Harbin Engineering University

About: Harbin Engineering University is a education organization based out in Harbin, Heilongjiang, China. It is known for research contribution in the topics: Control theory & Microstructure. The organization has 31149 authors who have published 27940 publications receiving 276787 citations. The organization is also known as: HEU.

Sliding Mode Control of Singular Stochastic Markov Jump Systems

Abstract: An investigation has been made into the sliding mode control (SMC) problem for singular stochastic Markov systems (SSMSs) in this paper. By using replacement of matrix variables, a new mean square admissibility criterion of SSMSs is proposed first. Based on this admissibility criterion, the desired state-feedback controller is designed to guarantee the closed-loop system to be mean-square admissible. Then, the obtained results given in the form of strict linear matrix inequalities are applied to solve SMC problem of SSMSs. In order to illustrate the workability and applicability of the theoretic results developed, numerical examples are provided.

Fabrication of ZIF-8@SiO2 Micro/Nano Hierarchical Superhydrophobic Surface on AZ31 Magnesium Alloy with Impressive Corrosion Resistance and Abrasion Resistance.

Abstract: Superhydrophobic coatings are highly promising for protecting material surfaces and for wide applications. In this study, superhydrophobic composites, comprising a rhombic-dodecahedral zeolitic imidazolate framework (ZIF-8@SiO2), have been manufactured onto AZ31 magnesium alloy via chemical etching and dip-coating methods to enhance stability and corrosion resistance. Herein, we report on a simple strategy to modify hydrophobic hexadecyltrimethoxysilan (HDTMS) on ZIF-8@SiO2 to significantly improve the property of repelling water. We show that various liquids can be stable on its surface and maintain a contact angle higher than 150°. The morphologies and chemical composition were characterized by means of scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FI-IR). In addition, the anticorrosion and antiattrition properties of the film were assessed by electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization and HT, respectively. Such a coating shows promisi...

Speech Emotion Recognition From 3D Log-Mel Spectrograms With Deep Learning Network

Abstract: Speech emotion recognition is a vital and challenging task that the feature extraction plays a significant role in the SER performance. With the development of deep learning, we put our eyes on the structure of end-to-end and authenticate the algorithm that is extraordinary effective. In this paper, we introduce a novel architecture ADRNN (dilated CNN with residual block and BiLSTM based on the attention mechanism) to apply for the speech emotion recognition which can take advantage of the strengths of diverse networks and overcome the shortcomings of utilizing alone, and are evaluated in the popular IEMOCAP database and Berlin EMODB corpus. Dilated CNN can assist the model to acquire more receptive fields than using the pooling layer. Then, the skip connection can keep more historic info from the shallow layer and BiLSTM layer are adopted to learn long-term dependencies from the learned local features. And we utilize the attention mechanism to enhance further extraction of speech features. Furthermore, we improve the loss function to apply softmax together with the center loss that achieves better classification performance. As emotional dialogues are transformed of the spectrograms, we pick up the values of the 3-D Log-Mel spectrums from raw signals and put them into our proposed algorithm and obtain a notable performance to get the 74.96% unweighted accuracy in the speaker-dependent and the 69.32% unweighted accuracy in the speaker-independent experiment. It is better than the 64.74% from previous state-of-the-art methods in the spontaneous emotional speech of the IEMOCAP database. In addition, we propose the networks that achieve recognition accuracies of 90.78% and 85.39% on Berlin EMODB of speaker-dependent and speaker-independent experiment respectively, which are better than the accuracy of 88.30% and 82.82% obtained by previous work. For validating the robustness and generalization, we also make an experiment for cross-corpus between above databases and get the preferable 63.84% recognition accuracy in final.