Search

With the development of information technology, intermodal transport research pays more attention to dynamic optimization and multi-role cooperation. The core issue of this paper was to realize container routing with dynamic adjustment, real-time optimization, and multi-role cooperation characteristics in the intermodal transport network. This paper first introduces the Intermodal Transport Cooperation Protocol (ITCP) that describes the operation and analysis of intermodal transport systems with the concept of encapsulation and layering. Then, a new network flow control method was built based on Model Predictive Control (MPC) in the ITCP framework. The method takes real-time information from all ITCP layers as input and generates flow control decisions for containers. To evaluate the method's effectiveness, a discrete event simulation experiment is applied. The results show that the proposed method outperforms the all-or-nothing method in scenarios with high freight volume, which means the method proposed in this paper can effectively balance the network transport load and reduce network operating costs. The research of this paper may throw some new light on intermodal transport research from the perspectives of digitization, multi-role cooperation, dynamic optimization, and system standardization., Author(s): Yuankai Huang (corresponding author) [1]; Qicai Zhou [1]; Xiaolei Xiong [1,2]; Jiong Zhao [1] 1. Introduction As an important part of logistics, transportation is of great importance to social [...]

The cooperative planning in intermodal transport networks can obtain the global optimal decision under the premise of ensuring the data privacy of each role in the cooperation and avoiding massive data transmission. For the control of container flow in intermodal transport networks, the distributed model predictive control (DMPC) method can effectively realize cooperative planning, but the convergence speed of the existing DMPC methods is slow. Therefore, this study attempts to construct faster DMPC methods for cooperative planning. The Jacobi proximal distributed model predictive control (JP‐DMPC) and dual consensus distributed model predictive control (DC‐DMPC) methods are constructed for container flow control based on two variants of alternating direction method of multipliers (ADMM). The simulation experiments prove that the convergence speed of JP‐DMPC and DC‐DMPC methods is higher than that of the state‐of‐the‐art method on the premise that the time cost and interaction data volume of each iteration do not change much, and the DC‐DMPC method improves planning speed particularly significantly. This study provides new methods for intermodal transport cooperative planning and has significance for the development of synchromodal transport. [ABSTRACT FROM AUTHOR]

Bridge cranes must be able to sense their working environment to achieve autonomous operation. An active visual mapping system is presented in this research to adapt the measurement range based on the crane's operational state. The rotation angles of the two servos are computed based on the crane's speed so that the binocular camera is deflected and a greater field of view is gained in front, improving the crane's safety. An experimental platform is developed to simulate the operation process of the active vision 3D mapping system, and experiments are carried out using the approach proposed in this paper. The experimental results indicate that the technology can successfully enhance the mapping scope of the bridge cranes' digital operating environment and improve crane operation safety. [ABSTRACT FROM AUTHOR]

Suppose that f : SX → SY is a surjective map between the unit spheres of two real ∞(Γ)-type spaces X and Y satisfying the following equation We show that such a mapping f is phase equivalent to an isometry, i.e., there exists a function ε : SX → {−1, 1} such that εf is an isometry. We further show that this isometry is the restriction of a linear isometry between the whole spaces. These results can be seen as a combination of Tingley's problem and Wigner's theorem for ∞(Γ)-type spaces. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Accurate degradation state recognition of rolling bearing is critical to effective condition based on maintenance for improving reliability and safety. In this work, a new architecture is proposed to recognize the degradation state of the rolling bearing. Firstly, the time-domain features including RMS, kurtosis, skewness and RMSEE, and Mel-frequency cepstral coefficients features are extracted from bearing vibration signals, which are then used as the input of k-means algorithm. These unlabeled features are clustered by k-means in order to define the different categories of the bearing degradation state. In this way, the original vibration signals can be labeled. Then, the convolutional neural network recognition model is built, which takes the bearing vibration signals as input, and outputs the degradation state category. So, interference brought by human factors can be eliminated, and further, the bearing degradation can be grasped so as to make maintenance plan in time. The proposed method was tested by bearing run-to-failure dataset provided by the Center for Intelligent Maintenance System, and the result proved the feasibility and reliability of the methodology. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The corrosion of grounding materials seriously threatens the safe operation of the power system. The corrosion resistance of four typical grounding materials as carbon steel, galvanized steel, Zn–Al-coated steel, copper was studied in acid red soil. The results show that carbon steel, galvanized steel, Zn–Al-coated steel, copper exhibit different corrosion resistance behaviors, respectively. The corrosion rate of these grounding materials usually increases first and then slowly decreases. Pitting corrosion is the main corrosion feature of carbon steel. The corrosion rate of carbon steel is the largest compared to the other three grounding materials. The corrosion rate of galvanized steel is higher than that of copper. Copper has a low corrosion rate and exhibits good corrosion resistance, but the cost of copper is high and it causes heavy metal pollution. The corrosion rate of Zn–Al-coated steel is the lowest compared to the other three grounding materials, and it has the best corrosion resistance. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]