JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY

Early Access
Current Issue
Archive

MORE+

Optimal Output Power Control of Switched Reluctance Generator at a Constant Speed

2020, 29(4): 435-444 . DOI: 10.15918/j.jbit1004-0579.20075

HTML

PDF

Dynamic Characteristics of the Jet Force on the Flapper of the Pilot Stage in a Flapper Nozzle Servo Valve Under the Flow-Solid Interaction

2020, 29(4): 445-455 . DOI: 10.15918/j.jbit1004-0579.20068

HTML

PDF

Resonance Suppression Strategy for Humanoid Robot Arm

2020, 29(4): 456-465 . DOI: 10.15918/j.jbit1004-0579.20020

HTML

PDF

Autonomous Tracking Control for Four-Wheel Independent Steering Robot Based on Improved Pure Pursuit

2020, 29(4): 466-473 . DOI: 10.15918/j.jbit1004-0579.19110

HTML

PDF

MORE+

Optimal Output Power Control of Switched Reluctance Generator at a Constant Speed

Liwei Shao, Lei Dong, Lulu Ling

2020, 29(4): 435-444. doi: 10.15918/j.jbit1004-0579.20075

[Abstract](32) [FullText HTML](19) [PDF 834KB](19)

Abstract:
In order to control the output power of a switched reluctance generator(SRG) at a constant speed, the output power of SRG is theoretically analyzed by using freewheeling control. First, through a theoretical analysis, a finite element simulation and an experiment, it was verified that the output power of SRG cannot be improved by using freewheeling control with a single pulse control method(SPCM). Then, the maximum output power can be obtained by optimizing the turn off angles of SPCM at a constant speed, and at the same time, the formula of the optimal turn-off angle was presented, which meets the criterion for the output power maximization. Finally, numerical simulation and experimental results demonstrated the validity of the theoretical analysis.

Object Recognition Algorithm Based on an Improved Convolutional Neural Network

Zheyi Fan, Yu Song, Wei Li

2020, 29(2): 139-145. doi: 10.15918/j.jbit1004-0579.19116

[Abstract](3) [FullText HTML](1) [PDF 662KB](1)

Abstract:
In order to accomplish the task of object recognition in natural scenes, a new object recognition algorithm based on an improved convolutional neural network (CNN) is proposed. First, candidate object windows are extracted from the original image. Then, candidate object windows are input into the improved CNN model to obtain deep features. Finally, the deep features are input into the Softmax and the confidence scores of classes are obtained. The candidate object window with the highest confidence score is selected as the object recognition result. Based on AlexNet, Inception V1 is introduced into the improved CNN and the fully connected layer is replaced by the average pooling layer, which widens the network and deepens the network at the same time. Experimental results show that the improved object recognition algorithm can obtain better recognition results in multiple natural scene images, and has a higher degree of accuracy than the classical algorithms in the field of object recognition.

Anisotropic Total Variation Regularization Based NAS-RIF Blind Restoration Method for OCT Image

Xuesong Fu, Jianlin Wang, Zhixiong Hu, Yongqi Guo, Kepeng Qiu, Rutong Wang

2020, 29(2): 146-157. doi: 10.15918/j.jbit1004-0579.20014

[Abstract](2) [FullText HTML](1) [PDF 1663KB](1)

Abstract:
Based on anisotropic total variation regularization (ATVR), a nonnegativity and support constraints recursive inverse filtering(NAS-RIF) blind restoration method is proposed to enhance the quality of optical coherence tomography (OCT) image. First, ATVR is introduced into the cost function of NAS-RIF to improve the noise robustness and retain the details in the image. Since the split Bregman iterative is used to optimize the ATVR based cost function, the ATVR based NAS-RIF blind restoration method is then constructed. Furthermore, combined with the geometric nonlinear diffusion filter and the Poisson-distribution-based minimum error thresholding, the ATVR based NAS-RIF blind restoration method is used to realize the blind OCT image restoration. The experimental results demonstrate that the ATVR based NAS-RIF blind restoration method can successfully retain the details in the OCT images. In addition, the signal-to-noise ratio of the blind restored OCT images can be improved, along with the noise robustness.

Electromagnetic Tomography System for Defect Detection of High-Speed Rail Wheel

Yu Miao, Xianglong Liu, Ze Liu, Yuanli Yue, Jianli Wu, Jiwei Huo, Yong Li

2020, 29(4): 474-483. doi: 10.15918/j.jbit1004-0579.20047

[Abstract](20) [FullText HTML](10) [PDF 1571KB](10)

Abstract:
A novel electromagnetic tomography(EMT) system for defect detection of high-speed rail wheel is proposed, which differs from traditional electromagnetic tomography systems in its spatial arrangements of coils. A U-shaped sensor array was designed, and then a simulation model was built with the low frequency electromagnetic simulation software. Three different algorithms were applied to perform image reconstruction, therefore the defects can be detected from the reconstructed images. Based on the simulation results, an experimental system was built and image reconstruction were performed with the measured data. The reconstructed images obtained both from numerical simulation and experimental system indicated the locations of the defects of the wheel, which verified the feasibility of the EMT system and revealed its good application prospect in the future.

News & Announcement

CALL FOR PAPERS
Special Issue On
Multi-Source Remote Sensing Data Processing and Applications

Dec.29,2020

MORE+

Submission & Review

Vol.29,No.4,(December,2020)

ISSN1004-0579

CN11-2916/T

Indexed by EI;Scopus

Eidtor-in-chief:Ran Tao,Beijing Institute of Technology,Beijing,China