Wuliang Yin

University of Manchester

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

A. J. Peyton

University of Manchester

156

Mingyang Lu

University of Manchester

Zhijie Zhang

North University of China

Ruochen Huang

Fuzhou University

Qian Zhao

Hebei Agricultural University

Xiaobai Meng

University of Manchester

Yuedong Xie

Beihang University

Zihan Xia

University of Manchester

Haoze Chen

North University of China

Zhigang Qu

Tianjin University of Science and Technology

Cooperative Institutions

Battery Park

167

University of Manchester

112

Chinese Academy of Sciences

Tianjin University

Zhejiang University

North University of China

Kunming University of Science and Technology

University of Chinese Academy of Sciences

Tianjin University of Science and Technology

Xi'an Jiaotong University

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

Depth Classification of Defects Based on Neural Architecture Search

IEEE Access (2021)

Haoze Chen Zhijie Zhang Chenyang Zhao Jiaqi Liu Wuliang Yin

As an important part of non-destructive testing, infrared thermography testing is widely used in various fields of industrial development for monitoring the quality of metal parts. Considering the problem of low detection rate of surface defects on steel parts, we explored the application of neural architecture search (NAS) in infrared thermography area for the first time. On the one hand, we compared different time-series temperature features of defect locations in infrared images and validate the performance of three different features such as heating, cooling and full process by machine learning methods. On the other hand, we searched for multilayer perceptron through NAS technology to classify defects with different depths. Experiments have proved that the time-series temperature feature is very effective when used in the depth classification of defects, and the accuracy rate can reach 93% under the verification of traditional machine learning methods. The NAS technique used in this paper can search 100 multilayer perceptrons in a minimum of 121s and achieve 100% defect classification accuracy.

Thermography

Perceptron

Feature (linguistics)

Multilayer perceptron

10.1109/access.2021.3077961

Cite

Citations (11)

Research on intelligent vibration damping base of washing machine based on a stiffness-variable magnetorheological elastomer

Sound&Vibration (2024)

Zihan Li Wuliang Yin Zefeng Li

Traditional washing machine vibration-damping bases have a fixed stiffness, which can lead to structural resonance when the motor’s excitation frequency aligns with the machine’s inherent vibration frequency during the washing and spinning processes. This not only amplifies the noise caused by vibrations but also accelerates the wear and tear of the internal components. In this study, a pioneering approach has been introduced, developing an intelligent vibration-damping base for drum washing machines based on the dynamics of a mass-spring-damped single-degree-of-freedom system. This innovative base is designed with variable stiffness magnetorheological elastomer, utilizing the dynamics of the mass-spring-damped system to adaptively counteract vibrations. A vibration transmission rate curve specific to drum washing machines has been derived, and an intelligent control strategy for the drum washing machine’s vibration-damping base has been proposed. Dynamic testing of the drum washing machine’s vibration-damping base was conducted to verify the effectiveness of the intelligent damping base. The intelligent damping base addresses the unavoidable low-frequency resonance issues of traditional passive damping bases in washing machines and overcomes the drawback that the performance parameters of traditional passive isolation structures cannot be changed once set, providing a new direction for the damping of household appliances.

Dynamic Vibration Absorber

Natural frequency

Damping torque

Base (topology)

Damping ratio

10.59400/sv2036

Cite

Citations (0)

Development of a Field Deployable EM Inspection Tool for the Evaluation of Power Station Steels

JW Wilson Noushin Karimian Wuliang Yin Jun Liu M. Strangwood

Source

Cite

Citations (1)

Evaluation of the effects of the screen based on an analytical solution of a simplified MIT system

Journal of Physics Conference Series (2010)

Wuliang Yin Bachir Dekdouk Christos Ktistis A. J. Peyton

Magnetic induction tomography (MIT) is a technology that reconstructs cross sectional conductivity distribution of an object from mutual impedance measurements of coils distributed around the object. In high frequency and low conductivity applications, an outer screen is generally used to confine the magnetic fields and to prevent electromagnetic interference from outside. However, the screen will alter the sensing and excitation field, hence the sensitivity distribution of the coil array. Therefore, the design parameters of the screen (thickness, distance to coil, materials) are important to the performance of the sensor system. This paper presents a simple method based on an analytical solution for the evaluation of the effects of the screen. The advantage of the approach includes efficient modelling of thin screens and physical insights into the effects of the screen.

