Leilei Wang¹, Li Chen², Haihang Cui², Xu Zheng^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.2, pp. 1-2, 2024, DOI:10.32604/icces.2024.011434

Abstract The emerging technique of artificial micro/nano-motors [1] provides a vivid example of the idea using tiny machines to finish jobs in microscopic world. Among many micro/nano-motors, microbubble driven micromotor is a unique type that can reach the highest propulsion speed [2, 3], owing to the high surface energy of the bubble and the focused hydrodynamic jet during bubble collapse that can significantly enhance micromotor’s propulsion. Recent progress has demonstrated that the microbubble itself can implement new functions for the micromotor based on bubble dynamics and induced hydrodynamic flow, rather than merely providing energy. For instance,… More >

Alaa U. Khawaja¹, Ahmad Shaf^2,*, Faisal Al Thobiani³, Tariq Ali⁴, Muhammad Irfan⁵, Aqib Rehman Pirzada², Unza Shahkeel²

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2399-2420, 2024, DOI:10.32604/cmes.2024.054257 - 31 October 2024

Abstract Electric motor-driven systems are core components across industries, yet they’re susceptible to bearing faults. Manual fault diagnosis poses safety risks and economic instability, necessitating an automated approach. This study proposes FTCNNLSTM (Fine-Tuned TabNet Convolutional Neural Network Long Short-Term Memory), an algorithm combining Convolutional Neural Networks, Long Short-Term Memory Networks, and Attentive Interpretable Tabular Learning. The model preprocesses the CWRU (Case Western Reserve University) bearing dataset using segmentation, normalization, feature scaling, and label encoding. Its architecture comprises multiple 1D Convolutional layers, batch normalization, max-pooling, and LSTM blocks with dropout, followed by batch normalization, dense layers, and More >

Zhuo Liu, Yecui Yan^*

Frontiers in Heat and Mass Transfer, Vol.22, No.5, pp. 1443-1460, 2024, DOI:10.32604/fhmt.2024.056091 - 30 October 2024

Abstract The external rotor hub motor adopts direct drive mode, no deceleration drive device, and has a compact structure. Its axial size is smaller than that of a deceleration-driven hub motor, which greatly reduces the weight of the vehicle and increases the cruising range of the vehicle. Because of the limited special working environment and performance requirements, the hub motor has a small internal space and a large heat generation, so it puts forward higher requirements for heat dissipation capacity. For the external rotor hub motor, a new type of in-tank water-cooled structure of hub motor… More >

Randeep Singh^1,*, Tomoki Oridate², Tien Nguyen²

Frontiers in Heat and Mass Transfer, Vol.22, No.3, pp. 703-717, 2024, DOI:10.32604/fhmt.2024.050744 - 11 July 2024

Abstract Electrification of vehicles intensifies their cooling demands due to the requirements of maintaining electronics/electrical systems below their maximum temperature threshold. In this paper, passive cooling approaches based on heat pipes have been considered for the thermal management of electric vehicle (EV) traction systems including battery, inverter, and motor. For the battery, a heat pipe base plate is used to provide high heat removal (180 W per module) and better thermal uniformity (<5°C) for the battery modules in a pack while downsizing the liquid cold plate system. In the case of Inverter, two phase cooling system… More > Graphic Abstract

Xin Xiong¹, Jing Zhang¹, Sanli Yi¹, Chunwu Wang², Ruixiang Liu³, Jianfeng He^1,*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4659-4681, 2024, DOI:10.32604/cmc.2024.050528 - 20 June 2024

Abstract The analysis of microstates in EEG signals is a crucial technique for understanding the spatiotemporal dynamics of brain electrical activity. Traditional methods such as Atomic Agglomerative Hierarchical Clustering (AAHC), K-means clustering, Principal Component Analysis (PCA), and Independent Component Analysis (ICA) are limited by a fixed number of microstate maps and insufficient capability in cross-task feature extraction. Tackling these limitations, this study introduces a Global Map Dissimilarity (GMD)-driven density canopy K-means clustering algorithm. This innovative approach autonomously determines the optimal number of EEG microstate topographies and employs Gaussian kernel density estimation alongside the GMD index for… More >

