Article
Automation & Control Systems
Fanli Meng, Tianyao Qi, Junjie Zhang, Hongmin Zhu, Zhenyu Yuan, Congyue Liu, Wenbo Qin, Mengning Ding
Summary: This article reports the synthesis of porous and hollow MoO3 microspheres for the detection of ammonia gas. The study found that p-h-MoO3 exhibits ultrahigh responsiveness to ammonia with significant selectivity. The composition and morphology of p-h-MoO3 were characterized using microscopic and spectroscopic techniques, and the sensing mechanism was investigated using X-ray photoelectron spectroscopy and diffuse reflectance Fourier transform infrared spectroscopy.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2022)
Article
Automation & Control Systems
Yong Feng, Jinglong Chen, Tianci Zhang, Shuilong He, Enyong Xu, Zitong Zhou
Summary: In this paper, a semi-supervised meta-learning network with attention mechanism is proposed for few-shot fault diagnosis in mechanical systems. The method utilizes unlabeled data to improve fault recognition and achieves outstanding adaptability in different situations, as demonstrated through experiments with bearing vibration datasets.
Article
Engineering, Electrical & Electronic
Guo Zhu, Yu Wang, Zhi Wang, Ragini Singh, Carlos Marques, Qiang Wu, Brajesh Kumar Kaushik, Rajan Jha, Bingyuan Zhang, Santosh Kumar
Summary: This work investigates the fabrication of sensor structures by splicing tapered/etched multicore fiber probes with multimode fiber and immobilizing them with gold nanoparticles and molybdenum disulfide nanoparticles to enhance sensitivity. The morphology of the nanoparticles is examined using high-resolution transmission electron microscopy, and the immobilized optical fiber sensor structures are characterized using scanning electron microscopy and SEM-EDX. The functionalization of the acetylcholinesterase enzyme over the nanoparticle-immobilized probe increases sensor specificity. The developed sensor probes are tested for detecting various concentrations of acetylcholine, and performance analyses are performed. The developed tapered fiber sensor exhibits a high sensitivity and wide detection range.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Automation & Control Systems
Farshid Naseri, Erik Schaltz, Daniel-Ioan Stroe, Alejandro Gismero, Ebrahim Farjah
Summary: This article introduces an efficient modeling approach based on the Wiener structure to enhance the capacity of classical equivalent circuit models in capturing the nonlinearities of Li-ion batteries. The proposed method combines a linear ECM with a static output nonlinearity block to achieve superior nonlinear mapping property while maintaining real-time efficiency. The observability of the battery model is analytically proven, and an efficient parameter estimator is introduced for real-time estimation of the Wiener model's parameters.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2022)
Article
Chemistry, Analytical
Khalid Abdulkhaliq M. Alharbi, Ahmed El-Sayed Ahmed, Maawiya Ould Sidi, Nandalur Ameer Ahammad, Abdullah Mohamed, Mohammed A. El-Shorbagy, Muhammad Bilal, Riadh Marzouki
Summary: This research reports the flow of an electroconductive incompressible ternary hybrid nanofluid with heat conduction in a boundary layer including metallic nanoparticles. A computational model is designed to enhance the mass and energy conveyance rate, improving the performance and efficiency of thermal energy propagation. The variation of ternary hybrid NPs significantly enhances the thermophysical features of the base fluid.
Article
Engineering, Multidisciplinary
Yinghao Zhao, Loke Kok Foong
Summary: This paper proposes a reliable predictive tool for the electrical power output of combined cycle power plants using novel soft computing methods. By combining the electrostatic discharge algorithm with an artificial neural network, the proposed hybrid outperforms conventional methods in both training and testing phases.
Article
Chemistry, Analytical
Kiran Jabeen, Muhammad Attique Khan, Majed Alhaisoni, Usman Tariq, Yu-Dong Zhang, Ameer Hamza, Arturas Mickus, Robertas Damasevicius
Summary: This paper proposes a framework for breast cancer classification from ultrasound images using deep learning and feature fusion. The framework achieves high accuracy in early cancer detection, outperforming recent techniques.
Article
Engineering, Multidisciplinary
Yinghao Zhao, Hesong Hu, Chaolin Song, Zeyu Wang
Summary: This study uses artificial neural network models to estimate the compressive strength of manufactured sand concrete. Two improved ANN models are developed using conventional algorithms and metaheuristic algorithms, respectively, and it is found that the improved models have higher accuracy. Additionally, the analysis reveals the significant impact of curing age and water to binder ratio on the compressive behavior of concrete.
Article
Engineering, Multidisciplinary
Caijiang Lu, Hai Zhou, Linfeng Li, Aichao Yang, Changbao Xu, Zhengyu Ou, Jingqi Wang, Xi Wang, Fei Tian
Summary: This paper proposed a split-core magnetoelectric current sensor with high detection sensitivity and stability for wireless measurement of 50 Hz current. The novel design concept provides a new idea for online current monitoring of the Internet of Things in power systems.