Induction coil

Electromagnetic induction

Skin effect

Inductive sensor

10.1088/1742-6596/224/1/012154

Cite

Citations (4)

Table of Content

Shashika Munasingha Keerthi Kodithuwakkuge Ravindu Priyankara Akira Gimantha Upasena Jie Fu

Table (database)

10.1109/ecbios54627.2022.9945046

Cite

Citations (0)

Off-line measurement of decarburization of steels using a multifrequency electromagnetic sensor

Scripta Materialia (2008)

X.J. Hao Wuliang Yin M. Strangwood A. J. Peyton P. F. Morris

Decarburization

Rod

10.1016/j.scriptamat.2008.01.042

Cite

Citations (61)

Biofriendly and Regenerable Emotional Monitor from Interfacial Ultrathin 2D PDA/AuNPs Cross-linking Films

ACS Applied Materials & Interfaces (2019)

Liming Chen Youju Huang Liping Song Wuliang Yin Linxi Hou

Well-designed 2D materials with ultrathin structures show great potential for humidity-sensing performance owing to their high surface-volume ratio and a great number of exposed atoms on the surface. However, some sensing elements employed for healthcare applications may be considered as potentially risky, such as inflammation, granuloma formation, and carcinogenesis. Herein, we explored biofriendly humidity-sensing characteristics inspired by the great biocompatibility and conductivity of hyperbranched polyethyleneimine-capped gold nanoparticles and cross-linked with polydopamine from the adhesive proteins in mussels. It was successfully employed into two kinds of wearable devices, sports watches and breathing masks, for real-time recording humidity's fluctuation in expiration and sweat with changes of individual's crying, laughing, nervous, sleeping, training, and cold states. The wearable devices allow us to monitor individual's physical activities and emotional states well, suggesting a promising prospect in safe, reusable, long term, and noncontact human health monitoring applications.

10.1021/acsami.9b11918

Cite

Citations (27)

Simulation of an Electromagnetic Acoustic Transducer Array by Using Analytical Method and FDTD

Journal of Sensors (2016)

Yuedong Xie S. Rodriguez Wenbo Zhang Zenghua Liu Wuliang Yin

Previously, we developed a method based on FEM and FDTD for the study of an Electromagnetic Acoustic Transducer Array (EMAT). This paper presents a new analytical solution to the eddy current problem for the meander coil used in an EMAT, which is adapted from the classic Deeds and Dodd solution originally intended for circular coils. The analytical solution resulting from this novel adaptation exploits the large radius extrapolation and shows several advantages over the finite element method (FEM), especially in the higher frequency regime. The calculated Lorentz force density from the analytical EM solver is then coupled to the ultrasonic simulations, which exploit the finite-difference time-domain (FDTD) method to describe the propagation of ultrasound waves, in particular for Rayleigh waves. Radiation pattern obtained with Hilbert transform on time-domain waveforms is proposed to characterise the sensor in terms of its beam directivity and field distribution along the steering angle, which can produce performance parameters for an EMAT array, facilitating the optimum design of such sensors.

Electromagnetic acoustic transducer

10.1155/2016/5451821

Cite

Citations (16)

Depth Evaluation for Metal Surface Defects by Eddy Current Testing using Deep Residual Convolutional Neural Networks

arXiv (Cornell University) (2021)

Meng Tian Yang Tao Ziqi Chen Jorge R. Salas Avila Qiaoye Ran

Eddy current testing (ECT) is an effective technique in the evaluation of the depth of metal surface defects. However, in practice, the evaluation primarily relies on the experience of an operator and is often carried out by manual inspection. In this paper, we address the challenges of automatic depth evaluation of metal surface defects by virtual of state-of-the-art deep learning (DL) techniques. The main contributions are three-fold. Firstly, a highly-integrated portable ECT device is developed, which takes advantage of an advanced field programmable gate array (Zynq-7020 system on chip) and provides fast data acquisition and in-phase/quadrature demodulation. Secondly, a dataset, termed as MDDECT, is constructed using the ECT device by human operators and made openly available. It contains 48,000 scans from 18 defects of different depths and lift-offs. Thirdly, the depth evaluation problem is formulated as a time series classification problem, and various state-of-the-art 1-d residual convolutional neural networks are trained and evaluated on the MDDECT dataset. A 38-layer 1-d ResNeXt achieves an accuracy of 93.58% in discriminating the surface defects in a stainless steel sheet. The depths of the defects vary from 0.3 mm to 2.0 mm in a resolution of 0.1 mm. In addition, results show that the trained ResNeXt1D-38 model is immune to lift-off signals.

Lift (data mining)

10.48550/arxiv.2104.02472

Cite

Citations (0)

Shape characterization and depth recognition of metal cracks based on laser infrared thermography and machine learning

Expert Systems with Applications (2023)

Haoze Chen Zhijie Zhang Wuliang Yin Guangyu Zhou Luxiang Wang

Thermography

10.1016/j.eswa.2023.122083

Cite

Citations (12)