Search

Tunnel settlement has a significant impact on property security and personal safety. Accurate tunnel-settlement predictions can quickly reveal problems that may be addressed to prevent accidents. However, each acquisition point in the tunnel is only monitored once daily for around two months. This paper presents a new method for predicting tunnel settlement via transfer learning. First, a source model is constructed and trained by deep learning, then parameter transfer is used to transfer the knowledge gained from the source model to the target model, which has a small dataset. Based on this, the training complexity and training time of the target model can be reduced. The proposed method was tested to predict tunnel settlement in the tunnel of Shanghai metro line 13 at Jinshajiang Road and proven to be effective. Artificial neural network and support vector machines were also tested for comparison. The results showed that the transfer-learning method provided the most accurate tunnel-settlement prediction.

To realize real-time detection of the operating environment of bridge cranes, this paper presents a three-dimensional mapping method using a binocular camera and laser ranging sensor. First, the left and right images are obtained by using the binocular camera, and the block matching is used for stereo correspondence to obtain the disparity image. Then, according to the reprojection matrix and the disparity image, the depth value of each pixel is obtained, and the depth image can be transformed into a three-dimensional point cloud of the current frame. Relying on the camera position data obtained by the laser ranging sensor, the three-dimensional point cloud generated from different positions is registered to obtain the three-dimensional point cloud of the craneʼs global operating environment. Finally, an octree map is used to represent the operating environment. To simulate the construction process of the operating environment of the bridge crane, an experimental platform was built, and experiments were carried out using the method proposed in this paper. The experimental results verified the feasibility of the method proposed in this paper.

An improved soft kill option (SKO) method is proposed to achieve the optimization design of tower crane boom. For a better optimization performance, a parabolic weight coefficient is suggested and the displacement constraint is taken into consideration. In order to eliminate the numerical instability phenomena like checkerboard and mesh dependence, element temperature filter function is added into SKO method. Through the optimization data comparison of rectangular cantilever plate, it is verified that the improved SKO method can achieve a better result with more uniform stress and higher efficiency. Based on the dimension and load parameters of QTZ63 tower crane boom, an optimization model is established, and a periodic SKO method is put forward to optimize this model. The optimization result of the crane boom can provide a new thought for tower crane boom design.

A new method combined continuum topology with discrete truss is proposed for truss optimization. The optimized material distribution is obtained by means of continuum topology optimization and then transformed into initial truss. Based on the element principal stress and 8-neighborhood elements of continuum, force flow path extraction method is presented to obtain the initial truss layout. For section size optimization, criterion is deduced from a mathematical model by Lagrange multiplier method. Leveraging the free-form nature of continuum, this method can avoid the restriction of traditional ground structure. And it can achieve a better solution with less mass, smaller deformation and more uniform stress.

Public research institutions are encouraged to engage in industry sustainable collaboration in China. We develop an analytical framework based on the factor-process-outputs model and use a mechanism model by incorporating four elements (innovation climate, strategic partnership, collaborative mechanism, and the degree of participation) associated with the research institutions and industry collaboration. Using data collected from a face-to-face interview survey of 533 experts located at research institutions in seven Chinese provinces and one municipality who have collaborated with the seed industry, we use a structural equation model to identify important factors that affect innovation behavior. Results show that the innovation climate does not directly affect the participation of research institutions in research industry collaboration, however, it has a direct effect on strategic partnership and the collaborative mechanism. We find that an innovation climate could indirectly influence the participation of research institutions via collaborative mechanism and strategic partnership. Furthermore, strategic partnership and collaborative mechanism are found to moderate the participation behavior of research institutions. Moreover, we find that policy support, knowledge innovation strategies, and resource sharing mechanisms are essential factors for sustainable and effective collaboration.

Deep learning methods have been widely used in the field of intelligent fault diagnosis due to their powerful feature learning and classification capabilities. However, it is easy to overfit depth models because of the large number of parameters brought by the multilayer-structure. As a result, the methods with excellent performance under experimental conditions may severely degrade under noisy environment conditions, which are ubiquitous in practical industrial applications. In this paper, a novel method combining a one-dimensional (1-D) denoising convolutional autoencoder (DCAE) and a 1-D convolutional neural network (CNN) is proposed to address this problem, whereby the former is used for noise reduction of raw vibration signals and the latter for fault diagnosis using the de-noised signals. The DCAE model is trained with noisy input for denoising learning. In the CNN model, a global average pooling layer, instead of fully-connected layers, is applied as a classifier to reduce the number of parameters and the risk of overfitting. In addition, randomly corrupted signals are adopted as training samples to improve the anti-noise diagnosis ability. The proposed method is validated by bearing and gearbox datasets mixed with Gaussian noise. The experimental result shows that the proposed DCAE model is effective in denoising and almost causes no loss of input information, while the using of global average pooling and input-corrupt training improves the anti-noise ability of the CNN model. As a result, the method combined the DCAE model and the CNN model can realize high-accuracy diagnosis even under noisy environment.

