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Liquid film thickness model formed by atomized droplets during sustainable cryogenic air MQL grinding

Frontiers of Mechanical engineering 2025年第1期20卷 45-68页

作者： Mingzheng LIU Changhe LI Qinglong AN Yanbin ZHANG Min YANG Xin CUI Teng GAO Yusuf Suleiman DAMBATTA Runze LI Key Laboratory of Industrial Fluid Energy Conservation and Pollution Control(Ministry of Education) Qingdao University of TechnologyQingdao 266520China Qingdao Jimo Qingli Intelligent Manufacturing Industry Research Institute Qingdao 266201China School of Mechanical Engineering Dalian University of TechnologyDalian 116081China Mechanical Engineering Department Ahmadu Bello UniversityZaria 810106Nigeria School of Engineering Department of Biomedical EngineeringMassachusetts Institute of TechnologyBoston 02139USA

Surface thermal damage in a difficult-to-process metal precision grinding workpiece has emerged as a technical bottleneck restricting machining *** an alternative to traditional pouring cooling,a green clean minimum-quantity lubrication technology still has defects,such as insufficient heat *** use of cryogenic air instead of normal temperature air,that is,the supply of low-temperature energized lubricant,can effectively improve oil film heat transfer and lubrication performance in a grinding *** the premise of ensuring the effective flow of lubricating oil in a grinding zone,the thickness of a liquid film in the wedge zone of a grinding wheel or workpiece is the key factor for determining its ***,the dynamic mechanism of droplet formation and distribution of liquid film thickness are still ***,the mechanism by which nozzle orientation influences the effective region of a liquid film was analyzed,and the range of nozzle inclination that helps to atomize droplets and enables them to enter the grinding zone was ***,the dynamic mechanism of atomized droplet film formation was analyzed,and the influence of normal and tangential momentum sources generated by gas impingement perturbation flow and droplet impingement steady flow on the driving effect of liquid film flow was *** thickness distribution model of a liquid film in the impact zone of gas–liquid two-phase flow under different cryogenic air temperatures was *** model results under different working conditions were obtained by numerical analysis,and validation experiments were carried *** show that the measured values agree with the theoretical *** 0.4 MPa air pressure,the thickness of the liquid film in the impact zone of the atomized droplets increases with decreasing cryogenic air ***−10 and−50°C,the thickness of the liquid film is 0.92 and 1.26 mm,***,on the basis of the surface topograph

关键词： sustainable grinding cryogenic cooling minimum quantity lubrication CAMQL liquid film thickness formation mechanism

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An Automated Diagnosis of Diabetic Retinopathy Grading Using DenseNet169 17

An Automated Diagnosis of Diabetic Retinopathy Grading Using...

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17th IEEE International Conference on Computer Research and Development, ICCRD 2025

作者： Yang, Cheng-Chung Li, Na Kwame Bensah, Kulevome Delanyo Khokhar, Salah-Ud-Din Mahrukh, Mahrukh Qilu Institute of Technology School of Intelligent Manufacturing and Control Engineering Jinan China

ISBN: (纸本)9798331531881

This study used DenseNet121, DenseNet169, DenseNet201, EfficientNetB0, EfficientNetB7, Inception ResNetV2, Inception V3, MobileNet, MobileNetV2, MobileNetV3Large, ResNet50, ResNet101 and ResNet152, 13 different deep learning neural network models were used for automatic grading of diabetes retinopathy(DR). The change of Validation Accuracy trend was initially observed with 10, 20, 30, 40 and 50, different EPOCHs. Before grading, pre-processing such as elimination, amplification, denoising, data enhancement, normalization, etc. were carried out for the problems in the retinal image data set in the experiment. Then we used the deep learning neural network model proposed in this study to embed the attention mechanism into DenseNet169 for automatic grading of DR, enable deep learning neural network models to focus on features such as exudates, thick blood vessels and microaneurysms in retinal images. In order to avoid vanishing gradients in the neural network model and ensure classification accuracy, DenseNet169 was adjusted by using a targeted swish function for processing, and classify retinal images into three levels and finally the classification accuracy of the neural network model proposed in this study reached 99.76%, macro avg precision reached 99.76%, macro avg recall reached 99.76% and macro avg f1-score reached 99.76%. It is significantly better than other models and on average. It can correctly identify and automatically grading more than 4600 images in the verification of 4,617 retinal images. The method proposed in this study has been validated to effectively improve the accuracy of DR classification and can be used for large-scale DR medical imaging screening. It can effectively assist doctors in achieving efficient diagnosis of DR grading. This work was supported by Research Program of Qilu Institute of Technology (***24NN032). © 2025 IEEE.

