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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Jung, Seungmin Zhou, Mianzhi Ma, Ji Yang, Ryan Cramer, Steven C. Dobkin, Bruce H. Yang, Lin F. Rosen, Jacob Department of Mechanical and Aerospace Engineering Los Angeles United States Department of Computer Science Engineering Los Angeles United States Department of Neurology Los Angeles United States Department of Electrical and Computer Engineering Los Angeles United States

ISBN: (纸本)9798350371499

Amidst the rising incidences of stroke and spinal cord injuries, this study introduces a Virtual Reality (VR) system integrated with wearable sensor-based motion capture technology to enhance rehabilitation and assessment of upper limb impairments. The proposed motion capture system utilizes Inertial Measurement Units (IMUs) engineered in a modular and portable fashion to best fit different rehabilitation needs of the motor control system. The wearable sensor system consists of 15 modules capable of capturing the entire human body motion, along with a pair of hand gloves including 11 miniature sensors measuring hand motion. The sensor accuracy test demonstrates a Root Mean Square Error below 1.78 degrees compared to measurements collected by a commercial robotic arm. Incorporating detailed data of human joints' motion collected by the array of wearable sensors, the proposed VR system provides additional tools for assessments of the motor control system function, including range of motion, reachable workspace, motor learning, along with rehabilitative intervention games. The developed system provides a foundation for an AI-driven autonomous rehabilitation system, integrating automated clinical assessments with quantitative tools and adaptive difficulty algorithms for personalized interventions, applicable in both clinical-based and home-based settings. © 2024 IEEE.

关键词： Robotic arms

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Deep-recurrent-neural-network-based adaptive sliding mode control for a 6-DOF serial robot 22

Deep-recurrent-neural-network-based adaptive sliding mode co...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Zhu, Ningyu Xie, Wen-Fang Toker, Onur Department of Mechanical Industrial and Aerospace Engineering Concordia University Montreal Canada Department of Electrical and Computer Engineering Florida Polytechnic University Lakeland United States

ISBN: (纸本)9798331527471

The efficient control of serial robots in the presence of dynamic uncertainties and external disturbances is significant in many industrial applications. In this work, an adaptive sliding mode control (ASMC) approach with deep recurrent neural network (DRNN) is proposed for a 6-degree-of-freedom (6-DOF) industrial serial robot in joint space. A model-based sliding mode controller is developed to maintain the strong robustness of the robotic system. A deep recurrent neural network is designed to estimate the lumped system uncertainties in the controller. It consists of a feedforward structure through two hidden layers and a feedback loop from the output layer to the input layer, which exhibits more powerful online learning ability and dynamic property than shallow feedforward neural networks. According to Lyapunov theorem, the adaptation laws of the neural network parameters are derived, and the stability of the controller can be guaranteed. Simulation results demonstrate the effectiveness and superiority of the DRNN-based ASMC strategy regarding estimation convergence speed and trajectory tracking accuracy. © 2024 IEEE.

关键词： Recurrent neural networks

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Heat Transfer Area Optimization for Heat Exchanger System

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computer Modeling in engineering & sciences 2025年第4期143卷 335-349页

作者： Yu-Cheng Liao Fu-I Chou Po-Yuan Yang Jyh-Horng Chou Department of Electrical Engineering National Kaohsiung University of Science and TechnologyKaohsiung807Taiwan Department of Intelligent Robotics National Pingtung UniversityPingtung900Taiwan Department of Mechanical and Computer-Aided Engineering Feng Chia UniversityTaichung407Taiwan

This paper presents an allowable-tolerance-based group search optimization(AT-GSO),which combines the robust GSO(R-GSO)and the external quality design planning of the Taguchi ***-GSO algorithm is used to optimize the heat transfer area of the heat exchanger *** R-GSO algorithm integrates the GSO algorithm with the Taguchi method,utilizing the Taguchi method to determine the optimal producer in each iteration of the GSO algorithm to strengthen the robustness of the search process and the ability to find the global *** conventional parameter design optimization,it is typically assumed that the designed parameters can be applied accurately and consistently throughout ***,for systems that are sensitive to changes in design parameters,even minor inaccuracies can substantially reduce overall system ***,the permissible variations of the design parameters are considered in the tolerance-optimized design to ensure the robustness of the *** optimized design of the heat exchanger system assumes that the system’s operating temperature parameters are ***,fixing the systemoperating temperature parameters at a constant value is *** paper assumes that the system operating temperature parameters have an uncertainty error when optimizing the heat transfer area of the heat exchanger *** results show that the AT-GSO algorithm optimizes the heat exchanger system and finds the optimal operating temperature in the absence of tolerance and under three tolerance conditions.

