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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Shaokun Zhang Xinde Li Chuanfei Hu Jianping Xu Huaping Liu School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China Nanjing Center for Applied Mathematics Nanjing China Southeast University Shenzhen Research Institute Shenzhen China Science and Technology on Information Systems Engineering Laboratory Nanjing China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

Recent transformer-based methods for estimating 3D human pose have gained widespread attention, achieving state-of-the-art results. Previous methods have primarily focused on capturing motion patterns of the human body at a single scale or cascading multiple scales, such as joints, bones, and body-parts. However, they are difficult to simultaneously capture spatial-temporal motion patterns of the human body at different scales due to the complex motion patterns. To address this issue, we propose Dual-scale Spatial and Temporal transFormer (DSTFormer), which can concurrently explore the spatial dependencies and temporal motion patterns of human joints and bones. Additionally, we introduce a Gcn-Spatial Transformer Block (GSTB), which introduces Graph Convolutional Networks (GCN) into transformer to enhance the exploitation of local relationships and global information between adjacent joints or bones. Extensive experiments are conducted on the Human3.6M benchmark dataset, and superior results are reported when comparing to other state-of-the-art methods. More remarkably, our model achieves to-date the best published performance, with P1 errors of 37.9 mm and 15.6 mm, respectively.

关键词： Solid modeling Three-dimensional displays Mechatronics Graph convolutional networks Computational modeling Pose estimation Transformers Bones Joints Robots

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Rock vs Mine Prediction and Detection for Aquatic Systems: A Comparative Analysis of Different Machine Learning and Deep Learning Algorithms

Rock vs Mine Prediction and Detection for Aquatic Systems: A...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Ayan Sar Tanupriya Choudhury Anvi Goel Ketan Kotecha Bhasker Pant Bhupesh Kumar Dewangan School of Computer Science University of Petroleum and Energy Studies (UPES) Dehradun Uttarakhand India Symbiosis Center for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International University Pune Maharashtra India CSE Dept Graphic Era Deemed to be University Dehradun Uttarakhand Department of Computer Science and Engineering OP Jindal University Raigarh Chhattisgarh India

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

Deep machine learning approaches can enhance the forecasting and detection of rocks and minerals in water defense systems. This research focused on applying deep machine learning techniques to increase accuracy and efficiency in rock and mineral prediction. The researchers employed a series of watermarks that appeared and then utilized a convolutional neural network (CNN) to extract significant characteristics. Using transfer learning approaches and state-of-the-art optimization algorithms, researchers suggested a multi-class solution to identify between rocks and minerals. The suggested technique accurately predicted and recognized rocks and mines in underwater imagery, even in complicated settings with complicated against and illumination circumstances. The finding was essential for water protection and maritime safety systems, as it can assist in the automatic identification of rocks and minerals, decreasing the time and effort necessary for manual inspection

关键词： Technological innovation Machine learning algorithms Transfer learning Watermarking Rocks Prediction algorithms Minerals

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A Multi-Objective Evolutionary Algorithm Based on Indicator and Performing Local Searches

A Multi-Objective Evolutionary Algorithm Based on Indicator ...

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Information science and Education (ICISE-IE), International Conference on

作者： Hanning Chen Xiangrui Zeng Kun Jia Yabao Hu Guopeng Wang School of Computer Science and technology Tiangong University Tianjin China Engineering Research Center of Integration and Application of Digital Learning Technology Ministry of Education Beijing China School of Artificial Intelligence Tiangong University Tianjin China Jingwei Textile Machinery Co. Ltd Beijing China College of Artificial Intelligence University of Science and Technology Tianjin China The Open University of China

The indicator-based multi-objective optimization evolutionary algorithm has gained significant attention for its strong performance across various optimization problems. We draw on the advantages and try to make improvements in the environmental selection process based on different selection criteria. From this, we developed a distinctive multi-objective evolutionary algorithm that based on indicator and performing local searches, namely INLS. The INLS algorithm includes a novel selection mechanism that does not rely on weight vectors, which employs a strategy choosing solutions with relatively poor convergence but good diversity to perturb the decision variables of the selected solutions, aiming to explore and discover solutions that demonstrate enhanced convergence and diversity characteristics. The experimental results obtained from conducting tests on the ZDT and DTLZ test suites provide clear evidence of the remarkable performance exhibited by the proposed INLS in comparison to various other well-known MOEAs. These results serve to underscore the competitiveness of the proposed method.

