A Theoretically Guaranteed Deep Optimization Framework for Robust Compressive Sensing MRI

Magnetic Resonance Imaging (MRI) is one of the most dynamic and safe imaging techniques available for clinical applications. However, the rather slow speed of MRI acquisitions limits the patient throughput and potential indi cations. Compressive Sensing (CS) has proven to be an efficient technique for accelerating MRI acquisition. The most widely used CS-MRI model, founded on the premise of reconstructing an image from an incompletely filled k-space, leads to an ill-posed inverse problem. In the past years, lots of efforts have been made to efficiently optimize the CS-MRI model. Inspired by deep learning techniques, some preliminary works have tried to incorporate deep architectures into CS-MRI process. Unfortunately, the convergence issues (due to the experience-based networks) and the robustness (i.e., lack real-world noise modeling) of these deeply trained optimization methods are still missing. In this work, we develop a new paradigm to integrate designed numerical solvers and the data-driven architectures for CS-MRI. By introducing an optimal condition checking mechanism, we can successfully prove the convergence of our established deep CS-MRI optimization scheme. Furthermore, we explicitly formulate the Rician noise distributions within our framework and obtain an extended CS-MRI network to handle the real-world nosies in the MRI process. Extensive experimental results verify that the proposed paradigm outperforms the existing state-of-the-art techniques both in reconstruction accuracy and efficiency as well as robustness to noises in real scene

A new crack diagnosis method on box structure based on empirical mode decomposition

A new crack diagnosis method on a two-story box structure based on empirical mode decomposition is proposed in this paper. According to the simulation analysis, it turns out that the model of the structure can be barely influenced by the crack. Response signals of swept sine vibration test are empirical mode decomposed into a set of intrinsic mode functions, from which tag vectors are constructed, then tag angles are defined to dignose the failure of the board. Combined with the load direction to the structure, the position and direction of the crack can be deduced using tag angles

The application of coordinate transformation matrix into the multi-degree of freedom vibration control

This paper describes the application of the coordinate transformation matrices into the multi-degree of freedom vibration control. An example with an aluminum beam supported by dual actuators is used to derive how to create both the input transformation matrix and the output transformation matrix. In order to achieve the synchronous movement of the dual actuators, the direct actuator control test and 2DOF control test have been performed. By comparing with the results of the direct actuator control test without using the transformation matrix, the 2DOF control test proves that the transformation matrix is a powerful tool for a significant improvement in test control accuracy

The effect of using group decision support system in Value Management studies: an experimental study

Abstract A group decision support system (GDSS) can be helpful to VM users overcome difficulties in value management (VM) workshops. A web-based GDSS known as interactive value management system (IVMS) is introduced in this paper. A comparative experimental study is undertaken to investigate the extent to which the use of IVMS can improve the performance of VM workshops by using a competing value approach (CVA). This study compares and contrasts the performance of a traditional VM workshop with an IVMS-supported VM workshop in three aspects: (1) process measures, (2) outcome measures, and (3) participants' satisfaction. The process measures indicate that IVMS is helpful in improving the efficiency, information reliability and supportability of decision and participation process, while the outcome measures show groups supported by IVMS perform better in ideas generations. The results also indicate that the use of GDSS results in increasing participant satisfaction

Study on multi-axis sine vibration test control techniques

This paper describes several key aspects about multi-axis sine vibration test control techniques including the identification of the system frequency response function, synchronization of the input and output signals, the generation of the sinewave, the control algorithm, etc. A multi-axis sine vibration controller is developed based on these key techniques and the major framework of the controller is introduced. Finally, a dual axial experiment is carried out by the controller. The test results show the feasibility of the control algorithm and the good control strategy of the multi-axis sine vibration controller in which the key techniques are realized

Implicit Identity Leakage: The Stumbling Block to Improving Deepfake Detection Generalization

In this paper, we analyse the generalization ability of binary classifiers for the task of deepfake detection. We find that the stumbling block to their generalization is caused by the unexpected learned identity representation on images. Termed as the Implicit Identity Leakage, this phenomenon has been qualitatively and quantitatively verified among various DNNs. Furthermore, based on such understanding, we propose a simple yet effective method named the ID-unaware Deepfake Detection Model to reduce the influence of this phenomenon. Extensive experimental results demonstrate that our method outperforms the state-of-the-art in both in-dataset and cross-dataset evaluation. The code is available at https://github.com/megvii-research/CADDM.Comment: Accepted by CVPR 202