An Optimized Convolutional Neural Network with Combination Blocks for Chinese Sign Language Identification

(Aim)Chinese sign language is an essential tool for hearing-impaired to live,learn and communicate in deaf communities.Moreover,Chinese sign language plays a significant role in speech therapy and rehabilitation.Chinese sign language identification can provide convenience for those hearing impaired people and eliminate the communication barrier between the deaf community and the rest of society.Similar to the research of many biomedical image processing(such as automatic chest radiograph processing,diagnosis of chest radiological images,etc.),with the rapid development of artificial intelligence,especially deep learning technologies and algorithms,sign language image recognition ushered in the spring.This study aims to propose a novel sign language image recognition method based on an optimized convolutional neural network.(Method)Three different combinations of blocks:Conv-BN-ReLU-Pooling,Conv-BN-ReLU,Conv-BN-ReLU-BN were employed,including some advanced technologies such as batch normalization,dropout,and Leaky ReLU.We proposed an optimized convolutional neural network to identify 1320 sign language images,which was called as CNN-CB method.Totally ten runs were implemented with the hold-out randomly set for each run.(Results)The results indicate that our CNN-CB method gained an overall accuracy of 94.88±0.99%.(Conclusion)Our CNN-CB method is superior to thirteen state-of-the-art methods:eight traditional machine learning approaches and five modern convolutional neural network approaches.

Defect detection of gear parts in virtual manufacturing

Gears play an important role in virtual manufacturing systems for digital twins;however,the image of gear tooth defects is difficult to acquire owing to its non-convex shape.In this study,a deep learning network is proposed to detect gear defects based on their point cloud representation.This approach mainly consists of three steps:(1)Various types of gear defects are classified into four cases(fracture,pitting,glue,and wear);A 3D gear dataset was constructed with 10000 instances following the aforementioned classification.(2)Gear-PCNet++introduces a novel Combinational Convolution Block,proposed based on the gear dataset for gear defect detection to effectively extract the local gear information and identify its complex topology;(3)Compared with other methods,experiments show that this method can achieve better recognition results for gear defects with higher efficiency and practicability.

Geometric positioning integrating optical satellite stereo imagery and a global database of ICESat-2 laser control points:A framework and key technologies

Block Adjustment(BA)is one of the essential techniques for producing high-precision geospatial 3D data products with optical stereo satellite imagery.For block adjustment with few ground-control points or without ground control,the vertical error of the model is the decisive factor that constrains the accuracy of 3D data products.The elevation data obtained by spaceborne laser altimeter have the advantages of short update periods,high positioning precision,and low acquisition cost,providing sufficient data support for improving the elevation accuracy of stereo models through the combined BA.This paper proposes a geometric positioning model based on the integration of Optical Satellite Stereo Imagery(OSSI)and spaceborne laser altimeter data.Firstly,we elaborate the principle and necessity of this work through a literature review of existing methods.Then,the framework of our geo-positioning models.Secondly,four key technologies of the proposed model are expounded in order,including the acquisition and management of global Laser Control Points,the association of LCPs and OSSI,the block adjustment model combining LCPs with OSSI,and the accuracy estimation and quality control of the combined BA.Next,the combined BA experiment using Ziyuan-3(ZY-3)OSSI and ICESat-2 laser data was carried out at the testing site in Shandong Province,China.Experimental results prove that our method can automatically select LCPs with high accuracy.The elevation deviation of the combined BA eventually achieved the Mean Error(ME)of 0.06 m and the Root Mean Square Error(RMSE)of 1.18 m,much lower than the ME of 13.20 m and the RMSE of 3.88 m before the block adjustment.A further research direction will be how to perform more adequate accuracy analysis and quality control using massive laser points as checkpoints.

Estimation of Fired Polynomial Effects of Mixed Models Based on PBIB

This paper discusses the estimation of fixed polynomial effects of mixed models based on PBIB, gives three concrete estimation, and analyses the condition of the design block while is orthogonal in these models. It also shows in mixed models that the three estimations of fixed polynomial effects T are identical under tile fact that the design block is orthogonal.