Deep fusion of multi-channel neurophysiological signal for emotion recognition and monitoring

How to fuse multi-channel neurophysiological signals for emotion recognition is emerging as a hot research topic in community of Computational Psychophysiology. Nevertheless, prior feature engineering based approaches require extracting various domain knowledge related features at a high time cost. Moreover, traditional fusion method cannot fully utilise correlation information between different channels and frequency components. In this paper, we design a hybrid deep learning model, in which the 'Convolutional Neural Network (CNN)' is utilised for extracting task-related features, as well as mining inter-channel and inter-frequency correlation, besides, the 'Recurrent Neural Network (RNN)' is concatenated for integrating contextual information from the frame cube sequence. Experiments are carried out in a trial-level emotion recognition task, on the DEAP benchmarking dataset. Experimental results demonstrate that the proposed framework outperforms the classical methods, with regard to both of the emotional dimensions of Valence and Arousal

Defect Detection for Patterned Fabric Images Based on GHOG and Low-Rank Decomposition

In contrast to defect-free fabric images with macro-homogeneous textures and regular patterns, the fabric images with the defect are characterized by the defect regions that are salient and sparse among the redundant background. Therefore, as an effective tool for separating an image into a redundant part (the background) and sparse part (the defect), the low-rank decomposition model provides an ideal solution for patterned fabric defect detection. In this paper, a novel patterned method for fabric defect detection is proposed based on a novel texture descriptor and the low-rank decomposition model. First, an efficient second-order orientation-aware descriptor, denoted as GHOG, is designed by combining Gabor and histogram of oriented gradient (HOG). In addition, a spatial pooling strategy based on human vision mechanism is utilized to further improve the discrimination ability of the proposed descriptor. The proposed texture descriptor can make the defect-free image blocks lay in a low-rank subspace, while the defective image blocks have deviated from this subspace. Then, a constructed low-rank decomposition model divides the feature matrix generated from all the image blocks into a low-rank part, which represents the defect-free background, and a sparse part, which represents sparse defects. In addition, a non-convex log det as a smooth surrogate function is utilized to improve the efficiency of the constructed low-rank model. Finally, the defects are localized by segmenting the saliency map generated by the sparse matrix. The qualitative results and quantitative evaluation results demonstrate that the proposed method improves the detection accuracy and self-adaptivity comparing with the state-of-the-art methods

Klasifikasi Citra Hiperspektral Pada Kasus Tutupan Lahan Menggunakan Metode Convolutional Neural Network (CNN)

Informasi tutupan lahan dengan citra penginderaan jauh (inderaja) berbasis hiperspektral sangat efektif dalam pengelolaan peruntukan penggunaan lahan secara tepat. Selain dapat memberikan informasi keragaman spasial secara luas, cepat dan mudah, citra ini memiliki ratusan band spektral yang dapat memberikan struktur informasi permukaan bumi berdasarkan reflektansi gelombang elektromagnetik yang diterimanya. Metode One Dimensional Convolutional Neural Network (1D CNN) menunjukkan performa yang cukup baik pada klasifikasi tutupan lahan berbasis citra hiperspektral. Pada penelitian ini akan dilakukan analisis performa metode 1D CNN pada dataset Indian Pines 16 kelas, dimana sebelumnya metode 1D CNN diimplementasikan pada dataset Indian Pines 9 kelas. Hasil klasifikasi terbaik diperoleh pada percobaan 10000 epoch dari lima percobaan epoch yang berbeda dengan Overall Accuracy (OA) 88.97% dan Kappa 87.4%

Machine Learning Approaches for the Prediction of Obesity using Publicly Available Genetic Profiles

This paper presents a novel approach based on the analysis of genetic variants from publicly available genetic profiles and the manually curated database, the National Human Genome Research Institute Catalog. Using data science techniques, genetic variants are identified in the collected participant profiles then indexed as risk variants in the National Human Genome Research Institute Catalog. Indexed genetic variants or Single Nucleotide Polymorphisms are used as inputs in various machine learning algorithms for the prediction of obesity. Body mass index status of participants is divided into two classes, Normal Class and Risk Class. Dimensionality reduction tasks are performed to generate a set of principal variables - 13 SNPs - for the application of various machine learning methods. The models are evaluated using receiver operator characteristic curves and the area under the curve. Machine learning techniques including gradient boosting, generalized linear model, classification and regression trees, K-nearest neighbours, support vector machines, random forest and multilayer neural network are comparatively assessed in terms of their ability to identify the most important factors among the initial 6622 variables describing genetic variants, age and gender, to classify a subject into one of the body mass index related classes defined in this study. Our simulation results indicated that support vector machine generated high accuracy value of 90.5%

