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21.
Akram Tavakoli Ali Akbar Babaluo Khadijeh Safaee 《Fullerenes, Nanotubes and Carbon Nanostructures》2017,25(5):312-317
In this work, multi-wall carbon nanotube (MWCNT) was successfully modified using aqueous solution of Oxone as a new oxidant. The effect of oxidation temperature on various characteristics of the treated MWCNTs was also investigated. FTIR and titration analysis proved the formation of carboxyl, carbonyl and epoxide groups at the surface of MWCNTs. The concentration of the functional groups increased as the modification temperature increased. The presence of such oxygen containing groups at the surface of MWCNTs justified the long time stability of the treated MWCNTs suspensions in water and methanol. The modified MWCNTs showed higher entanglement compared to row MWCNT due to the cross-links adjacent effect of pendant functional groups. Finally, it was concluded that Oxone oxidation process destroys the structure of the MWCNTs, but not severe enough to unzip the MWCNTs. 相似文献
22.
Microfluidic Manipulation of Core/Shell Nanoparticles for Oral Delivery of Chemotherapeutics: A New Treatment Approach for Colorectal Cancer
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Mohammad Mahdi Hasani‐Sadrabadi Shahrouz Taranejoo Erfan Dashtimoghadam Ghasem Bahlakeh Fatemeh Sadat Majedi Jules John VanDersarl Mohsen Janmaleki Fatemeh Sharifi Arnaud Bertsch Kerry Hourigan Lobat Tayebi Philippe Renaud Karl I. Jacob 《Advanced materials (Deerfield Beach, Fla.)》2016,28(21):4134-4141
23.
Stochastic chaos synchronization using Unscented Kalman-Bucy Filter and sliding mode control 总被引:1,自引:0,他引:1
Mahdi HeydariHassan Salarieh Mehdi Behzad 《Mathematics and computers in simulation》2011,81(9):1770-1784
This paper presents an algorithm for synchronizing two different chaotic systems by using a combination of Unscented Kalman-Bucy Filter (UKBF) and sliding mode controller. It is assumed that the drive chaotic system is perturbed by white noise and shows stochastic chaotic behavior. In addition the output of the system does not contain the whole state variables of the system, and it is also affected by some independent white noise. By combining the UKBF and the sliding mode control, a synchronizing control law is proposed. Simulation results show the ability of the proposed method in synchronizing chaotic systems in presence of noise. 相似文献
24.
Mahdi JaliliAuthor Vitae 《Neurocomputing》2011,74(10):1551-1556
Although diffusive electrical connections in neuronal networks are instantaneous, excitatory/inhibitory couplings via chemical synapses encompass a transmission time-delay. In this paper neural networks with instantaneous electrical couplings and time-delayed excitatory/inhibitory chemical connections are considered and scaling of the spike phase synchronization with the unified time-delay in the network is investigated. The findings revealed that in both excitatory and inhibitory chemical connections, the phase synchronization could be enhanced by introducing time-delay. The role of the variability of the neuronal external current in the phase synchronization is also investigated. As individual neuron models, Hindmarsh-Rose model is adopted and the network structure of the electrical and chemical connections is considered to be Watts-Strogatz and directed random networks, respectively. 相似文献
25.
The use of fuzzy logic and neural networks models for sensory properties prediction from process and structure parameters of knitted fabrics 总被引:1,自引:1,他引:0
Selsabil?El-Ghezal JeguirimEmail author Amal?Babay?Dhouib Mahdi?Sahnoun Morched?Cheikhrouhou Laurence?Schacher Dominique?Adolphe 《Journal of Intelligent Manufacturing》2011,22(6):873-884
In a competitive business environment, the textile industrialists intend to propose diversified products according to consumers
preference. For this purpose, the integration of sensory attributes in the process parameters choice seems to be a useful
alternative. This paper provides fuzzy and neural models for the prediction of sensory properties from production parameters
of knitted fabrics. The prediction accuracy of these models was evaluated using both the root mean square error (RMSE) and
mean relative percent error (MRPE). The results revealed the models ability to predict tactile sensory attributes based on
the production parameters. The comparison of the prediction performances showed that the neural models are slightly powerful
than the fuzzy models. 相似文献
26.
27.