关键词： Deep neural networks

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A Seamless Pedestrian Localization System Based on GNSS/IμUWB/MAP Integration

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IEEE Internet of Things Journal 2025年第11期12卷 16653-16667页

作者： Zhang, Shuning Tang, Hongjing Chen, Liangming Gao, Yufeng Southern University of Science and Technology School of Automation and Intelligent Manufacturing Shenzhen518055 China Southern University of Science and Technology Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and Technology Shenzhen518055 China Beijing Institute of Spacecraft System Engineering Beijing100094 China

Pedestrian localization has broad applications. However, achieving seamless positioning in complex environments remains a significant challenge due to factors like environmental occlusion and indoor-outdoor transitions. This paper proposes a combined localization framework using Global Navigation Satellite System (GNSS), Inertial Measurement Unit (IMU), Ultra-Wideband (UWB), and MAP data to address this issue. In the framework, a three-step GNSS localization method is proposed to enhance accuracy and stability by eliminating Non-Line-of-Sight (NLOS) effects and filtering anomalies. Inertial Navigation System (INS) and Pedestrian Dead Reckoning (PDR) models are integrated to improve local precision and stability. UWB, known for its superior localization capabilities, has been integrated into some smartphones and is promising for large-scale indoor applications. The paper presents a gradient descent-based optimal anchor deployment algorithm and localization methods for varying anchor quantities. Additionally, a non-stationary Markov model fuses map data for trajectory prediction and correction. Finally, anomaly detection filtering and low-pass filtering are applied to the corrected trajectories, yielding high accuracy and stability. Experiments on the Honor V20 and iQOO U3 smartphones showed significant improvements compared to the baseline algorithm. Outdoor Root Mean Square Error (RMSE) was reduced by 44.4% (10.83m) for the Honor V20 and 40.0% (10.77m) for the iQOO U3. Additionally, outdoor Standard Deviation (STD) improved by 43.9% (4.80m) and 43.5% (5.50m), respectively. In transitional areas, RMSE improved by 4.61% (10.77m) and 19.6% (4.76m), respectively. Indoor RMSE also showed improvements, with the Honor V20 reducing it by 8.00% (3.79m) and the iQOO U3 by 5.78% (3.91m). © 2025 IEEE.

关键词： Location awareness Global navigation satellite system Accuracy Pedestrians Filtering Sensors Prediction algorithms Predictive models Smart phones Kalman filters

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An Automated Diagnosis of Diabetic Retinopathy Grading Using DenseNet169

An Automated Diagnosis of Diabetic Retinopathy Grading Using...

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International Conference on Computer Research and Development (ICCRD)

作者： Cheng-Chung Yang Na Li Kulevome Delanyo Kwame Bensah Salah-Ud-Din Khokhar Mahrukh Mahrukh School of Intelligent Manufacturing and Control Engineering Qilu Institute of Technology Jinan China

ISBN: (数字)9798331531881

ISBN: (纸本)9798331531898

关键词： Deep learning Accuracy Attention mechanisms Neural networks Noise reduction Medical services Retina Research and development Biomedical imaging Residual neural networks

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Structure design and analysis of a rigid-soft robotic hand with large load capacity 3

Structure design and analysis of a rigid-soft robotic hand w...

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3rd International Conference on Electrical, Electronics, and Information engineering, EEIE 2024

作者： Wu, Peichen Lan, Fuming Hong, Wen Chen, Xiaoping Shuai, Wei Cui, Guowei Gao, Yang Institute of Intelligent Manufacturing Guangdong Academy of Sciences Guangdong Key Laboratory of Modern Control Technology Guangzhou510070 China Shanghai Institute of Aerospace System Engineering Shanghai201100 China Intelligent Manufacturing Institute of HFUT HeFei230041 China School of Computer Science and Technology University of Science and Technology of China Hefei230026 China