Background China’s universal medical insurance system (UMIS) is designed to promote social fairness through improving access to medical services and reducing out-of-pocket (OOP) costs for all Chinese. However, it is still not known whether UMIS has a significant impact on the accessibility of medical service supply and the affordability, as well as the seeking-care choice, of patients in China. Methods Segmented time-series regression analysis, as a powerful statistical method of interrupted time series design, was used to estimate the changes in the quantity and quality of medical service supply before and after the implementation of UMIS. The rates of catastrophic payments and seeking-care choices for UMIS beneficiaries were selected to measure the affordability and medical service flow of patients after the implementation of UMIS. Results China’s UMIS was established in 2008. After that, the trending increase of the expenditure of the UMIS was higher than that of increase in revenue compared to previous years. Up to 2014, the UMIS had covered 97.5% of the entire population in China. After introduction of the UMIS, there were significant increases in licensed physicians, nurses, and hospital beds per 1000 individuals. In addition, hospital outpatient visits and inpatient visits per year increased compared to the pre-UMIS period. The average fatality rate of inpatients in the overall hospital and general hospital and the average fatality rate due to acute myocardial infarction (AMI) in general hospitals was significantly decreased. In contrast, no significant and prospective changes were observed in rural physicians per 1000 individuals, inpatient visits and inpatient fatality rate in the community centers and township hospitals compared to the pre-UMIS period. After 2008, the rates of catastrophic payments for UMIS inpatients at different income levels were declining at three levels of hospitals. Whichever income level, the rate of catastrophic payments for inpatients of Urban Employee’s Basic Medical Insurance was the lowest. For the low-income patients, a single hospitalization at a tertiary hospital can lead to catastrophic payments. It is needless to say what the economic burden could be if patients required multiple hospitalizations within a year. UMIS beneficiaries showed the intention of growth to seek hospitalization services in tertiary hospitals. Conclusions Introduction of the UMIS contributed to an increase in available medical services and the use thereof, and a decrease in fatality rate. The affordability of UMIS beneficiaries for medical expenses was successfully ameliorated. The differences in patients’ affordability are mainly manifested in different medical insurance schemes and different seeking-care choices. The ability of the poor patients covered by UMIS to resist catastrophic medical payments is still relatively weak. Therefore, the current UMIS should reform the insurance payment model to promote the integration of medical services and the formation of a tiered treatment system. UMIS also should establish supplementary medical insurance packages for the poor.

In view of the problem of the operational risk on distribution center, a technical framework of logistics equipments that adapted to the risk-based maintenance decision making is proposed. The frame includes three modules: risk analysis, risk evaluation, and risk control. The reliability of equipment and consequence of malfunction are considered. The risk is quantitatively analyzed, by which maintenance strategy is built for decision objective, so that we can build the management system of logistics equipment. An instance is taken to explain how to use the risk-based maintenance decision technique to logistics equipment.

Some adverse conditions exist in the high-speed railway tunnel lining, such as lining insufficient thickness, lining cavity, unreasonable steel reinforcement layout, etc. In this paper, simulation of the typical adverse lining condition was implemented by using the simulation software GPRMax2D. The analysis of the typical radar images based on detection examples of a high-speed railway tunnel was described, and the summary of the features and rules of the adverse lining conditions was introduced in order to improve ground-penetrating radar data feature library, which provides the effective basis for the highspeed railway tunnel non-destructive testing.

The course Automatic Control Theory is a fundamental subject for machinery design, manufacturing and automation students of bachelor degree programs in College of Mechanical Engineering, Tongji University. The course construction under the framework of Tongji’s “Excellent Talent Program” has been proposed revolving around teaching content adjustment, teaching methods improving, bilingual teaching developing, and staff construction. This paper discusses the course content adjustment focused on typical theories and widely used engineering techniques. Teaching methods improving faces to the problem of quite diversified student population in university today and present some active methods such as problem-based teaching to improve the students’ ability of independent analysis and solving problems. About 5 years bilingual teaching experience of this course shows that suitable textbook, student-based teaching method and small scale class will benefit the teaching effect. The course Automatic Control Theory is developing to satisfy the demands of advanced engineering education.

The micro-electro-mechanical systems (MEMS) technology has broadened the horizon for sensors design, and MEMS devices, such as sensors and actuators, with low manufacturing cost, compact size, low weight and power consumption, are widely used in many industrial fields. A new type of MEMS accelerometer designed for the application in civil engineering is introduced in this paper. The some characteristics parameters of the sensor, such as reliable response frequency range and cross-axis sensitivity ratio, are tested through the calibration experiments designed in the paper, with the consideration based on application fields of the accelerometer.