关键词： Heat exchanger system allowable tolerance-based group search optimization Taguchi method tolerance design

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Self-supervised Orientation-Guided Deep Network for Segmentation of Carbon Nanotubes in SEM Imagery 17th

Self-supervised Orientation-Guided Deep Network for Segment...

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17th European Conference on computer Vision, ECCV 2022

作者： Nguyen, Nguyen P. Surya, Ramakrishna Maschmann, Matthew Calyam, Prasad Palaniappan, Kannappan Bunyak, Filiz Department of Electrical Engineering and Computer Science Columbia United States Department of Mechanical and Aerospace Engineering University of Missouri-Columbia ColumbiaMO United States

ISBN: (纸本)9783031250842

Electron microscopy images of carbon nanotube (CNT) forests are difficult to segment due to the long and thin nature of the CNTs;density of the CNT forests resulting in CNTs touching, crossing, and occluding each other;and low signal-to-noise ratio electron microscopy imagery. In addition, due to image complexity, it is not feasible to prepare training segmentation masks. In this paper, we propose CNTSegNet, a dual loss, orientation-guided, self-supervised, deep learning network for CNT forest segmentation in scanning electron microscopy (SEM) images. Our training labels consist of weak segmentation labels produced by intensity thresholding of the raw SEM images and self labels produced by estimating orientation distribution of CNTs in these raw images. The proposed network extends a U-net-like encoder-decoder architecture with a novel two-component loss function. The first component is dice loss computed between the predicted segmentation maps and the weak segmentation labels. The second component is mean squared error (MSE) loss measuring the difference between the orientation histogram of the predicted segmentation map and the original raw image. Weighted sum of these two loss functions is used to train the proposed CNTSegNet network. The dice loss forces the network to perform background-foreground segmentation using local intensity features. The MSE loss guides the network with global orientation features and leads to refined segmentation results. The proposed system needs only a few-shot dataset for training. Thanks to it’s self-supervised nature, it can easily be adapted to new datasets. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Semantic Segmentation

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A Design for Testability (DFT) strategy for the development of highly complex safety-critical system using a Model-Based Systems engineering (MBSE) approach 10

A Design for Testability (DFT) strategy for the development ...

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10th IEEE International Symposium on Systems engineering, ISSE 2024

作者： Ramirez, Clara A. Thompson, Amy E. Gorthala, Ravi University of Connecticut School of Mechanical Aerospace and Manufacturing Engineering CT United States University of Connecticut Department of Electrical and Computer Engineering CT United States

ISBN: (纸本)9798350353723

The concept of Design for Testability (DFT) involves enhancing a system's design with functionalities and features that facilitate testing during development and lifecycle stages, including system design, implementation, integration, verification, validation, production, and testing. When employing a Model-Based Systems engineering (MBSE) approach for system design, it becomes essential to formulate an MBSE methodology to define how the system will address DFT requirements and strategies, seamlessly integrating them into the overall system design model. This work proposes an MBSE methodology that defines a DFT approach where system functions and physical features planned for testing, operations, maintenance and support and their relations are captured in a systems model using the systems modeling language (SysML). While testability in software has undergone extensive study, often driven by error occurrences (induced error), complex safety-critical systems primarily comprised of highly coupled hardware call for a different approach. The proposed DFT methodology includes failure mode and effect analysis (FMEA) artifacts that allow addressing the current constraints in the identification of multiple failures, and the roll-up of these failures to other elements at the same level or a higher level. The work also defines DFT metrics based on the current state of practice and a thorough literature review. These DFT metrics are defined as measures of effectiveness and performance in the system model and are demonstrated with an example for the development of a common hydromechanical system. A comprehensive framework, approach, and methodology that enables engineers to perform DFT while they are still early in the engineering design phase are proposed, considering the development of a system, its validation test system, and production test system. © 2024 IEEE.

