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SSRN 2022年

作者： Yang, Li Liu, Chaosai Yuan, Wenjing Meng, Chuizhou Dutta, Ankan Chen, Xue Guo, Langang Niu, Guangyu Cheng, Huanyu State Key Laboratory of Reliability and Intelligence of Electrical Equipment School of Health Sciences and Biomedical Engineering Hebei University of Technology Tianjin300130 China Tianjin Key Laboratory of Materials Laminating Fabrication and Interface Control Technology School of Materials Science & Engineering Hebei University of Technology Tianjin300130 China State Key Laboratory for Reliability and Intelligence of Electrical Equipment Hebei Key Laboratory of Smart Sensing and Human-Robot Interaction School of Mechanical Engineering Hebei University of Technology Tianjin300401 China Department of Engineering Science and Mechanics The Pennsylvania State University University Park 16802 United States State Key Laboratory of Reliability and Intelligence of Electrical Equipment Key Laboratory of Bioelectromagnetics and Neuroengineering of Hebei Province School of Electrical Engineering Hebei University of Technology Tianjin300130 China School of Architecture and Art Hebei University of Technology Tianjin300130 China

The growing demand for intelligent wearable electronic devices has spurred the rapid developments of high-performance deformable power supplies such as triboelectric nanogenerators (TENGs) with high output performance. However, the intrinsically stretchable TENGs especially those prepared with low-cost manufacturing approaches still suffer from poor performance. To address the challenge, this paper presents a fully stretchable TENG consisting of an intrinsically stretchable MXene/silicone elastomer and silver nanowires (Ag NWs)-graphene foam nanocomposite. The intrinsically stretchable TENG exhibits high output performance (voltage, current, and power of 73.6 V, 7.75 μA, and 2.76 W m -2 ), long-term reliability, and stable electrical output under various extreme deformation conditions. In addition to the application on the human skin and clothing for human motion monitoring and detecting the strength training postures, the intrinsically stretchable TENG can also harvest the intermittent mechanical energy from human bodies to charge various energy storage units such as commercial capacitors for driving wearable electronic devices. The resulting systems have been demonstrated in applications from home anti-theft to water resources early warning systems, which provide the proof-of-the-concept demonstrations for the next-generation standalone device platforms. © 2022, The Authors. All rights reserved.

关键词： Nanogenerators

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Research on temperature field of non-uniform heat source model in surface grinding by cup wheel

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Advances in Manufacturing 2019年第3期7卷 326-342页

作者： Shi-Jie Dai Xiao-Qiang Li Hui-Bo Zhang State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of TechnologyTianjin 300130People's Republic of China School of Mechanical Engineering Hebei University of TechnologyTianjin 300130People's Republic of China Hebei Key Laboratory of Robot Perception and Human-Robot Interaction Tianjin 300130People's Republic of China

To address the problems of thermal damage to a workpiece surface caused by the instantaneous high temperature during grinding and the difficulty in monitoring temperature in real time,the temperature field in the case of composite surface grinding by a cup wheel is *** order to predict the grinding temperature,considering material removal and grinding force distribution,a nonuniform heat source model with different function distributions in the circumferential and radial directions in the cylindrical coordinate system is first proposed;then,the analytical model is deduced and the numerical model of the temperature field is established based on the heat source *** validation experiments for grinding temperature field are carried out using a high-definition infrared thermal imager and an artificial *** to the temperature field based on the uniform heat source model,the results based on the non-uniform heat source model are in better agreement with the actual temperature field,and the temperature prediction error is reduced from approximately 23% to 6%.Thus,the present study provides a more accurate theoretical basis for preventing bums in cup wheel surface grinding.

关键词： Non-uniform heat source Temperature filed Surface grinding Cup wheel

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Image-guided Breast Biopsy of MRI-visible Lesions with a Hand-mounted Motorised Needle Steering Tool

Image-guided Breast Biopsy of MRI-visible Lesions with a Han...

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International Symposium on Medical robotics (ISMR)

作者： Marta Lagomarsino Vincent Groenhuis Maura Casadio Marcel K. Welleweerd Françoise J. Siepel Stefano Stramigioli Robotics and Mechatronics (RAM) Laboratory Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede The Netherlands University of Genoa Genoa Italy Human-Robot Interfaces and Physical Interaction (HRII) Laboratory Istituto Italiano di Tecnologia Genoa Italy Politecnico di Milano Milan Italy

ISBN: (纸本)9781665406239

A biopsy is the only diagnostic procedure for accurate histological confirmation of breast cancer. When sonographic placement is not feasible, a Magnetic Resonance Imaging(MRI)-guided biopsy is often preferred. The lack of real-time imaging information and the deformations of the breast make it challenging to bring the needle precisely towards the tumour detected in pre-interventional Magnetic Resonance (MR) images. The current manual MRI-guided biopsy workflow is inaccurate and would benefit from a technique that allows real-time tracking and localisation of the tumour lesion during needle insertion. This paper proposes a robotic setup and software architecture to assist the radiologist in targeting MR-detected suspicious tumours. The approach benefits from image fusion of preoperative images with intraoperative optical tracking of markers attached to the patient’s skin. A hand-mounted biopsy device has been constructed with an actuated needle base to drive the tip toward the desired direction. The steering commands may be provided both by user input and by computer guidance. The workflow is validated through phantom experiments. On average, the suspicious breast lesion is targeted with a radius down to 2.3 mm. The results suggest that robotic systems taking into account breast deformations have the potentials to tackle this clinical challenge.

