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Prediction model of bone drilling temperature based on heat source method in surgical rehabilitation

Procedia CIRP 2020年 89卷 263-269页

作者： Yahui Hu Zhiqiang Yan Xiangjun Li Chunqiu Zhang Qingchun Zheng Tianjin Key Laboratory of the Design and Intelligent Control of the Advanced Mechatronical System Tianjing University of Technology Tianjin China School of Mechanical Engineering Tianjin University of Technology Tianjin China

In order to feedback the cutting temperature in real time during the operation with the surgical robot, this study established a temperature prediction model to analyze the temperature distribution of the cortex. In this work, a three-dimensional governing equation for heat transfer of bone tissue during drilling is proposed, and the heat transfer of cortical bone is evaluated by a reverse heat transfer method. The prediction model was validated by experimental methods and the heat affected zone (HAZ) of cortical bone was analyzed. The results show that the prediction results are in good agreement with the relevant experimental results. HAZ increases with increasing rate of penetration; the closer the bone hole is, the earlier the temperature rises; and the temperature at which the cortical bone is difficult to measure (radial distance x < 0.5 mm) is predicted. The predictive model can accurately predict the temperature distribution of the cortical bone and prevent the occurrence of thermal damage. Provide technical support for the development of medical rehabilitation robots. And reduce the patient’s secondary injury, which is conducive to rapid recovery after surgery.

关键词： Cortical bone drilling Temperature estimation Inverse heat transfer problem Thermal damage rehabilitative

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Thermal network model and experimental validation for a motorized spindle including thermal–mechanical coupling effect

Research Square

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Research Square 2021年

作者： Zhou, Changjiang Qu, Zefeng Hu, Bo Li, Shengbo State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China Hunan Provincial Key Laboratory of Intelligent Manufacturing Technology for High-performance Mechanical Equipment Changsha University of Science and Technology Changsha410114 China College of Mechanical and Vehicle Engineering Xiamen University of Technology Xiamen361024 China

Thermal deformation caused by temperature rise have an influence on the dynamic performance of a motorized spindle. In turn, the change in the dynamic performance will affect the temperature rise and thermal deformation of the system. However, the latter was rarely focused on in the previous literature. Therefore, a thermal network model of motorized spindle is enhanced by considering the thermal–mechanical coupling effect. Then, an iterative method is presented to solve the coupled equations, and a temperature test rig of the motorized spindle is set up to verify the proposed model. The relative error between the predicted and experimental results at two test points decreases by 9.56% and 3.44% after considering the thermal–mechanical coupling effect. The comparison with the experimental results shows that the proposed model with thermal–mechanical coupling effect can obtain a more accurate temperature field than the previous model. © 2021, CC BY.

关键词： Iterative methods

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A safety-reinforced mutual pulse heating strategy based on microscopic-state estimation for power-redistributable lithium-ion battery pack

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Energy 2025年 330卷

作者： Hao Zhong Fei Lei Jie Liu Fei Ding Wenhao Zhu Haijun Wang School of Intelligent Engineering and Intelligent Manufacturing Hunan University of Technology and Business Changsha 410205 PR China State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha 410082 PR China College of Automotive and Mechanical Engineering Changsha University of Science and Technology Changsha 410114 PR China

advanced heating is essential for mitigating temporary losses in battery capacity and peak power at low temperatures, thereby enhancing utilization efficiency and reducing safety risks. Expanding upon our prior research, this paper proposes a safety-reinforced mutual pulse heating (MPH) strategy based on microscopic-state estimation, in which multiplexing converter-based drivers are reused as on-board heating topologies. An adaptive super-twisting sliding mode observer is utilized to capture battery solid-liquid phase Li + concentration, electrode and electrolyte potentials, and side-reaction overpotentials. Then, a microscopic-state constraints-based MPH current prediction method is formulated to delineate the safe operational area of battery micro-states. The MPH current amplitude is updated by an online negative feedback-based optimization controller, while the heating duty ratio is still adjusted using fuzzy feedback mechanisms. Experimental validation of the proposed method is conducted across five critical metrics: heating duration, temperature-rise rate, safety risk, temperature uniformity, and degradation. The method demonstrated its efficacy by elevating the battery temperature from −20 °C to 0 °C within 178.3 s, while keeping the microscopic states within safe thresholds, with a peak temperature gradient of <2 °C. During 800 heating cycles, battery integrity remains intact as evidenced by stable capacity, coulomb efficiency, internal resistance, and differential voltage curves.

