Lei Ye

In the project of supergravity centrifugal simulation and experimental device, the BIM application is carried out throughout the process in order to improve the construction quality. In this paper, a hyper-gravity BIM platform is developed, and 3d collaborative design results are published on the web in light weight. Combining the requirements of the project application, this paper sorts out the relationship among the 3D model, design files, and technical disclosure, develops design clarification and design data management modules, which practices in deepening the application of BIM. With the help of this hyper-gravity BIM platform, online technical disclosure and design file management based on lightweight BIM are realized for the first time, forming complete BIM-oriented design data assets. The practical results are of great significance for promoting the application of BIM and improving the quality of project construction.

The accuracy of travel time prediction is not only decided by the state of the current journey but also affected by that of the transfer journey. This paper focuses on the transit transfer problem of urban public transport and aims to improve the performance of multi-route bus travel time prediction. In this paper, two kinds of infect factors, which are macro infect factors and local infect factors, are defined. Then, a pre-processing approach is proposed to construct a travel chain, which is used to compensate the shortage of raw data set. Multi-route bus travel time is divided into four parts, which are travel time of the current driving segment (CDS), bus stop dwelling time module, stop-stop travel time, and transfer point waiting time. A parrel processing architecture, which includes four learning blocks, is designed to fulfill the prediction process of the above four time parts. The classic LSTM model is used to predict the CDS travel time and transfer point waiting time. An improved DA-RNN model, the MDARNN model, is proposed to predict the bus stop dwelling time module, stop-stop travel time. Moreover, a real-time traffic flow model is used to calibrate the prediction time of all four sub-models. Experiment results prove the validity of the proposed methods.

The nonhomogeneous clutter is one of the biggest challenge for ocean remote sensing radar system such as high frequency hybrid sky-surface wave radar. Space-time adaptive processing (STAP) is a powerful method to suppress the clutter but the training samples in adjacent range gates are not always independent and identically distributed with the cell under test in the heterogeneous environment. So the estimation of the clutter covariance matrix will be inaccurate and degrade the STAP performance. In this paper, a novel training sample selection method based on information geometric means is proposed to choose the more homogeneous secondary data containing the precise clutter information of cell under test. The simulation results of selection performance is conducted to show the advantages of the Information geometric mean based estimators compared with the existing estimators. Combined with joint domain localized STAP, the effectiveness of the proposed method is verified by experimental data. The results show the proposed method outperforms conventional STAP method and the nonhomogeneous clutter can be greatly suppressed.

The nonhomogeneous clutter is a major challenge for ship detection in high-frequency mixed-mode surface wave radar. In this paper, a geometric barycenter-based reduced-dimension space-time adaptive processing method is proposed to suppress the clutter. Given the measured dataset, the range correlation of sea clutter is first investigated. Then, joint domain localized processing is applied to solve the training samples starve scenario in a practical system. The geometric barycenter-based training data selector is presented to select valid training samples and improve the accuracy of the clutter covariance matrix estimation. Finally, the validity of the proposed method is verified using the experimental data and the results show that it outperforms the conventional method in the nonhomogeneous environment of a practical system.

A performance bound—Cramér-Rao lower bound (CRLB) for target estimation and detection in sparse stepped frequency radars is presented. The vector formulation of this CRLB is used to obtain a lower bound on the estimation error. The estimation performance can be transformed into different types of CRLB structures. Therefore, the expressions of bounds under three equivalent models are derived separately: time delay and Doppler stretch estimator, joint multiple parameter estimator, and sparse-based estimator. The variables to be estimated include the variances of unknown noise, range, velocity, and the real and imaginary parts of the amplitude. A general performance expression is proposed by considering the echo of the target in the line-of-sight. When the relationship between CRLB and various parameters are discussed in detail, the specific effect of waveform parameters on a single CRLB is compared and analyzed. Numerical simulations demonstrated that the resulting CRLB exhibits considerable theoretical and practical significance for the selection of optimal waveform parameters.

The paper proposes a joint domain localized matrix constant false alarm rate detector based on the space time adaptive processing and information geometry method to improve the detection performance for high frequency surface wave radar. Unlike traditional target detectors that use onedimensional or two-dimensional amplitude information, the proposed detector considers multidimensional information of the signal to easily distinguish the target from the noise or clutter. The multidimensional information is obtained by joint domain localized processing, a reduced dimension space time adaptive algorithm. Then using the information geometry method, we map the detection problem to the geometric manifold, and detect the targets by geometric measures. This paper proposes different detectors based on different geometric measures. The experiments and results show that the proposed detectors can enhance the detection performance compared with classical cell average constant false alarm rate detector.

Wave‐based methods for acoustic simulations within enclosures suffer the numerical dispersion and then usually have evident dispersion error for problems with high wave numbers. To improve the upper limit of calculating frequency for 3D problems, a hybrid smoothed finite element method (hybrid SFEM) is proposed in this paper. This method employs the smoothing technique to realize the reduction of the numerical dispersion. By constructing a type of mixed smoothing domain, the traditional node‐based and face‐based smoothing techniques are mixed in the hybrid SFEM to give a more accurate stiffness matrix, which is widely believed to be the ultimate cause for the numerical dispersion error. The numerical examples demonstrate that the hybrid SFEM has better accuracy than the standard FEM and traditional smoothed FEMs under the condition of the same basic elements. Moreover, the hybrid SFEM also has good performance on the computational efficiency. A convergence experiment shows that it costs less time than other comparison methods to achieve the same computational accuracy.

COMPASS (Beidou) Navigation System is an important high-technology investment of China, the second generation of the system is designed consisting of 35 satellites and will provide 24-hour, global satellite navigation capability, and offered public regional service from 2010. One key advantage of Beidou is the low service cost in marine positioning and communication services. One can expect that Beidou system will create a huge potential market of global positioning services, especially in greater China region. One key challenge of using Beidou in marine positioning and communication services is the difficulties in integrating it with other existing marine positioning systems including Global Positioning System (GPS), Automatic Identification System (AIS), Radar and Radio Frequency Identification (RFID) systems etc. As such, this paper aims to describe the development of key data interfacing, communication and processing technologies and an application platform for the seamless integration of the multiple navigation data sources. Integrating Beidou to the existing VMS (Vessel Monitoring Systems) will greatly reduce the investment costs, and provide the opportunities for the fishery industries, maritime traffic management departments to experience the unique advantages of Beidou and explore the huge business potentials.