Biblio

Tensors, as the multidimensional generalization of matrices, are naturally suited for representing and processing high-dimensional data. To date, tensors have been widely adopted in various data-intensive applications, such as machine learning and big data analysis. However, due to the inherent large-size characteristics of tensors, tensor algorithms, as the approaches that synthesize, transform or decompose tensors, are very computation and storage expensive, thereby hindering the potential further adoptions of tensors in many application scenarios, especially on the resource-constrained hardware platforms. In this paper, we propose a reduced-complexity SVD (Singular Vector Decomposition) scheme, which serves as the key operation in Tucker decomposition. By using iterative self-multiplication, the proposed scheme can significantly reduce the storage and computational costs of SVD, thereby reducing the complexity of the overall process. Then, corresponding hardware architecture is developed with 28nm CMOS technology. Our synthesized design can achieve 102GOPS with 1.09 mm2 area and 37.6 mW power consumption, and thereby providing a promising solution for accelerating Tucker decomposition.

Neural networks are vulnerable to adversarial examples, which poses a threat to their application in security sensitive systems. We propose high-level representation guided denoiser (HGD) as a defense for image classification. Standard denoiser suffers from the error amplification effect, in which small residual adversarial noise is progressively amplified and leads to wrong classifications. HGD overcomes this problem by using a loss function defined as the difference between the target model's outputs activated by the clean image and denoised image. Compared with ensemble adversarial training which is the state-of-the-art defending method on large images, HGD has three advantages. First, with HGD as a defense, the target model is more robust to either white-box or black-box adversarial attacks. Second, HGD can be trained on a small subset of the images and generalizes well to other images and unseen classes. Third, HGD can be transferred to defend models other than the one guiding it. In NIPS competition on defense against adversarial attacks, our HGD solution won the first place and outperformed other models by a large margin.1

This paper presents a novel sensor parameter fault diagnosis method for generally multiple-input multiple-output (MIMO) affine nonlinear systems based on adaptive observer. Firstly, the affine nonlinear systems are transformed into the particular systems via diffeomorphic transformation using Lie derivative. Then, based on the techniques of high-gain observer and adaptive estimation, an adaptive observer structure is designed with simple method for jointly estimating the states and the unknown parameters in the output equation of the nonlinear systems. And an algorithm of the fault estimation is derived. The global exponential convergence of the proposed observer is proved succinctly. Also the proposed method can be applied to the fault diagnosis of generally affine nonlinear systems directly by the reversibility of aforementioned coordinate transformation. Finally, a numerical example is presented to illustrate the efficiency of the proposed fault diagnosis scheme.

With the rapid development of bulk power grid under extra-high voltage (EHV) AC/DC hybrid power system and extensive access of distributed energy resources (DER), operation characteristics of power grid have become increasingly complicated. To cope with new severe challenges faced by safe operation of interconnected bulk power grids, an in-depth analysis of bulk power grid security defense system under the background of EHV and new energy resources was implemented from aspects of management and technology in this paper. Supported by big data and cloud computing, bulk power grid security defense system was divided into two parts: one is the prevention and control of operation risks. Power grid risks are eliminated and influence of random faults is reduced through measures such as network planning, power-cut scheme, risk pre-warning, equipment status monitoring, voltage control, frequency control and adjustment of operating mode. The other is the fault recovery control. By updating “three defense lines”, intelligent relay protection is used to deal with the challenges brought by EHV AC/DC hybrid grid and new energy resources. And then security defense system featured by passive defense is promoted to active type power grid security defense system.