关键词： Integration testing

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Flexible pressure sensors with ultrahigh stress tolerance enabled by periodic microslits

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Microsystems & Nanoengineering 2024年第1期10卷 301-311页

作者： Song Wang Chenying Wang Yifan Zhao Yujing Zhang Yaxin Zhang Xiangyue Xu Qijing Lin Kai Yao Yuheng Wang Feng Han Yu Sun Zhuangde Jiang State Key Laboratory for Manufacturing Systems Engineering International Joint Laboratory for Micro/Nano Manufacturing and Measurement TechnologiesSchool of Mechanical EngineeringXi’an Jiaotong UniversityXi’anChina State Key Laboratory for Manufacturing Systems Engineering International Joint Laboratory for Micro/Nano Manufacturing and Measurement TechnologiesSchool of Instrument Science and TechnologyXi’an Jiaotong UniversityXi’anChina The Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingboChina Department of Mechanical and Industrial Engineering University of TorontoTorontoCanada

Stress tolerance plays a vital role in ensuring the effectiveness of piezoresistive sensing films used in flexible pressure ***,existing methods for enhancing stress tolerance employ dome-shaped,wrinkle-shaped,and pyramidal-shaped microstructures in intricate molding and demolding processes,which introduce significant fabrication challenges and limit the sensing *** address these shortcomings,this paper presents periodic microslits in a sensing film made of multiwalled carbon nanotubes and polydimethylsiloxane to realize ultrahigh stress tolerance with a theoretical maximum of 2.477 MPa and a sensitivity of 18.092 kPa−*** periodic microslits permit extensive deformation under high pressure(e.g.,400 kPa)to widen the detection ***,the periodic microslits also enhance the sensitivity based on simultaneously exhibiting multiple synapses within the sensing interface and between the periodic sensing *** proposed solution is verified by experiments using sensors based on the microslit strategy for wind direction detection,robot movement sensing,and human health *** these experiments,vehicle load detection is achieved for ultrahigh pressure sensing under an ultrahigh pressure of over 400 kPa and a ratio of the contact area to the total area of 32.74%.The results indicate that the proposed microslit strategy can achieve ultrahigh stress tolerance while simplifying the fabrication complexity of preparing microstructure sensing films.

关键词： robot shaped stress

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A lightweight deep learning model with knowledge distillation for pulmonary diseases detection in chest X-rays

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Multimedia Tools and Applications 2025年第15期84卷 14885-14913页

作者： Asham, Mohammed A. Al-Shargabi, Asma A. Al-Sabri, Raeed Meftah, Ibrahim School of Computer Science and Engineering Central South University Hunan Changsha410083 China Department of Information Technology Collage of Computer Qassim University Buraydah51452 Saudi Arabia College of Mechanical and Electrical Engineering Central South University Changsha410017 China

Accurate and timely diagnosis of pulmonary diseases is critical in the field of medical imaging. While deep learning models have shown promise in this regard, the current methods for developing such models often require extensive computing resources and complex procedures, rendering them impractical. This study focuses on the development of a lightweight deep-learning model for the detection of pulmonary diseases. Leveraging the benefits of knowledge distillation (KD) and the integration of the ConvMixer block, we propose a novel lightweight student model based on the MobileNet architecture. The methodology begins with training multiple teacher model candidates to identify the most suitable teacher model. Subsequently, KD is employed, utilizing the insights of this robust teacher model to enhance the performance of the student model. The objective is to reduce the student model's parameter size and computational complexity while preserving its diagnostic accuracy. We perform an in-depth analysis of our proposed model's performance compared to various well-established pre-trained student models, including MobileNetV2, ResNet50, InceptionV3, Xception, and NasNetMobile. Through extensive experimentation and evaluation across diverse datasets, including chest X-rays of different pulmonary diseases such as pneumonia, COVID-19, tuberculosis, and pneumothorax, we demonstrate the robustness and effectiveness of our proposed model in diagnosing various chest infections. Our model showcases superior performance, achieving an impressive classification accuracy of 97.92%. We emphasize the significant reduction in model complexity, with 0.63 million parameters, allowing for efficient inference and rapid prediction times, rendering it ideal for resource-constrained environments. Outperforming various pre-trained student models in terms of overall performance and computation cost, our findings underscore the effectiveness of the proposed KD strategy and the integration of the Conv

关键词： Diagnosis

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VISION-iT:A Framework for Digitizing Bubbles and Droplets

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Energy and AI 2024年第1期15卷 26-35页

