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IEEE International Conference on Cloud Computing and Intelligence systems (CCIS)

作者： Shuaibing Wang Shunli Wang HaoPeng Kuang Fang Li Ziyun Qian Mingcheng Li Lihua Zhang Academy for Engineering & Technology Fudan University Engineering Research Center of AI and Robotics Ministry of Education China Jilin Provincial Key Laboratory of Intelligence Science & Engineering China AI & Unmanned Systems Engineering Research Center of Jilin Province China

ISBN: (纸本)9781665456579

Hand pose estimation is one of the representative tasks in computer vision. Solving the hand pose estimation problem is essential for various fields such as virtual reality, augmented reality, mixed reality, and human-computer interaction. Due to the significant development of deep learning techniques, the hand pose estimation task has reached significant performance on many hand pose estimation datasets. However, the hand pose estimation task still faces many challenges due to the lack of large-scale labeled data, severe occlusion, low hand resolution, and background clutter. To better understand the hand pose estimation task, this paper presents a comprehensive survey of outstanding papers over the last five years. The paper first introduces 19 common hand pose estimation datasets, then extensively discusses some of the mainstream approaches in hand pose estimation, including fully supervised, semi-supervised, weakly supervised, and self-supervised learning methods. Finally, we extensively discuss the future evolution trends of hand pose estimation.

关键词： Human computer interaction Computer vision Solid modeling Pose estimation Supervised learning Mixed reality Self-supervised learning

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Variation of sEMG with Muscle Fatigue for Lower Limb Stroke Patient During Walking

Variation of sEMG with Muscle Fatigue for Lower Limb Stroke ...

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IEEE International Conference on robotics and Biomimetics

作者： Wenze Shang Ruisen Huang Wei-Hsin Liao Xinyu Wu Fei Gao Southern University of Science and Technology Shenzhen China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

sEMG signal is widely used and explored in control strategies of powered assistive human-robot interaction systems due to its non-invasive nature and ability to estimate motion intention well. However, prolonged use of the sEMG-based systems leads to muscle fatigue, contributing to unstable sEMG signals and regarding the performance of sEMG-based control strategies. In this study, three subjects, including two healthy subjects and one stroke subject, participated in our experiment, and the time domain, frequency domain, and complex features extracted from their $sEMG$ signals were utilized to evaluate muscle fatigue. The results indicate that $sEMG$ from the stroke side of the stroke subject contains more non-linear and chaotic parts, and the statistical differences between different trials are more significant, indicating that stroke has a more significant impact on the stroke side of the stroke subject. This study suggests that RMS in the stroke side of stroke subjects is less than that in others, whereas FuzzyEn in the stroke side of stroke subjects is greater than that in others, indicating that stroke leads to different motion patterns and more susceptibility to muscle fatigue.

关键词： Legged locomotion Limbs Frequency-domain analysis Biomimetics Human-robot interaction Muscles Fatigue Feature extraction Time-domain analysis Robots

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Novel Surface Design of Deployable Reflector Antenna Based on Polar Scissor Structures

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Chinese Journal of Mechanical engineering 2020年第5期33卷 4-18页

作者： Pengyuan Zhao Jinguo Liu Chenchen Wu Yangmin Li Keli Chen State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of ScienceShenyang 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China University of Chinese Academy of Sciences Beijing 100049China Department of Industrial and Systems Engineering The Hong Kong Polytechnic UniversityHong Kong 999077China

Space-deployable mechanisms can be used as supporting structures for large-diameter antennas in space *** study proposes a novel method for constructing the surface design of space reflector antennas based on polar scissor *** concurrency and deployability equations of the space scissor unit with definite surface constraints are derived using the rod and vector *** equations of the spatial transformation for space n-edge polar scissor units are summarized.A new closed-loop deployable structure,called the polar scissor deployable antenna(PSDA),is designed by combining planar polar scissor units with spatial polar scissor *** overconstrained problem is solved by releasing the curve constraint that locates at the end-point of the planar scissor *** simulation and error analysis are *** results show that the PSDA can effectively fit the paraboloid of ***,deployment experiments verify the validity and feasibility of the proposed design method,which provides a new idea for the construction of large space-reflector antennas.