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An Automatic Method for Brain Tumors Segmentation Based on Deep Convolutional Neural Network

An Automatic Method for Brain Tumors Segmentation Based on D...

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IEEE International Conference on Medical Imaging Physics and Engineering

作者： Hui Lin Benjian Zhang Xin Guo Dong Guo Jia Jing Luo Huiyuan Zhao Yumeng Min Hu Fuji Ren School of Physics Hefei University of Technology Hefei China Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei China School of Computer and Information Hefei University of Technology Hefei China Graduate School of Advanced Technology and Science University of Tokushima Tokushima Japan

ISBN: (纸本)9781665426091

[Purpose] Accurate outline of tumor targets is critical to a high quality radiotherapy plan. Manual segmentation is of great workload and has a strong artificial subjectivity. Using deep learning method to assist automatically segmenting of tumor target is the penetration and application of artificial intelligence in medicine. [Methods] A 6-layer model of deep Convolution Neural Network (CNN) has been constructed by taking advantage of different types of layers for brain tumor segmentation. This model is a 6 layer CNN model (6-CNN) composed of three convolution layers, two pool layers and one full connection layer. To obtain enough samples for 6-CNN model training, a patch-based technology has been adopted. That is to successively extract a local area from the whole image as a patch. And the center pixel value is taken as the pixel value of the whole patch. Similarly, the label of the center pixel is also taken as the label of the whole patch. Thus the 6-CNN model transforms the brain tumor image segmentation into patch classification based on the excellent classification characteristics of deep convolution neural network. The model combines the local features of patch, the information extracted from shallow network and the global features to predict the category label of the central pixel of patch. [Results] The model is validated on BRATS 2015 dataset and results show that the segmentation accuracy can be up to Dice Similarity Coefficient (DSC) $90\%\pm 4\%$ . [Conclusions] An automatic deep CNN segmentation model for brain tumors has been constructed based on MRI image patches, which is expected to assist or even substitute the manual segmentation of brain tumors.

关键词： Image segmentation Convolution Magnetic resonance imaging Transforms Predictive models Brain modeling Feature extraction

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Exploring the Spatial Correlation of Shadowing in RF-based Device-Free Localization by Block Sparse Bayesian Learning

Exploring the Spatial Correlation of Shadowing in RF-based D...

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IEEE International Conference on Robotics and Biomimetics

作者： Jiaju Tan Xin Zhao Xuemei Guo Guoli Wang School of Mathematics and Computational Science Wuyi University Jiangmen China Tianjin Key Laboratory of Intelligent Robotic (tjKLIR) Institute of Robotics and Automatic Information System (IRAIS) Nankai University Tianjin China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education School of Data and Computer Science Sun Yat-sen University Guangzhou China

ISBN: (纸本)9781665405362

Device-Free Localization (DFL) realized by the Radio Frequency (RF) signal sensing, is an emerging technology to obtain the target’s position without needing the target carrying any electronic devices or tags. Due to its through-wall detectability and privacy protection, the RF-based DFL is becoming a promising research topic in the Location-based Services (LBS) applications based on Internet of Things (IoT). Among the RF-based DFL approaches, Radio Tomographic Imaging (RTI) is a low-cost and novel computational imaging method to reconstruct the target-induced shadowing effect in the RF monitored area and then estimate the target’s position. However, the multipath interference in the RF sensing network often induces the reconstruction degradation in the RTI system and leads to the position misestimation in DFL applications. Addressing this problem, considering that the target’s shadowing may occupy a small spatial range and then express some spatial structure, this aricle focuses on the spatial correlation of the target’s shadowing. Then an RTI reconstruction methods based on the Block Sparse Bayesian Learning is proposed to explore the spatial correlation in the sparse target’s shadowing. Moreover, the actual localization experiments are conducted to validate the spatial correlation in shadowing is an important property to improve the RTI reconstruction performance and DFL accuracy.

