Rubik's Cube is a widely popular mechanical puzzle that has attracted attention around the world because of its unique characteristics. As a classic brain-training toy well known to the public, Rubik's Cube was used for scientific research and technology development by many scholars. This paper provides a basic understanding of the Rubik's Cube and shows its mechanical art from the aspects of origin and development, characteristics, research status and especially its mechanical engineering design, as well as making a vision for the application in mechanism. First, the invention and origin of Rubik's Cube are presented, and then the special characteristics of the cube itself are analyzed. After that, the present researches of Rubik's Cube are reviewed in various disciplines at home and abroad, including the researches of Rubik's Cube scientific metaphors, reduction algorithms, characteristic applications, and mechanism issues. Finally, the applications and prospects of Rubik's Cube in the field of mechanism are discussed.
Recent years have witnessed a growing interest in the use of U-Net and its improvement. It is one of the classic semantic segmentation networks with an encoder-decoder architecture and is widely used in medical image segmentation. In the series versions of U-Net, U-Net++ has been developed as an improved U-Net by designing an architecture with nested and dense skip connections, and U-Net 3+ has been developed as an improved U-Net++ by taking advantage of full-scale skip connections and deep supervision on full-scale aggregated feature maps. Each network architecture has its own advantages in the use of the encoder and decoder. In this paper, we propose an efficient and lightweight U-Net (ELU-Net) with deep skip connections. The deep skip connections include same-and large-scale skip connections from the encoder to fully extract the features of the encoder. In addition, the proposed ELU-Net with different loss functions is discussed to improve the effect of brain tumor learning including WT (whole tumor), TC (tumor core) and ET (enhance tumor) and a new loss function DFK is designed. The effectiveness of the proposed method is demonstrated for a brain tumor dataset used in the BraTS 2018 Challenge and liver dataset used in the ISBI LiTS 2017 Challenge.
Less degrees of freedom parallel mechanism is widely used in many fields with its unique advantages. A decoupled parallel mechanism with 2 degree-of-freedom translation and 1 degree-of-freedom rotation is presented, and its performance evaluation indices analysis is performed. By the constraint screw method, the motion feature of the mechanism and its number of degree of freedom are analyzed. The constrained equations of the mechanism are established according to the constrained conditions of the pole length. The analytical expression of the forward and inverse position for the mechanism is deduced, and the expression of the Jacobian matrix is derived, which validated the decoupling feature of the mechanism. The singularity of the mechanism is also carried out. The performance evaluation indices for the decoupled parallel mechanism are discussed and the corresponding performance indices analysis of the proposed decoupled parallel mechanism is executed. The novel decoupled parallel mechanism presented herein enriches the parallel mechanism structure, and the definition and analysis of the performance evaluation indices should be meaningful for the further design and optimization of the decoupled parallel mechanism.
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