In seismic exploration, effective seismic signals can be seriously distorted by and interfered with noise, and the performance of traditional seismic denoising approaches can hardly meet the requirements of high-precision seismic exploration. To remarkably enhance signal-to-noise ratios (SNR) and adapt to high-precision seismic exploration, this work exploits the non-subsampled contourlet transform (NSCT) and threshold shrink method to design a new approach for suppressing seismic random noise. NSCT is an excellent multiscale, multidirectional, and shift-invariant image decomposition scheme, which can not only calculate exact contourlet transform coefficients through multiresolution analysis but also give an almost optimized approximation. It has better high-frequency response and stronger ability to describe curves and surfaces. Specifically, we propose to utilize the superior performance NSCT to decomposing the noisy seismic data into various frequency sub-bands and orientation response sub-bands, obtaining fine enough transform high frequencies to effectively achieve the separation of signals and noises. Besides, we use the adaptive Bayesian threshold shrink method instead of traditional handcraft threshold scheme for denoising the high-frequency sub-bands of NSCT coefficients, which pays more attention to the internal characteristics of the signals/data itself and improve the robustness of method, which can work better for preserving richer structure details of effective signals. The proposed method can achieve seismic random noise attenuation while retaining effective signals to the maximum degree. Experimental results reveal that the proposed method is superior to wavelet-based and curvelet-based threshold denoising methods, which increases synthetic seismic data with lower SNR from −8.2293 dB to 8.6838 dB, and 11.8084 dB and 9.1072 dB higher than two classic sparse transform based methods, respectively. Furthermore, we also apply the proposed method to process field data, which achieves satisfactory results.
Hybrid cloud and heterogeneous cloud have become industry hotspots in recent years, and are also one of the main growth points of cloud computing market in the coming years. We hope to investigate container virtual network solutions and virtual machine container fusion solutions to manage hybrid and heterogeneous cloud. In this paper, a performance comparison of different container networking solutions, including Flannel, Calico, Weave and SR-IOV+DPDK, are presented. We aim to provide a fair comparison of these solutions for management problems of hybrid cloud and heterogeneous cloud. Results show compared with other solutions at the same packet size, SRIOV has a much better performance even when the packet size is 256 bytes, it can reach linear speed. Then, we put forward some advice on application based on the results.
The relay protection device is the core equipment that ensures the safe and stable operation of a power grid. With the open access of a large number of distributed generation, DC transmission and electric vehicles, a new deep low-carbon power system dominated by power electronic devices has gradually been formed. It is difficult for the traditional control and protection architecture, methods, and technology to meet the business characteristics and functional requirements of a diversified interaction, agile response, safety, and credibility of the power grid in the future. This paper presents a chip-based relay protection technology based on system-on-chip (SoC), which is described from four aspects, namely, the architectural design of the relay protection SoC, software and hardware cooperative relay protection based on the SoC IP core, experimental verification, and engineering application. The results show that the relay protection SoC proposed in this paper has significantly improved the performance of high-speed data acquisition and interaction through hardware algorithm engine acceleration and software and hardware collaborative computing, which is helpful to realize the local equipment operation and the integration of primary and secondary equipment, shorten the protection action time, and improve the speed, reliability, and stability of relay protection devices.
Objective:Diagnosing Mediastinal tumor through the characteristics of x-ray and CT performance.Methods:Retrospectively,38 cases are chosen for analysis who have been diognosed with mediastinal tumor combining with surgery and pathology.Analysing the performance of their image,by the combination of the literature on the diagnosis of mediastinal tumor and promoting a valuable imaging characteristics in diagnosis.Results:20 cases are diagnosed former mediastinal tumors,including 12 cases of intrathoracic thyroid,5 cases of thymus Liu,3 cases of teratoma.8 cases are diagnosed middle mediastinal tumors,including 3 cases of malignant lymphoma,5 cases of bronchial cyst,10 cases of after mediastinal tumors.All cases are nerve tumors.Conclusion:Generaly,we can make a diagnosis of mediastinal tumor By means of mediastinal tumor imaging combining with clinical characteristics.
