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Developing waterway-waterway transfer is an important path for Shanghai's container logistics to innovate service models. Taicang Express Line, a typical case of service model innovation, plays an important role in elevating the standing of Shanghai Port as a container hub port and in developing China (Shanghai) Pilot Free Trade Zone. From the three dominant transfer service models, the waterway-waterway transfer for container logistics of Taicang Express Line has the traits and experience in streamlining logistics processes, innovating logistics clearance models, saving logistics operating costs, offering port logistics cooperation experience for replications and promoting integration of regional port logistics resources. However, it also harbors issues in infrastructure construction, staffing, container resources allocation and transportation, transportation efficiency and policy innovation. In the future, efforts should be invested to strengthening the construction and staffing of port logistics infrastructure, optimizing the container resources allocation and transport of port logistics systems, improving the logistics transportation efficiency of Taicang Express Line, and pushing forward innovation of the synergistic policy mechanism for regional port logistics.

Developing waterway-waterway transfer is an important path for Shanghai's container logistics to innovate service models. Taicang Express Line, a typical case of service model innovation, plays an important role in elevating the standing of Shanghai Port as a container hub port and in developing China (Shanghai) Pilot Free Trade Zone. From the three dominant transfer service models, the waterway-waterway transfer for container logistics of Taicang Express Line has the traits and experience in streamlining logistics processes, innovating logistics clearance models, saving logistics operating costs, offering port logistics cooperation experience for replications and promoting integration of regional port logistics resources. However, it also harbors issues in infrastructure construction, staffing, container resources allocation and transportation, transportation efficiency and policy innovation. In the future, efforts should be invested to strengthening the construction and staffing of port logistics infrastructure, optimizing the container resources allocation and transport of port logistics systems, improving the logistics transportation efficiency of Taicang Express Line, and pushing forward innovation of the synergistic policy mechanism for regional port logistics.

This study aims to ensure the operation safety of high speed trains, it is necessary to carry out nondestructive monitoring of the tensile damage of the gearbox housing material in rail time, yet the traditional tests of mechanical property can hardly meet this requirement.

In this study the acoustic emission (AE) technology is applied in the tensile tests of the gearbox housing material of an high-speed rail (HSR) train, during which the acoustic signatures are acquired for parameter analysis. Afterward, the support vector machine (SVM) classifier is introduced to identify and classify the characteristic parameters extracted, on which basis the SVM is improved and the weighted support vector machine (WSVM) method is applied to effectively reduce the misidentification of the SVM classifier. Through the study of the law of relations between the characteristic values and the tensile life, a degradation model of the gearbox housing material amid tensile is built.

The results show that the growth rate of the logarithmic hit count of AE signals and that of logarithmic amplitude can well characterize the stage of the material tensile process, and the WSVM method can improve the classification accuracy of the imbalanced data to above 94%. The degradation model built can identify the damage occurred to the HSR gearbox housing material amid the tensile process and predict the service life remains.

The results of this study provide new concepts for the life prediction of tensile samples, and more further tests should be conducted to verify the conclusion of this research.

The purpose is using generative adversarial network (GAN) to solve the problem of sample augmentation in the case of imbalanced bearing fault data sets and improving residual network is used to improve the diagnostic accuracy of the bearing fault intelligent diagnosis model in the environment of high signal noise.

A bearing vibration data generation model based on conditional GAN (CGAN) framework is proposed. The method generates data based on the adversarial mechanism of GANs and uses a small number of real samples to generate data, thereby effectively expanding imbalanced data sets. Combined with the data augmentation method based on CGAN, a fault diagnosis model of rolling bearing under the condition of data imbalance based on CGAN and improved residual network with attention mechanism is proposed.

The method proposed in this paper is verified by the western reserve data set and the truck bearing test bench data set, proving that the CGAN-based data generation method can form a high-quality augmented data set, while the CGAN-based and improved residual with attention mechanism. The diagnostic model of the network has better diagnostic accuracy under low signal-to-noise ratio samples.

A bearing vibration data generation model based on CGAN framework is proposed. The method generates data based on the adversarial mechanism of GAN and uses a small number of real samples to generate data, thereby effectively expanding imbalanced data sets. Combined with the data augmentation method based on CGAN, a fault diagnosis model of rolling bearing under the condition of data imbalance based on CGAN and improved residual network with attention mechanism is proposed.