Article
Engineering, Multidisciplinary
Yang Yu, Maria Rashidi, Bijan Samali, Masoud Mohammadi, Thuc N. Nguyen, Xinxiu Zhou
Summary: This study proposes a vision-based crack diagnosis method using deep convolutional neural network (DCNN) and enhanced chicken swarm algorithm (ECSA) for model training and optimization. The method is tested on image patches cropped from damaged concrete samples, and its performance is evaluated using a group of statistical evaluation indicators.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2022)
Article
Chemistry, Analytical
Dongyue Wang, Dongzhi Zhang, Qiannan Pan, Tian Wang, Fengjiao Chen
Summary: This paper introduces a high-efficiency CO gas sensor based on ZnO/SnSe2 composite film, and studies its structural characteristics, response performance, and sensing mechanism. The study found that the ZnO/SnSe2 sensor with a loading rate of 25% SnSe2 showed the highest response performance, and UV light can improve the gas-sensing characteristics of the sensor.
SENSORS AND ACTUATORS B-CHEMICAL
(2022)
Article
Instruments & Instrumentation
Guang Zhang, Junyu Chen, Zheng Zhang, Min Sun, Yang Yu, Jiong Wang, Shibo Cai
Summary: This study describes the magnetic analysis of a novel double cup-shaped gap magnetorheological (MR) clutch using four kinds of Halbach array to excite the MR gel. The distribution of magnetic flux density, shear yield stress, dynamic viscosity, and shear stress inside the MR gel is obtained and carefully studied. The structure of the prototype is optimized based on multi-physics analysis and the optimal MR clutch is developed and tested for magneto-static torque.
SMART MATERIALS AND STRUCTURES
(2022)
Article
Engineering, Multidisciplinary
Chen Zhao, Jun Lv, Shichang Du
Summary: This paper proposes a spherical multi-output Gaussian process (S-MOGP) method to model and monitor geometrical deviations on 3D surfaces. By mapping the 3D surface to a 2D parameter domain and establishing a state equation based on the multi-output Gaussian process, statistics are calculated and control charts are presented to effectively model and monitor the geometrical deviations.
Article
Automation & Control Systems
Shengnan Tang, Yong Zhu, Shouqi Yuan
Summary: This article investigates the problem of fault diagnosis in hydraulic pumps, using a deep learning method for fault identification and introducing the Bayesian optimization algorithm for selecting hyperparameters. By comparing with traditional methods, the results show that CNN-BO can accurately achieve intelligent fault diagnosis of hydraulic pumps.
Article
Engineering, Electrical & Electronic
Liang Zhang, Hao Zhang, Guowei Cai
Summary: A novel multiclass wind turbine bearing fault diagnosis strategy based on the conditional variational generative adversarial network (CVAE-GAN) model combining multisource signals fusion is proposed in this study. The strategy increases diagnostic accuracy by converting multisource vibration signals into 2-D signals and fusing them.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Chemistry, Analytical
Akinrinade George Ayankojo, Roman Boroznjak, Jekaterina Reut, Andres Opik, Vitali Syritski
Summary: The study presents an electrochemical sensor based on a molecularly imprinted polymer receptor for quantitatively detecting the S1 subunit of the spike protein of SARS-CoV-2. It has a quick response time and low detection limit, showing promise as a point-of-care testing platform for early diagnosis of COVID-19.
SENSORS AND ACTUATORS B-CHEMICAL
(2022)
Article
Automation & Control Systems
Rui Yao, Chen Guo, Wu Deng, Huimin Zhao
Summary: This study proposes a scale-adaptive mathematical morphology spectrum entropy (AMMSE) to improve the scale selection for signal feature extraction. By reducing feature loss and automatically determining the scale, AMMSE achieves better results compared to existing methods.
Article
Engineering, Electrical & Electronic
Gyan Prakash Mishra, Dharmendra Kumar, Vijay Shanker Chaudhary, Santosh Kumar
Summary: This paper presents a gas detection sensor based on microstructured-core photonic crystal fiber, and analyzes the quantitative dependence of its guiding properties on geometrical parameters at different wavelengths. The sensor shows high sensitivity and minimal confinement loss, making it suitable for detecting gases such as methane and hydrogen fluoride.
IEEE SENSORS JOURNAL
(2022)
Article
Engineering, Multidisciplinary
Jun Li, Yongbao Liu, Qijie Li
Summary: The paper introduces a novel fault diagnosis approach for rolling bearings, combining DA-RNN and CBAM technologies, which enhances diagnostic accuracy by handling imbalanced datasets and utilizing convolutional neural networks.
Article
Automation & Control Systems
Yunjia Dong, Yuqing Li, Huailiang Zheng, Rixin Wang, Minqiang Xu
Summary: This paper proposes an intelligent fault diagnosis framework based on dynamic model and transfer learning for rolling element bearings. It addresses the small sample problem by generating simulation data using a dynamic model and applying the diagnosis knowledge gained from the simulation data to real scenarios through parameter transfer strategies, ultimately improving the fault identification performance.
