Acoustic emission and moving window-improved kernel entropy component analysis for structural condition monitoring of hoisting machinery under various working conditions

Acoustic emission (AE) has been widely used to the nondestructive evaluation (NDE) and structural health monitoring (SHM) of hoisting machinery recently. Kernel entropy component analysis (KECA) is generally applied to extract the AE features based on its excellent nonlinear ability. However, traditional KECA specifically requires a considerable number of components (e.g. eigenvalues and eigenvectors) to excellently describe the original data, which leads to a reduction in the effect of approximate dimensionality reduction of high-dimensional data, thus causing readily unacceptable condition monitoring result. To overcome this weakness, a novel method named moving window-improved kernel entropy component analysis (MW-IKECA) is proposed in this study for structural condition monitoring of hoisting machinery, which is aimed at extracting more AE feature information and improving the condition identification accuracy. Firstly, a twiddle factor is introduced in the KECA model for the purpose of breaking the restriction that the projection axes originate only from the feature vectors and maximizing the independence between the components. Meanwhile, the moving window local strategy is incorporated into the proposed IKECA to extract more rich and effectiveness AE feature information at different scales. Finally, the Cauchy–Schwarz (CS) statistic is utilized to calculate the similarity between probability density functions and maintain the angular structure of the MW-IKECA feature space for the task of improving the monitoring accuracy and shortening the monitoring time-delay of MW-IKECA. Results of the experimental and practical engineering application validate the effectiveness and superiority of the proposed method in AE-based crane SHM under different working conditions compared with the traditional KECA and some combinatorial methods.

Facial Expression Recognition Using Kernel Entropy Component Analysis Network and DAGSVM

Facial expression recognition (FER) plays a significant part in artificial intelligence and computer vision. However, most of facial expression recognition methods have not obtained satisfactory results based on low-level features. The existed methods used in facial expression recognition encountered the major issues of linear inseparability, large computational burden, and data redundancy. To obtain satisfactory results, we propose an innovative deep learning (DL) model using the kernel entropy component analysis network (KECANet) and directed acyclic graph support vector machine (DAGSVM). We use the KECANet in the feature extraction stage. In the stage of output, binary hashing and blockwise histograms are adopted. We sent the final output features to the DAGSVM classifier for expression recognition. We test the performance of our proposed method on three databases of CK+, JAFFE, and CMU Multi-PIE. According to the experiment results, the proposed method can learn high-level features and provide more recognition information in the stage of training, obtaining a higher recognition rate.

Lower limb motor function assessment based on TensorFlow convolutional neural network and kernel entropy component analysis–local tangent space alignment

Motor function assessment of patients and the elderly is crucial to gait assessment and gait rehabilitation. Accuracy of the assessment is affected by clinician’s experience. To solve the problem, this article proposes motor function assessment index to assess the motor function of patients. VICON system collects video of subjects when they are walking. And the original gait videos are pre-processed by the pixel-based adaptive segmenter and extracted by the convolutional neural network. The kernel entropy component analysis and local tangent space alignment reduced the dimensions of extracted features, and motor function assessment index is obtained. The Pearson correlation analysis shows that the motor function assessment index and modified gait abnormality rating scale are significantly correlated, and Pearson correlation coefficient is 0.92. These effectiveness results demonstrate that the proposed method has the considerable potential to promote the future design of automatic motor function assessment for clinical rehabilitation research.

Class-information–incorporated kernel entropy component analysis with application to bearing fault diagnosis

Rolling bearings are present ubiquitously in industrial fields; timely fault diagnosis is of crucial significance in avoiding serious catastrophe. The extraction of ideal fault feature is a challenging task in vibration-based bearing fault detection. In this article, a novel method called class-information–incorporated kernel entropy component analysis is proposed for bearing fault diagnosis. The method is developed based on the Hebbian learning theory of neural network and the kernel entropy component analysis which attempts to compress the most Renyi quadratic entropy of input dataset after dimension reduction and presents a good performance for nonlinear feature extraction. Class-information–incorporated kernel entropy component analysis can take advantage of the label information of training samples to guide dimensional reduction and still follow the same simple mathematical formulation as kernel entropy component analysis. The high-dimensional feature dataset including time-domain, frequency-domain, and time–frequency domain characteristic parameters is first derived from the vibration signals. Then, the intrinsic geometric features are extracted by class-information–incorporated kernel entropy component analysis, and a classification strategy based on fusion information is applied to recognize different operating conditions of bearings. The experimental results demonstrated the feasibility and effectiveness of the proposed method.

Conformational entropy and specific affinity of M-cholinolytics and ligands of µ-opioid and D2-dopamine receptors

The computation model for evaluation of conformational entropy changes upon binding ligands to receptors is described. Then, changes of conformational entropy component and of binding free energy are compared. Interest to conformational entropy arises from developing new drugs as it might be changed purposefully. It is shown that conformational entropy may be used for prediction of affinity to a certain receptor. Examples of directed affinity change under the modification of substances’ conformational flexibility are given. The specific role of the conformational entropy in the receptor’s protection from the irreversible inactivation is identified.