Corrosion in complex coupling environments is an important issue in corrosion field, because it is difficult to take into account a large number of environment factors and their interactions. Design of Experiment(DOE)...Corrosion in complex coupling environments is an important issue in corrosion field, because it is difficult to take into account a large number of environment factors and their interactions. Design of Experiment(DOE) can present a methodology to deal with this difficulty, although DOE is not commonly spread in corrosion field. Thus, modeling corrosion of Ni-Cr-Mo-V steel in deep sea environment was performed in order to provide example demonstrating the advantage of DOE. In addition, an artificial neural network mapping using back-propagation method was developed for Ni-Cr-Mo-V steel such that the ANN model can be used to predict polarization curves under different complex sea environments without experimentation. Furthermore, roles of environment factors on corrosion of Ni-Cr-Mo-V steel in deep sea environment were discussed.展开更多
This paper describes the implementation of a data logger for the real-time in-situ monitoring of hydrothermal systems. A compact mechanical structure ensures the security and reliability of data logger when used under...This paper describes the implementation of a data logger for the real-time in-situ monitoring of hydrothermal systems. A compact mechanical structure ensures the security and reliability of data logger when used under deep sea. The data logger is a battery powered instrument, which can connect chemical sensors （pH electrode, H2S electrode, H2 electrode） and temperature sensors. In order to achieve major energy savings, dynamic power management is implemented in hardware design and software design. The working current of the data logger in idle mode and active mode is 15 μA and 1.44 mA respectively, which greatly extends the working time of battery. The data logger has been successftdly tested in the first Sino-American Cooperative Deep Submergence Project from August 13 to September 3, 2005.展开更多
Based on analyses of the theories of groundwater unsteady flow in deep well dewatering in the deep foundation pit, Theis equations are chosen to calculate and analyze the relationship between water level drawdown of c...Based on analyses of the theories of groundwater unsteady flow in deep well dewatering in the deep foundation pit, Theis equations are chosen to calculate and analyze the relationship between water level drawdown of confined aquifer and dewatering duration. In order to reduce engineering cost and diminish detrimental effect on ambient surrounding, optimization design target function based on the control of confined water drawdown and four restriction requisitions based on the control of safe water level, resistance to throwing up from the bottom of foundation pit, avoiding excessively great subsidence and unequal surface subsidence are proposed. A deep well dewatering project in the deep foundation pit is optimally designed. The calculated results including confined water level drawdown and surface subsidence are in close agreement with the measured results, and the optimization design can effectively control both surface subsidence outside foundation pit and unequal subsidence as a result of dewatering.展开更多
In this paper, design, re-design, and performance of a long-standing very deep excavation, which was originally planned to depth of 38 m, are presented. Over-digging was not planned in the original design,thus the rea...In this paper, design, re-design, and performance of a long-standing very deep excavation, which was originally planned to depth of 38 m, are presented. Over-digging was not planned in the original design,thus the reassessment was performed. Two main topics were followed: deepening to increase the maximum depth of an existent excavation from 38 m to 42.5 m, and feasibility for upgrading a predesigned support system from temporary to permanent support system. The geological investigations in the project site illustrated a type of stiff and cemented coarse-grained alluvium. An observational approach with additional geotechnical investigations and in situ tests was applied. Back analyses of stability of an unsupported access ramp, as well as deformation monitoring of walls, were used in order to review geotechnical design parameters that represent the full-scale behavior of the ground. Additional nails and soldier piles together with building mat foundation were implemented as a complementary lateral support in the retaining system. From an engineering point of view, by assuming a corrosion rate of 0.065 mm/a for existent rebars, according to chemical and electrical resistivity tests, the long-term performance of the revised retaining system was verified by static and pseudo-dynamic ultimate limit state analyses. Performance monitoring during the construction shows that the measured deformation is in the lower limit of the prediction.展开更多
