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Shuang Wang

Researcher at Civil Aviation University of China

Publications -  6
Citations -  8

Shuang Wang is an academic researcher from Civil Aviation University of China. The author has contributed to research in topics: Machining & Surface roughness. The author has an hindex of 1, co-authored 5 publications receiving 3 citations.

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Transient separation cutting characteristic of axial ultrasonic vibration–assisted cutting

TL;DR: In this article, the transient separation cutting characteristic of axial ultrasonic vibration-assisted cutting and its influence on the cutting performance was investigated. But the transient signals of different duty ratios 0.55 to 1 were obtained, and 10 to 40% reductions of feed thrust were measured both by a PCB sensor and a Kistler dynamometer.
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Cybersecurity risk assessment method of ICS based on attack-defense tree model

TL;DR: Applying this model to the airport oil supply automatic control system can comprehensively evaluate risk, solve the practical problems faced by the airport, and also provide an important basis for the cybersecurity protection scheme of the energy industry.
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Feasibility study on machining extra-large aspect ratio aviation deep-hole Ti6Al4V part with axial ultrasonic vibration-assisted boring

TL;DR: In this article, the axial ultrasonic vibration-assisted boring (AUVB) method has been used for aviation deep-hole machining and the results demonstrate that AUVB has obvious advantages in reducing boring force, improving boring accuracy, suppressing vibration and promoting surface quality.
Posted ContentDOI

Theoretical and experimental investigation into the machining performance in axial ultrasonic vibration-assisted cutting of Ti6Al4V

TL;DR: In this paper, the effect of various factors on AUVC machining performance has been investigated, such as machining parameters, vibration parameters, tool choice, and cooling conditions, and the relationship between these factors in terms of their effect on machining performances is established theoretically.
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GAN-SR Anomaly Detection Model Based on Imbalanced Data

TL;DR: Wang et al. as discussed by the authors proposed a GAN-SR-based intrusion detection model for industrial control systems, which corrected the imbalance of minority classes in the dataset to reconstruct new minority class training samples accordingly, and high-dimensional feature extraction is completed using stacked asymmetric depth self-encoder to address the issues of low reconstruction error and lengthy training times.