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A Flexible-Integrated Multimodal Hydrogel-Based Sensing Patch

Nano-Micro Letters 2025年第7期 115-133页

作者： Peng Wang Guoqing Wang Guifen Sun Chenchen Bao Yang Li Chuizhou Meng Zhao Yao School of Mechanical Engineering University of Jinan School of Information Science and Engineering University of Jinan State Key Laboratory for Reliability and Intelligence of Electrical Equipment Engineering Research Center of Ministry of Education for Intelligent Rehabilitation Device and Detection TechnologyHebei Key Laboratory of Smart Sensing and Human-Robot InteractionSchool of Mechanical EngineeringHebei University of Technology School of Integrated Circuits Shandong University State Key Laboratory of Integrated Chips and Systems Fudan University College of Electronics and Information Qingdao University

Sleep monitoring is an important part of health management because sleep quality is crucial for restoration of human health. However, current commercial products of polysomnography are cumbersome with connecting wires and state-of-the-art flexible sensors are still interferential for being attached to the body. Herein, we develop a flexible-integrated multimodal sensing patch based on hydrogel and its application in unconstraint sleep monitoring. The patch comprises a bottom hydrogel-based dualmode pressure–temperature sensing layer and a top electrospun nanofiber-based non-contact detection layer as one integrated device. The hydrogel as core substrate exhibits strong toughness and water retention, and the multimodal sensing of temperature, pressure, and non-contact proximity is realized based on different sensing mechanisms with no crosstalk interference. The multimodal sensing function is verified in a simulated real-world scenario by a robotic hand grasping objects to validate its practicability. Multiple multimodal sensing patches integrated on different locations of a pillow are assembled for intelligent sleep monitoring. Versatile human–pillow interaction information as well as their evolution over time are acquired and analyzed by a one-dimensional convolutional neural network. Track of head movement and recognition of bad patterns that may lead to poor sleep are achieved, which provides a promising approach for sleep monitoring.

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Practical Implementation of Real-Time Waste Detection and Recycling based on Deep Learning for Delta Parallel robot

Practical Implementation of Real-Time Waste Detection and Re...

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International eConference on computer and Knowledge Engineering (ICCKE)

作者： Hasan Jalali Shaya Garjani Ahmad Kalhor Mehdi Tale Masouleh Parisa Yousefi School of Electrical and Computer Engineering Human and Robot Interaction Laboratory University of Tehran Tehran Iran School of Computer Engineering Imam Reza International University Mashhad Iran

Intelligent robots play an essential role in waste management and recycling due to their high speed and a wide variety of applications. In this paper, two methods for waste detection and accurate pick-and-place based on computer vision and neural networks are presented. The suggested methods have been put into practical application on a 3-DOF Delta parallel robot to show the accuracy and fastness of the foregoing method for real intelligence systems. The first method, Multi-Stage Detection, consists of two stages to detect the waste objects, namely, object localization and segmentation, and classification. The second method, known as One-Stage object detectors, such as YOLOv5, has the capability to simultaneously localize and classify the waste objects. The dataset utilized in this paper relies on the TrashNet dataset as its foundation. In order to improve the classification capabilities in the multi-stage method, a larger dataset was created by utilizing data augmentation. Also, for the one-stage method, a new multi-label dataset is constructed based on the TrashNet dataset. Additionally, the results of the experimental implementation were compared based on time and evaluation metrics for detection and classification. The ResNet50 model achieved the highest accuracy in the multi-stage method, with 99.31% accuracy. In the one-stage detection method, the YOLOv5x model achieved the best mAP (@IoU = 0.75) of 97.4%, which outperformed the YOLOv5s model by 0.8 percent; however, the inference speed of the YOLOv5x in comparison with the YOLOv5s models was six times as slow. Therefore, the YOLOv5s model was employed in real-time online waste detection, which resulted in 82.1% mAP (@IoU = 0.5) after being trained on real images from the waste-sorting platform.

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Designing Controller for the 2-DoF Cable-Driven Serial robot with Variable Configuration based on Adaptive Neuro-Fuzzy Systems

Designing Controller for the 2-DoF Cable-Driven Serial Robot...