关键词： Deployable structures Polar scissor unit Reflector antenna Surface design

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DDCM: Cracking Anonymized Facial Images Using Denoising Diffusion Cryptanalytic Model

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IEEE Transactions on Consumer Electronics 2025年

作者： Jiang, Donghua Ni, Jiangqun Alasbali, Nada Nafea, Ibtehal Liu, Qingliang Ahmad, Jawad Boulila, Wadii Sun Yat-sen University School of Computer Science and Engineering Guangzhou510006 China Sun Yat-sen University School of Cyber Science and Technology Shenzhen518107 China Peng Cheng Laboratory Department of New Networks Shenzhen518052 China King Khalid University College of Computer Science Department of Informatics and Computing Systems Abha61421 Saudi Arabia Taibah University College of Computer Science and Engineering Medina41477 Saudi Arabia Qingdao University of Science and Technology School of Mathematics and Physics Qingdao266061 China Prince Mohammad Bin Fahd University Cybersecurity Center Alkhobar Saudi Arabia Prince Sultan University Robotics and Internet-of-Things Laboratory Riyadh12435 Saudi Arabia

Currently, in consumer electronics industry, quite a number of E-healthcare products highly depend on consumers' facial biometrics for both disease diagnosis and identity authen-tication. In light of this, various facial privacy-preserving approa-ches oriented towards the internet of medical things have been proposed, including visual cryptography and desensitization tech-nique. Nevertheless, few attentions have been paid to investigating their countermeasure, i.e., facial privacy analysis, owning to significant distribution discrepancy between the original and anony-mized facial images. To this end, a general Denoising Diffusion Cryptanalytic Model (DDCM) is proposed in this paper to recon-struct the original facial images in a coarse-to-fine manner from their encrypted ones with diverse anonymous approaches. Spec-ifically, to accurately reconstruct the original facial image without any prior knowledge, a customized initial prediction module with an encoder-decoder structure is introduced by taking advantage of its leaked visual cues to generate the coarse face. Furthermore, the residual map between the original and coarse facial images is explicitly modeled with the output of the customized prediction module as a conditional prior, thereby obtaining a refined facial image. Finally, extensive experiments demonstrate that the propo-sed DDCM could not only effectively crack multiple handcrafted facial anonymization approaches, such as blurring, pixelation and thumbnail-preserving encryption, but also outperform the prev-ious works (e.g., Pix2Pix) in terms of image fidelity and attack success rate. © 1975-2011 IEEE.

关键词： Anonymity

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Towards Autonomous Robotic Ultrasound Scanning Using the Reinforcement Learning-Based Volumetric Data Navigation Method

Towards Autonomous Robotic Ultrasound Scanning Using the Rei...

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WRC Symposium on Advanced robotics and Automation (WRC SARA)

作者： Cuifeng Shen Zhaokun Deng Jinzhi Wang Shuangyi Wang Chen Chen Department of Mechanics and Engineering Science College of Engineering State Key Laboratory for Turbulence and Complex Systems Peking University Beijing P. R. China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Chinese Academy of Sciences Hong Kong China

Ultrasound scanning is an indispensable diagnostic tool in modern medicine, but obtaining an accurate standard plane relies heavily on the sonographer’s skill and expertise. This paper presents a novel approach toward autonomous robotic ultrasound scanning using a reinforcement learning-based navigation method with volumetric ultrasound data. The proposed method enables an ultrasound robot to autonomously acquire high-quality 2D standard planes from patients, thereby reducing the need for human intervention. The core of the approach is a deep reinforcement learning algorithm that detects 2D standard planes within the 3D ultrasound volume and guides the ultrasound probe to extract high-quality standard planes. To assess the effectiveness of the 2D standard plane detection method, an evaluation was conducted using a realworld ultrasound dataset, and a comparison was made between its performance and that of other state-of-the-art autonomous approaches. The simulation experiment was also conducted to validate the autonomous robotic ultrasound acquisition workflow. Results demonstrate that the reinforcement learning-based volumetric data navigation method outperforms existing techniques in terms of standard plane accuracy and is very effective for volumetric data-based robotic navigation.