关键词： Target tracking Biopsy Phantoms Breast Needles Real-time systems Skin

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QL-PGD: An efficient defense against membership inference attack

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Journal of Information Security and Applications 2025年 92卷

作者： Pham, Tuan Dung Nguyen, Bao Dung Mai, Son T. Ta, Viet Cuong Human Machine Interaction Laboratory VNU University of Engineering and Technology 144 Xuan Thuy Street Cau Giay District Hanoi100000 Viet Nam School of Electronics Electrical Engineering and Computer Science Queen's University Belfast United Kingdom

In membership inference attacks (MIAs), attackers exploit the overfitting phenomenon when training deep learning models to identify whether a specific data sample was used to train a victim model. Consequently, many defenses have been introduced to mitigate the risk of being attacked by MIAs. However, state-of-the-art defenses against MIAs often suffer from poor privacy-utility balance and high training or inference computational time. To overcome these limitations, we propose Quantized Layer-wise Perturbed Gradient Descent (QL-PGD), a novel, lightweight and effective generalization method to protect machine learning models from Membership Inference Attacks. The key idea of QL-PGD is to regularize the model to prevent overfitting by adjusting the injected noise added to the gradient at each layer which explicitly regularizes the gradient passing through it to achieve a stronger privacy defense while maintaining similar levels of accuracy. Moreover, quantization is further performed on both model weight and gradient to reduce computational overhead. Extensive experiments are conducted to evaluate the performance of our method compared to other state-of-the-art generalization defenses against multiple attacks. The results show that QL-PGD can withstand both black-box and white-box attacks and preserve the target model's utility with efficiency in terms of speed, memory and energy. © 2025

关键词： Deep learning

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Bayesian deep multi-instance learning for student performance prediction based on campus big data

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Neurocomputing 2025年 647卷

作者： Jiayang Huang Keyi Yang Quan Wang Pengfei Yang Ziling Ruan Jiaxi Wang Zhi-Qiang Zhang School of Computer Science and Technology Xidian University Xi’an 710126 China The Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shaanxi Province Xi’an 710126 China School of Electronic and Electrical Engineering Institute of Robotics Autonomous Systems and Sensing University of Leeds Leeds UK

Problem: Predicting student performance using campus big data and deep learning methods has emerged as a promising alternative to traditional psychological assessments, which are often delayed and subjective. However, existing deep learning approaches face significant challenges, including the sparsity of campus big data and susceptibility to overfitting. Objective: To address these issues, this study aims to develop a robust and effective framework for predicting student performance by overcoming data sparsity and overfitting problems. Method: We propose a novel Bayesian Deep Multi-Instance Learning (Bay-DeepMIL) framework. The method integrates multi-instance learning (MIL) to handle data sparsity and incorporates a Bayesian framework to treat network parameters as probabilistic distributions, enhancing robustness and mitigating overfitting. Additionally, a Bayesian multi-head attention mechanism is introduced to dynamically assign importance to different instances, improving the extraction of key information from multi-instance data. Results: Extensive experiments demonstrate that Bay-DeepMIL outperforms state-of-the-art methods in prediction accuracy. The framework also provides uncertainty estimates for each prediction, offering valuable confidence measures and decision support for educational stakeholders. Conclusion: The proposed Bay-DeepMIL framework not only advances the technical capabilities of predictive models but also provides practical tools for enhancing educational decision-making. This study underscores the effectiveness of integrating Bayesian inference with multi-instance learning to address core challenges such as data sparsity and model overfitting in student performance prediction.

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An Experimental Study on the Design & Construction of a Torque Measurement Unit with Tactile Sensors

An Experimental Study on the Design & Construction of a Torq...

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Iranian Conference on electrical engineering (ICEE)

作者： Fatemeh Zargarbashi Hamed Nazemi Harandi Amin Hamed Mehdi Tale Masouleh School of Mechanical Engineering Sharif University of Technology Tehran Iran School of Mechanical Engineering Amirkabir University of Technology Tehran Iran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (数字)9781728172965

ISBN: (纸本)9781728172972

This paper presents the state of the art of the design and construction of a torque measurement unit based on a new modification in balancing method. This unit consists of six soft tactile sensors that each include a MEMS barometer covered by silicone rubber. Sensors are mounted in an array under the body of a servo motor to read the force from which motor torque can be obtained. Correct performance of sensor was validated in experimental tests by applying different loads on a Kinco servo motor and comparison of sensor output with value of actual torque obtained from motor driver software. The sensor was able to measure torque with less than 0.1 N.m uncertainty. The main advantage of this torque measurement method is its accuracy, simple construction, precision, real-time measurement and low cost compared to expensive strain gauge based sensors. Due to its soft surface, this torque sensor can be safely employed in human-robot interaction purposes and one can instantly find the force exerted on the environment by the end effector of robot due to the low delay time of the sensors.