ISBN: (纸本)9781510688179

In recent years, soft grippers have gained significant attention due to their adaptability and safety. However, when performing horizontal object grasping, their limited structural stiffness prevents them from withstanding the torque generated by friction between the gripper and the object, leading to a loss of contact and limiting their horizontal load capacity. To address this limitation while preserving the adaptability of soft grippers, this paper proposes a gripper structure called the rigid-soft robotic hand, which incorporates lateral rigid connecting rods outside the soft joints. The paper analyzes the bending capacity of the rigid-soft joint using kinematic methods and investigates the influence of link design parameters on maximum bending angle. Subsequently, a finite element analysis is conducted to study the finger structure with the added connecting rods, aiming to optimize the link design parameters. Experimental results demonstrate the grasping capabilities of the rigid-soft gripper in both vertical and horizontal orientations. The findings show that the rigid-soft gripper significantly improves the load-carrying capacity during horizontal object grasping while maintaining the adaptability of the soft gripper. © The Authors.

关键词： Robotic arms

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Finite-Time Consensus of Second-Order Multiagent Systems With Input Saturation via Hybrid Sliding-Mode control

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IEEE Transactions on Automation Science and engineering 2025年 22卷 14623-14632页

作者： Jiang, Xiaowei Jiao, Ranran Li, Bo Zhang, Xianhe Yan, Huaicheng China University of Geosciences School of Automation Wuhan430079 China Ministry of Education Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems Engineering Research Center of Intelligent Geodetection Technology Wuhan430074 China Ministry of Education Key Laboratory of System Control and Information Processing Shanghai200240 China Anhui University of Finance and Economics School of Finance Bengbu233030 China Hubei Normal University School of Electrical Engineering and Automation Huangshi435002 China Ministry of Education East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Shanghai200237 China

This paper addresses the finite-time consensus (FTC) issue for second-order multi-agent systems (MASs) with nonlinear disturbances. To tackle the challenges posed by increasingly complex communication environments, an innovative integral sliding-mode surface is designed using event-triggered control. Furthermore, the study explores communication constraints between the leader and followers, employing impulsive control to facilitate communication only at specific intervals. A novel hybrid integral sliding-mode control protocol is advanced for the second-order MASs, which effectively eliminates the "Zeno phenomenon" and notably reduces both communication frequency and energy consumption, thereby enhancing overall communication efficiency. Ultimately, the efficacy of the put forward protocol is verified through simulation examples including comparison experiment. © 2004-2012 IEEE.

关键词： Protocols Sliding mode control Event detection Multi-agent systems Costs Training Topology Switches Sufficient conditions Stability analysis

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Design and testing research of LiDAR for detecting atmospheric turbulence

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Optoelectronics Letters 2025年第3期21卷 172-176页

作者： QIU Duoyang LI Xianyang YANG Hao ZHU Xiaomeng FANG Zhiyuan XU Xiang School of Advanced Manufacturing Engineering Hefei UniversityHefei230601China Anhui Provincial Engineering Technology Research Center of Intelligent Vehicle Control and Integrated Design Technology Hefei UniversityHefei230601China Science Island Branch of Graduate School University of Science and Technology of ChinaHefei230026China School of Mechanical and Energy Engineering Ningbo Tech UniversityNingbo315100China Key Laboratory of Atmospheric Optics Anhui Institute of Optics and Fine MechanicsChinese Academy of SciencesHefei230031China

Atmospheric turbulence is an important parameter affecting laser atmospheric *** paper reports on a self-developed atmospheric turbulence detection Li DAR system(scanning differential image motion Li DAR(DIM-Li DAR)system).By designing and simulating the optical system of atmospheric turbulence detection Li DAR,the basic optical imaging accuracy has been determined.

关键词： li dar designing simulating optical system lidar scanning differential image motion lidar differential image motion laser atmospheric transmissionthis atmospheric turbulence atmospheric turbulence detection li darthe

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RDW-YOLOv8n: a deeply focused algorithm for small object traffic sign detection under complex weather conditions

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Journal of Electronic Imaging 2025年第2期34卷

作者： Guo, Hao He, Guodong Fang, Mingxing Xie, Shaoguo Ge, Jianfeng Anhui Normal University School of Physics and Electronic Information Wuhu China Anhui Engineering Research Center on Information Fusion and Control of Intelligent Robot Wuhu China Anqing Normal University School of Electronic Engineering and Intelligent Manufacturing Anqing China

