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Smart robots and smart services using robots are promising research fields in academia and industry. However, those smart services are based on basic motions of the robot, such as grabbing objects, and moving them to a designated place. In this paper, we propose a way to produce new motions without programming, from existing motions, through a motion composition method. Our motion composition method utilizes an Action Petri net, which is a variance of a Petri net, with both interpolation and composition operations on a transition. In the Action Petri net, a place is a posture or a moving action of a robot, and it is represented as a diagonal matrix with the robot's joint motor values. Robot motions can be generated from one posture to another posture, and from composing different postures and moving actions. All operations performed to generate new motions are carried out as matrix manipulation operations. Our approach provides a formal method to generate new motions from existing motions, and a practical method to create new motions in low level motion control, without programming.

In this paper, we present a matrix-based representation of rough approximations in multi-source hybrid information systems (MHIS). Besides, an incremental method for updating rough approximations is proposed in MHIS under the variation of features, objects and feature values simultaneously. An example is illustrated to show its effectiveness.

In this paper, we first propose a general composite ordered rough set model, which can deal with various types of attributes with a preference order. Then, we present a matrix method for computing approximations in composite ordered decision systems. Finally, we introduce an incremental method for updating approximations in composite ordered decision systems under the variation of attributes.

In this paper, a super-hyper network model based on Khatri-Rao Product on the correlation matrix of a series of hypergraphs is proposed. Both marginal and joint node degree, node hyperdegree, hyperedge degree and their corresponding polynomials are introduced to describe this super-hyper network model. It is shown that it is fractal since its correlation matrix is a fractal matrix. The fractal parameter is then provided. It is also validated that it is small-world for its diameter won't exceed twice the summation of the diameter of primitive hypergraphs. By a novel product of either marginal or joint node degree polynomial, node hyperdegree polynomial and hyperedge degree polynomial, the corresponding marginal and joint node degree, node hyperdegree and hyperedge degree are obtained.