关键词： Performance evaluation Location awareness Radio frequency Correlation Robot sensing systems Bayes methods Sensors

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In-Situ Biaxial Cyclic Loading Behavior of Articular Cartilage and its Microstructure Analysis

SSRN

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SSRN 2022年

作者： Gao, Lilan Feng, Luming Tan, Yansong Liu, Gang Li, Ruixin Gao, Hong Zhang, Chunqiu Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin300384 China Tianjin Stomatological Hospital Tianjin300041 China School of Chemical Engineering and Technology Tianjin University Tianjin300350 China

Altered cyclic loading may change the mechanical properties, further inducing damage to articular cartilage. In this study, we studied the biaxial cyclic loading behavior of knee articular cartilage and analyzed the changes of microstructure at fibril level. Firstly, the in-plane biaxial cyclic behaviors of cartilage were investigated with an emphasis on the effects of loading paths to account for complicated loading states in daily activities. The results show that the strain evolution in X-direction is obviously affected by the constraints in Y-direction (orthogonal direction). The loading path has a great influence on the biaxial mean strain, while due to no constraint and the largest stress constraint in Y direction, proportional loading path and uniaxial loading path show the highest and lowest mean strain in X-direction, respectively. Subsequently, the effects of stress amplitude and stress peak holding time on mean strain were probed under square path. It was found that the mean strain of cartilage increases with the increase of stress amplitude or peak stress holding time. In addition, to reveal the inherent deformation mechanism, the collagen fibrils networks before and after biaxial cyclic loading were analyzed. The rearrangement of collagen fibrils networks is related to the stress state of cartilage. Larger stress leads to severer fiber damage in cartilage. © 2022, The Authors. All rights reserved.

关键词： Cartilage

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On Modeling of Extruded Thin-Walled Multi-Cell Structures Incorporating Anisotropy Yielding, Strain Hardening and Damage Failure

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Thin-Walled Structures 2025年

作者： Ruyang Yao Bei Zhang Qiang Niu Qing Li Guangyong Sun State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle Hunan University Changsha 410082 China Zhejiang Key Laboratory of Intelligent Vehicle Comprehensive Safety Geely Automobile Research Institute (Ningbo) Co. Ltd. Ningbo 315336 China School of Aerospace Mechanical and Mechatronic Engineering The University of Sydney Sydney NSW 2006 Australia

Thin-walled multi-cell structures (TWMCS) have been extensively utilized in a range of transportation facilities, including automobiles, railways, aircraft, and marine vessels, due to their exceptional lightweight and energy absorption capabilities. However, current numerical models often oversimplify the material behaviors, significantly limiting their reliable applications in engineering practice. This study aims at developing a systematic modeling framework to address this issue, through simulating the extruded AA6063-T6 TWMCS against axial crushing process. Comprehensive experimental characterization of the base material AA6063-T6 was conducted first to investigate its yielding, strain hardening, and damage fracture behaviors under complex stress states. Constitutive models for the base material were established by incorporating the Hill 90 anisotropic yield, Voce strain hardening, and GISSMO damage failure models. The material parameters of these models were precisely calibrated through an in-house inverse identification approach. Based on this, a reliable numerical model was then developed to achieve good correlation with the experimental results. The influence of constitutive models on numerical results at a structural-scale was thoroughly investigated, highlighting the importance of understanding the intrinsic mechanical properties of materials and making rational constitutive models in engineering design. Finally, this study provides perspectives for future research to further enhance the safety and lightweight performance of protective structures.

