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Yan Guangwen,Zhang Desheng,Xu Jinting,Sun Yuwen 한국CDE학회 2023 Journal of computational design and engineering Vol.10 No.4
Corner rounding methods have been widely developed to pursue the smooth motions of machine tools. However, most corner rounding methods, which adopt the double inscribed transitions, still remain an inherent issue of retaining large curvatures of transition curves. Even for those double circumscribed transitions-based methods with relatively small curvatures, they also constrain excessively the transition lengths and are limited to a low-order continuity, deteriorating the feedrate and jerk of machine tools. For addressing these problems, a C3 continuous double circumscribed corner rounding (DCCR) method is proposed for five-axis linear tool path. In this method, the C3 continuous double circumscribed B-splines are specially designed to round the corners of tool position and tool orientation, whose transition lengths are analytically determined by jointly constraining the approximation errors, overlaps elimination, and parameter synchronization. Moreover, the excessive constrains of transition lengths imposed by traditional methods are alleviated by fully considering the effects of overlaps and parameter synchronization, and the jerk of rotary axes is also limited with a high-order continuity. Compared to the existing double inscribed corner rounding (DICR) and DCCR methods, experiment results demonstrate that our method can improve further the feedrate while limiting the jerk of machine tools.
Kaihang Wang,Kaili Sun,Zihan Li,Zunhang Lv,Tianpeng Yu,Xin Liu,Guixue Wang,Guangwen Xie,Luhua Jiang 대한금속·재료학회 2020 ELECTRONIC MATERIALS LETTERS Vol.16 No.2
The development of electrocatalysts with high activity and low Tafel slope for overall water splitting has become a crucialchallenge to exploit the sustainable energy. Herein, we construct a Fe–Co–P–Gr catalyst on nickel foam (NF) support throughelectroless composite plating to realize the co-deposition of Fe–Co–P alloys and graphene quantum dots. Interestingly,graphene quantum dots exhibit obvious eff ects on electron mobility and active sites of Fe–Co–P–Gr/NF catalyst. In oxygenevolution reaction, the Fe–Co–P–Gr/NF catalyst exhibits a small overpotential of 230 mV at 10 mA cm −2 and fast kineticswith Tafel slope of 37.8 mV dec −1 . Meanwhile, the Fe–Co–P–Gr/NF also has a superior hydrogen evolution reaction performancein 1.0 M KOH. Compared with the Fe–Co–P alloys, the Fe–Co–P–Gr/NF both as the anode and cathode requireonly 1.58 V to reach a current density of 10 mA cm −2 . The successful preparation of Fe–Co–P–Gr/NF electrode throughelectroless composite deposition provides a new path to manufacture electrocatalysts for overall water splitting.
Visual Tracking with Online Incremental Deep Learning and Particle Filter
Shuai Cheng,Yonggang Cao,Junxi Sun,Guangwen Liu 보안공학연구지원센터 2015 International Journal of Signal Processing, Image Vol.8 No.12
To solve the problem of tracking the trajectory of a moving object and learning a deep compact image representation in the complex environment, a novel robust incremental deep learning tracker is presented under the particle filter framework. The incremental deep classification neural network was composed of stacked denoising autoencoder, incremental feature learning and support vector machine to achieve the feature-extracting and classification of particle set. Deep learning is successfully taken to express the image representations obtained effectively. Unsupervised feature learning is used to learn generic image features and transfer learning transforms knowledge from offline training to the online tracking process. The incremental feature learning was consisted of adding features and merging features to online learn compact feature set. Linear support vector machine increases the discretion for target with similar appearance and is further tuned to adapt to appearance changes of the moving object. Compared with the state-of-the-art trackers in the complex environment, the results of experiments on variant challenging image sequences show that incremental deep learning tracker solves the problem of existent trackers more efficiently, it has better robust and more accurate, especially for occlusions, background clutter, illumination changes and appearance changes.