Estimation and Analysis of Wave Spectrum Parameter using HeMOSU-2 Observation Data

In this study, wave spectrum data were calculated using the water surface elevation data observed at 5Hz intervals from the HeMOSU-2 meteorological tower installed on the west coast of Korea, and wave parameters were estimated using wave spectrum data. For all significant wave height ranges, the peak enhancement parameter (γ opt ) of the JONSWAP spectrum and the scale parameter (α) and shape parameter (β) of the modify BM spectrum were estimated based on the observed spectrum, and the distribution of each parameter was confirmed. As a result of the analysis, the peak enhancement parameter (γ opt ) of the JONSWAP spectrum was calculated to be 1.27, which is very low compared to the previously proposed 3.3. And in the range of all significant wave heights, the distribution of the peak enhancement parameter (γ opt ) was shown as a combined distribution of probability mass function (PMF) and probability density function (PDF). In addition, the scale parameter (α) and shape parameter (β) of the modify BM spectrum were estimated to be [0.245, β1.278], which are lower than the existing [0.300, -1.098], and the result of the linear correlation analysis between the two parameters was β = =3.86α.

UAV-Assisted Three-Dimensional Spectrum Mapping Driven by Spectrum Data and Channel Model

As the number of civil aerial vehicles increase explosively, spectrum scarcity and security become an increasingly challenge in both the airspace and terrestrial space. To address this difficulty, this paper presents an unmanned aerial vehicle-assisted (UAV-assisted) spectrum mapping system and a spectrum data reconstruction algorithm driven by spectrum data and channel model are proposed. The reconstruction algorithm, which includes a model-driven spectrum data inference method and a spectrum data completion method with uniformity decision mechanism, can reconstruct limited and incomplete spectrum data to a three-dimensional (3D) spectrum map. As a result, spectrum scarcity and security can be achieved. Spectrum mapping is a symmetry-based digital twin technology. By employing an uniformity decision mechanism, the proposed completion method can effectively interpolate spatial data even when the collected data are unevenly distributed. The effectiveness of the proposed mapping scheme is evaluated by comparing its results with the ray-tracing simulated data of the campus scenario. Simulation results show that the proposed reconstruction algorithm outperforms the classical inverse distance weighted (IDW) interpolation method and the tensor completion method by about 12.5% and 92.3%, respectively, in terms of reconstruction accuracy when the collected spectrum data are regularly missing, unevenly distributed and limited.

A Nuclide Recognition Method for Nuclear Robot System

In the real energy spectrum attenuation environment, many traditional nuclide identification methods for nuclear robot systems have problems such as using only part of the energy spectrum curve, being susceptible to noise, and having low recognition accuracy. Proposes an energy spectrum nuclide recognition method based on S-transform (ST) and Mahalanobis distance-based support vector machine (MSVM). Regarding the energy spectrum curve as a non-stationary signal, combined with the widely used S transformation method in signal transformation, the energy spectrum data is two-dimensional, Then use two-dimensional principal component analysis(2D-PCA) to reduce the dimension of the two-dimensional energy spectrum data for feature extraction, and design a support vector machine (SVM) classifier based on Mahalanobis distance to realize the identification of energy spectrum nuclides. Finally, experiments are carried out with simulated nuclide energy spectrum data based on Geant4. The experimental results show that this method effectively improves the accuracy of energy spectrum nuclide recognition by using full spectrum information. At the same time, experiments are carried out on the nuclide energy spectrum data of different detection distances obtained by the NaI detector in the real environment, and it is verified that the algorithm proposed in this paper also has a good recognition performance for the nuclide energy spectrum collected in the real environment.

Research and Development of Gearbox Load Spectrum Database under Variable Working Conditions

Gearbox load spectrum is the data basis for gearbox fatigue life prediction and strength verification. As the amount of load spectrum data increases exponentially and the demand for sharing increases, it is necessary to introduce database technology to store and manage load spectrum data. In this paper, the collection and processing of load data and the compilation of load spectrum are introduced in turns firstly. Then it sums the development and research status of database technology and the technical route of combining load spectrum database up secondly. Moreover, the current status of load spectrum database and the design steps of load spectrum database are summarized. Finally, the development trend of load spectrum database research is generalized and the prospect is put forward.

Radio Station Background Noise Detection Based on Time-Frequency Domain Electromagnetic Spectrum

The electromagnetic spectrum resource is one of the important national resources. It is a physical channel for wireless communication between ships and between ships and radio stations. Good communication quality must be guaranteed, so it is urgent to monitor and analyze the environmental background noise of the electromagnetic spectrum. The estimation of the radio frequency signal coverage in the target area during the monitoring process is of great significance to the study of electromagnetic spectrum resource management and control. This paper estimates the upper envelope and lower envelope of the background noise of the target frequency band based on the electromagnetic spectrum data in the time-frequency domain and combines the forward difference algorithm to estimate the background noise envelope curve. We set up fixed detection nodes and mobile detection nodes for specific construction areas and collect time-frequency spectrum data of electromagnetic spectrum in multiple locations. The instantaneous frequency spectrum and the collected data of a specific frequency point are compared, and it is difficult to judge whether there is a valid signal. This paper is based on the time-frequency domain electromagnetic spectrum data in the construction area of the project and estimates the background noise of the coast station frequency band in the current environment. It is based on the energy gradient estimation of the time-frequency domain spectrum, and the effective signal of the target frequency band is obtained and combines the noise envelope and the effective signal location to improve the estimation result of the background noise envelope. The experimental results show that the background noise estimation algorithm can reflect the changes in the noise floor of different target frequency bands.

Majority scoring with backward elimination in PLS for high dimensional spectrum data

Variable selection is crucial issue for high dimensional data modeling, where sample size is smaller compared to number of variables. Recently, majority scoring of filter measures in PLS (MS-PLS) is introduced for variable selection in high dimensional data. Filter measures are not greedy for optimal performance, hence we have proposed majority scoring with backward elimination in PLS (MSBE-PLS). In MSBE-PLS we have considered variable importance on projection (VIP) and selectivity ratio (SR). In each iteration of backward elimination in PLS variables are considered influential if they were selected by both filter indicator. The proposed method is implemented for corn’s and diesel’s content prediction. The corn contents include protein, oil, starch and moisture while diesel contents include boiling point at 50% recovery, cetane number, density, freezing temperature of the fuel, total aromatics, and viscosity. The proposed method outperforms in terms of RMSE when compared with reference methods. In addition to validating the spectrum models, data properties are also examined for explaining prediction behaviors. Moreover, MSBE-PLS select the moderate number of influential variables, hence it presents the parsimonious model for predicting contents based on spectrum data.

Design and Validation of the Scientific Data Products for China’s Tianwen-1 Mission

The Tianwen-1 mission is China’s first Mars exploration mission. To complete the scientific objectives of “orbiting, landing, and patrolling” in one mission, thirteen instruments for acquiring multi-disciplinary data are configured. The data products with rich ancillary data and flexible structures will facilitate the data application. To integrate the requirements of experts from disparate disciplines and leverage new information technologies, a complete redesign of data products is promoted in the Tianwen-1 mission. We discuss the whole process of data product generation from the data pipeline design to data validation. We design the various data products according to the application requirements of each data type, including radar echo data, spectrum data, image data, energy spectrum data, magnetic field data, meteorological data, and acoustic data. Since various error sources will exist in the generation chain of the data products, data validation is essential before release to the public; the validation activities are discussed at the end. We deliver these scientific products to the community in a timely manner, with ancillary information and quality information. This paper can provide practical reference for Tianwen-1 data application.