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CIR-Net: Cross-modality Interaction and Refinement for RGB-D Salient Object Detection

arXiv

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arXiv 2022年

作者： Cong, Runmin Lin, Qinwei Zhang, Chen Li, Chongyi Cao, Xiaochun Huang, Qingming Zhao, Yao Institute of Information Science Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China School of Computer Science and Engineering Nanyang Technological University Singapore School of Cyber Science and Technology Shenzhen Campus Sun Yat-sen University 518107 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Peng Cheng Laboratory Shenzhen518055 China

Focusing on the issue of how to effectively capture and utilize cross-modality information in RGB-D salient object detection (SOD) task, we present a convolutional neural network (CNN) model, named CIR-Net, based on the novel cross-modality interaction and refinement. For the cross-modality interaction, 1) a progressive attention guided integration unit is proposed to sufficiently integrate RGB-D feature representations in the encoder stage, and 2) a convergence aggregation structure is proposed, which flows the RGB and depth decoding features into the corresponding RGB-D decoding streams via an importance gated fusion unit in the decoder stage. For the cross-modality refinement, we insert a refinement middleware structure between the encoder and the decoder, in which the RGB, depth, and RGB-D encoder features are further refined by successively using a self-modality attention refinement unit and a cross-modality weighting refinement unit. At last, with the gradually refined features, we predict the saliency map in the decoder stage. Extensive experiments on six popular RGB-D SOD benchmarks demonstrate that our network outperforms the state-of-the-art saliency detectors both qualitatively and quantitatively. The code and results can be found from the link of https://***. io/proj_***. © 2022, CC BY-NC-SA.

关键词： Object detection

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Implementation of Hodgkin-Huxley Spiking Neuron Model Using FPGA 19

Implementation of Hodgkin-Huxley Spiking Neuron Model Using ...

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19th IEEE International Conference on Communication Technology, ICCT 2019

作者： Yi, Zexuan Fu, Wenlong Cao, Lihong Neuroscience and Intelligent Media Institute Communication University of China Beijing China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China

ISBN: (纸本)9781728105352

For the study of new computer architecture inspired by brain, implementing neuron function on hardware has become an emerging research direction. To study the feasibility of biological neuron implementation and acceleration characteristics on the hardware platform, the basic Hodgkin-Huxley (HH) neuron model and a small scale of synapse connection model is implemented on Xilinx Zynq7 Zedboard FPGA, high-level-synthesis method is used to reduce the burden of hardware design. The Hodgkin-Huxley model simulation result of FPGA are validated through high level software simulation tool in Brian2, furthermore, resource utilization and calculation time cost are provided in this paper, finally, a pipeline design of HH neuron model is used to accelerate calculations and reduce resource utilization, the result proves that this design is suitable for HH model simulation. © 2019 IEEE.

关键词： Neurons

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Training-free image style alignment for self-adapting domain shift on handheld ultrasound devices

arXiv

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arXiv 2024年

作者： Zeng, Hongye Zou, Ke Chen, Zhihao Gao, Yuchong Chen, Hongbo Zhang, Haibin Zhou, Kang Wang, Meng Goh, Rick Siow Mong Liu, Yong Jiang, Chang Zheng, Rui Fu, Huazhu School of Information Science and Technology ShanghaiTech University Shanghai China Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai China University of Chinese Academy of Sciences Beijing China National Key Laboratory of Fundamental Science on Synthetic Vision College of Computer Science Sichuan University Chengdu China College of Intelligence and Computing Tianjin University Tianjin China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Beth Israel Deaconess Medical Center Harvard Medical School Boston United States Institute of High Performance Computing Agency for Science Technology and Research Singapore Singapore Department of Orthopedics Zhongshan Hospital Fudan University Shanghai China Shanghai Engineering Research Center of Intelligent Vision and Imaging Shanghai China

Handheld ultrasound devices face usage limitations due to user inexperience and cannot benefit from supervised deep learning without extensive expert annotations. Moreover, the models trained on standard ultrasound device data are constrained by training data distribution and perform poorly when directly applied to handheld device data. In this study, we propose the Training-free Image Style Alignment (TISA) framework to align the style of handheld device data to those of standard devices. The proposed TISA can directly infer handheld device images without extra training and is suited for clinical applications. We show that TISA performs better and more stably in medical detection and segmentation tasks for handheld device data. We further validate TISA as the clinical model for automatic measurements of spinal curvature and carotid intima-media thickness. The automatic measurements agree well with manual measurements made by human experts and the measurement errors remain within clinically acceptable ranges. We demonstrate the potential for TISA to facilitate automatic diagnosis on handheld ultrasound devices and expedite their eventual widespread use. © 2024, CC BY-NC-SA.

关键词： Deep learning

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