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检索条件"任意字段=International Conference on Methods and Models in Automation and Robotics"

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An Electrostatic Locking Mechanism For Tendon-driven Surgical Robot

An Electrostatic Locking Mechanism For Tendon-driven Surgica...

引用

2023 international conference on Biomimetic Intelligence and robotics

作者： Lin, Chengye Lin, Botao Lyu, Erli Song, Shuang Wang, Jiaole School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China Faculty of Applied Sciences Macao Polytechnic University China

Variable stiffness surgical robots are capable of adapting to different surgical scenarios, providing enhanced stability during minimally-invasive surgeries. Prevalent methods for achieving variable stiffness, such as lockable mechanisms, granular jamming, and phase transition materials, have inherent weaknesses including difficulties in downsizing and issues related to excessive temperatures. In this paper, a novel lockable mechanism based on electrostatics is proposed, along with a tendon-driven surgical robot prototype that utilizes this locking mechanism to achieve variable stiffness. The proposed mechanism involves the installation of graphite electrode sheets at each joint, with silica gel being used as the dielectric material. By applying a high voltage to the electrode sheets, adjacent joints automatically absorb and restrict relative sliding, effectively preventing joint rotation and ensuring complete joint locking. An electrostatic adhesive braking force model for the robot is presented. Simulations and experiments conducted based on the robot prototype have been carried out to validate the locking capability of the proposed mechanism and the accuracy of the electrostatic adhesive braking force model. © 2023 The Authors. Published by Elsevier B.V.

关键词： Tendons

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A Multimodal Deep Neural Network for ECG and PCG Classification With Multimodal Fusion 13

A Multimodal Deep Neural Network for ECG and PCG Classificat...

引用

13th international conference on Information Science and Technology, ICIST 2023

作者： Han, Haozhan Xiang, Muchao Lian, Cheng Liu, Dan Zeng, Zhigang School of Automation Wuhan University of Technology Wuhan China State Grid Hubei Electric Power Research Institute Wuhan China School of Instrument and Electronics North University of China Taiyuan China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350313925

The growing trend of employing deep learning-based models in electrocardiogram (ECG) and phonocardiogram (PCG) classification is evident. However, the weakness and susceptibility to noise in ECG and PCG signals present challenges for accurate analysis and classification using existing methods. This paper addresses this issue by introducing a multimodal network called MCT for ECG and PCG classification. MCT combines a convolutional neural network (CNN) and Transformer, allowing the model to effectively capture both local and global information from ECG and PCG signals. Furthermore, this paper propose a novel attention mechanism-based multimodal fusion method. This fusion method adjusts the weights on both intra-modal and inter-modal patterns, enabling effective fusion of multimodal features. By exploiting complementary information across modalities, MCT improves the accuracy of ECG and PCG classification. The evaluation of MCT is conducted on both a public human motion state recognition ECG and PCG multimodal dataset and a real-world cardiovascular disease diagnosis ECG and PCG multimodal dataset. The experimental results reveal that MCT outperforms previous models, achieving better performance in ECG and PCG classification. © 2023 IEEE.

关键词： Electrocardiograms

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Semantic Traffic Element Removal by Learning to Follow Linguistic Instructions

Semantic Traffic Element Removal by Learning to Follow Lingu...

引用

2023 IEEE international conference on Unmanned Systems, ICUS 2023

作者： Feng, Xiaodan Su, Yuanqi Liu, Yuehu Zhang, Chi School of Computer Science and Technology Xi'an Jiaotong University Shaanxi Xi'an China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Shaanxi Xi'an China

ISBN: (纸本)9798350316308

We propose a novel task of semantic object removal in traffic scenes through human-guided image editing. Unlike traditional methods, our model does not require image masks;instead, it effectively removes semantic elements from a traffic scene image based solely on an input image and linguistic instruction. To achieve this objective, we use large-scale pre-trained models to construct a traffic scene image dataset suitable for human-guided image editing. We train a text-guided image translation model on this dataset. During inference, our model quickly generalizes to photorealistic traffic scene images and user-written instructions, obviating the need for fine-tuning and enabling rapid removal of semantic object elements from images within seconds. Experimental results demonstrate that our model achieves satisfying removal performance in the traffic scene semantic object removal task, showing the potential advantages of text guidance in image processing tasks. © 2023 IEEE.

关键词： Semantics

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Deep Learning-Based Approach for Frost Quantification in Domestic Refrigerators

Deep Learning-Based Approach for Frost Quantification in Dom...