Jiajie Shen¹, Yan Wang^1,*, Dongxu Zhang²

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4349-4370, 2024, DOI:10.32604/cmc.2024.047903 - 20 June 2024

Abstract Locomotor intent classification has become a research hotspot due to its importance to the development of assistive robotics and wearable devices. Previous work have achieved impressive performance in classifying steady locomotion states. However, it remains challenging for these methods to attain high accuracy when facing transitions between steady locomotion states. Due to the similarities between the information of the transitions and their adjacent steady states. Furthermore, most of these methods rely solely on data and overlook the objective laws between physical activities, resulting in lower accuracy, particularly when encountering complex locomotion modes such as transitions.… More >

Tengfei Li¹, Wenhui Zhang¹, Ke Mi¹, Qingming Lin¹, Shuangwei Zhao^2,*, Jiayi Song²

Energy Engineering, Vol.121, No.7, pp. 1991-2007, 2024, DOI:10.32604/ee.2024.049460 - 11 June 2024

Abstract Aiming at the problem of energy storage unit failure in the spring operating mechanism of low voltage circuit breakers (LVCBs). A fault diagnosis algorithm based on an improved Sparrow Search Algorithm (ISSA) optimized Backpropagation Neural Network (BPNN) is proposed to improve the operational safety of LVCB. Taking the 1.5kV/4000A/75kA LVCB as an example. According to the current operating characteristics of the energy storage motor, fault characteristics are extracted based on Empirical Wavelet Transform (EWT). Traditional BPNN has problems such as difficulty adjusting network weights and thresholds, being sensitive to initial weights, and quickly falling into More >

Shuai Zhou¹, Dazhi Wang^1,*, Yongliang Ni², Keling Song², Yanming Li²

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2187-2207, 2024, DOI:10.32604/cmc.2024.048859 - 15 May 2024

Abstract In the process of identifying parameters for a permanent magnet synchronous motor, the particle swarm optimization method is prone to being stuck in local optima in the later stages of iteration, resulting in low parameter accuracy. This work proposes a fuzzy particle swarm optimization approach based on the transformation function and the filled function. This approach addresses the topic of particle swarm optimization in parameter identification from two perspectives. Firstly, the algorithm uses a transformation function to change the form of the fitness function without changing the position of the extreme point of the fitness… More >

Yongqiu Liu¹, Shaohui Zhong^2,*, Nasreen Kausar³, Chunwei Zhang^4,*, Ardashir Mohammadzadeh⁴, Dragan Pamucar^5,6

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 793-812, 2024, DOI:10.32604/cmes.2023.028175 - 22 September 2023

Abstract In this paper, a stable and adaptive sliding mode control (SMC) method for induction motors is introduced. Determining the parameters of this system has been one of the existing challenges. To solve this challenge, a new self-tuning type-2 fuzzy neural network calculates and updates the control system parameters with a fast mechanism. According to the dynamic changes of the system, in addition to the parameters of the SMC, the parameters of the type-2 fuzzy neural network are also updated online. The conditions for guaranteeing the convergence and stability of the control system are provided. In More >

Kun Cai^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.27, No.3, pp. 1-2, 2023, DOI:10.32604/icces.2023.09777

Abstract Rotary nanomotor, as the essential component of a dynamic nanomachine, can output rotation via its rotor. So far, several techniques e.g., electric-, nanofluid-, laser-, chemical-, and thermally-driven models, have been proposed to actuate a rotary nanomotor,. Among the techniques, the thermally-driven rotary nanomotor (TDRM) models are the simplest technique that does not require an accurate external field as into energy. The model says that the thermal vibration of the atoms in the nanomotor can transmitted into rotationally kinetic energy via a rotor. Cai et al. [1] discovered the TDRM when relaxing double-walled carbon nanotubes (CNTs)… More >