Maryam Amiri Hassan Bakhshandeh Amnieh Mahdi Hasanipanah Leyli Mohammad Khanli 《Engineering with Computers》2016,32(4):631-644
Blasting operation is widely used method for rock excavation in mining and civil works. Ground vibration and air-overpressure (AOp) are two of the most detrimental effects induced by blasting. So, evaluation and prediction of ground vibration and AOp are essential. This paper presents a new combination of artificial neural network (ANN) and K-nearest neighbors (KNN) models to predict blast-induced ground vibration and AOp. Here, this combination is abbreviated using ANN-KNN. To indicate performance of the ANN-KNN model in predicting ground vibration and AOp, a pre-developed ANN as well as two empirical equations, presented by United States Bureau of Mines (USBM), were developed. To construct the mentioned models, maximum charge per delay (MC) and distance between blast face and monitoring station (D) were set as input parameters, whereas AOp and peak particle velocity (PPV), as a vibration index, were considered as output parameters. A database consisting of 75 datasets, obtained from the Shur river dam, Iran, was utilized to develop the mentioned models. In terms of using three performance indices, namely coefficient correlation (R 2), root mean square error and variance account for, the superiority of the ANN-KNN model was proved in comparison with the ANN and USBM equations. 相似文献
28.
Multiple kernel learning (MKL) approach has been proposed for kernel methods and has shown high performance for solving some real-world applications. It consists on learning the optimal kernel from one layer of multiple predefined kernels. Unfortunately, this approach is not rich enough to solve relatively complex problems. With the emergence and the success of the deep learning concept, multilayer of multiple kernel learning (MLMKL) methods were inspired by the idea of deep architecture. They are introduced in order to improve the conventional MKL methods. Such architectures tend to learn deep kernel machines by exploring the combinations of multiple kernels in a multilayer structure. However, existing MLMKL methods often have trouble with the optimization of the network for two or more layers. Additionally, they do not always outperform the simplest method of combining multiple kernels (i.e., MKL). In order to improve the effectiveness of MKL approaches, we introduce, in this paper, a novel backpropagation MLMKL framework. Specifically, we propose to optimize the network over an adaptive backpropagation algorithm. We use the gradient ascent method instead of dual objective function, or the estimation of the leave-one-out error. We test our proposed method through a large set of experiments on a variety of benchmark data sets. We have successfully optimized the system over many layers. Empirical results over an extensive set of experiments show that our algorithm achieves high performance compared to the traditional MKL approach and existing MLMKL methods. 相似文献
29.
Elnaz Bigdeli Mahdi Mohammadi Bijan Raahemi Stan Matwin 《Pattern Analysis & Applications》2017,20(1):183-199
Clustering, while systematically applied in anomaly detection, has a direct impact on the accuracy of the detection methods. Existing cluster-based anomaly detection methods are mainly based on spherical shape clustering. In this paper, we focus on arbitrary shape clustering methods to increase the accuracy of the anomaly detection. However, since the main drawback of arbitrary shape clustering is its high memory complexity, we propose to summarize clusters first. For this, we design an algorithm, called Summarization based on Gaussian Mixture Model (SGMM), to summarize clusters and represent them as Gaussian Mixture Models (GMMs). After GMMs are constructed, incoming new samples are presented to the GMMs, and their membership values are calculated, based on which the new samples are labeled as “normal” or “anomaly.” Additionally, to address the issue of noise in the data, instead of labeling samples individually, they are clustered first, and then each cluster is labeled collectively. For this, we present a new approach, called Collective Probabilistic Anomaly Detection (CPAD), in which, the distance of the incoming new samples and the existing SGMMs is calculated, and then the new cluster is labeled the same as of the closest cluster. To measure the distance of two GMM-based clusters, we propose a modified version of the Kullback–Libner measure. We run several experiments to evaluate the performances of the proposed SGMM and CPAD methods and compare them against some of the well-known algorithms including ABACUS, local outlier factor (LOF), and one-class support vector machine (SVM). The performance of SGMM is compared with ABACUS using Dunn and DB metrics, and the results indicate that the SGMM performs superior in terms of summarizing clusters. Moreover, the proposed CPAD method is compared with the LOF and one-class SVM considering the performance criteria of (a) false alarm rate, (b) detection rate, and (c) memory efficiency. The experimental results show that the CPAD method is noise resilient, memory efficient, and its accuracy is higher than the other methods. 相似文献
30.
The structural properties of networked control systems with both bandwidth limitations and delays are investigated. Sufficient conditions are given for controllability (stabilizability) and reconstructibility (detectability). Our results enhance previous works by capturing bandwidth limitations and delays simultaneously. The adopted modeling framework could be readily used in control and estimation methods, including optimal and predictive schemes. It also facilitates the use of scheduling optimization algorithms in conjunction with the control scheme presented. 相似文献