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A biosensing system using a multiparameter nonlinear dynamic analysis of cardiomyocyte beating for drug-induced arrhythmia recognition

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Microsystems & Nanoengineering 2022年第3期8卷 277-290页

作者： Hao Wang Yue Wu Quchao Zou Wenjian Yang Zhongyuan Xu Hao Dong Zhjing Zhu Depeng Wang Tanxing Wang Ning Hu Diming Zhang State Key Laboratory of Optoelectronic Materials and Technologies Guangdong Province Key Laboratory of Display Material and TechnologySchool of Electronics and Information TechnologySun Yat-sen UniversityGuangzhou 510006China Research Center for Itelligent Sensing Systems Zhejiang LabHangzhou 311121China ZJU-Hangzhou Global Scientific and Technological Innovation Center Department of ChemistryThe Second Affiliated Hospital Zhejiang University School of MedicineDepartment of Clinical Medical EngineeringZhejiang UniversityHangzhou 310058China Key Laboratory of Novel Target and Drug Study for Neural Repair of Zhejang Province School of MedicineSchool of Computer&Computing ScienceZhejiang University City CollegeHangzhou 310015China School of Brain Science and Brain Medicine Zhejiang UniversityHangzhou 310058China College of Energy and Power Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016China E-LinkCare Meditech Co LtdHangzhou 310011China State Key Laboratory of Transducer Technology Chinese Academy of SciencesShanghai 200050China

Cardiovascular disease is the number one cause of death in ***,cardiotoxicity is one of the most important adverse effects assessed by arrhythmia recognition in drug ***,cell-based techniques developed for arhythmia recognition primarily employ linear methods such as time-domain analysis that detect and compare individual waveforms and thus fall short in some applications that require automated and efficient arrhythmia recognition from large *** carried out the frst report to develop a biosensing system that integrated impedance measurement and multiparameter nonlinear dynamic algorithm(MNDA)analysis for druginduced arrhythmia recognition and *** biosensing system cultured cardiomyocytes as physiologically relevant models,used interdigitated electrodes to detect the mechanical beating of the cardiomyocytes,and employed MNDA analysis to recognize drug-induced arrhythmia from the cardiomyocyte beating *** best performing MNDA parameter,approximate entropy,enabled the system to recognize the appearance of sertindoleand norepinephrine-induced arrhythmia in the *** MNDA reconstruction in phase space enabled the system to classify the different arrhythmias and quantify the severity of *** new biosensing system utilizing MNDA provides a promising and alternative method for drug-induced arrhythmia recognition and classification in cardiological and pharmaceutical applications.

关键词： arrhythmia cardio analysis

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Web-enabled Vision-based Person Counting System for Smart Space Management

Web-enabled Vision-based Person Counting System for Smart Sp...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Jasgurpreet Singh Chohan Zamen Latef Naser Hayder Sharif Laith H. A. Fezaa Krishna Kant Dixit Yashwant Singh Bisht University Center for Research and Development Chandigarh University Mohali Punjab India National University of Science and Technology Dhi Qar Iraq Medical Technical College Al-Farahidi University Iraq Dept. of Computer Engineering and Applications GLA University Mathura India Department of Mechanical Engineering Uttaranchal Institute of Technology Dehradun India

