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Transportation Engineering—Special Issue for Intelligent Transportation
Intelligent scheduling technology of highway emergency rescue vehicle
Xiuguang SONG,Xinming GUO,Fang YAN,Guoqiang LI,Yuan TIAN
2023, 53(4):  1-17.  doi:10.6040/j.issn.1672-3961.0.2023.045
Abstract ( 133 )   HTML( 83 )   ( 2 )   PDF (2239KB) ( 83 )   Save
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Traffic accidents had suddenness, complexity and severity. Efficient and reasonable dispatch of emergency rescue vehicles played a vital role in reducing casualties and property losses. The influencing factors of rescue route planning were introduced. The model was described from the single objective model and the multi-objective model. Combined with the characteristics of emergency rescue in sudden traffic accidents, the route optimization algorithm of emergency rescue vehicles was summarized from two aspects: precise algorithm and meta-heuristic algorithm. The current emergency rescue vehicle scheduling technology and development trend were summarized and prospected.

Research review of highway differentiated toll collection
Jianqing WU,Yanqiang HUO,Jianzhu WANG,Hongyu GUO
2023, 53(4):  18-29.  doi:10.6040/j.issn.1672-3961.0.2023.063
Abstract ( 102 )   HTML( 52 )   ( 2 )   PDF (1386KB) ( 52 )   Save
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In order to formulate a scientific and reasonable scheme for highway differentiated toll collection, the background, realization manners, related theories and key technologies are systematically described, and the cases upgraded in Guangxi, Tianjin and Hebei are briefly introduced with design essentials and application effects, and outlooks on the research trend of highway differentiated toll collection are given.

A snow point cloud denoising algorithm based on roadside LiDAR
ZHOU Yong, LAN Xiaowei, LÜ Bin, LI Jian
2023, 53(4):  30-36.  doi:10.6040/j.issn.1672-3961.0.2022.139
Abstract ( 100 )   PDF (3994KB) ( 30 )   Save
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Signal control method integrating history and current traffic flow
ZHOU Xiaoxin, LIAO Zhuhua, LIU Yizhi, ZHAO Yijiang, FANG Yijie
2023, 53(4):  48-55.  doi:10.6040/j.issn.1672-3961.0.2022.302
Abstract ( 62 )   PDF (4555KB) ( 18 )   Save
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Vehicle coordinate conversion model of virtual rail system of superhighway
HE Yongming, QUAN Cong, WEI Kun, FENG Jia, WAN Yanan, CHEN Shisheng
2023, 53(4):  56-64.  doi:10.6040/j.issn.1672-3961.0.2023.014
Abstract ( 59 )   PDF (3183KB) ( 16 )   Save
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Machine Learning & Data Mining
The classification of mild cognitive impairment based on supervised graph regularization and information fusion
Ying LI,Jiankun WANG
2023, 53(4):  65-73.  doi:10.6040/j.issn.1672-3961.0.2023.025
Abstract ( 79 )   HTML( 30 )   ( 3 )   PDF (3086KB) ( 30 )   Save
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To precisely distinguish progressive and stable mild cognitive impairment (MCI). The projection matrix was learned from Alzheimer′s disease samples and normal control samples. The supervised graph regularization was used to optimize the local nearest neighbor relationship of the samples. Based on the projection matrix, the spatial transformation of the MCI samples was carried out to extract the discriminative features of progressive and stable MCI. The proposed features were fused with the scores of Mini-Mental State Examination and apolipoprotein E4. The SVM classifier was trained using the fused features for the MCI classification. The experiments were conduct on the Alzheimer′s Disease Neuroimaging Initiative (ADNI) database. The classification accuracy reached to 73.33%. Compared with the existing approaches, the proposed method significantly improved the classification accuracy, sensitivity and specificity.

