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IEEE International Conference on Mechatronics and Automation

作者： Haotian Li Shuxiang Guo Chunying Li Jipeng Long National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China The Aerospace Center Hospital School of Life Science and the Key Laboratory of Convergence Medical Engineering System and Healthcare Technology Ministry of Industry and Information Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

With the growing demand for autonomous underwater vehicles (AUVs) capable of precise navigation in intricate environments, achieving accurate depth control becomes pivotal. This paper introduced a pioneering study on depth control for an "Egg-shaped" underwater robot (EUR) utilizing the backstepping sliding mode control (BSMC) algorithm. By analyzing the kinematic and kinetic models of the EUR, a specialized control system was devised. This paper significantly advanced underwater robotics by presenting a robust and efficient solution for depth control, employing Lyapunov function in stability analysis. Simulation and experimental results validated the performance of the proposed algorithm in regulating the robot's depth across diverse operating conditions, surpassing traditional control methods in terms of stability and accuracy.

关键词： Autonomous underwater vehicles Backstepping Accuracy Mechatronics Navigation Stability analysis Robustness

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RoboNet: a Neural Network Based Kinematic Parameter Identification Model

RoboNet: a Neural Network Based Kinematic Parameter Identifi...

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IEEE International Conference on Networking, Sensing and Control

作者： Diwen Xiong Mingsheng Shang Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China

ISBN: (纸本)9781665440493

The positioning accuracy of a robot is crucial for its industrial applications. A common way to improve such accuracy is through robot calibration, which mostly aims to identify the errors of a robot’s kinematic parameters. However, while a robot kinematic model being a highly nonlinear system with non-Gaussian noises, approaches could cause truncation error when approximating such nonlinearity, resulting in a less sufficient calibration result. To address this issue, this paper innovatively proposes a neural network-based approach: the RoboNet, which identifies the kinematic parameters by nonlinearly approximating the error function of each parameter with neurons and nonlinear activation functions. It also provides a unified architecture for kinematic parameters identification of the robot calibration process, so that with simple modifications, RoboNet could be applied to the various robot calibration processes. And supported by the simulation results, RoboNet can accurately identify the errors in kinematic parameters and is robust against flawed data.

关键词： Fault diagnosis Parameter estimation Service robots Simulation Kinematics Robot sensing systems Calibration

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Large Language Models for Sexual, Reproductive, and Maternal Health Rights*

Large Language Models for Sexual, Reproductive, and Maternal...

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IEEE International Conference on Healthcare Informatics (ICHI)

作者： Walelign Sewunetie Assefa Beza Hailemariam Abebe Tesfamariam M Abuhay Wasyihun Admass Hayat Hassen Tsion Haile Hana Hailemariam Lydia Debebe Nurlign Moges Nathnael Bekele Surafel L Tilahun Mahlet Berta Mahlet Mammo Seid Muhie Yimam Kovacs Laszlo School of Computing Debre Markos University Ethiopia Faculty of Computing Bahir Dar University Ethiopia University of Gondar Ethiopia Queen's University Canada School of Computing Mekdela Amba University Mekdela Ethiopia College of Medicine and Health Sciences University of Gondar Ethiopia Saint Paul's Hospital Millennium Medical College AA Ethiopia College of Medicine and Health Sciences Bahir Dar University Ethiopia College of Medicine and Health Sciences Debre Markos University Ethiopia Addis Ababa Science and Technology University Ethiopia HPC & Big Data Analytics CoE Addis Ababa Science and Technology University Ethiopia House of Computing and Data Science University of Hamburg Hamburg Germany Institute of Informatics Univerisity of Miskolc Miskolc Hungary

ISBN: (数字)9798350383737

ISBN: (纸本)9798350383744

This research explores the potential of Large Language Models in the context of healthcare solutions, with a specific focus on Sexual, Reproduc-tive, and Maternal Health Rights (SRMHR) Question Answering (QA) in the low-resource language, Amharic (““'Ie;;:). To construct the dataset, we first collected data from medical textbooks and guidelines authored by reputable medical institutions and organizations. U ti-lizing automatic question-and-answer generation techniques, we then generated pairs for the dataset. Sub-sequently, the dataset underwent annotation, translation, and evaluation processes, resulting in a refined collection of 2.8k Amharic datasets. We use the dataset to fine-tune the LLaMA-2-Amharic model, with test results assessed using BLEU scores and human-level evaluations, demonstrating promising outcomes. The curated Amharic SRMHRQA dataset serves as a foun- dational resource for future research. However, further enhancements are necessary to optimize its efficacy, particularly within the realm of SRMHR for low- resource languages like Amharic. Future research could involve scaling up the dataset in terms of size, quality, and domain coverage.

