Information Technology Club Management System

Submerged floating tunnel (SFT for short) is a special underwater traffic structure, and wave load is one of the main environmental loads of SFT structure. In this paper, the 1:60 physical model test of three kinds of SFT in a two-dimensional wave flume is tested. The effects of random irregular waves on the SFT structure under different wave heights and periods are discussed. The study shows that: (1) Compared with circular and polygonal sections, there are multiple local peaks in the elliptical section during the period. with the increase of wave height, the number of local peaks also increases. It suggests that the rotational moment plays an important role in the elliptical section which has a relatively small depth-width ratio. (2) The position of the maximum and minimum pressure in the three kinds of SFT sections is consistent. Their vertical wave forces are all larger than their horizontal wave forces. The increase of vertical wave force relative to horizontal wave force in polygon section is larger than that in elliptical section, and the difference in the circular section is the smallest. (3) Under the same traffic condition, the wave force of the elliptical and polygon section is smaller, but they are more sensitive to the change of wave height, and the increase is obvious. The distribution of wave force in the circular section is more uniform.

Section: Test Analysismentioning

confidence: 99%

Experiment on Pressure Characteristics of Submerged Floating Tunnel with Different Section Types Under Wave Condition

Qinxi

Jiang

Chen

2018

“…It is difficult to calculate the expected risk. The principle of empirical risk minimization is as follows [12]:…”

Section: Port Logistics Demand Forecasting Model Based On Pca-svmmentioning

confidence: 99%

Application of Evaluation Algorithm for Port Logistics Park Based on Pca-Svm Model

2018

To predict the logistics needs of the port, an evaluation algorithm for the port logistics park based on the PCA-SVM model was proposed. First, a quantitative indicator set for port logistics demand analysis was established. Then, based on the grey correlation analysis method, the specific indicator set of port logistics demand analysis was selected. The advantages of both principal component analysis and support vector machine algorithms were combined. The PCA-SVM model was constructed as a predictive model of the port logistics demand scale. The empirical analysis was conducted. Finally, from the perspective of the structure, demand, flow pattern and scale of port logistics demand, the future logistics demand of Shenzhen port was analysed. Through sensitivity analysis, the main influencing factors were found out, and the future development proposals of Shenzhen port were put forward. The results showed that the port throughput of Shenzhen City in 2016 was 21,328,200 tons. Compared with the previous year, it decreased by about 1.74 %. In summary, the PCA-SVM model accurately predicts the logistics needs of the port.

“…In this study, we used an artificial long channel which requires four independent observation stations. There is some minor relevance among the tide height information, collected by each observation station [19]. During the prediction process of tide height, four artificial neural network architectures are established for the corresponding tide height prediction of four observation stations.…”

Section: Computing Architecture For Tidal Window Period Based On Artimentioning

confidence: 99%

Calculation and Measurement of Tide Height for the Navigation of Ship at High Tide Using Artificial Neural Network

Bing-Dong²,

Bao

2018

Accurate tide height is crucial for the safe navigation of large deep-draft ships when they enter and leave the port. We have proposed an accurate forecasting method for the tide heights from the observation data and neural networks, which can easily calculate the tidal window period of large deep-draft ships' navigation through long channels at high tide. Moreover, an artificial neural network is established for the tide height from the observation of tide heights before their current time node. For an ideal forecast, the neural network was optimized for one year with the tide height data of Huanghua Port. In case of large ships, their tidal characteristics of channels for are complex. A new method is proposed for the observation of multiple stations and artificial neural networks of each observation station. When ships are navigating through the port, the tide height is predicted from the observed data and forecast tide heights of multiple observation stations. Thus, a valid tidal window period is secured when the ships enter the port. Comparative analysis of the ship's tidal window period with that of the measured one can lead us to conclude that the forecasted data has a strong correlation with the measurement. So, our proposed algorithm can accurately predict the tide height and calculate the node timing when the ship enters and depart the port. Finally, these results can be applied for the safe navigation of large deep-draft ships when the port is at high tide.