In this paper, in order to improve the green remanufacturing capacity of heavy duty machine tools, the design domain of axiomatic design is taken as the main line and extended it to the regeneration domain innovatively. The design structure matrix is used to consider the correlation and similarity of design parameters between structural domain and regeneration domain. The particle swarm optimization algorithm based on the minimal description length is used to find the ideal modular design method for heavy machine tools.
The double ball bar is widely used because it can quickly, easily and cost-effectively detect and evaluate the accuracy of CNC machine tools. But since the error recognition algorithm based on the double ball bar ignores the quadratic item, its recognition accuracy would be reduced. In this paper, an improved CNC verticality error and position error identification formula, combined with the machine tool error model to deduce a new error recognition model of double ball bar measurement is proposed. It can be drawn that the accuracy of the model are better than the existing methods because it keeps the second item in the derivation process of the model.
This paper proposes a hierarchical support vector machine recognition algorithm based on a finite state machine (FSM-HSVM) to accurately and reliably recognize the locomotion mode recognition of an exoskeleton robot. As input signals, this method utilizes the angle information of the hip joint and knee joint collected by inertial sensing units (IMUs) on the thighs and shanks of the exoskeleton and the plantar pressure information collected by force sensitive resistors (FSRs) are used as input signals. This method establishes a framework for mode transition by combining the finite state machine (FSM) with the common locomotion modes. The hierarchical support vector machine (HSVM) recognition model is then tightly integrated with the mode transition framework to recognize five typical locomotion modes and eight locomotion mode transitions in real-time. The algorithm not only reduces the abrupt change in the recognition of locomotion mode, but also significantly improves the recognition efficiency. To evaluate recognition performance, separate experiments are conducted on six subjects. According to the results, the average accuracy of all motion modes is 97.106% ± 0.955%, and the average recognition delay rate is only 25.017% ± 6.074%. This method has the benefits of a small calculation amount and high recognition efficiency, and it can be applied extensively in the field of robotics.
The gravity center of integration about the upper extremity exoskeleton and loads has a great impact on the balance control of the systemic exoskeleton system. In this paper, a new method is presented to calculate the gravity of the integration. First, build a model of the upper extremity exoskeleton by standard D-H method, analyze the model by kinematics and then obtain the function of the gravity center by analyzing the relationship between the structure parameters, joint angles and gravity center of the integration. Second, simulate each curve of the gravity center with lifting the load of 0, 10KG and 20KG under a specific trajectory determined by cycloid interpolation method. This paper lays a foundation to study on the balance control of the systemic exoskeleton system.
Text detection methods based on grouping characters have emerged and have achieved promising performance. Nevertheless, previous methods that grouped characters by learning the relation of adjacent characters or used the heuristic clustering method with a handcrafted feature are unsuitable for dense, curved, or long texts. An effective manner of grouping characters is proposed by introducing K-Means that is modified by the law of universal gravitation, an outlier detection mechanism and sufficient context information. Based on that, corresponding text detector is presented, named the Text Detector, based on modified K-Means (KText), which can generate the bounding boundary of word-level texts with an arbitrary shape. In the experimental stage, two novel stratagems are presented to replenish character-level annotations to several datasets that provide only wordlevel annotations. To evaluate the effectiveness of the method, experiments are carried out on three benchmarks, ICDAR2013, ICDAR2015 and Total-Text, which contain horizontal, oriented and curved text. The results show that KText performs more competently than most state-of-the-art text detectors when handling dense texts with an arbitrary shape.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
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