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

作者： Vahid Bahrami Ahmad Kalhor Mehdi Tale Masouleh School of Electrical and Computer Engineering University of Tehran Human and Robot Interaction Laboratory Tehran Iran

ISBN: (纸本)9781665454537

This paper addresses the problem of actuation and design of a controller for a 2-DoF Cable-Driven Serial Chain (CDSC) with variable configuration based on adaptive neuro-fuzzy systems. The mentioned structure uses several light cables to actuate rigid links of a passive planar serial robot, performing a 2-DoF motion pattern, which can apply tensile forces for moving the links and thus produce the necessary torque to track the desired reference model by the robot’s end-effector. Additionally, cable attachment point coordinates of the CDSC are considered variable configurations. So, a novel concept is expressed in the calculation of the CDSC’s Jacobian matrix. In this mechanism, upon formulating the sliding mode error dynamics, an Adaptive Neural Network (ANN) is used to cope with complex mathematical calculations of the CDSC dynamics. Then, the coordinates of cables are considered as variables that depend on the main states of the plant. These co-states are adaptively determined by applying an appropriate fuzzy system. As a result, an Adaptive Neuro-Fuzzy Controller (ANFC) algorithm is introduced for the mentioned redundant mechanism. Finally, by defining a new Cost of Redundancy (CoR), an optimal configuration of the cables is found, so the maximum Degrees of Redundancy (DoR) is obtained. The simulation results indicate that using the ANFC algorithm leads to a reduction of 2.23% in the CoR for the 2-DoF CDSC.

关键词： Adaptive systems Costs Torque Tracking robot kinematics Heuristic algorithms Simulation

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Safe Deep Learning-Based Global Path Planning Using a Fast Collision-Free Path Generator

SSRN

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SSRN 2022年

作者： Chehelgami, Shirin Ashtari, Erfan Basiri, Mohammad Amin Masouleh, Mehdi Tale Kalhor, Ahmad Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran

In this research, a global path planning method based on recurrent neural networks by means of a new loss function is presented, which regardless of the complexity of the configuration space, generates the path in a relatively constant time. The new loss function is defined in such a way that in addition to learning the input data of the network, it creates an adjustable safety margin around the obstacles and ultimately creates a safe path. Moreover, a new global path planning method is also introduced, which is used to create the dataset needed to train the proposed neural network. The convergence of this method is mathematically proven and it is shown that this method can also produce a suboptimal path in a much shorter time than the common methods of global path planning reported in the literature. In short, the main purpose of this research consists in providing a method which can create a suboptimal, fast and safe path for a mobile robot from any random starting point to any random destination in a known environment. First, the proposed methods will be implemented for different two-dimensional environments consisting of convex and non-convex obstacles, considering the robot as a pointmass, and then it will be implemented in a 3D simulation environment, *** to classical global path planning algorithms, such as RRT and A*, the proposed approach demonstrates better performance in complex and challenging environments. © 2022, The Authors. All rights reserved.

关键词： Recurrent neural networks

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Ennet: Enhanced Interactive Information Network with Zero-Order Optimization

SSRN

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SSRN 2024年

作者： Yang, Pengfei Chen, Yanxin Cheng, Fei Wang, Quan Sun, Jianguo School of Computer Science and Technology Xidian University Xi’an710071 China The Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shaanxi Province Xi’an710071 China

Interactive image segmentation extremely accelerates the generation of high-quality annotation image datasets, which are the pillars of the applications of deep learning. However, these methods suffer from the insignificance of interaction information and excessively high optimization costs, resulting in unexpected segmentation outcomes and increased computational burden. To address these issues, this paper focuses on interactive information mining from the network architecture and optimization procedure. In terms of network architecture, the issue mentioned above arises from two perspectives: less representative feature of interactive region in each layer, and the interactive information weakened by the network hierarchy structure. Therefore, the paper proposes a network called EnNet. The network addresses the two aforementioned issues by employing attention mechanisms to integrate user interaction information across the entire image and incorporating interaction information twice in a design that progresses from coarse to fine. In terms of optimization, this paper proposes an optimization method that utilizes zero-order gradient estimation to compute the gradient during the first four iterations, concurrently enhancing the efficiency of model segmentation for challenging cases while reducing optimization costs. The experimental results on GrabCut, Berkeley, DAVIS, and SBD datasets validate the effectiveness of the proposed method, with our approach achieving an average NOC@90 that surpasses RITM by 0.35. © 2024, The Authors. All rights reserved.

