates the problem of global exponential anti-synchronization of a class of switched neural networks with time-varying delays and lag signals. Considering the packed circuits, the controller is dependent on the output of the system as the inner states are very hard to measure. Therefore, it is necessary to investigate the controller based on the output of the neuron cell. Through theoretical analysis, it is obvious that the obtained ones improve and generalize the results derived in the previous literature. To illustrate the effectiveness, a simulation example with applications in image encryptions is also presented in the paper.
Inspired by the fact that in most existing swarm models of multi-agent systems the velocity of an agent can be infinite, which is not in accordance with the real applications, we propose a novel swarm model of multi-a...
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Inspired by the fact that in most existing swarm models of multi-agent systems the velocity of an agent can be infinite, which is not in accordance with the real applications, we propose a novel swarm model of multi-agent systems where the velocity of an agent is finite. The Lyapunov function method and LaSalle＇s invariance principle are employed to show that by using the proposed model all of the agents eventually enter into a bounded region around the swarm center and finally tend to a stationary state. Numerical simulations are provided to demonstrate the effectiveness of the theoretical results.
Spiking neural P systems with astrocytes (SNPA, for short) are a class of distributed parallel computing devices inspired from the way spikes pass through the synapses between the neurons. In the present work, we disc...
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In this paper, we develop a novel application of independent component analysis (ICA) based auto-regression forecasting model(ICAARF). The method can noninvasively, continuously and conveniently derive ambulatory bloo...
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This paper investigates the maintenance scheduling problem in a flow line system consisting of two series machines with a finite buffer in between. The machines deteriorate with age and have multiple deteriorating qua...
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This paper presents a memory crossbar based on two serial memristors with threshold characteristic to eliminate the effect of sneak paths, which is a key issue in crossbar memory system leading to great degradation in...
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ISBN:
(纸本)9781479919611
This paper presents a memory crossbar based on two serial memristors with threshold characteristic to eliminate the effect of sneak paths, which is a key issue in crossbar memory system leading to great degradation in their performance and power efficiency. At first, we analyze the threshold characteristic of memristor and propose a memristor model with threshold. Based on this model, the paper presents the design and simulation of a non-volatile memory system utilizing two serial memristors with different polarities as a memory cell. This scheme solves the sneak-path problem by taking advantage of the threshold characteristic and the performance with having always high resistance state in all the memory cells, which is validated by simulation results. The scheme also possesses the superior properties of remarkable compatibility and high density.
With the wide use of power conversion devices - 'nonlinear loads' - many harmonic currents are being injected into the power grid. Shunt Active Power Filters (SAPF) are the power electronic equipment to compen...
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This paper investigates containment control of multi-agent systems with intermittent communications and input saturation on fixed undirected networks . Under the assumption that each agent is asymptotically null contr...
This paper investigates containment control of multi-agent systems with intermittent communications and input saturation on fixed undirected networks . Under the assumption that each agent is asymptotically null controllable with bounded controls and there exists at least one leader that has directed path to each followers, both state feedback and output feedback control protocols are designed by utilizing the algebraic Riccati equation . For any a priori given bounded set , semi-global state feedback and output feedback containment control of multi-agent systems with intermittent communication can be attained. Numerical simulations are provided to ensure the effectiveness of results.
Mass localization is a crucial problem in computer-aided detection (CAD) system for the diagnosis of suspicious regions in mammograms. In this paper, a new automatic mass detection method for breast cancer in mammogra...
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Mass localization is a crucial problem in computer-aided detection (CAD) system for the diagnosis of suspicious regions in mammograms. In this paper, a new automatic mass detection method for breast cancer in mammographic images is proposed. Firstly, suspicious regions are located with an adaptive region growing method, named multiple concentric layers (MCL) approach. Prior knowledge is utilized by tuning parameters with training data set during the MCL step. Then, the initial regions are further refined with narrow band based active contour (NBAC), which can improve the segmentation accuracy of masses. Texture features and geometry features are extracted from the regions of interest (ROI) containing the segmented suspicious regions and the boundaries of the segmentation. The texture features are computed from gray level co-occurrence matrix (GLCM) and completed local binary pattern (CLBP). Finally, the ROIs are classified by means of support vector machine (SVM), with supervision provided by the radiologist׳s diagnosis. To deal with the imbalance problem regarding the number of non-masses and masses, supersampling and downsampling are incorporated. The method was evaluated on a dataset with 429 craniocaudal (CC) view images, containing 504 masses. Among them, 219 images containing 260 masses are used to optimize the parameters during MCL step, and are used to train SVM. The remaining 210 images (with 244 masses) are used to test the performance. Masses are detected with 82.4% sensitivity with 5.3 false positives per image (FPsI) with MCL, and after active contour refinement, feature analysis and classification, it obtained 1.48 FPsI at the sensitivity 78.2%. Testing on 164 normal mammographic images showed 5.18 FPsI with MCL and 1.51 FPsI after classification. Experiments on mediolateral oblique (MLO) images have also been performed, the proposed method achieved a sensitivity 75.6% at 1.38 FPsI. The method is also analyzed with free response operating characteristi
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