作者： Youngjoon Suh Sanghyeon Chang Peter Simadiris Tiffany B.Inouye Muhammad Jahidul Hoque Siavash Khodakarami Chirag Kharangate Nenad Miljkovic Yoonjin Won Department of Mechanical and Aerospace Engineering University of CaliforniaIrvine4200 Engineering GatewayCA 92617-2700USA Department of Electrical Engineering and Computer Science University of CaliforniaIrvine5200 Engineering HallCA 92617-2700USA Department of Mechanical Science and Engineering University of Illinois at Urbana-ChampaignUrbanaIL 61801USA Mechanical and Aerospace Engineering Department Case Western Reserve UniversityClevelandOH44106USA Department of Electrical and Computer Engineering University of Illinois at Urbana-ChampaignUrbanaIL61801USA Materials Research Laboratory University of Illinois at Urbana-ChampaignUrbanaIL61801USA International Institute for Carbon Neutral Energy Research(WPI-12CNER) Kyushu University744 Moto-okaNishi-kuFukuoka 819-0395Japan

Quantifying the nucleation processes involved in liquid-vapor phase-change phenomena,while dauntingly challenging,is central in designing energy conversion and thermal management *** technological advances in the deep learning and computer vision field offer the potential for quantifying such complex two-phase nucleation processes at unprecedented *** leveraging these new technologies,a multiple object tracking framework called“vision inspired online nuclei tracker(VISION-iT)”has been proposed to extract large-scale,physical features residing within boiling and condensation ***,extracting high-quality features that can be integrated with domain knowledge requires detailed discussions that may be field-or case-specific *** this regard,we present a demonstration and discussion of the detailed construction,algorithms,and optimization of individual modules to enable adaptation of the framework to custom *** concepts and procedures outlined in this study are transferable and can benefit broader audiences dealing with similar problems.

关键词： Deep learning computer vision Nucleation Heat transfer Phase-change phenomena

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Incorporating Lasso Regression to Physics-Informed Neural Network for Inverse PDE Problem

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computer Modeling in engineering & sciences 2024年第10期141卷 385-399页

作者： Meng Ma Liu Fu Xu Guo Zhi Zhai National Key Lab of Aerospace Power System and Plasma Technology Xi’an Jiaotong UniversityXi’an710049China School of Mechanical Engineering Xi’an Jiaotong UniversityXi’an710049China Department of Electrical and Computer Engineering University of Massachusetts LowellLowellMA 01854USA

Partial Differential Equation(PDE)is among the most fundamental tools employed to model dynamic *** PDE modeling methods are typically derived from established knowledge and known phenomena,which are time-consuming and ***,discovering governing PDEs from collected actual data via Physics Informed Neural Networks(PINNs)provides a more efficient way to analyze fresh dynamic systems and establish *** study proposes Sequentially Threshold Least Squares-Lasso(STLasso),a module constructed by incorporating Lasso regression into the Sequentially Threshold Least Squares(STLS)algorithm,which can complete sparse regression of PDE coefficients with the constraints of l0 *** further introduces PINN-STLasso,a physics informed neural network combined with Lasso sparse regression,able to find underlying PDEs from data with reduced data requirements and better *** addition,this research conducts experiments on canonical inverse PDE problems and compares the results to several recent *** results demonstrated that the proposed PINN-STLasso outperforms other methods,achieving lower error rates even with less data.

关键词： Physics-informed neural network inverse partial differential equation Lasso regression scientific machine learning

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Design Study Evaluating Impact of Gap Loss on Nanocrystalline Inductor Cores with Experimental Validation 14

Design Study Evaluating Impact of Gap Loss on Nanocrystallin...

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14th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2025

作者： Sturdivant, Maurice Grainger, Brandon Bracken, Christopher Ohodnicki, Paul University of Pittsburgh Department of Electrical and Computer Engineering United States University of Pittsburgh Department of Mechanical Engineering and Materials Science United States

ISBN: (纸本)9798331516116

As electric vehicle (EV) technology matures, the need for compact and efficient power electronics will continue to grow. To increase power density and efficiency, it is ideal to minimize the size and power loss of components, including inductors. A standard inductor design technique is placing an air gap in the magnetic core, allowing designers to tune the inductance and allow higher levels of magnetic field without saturation. The inclusion of gaps can increase the total magnetic core loss beyond those predicted by common models such as the Steinmetz equation, creating the need for accurate gap loss models. In this study, the losses of a nanocrystalline inductor core are experimentally measured and compared to an existing gap loss model, showing significant agreement with predicted values. The model was then integrated into a multi-objective optimization framework to evaluate the fitness of nanocrystalline cores against ferrite cores at different switching frequencies. This work finds that gap losses can increase the total loss of nanocrystalline inductors by up to an order of magnitude as frequency increases, implying that consideration of gap losses is vital to selecting the appropriate core material for design applications. © 2025 IEEE.

关键词： Magnetic cores

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