With the deepening of the“Belt and Road”initiative,Chinese enterprises are facing unprecedented opportunities and challenges for international cooperation.This paper mainly analyzes the current situation,existing pr...With the deepening of the“Belt and Road”initiative,Chinese enterprises are facing unprecedented opportunities and challenges for international cooperation.This paper mainly analyzes the current situation,existing problems,and coping strategies of enterprise international cooperation management in the context of the“Belt and Road”.This article expounds on the importance of the“Belt and Road”initiative for international cooperation of enterprises and analyzes the key links of international cooperation management of enterprises from the aspects of internationalization strategy,cross-cultural management,risk prevention and control,and resource integration.Finally,combined with the case,this paper puts forward the management strategies and suggestions that enterprises should adopt in international cooperation,to provide a useful reference for Chinese enterprises in international cooperation in the construction of the“Belt and Road.”展开更多
针对宽带多极化雷达,提出将高分辨一维距离像(high resolution range profile,HRRP)与极化信息相结合的算法,获得目标在4种极化组态下的一维距离像并将其组成极化距离矩阵.该算法对目标进行全方位的特征抽取与建模,以适应不同的姿态,有...针对宽带多极化雷达,提出将高分辨一维距离像(high resolution range profile,HRRP)与极化信息相结合的算法,获得目标在4种极化组态下的一维距离像并将其组成极化距离矩阵.该算法对目标进行全方位的特征抽取与建模,以适应不同的姿态,有助于减少高分辨一维距离像方位敏感性带来的影响.然后提出了直接基于极化距离矩阵、Pauli分解和Freeman分解三种特征提取方式对极化距离矩阵进行目标特征的提取,并将获得的目标特征向量结合起来送入搭建的深度卷积神经网络进行训练学习.该方法不仅结合了不同的特征提取方式以对极化距离矩阵进行更全面的特征提取,而且深度卷积神经网络的运用又对目标特征向量进行了深层学习,仿真结果验证了该方法的有效性.展开更多
Quantum error correction technology is an important solution to solve the noise interference generated during the operation of quantum computers.In order to find the best syndrome of the stabilizer code in quantum err...Quantum error correction technology is an important solution to solve the noise interference generated during the operation of quantum computers.In order to find the best syndrome of the stabilizer code in quantum error correction,we need to find a fast and close to the optimal threshold decoder.In this work,we build a convolutional neural network(CNN)decoder to correct errors in the toric code based on the system research of machine learning.We analyze and optimize various conditions that affect CNN,and use the RestNet network architecture to reduce the running time.It is shortened by 30%-40%,and we finally design an optimized algorithm for CNN decoder.In this way,the threshold accuracy of the neural network decoder is made to reach 10.8%,which is closer to the optimal threshold of about 11%.The previous threshold of 8.9%-10.3%has been slightly improved,and there is no need to verify the basic noise.展开更多
A dynamic modeling framework based on an intelligent approach is proposed to identify the complex behaviors of solid-gas sorption systems.An experimental system was built and tested to assist in developing a model of ...A dynamic modeling framework based on an intelligent approach is proposed to identify the complex behaviors of solid-gas sorption systems.An experimental system was built and tested to assist in developing a model of the system performance during the adsorption and desorption processes.The variations in the thermal effects and gaseous environment accompanying the reactions were considered when designing the model.An optimization platform based on a multi-population genetic algorithm and artificial criteria was established to identify the mod-eling coefficients and quantify the effects of condition changes on the reactions.The calibration of the simulation results against the tested data showed good accuracy,where the coefficient of determination was greater than 0.988.The outcome of this study could provide a modeling basis for the optimization of solid-gas sorption systems and contribute a potential tool for uncovering key characteristics associated with materials and components.展开更多
文摘With the deepening of the“Belt and Road”initiative,Chinese enterprises are facing unprecedented opportunities and challenges for international cooperation.This paper mainly analyzes the current situation,existing problems,and coping strategies of enterprise international cooperation management in the context of the“Belt and Road”.This article expounds on the importance of the“Belt and Road”initiative for international cooperation of enterprises and analyzes the key links of international cooperation management of enterprises from the aspects of internationalization strategy,cross-cultural management,risk prevention and control,and resource integration.Finally,combined with the case,this paper puts forward the management strategies and suggestions that enterprises should adopt in international cooperation,to provide a useful reference for Chinese enterprises in international cooperation in the construction of the“Belt and Road.”
文摘针对宽带多极化雷达,提出将高分辨一维距离像(high resolution range profile,HRRP)与极化信息相结合的算法,获得目标在4种极化组态下的一维距离像并将其组成极化距离矩阵.该算法对目标进行全方位的特征抽取与建模,以适应不同的姿态,有助于减少高分辨一维距离像方位敏感性带来的影响.然后提出了直接基于极化距离矩阵、Pauli分解和Freeman分解三种特征提取方式对极化距离矩阵进行目标特征的提取,并将获得的目标特征向量结合起来送入搭建的深度卷积神经网络进行训练学习.该方法不仅结合了不同的特征提取方式以对极化距离矩阵进行更全面的特征提取,而且深度卷积神经网络的运用又对目标特征向量进行了深层学习,仿真结果验证了该方法的有效性.
基金the National Natural Science Foundation of China(Grant Nos.11975132 and 61772295)the Natural Science Foundation of Shandong Province,China(Grant No.ZR2019YQ01)the Project of Shandong Province Higher Educational Science and Technology Program,China(Grant No.J18KZ012).
文摘Quantum error correction technology is an important solution to solve the noise interference generated during the operation of quantum computers.In order to find the best syndrome of the stabilizer code in quantum error correction,we need to find a fast and close to the optimal threshold decoder.In this work,we build a convolutional neural network(CNN)decoder to correct errors in the toric code based on the system research of machine learning.We analyze and optimize various conditions that affect CNN,and use the RestNet network architecture to reduce the running time.It is shortened by 30%-40%,and we finally design an optimized algorithm for CNN decoder.In this way,the threshold accuracy of the neural network decoder is made to reach 10.8%,which is closer to the optimal threshold of about 11%.The previous threshold of 8.9%-10.3%has been slightly improved,and there is no need to verify the basic noise.
基金funding from the European Union’s Horizon 2020 research and innovation program under Marie Sklodowska-Curie(Grant No.:101007976)The authors also express their sincere grati-tude to the Engineering and Physical Sciences Research Council(EPSRC)for the funding provided to this project(Grant Nos.:EP/V041665/1 and EP/T022701/1)This work was also financially supported by the National Key Research and Development Program of China(Grant No.:2021YFE0112500).
文摘A dynamic modeling framework based on an intelligent approach is proposed to identify the complex behaviors of solid-gas sorption systems.An experimental system was built and tested to assist in developing a model of the system performance during the adsorption and desorption processes.The variations in the thermal effects and gaseous environment accompanying the reactions were considered when designing the model.An optimization platform based on a multi-population genetic algorithm and artificial criteria was established to identify the mod-eling coefficients and quantify the effects of condition changes on the reactions.The calibration of the simulation results against the tested data showed good accuracy,where the coefficient of determination was greater than 0.988.The outcome of this study could provide a modeling basis for the optimization of solid-gas sorption systems and contribute a potential tool for uncovering key characteristics associated with materials and components.
