A polygon-scaling mechanism is a single DOF (degree-of-freedom) mechanism for scaling a polygon. This paper presents a tetragon-elements based synthesis method of polygon-scaling mechanisms. According to movable conditions of radial scaling elements, four basic tetragon elements (rhombus element, parallelogram element, kite element and general tetragon element) are proposed. For a given polygon, these four types of elements can be selected based on the characteristics of target polygons to construct polygon-scaling mechanisms in a straightforward manner. Using this synthesis method, some planar 1-DOF scaling mechanisms are obtained with the characteristics of retracting and deploying. Their 3D models are also presented to proof the validity of the proposed method. Finally, a table of tetragon elements with the characteristics of their associated polygon-scaling mechanisms is summarized using which polygon-scaling mechanisms can be easily constructed.
The motor imagery electroencephalography (MI-EEG) reflects the subjective motor intention, which has received increasing attention in rehabilitation. How to extract the features of MI-EEG accurately and quickly is the key to its successful application. Based on the analysis and comparison of the existing feature extraction algorithms, a feature extraction method based on principal component analysis (PCA) and deep belief networks (DBN) is proposed, namely PCA-DBN. Firstly, the second-order moment is used to analyze the time-domain of MI-EEG, select the effective time interval. Secondly, PCA is used to analyze the selected time-domain interval and obtain the principal component feature points. Then, feature points are imported into DBN to realize the final feature extraction. Finally, use the softmax classifier to complete task classification. Perform algorithm validation on the BCI Competition II Data set III and BCI Competition IV Data sets 2b, classification accuracies are 96.25% and 91.71%, kappa values are 0.925 and 0.8342. The paired-sample t-test with FDR correction is carried out on the verification results, and the comparison with some better classification algorithms shows that the algorithm has better performance. In the end, this method is used to extract the features of laboratory data, the optimal classification accuracy is 97.69% and kappa value is 0.9538, the validity of the method is further verified. INDEX TERMS Deep belief networks, motor imagery electroencephalogram, principal component analysis, second-order moment, softmax classifier.
This paper deals with a 2-DOF (degrees-of-freedom) 3-4R parallel manipulator (PM) with planar base and platform—a novel PM with multiple operation modes (or disassembly free reconfigurable PM) that can use the minimum number of actuated joints. At first, a set of constraint equations of the 3-4R PM are derived with the orientation of the moving platform represented using a Euler parameter quaternion (also Euler–Rodrigues quaternion) and then solved using the algebraic geometry method. It is found that this 3-4R PM has six 2-DOF operation modes, including the two expected spherical translation mode and sphere-on-sphere rolling mode when the PM was synthesized. The motion characteristics of the moving platform are obtained using the kinematic interpretation of Euler parameter quaternions with certain number of constant zero components, which was presented in a recent paper by the first author of this paper, instead of the eigenspace-based approach in the literature. The transition configurations, which are constraint singular configurations, among different operation modes are also presented. This work provides a solid foundation to the development and control of the 2-DOF 3-4R PM with both 2-DOF spherical translation mode and 2-DOF sphere-on-sphere rolling mode.
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