关键词： Network architecture

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In Vivo Bioelectronic Nose Based on a Bioengineered Rat for the Explosive Odors with Benzene Ring Detection and Identification 24

In Vivo Bioelectronic Nose Based on a Bioengineered Rat for ...

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11th International Conference on Biomedical and Bioinformatics Engineering, ICBBE 2024

作者： Ma, Jing Wang, Yimeng Yang, Jun Sun, Biao Yang, Qing Zheng, Chenguang Academy of Medical Engineering and Translational Medicine Medical College Tianjin University Tianjin China College of Precision Instruments & Optoelectronics Engineering Tianjin University Tianjin China School of Electrical and Information Engineering Tianjin University Tianjin China Tianjin Key Laboratory of Brain Science and Neuroengineering Tianjin China Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration Tianjin China

ISBN: (纸本)9798400718274

Accidents caused by explosives occur frequently, which seriously threaten people's lives and health, so it is urgent to develop new technologies for sensitively and rapidly detecting trace explosives. In this study, a bioengineered rat with the olfactory receptor (OR3) overexpression in the olfactory epithelium (OE) was successfully developed, and the unique features of three representative explosives (NT, DNT, and TNT) were conducted by recording electrophysiological-responsive signals from the olfactory bulb (OB) with implanted multichannel microelectrodes during awake-state-independent sniffing behavior induced by explosives and non-explosives. These three explosives were regarded as goal odors, while citral and air were no-goal odors. The analysis of local field potential (LFP) power changes revealed that, goal, and no-goals could be distinguished in both the vector group and the OR3 group at concentrations as low as 10-12 mol/L, and the classification performance of the OR3 group was superior to that of the vector group overall. Our findings suggest that OR3 bioengineered rats can reliably and accurately identify trace explosives, which provides a novel approach for detecting and identifying explosives with promising applications in odor discrimination in complex environments. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Electronic nose

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Imperceptible and Sparse Adversarial Attacks via a Dual-Population-Based Constrained Evolutionary Algorithm

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第2期4卷 268-281页

作者： Tian, Ye Pan, Jingwen Yang, Shangshang Zhang, Xingyi He, Shuping Jin, Yaochu Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Hefei230601 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230088 China Anhui University School of Computer Science and Technology Hefei230601 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Hefei230601 China Anhui University Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and Automation Hefei230601 China Bielefeld University Faculty of Technology Bielefeld33619 Germany

The sparse adversarial attack has attracted increasing attention due to the merit of a low attack cost via changing a small number of pixels. However, the generated adversarial examples are easily detected in vision since the perturbation to each pixel is relatively large. To achieve imperceptible and sparse adversarial attacks, this article formulates a bi-objective constrained optimization problem, simultaneously minimizing the 0 and 2 distances to the original image, and proposes a dual-population-based constrained evolutionary algorithm to solve it. The proposed method solves the optimization problem by evolving two populations, where one population is responsible for finding feasible solutions (i.e., successful attacks) and the other one is to minimize both the 0 and 2 distances. Moreover, a population initialization strategy and two genetic operators are customized to accelerate the convergence speed. Experimental results indicate that the proposed method can achieve high success rates with low attack costs, and strikes a better balance between the 0 and 2 distances than state-of-the-art methods. © 2020 IEEE.

关键词： Evolutionary algorithms

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Collaborative Multi-Agent Reinforcement Learning Control of Parallel robots

Collaborative Multi-Agent Reinforcement Learning Control of ...

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

作者： Amirhossein Afkhami Ardekani Mehdi Tale Masouleh Mohammad Reza Hairi Yazdi Human and Robot Interaction Laboratory School of Mechanical Engineering University of Tehran Tehran Iran Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran School of Mechanical Engineering College of Engineering University of Tehran Tehran Iran

