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        The Improvement of China’s Nuclear Safety Supervision Technical Support Ability

        Han Wu,Guoxin Yu,Xiangyang Zheng,Keyan Teng 한국방사성폐기물학회 2022 방사성폐기물학회지 Vol.20 No.4

        The International Atomic Energy Agency (IAEA) entails independent decision-making for the safety supervision of civil nuclear facilities. To evaluate and review the safety of nuclear facilities, the national regulatory body usually consults independent institutions or external committees. Technical Support Organizations (TSOs) include national laboratories, research institutions, and consulting organizations. Support from professional organizations in other countries may also be required occasionally. Most of the world’s major nuclear power countries adopt an independent nuclear safety supervision model. Accordingly, China has continuously improved upon the construction of such a system by establishing the National Nuclear Safety Administration (NNSA) as the decision-making department for nuclear and radiation safety supervision, six regional safety supervision stations, the Nuclear and Radiation Safety Center (NSC), a nuclear safety expert committee, and the National Nuclear and Radiation Safety Supervision Technology R&D Base, which serves as the test, verification, and R&D platform for providing consultation and technical support. An R&D system, however, remains to be formed. Future endeavors must focus on improving the technical support capacity of these systems. As an enhancement from institutional independence to capability independence is necessary for ensuring the independence of China’s nuclear safety regulatory institution, its regulatory capacity must be improved in the future.

      • KCI등재

        Investigation of Microscale Laser Shock Flat Hole Clinching

        Yaxuan Hou,Kexin Ding,Guoxin Lu,Chao Zheng,Zhong Ji 한국정밀공학회 2022 International Journal of Precision Engineering and Vol.23 No.9

        This paper proposes an improved laser shock flat hole clinching process for joining ductile and brittle materials to obtain single-sided flat joints in microscale. A copper foil is clinched with a perforated sheet and a mechanical joining is achieved by this process. Experiments and numerical simulations are conducted for three thickness combinations of 50 μm/50 μm, 30 μm/50 μm and 20 μm/100 μm to investigate the joinability of this process. The results show that a step-by-step laser shock process with a low-energy pre-shock and a high-energy secondary shock can effectively join the copper foil and the perforated sheet. The three thickness combinations of the upper and lower sheets result in three clinching structures, namely stacked, intermediate and thinning joints. And the joint strength mainly depends on the thinner sheet in a successful joining even for the single joint or the double-joint.

      • SCISCIESCOPUSKCI등재

        Channel modeling based on multilayer artificial neural network in metro tunnel environments

        Jingyuan Qian,Asad Saleem,Guoxin Zheng Electronics and Telecommunications Research Instit 2023 ETRI Journal Vol.45 No.4

        Traditional deterministic channel modeling is accurate in prediction, but due to its complexity, improving computational efficiency remains a challenge. In an alternative approach, we investigated a multilayer artificial neural network (ANN) to predict large-scale and small-scale channel characteristics in metro tunnels. Simulated high-precision training datasets were obtained by combining measurement campaign with a ray tracing (RT) method in a metro tunnel. Performance on the training data was used to determine the number of hidden layers and neurons of the multilayer ANN. The proposed multilayer ANN performed efficiently (10 s for training; 0.19 ms for prediction), and accurately, with better approximation of the RT data than the single-layer ANN. The root mean square errors (RMSE) of path loss (2.82 dB), root mean square delay spread (0.61 ns), azimuth angle spread (3.06°), and elevation angle spread (1.22°) were impressive. These results demonstrate the superior computing efficiency and model complexity of ANNs.

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