关键词： Annotations Large language models Medical services Organizations Question answering (information retrieval) Informatics Guidelines

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Evaluation of the Dynamic Performance of a Novel Egg-shaped Underwater Robot

Evaluation of the Dynamic Performance of a Novel Egg-shaped ...

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IEEE International Conference on Mechatronics and Automation

作者： Jipeng Long Shuxiang Guo Chunying Li National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China The Aerospace Center Hospital School of Life Science and the Key Laboratory of Convergence Medical Engineering System and Healthcare Technology Ministry of Industry and Information Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

With the increasing demand for ocean exploration and underwater operations, the development of new autonomous underwater vehicles (AUVs) to adapt to specific marine environments has become crucial. This paper designed an innovative Egg-shaped Underwater Robot (EUR) aiming to optimize its mobility, stability and operational efficiency in complex underwater environments. Compared to traditional simulation robots of various types, the structure possessed the stability of a ball-shaped robot and the characteristics of a streamlined robot. The EUR adopted a unique elliptic structure to reduce underwater drag, improve maneuverability and enhance load capacity. Then, the overall design of the EUR was presented with detailed descriptions of the mechanical structure and electrical system respectively. Next, various motion states of the EUR and experimental validation were simulated by Computational Fluid Dynamics (CFD), and the experimental results showed that the motion characteristics of the EUR are acceptable, and the design was worthy of further study.

关键词： Autonomous underwater vehicles Mechatronics Computational fluid dynamics Oceans Propulsion Stability analysis Vectors

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Effect of Hyperparameter Tuning in Long Short-Term Memory for Crude Oil Price Prediction

Effect of Hyperparameter Tuning in Long Short-Term Memory fo...

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IEEE Symposium on Computers & Informatics (ISCI)

作者： Marina Yusoff Muhammad Yusof Sharif Mohamad Taufik Mohd Sallehud-din Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Shah Alam Selangor Malaysia College of Computing Informatics and Mathematics Kompleks Al-Khawarizmi Universiti Teknologi MARA (UiTM) Shah Alam Selangor Malaysia Faculty of Business Sohar University Oman PETRONAS Research Sdn Bhd Jln Ayer Hitam Kawasan Institusi Bangi Bandar Baru Bangi Selangor

ISBN: (数字)9798350353853

ISBN: (纸本)9798350353860

Forecasting crude oil prices is significant in finance, energy, and economics due to its profound influence on global markets and socioeconomic balance. Using Long Short-Term Memory (LSTM) neural networks has demonstrated remarkable success in time series forecasting, particularly in accurately predicting crude oil prices. However, LSTM models often rely on the manual tuning of hyperparameters, which can be difficult and time-consuming. This study evaluates LSTM networks to optimize the network architecture by looking at lookback variations and train-test split ranges. This study employs historical data on crude oil prices to explore and identify a suitable lookback number by comparing it with the previous study using the same datasets. The experimental findings demonstrate the superiority of the LSTM network using the number of lookbacks equal to 5. It is also compared with statistical time series methods regarding their predictive accuracy. The results indicate that the LSTM model is a valuable resource for financial industries, particularly in oil and gas.

关键词： Oils Biological system modeling Time series analysis Neural networks Stochastic processes Predictive models Forecasting

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Research on the Distribution Characteristics of Standardized big data Resources Based on data Visualization——Taking Standardization Practice in Shandong Province as an Example

Research on the Distribution Characteristics of Standardized...

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IEEE International Conference on data Science in Cyberspace (DSC)

作者： Jinyang Sun Dongdong Peng Zhe Lu Mengran Zhai Ning Qi Yuanhua Qi SHANDONG SCICOM Information and Economy Research Institute Co. Ltd Jinan China Jinan Municipal Bureau of Big Data Jinan China Shandong Institute of Economy and Informatization Development Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology Shandong Academy of Sciences Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

Standardized big data, as a product of the combination of big data and standardization work, objectively reflects the status quo and trends of standardization work. An in-depth study of the distribution characteristics of standardized big data resources has important value for improving the efficiency of standardization. Therefore, this paper explores the characteristics of the distribution of standardization resources from the dimensions of spatial and temporal distribution, industry distribution, standard type distribution, and distribution of think tank experts through visual analysis methods. The study shows that: (1) the distribution of standardization resources in Shandong Province presents significant hierarchical and geographical characteristics; (2) from the viewpoint of the distribution of industry types and standard types, the distribution of standardization resources in Shandong Province presents a typical "long-tailed distribution"; (3) the distribution of think-tank experts presents obvious "agglomeration effect".