A rough K-means clustering algorithm optimized by mutation firefly algorithm
LI Zhaobin, YE Jun, ZHOU Haoyan, LU Lan, XIE Li
2023, 53(4):  74-82.  doi:10.6040/j.issn.1672-3961.0.2022.268
Abstract ( 59 )   PDF (1321KB) ( 24 )   Save
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Boosting classification algorithm for imbalanced drift data stream based on dynamic ensemble selection
ZHANG Xilong, HAN Meng, CHEN Zhiqiang, WU Hongxin, LI Muhang
2023, 53(4):  83-92.  doi:10.6040/j.issn.1672-3961.0.2022.126
Abstract ( 57 )   PDF (1310KB) ( 22 )   Save
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Aquila optimizer based on hybrid improved strategies
LIU Qingxin, QI Qi, JIA Heming, LI Ni
2023, 53(4):  93-103.  doi:10.6040/j.issn.1672-3961.0.2022.128
Abstract ( 69 )   PDF (4774KB) ( 27 )   Save
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Electrical Engineering
Data fitting method for electricity consumption of power market users considering behavioral characteristics
Hong YU,Juan DU,Lin WEI,Li ZHANG
2023, 53(4):  113-119.  doi:10.6040/j.issn.1672-3961.0.2023.077
Abstract ( 56 )   HTML( 22 )   ( 2 )   PDF (2949KB) ( 22 )   Save
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To address the problem that the electricity consumption behavior of market-based users was complex and variable, and the laws of electricity data were difficult to be accurately characterized, a market-based user electricity data fitting method considering behavioral characteristics was proposed. The K-means clustering algorithm was used to classify the electricity consumption behavior of customers and clarify the typical characteristics of each type of customers; the neural network model based on orthogonal polynomials was constructed, in which the neural network weight coefficients were trained by gradient descent algorithm and the orthogonal polynomials were Chebyshev polynomials, Hermite polynomials, Legendre polynomials and Laguerre polynomials. The simulation analysis was carried out using the electricity data of users in Jinan, Shandong Province, and four different orthogonal polynomials were used to fit the electricity data and calculate the evaluation indexes for different categories of users, so as to summarize the most suitable fitting methods for users with different behavioral characteristics. The simulation results showed that the power data fitting effect differed significantly among different implementation methods for similar users, and the fitting accuracy of the neural network models based on Hermite polynomials and Laguerre polynomials was relatively high, but the polynomial models with the highest power data fitting accuracy for different categories of users were different. Selecting the corresponding orthogonal polynomials to form a neural network fitting model according to the type of electricity consumption behavior was an effective way to achieve accurate fitting of user electricity data.

The galloping characteristics and mechamism of wire wear of quad-bundled conductor
ZHANG Sixiang, ZHANG Yong, JIANG Weiguo, YUAN Chunyuan, WANG Xiaoyang, TIAN Li
2023, 53(4):  120-127.  doi:10.6040/j.issn.1672-3961.0.2022.355
Abstract ( 57 )   PDF (8791KB) ( 11 )   Save
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Analytical analysis of small disturbance stability of grid-connected CIG system
WENG Hua, ZHU Weijun, LI Yujun, YU Dan, ZHANG Yumeng, HUA Fenglin
2023, 53(4):  128-139.  doi:10.6040/j.issn.1672-3961.0.2022.265
Abstract ( 57 )   PDF (5743KB) ( 6 )   Save
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Static equivalent of distribution network with distributed photovoltaic based on convolutional neural network
FAN Haiwen, HAO Xudong, ZHAO Kang, XING Facai, JIANG Zhe, LI Changgang
2023, 53(4):  140-148.  doi:10.6040/j.issn.1672-3961.0.2022.308
Abstract ( 51 )   PDF (5186KB) ( 18 )   Save
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Optimization of manufacturing parameters for optical fiber preform core based on intelligent algorithm
Haoyuan LI,Jingming YU,Guilin ZHANG,Bin ZHANG
2023, 53(4):  149-156.  doi:10.6040/j.issn.1672-3961.0.2022.264
Abstract ( 64 )   HTML( 20 )   ( 1 )   PDF (3813KB) ( 20 )   Save
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Combined with back propagation (BP) neural network and genetic algorithm, a high-quality and low-cost process parameter optimization method was proposed. The flow rate of two blowtorch gases (H2-1, H2-2, H2-3, Ar-1, Ar-2, Ar-3, O2-1, O2-2, SiCl4) during the preparation of optical fiber preform core layer by vapor axial deposition (VAD) was selected as the input variable. The quality of the prepared optical fiber preform core layer was taken as the output variable in the established neural network model. The trained neural network model was combined with the genetic algorithm with global optimization ability, and the high quality core layer of optical fiber preform was taken as the optimization objective to obtain high quality gas parameters. The obtained parameters were selected at low cost, and the high quality and low cost process parameters were obtained. The experimental results showed that compared with the manual optimization results before optimization, the prepared optical fiber preform core layer met the high quality requirements and the cost was reduced by 22.19%.

Collaborative transportation for bulky items based on multi-robot formation control
ZHANG Haisen, ZHANG Huang, WANG Changshun
2023, 53(4):  157-162.  doi:10.6040/j.issn.1672-3961.0.2022.311
Abstract ( 63 )   PDF (2448KB) ( 18 )   Save
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Design of intelligent auxiliary construction system for excavator
ZHAO Tianhuai, WANG Mushu, PAN Weigang, KANG Chao, QIN Shiming, XU Fei
2023, 53(4):  163-172.  doi:10.6040/j.issn.1672-3961.0.2022.020
Abstract ( 49 )   PDF (6539KB) ( 10 )   Save
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