Graphical interface development for the position control of a 3RRR planar parallel manipulator

This paper presents the development of a graphical interface implemented in a 3-RRR planar parallel manipulator. The graphical interface allows the control, in open loop, of the position of the manipulator, allowing to locate in the plane the end actuator and define its pose. The graphical interface allows the visualization of variables such as angular position, torque, voltage of the servomotors that drive the input links. The theoretical and real comparison of the angular displacement and the torque in the motors during the chosen path is shown, obtaining very close results between both.This paper presents the development of a graphical interface implemented in a 3-RRR planar parallel manipulator. The graphical interface allows the control, in open loop, of the position of the manipulator, allowing to locate in the plane the end actuator and define its pose. The graphical interface allows the visualization of variables such as angular position, torque, voltage of the servomotors that drive the input links. The theoretical and real comparison of the angular displacement and the torque in the motors during the chosen path is shown, obtaining very close results between both

Graphical interface development for the position control of a 3RRR planar parallel manipulator

This paper presents the development of a graphical interface implemented in a 3-RRR planar parallel manipulator. The graphical interface allows the control, in open loop, of the position of the manipulator, allowing to locate in the plane the end actuator and define its pose. The graphical interface allows the visualization of variables such as angular position, torque, voltage of the servomotors that drive the input links. The theoretical and real comparison of the angular displacement and the torque in the motors during the chosen path is shown, obtaining very close results between both.This paper presents the development of a graphical interface implemented in a 3-RRR planar parallel manipulator. The graphical interface allows the control, in open loop, of the position of the manipulator, allowing to locate in the plane the end actuator and define its pose. The graphical interface allows the visualization of variables such as angular position, torque, voltage of the servomotors that drive the input links. The theoretical and real comparison of the angular displacement and the torque in the motors during the chosen path is shown, obtaining very close results between both

Graphical interface development for the position control of a 3RRR planar parallel manipulator

This paper presents the development of a graphical interface implemented in a 3-RRR planar parallel manipulator. The graphical interface allows the control, in open loop, of the position of the manipulator, allowing to locate in the plane the end actuator and define its pose. The graphical interface allows the visualization of variables such as angular position, torque, voltage of the servomotors that drive the input links. The theoretical and real comparison of the angular displacement and the torque in the motors during the chosen path is shown, obtaining very close results between both.This paper presents the development of a graphical interface implemented in a 3-RRR planar parallel manipulator. The graphical interface allows the control, in open loop, of the position of the manipulator, allowing to locate in the plane the end actuator and define its pose. The graphical interface allows the visualization of variables such as angular position, torque, voltage of the servomotors that drive the input links. The theoretical and real comparison of the angular displacement and the torque in the motors during the chosen path is shown, obtaining very close results between both

A data-driven method for total organic carbon prediction based on random forests

The total organic carbon (TOC) is an important parameter for shale gas reservoir exploration. Currently, predicting TOC using seismic elastic properties is challenging and of great uncertainty. The inverse relationship, which acts as a bridge between TOC and elastic properties, is required to be established correctly. Machine learning especially for Random Forests (RF) provides a new potential. The RF-based supervised method is limited in the prediction of TOC because it requires large amounts of feature variables and is very onerous and experience-dependent to derive effective feature variables from real seismic data. To address this issue, we propose to use the extended elastic impedance to automatically generate 222 extended elastic properties as the feature variables for RF predictor training. In addition, the synthetic minority oversampling technique is used to overcome the problem of RF training with imbalanced samples. With the help of variable importance measures, the feature variables that are important for TOC prediction can be preferentially selected and the redundancy of the input data can be reduced. The RF predictor is finally trained well for TOC prediction. The method is applied to a real dataset acquired over a shale gas study area located in southwest China. Examples illustrate the role of extended variables on improving TOC prediction and increasing the generalization of RF in prediction of other petrophysical properties