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Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review
The brain–computer interface (BCI) is an emerging technology that has the potential to revolutionize the world, with numerous applications ranging from healthcare to human augmentation. Electroencephalogram (EEG) motor imagery (MI) is among the most common BCI paradigms that have been used extensively in smart healthcare applications such as post-stroke rehabilitation and mobile assistive robots. In recent years, the contribution of deep learning (DL) has had a phenomenal impact on MI-EEG-based BCI. In this work, we systematically review the DL-based research for MI-EEG classification from the past ten years. This article first explains the procedure for selecting the studies and then gives an overview of BCI, EEG, and MI systems. The DL-based techniques applied in MI classification are then analyzed and discussed from four main perspectives: preprocessing, input formulation, deep learning architecture, and performance evaluation. In the discussion section, three major questions about DL-based MI classification are addressed: (1) Is preprocessing required for DL-based techniques? (2) What input formulations are best for DL-based techniques? (3) What are the current trends in DL-based techniques? Moreover, this work summarizes MI-EEG-based applications, extensively explores public MI-EEG datasets, and gives an overall visualization of the performance attained for each dataset based on the reviewed articles. Finally, current challenges and future directions are discussed.
Journal Article
Risk prediction in financial management of listed companies based on optimized BP neural network under digital economy
In the era of \"Internet plus,\" the world economy is becoming more and more globalized and informationalized. China's enterprises are facing unprecedented opportunities for their operation and development. However, it is also facing the financial uncertainties brought about by the fluctuations of the general economic environment, and the company is facing increasing financial risks. The reason why most enterprises encounter a serious financial crisis or even close down in the later stage is that they do not pay full attention to the initial financial problems and do not take effective measures to deal with the crisis in time. Financial risk warning has become an important part of modern enterprise financial management. This paper mainly puts forward the optimized BP neural system as the financial early warning model and ensures its high prediction accuracy. In the research, the operation principle and related reasoning process of the model are described, its shortcomings are analyzed, and solutions are put forward. Through the financial risk analysis of listed companies from 2017 to 2020, we find that the correct rate of the prediction results of the financial distress of normal companies in the selected companies based on the optimized BPNN has reached more than 80%, which proves the effectiveness of the optimized BPNN.
Journal Article
Multi-scale memory-enhanced method for predicting the remaining useful life of aircraft engines
To guarantee the safe operation of machinery and reduce its maintenance costs, estimating its remaining useful life (RUL) is a crucial task. Hence, in this study, a multi-scale memory-enhanced prediction method is proposed to describe fully characteristics of the data. This method is based on a deep learning algorithm and is designed to estimate the RUL of aircraft engines. To handle the complex and multi-fault operating conditions with uncertain properties in RUL estimation, a hybrid model that combines a multi-scale deep convolutional neural network and long short-term memory is presented. Experimental verification was carried out with the Commercial Modular Aero-Propulsion System Simulation dataset from NASA. Compared with multi-scale deep convolutional and long short-term memory networks, the hybrid model performed more efficiently. Furthermore, compared with other state-of-the-art methods, the multi-scale memory-enhanced prediction method can achieve better prognostics, especially for equipment with multiple operating conditions and failure modes.
Journal Article
Vehicle detection and tracking based on video image processing in intelligent transportation system
As an integral part of intelligent transportation system, vehicle detection and tracking system is of great research significance and practical application value. In this paper, based on the mixed Gaussian background model, the detection target is segmented by the different methods, and the most matching target track is found by using the location information and color information of the detection target, so as to realize the vehicle tracking. The experiment results show that for the same target, the centroid distance is less than 0.2, the color distance of HSV (hue saturation value) is less than 0.3, the centroid distance of different targets is less than 0.2, the HSV distance is less than 0.3, and the rest are distributed to some extent. When the centroid distance is 0.01, 0.02, 0.03, 0.04, 0.05 and 0.06, respectively, the matching results are 250, 150, 100, 50, 25 and 10, respectively; when the HSV color distance is 0.02, 0.06, 0.1, 0.14 and 0.18, respectively, the matching results are 160, 200, 100, 80 and 50, respectively. Therefore, for the normalized distance between the same targets, including the centroid distance and HSV color, in each possible matching area, the greater the distance is, the less the distribution of matching results. Experimental verification shows that when the vehicle is detected in the detection area, the effective contour is sequentially accessed and tracked through the memory pointer, and the relatively accurate contour of the moving vehicle can be obtained through the improved Gaussian mixture model. The vehicle detection algorithm based on regional method has high real-time accuracy and strong practical value, can meet the needs of intelligent transportation system, and has strong practical value.
Journal Article
Research on deep learning image processing technology of second-order partial differential equations
Image classification can effectively manage and organize images, laying a good foundation for the work in multiple fields of image processing. With the rise of Internet technology and social networks, the number of digital images has increased dramatically and there are more and more applications. People also use intuitive pictures instead of words when expressing emotions and information. A large number of digital images need to be managed, analyzed, and retrieved. This urgently requires more efficient and accurate image classification technology. Deep learning is a learning method that extends traditional neural networks, simulating the process of gradual abstraction of human brain cognition. The number of hidden layers is deepened, and features can be learned automatically. This paper has completed the traditional image noise detection and segmentation experiment based on convolutional neural network. We introduced the various parts of the convolutional neural network, including the convolutional layer, activation function, fully connected layer, etc. Noise detection is completed based on Fast RCNN on the MSTAR data set containing the background. The model fully combines the advantages of the isotropic model, the Perona-Malik model, and the second-order directional derivative. In order to better maintain the edge structure information of the image, the structure information is extracted based on the original noise image, and the improved ID model and the improved PM model are directionally diffused along the edge tangent direction of the original noise image. Considering the local structure information of the image, we use the slice similarity modulus value as the edge detector of the improved PM model, and use the slice similarity modulus value to construct a new weighting function to adaptively balance the relative weights of the improved ID model and the improved PM model. Simulation and measured data verify the effectiveness of this network in removing image coherent speckle noise. We compare and analyze it with existing denoising methods. The use of visual evaluation and objective evaluation indicators to evaluate the denoising effect and calculation efficiency shows the advantages of the network in this paper in terms of denoising effect, calculation time and space complexity.
Journal Article