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Lower Limb Joint Torque Estimation via Bayesian Regularized Backpropagation Neural Networks 2

Lower Limb Joint Torque Estimation via Bayesian Regularized ...

2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Narayan, Jyotindra Gritli, Hassène Dwivedy, Santosha K. Indian Institute of Technology Guwahati Mechatronics and Robotics Laboratory Department of Mechanical Engineering Assam Guwahati781039 India National Engineering School of Tunis Le Belvédère Tunis1002 Tunisia

ISBN: (纸本)9798350360523

Prior gait analysis has played a crucial role in assessing overall health and well-being, particularly in individuals of different age groups. Nevertheless, acquiring gait data has necessitated the in-person presence of participants, incurring potential expenses. We introduce a deep learning technique called Bayesian regularized backpropagation neural network (BR-BPNN) to estimate joint torques in lower-limb motion based on anthropometric data and joint movements. The study collects joint movement data from 40 healthy participants (25 males and 15 females) spanning ages 7 to 65, utilizing an established motion-capture system. The input data encompassed various parameters such as age, gender, total body mass, height, thigh length, calf length, ankle-foot length, thigh mass, calf mass, ankle mass, and joint angles for the hip, knee, and ankle. The output data included joint torques calculated for a simplified leg model using the inverse dynamics method derived from the Euler-Lagrangian principle. We presented the conceptual details of the proposed BR-BPNN model and contrast model named Levenberg-Marquardt-based BPNN (LM-BPNN). Performing a comparative analysis, the results revealed that the BR-BPNN model (Rtrain = 0.985, Rtest = 0.955) excelled in accurately estimating joint torques compared to the LM-BPNN model (Rtrain = 0.973, Rtest = 0.924). Furthermore, the BR-BPNN model is found to be effective by 87.19%, 77.87%, and 72.73% in estimating hip, knee, and ankle joint torques for a 51-year-old male subject from the testing dataset. © 2024 IEEE.

关键词： Neural network models

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Leakage Source Localization Based on Improved-Infotaxis 16th

Leakage Source Localization Based on Improved-Infotaxis

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16th International Conference on Modelling, Identification and Control, ICMIC 2024

作者： Tao, Xiang Cheng, Lei Yao, Tianxiao Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Eduction Wuhan430081 China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan430081 China

ISBN: (纸本)9789819617760

In recent years, with the rapid development of the chemical industry, the risks in this field are also increasing, in which the leakage of hazardous gases can cause serious harm to both people and the environment, so it is crucial to locate the leakage source quickly and accurately. For pollution source traceability, the traditional gradient algorithm is prone to fall into local optimization, and the infotaxis algorithm is biased towards exploration, which is prone to waste time cost and control cost. We propose a new strategy to balance the exploration and utilization in the search process by studying the effects of multiple algorithms and setting different experimental parameters (gas release rate and wind speed) on the overall performance, and the experimental results show that the algorithm has a strong performance under different environmental variables with strong robustness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Chemical industry

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Effectiveness of artificial neural network for forecasting of fracture toughness of concrete specimens

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Asian Journal of Civil engineering 2024年第6期25卷 4697-4712页

作者： Pathak, Sudhanshu S. Vesmawala, Gaurang R. Mane, Sachin J. Sarnobat, Sandeep S. Department of Civil Engineering D Y Patil College of Engineering Akurdi Pune India Department of Civil Engineering Sardar Vallabhbhai National Institute of Technology Gujarat Surat India Department of Robotics and Automation D Y Patil College of Engineering Akurdi Pune India

Since fracture energy and fracture toughness are directly linked to failure behavior, crack initiation and propagation assumes a significant importance in the study of fracture mechanics. The current study attempts to develop a prediction model for fracture toughness by employing artificial neural network (ANN). Data sets of previous research work obtained from 210 fracture tests have been used to train, test and validate the ANN Model. Experimental results are further used check the feasibility of the model. The test specimen were concrete beams made using Nano TiO₂ (1, 2, 3 and 4%) and ground granulated blast furnace slag (30%). Compressive strength, maximum aggregate size and water to cement ratio are used as input parameters while fracture toughness, tensile strength, Compressive strength and flexure strength are the measured outputs for experimentation. The experimental results indicate the fracture toughness, tensile strength, compressive strength increase with the increase in the NT percentage by 46.15, 10.22, 16.07 and 14.83% respectively. The ANN predicted values of fracture toughness are in good agreement with the experimental results. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Artificial neural network Fracture toughness Ground granulated blast furnace slag Nano TiO2

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Localization of Mobile Robot Aided for Large-Scale Construction Based on Optimized Artificial Landmark Map in Ongoing Scene

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Computer Modeling in engineering & Sciences 2022年第3期130卷 1853-1882页