With the increase in research on AI(Artificial Intelligence),the importance of DL(Deep Learning)in various fields,such as materials,biotechnology,genomes,and new drugs,is increasing significantly,thereby increasing th...With the increase in research on AI(Artificial Intelligence),the importance of DL(Deep Learning)in various fields,such as materials,biotechnology,genomes,and new drugs,is increasing significantly,thereby increasing the number of deep-learning framework users.However,to design a deep neural network,a considerable understanding of the framework is required.To solve this problem,a GUI(Graphical User Interface)-based DNN(Deep Neural Network)design tool is being actively researched and developed.The GUI-based DNN design tool can design DNNs quickly and easily.However,the existing GUI-based DNN design tool has certain limitations such as poor usability,framework dependency,and difficulty encountered in changing GUI components.In this study,a deep learning algorithm that solves the problem of poor usability was developed using a template to increase the accessibility for users.Moreover,the proposed tool was developed to save and share only the necessary parts for quick operation.To solve the framework dependency,we applied ONNX(Open Neural Network Exchange),which is an exchange standard for neural networks,and configured it such that DNNs designed with the existing deep-learning framework can be imported.Finally,to address the difficulty encountered in changing GUI components,we defined and developed the JSON format to quickly respond to version updates.The developed DL neural network designer was validated by running it with KISTI’s supercomputer-based AI Studio.展开更多
In recent years, there has been a revolution in the way that we transmit information through optical communication systems, allowing for fast and high-capacity data transmission using optical communication systems. Du...In recent years, there has been a revolution in the way that we transmit information through optical communication systems, allowing for fast and high-capacity data transmission using optical communication systems. Due to the growing demand for higher-capacity and faster networks, traditional optical communication systems are reaching their limits due to the increasing demand for faster and higher-capacity networks. The advent of machine learning and deep learning approaches has led to the emergence of powerful tools that can dramatically enhance the performance of optical communication systems with significant efficiency improvements. In this paper, we provide an overview of the role that machine learning (ML) and deep learning can play in enhancing the performance of various aspects of optical communication systems, including modulation techniques, channel modelling, equalization, and system optimization methods. The paper discusses the advantages of these approaches, such as improved spectral efficiency, reduced latency, and improved robustness to impairments in the channel, such as spectrum degradation. Additionally, a discussion is made regarding the potential challenges and limitations associated with using machine learning and deep learning in optical communication systems as well as their potential benefits. The purpose of this paper is to provide insight and highlight the potential of these approaches to improve optical communication in the future.展开更多
基金the financial support of the National Natural Science Foundation of China(No.51371182)the National Program for the Young Top-notch Professionals and the Fundamental Research Funds for the Central Universities(N170205002)
文摘Corrosion in complex coupling environments is an important issue in corrosion field, because it is difficult to take into account a large number of environment factors and their interactions. Design of Experiment(DOE) can present a methodology to deal with this difficulty, although DOE is not commonly spread in corrosion field. Thus, modeling corrosion of Ni-Cr-Mo-V steel in deep sea environment was performed in order to provide example demonstrating the advantage of DOE. In addition, an artificial neural network mapping using back-propagation method was developed for Ni-Cr-Mo-V steel such that the ANN model can be used to predict polarization curves under different complex sea environments without experimentation. Furthermore, roles of environment factors on corrosion of Ni-Cr-Mo-V steel in deep sea environment were discussed.
基金supported by the International Cooperative Key Project(Grant No.2004DFA04900)Ministry of Sciences and Technology of PRC,and the National Natural Science Foundation of China (Grant Nos.40637037 and 50675198)
文摘This paper describes the implementation of a data logger for the real-time in-situ monitoring of hydrothermal systems. A compact mechanical structure ensures the security and reliability of data logger when used under deep sea. The data logger is a battery powered instrument, which can connect chemical sensors （pH electrode, H2S electrode, H2 electrode） and temperature sensors. In order to achieve major energy savings, dynamic power management is implemented in hardware design and software design. The working current of the data logger in idle mode and active mode is 15 μA and 1.44 mA respectively, which greatly extends the working time of battery. The data logger has been successftdly tested in the first Sino-American Cooperative Deep Submergence Project from August 13 to September 3, 2005.
基金This paper is supported by the Hubei Construct Science Foundation of China (G200013).