Efficient space management has emerged as a crucial issue in the era of smart spaces and IoT-enabled surroundings. In this study, an innovative method is proposed to solve the difficulties of real-time occupancy monitoring and resource optimization in various public and private venues. The system makes use of an ESP32-CAM module with a built-in camera to capture real-time images of the monitored region. The recorded images are analyzed using computer vision techniques and algorithms to track and detect people entering and leaving the area. The basis of the system is the person counting algorithm, which delivers precise and immediate occupancy statistics. The system allows users to log in from any location and monitor space occupancy in real time. The study was conducted based on the performance and accuracy of the system under various circumstances, indicating its appropriateness for a range of applications, from offices and retail outlets to public spaces and smart homes. The importance of this research is found in its contribution to the subject of smart space management, which is still in its infancy but where data-driven choices are essential for maximizing resource usage, guaranteeing social distance compliance, and improving user experiences. This web-enabled vision-based person counting system can help advance smart space management in the context of the Internet of Things (IoT) and smart cities as a creative and useful solution. In the end, the study provides a thorough description outlining its goals, approach, salient characteristics, and prospective effects on smart space management.

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Enhancing Crop Management: Ensemble Machine Learning for Real-Time Crop Recommendation System from Sensor Data

Enhancing Crop Management: Ensemble Machine Learning for Rea...

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Smart Systems for applications in Electrical sciences (ICSSES), International Conference on

作者： Sadia Hossain Nuzhat Noor Islam Prova Md Rezwane Sadik Abdullah Al Maruf Internet of Things and Robotics Engineering Bangabandhu Sheikh Mujibur Rahman Digital University Gazipur Bangladesh Seidenberg School of CSIS One Pace Plaza Pace University New York City NY USA Department of Economics & Decision Sciences University of South Dakota Vermillion SD United States Department of Computer Science & Engineering Bangladesh University of Business & Technology (BUBT) Dhaka Bangladesh

ISBN: (数字)9798350364040

ISBN: (纸本)9798350364057

The agricultural industry is essential to the world’s food production, and it is critical to use cutting-edge technologies to increase crop productivity. We provide a revolutionary Crop Recommendation System (CRS) that utilizes cutting-edge technology to maximize crop output in response to the pressing need for improvement. Our study incorporates real-time monitoring of soil conditions, made possible by a custom hardware configuration that includes sensors for temperature, humidity, phosphorus, potassium, nitrogen, and pH measurements. First, we assembled a large dataset with 22 kinds of agricultural production components. Using many machine learning models, such as ensemble methods and baseline classifiers, we were able to classify crops with an astounding $99 \%$ accuracy rate. With the application of these insights, the CRS provides customized recommendations through an easy-to-use user interface for appropriate crops under particular climatic conditions. Our system’s innovative combination of hardware sensing capabilities and AI-driven decision-making promises to revolutionize crop management practices, offering actionable insights for agricultural stakeholders. Our system’s novel integration of AI-driven decision-making and hardware sensing capabilities promises to transform crop management techniques and provide agricultural stakeholders with useful insights.

关键词： Temperature sensors Accuracy Crops Transforms Soil Hardware Real-time systems

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Generalizable Cervical Cancer Screening via Large-scale Pretraining and Test-Time Adaptation

arXiv

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arXiv 2025年

作者： Jiang, Hao Jin, Cheng Lin, Huangjing Zhou, Yanning Wang, Xi Ma, Jiabo Ding, Li Hou, Jun Liu, Runsheng Chai, Zhizhong Luo, Luyang Shi, Huijuan Qian, Yinling Wang, Qiong Li, Changzhong Han, Anjia Chan, Ronald Cheong Kin Chen, Hao Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Tencent AI Lab Shenzhen China Department of Pathology The First Affiliated Hospital Sun Yat-sen University Guangzhou China Center of Obstetrics and Gynecology Peking University Shenzhen Hospital Shenzhen China Institute of Obstetrics and Gynecology Shenzhen PKU-HKUST Medical Center Shenzhen China Shenzhen Key Laboratory on Technology for Early Diagnosis of Major Gynecologic Diseases Shenzhen China Department of Biomedical Informatics Harvard University United States Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Anatomical and Cellular Pathology The Chinese University of Hong Kong Hong Kong Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute Shenzhen China Division of Life Science The Hong Kong University of Science and Technology Hong Kong State Key Laboratory of Molecular Neuroscience The Hong Kong University of Science and Technology Hong Kong