Network security situational awareness is based on the extraction and analysis of big data, and by understanding these data to evaluate the current network security status and predict future development trends, provide feedback to decision-makers to make corresponding countermeasures, and achieve security protection for the network environment. This article focuses on artificial intelligence, summarizes the related definitions and classic models of network security situational awareness, and provides an overview of artificial intelligence. Starting from the method of machine learning, it specifically introduces the research status of neural-network-based network security situational awareness and summarizes the research work in recent years. Finally, the future development trends of network security situational awareness are summarized, and its prospects.
Deepfake techniques can forge the visual or audio signals in the video, which leads to inconsistencies between visual and audio (VA) signals. Therefore, multimodal detection methods expose deepfake videos by extracting VA inconsistencies. Recently, deepfake technology has started VA collaborative forgery to obtain more realistic deepfake videos, which poses new challenges for extracting VA inconsistencies. Recent multimodal detection methods propose to first extract natural VA correspondences in real videos in a self-supervised manner, and then use the learned real correspondences as targets to guide the extraction of VA inconsistencies in the subsequent deepfake detection stage. However, the inherent VA relations are difficult to extract due to the modality gap, which leads to the limited auxiliary performance of the aforementioned self-supervised methods. In this paper, we propose Predictive Visual-audio Alignment Self-supervision for Multimodal Deepfake Detection (PVASS-MDD), which consists of PVASS auxiliary and MDD stages. In the PVASS auxiliary stage in real videos, we first devise a three-stream network to associate two augmented visual views with corresponding audio clues, leading to explore common VA correspondences based on cross-view learning. Secondly, we introduce a novel cross-modal predictive align module for eliminating VA gaps to provide inherent VA correspondences. In the MDD stage, we propose to the auxiliary loss to utilize the frozen PVASS network to align VA features of real videos, to better assist multimodal deepfake detector for capturing subtle VA inconsistencies. We conduct extensive experiments on existing widely used and latest multimodal deepfake datasets. Our method obtains a significant performance improvement compared to state-of-the-art methods.
To measure the cooperation effect between civil aviation and high-speed rail, the paper quantifies the impact of the hub integration between airports and high-speed rail stations on airport passenger throughput and proposes a hub-integrated model based on the difference in differences method. Airports in China mainland with complete annual passenger throughput from 2007 to 2018 are selected into the sample. And for the requirements of the non-randomized controlled trial, those which integrate with stations are divided into the treated group and the rest are in the control group. Then a balanced panel dataset about all chosen airports and the cities where those are located is created for econometric analysis. To control the influence of confounding factors and satisfy the parallel trend assumption which is the basic condition of the difference in differences method application, propensity score matching is carried out by using the nearest neighbour matching within a caliper. Results show that the annual passenger throughput of the treated airports increases by 17.5% on average than before. Since most of the new airport passengers are transferred to or from high-speed rail stations, it is beneficial for both transport modes to construct the hub integration.
Recently, AI-manipulated face techniques have developed rapidly and constantly, which has raised new security issues in society. Although existing detection methods consider different categories of fake faces, the performance on detecting the fake faces with "unseen" manipulation techniques is still poor due to the distribution bias among cross-manipulation techniques. To solve this problem, we propose a novel framework that focuses on mining intrinsic features and further eliminating the distribution bias to improve the generalization ability. Firstly, we focus on mining the intrinsic clues in the channel difference image (CDI) and spectrum image (SI) from the camera imaging process and the indispensable step in AI manipulation process. Then, we introduce the Octave Convolution (OctConv) and an attention-based fusion module to effectively and adaptively mine intrinsic features from CDI and SI. Finally, we design an alignment module to eliminate the bias of manipulation techniques to obtain a more generalized detection framework. We evaluate the proposed framework on four categories of fake faces datasets with the most popular and state-of-the-art manipulation techniques, and achieve very competitive performances. To further verify the generalization ability of the proposed framework, we conduct experiments on cross-manipulation techniques, and the results show the advantages of our method.
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