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3 results on '"Lingling Lu"'

1. The Effects of Professional Identity, Empathy with Nature, and Consideration of Future Consequences on Sustainability Consciousness among Chinese Undergraduate Engineering Students.

Author

LINGLING LU, FADZIL, HIDAYAH MOHD, PENG YEN LIEW, AN JIE LYE, QUANWANG WANG, and CHAN CHOONG FOONG

Subjects
PROFESSIONAL identity, EMPATHY, ENGINEERING students, UNDERGRADUATES, SUSTAINABILITY
Abstract

Sustainability consciousness is important for engineers' effective execution of sustainable development goals. Today's students are tomorrow's engineers. Hence. exploring fuctors that influence undergraduate engineering students' sustainability consciousness is essential. This study explored the effects of professional identity, empathy with nature. and consideration of future consequences on three dimensions of sustainability consciousness, namely knowingness, attitudes and behaviors. A survey was conducted among 263 first-year undergraduate mechanical engineering students in Shanxi, China. Using SPSS AMOS. the measurement model verified that the questionnaires were valid and reliable. Subsequently, the structural model analysis reported that (1) professional identity positively influenced three dimensions of sustainability consciousness; (2) empathy with nature was a positive predictor of three dimensions of sustainability consciousness; (3) consideration of future consequences significantly predicted sustainability behaviors but did not predict sustainability knowingness and attitudes. The present study provides justifications for integrating professional identity and empathy with nature in the engineering curriculum. [ABSTRACT FROM AUTHOR]

Published
2024

2. Application of model reduction technique and structural subsection technique on optimal sensor placement of truss structures.

Author

Lingling Lu, Xi Wang, Lijuan Liao, Yanpeng Wei, Chenguang Huang, and Yanchi Liu

Subjects
TRUSSES -- Design & construction, SENSOR placement, GENETIC algorithms, KINETIC energy, MECHANICAL vibration research
Abstract

An optimal sensor placement (OSP) method based on structural subsection technique (SST) and model reduction technique was proposed for modal identification of truss structures, which was conducted using genetic algorithm (GA). The constraints of GA variables were determined by SST in advance. Subsequently, according to model reduction technique, the optimal group of master degrees of freedom and the optimal objective function value were obtained using GA in a case of the given number of sensors. Correspondingly, the optimal number of sensors was determined according to optimal objective function values in cases of the different number of sensors. The proposed method was applied on a scaled jacket offshore platform to get its optimal number of sensors and the corresponding optimal sensor layout. Then modal kinetic energy and modal assurance criterion were adopted to evaluate vibration energy and mode independence property. The experiment was also conducted to verify the effectiveness of the selected optimal sensor layout. The results showed that experimental modes agreed reasonably well with numerical results. Moreover the influence of the proposed method using different optimal algorithms and model reduction technique on optimal results was also compared. The results showed that the influence was very little. [ABSTRACT FROM AUTHOR]

Published
2015
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3. Using Deep Learning Techniques for Sandwich Panels with Truss Core Damage Detection

Author

Wang, Yabo, and, Lingling Lu, and Song, Hongwei

Abstract

In the previous study, the accuracy of damage detection for the sandwich structures with truss core (SPTC) was affected by the selected damage index, Other than this, human subjective judgment could also not directly determine the degree and the location of damage for SPTC. In this paper, the deep learning method is applied to identify the damage for SPTC, and the dataset of the training deep learning model is obtained based on the dynamic method. This paper adopts to the Caffe, which is a deep learning open source framework, object detection model Faster R-CNN is utilized to study the lattice sandwich plate. The damage data set, the optimal hyperparameters for training the deep learning model, and the optimal ratios of the test set and training set for damage dataset are also studied. It is difficult to detect the damage of SPTC applying to the deep learning algorithms, so the good results cannot be gotten. In this paper, the method of Faster R-CNN has used extracts the deep features of the defective target by ZF that is a kind of Convolutional Neural Network (CNN), the method effectively solves the problem that the traditional algorithm cannot effectively detect the damage. As to the damage of SPTC that the traditional algorithms could also identify, the deep learning algorithm is excelled, the experimental mean average precision(mAP) can be raised to 90%. At the same time, the deep learning method can effectively identify locations and size of the damage in SPTC, the method is proven that the accuracy is higher and the speed is faster for damage detection. In the future, a real-time damage monitoring system is possible, and the theory is worth exploring further.

Published
2019

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