Mr. Teng Zhou Profile

Teng Zhou

at Shantou Univ.

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Proceedings Article | 10 November 2021 Paper

A noise-immune extreme learning machine for short-term traffic flow forecasting

Yuqi Wei, Shiqiang Zheng, Xi Yang, Boyu Huang, Guanru Tan, Teng Zhou

Proceedings Volume 12050, 120502A (2021) https://doi.org/10.1117/12.2614149

KEYWORDS: Data modeling, Performance modeling, Roads, Optimization (mathematics), Particles, Neurons, Intelligence systems, Error analysis, Data acquisition, Time series analysis

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Traffic flow prediction is an essential foundation of intelligent traffic management, and its accuracy and timeliness are essential indicators for effective traffic diversion and alleviation of traffic congestion. Aiming at the nonlinear relationship affecting traffic flow forecasting effect, a noise-immune extreme learning machine is proposed for shortterm traffic flow forecasting, which takes advantage of the gravitational search algorithm to search for an optimal global solution and used an extreme learning machine to forecast traffic flow. Extreme Learning Machine algorithm has high learning efficiency and strong generalization ability, which is widely used in regression, classification, and feature learning problems. However, due to the random setting of the input weights and the parameters of the bias matrix, the accuracy is not high, and the generalization ability is not strong. Therefore, the gravitational search algorithm is used to optimize the input weights and bias matrix to improve the accuracy of the prediction model. Based on the experimental data of Amsterdam Ring Road, the mean square error and mean absolute percentage error of the optimized model is reduced, which proves the effectiveness of the optimization. The noise-immune extreme learning machine model demonstrated superior performance and high prediction accuracy and can be well used in short-term traffic flow prediction.

Proceedings Article | 1 June 2021 Paper

CNN-HKNN for osteoporosis magnetic resonance imaging classification with data augmentation

Shuting Lin, Lin Zou, Yan Li, Zhuozhi Dai, Teng Zhou, Gang Xiao, Gang Guo, Renhua Wu, Guishan Zhang, Yaowen Chen

Proceedings Volume 11848, 118481E (2021) https://doi.org/10.1117/12.2600463

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Osteoporosis is a systemic bone disease that characterized by an increase in bone fragility due to bone microstructure damage. Currently, osteoporosis is diagnosed clinically and confirmed by Dual-energy X-ray absorptiometry (DXA), which mainly depends on bone density and somehow being subjective. This study aimed to develop a deep learning method combined with bone tissue microstructure for the early diagnosis of osteoporosis. First, we applied Gabor filters to preprocess the raw osteoporotic MRI images in three scales and three directions for data augmentation. Second, we proposed a novel hybrid CNN-HKNN system which combines convolutional neural network (CNN) with k-local hyperplane distance nearest neighbour algorithm (HKNN) for osteoporotic MRI classification. Third, we introduced a transfer learning technique by pre-training the CNN model with the augmented dataset to improve the robustness of the proposed model. Experiments under 10-fold cross-validation showed accuracy of the system is 0.963, and the area under the receiver operating characteristic curve (AUC) was 0.980. In conclusion, the proposed method has an excellent ability to diagnose osteoporosis, which has certain clinical application prospects.

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