关键词： Torque Sensors Torque measurement Force Force measurement Mathematical model Tactile sensors

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Design and Prototyping of One Degree-of-Freedom Rehabilitation robotic Arm with Variable Weights

Design and Prototyping of One Degree-of-Freedom Rehabilitati...

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RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： Mahdi Atashi Gw.olestan Amin Hamed Mehdi Tale Masouleh School of Electrical & Computer Engineering University of Tehran Human & robot interaction Laboratory Tehran Iran

ISBN: (数字)9781728166049

ISBN: (纸本)9781728166056

In this paper, a novel concept is proposed to design a robotic arm for rehabilitating individuals with muscle disability. Wearable robots are used for dealing with this challenge. Many of these robots can only reduce certain amounts of hand weight and whatever it grabs, and the system force does not change with the weight of hand. The existing systems are designed for certain weights and with any variation in length, the device should be set to static balance manually; a process which takes time and expertise. The proposed mechanism, when the device is connected to the individual's hand, it synchronizes itself with the weight and torque automatically using a novel force sensor, which is based on a barometer device, and the reaches statistic balance. In addition, if a weight is lifted, the device rebalances with the new weight and reaches statistic balance accordingly. In order to provide statistic balance in this system, counterweight is used which neutralizes 98% of sum of weight of hand and the weight.

关键词： robot sensing systems Manipulators Force Torque Skin Force sensors

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Generating Synthetic Medical Images by Using GAN to Improve CNN Performance in Skin Cancer Classification

Generating Synthetic Medical Images by Using GAN to Improve ...

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RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： Pooyan Sedigh Rasoul Sadeghian Mehdi Tale Masouleh Science and Research Branch Islamic Azad University Tehran Iran Human and Robot Interaction Laboratory Tehran Iran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (数字)9781728166049

ISBN: (纸本)9781728166056

One of the main reasons of slow progress in using deep learning methods for cancer detection is the lack of data, especially the annotated data which is usually used for supervised learning algorithms. This paper presents a Convolutional Neural Network (CNN) to detect skin cancer. The primary database which is used to train the designed CNN algorithm has 97 members (50 benign and 47 malignant), which are collected from the International Skin Imaging Collaboration (ISIC). In order to compensate the lack of data for training the proposed CNN algorithm, a Generative Adversarial Network (GAN) is designed to produce synthetic skin cancer images. The classification performance of the designed trained CNN without the obtained synthetic images is near 53%, but by adding the synthetic images to the primary database the performance of the model is increased to 71%.

关键词： Gallium nitride Generative adversarial networks Databases Skin cancer Medical diagnostic imaging

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Design a 3-DOF Delta Parallel robot by One Degree Redundancy along the Conveyor Axis, A Novel Automation Approach 5

Design a 3-DOF Delta Parallel Robot by One Degree Redundancy...

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5th IEEE Conference on Knowledge Based engineering and Innovation, KBEI 2019

作者： Ahangar, Sajjad Mehrabani, Mehdi Valizadeh Pouransari Shorijeh, Alireza Masouleh, Mehdi Tale Mechanical Eng. Dept. of Islamic Azad University Central Tehran Branch Tehran Iran School of Mechanical Eng. University of Tehran Tehran Iran Human Robot Interaction Laboratory School of Electrical and Computer Eng. University of Tehran Tehran Iran

ISBN: (纸本)9781728108728

This paper addresses the design of a 3 degree-of-freedoms (DOF) Delta parallel robot with one DOF redundancy attached to its base. This paper aims at presenting the state-of-the-art of the design and control for the proposed redundant robot for improving time and saving energy consumption. The main idea behind the proposed design consists in examining the performance of a movable Delta robot for pick-and-place purpose. To this end, some improvement has been made on the design of the robot. Spherical magnetic joints are used for the sake of easing the assembling and disassembling of components. At the end, kinematic properties of the robot are investigated. © 2019 IEEE.

关键词： Energy utilization

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Design a 3-DOF Delta Parallel robot by One Degree Redundancy along the Conveyor Axis, A Novel Automation Approach

Design a 3-DOF Delta Parallel Robot by One Degree Redundancy...

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International Conference on Knowledge-Based engineering and Innovation (KBEI)

作者： Sajjad Ahangar Mehdi Valizadeh Mehrabani Alireza Pouransari Shorijeh Mehdi Tale Masouleh Mechanical Eng. dept. of Islamic Azad University Central Tehran Branch Tehran Iran School of Mechanical Eng. University of Tehran Iran Human & Robot interaction Laboratory School of Electrical and Computer Eng. University of Tehran Iran

关键词： Kinematics Parallel robots Service robots Energy consumption End effectors

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