The geomagnetic field is the main magnetic field on the surface of the Earth, and its value is generally much larger than that of ferromagnetic objects. The existence of a geomagnetic field makes the ferromagnetic material magnetized, and the magnetized field will make the local total magnetic field abnormal, so it is called an anomalous magnetic field. This unusual magnetic field is a necessary condition for conducting magnetic anomaly detection (MAD). MAD is a widely used passive method for magnetic target detection, and its applications include surface ship target detection, the monitoring of underwater moving targets, land target detection and the identification of seismic activity for metal mining. MAD technology uses a high-sensitivity magnetometer to measure the target magnetic field. The magnetic field data are used to calculate the position, velocity, volume and other parameters of the target to identify and localize the ferromagnetic target. It is of great significance to study MAD data based on geomagnetic background. This paper reviews the MAD methods proposed by researchers in recent years and summarizes them into two categories. One is target based, and the other is noise based. The target-based group of detection methods involves typical magnetic search systems based on the assumption that the magnetometer and the target move relative to each other, which applies to the case where the target motion obeys a specific tracking time mode. The noise-based detection methods are based on statistical analyses of magnetometer noise and are suitable for situations in which assumptions about the mutual motion of the target and the magnetometer cannot be made. The magnetic dipole model is introduced in the second part of the paper, and then an algorithm based on the standard orthogonal basis function (OBF) decomposition is proposed. The algorithm parallels the target to a magnetic dipole and decomposes it into a linear combination of several standard OBFs. Solving for the coefficients of the basis function yields the signal energy function in the basis function space. The results show that the signal-to-noise ratio of the data processed by the OBF algorithm is significantly improved. The OBF can be further optimized; for example, when using a single magnetometer to conduct MAD, the five OBFs can be simplified to three OBFs; to locate the target more accurately when using two magnetometers to form the gradient magnetometer, the five OBFs can be simplified into four OBFs. The OBF algorithm is not very effective in the detection of non-Gaussian white noise, so
Enjoying local food could be one of the motives for tourism, and local food and restaurant recommendation information would be important for tourists to decide their destination. Recommendation agents are the sorting and searching function to find the best local food and restaurant among the complexity of information, and they could also be helpful for the tourist to decide their destination. Online tourism websites (e.g., Ctrip.com ) started to provide restaurant recommendations containing food-related information and recommendation agents to attract tourists. However, few studies have investigated their impact on the destination visit intention of potential Chinese tourists. This study aims to empirically validate how restaurant recommendation information, including food-related information and recommendation agents, could impact online tourists’ reactions, such as satisfaction, continuous website usage, and destination visits. We developed our hypothesis based on the information system (IS) success model. We gathered 202 data points from potential tourists using quasi-experimental methods, and these data were analyzed by the PLS algorithm. The results indicate that restaurant recommendation information and recommendation agents significantly increase the perceived information quality and perceived system quality. Increased perceived information quality and system quality could significantly increase potential tourists’ satisfaction, website continuous usage intention, and destination visit intention. The results of this study could contribute to making tourism websites more attractive by using local food and restaurant information and recommendation agents.
This paper uses the Analytics Hierarchy Process to realize the analysis of cultural tourism competitiveness. First of all, the authors design the questionnaire and carry out the research on valid objects, which realizes the collection of valid information about influential factors on cultural tourism competitiveness. Then the authors categorize and draw out the concept of influential factors for cultural tourism development, determine the importance degree between the two factors through comparison, establish the Analytic Hierarchy Process of cultural tourism competitiveness, and then judge the validity of the analysis result through consistency test. The research results show that in the construction stage, in order to improve the cultural tourism competitiveness, it is necessary to adopt the mode of "moderate development" and "complete development" respectively according to the types of cultural tourism resources. When designing, it's possible to refer to the mature modes of historic sites and cultural towns. In addition, an effective way to realize users' cultural experience and obtain identity recognition should be provided.
Threshold segmentation method was widely applied in image process and the selection of threshold affected the final results of image segmentation to a large extent. In order to improve the accuracy and the calculation speed of image segmentation, an Otsu threshold segmentation method based on genetic algorithm was offered. According to the threshold and the gray scale values of pixels, the pixels were divided into two categories, and then the genetic algorithm was used to find the maximum variance between clusters and obtain the optimal threshold of segmentation image. The experimental results show that this method can be used to segment the image effectively, which make the basis for image processing and analysis in the next step.
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