作者： Zhen Xu Shuai Guo Tao Song Yuwen Li Lingdong Zeng Shanghai Key Laboratory of IntelligentManufacturing and Robotics School of Mechatronic Engineering and AutomationShanghai UniversityShanghai200444China National Demonstration Center for Experimental Engineering Training Education Shanghai UniversityShanghai200444China

The effectiveness of mobile robot aided for architectural construction depends strongly on its accurate localization *** of mobile robot is increasingly important for the printing of buildings in the construction *** many available studies on the localization have been conducted,only a few studies have addressed the more challenging problem of localization for mobile robot in large-scale ongoing and featureless *** realize the accurate localization of mobile robot in designated stations,we build an artificial landmark map and propose a novel nonlinear optimization algorithm based on graphs to reduce the uncertainty of the whole ***,the performances of localization for mobile robot based on the original and optimized map are compared and ***,experimental results show that the average absolute localization errors that adopted the proposed algorithm is reduced by about 21%compared to that of the original map.

关键词： Large-scale construction artificial landmark map localization mobile robot non-linear optimization

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Quantitative Analysis of the Method of Myoelectric Hand Prosthesis Hand Gesture Conversion

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Journal of institute of Control, robotics and Systems 2022年第1期28卷 13-21页

作者： Kim, Seung-Gi Jung, Sung-Yoon Kim, Kyoung-Ho Park, Se-Hoon Korea Orthopedics and Rehabilitation Engineering Center Korea Workers' Compensation & Welfare Service Korea Republic of Korea Institute of Robotics & Technology Convergence Korea Republic of

A control algorithm is applied to commercial myoelectric hand prostheses for converting various hand gestures. However, since the conversion efficiency of these algorithms has never been quantitatively analyzed, it is necessary to do so. Therefore, herein, we quantitatively analyze conversion algorithms applied to the commercial myoelectric hand prosthesis and the recently proposed intuitive conversion algorithm to verify its usability. First, the structure of each mode conversion algorithm is described, as well as its association with the final hand gesture state for efficient conversion. Subsequently, the usability of the proposed algorithm is verified by comparing the number of conversions to the number of hand gestures used by each algorithm. According to the comparison results, the algorithm is easily used by upper limb amputees because the number of conversions is relatively reduced. From the experimental results, we demonstrated the usability of the proposed algorithm and its relevance to upper limb amputees. © ICROS 2022.

关键词： Artificial limbs

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Geometric-based Bimodal Control for Hybrid Aerial/Ground Vehicles

Geometric-based Bimodal Control for Hybrid Aerial/Ground Veh...

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2024 IEEE International Conference on Real-Time Computing and robotics, RCAR 2024

作者： Tang, Haoming Miao, Zhiqiang Wang, Haoyu Chen, Yizong Zhang, Hui Wang, Yaonan Hunan University College of Electrical and Information Engineering Changsha410082 China National Engineering Research Center for Robot Visual Perception and Control Changsha China School of Robotics Hunan University Changsha410082 China

ISBN: (纸本)9798350372601

Hybrid ground/aerial vehicles, capable of adapting to both terrestrial and aerial environments simultaneously, can accomplish more complex tasks, holding promising applications across various domains. Furthermore, designing a highly real-time multi-modal controller to adapt to environmental and modal changes, is currently a critical issue to be addressed. Based on this requirement, a geometric-based bimodal control structure is proposed in this study. We initially design the aerial controller in the Lie algebra space, based on a geometric attitude controller. Moreover, inspired by the characteristics of ground locomotion, we modify the aerial controller to satisfy the constraints of this mode. Through this approach, we can simplify the design process of the ground controller. Both controllers feature a similar and simpler structure, meet real-time control requirements, and allow them to use the same parameters. Ultimately, the proposed method's efficacy and performance are validated through ROS-Gazebo simulation environment. © 2024 IEEE.

关键词： Antenna grounds

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A study on the noise reduction method in turbid water environments using Spatial frequency characteristics 9

A study on the noise reduction method in turbid water enviro...

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9th International Conference on Intelligent Informatics and BioMedical Sciences, ICIIBMS 2024

作者： Koura, Ayumu Kim, Hyun-Woo Cho, Myungjin Lee, Min-Chul Kyushu Institute of Technology Graduate School of Computer Science and Systems Engineering Iizuka city Japan Hankyong National University School of ICT Robotics and Mechanical Engineering Iitc Anseong-si Korea Republic of

ISBN: (纸本)9798350363043

Scattering noise reduction is a challenging project to remove noise under scattering media conditions such as fog or turbid water. In previous study, blurring caused by scattering medium particles in fog or turbid water was removed by low-pass filtering. However, in this method, the accurate color information of the object may be lost due to rough low-frequency filtering. In addition, it is difficult to the obtain object shape information in heavy turbid environments. To solve this problem, we propose a new de-scattering algorithm that combines Fourier-domain filtering and integral imaging by creating a filter tailored to the frequency to be removed. To prove the proposed method, we perform a numerical analysis of experimental results using conventional and proposed methods. © 2024 IEEE.

