Flight track pattern recognition based on few labeled data with outliers

Fan, Yuqi; Shen, Guangming; Xu, Xiong; Xu, Juan; Yuan, Xiaohui

doi:10.1117/1.jei.30.3.031204

Cited by 7 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The laser emitting system emits a laser beam with a small divergence angle and concentrated energy; the laser receiving system detects and receives echo signals such as reflection and scattering that are irradiated onto the target. 15 The basic technology of laser radar is derived from microwave radar, and there is no essential difference between the two. Conventional radar is a radar with microwave and millimeter wave carrier.…”

Section: Introductionmentioning

confidence: 99%

(Retracted) Laser radar-based intelligent vehicle target recognition and detection system using image detection technology

Zheng

2022

J. Electron. Imag.

View full text Add to dashboard Cite

.In recent years, with the progress of society and the development of the economy, the number of cars in China has been increasing. In wireless communication networks, the choice of wireless nodes has a greater impact on the improvement of system performance (such as channel capacity, coverage area, etc.). The intelligent vehicle target detection system can perceive and recognize the surrounding objects such as pedestrians and vehicles through sensors, which is the basis for realizing the unmanned driving of intelligent vehicles. In a wireless environment where multiple wireless nodes coexist, current research focuses on how long it takes to re-plan the selection of wireless nodes (for mobile environments) and how to allocate and manage wireless nodes, such as where under the conditions of wireless, who will wireless with whom, and in what way (such as auto focus, digital fusion, or other) wireless, etc.; the dedicated centralized controller (such as the base station of the infrastructure-based wireless access network) determines the wireless partner (i.e., centralized type selection), or the wireless node decides by itself (i.e., distributed selection); select the appropriate number of wireless nodes to take into account the system performance gain and implementation complexity. The popularity of automobiles has brought great convenience to people’s lives, but it has also brought greater traffic pressure. At present, the amount of domestic automobile traffic has increased exponentially, and urban traffic congestion has become more serious, and even caused serious traffic accidents, directly affecting people’s quality of life. The emergence of intelligent transportation systems has effectively alleviated road traffic pressure and reduced the incidence of traffic accidents. As an important part of the intelligent transportation system, the research of intelligent vehicles has received extensive attention. Intelligent vehicles use a variety of sensors installed on the body to sense the environment, and realize intelligent driving functions such as lane line detection, obstacle detection, dynamic cruise control, and unmanned driving, which is conducive to reducing the incidence of traffic accidents and improving the safety of vehicle driving. At present, in the research of target recognition and tracking of intelligent vehicles, the traditional target detection method is mainly based on artificial feature extraction, which is difficult to describe more complex or higher-order image features, and the tracking effect is not good, which limits the target detection. An intelligent vehicle is an intelligent system with functions such as environment perception, planning decision-making, and operation control, and is an important part of the intelligent transportation system. Identify the effect. In response to these problems, we present a study of the target recognition and tracking of intelligent vehicles based on grid map and lidar sensor technology. To verify the proposed method in the actual scene, we identify ...

show abstract

Section: Introductionmentioning

confidence: 99%

(Retracted) Laser radar-based intelligent vehicle target recognition and detection system using image detection technology

Zheng

2022

J. Electron. Imag.

View full text Add to dashboard Cite

show abstract

Zero-shot learning for compound fault diagnosis of bearings

Zhou

Zhao

et al. 2022

Expert Systems with Applications

View full text Add to dashboard Cite

A novel linetype recognition algorithm based on track geometry detection data

Cheng,

Han,

Han

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

The detection and recognition of track geometry are crucial for ensuring railway transportation safety and maintenance efficiency. Traditional linetype recognition algorithms struggle to effectively handle the complexities of variable track geometry and noise interference. To address these challenges, a novel linetype recognition algorithm (NLRA) is proposed, which thoroughly analyzes the characteristics of track curvature across straight lines, transition curves, and circular segments. By employing partial differential equations for data preprocessing, NLRA effectively filters out noise. Utilizing curve design parameters and integrating line anti-noise recognition with multi-segment line fitting techniques, the algorithm accurately identifies each line segment and incorporates a single-point recognition method for data gaps. Experimental results demonstrate that NLRA outperforms traditional algorithms in terms of real-time performance, recognition accuracy, anti-interference capability, and noise handling. Notably, NLRA enhances accuracy by at least 35.1% and recall by at least 17.4% on selected typical routes. This work underscores the algorithm's potential to provide efficient and accurate tools for track maintenance, facilitate advancements in track detection software, and significantly contribute to railway transportation safety.

show abstract

Flight track pattern recognition based on few labeled data with outliers

Cited by 7 publications

References 0 publications

(Retracted) Laser radar-based intelligent vehicle target recognition and detection system using image detection technology

(Retracted) Laser radar-based intelligent vehicle target recognition and detection system using image detection technology

Zero-shot learning for compound fault diagnosis of bearings

A novel linetype recognition algorithm based on track geometry detection data

Contact Info

Product

Resources

About