关键词： Industries Technological innovation Heavily-tailed distribution Animals Urban areas data visualization Forestry big data Fisheries Standards

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UniTSFace: Unified Threshold Integrated Sample-to-Sample Loss for Face Recognition

arXiv

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arXiv 2023年

作者： Li, Qiufu Jia, Xi Zhou, Jiancan Shen, Linlin Duan, Jinming National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Computer Vision Institute Shenzhen University China School of Computer Science University of Birmingham United Kingdom Aqara Lumi United Technology Co. Ltd China Alan Turing Institute United Kingdom SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China

Sample-to-class-based face recognition models can not fully explore the cross-sample relationship among large amounts of facial images, while sample-to-sample-based models require sophisticated pairing processes for training. Furthermore, neither method satisfies the requirements of real-world face verification applications, which expect a unified threshold separating positive from negative facial pairs. In this paper, we propose a unified threshold integrated sample-to-sample based loss (USS loss), which features an explicit unified threshold for distinguishing positive from negative pairs. Inspired by our USS loss, we also derive the sample-to-sample based softmax and BCE losses, and discuss their relationship. Extensive evaluation on multiple benchmark datasets, including MFR, IJB-C, LFW, CFP-FP, AgeDB, and MegaFace, demonstrates that the proposed USS loss is highly efficient and can work seamlessly with sample-to-class-based losses. The embedded loss (USS and sample-to-class Softmax loss) overcomes the pitfalls of previous approaches and the trained facial model UniTSFace exhibits exceptional performance, outperforming state-of-the-art methods, such as CosFace, ArcFace, VPL, AnchorFace, and UNPG. Our code is available at https://***/CVI-SZU/UniTSFace. © 2023, CC BY.

关键词： Face recognition

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Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Learning 22

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Lea...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Cao, Weipeng Yao, Xuyang Xu, Zhiwu Pan, Yinghui Sun, Yixuan Li, Dachuan Qiu, Bohua Wei, Muheng Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (纸本)9798331527471

Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model’s capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods. © 2024 IEEE.

关键词： Zero-shot learning

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Study on the Path Planning Based on A* Algorithm for Vascular Intervention Robots

Study on the Path Planning Based on A* Algorithm for Vascula...

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IEEE International Conference on Mechatronics and Automation

作者： Huiyin Xu Shuxiang Guo Chunying Li Sheng Cao National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China The Aerospace Center Hospital School of Life Science and the Key Laboratory of Convergence Medical Engineering System and Healthcare Technology Ministry of Industry and Information Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

In vascular interventional surgery, accurate path planning is essential to improve surgical success and reduce risk. This paper aimed to verify the effectiveness and practicability of the A* algorithm in the shortest path planning of vascular interventional surgical robot systems. Computed tomography angiography (CTA) vascular images were processed in advance to extract features and calibrate obstacles, simulating the path planning problem in vascular interventional surgery. The A* algorithm was then employed to search for the shortest path from the starting point to the endpoint. Based on the planned path, assistance could be provided to surgeons during operative procedures. The experimental results indicated that the A* algorithm effectively navigated obstacles and identified the shortest path. Moreover, through user-interactive design, this paper offered an intuitive operational experience, allowing users to interactively choose start and end points and display the algorithm’s search process and outcomes in real-time. This paper demonstrated the potential application of the A* algorithm within vascular structures and laid the groundwork for the future development of more efficient and intelligent systems for planning and navigating vascular interventional surgeries.

关键词： Medical robotics Mechatronics Navigation Computed tomography Surgery Feature extraction Path planning

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Real-time bottleneck matching in spatial crowdsourcing

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Science China(Information Sciences) 2021年第8期64卷 233-234页

作者： Long LI Lingling WANG Weifeng LV State Key Laboratory of Software Development Environment (SKLSDE Lab) and Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC) Beihang University School of Management and Economics Beijing Institute of Technology

Dear editor,Spatial crowdsourcing(SC) services(e.g., Uber, DiDi, and Meituan) have become popular with smart-phone ***, the online matching problems in real-time spatial data are a key issue in SC [1–4]. Unlike the current one-sided online matching study in real-time spatial data [5], which focuses on minimizing the overall cost of the matching,

关键词： Real-time bottleneck matching in spatial crowdsourcing

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