关键词： Noise abatement

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Hidden States in LLMs Improve EEG Representation Learning and Visual Decoding 27

Hidden States in LLMs Improve EEG Representation Learning an...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Liu, Aoyang Jing, Haodong Liu, Yulong Ma, Yongqiang Zheng, Nanning National Key Laboratory of Human-Machine Hybrid Augmented Intelligence China National Engineering Research Center of Visual Information and Applications China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University China The Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9781643685489

Analyzing brain signals and reconstructing visual stimuli from the brain can facilitate further exploration on cognitive functions of the human brain, which have attracted strong interest in neuroscience and artificial intelligence. However, due to defects such as complex noises and the lack of alignment accuracy, efficient methods for extracting information from Electroencephalogram (EEG) signals are still very limited, making it difficult to perform EEG visual decoding tasks. Our study shows a way to handle the issues by proposing a new method for EEG representation learning and visual decoding, thus completing end-to-end image reconstruction tasks from EEG signals. We utilize the ability of semantic extraction and prediction of large language models (LLMs) to enhance the performance of EEG feature extraction. For semantic representation learning, we align EEG signals with target semantic embeddings, which are obtained from hidden states of Large Language Model Meta AI 2 (LLaMa-2) by inputting descriptions of images into the model. We also extract visual features from EEG signals to improve the quantity of the reconstructed images at low levels. Then we fuse semantic features and visual features by applying a pre-trained diffusion model and finally generate the corresponding images. We are the first to incorporate the LLM into EEG visual decoding tasks. Our method achieves the state-of-the-art result of EEG classification accuracy and the quality of reconstructed images on ImageNet-EEG datasets. In one word, our work is an important step forward in the field of exploiting the relationship between language models and human visual cognition. Our codes are available at https://***/lay-atsa/llm4eeg. © 2024 The Authors.

关键词： Semantics

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A study on Image Processing Method for Noise Reduction in Digital Holographic Microscopy 9

A study on Image Processing Method for Noise Reduction in Di...

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9th International Conference on Intelligent Informatics and BioMedical Sciences, ICIIBMS 2024

作者： Nagashima, Masaharu Kim, Hyun-Woo Cho, Myungjin Lee, Min-Chul Kyushu Institute of Technology Graduate School of Computer Science and Systems Engineering Iizuka city Japan Iitc Hankyong National University School of Ict Robotics and Mechanical Engineering Anseong-si Korea Republic of

ISBN: (纸本)9798350363043

Digital Holographic Microscopy (DHM) can obtain three-dimensional (3D) information about the fine structure of an object by utilizing the phase information of coherent light. In DHM, during the process of reconstructing the shape of an object, phase information is extracted by windowing the sideband containing the phase information. The 3D information of the object is then obtained from the acquired phase information. When windowing the sideband in a narrow range, it can remove noise caused by the DC spectrum, but detailed shape information of the object is lost. On the other hand, when windowing in a wide range, detailed shape information of the object can be obtained, but noise from the DC spectrum increases. To solve this trade-off relationship, we propose a method of determining the range for windowing the sideband using a function and then averaging the windowed sidebands. This proposed method can reduce the impact of noise from the DC spectrum and enables the acquisition of the detailed shape information. In this paper, we explain the reconstruction algorithm of this method and compare it with conventional methods and the previous study, phase differences averaging (PDA). © 2024 IEEE.

关键词： Noise abatement

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V-DETR: DETR WITH VERTEX RELATIVE POSITION ENCODING FOR 3D OBJECT DETECTION 12

V-DETR: DETR WITH VERTEX RELATIVE POSITION ENCODING FOR 3D O...

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12th International Conference on Learning Representations, ICLR 2024

作者： Shen, Yichao Geng, Zigang Yuan, Yuhui Lin, Yutong Liu, Ze Wang, Chunyu Hu, Han Zheng, Nanning Guo, Baining National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Informationand Applications Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University China University of Science and Technology of China China Microsoft Research Asia China

We introduce a highly performant 3D object detector for point clouds using the DETR framework. The prior attempts all end up with suboptimal results because they fail to learn accurate inductive biases from the limited scale of training data. In particular, the queries often attend to points that are far away from the target objects, violating the locality principle in object detection. To address the limitation, we introduce a novel 3D Vertex Relative Position Encoding (3DV-RPE) method which computes position encoding for each point based on its relative position to the 3D boxes predicted by the queries in each decoder layer, thus providing clear information to guide the model to focus on points near the objects, in accordance with the principle of locality. Furthermore, we have systematically refined our pipeline, including data normalization, to better align with the task requirements. Our approach demonstrates remarkable performance on the demanding ScanNetV2 benchmark, showcasing substantial enhancements over the prior state-of-the-art CAGroup3D. Specifically, we achieve an increase in AP25 from 75.1% to 77.8% and in AP50 from 61.3% to 66.0%. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Object detection

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