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Automated Leukemia Screening and Sub-types Classification Using Deep Learning

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Computer systems science & engineering 2023年第9期46卷 3541-3558页

作者： Chaudhary Hassan Abbas Gondal Muhammad Irfan Sarmad Shafique Muhammad Salman Bashir Mansoor Ahmed Osama M.Alshehri Hassan H.Almasoudi Samar M.Alqhtani Mohammed M.Jalal Malik A.Altayar Khalaf F.Alsharif Department of Computer Science COMSATS University Islamabad44000Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Control Automotive and Robotics LabNational Center of Robotics and Automation(NCRA HEC)MirpurPakistan Mirpur University of Science and Technology(MUST)MirpurPakistan Department of Computer Science Virtual University of PakistanLahore54000Pakistan Department of Clinical Laboratory Sciences College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Medical Laboratory Technology Faculty of Applied Medical SciencesUniversity of TabukTabukSaudi Arabia Department of Clinical Laboratory Sciences College of Applied Medical SciencesTaif UniversityP.O.Box 11099Taif21944Saudi Arabia

Leukemia is a kind of blood cancer that damages the cells in the blood and bone marrow of the human *** produces cancerous blood cells that disturb the human’s immune system and significantly affect bone marrow’s production ability to effectively create different types of blood cells like red blood cells(RBCs)and white blood cells(WBC),and *** can be diagnosed manually by taking a complete blood count test of the patient’s blood,from which medical professionals can investigate the signs of leukemia ***,two other methods,microscopic inspection of blood smears and bone marrow aspiration,are also utilized while examining the patient for ***,all these methods are labor-intensive,slow,inaccurate,and require a lot of human experience and *** authors have proposed automated detection systems for leukemia diagnosis to overcome these *** have deployed digital image processing and machine learning algorithms to classify the cells into normal and blast ***,these systems are more efficient,reliable,and fast than previous manual diagnosing ***,more work is required to classify leukemia-affected cells due to the complex characteristics of blood images and leukemia cells having much intra-class variability and inter-class *** this paper,we have proposed a robust automated system to diagnose leukemia and its *** have classified ALL into its sub-types based on FAB classification,i.e.,L1,L2,and L3 types with better *** have achieved 96.06%accuracy for subtypes classification,which is better when compared with the state-of-the-art methodologies.

关键词： Healthcare cancer detection deep learning convolutional neural network

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IEEE International Conference on Image Processing

作者： Chaowei Han Gaofeng Meng Chunlei Huo State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Centre for Artificial Intelligence and Robotics HK Institute of Science & Innovation Chinese Academy of Sciences School of Information Engineering Capital Normal University Beijing China

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Content-based video retrieval aims to retrieve near-duplicate entries from a database of a given query video. It plays an important role in combating video piracy. Robustness to video temporal dynamics is crucial for a representation model in video retrieval, as frames extracted from two copied videos are hardly temporally aligned in actual situations. However, current image retrieval datasets have difficulty in evaluating this robustness. To address this issue, we collect Similar Frame Dataset (SFD), which consists of 32,923 query-target pairs with 128,240 distraction images. The task of SFD is to retrieve the target frame from all items given a query frame. SFD is constructed by sampling frames from Kinetics-700 action classification dataset. An object detection model (Faster R-CNN) and a Multimodal Large Language Model (BLIP2) are used during sampling to select those valid frames. Besides, we propose Adjacent Frames Contrastive Learning (AFCL) framework. In AFCL, adjacent frames are sampled from unlabeled videos as positive pairs. An image representation model with robustness to changing frames can be trained under AFCL framework and achieve the state-of-the-art performance on SFD. The code will be released at https://***/Chuan-shanjia/Similar-Frame-Dataset.

关键词： Codes Databases Scalability Large language models Image retrieval Contrastive learning Object detection

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On Embeddings and Inverse Embeddings of Input Design for Regularized System Identification

arXiv

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arXiv 2022年

作者： Mu, Biqiang Chen, Tianshi Kong, He Jiang, Bo Wang, Lei Wu, Junfeng Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen518172 China Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen518055 China Key Laboratory for NSLSCS of Jiangsu Province School of Mathematical Sciences Nanjing Normal University Nanjing China College of Control Science and Engineering Zhejiang University Hangzhou China