Cervical cancer is a leading malignancy in female reproductive system. While AI-assisted cytology offers a cost-effective and non-invasive screening solution, current systems struggle with generalizability in complex clinical scenarios. To address this issue, we introduced Smart-CCS, a generalizable Cervical Cancer Screening paradigm based on pretraining and adaptation to create robust and generalizable screening systems. To develop and validate Smart-CCS, we first curated a large-scale, multi-center dataset named CCS-127K, which comprises a total of 127,471 cervical cytology whole-slide images collected from 48 medical centers. By leveraging large-scale self-supervised pretraining, our CCS models are equipped with strong generalization capability, potentially generalizing across diverse scenarios. Then, we incorporated test-time adaptation to specifically optimize the trained CCS model for complex clinical settings, which adapts and refines predictions, improving real-world applicability. We conducted large-scale system evaluation among various cohorts. In retrospective cohorts, Smart-CCS achieved an overall area under the curve (AUC) value of 0.965 and sensitivity of 0.913 for cancer screening on 11 internal test datasets. In external testing, system performance maintained high at 0.950 AUC across 6 independent test datasets. In prospective cohorts, our Smart-CCS achieved AUCs of 0.947, 0.924, and 0.986 in three prospective centers, respectively. Moreover, the system demonstrated superior sensitivity in diagnosing cervical cancer, confirming the accuracy of our cancer screening results by using histology findings for validation. Interpretability analysis with cell and slide predictions further indicated that the system’s decision-making aligns with clinical practice. Smart-CCS represents a significant advancement in cervical cancer screening and highlights the potential for generalizable screening in real-word practice across diverse clinical contexts. Copyright ©

关键词： Diseases

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Optimizing Renewable Energy Distribution in Smart Microgrids Using IoT and Reinforcement Learning

Optimizing Renewable Energy Distribution in Smart Microgrids...

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Device Intelligence, Computing and Communication Technologies, (DICCT), International Conference on

作者： P L Kishan Kumar Reddy Gurumoorthi Gurulakshmanan E. Sivanantham Dillibai. R Vishwa Gopalakrishnan S. Murugan Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation K L University (Has to be deemed) Guntur Andhra Pradesh India Mphasis Corporation Texas USA Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Tamil Nadu India New York University Tandon School of Engineering MetroTech Center Brooklyn NY United States Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

Smart microgrids improve energy efficiency by integrating renewable energy sources, but their intermittent nature complicates steady power distribution. This research introduces an IoT-enabled Reinforcement Learning (RL) framework for optimizing renewable energy distribution in smart microgrids. IoT sensors collect real-time data on energy production, use, and environmental variables, enabling adaptive management. The RL model adaptively acquires optimum energy distribution techniques to attain load equilibrium, energy storage enhancement, and grid stability. The system uses autonomous decision-making to reduce energy waste, increase cost efficiency, and enhance resilience. The proposed system facilitates real-time demand response, optimizes power distribution, and assures effective use of distributed energy resources. Experimental findings illustrate the efficacy of RL in mitigating uncertainty in energy supply and demand. This research advances the development of intelligent, self-adaptive microgrids, enabling a more sustainable and efficient energy infrastructure. Previous studies will investigate hybrid AI models to enhance performance further.

关键词： Renewable energy sources Supply and demand Power distribution Microgrids Reinforcement learning Power system stability Real-time systems Energy efficiency Stability analysis Smart grids

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Optimized Security Mechanism for publicly Secret Key Sharing over Cloud using Blockchain

Optimized Security Mechanism for publicly Secret Key Sharing...