文摘Based on analyses of the theories of groundwater unsteady flow in deep well dewatering in the deep foundation pit, Theis equations are chosen to calculate and analyze the relationship between water level drawdown of confined aquifer and dewatering duration. In order to reduce engineering cost and diminish detrimental effect on ambient surrounding, optimization design target function based on the control of confined water drawdown and four restriction requisitions based on the control of safe water level, resistance to throwing up from the bottom of foundation pit, avoiding excessively great subsidence and unequal surface subsidence are proposed. A deep well dewatering project in the deep foundation pit is optimally designed. The calculated results including confined water level drawdown and surface subsidence are in close agreement with the measured results, and the optimization design can effectively control both surface subsidence outside foundation pit and unequal subsidence as a result of dewatering.
文摘In this paper, design, re-design, and performance of a long-standing very deep excavation, which was originally planned to depth of 38 m, are presented. Over-digging was not planned in the original design,thus the reassessment was performed. Two main topics were followed: deepening to increase the maximum depth of an existent excavation from 38 m to 42.5 m, and feasibility for upgrading a predesigned support system from temporary to permanent support system. The geological investigations in the project site illustrated a type of stiff and cemented coarse-grained alluvium. An observational approach with additional geotechnical investigations and in situ tests was applied. Back analyses of stability of an unsupported access ramp, as well as deformation monitoring of walls, were used in order to review geotechnical design parameters that represent the full-scale behavior of the ground. Additional nails and soldier piles together with building mat foundation were implemented as a complementary lateral support in the retaining system. From an engineering point of view, by assuming a corrosion rate of 0.065 mm/a for existent rebars, according to chemical and electrical resistivity tests, the long-term performance of the revised retaining system was verified by static and pseudo-dynamic ultimate limit state analyses. Performance monitoring during the construction shows that the measured deformation is in the lower limit of the prediction.
基金This research was supported by the KISTI Program(No.K-20-L02-C05-S01)the EDISON Program through the National Research Foundation of Korea(NRF)(No.NRF-2011-0020576).A grant was also awarded by the Ministry of Science and ICT(MSIT)under the Program for Returners for R&D.
文摘With the increase in research on AI(Artificial Intelligence),the importance of DL(Deep Learning)in various fields,such as materials,biotechnology,genomes,and new drugs,is increasing significantly,thereby increasing the number of deep-learning framework users.However,to design a deep neural network,a considerable understanding of the framework is required.To solve this problem,a GUI(Graphical User Interface)-based DNN(Deep Neural Network)design tool is being actively researched and developed.The GUI-based DNN design tool can design DNNs quickly and easily.However,the existing GUI-based DNN design tool has certain limitations such as poor usability,framework dependency,and difficulty encountered in changing GUI components.In this study,a deep learning algorithm that solves the problem of poor usability was developed using a template to increase the accessibility for users.Moreover,the proposed tool was developed to save and share only the necessary parts for quick operation.To solve the framework dependency,we applied ONNX(Open Neural Network Exchange),which is an exchange standard for neural networks,and configured it such that DNNs designed with the existing deep-learning framework can be imported.Finally,to address the difficulty encountered in changing GUI components,we defined and developed the JSON format to quickly respond to version updates.The developed DL neural network designer was validated by running it with KISTI’s supercomputer-based AI Studio.
文摘In recent years, there has been a revolution in the way that we transmit information through optical communication systems, allowing for fast and high-capacity data transmission using optical communication systems. Due to the growing demand for higher-capacity and faster networks, traditional optical communication systems are reaching their limits due to the increasing demand for faster and higher-capacity networks. The advent of machine learning and deep learning approaches has led to the emergence of powerful tools that can dramatically enhance the performance of optical communication systems with significant efficiency improvements. In this paper, we provide an overview of the role that machine learning (ML) and deep learning can play in enhancing the performance of various aspects of optical communication systems, including modulation techniques, channel modelling, equalization, and system optimization methods. The paper discusses the advantages of these approaches, such as improved spectral efficiency, reduced latency, and improved robustness to impairments in the channel, such as spectrum degradation. Additionally, a discussion is made regarding the potential challenges and limitations associated with using machine learning and deep learning in optical communication systems as well as their potential benefits. The purpose of this paper is to provide insight and highlight the potential of these approaches to improve optical communication in the future.
