Search

The motion of many robotics systems, such as the rotation of unmanned aerial vehicles, can be modeled by SO(3). However, the difficulties in the parameterization of the SO(3) makes it hard to implement the state-of-the-art collision avoidance algorithm. In this paper, we present a new method for control on SO(3) via the combination of the nonlinear state constraints and geometric control technique. We first define the nonlinear constraints for trajectory optimization on SO(3). Then we solve the trajectory optimization problem by constrained Differential Dynamic Programming (DDP) in a Riemannian geometry framework. The first and second-order expansion of the cost function and dynamics are derived in a coordinate-free manner. The safety condition represented by nonlinear constraints is incorporated into the DDP via an active set method, where we find the active set in the backward path to obtain the optimal control policy. We validated our methods on motion planning of rigid model on SO(3) manifold where pose constraints are imposed. Our methods outperform the baseline methods in terms of convergence speed and numerical robustness to disturbance. The numerical robustness is essential when the system is initialized far from the local optimum.

For the surface-mount permanent magnet synchronous motors (SPMSM) with fractional slot concentrated windings, the winding distribution of per phase under different poles is uneven, which results in unbalanced magnetic pull (UMP) between the stator and rotor. UMP has an important impact on the performance and service life of the motor. In this paper, a new permanent magnet (PM) structure is proposed to reduce UMP and torque fluctuation of the fractional slot SPMSM. Furthermore, a 48p/54s SPMSM is taken as an example, the proposed PM structure is optimized by the Taguchi method which can comprehensively consider the control factors and noise factors. The effects of various variables and process error on UMP, torque fluctuation and robustness of results are analyzed respectively. Finally, the optimized PM structure is obtained, which improves the operation stationarity.

Maximum rank distance (MRD for short) codes lately attract more attention due to their various applications in storage systems, network coding, cryptography and space time coding. Similar to Reed-Solomon codes in classical coding theory, Gabidulin codes are the most prominent family of MRD codes. Due to their poor performance in list decoding or in constructing McEliece-type cryptosystems, the focus moves from Gabidulin codes to other non-Gabidulin codes. A natural following challenge is then to see if we can construct an infinite family of MRD codes that are not equivalent to Gabidulin codes. In this paper, we utilize Dickson matrices to construct an infinite family of Fq-linear MRD codes. Our codes are characterized by each of their codewords corresponding to a linearized polynomial with leading coefficient determined by one of any other coefficients. The family of codes corresponding to the set of linearized polynomials with leading coefficients dependent on the linear terms provides an extension to both Twisted Gabidulin codes and generalized Twisted Gabidulin codes for dimensions 1 and n-1. Lastly, we also provide some analysis on the equivalence between our proposed codes with some known families of MRD codes.

Image emotion analysis attracts considerable attention with the increasing demanding of opinion mining in social networks. Emotion evoked by an image is always ambiguous for emotion's subjectivity. Different from previous researches on image emotion classification, Label Distribution Learning framework which assigns a set of labels with degree value to an instance, describes emotions more explicitly. However, in our study, we find that some labels have co-occurrence relation with others and all the labels together appear some structural forms. To make use of these relations as complementary information to the holistic distribution of labels, we analysis the correlations among emotion labels and then propose a method based on Structural Learning framework, which learns a mapping from images to the distribution labels with correlations. On the other hand, images usually contain some emotion-unrelated contents, to extract features that can represent image emotion at utmost, we propose a cropping method to select the emotional region from the images with the help of Fully Convolutional Networks. Extensive experiments on two widely used datasets show the advantages of our methods.

With the development of the Internet, malware has become one of the most significant threats. Recognizing specific types of malware is an important step toward effective removal. Malware visualization is an important branch of malware static analysis techniques, where a piece of malware is turned into an image for visualization and classification. Despite great success, it is still difficult to extract effective texture feature representations for challenging datasets. The existing methods use global image features which are sensitive to relative code locations. In this paper, we present a new learning framework to obtain more discriminative and robust feature descriptors. The proposed method works with the existing local descriptors such as local binary patterns and dense scale-invariant feature transform, by grouping them into blocks and by using a new bag-of-visual-words model to obtain robust features, which are more flexible than global features and more robust than local features. We evaluate the proposed method on three malware databases. The experimental results demonstrate that the obtained descriptors lead to the state-of-the-art classification performance.