Input design is an important problem for system identification and has been well studied for the classical system identification, i.e., the maximum likelihood/prediction error method. For the emerging regularized system identification, the study on input design has just started, and it is often formulated as a non-convex optimization problem that minimizes a scalar measure of the Bayesian mean squared error matrix subject to certain constraints, and the state-of-art method is the so-called quadratic mapping and inverse embedding (QMIE) method, where a time domain inverse embedding (TDIE) is proposed to find the inverse of the quadratic mapping. In this paper, we report some new results on the embeddings/inverse embeddings of the QMIE method. Firstly, we present a general result on the frequency domain inverse embedding (FDIE) that is to find the inverse of the quadratic mapping described by the discrete-time Fourier transform. Then we show the relation between the TDIE and the FDIE from a graph signal processing perspective. Finally, motivated by this perspective, we further propose a graph induced embedding and its inverse, which include the previously introduced embeddings as special cases. This deepens the understanding of input design from a new viewpoint beyond the real domain and the frequency domain viewpoints. Copyright © 2022, The Authors. All rights reserved.

关键词： Embeddings

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Platoon-Based Distributed Control for Automated Material Handling systems

Platoon-Based Distributed Control for Automated Material Han...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Alessandro Bozzi Jose-Fernando Jimenez Camilo Hernandez-Rodriguez Eliana-Maria Gonzalez-Neira Damien Trentesaux Department of Informatics Bioengineering Robotics and Systems Engineering (DIBRIS) University of Genova Italy Laboratory System and Materials for Mechatronics (SYMME) Université Savoie Mont Blanc Annecy France Industrial Engineering Department Pontificia Universidad Javeriana Bogota Columbia Laboratory of Industrial and Human Automation Control Mechanical Engineering and Computer Science (LAMIH) Université Polytechnique Hauts-de-France Valenciennes France

Smart manufacturing systems combine collaborative and autonomous control systems to provide optimal performance and respond to volatile demands. A key component of smart manufacturing is material management, as raw materials, work-in-progress, and finished products must be transported to the right location at the right time. However, during the production phase, it is imperative to establish an optimal traffic control scheme in order to avoid congestion and optimize material flow. This paper proposes a platooning-based control procedure implemented in a virtual agent-based manufacturing environment to increase the material flow efficiency on a manufacturing shop floor. An analysis of the production makespan and an indicator of the level of congestion in the material handling system was conducted in order to determine productivity improvements. An agent-based simulation is used to test the effectiveness of the novel platooning approach versus a traditional basic approach. A multivariate analysis of variance (MANOVA) reveals that the inclusion of a platoon-based procedure improves production performance and reduces congestion risks.

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Joint Routing and Charging Problem of Electric Vehicles with Incentive-Aware Customers Considering Spatio-Temporal Charging Prices

SSRN

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SSRN 2022年

作者： Yao, Canqi Chen, Shibo Salazar, Mauro Yang, Zaiyue School of Mechatronics Engineering Harbin Institute of Technology Harbin150000 China Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy Engineering The Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen518055 China Control Systems Technology Group Department of Mechanical Engineering Eindhoven University of Technology EindhovenMB 5600 Netherlands

This paper investigates the scheduling problem of a fleet of electric vehicles, providing mobility as a service to a set of time-specified customers, where the operator needs to solve the routing and charging problem jointly for each EV. Hereby we consider incentive-aware customers and propose that the operator offers monetary incentives to customers in exchange for time flexibility. In this way, the fleet operator can achieve a routing and charging schedule with lower costs, whilst the customers receive monetary compensation for their flexibility. Specifically, we first propose a bi-level optimization model whereby the fleet operator optimizes the routing and charging schedule accounting for the spatio-temporal varying charging price, jointly with a monetary incentive to reimburse the delivery time flexibility experienced by the customers. Concurrently the customers choose their own time flexibility by minimizing their own cost. Second, we cope with the computational burden coming from this nonlinear bi-level optimization model with an accurate reformulation approach consisting of the KKT optimality conditions, a Big-M-based linearization method, and the zero duality gap of convex optimization problems. This way, we convert the proposed problem into a single-level optimization problem, which can be solved by a strengthened generalized Benders decomposition method holding a faster convergence rate than the generalized Benders decomposition method. To evaluate the effectiveness of the proposed mathematical model, we carry out numerous simulation experiments by using the VRP-REP data of Belgium. The numerical results showcase that the proposed mathematical model can reduce the delivery fees for the customers together with the cost of operation incurred by the fleet operator. © 2022, The Authors. All rights reserved.

关键词： Sales

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