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BackgroundAutomated seizure detection from clinical EEG data can reduce the diagnosis time and facilitate targeting treatment for epileptic patients. However, current detection approaches mainly rely on limited features manually designed by domain experts, which are inflexible for the detection of a variety of patterns in a large amount of patients’ EEG data. Moreover, conventional machine learning algorithms for seizure detection cannot accommodate multi-channel Electroencephalogram (EEG) data effectively, which contains both temporal and spatial information. Recently, deep learning technology has been widely applied to perform image processing tasks, which could learns useful features from data and process multi-channel data automatically. To provide an effective system for automatic seizure detection, we proposed a new three-dimensional (3D) convolutional neural network (CNN) structure, whose inputs are multi-channel EEG signals.MethodsEEG data of 13 patients were collected from one center hospital, which has already been inspected by experts. To represent EEG data in CNN, firstly time series of each channel of EEG data was converted into the two-dimensional image. Then all channel images were combined into 3D images according to the mutual correlation intensity between different electrodes. Finally, a CNN was constructed using 3D kernels to predict different stages of EEG data, including inter-ictal, pre-ictal, and ictal stages. The system performance was evaluated and compared with the traditional feature-based classifier and the two-dimensional (2D) deep learning method.ResultsIt demonstrated that multi-channel EEG data could provide more information for increasing the specificity and sensitivity in cpmparison result between the single and multi-channel. And the 3D CNN based on multi-channel outperformed the 2D CNN and traditional signal processing methods with an accuracy of more than 90%, an sensitivity of 88.90% and an specificity of 93.78%.ConclusionsThis is the first effort to apply 3D CNN in detecting seizures from EEG. It provides a new way of learning patterns simultaneously from multi-channel EEG signals, and demonstrates that deep neural networks in combination with 3D kernels can establish an effective system for seizure detection.

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Recent progress in material data mining has been driven by high-capacity models trained on large datasets. However, collecting experimental data (real data) has been extremely costly owing to the amount of human effort and expertise required. Here, we develop a novel transfer learning strategy to address problems of small or insufficient data. This strategy realizes the fusion of real and simulated data and the augmentation of training data in a data mining procedure. For a specific task of grain instance image segmentation, this strategy aims to generate synthetic data by fusing the images obtained from simulating the physical mechanism of grain formation and the “image style” information in real images. The results show that the model trained with the acquired synthetic data and only 35% of the real data can already achieve competitive segmentation performance of a model trained on all of the real data. Because the time required to perform grain simulation and to generate synthetic data are almost negligible as compared to the effort for obtaining real data, our proposed strategy is able to exploit the strong prediction power of deep learning without significantly increasing the experimental burden of training data preparation.

Purpose This paper aims to explore the evolution mechanism of resources in a standard alliance that are matched with resources required at different standardization stages from the viewpoint of dynamic matching. How core enterprises in an alliance allocate resources, select member enterprises and maintain the normal operation of an alliance, according to the resource evolution of a standard alliance, is an important issue when dealing with the implementation of technology standardization. Design/methodology/approach The authors have chosen the Intelligent Grouping and Resource Sharing (IGRS) standard alliance of computer companies in China as the object of this study. The authors have built indices to identify core enterprises in the alliance from the viewpoint of network organization. The authors also collected data from authoritative news websites concerning patents and cooperative projects undertaken by 216 enterprises in the IGRS alliance during the period from 2002 to 2016, and they have computed and analyzed these data by using UCINET 6.0 software and social network analysis methodology to identify core enterprises at different standardization stages, thus revealing the evolution mechanism for resources in the standard alliance. Findings Technology standardization is divided into R&D, industrialization and marketization stages, and the standard alliance requires different resources to satisfy what is required at each of those different standardization stages. While technology standardization is a process during which technology systems standards are continuously being perfected and the standard product market is continuously expanding, the development of technology standardization affects the evolutionary processes of the core enterprises and affects the selection of member enterprises in the standard alliance. Practical implications The results obtained will assist the standard alliance to select proper member enterprises and dynamically match the alliance’s resources with the resources required at different standardization stages to speed up the implementation of independent standardization in China. Originality/value This study demonstrates the evolution mechanism of resources in technology standard alliances at different standardization stages by using quantitative analysis methodology, and it enriches the research on which elements are influential for technology standardization’s development in the context of China’s social, economic and cultural characteristics.

Due to the wide availability of the tools used to produce manipulated images, a large number of digital images have been tampered with in various media, such as newspapers and social networks, which makes the detection of tampered images particularly important. Therefore, an image manipulation detection algorithm leveraged by the Faster Region-based Convolutional Neural Network (Faster R-CNN) model combined with edge detection was proposed in this paper. In our algorithm, first, original tampered images and their detected edges were sent into symmetrical ResNet101 networks to extract tampering features. Then, these features were put into the Region of Interest (RoI) pooling layer. Instead of the RoI max pooling approach, the bilinear interpolation method was adopted to obtain the RoI region. After the RoI features of original input images and edge feature images were sent into bilinear pooling layer for feature fusion, tampering classification was performed in fully connection layer. Finally, Region Proposal Network (RPN) was used to locate forgery regions. Experimental results on three different image manipulation datasets show that our proposed algorithm can detect tampered images more effectively than other existing image manipulation detection algorithms.