ISBN: (纸本)9781665454537

In this paper, a novel algorithm based on Multi-Agent Reinforcement Learning for controlling parallel robots has been suggested. The dynamic models of parallel robots are complex and full of uncertainties, and deriving them requires deep knowledge of the mechanism of the robot. Therefore, the proposed algorithm is designed model-free to be independent of prior knowledge about the system from the outset. Moreover, this algorithm comprises two primary components, making it efficient in training and convergence. The proposed algorithm takes each loop or limb in parallel robots as a separate agent. These agents then learn to collaborate to fulfill the robot’s defined task by producing appropriate control signals from a decentralized point of view. For studying the performance of the proposed algorithm, a 3-DOF parallel robot called Agile Eye is taken into account as a case study which is simulated in CoppeliaSim simulation environment for the task of reference tracking. Two other controllers, including the classic Proportional Integral Derivative (PID) controller and the single-agent counterpart of the suggested algorithm, have been implemented for a better performance comparison of the proposed algorithm. Using the Root Mean Square Error (RMSE) index, the recommended algorithm with an RMSE value of 0.0553 is superior to its single-agent counterpart with an RMSE of 0.1105. On the other hand, the proposed algorithm is inferior to the PID controller with an RMSE of 0.0275, mainly due to the fact that the PID Controller is in velocity control mode, while the proposed algorithm manipulates the robot in torque control mode, which is less stable.

关键词： Training Parallel robots Torque Uncertainty Heuristic algorithms Torque control Reinforcement learning

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VAction: A Lightweight and Integrated VR Training System for Authentic Film-Shooting Experience 25

VAction: A Lightweight and Integrated VR Training System for...

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2025 CHI Conference on human Factors in Computing Systems, CHI 2025

作者： Wang, Shaocong Qu, Che Yu, Minjing Zhou, Chao Wang, Yuntao Wen, Yu-Hui Shi, Yuanchun Liu, Yong-Jin Department of Computer Science and Technology Tsinghua University Beijing China Film & TV Technology Department Beijing Film Academy Beijing China College of Intelligence and Computing Tianjin University Tianjin China Human Computer Interaction Technology Intelligent Information Processing Laboratory Institute of Software Chinese Acadamy of Sciences Beijing China School of Computer Science and Technology Beijing Jiaotong University Beijing China

ISBN: (纸本)9798400713941

The film industry exerts significant economic and cultural influence, and its rapid development is contingent upon the expertise of industry professionals, underscoring the critical importance of film-shooting education. However, this process typically necessitates multiple practice in complex professional venues using expensive equipment, presenting a significant obstacle for ordinary learners who struggle to access such training environments. Despite VR technology has already shown its potential in education, existing research has not addressed the crucial learning component of replicating the shooting process. Moreover, the limited functionality of traditional controllers hinder the fulfillment of the educational requirements. Therefore, we developed VAction VR system, combining high-fidelity virtual environments with a custom-designed controller to simulate the real-world camera operation experience. The system's lightweight design ensures cost-effective and efficient deployment. Experiment results demonstrated that VAction significantly outperforms traditional methods in both practice effectiveness and user experience, indicating its potential and usefulness in film-shooting education. © 2025 Copyright held by the owner/author(s).

关键词： Personnel training

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Deep-HFID: Deep Neural Network Based Hands and Face bio-metric Identification System Using Metric Learning

Deep-HFID: Deep Neural Network Based Hands and Face bio-metr...

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

作者： Zahra Parvin Ashtyani Sara Ghanbari Navid Zare Mehdi Tale Masouleh Ahmad Kalhor School of Electrical and Computer Engineering Human and Robot Interaction Laboratory University of Tehran Tehran Iran School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (纸本)9781665454537

This paper presents a device for person identification based on hand bio-metrics. The main objective of a person recognition device is to perform accurate identification at a reasonable production cost. The so-called Deep-HFID device is designed to meet these objectives as well as to provide users with ease of use. For the purpose of identifying individuals, this device uses hand and face bio-metrics. In terms of dimensions, this device has been designed to facilitate the use of a variety of individuals. For ease of deployment, the Deep-HFID system relies on a server for most of its processes. The routine tasks, like showing the graphical user interface are handled by a Raspberry Pi board. A dataset from the palm side of both hands from 110 individuals has been created. Using transfer learning, the six pre-trained state-of-the-art neural network architecture was fine-tuned and calibrated based on the F1 score and precision criteria. An augmented dataset containing samples from the palm side of 30 individuals with between fingers angles variation is collected. By applying the main and augmented dataset and using the center-based separation index as a complexity measure algorithm to rank pre-trained deep neural networks, a compression on the last layer of the best performed neural network, EfficientNetV2B1, is done which results in a more robust system without reducement in accuracy.

关键词： Performance evaluation Training Face recognition Neurons Liquid crystal displays Libraries Servers

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