This paper investigates the finite-time anti-synchronization problem of memristive neural networks(MNNs) with time-varying delays. First, a delayed-feedback controller is constructed to realize the anti-synchronizat...
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This paper investigates the finite-time anti-synchronization problem of memristive neural networks(MNNs) with time-varying delays. First, a delayed-feedback controller is constructed to realize the anti-synchronization. Then, by employing appropriate Lyapunov-Krasovskii functional and the designed controller, new sufficient criteria are derived for the finite-time anti-synchronization of MNNs. Finally, an example is given to demonstrate the effectiveness of the theoretical results.
The research area of industrial alarm monitoring and management has been attracting increasing studies in the past decade. A variety of advanced alarm management techniques have been proposed. However, the validation ...
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The research area of industrial alarm monitoring and management has been attracting increasing studies in the past decade. A variety of advanced alarm management techniques have been proposed. However, the validation of the results were mostly based on real industrial data while such data was not always accessible to all researchers. Even if obtained, the privacy policies of industrial corporations may lead to difficulty in publishing new results. Thus, there is a great demand to construct a large scale simulated alarm system and create standard alarm & event data for the test of new methods. This paper proposes the design of a simulated alarm system by extending the public model of a Vinyl Acetate Monomer(VAM) process and collects the alarm & event data through a long term alarm monitoring process. An example alarm & event dataset is created and analyzed to illustrate the usability of the simulated alarm system.
This paper presents a control strategy for a planar three-link underactuated manipulator(UM) with a passive first link based on a wavelet neural network(WNN) model. Firstly, by using the particle swarm optimizatio...
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This paper presents a control strategy for a planar three-link underactuated manipulator(UM) with a passive first link based on a wavelet neural network(WNN) model. Firstly, by using the particle swarm optimization(PSO) algorithm, the target angles of all links are calculated according to the established kinematic model and the given target position. Then, a WNN model is trained to describe the coupling relationship between the passive link and the second link. The difference between the current angle and the target angle of the passive link is converged to zero by repeatedly controlling the second link to rotate an angle which is calculated by the trained WNN model. Next, the active links are controlled to rotate to their target angles with low speeds. Finally, the effectiveness of the proposed control strategy is verified through experimental results.
This paper is concerned with the stability analysis of discrete time-delay system. Firstly, an improved augmented functional form is proposed and the positive definite condition of functional is derived. Then, the for...
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This paper is concerned with the stability analysis of discrete time-delay system. Firstly, an improved augmented functional form is proposed and the positive definite condition of functional is derived. Then, the forward difference of functional is estimated by applying summation inequalities and a state-connecting-based zero-value equation. As a result, an improved stability criterion is established. Finally, a numerical example is given to show the efficiency and merit of the proposed method.
Speech emotion recognition helps enrich next-generation AI with emotional intelligence abilities by grasping the emotion from voice and words. At the current stage, speech emotion recognition(SER) is only used withi...
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Speech emotion recognition helps enrich next-generation AI with emotional intelligence abilities by grasping the emotion from voice and words. At the current stage, speech emotion recognition(SER) is only used within experimental *** current challenge facing the SER research is the lack of robustness across cultures, languages and even minor differences such as age-gaps of speakers. To create a more adaptable SER in adversarial circumstances, we propose hybrid neural networks architecture that creates a holistic model by embedding the Mel Frequency Cepstrum Coefficients as one-hot inputs such that differences in coefficients in each emotional category are inflated according to their importance. We performed experiments on three different databases to test the cross-corpus effectiveness of the proposed model.
The study of bit-rock interaction model is essential to describe the rock breaking process. In practice, it is difficult to get downhole measurement, and the downhole rock-breaking data is difficult to obtain. Therefo...
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The study of bit-rock interaction model is essential to describe the rock breaking process. In practice, it is difficult to get downhole measurement, and the downhole rock-breaking data is difficult to obtain. Therefore, this paper uses finite element simulation to obtain the kinetic data of bit-rock interaction, based on the analysis and comparison of existing models, an effective analysis method is provided for bit-rock interaction. Firstly, by using the Drucker-Prager rock criterion, actual bit and rock parameters, we develop the finite element bit-rock interaction experiments, and we obtain the data of rotating speed, rate-ofpenetration, weight-on-bit. Then, based on multiple nonlinear regression method, we identify the existing Young model, Jorden and Shirley model, Richard model, Ritto model parameters. Through the analysis and comparison of identification effects and characteristics of each model, we obtain the relationship among parameters of the bit-rock interaction.
Wind power forecasting is of great significance in grid dispatching. This paper proposes a statistical model based on feature classification least squares support vector machine, which can predict short-term wind powe...
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Wind power forecasting is of great significance in grid dispatching. This paper proposes a statistical model based on feature classification least squares support vector machine, which can predict short-term wind power. First of all, this paper analyzes the data of an actual power plant. After analyzing the data, it is found that there is uncertainty in the existence of multiple powers at the same wind speed. Then, in order to resolve this uncertainty, the wind speed and wind speed trend samples are density clustered according to the DBSCAN method. The clustering results are divided into several categories, and the samples of different categories are modeled by least squares support vector machines. Finally, the effectiveness of the proposed prediction model is compared with that of unclassified samples through the prediction model. Simulation results show that the designed model has higher prediction power accuracy.
An improved method for spectral reflectance reconstruction from digital camera raw RGB responses of pixels is proposed by adaptively weighting training samples considering colorimetric and lightness similarities. The ...
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An improved method for spectral reflectance reconstruction from digital camera raw RGB responses of pixels is proposed by adaptively weighting training samples considering colorimetric and lightness similarities. The proposed method was based on an adaptive local weighted linear regression model by using a Gaussian function in weighting matrix *** novelty of our method is designing the weighting matrix combining colorimetric and lightness similarities. The proposed method was tested using two different standard color charts, with a simulated digital camera based on the camera spectral sensitivity. Experimental results indicate that the proposed method exhibits considerable improvements in terms of the spectral reflectance and the colorimetric values in comparison with existing methods.
This paper is concerned with the problem of asymptotical synchronization of chaotic Lur’e systemscontrolled via PD controller with time-varying delay. Firstly, a new Lyapunov-Krasovskii functional(LKF) with more i...
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This paper is concerned with the problem of asymptotical synchronization of chaotic Lur’e systemscontrolled via PD controller with time-varying delay. Firstly, a new Lyapunov-Krasovskii functional(LKF) with more information of time-varying delay is constructed. Then, by applying the Wirtinger-based integral inequality and the extended reciprocally convex combination lemma(RCCL), a new synchronization criterion for time-varying delay is obtained, and a less conservatism corollary for the constant delay is established by weakening some terms of LKF. Finally, a numerical example is given to show the better performance of the proposed criteria.
Feature extraction and matching of images is a key step in 3D reconstruction, and its accuracy directly affects the accuracy of 3D reconstruction. In this paper, aiming at the mismatch caused by the high similarity be...
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Feature extraction and matching of images is a key step in 3D reconstruction, and its accuracy directly affects the accuracy of 3D reconstruction. In this paper, aiming at the mismatch caused by the high similarity between screws, proposes a feature matching algorithm based on median filtering, Lowes algorithm and scale-invariant feature transform(SURF), called M-L-SURF algorithm. First, the median filtering is performed on the screw image to remove noise, then the SURF algorithm is used for feature extraction and matching, and finally, the Lowe's algorithm is used to filter the matching results. The results of experiments show that the M-L-SURF algorithm can achieve a 97.4% correct rate of screw image matching. The matching results obtained in this paper can be better applied to the subsequent work of 3D reconstruction.
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