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2024 International Symposium on Securing Next Generation Systems Using Future Artificial Intelligence Technologies, SNSFAIT 2024

作者： Byeon, Haewon Quraishi, Aadam Thuniki, Praveen Keshta, Ismail Soni, Mukesh Shabaz, Mohammad Department of AI and Software Inje University Gimhae50834 Korea Republic of Inje University Medical Big Data Research Center Gimhae50834 Korea Republic of Intervention Treatment Institute HoustonTX United States Sr Program Analyst Georgia CummingGA30040 United States Computer Science and Information Systems Department College of Applied Sciences Al Maarefa University Riyadh Saudi Arabia Dr. D. Y. Patii Vidyapeeth Pune Dr. D. Y. Patii School of Science & Technology Tathawade Pune India Model Institute of Engineering and Technology J&K Jammu India

A publicly verifiable key sharing mechanism based on threshold key sharing is provided to explore the security of users' private keys on the blockchain. Participating nodes check the key fragment after receiving it, effectively preventing it from being abused. The crucial sections of the nodes that participated in the critical splicing are made public during the critical recovery stage to prevent them from performing harmful things during the critical recovery stage. Add IDs to the nodes that participated in the crucial splicing during the key distribution stage;a dynamic threshold system is intended to track and update the status of malicious nodes in real time. When the node that possesses the crucial component fails, the owner of the critical component and the main node relocate a key element to the new participating nodes. To safeguard sensitive information. The experimental results show that this system has a key recovery rate of 80% and threshold qualities such as traceability, enforceability, and recoverability. © 2024 Copyright for this paper by its authors.

关键词： Blockchain Encryption Key sharing mechanism Privacy Secret key Security

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Shear Stiffening and Magneto-Induced Properties of Magnetorheological Elastomer Based on Self-Healing Poly(Urethane-Urea) Matrix

SSRN

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SSRN 2024年

作者： Guo, Fei Li, Zhi Du, Chengbin Yu, Guojun Mo, Zongyun Cui, Hanbo Xu, Zhedong Ye, Jisen School of Civil Engineering and Architecture Anhui Polytechnic University Wuhu241000 China Anhui Engineering Research Center of Green Building and Digital Construction Wuhu241000 China Department of Engineering Mechanics Hohai University Nanjing210098 China Faculty of Civil Engineering and Mechanics Jiangsu University Zhenjiang212013 China Audit Office Anhui University of Science and Technology Huainan232001 China

A novel type of multifunctional composite, defined as self-healing magnetorheological elastomer (SH-MRE), exhibiting good shear stiffening (ST) and magnetorheological (MR) effects, as well as self-healing ability, is achieved by dispersion of carbonyl iron particles (CIPs) into poly(urethane-urea) (PUU) matrix. The mechanical properties of this smart composite can be obtained by changing the content of CIP particles and the type of PUU matrix. The storage modulus of the prepared PUU matrix exhibits an ascending trend from 0.085 MPa to 2.03 MPa as the shear frequency rises from 0.1 Hz to 100 Hz, which demonstrates a good relative ST effect of 2288.24%. With the magnetic flux density from 0 to 0.894 T, the MR effect of isotropic SH-MRE with only 40% mass fraction of filled CIPs is obtained as 131.64%. Notably, the PUU matrix filled with more CIPs is able to affect the self-healing efficiency of the relative MR effect with destructive shear damage after 2 h healing. In addition, with the assistance of trace solvent, SH-MREs exhibit a recovery efficiency higher than 97% of the storage modulus after 0.5 h, and both the relative MR and ST effects for SH-MREs are slightly higher than the initial values. Finally, based on the experimental results, a possible mechanism to explain the ST properties, MR effect, and self-healing performance of SH-MRE is rationally proposed and discussed. The hydrogen bonds (H-bonds) in the dynamic hard domains and the magnetic interaction forces between the CIPs induced by the magnetic field are believed to be due to the good multifunctional properties. The present work could provide a new idea to balance the MR effect and mechanical properties by adjusting the soft and hard segments of the matrix during the magnetorheological elastomer preparation process. © 2024, The Authors. All rights reserved.

关键词： Elastic moduli

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