In autonomous driving technology, accurate detection of traffic signs is crucial for vehicles to perceive their surrounding road conditions. However, the detection of small traffic signs is often affected by changes in lighting and obstruction from obstacles under complex weather conditions, resulting in inaccurate localization and missed detections. To address these challenges, we introduce a new optimized algorithm - RDW-YOLOv8n. We utilize an enhanced small object layer integrated with the ResCBAM module to replace the original large object layer. Unlike traditional methods of introducing small object layers, this method improves detection accuracy by enhancing the network's perception of small objects through residual attention while effectively addressing the issue of missed detections present in the original algorithm and significantly reducing the number of model parameters. In addition, lightweight depthwise convolution (DWConv) is introduced to further streamline the model without sacrificing accuracy. Finally, the original loss function is replaced with the WIoU loss function to enable the model to focus on the average quality of anchor boxes, balancing the learning of high- and low-quality samples. Experiments conducted on the CCTSDB 2021 dataset show that the RDW-YOLOv8n algorithm achieves a mean average precision (mAP50) of 84.7% and a recall rate (R) of 75.3%, which represent improvements of 6.9 and 5.2 percentage points over the baseline model, respectively. Meanwhile, the model parameter count is reduced by 68.11%, and the F1 score increases by 3.1%. These results effectively demonstrate the detection performance of this algorithm under complex weather. When compared with recent advanced algorithms, RDW-YOLOv8n achieves the highest mAP50 with the smallest number of parameters, highlighting the strong competitiveness and superiority of this algorithm. © 2025 SPIE and IS&T.

关键词： Road and street markings

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Active Fault Tolerant control for PEM Fuel Cells Under Cathode Flooding

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IEEE Transactions on Transportation Electrification 2025年

作者： Chen, Jian Yan, Chizhou Kumar, Lalitesh Jin, Lei Li, Xinyu Southern University of Science and Technology School of Automation and Intelligent Manufacturing Shenzhen518055 China Zhejiang University School of Mechanical Engineering Hangzhou310058 China Zhejiang University College of Control Science and Engineering Hangzhou310027 China

Proper fault tolerant control is a key issue to extend the lifetime of proton exchange membrane fuel cells. This paper proposes a fault tolerant control algorithm to realize performance recovery under flooding. A water transfer model of the PEM fuel cells is established and verified, and a flooding fault indicator is developed for diagnosing the fault in real time. Thereafter, an active fault tolerant controller based on the water transfer model and the fault indicator is developed in order to recover the performance characteristics of the PEM fuel cells. Experimental results show that the proposed fault indicator can accurately identify the flooding, and the fault tolerant controller can effectively suppress the water management failure. Thereby improving the reliability of the PEM fuel cells. © 2015 IEEE.

关键词： Fault detection

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MorNet: Asymmetric UNet-like Network with Morphological Opening and Closing for Image Segmentation 17th

MorNet: Asymmetric UNet-like Network with Morphological Open...

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17th International Conference on intelligent Robotics and Applications, ICIRA 2024

作者： Li, Meng Yun, Juntong Jiang, Du Tao, Bo Li, Gongfa Liu, Rong Liu, Ying Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Wuhan430081 China Research Center for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan430081 China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan430081 China Precision Manufacturing Research Institute Wuhan University of Science and Technology Wuhan430081 China School of Medicine Wuhan University of Science and Technology No. 1 Huangjia Lake University Town Wuhan430065 China School of Mechanical Engineering Hubei Engineering University Xiaogan432000 China

ISBN: (纸本)9789819607761

Image segmentation plays a crucial role in target recognition as it provides an essential prerequisite for identifying targets. However, UNet cannot acquire comprehensive information about the image, which limits its ability to effectively segment small and discrete objects. A new network structure, called MorNet, is proposed. It applies dilation and erosion operations in morphological opening and closing for connected and discrete region segmentation. MorNet effectively integrates global segmentation images, emphasizing small and discrete area segmentation and enhancing the segmentation of large continuous areas. This method enhances the understanding of the location of organs and diseased sites, enabling accurate segmentation. A side-out (SO) monitoring mechanism is introduced into the decoder to provide monitoring information for the original image and the predicted results. Compared with current UNet-like networks, our proposed method achieved the highest Dice scores (96.4635%, 94.0514%, 94.3444%) and PPV scores (94.2226%, 90.2361%, 91.5582%) for liver tumor, brain tumor, and liver segmentation tasks, respectively. The experimental results show that MorNet successfully combines the segmentation of connected and discrete regions. MorNet locates organs and lesions more accurately than current state-of-the-art medical image segmentation models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image segmentation

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