Steganography technology has been widely used in data transmission with secret information.However,the existing steganography has the disadvantages of low hidden information capacity,poor visual effect of cover images...Steganography technology has been widely used in data transmission with secret information.However,the existing steganography has the disadvantages of low hidden information capacity,poor visual effect of cover images,and is hard to guarantee security.To solve these problems,steganography using reversible texture synthesis based on seeded region growing and LSB is proposed.Secret information is embedded in the process of synthesizing texture image from the existing natural texture.Firstly,we refine the visual effect.Abnormality of synthetic texture cannot be fully prevented if no approach of controlling visual effect is applied in the process of generating synthetic texture.We use seeded region growing algorithm to ensure texture’s similar local appearance.Secondly,the size and capacity of image can be decreased by introducing the information segmentation,because the capacity of the secret information is proportional to the size of the synthetic texture.Thirdly,enhanced security is also a contribution in this research,because our method does not need to transmit parameters for secret information extraction.LSB is used to embed these parameters in the synthetic texture.展开更多
The segment erector is a key part of the shield machines for tunnel engineering. The available segment erectors are all of serial configuration which is suffering from the problems of low rigidity and accumulative mot...The segment erector is a key part of the shield machines for tunnel engineering. The available segment erectors are all of serial configuration which is suffering from the problems of low rigidity and accumulative motion errors. The current research mainly focuses on improving assembly accuracy and control performance of serial segment erectors. An innovative design method is proposed featuring motion group-decoupling, based on which a new type of segment erector is developed and investigated. Firstly, the segment installation manipulation is analyzed and decomposed into three motion groups that are decoupled. Then the type synthesis for the 4-DOF motion group is performed based on the general function（GF） set theory and a new configuration of （1T？1R？1PS3UPS） is attained according to the segment manipulation requirements. Consequently, the kinematic models are built and the reducibility and accuracy are analyzed. The dexterity is verified though numerical simulation and no singular points appear in the workspace. Finally, a positioning experiment is carried out by using the prototype developed in the lab that demonstrates a 13.1% improvement of positioning accuracy and the feasibility of the new segment erector. The presented group-decoupling design method is able to invent new type of hybrid segment erectors that avoid the accumulative motion error of erecting.展开更多
Intelligent identification of sandstone slice images using deep learning technology is the development trend of mineral identification,and accurate mineral particle segmentation is the most critical step for intellige...Intelligent identification of sandstone slice images using deep learning technology is the development trend of mineral identification,and accurate mineral particle segmentation is the most critical step for intelligent identification.A typical identification model requires many training samples to learn as many distinguishable features as possible.However,limited by the difficulty of data acquisition,the high cost of labeling,and privacy protection,this has led to a sparse sample number and cannot meet the training requirements of deep learning image identification models.In order to increase the number of samples and improve the training effect of deep learning models,this paper proposes a tight sandstone image data augmentation method by combining the advantages of the data deformation method and the data oversampling method in the Putaohua reservoir in the Sanzhao Sag of the Songliao Basin as the target area.First,the Style Generative Adversarial Network(StyleGAN)is improved to generate high-resolution tight sandstone images to improve data diversity.Second,we improve the Automatic Data Augmentation(AutoAugment)algorithm to search for the optimal augmentation strategy to expand the data scale.Finally,we design comparison experiments to demonstrate that this method has obvious advantages in generating image quality and improving the identification effect of deep learning models in real application scenarios.展开更多
基金This work was mainly supported by National Natural Science Foundation of China(No.61370218)Public Welfare Technology and Industry Project of Zhejiang Provincial Science Technology Department(No.2016C31081,No.LGG18F020013)。
文摘Steganography technology has been widely used in data transmission with secret information.However,the existing steganography has the disadvantages of low hidden information capacity,poor visual effect of cover images,and is hard to guarantee security.To solve these problems,steganography using reversible texture synthesis based on seeded region growing and LSB is proposed.Secret information is embedded in the process of synthesizing texture image from the existing natural texture.Firstly,we refine the visual effect.Abnormality of synthetic texture cannot be fully prevented if no approach of controlling visual effect is applied in the process of generating synthetic texture.We use seeded region growing algorithm to ensure texture’s similar local appearance.Secondly,the size and capacity of image can be decreased by introducing the information segmentation,because the capacity of the secret information is proportional to the size of the synthetic texture.Thirdly,enhanced security is also a contribution in this research,because our method does not need to transmit parameters for secret information extraction.LSB is used to embed these parameters in the synthetic texture.
基金supported by National Natural Science Foundation of China(Grant No. 51275284)Program for New Century Excellent Talents in University of China(Grant No. NCET-10-0567)the Research Fund of State Key Lab of Mechanical Systems and Vibration(Grant No.MSV-ZD-2010-02)
文摘The segment erector is a key part of the shield machines for tunnel engineering. The available segment erectors are all of serial configuration which is suffering from the problems of low rigidity and accumulative motion errors. The current research mainly focuses on improving assembly accuracy and control performance of serial segment erectors. An innovative design method is proposed featuring motion group-decoupling, based on which a new type of segment erector is developed and investigated. Firstly, the segment installation manipulation is analyzed and decomposed into three motion groups that are decoupled. Then the type synthesis for the 4-DOF motion group is performed based on the general function（GF） set theory and a new configuration of （1T？1R？1PS3UPS） is attained according to the segment manipulation requirements. Consequently, the kinematic models are built and the reducibility and accuracy are analyzed. The dexterity is verified though numerical simulation and no singular points appear in the workspace. Finally, a positioning experiment is carried out by using the prototype developed in the lab that demonstrates a 13.1% improvement of positioning accuracy and the feasibility of the new segment erector. The presented group-decoupling design method is able to invent new type of hybrid segment erectors that avoid the accumulative motion error of erecting.
基金This research was funded by the National Natural Science Foundation of China(Project No.42172161)Heilongjiang Provincial Natural Science Foundation of China(Project No.LH2020F003)+1 种基金Heilongjiang Provincial Department of Education Project of China(Project No.UNPYSCT-2020144)Northeast Petroleum University Guided Innovation Fund(2021YDL-12).
文摘Intelligent identification of sandstone slice images using deep learning technology is the development trend of mineral identification,and accurate mineral particle segmentation is the most critical step for intelligent identification.A typical identification model requires many training samples to learn as many distinguishable features as possible.However,limited by the difficulty of data acquisition,the high cost of labeling,and privacy protection,this has led to a sparse sample number and cannot meet the training requirements of deep learning image identification models.In order to increase the number of samples and improve the training effect of deep learning models,this paper proposes a tight sandstone image data augmentation method by combining the advantages of the data deformation method and the data oversampling method in the Putaohua reservoir in the Sanzhao Sag of the Songliao Basin as the target area.First,the Style Generative Adversarial Network(StyleGAN)is improved to generate high-resolution tight sandstone images to improve data diversity.Second,we improve the Automatic Data Augmentation(AutoAugment)algorithm to search for the optimal augmentation strategy to expand the data scale.Finally,we design comparison experiments to demonstrate that this method has obvious advantages in generating image quality and improving the identification effect of deep learning models in real application scenarios.