引用

international conference on robotics and Artificial Intelligence (ICRAI)

作者： Hassan Akbar Huzzam Hassan Anjum Naeem Malik Tahir Habib Nawaz National Centre of Robotics and Automation Department of Mechatronics Engg. CEME NUST Islamabad Pakistan Department of Mechatronics Engg. CEME NUST Islamabad Pakistan

ISBN: (数字)9798350355703

ISBN: (纸本)9798350355710

Frost formation in refrigerators disrupts heat ex-change, lowering system efficiency and causing operational is-sues, making defrosting essential for maintaining performance. However, modern defrost systems rely on sensors for frost detection. The sensory approach is reliable but it could degrade over time in changing environmental conditions. To address this limitation, the paper proposes a real-time frost quantification method using digital image processing and deep learning. A custom Convolutional Neural Network (CNN) model is designed to classify frost thickness in RGB images captured inside a refrigerator. We have used a custom dataset, collected from a real refrigeration environment and made a comparison with other methods previously used for frost thickness estimation. The dataset is collected from several experiments to ensure its diversity such as illumination and environmental changes. This method enables precise frost detection, targeted for optimal defrost control for improved efficiency. Results indicate that the CNN model achieves a high test accuracy of 98.10%. The model's performance surpasses traditional methods, demonstrating its robustness in distinguishing varying frost levels from 0 to 4 (mm). This method enables precise frost thickness estimation and is a suitable choice for defrost control to improve the energy efficiency of the refrigerator.

关键词： Deep learning Accuracy Thresholding (Imaging) System performance Estimation Lighting Sensor systems Sensors Refrigeration Convolutional neural networks

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DC-MOT: Motion Deblurring and Compensation for Multi-Object Tracking in UAV Videos

DC-MOT: Motion Deblurring and Compensation for Multi-Object ...

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IEEE international conference on robotics and automation (ICRA)

作者： Song Cheng Meibao Yao Xueming Xiao Intelligent Robotics Lab (IRL) School of Artificial Intelligence Jilin University Changchun China Engineering Research Center of Knowledge-Driven Human-Machine Intelligence Ministry of Education China CVIR lab Changchun University of Science and Technology China Key Lab of Optoelectronic Measurement and Optical Information Transmission Technology Ministry of Education China

In this paper, we propose a multi-object tracking framework for videos captured by UAVs, considering motion imperfection in the following two aspects: 1) motion blurring of objects due to high-speed motion of the UAV and the objects, deteriorating the performance of the detector; 2) motion coupling of the global movement of the UAV camera with the object motion, resulting in the nonlinearity of objects trajectories in adjacent frames and further more difficult to predict. For motion blurring, this paper proposes a hybrid deblurring module that deals with the blurred frames while retaining the clear frames, trading off between video tracking performance and spatio-temporal consistency. For motion coupling, we proposed a motion compensation module to align adjacent frames by feature matching, and the corrected target position is obtained in the next frame to alleviate the interference of camera movement with tracking. We evaluate the proposed methods on VisDrone dataset and validate that our framework achieves new state-of-the-art performance on UAV-based MOT systems.

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Coordinated Control of Precision Conveyor Based on Array Piezoelectric Actuators

Coordinated Control of Precision Conveyor Based on Array Pie...

引用

automation, Control and robotics Engineering (CACRE), international conference on

作者： Yujuan Tang Yongquan Zhao School of Intelligent Science and Control Engineering Jinling Institute of Technology Nanjing China School of Mechanical Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9798350350302

ISBN: (纸本)9798350350319

Due to the problems of the traditional transmission platform device, such as low transmission accuracy and single transmission path, a precision conveyor based on array piezoelectric actuators is present. In order to meet the requirements of the two-DOF drive motion, a structure of double-V orthogonal two-DOF piezoelectric actuator is adopted. The advantages and disadvantages of three different layout methods are discussed, and the layout of four ultrasonic drivers with regular hexagon for conveyor is determined. The working principle of the ultrasonic piezoelectric transmission platform module is classified and discussed according to the regular hexagon layout, and the output speed of each ultrasonic driver under different required motion angles is obtained, so as to achieve the effect of coordinated control. According to the layout and working principle of the GSA-based fuzzy PID controller and piezoelectric precision conveyor, the coordinated control simulation model of the precision conveyor is built on the Simulink. A coordinated control experimental platform is built to validate whether the piezoelectric conveyor can move things translationally and rotationally. The results show that the translation and rotation displacement of the carried objects can be realized.

关键词： automation Accuracy Software packages Robot kinematics Layout 2-DOF Piezoelectric actuators

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Applications Research of Artificial Intelligence Technology in Electric automation Control 22

Applications Research of Artificial Intelligence Technology ...

引用

2nd international conference on Control and Intelligent robotics, ICCIR 2022

作者： Dong, Hai Tarim Oil and Gas Transportation Branch Company of West Pipeline Co. Ltd China Oil and Gas Pipeline Network Corporation Xinjiang Korla841000 China

ISBN: (纸本)9781450397179

With the rapid development of computer science and technology, artificial intelligence technology has been widely used in various fields of society. Because of its high level of intelligence, the control effect can meet the needs of general electrical automatic control. The control effect and control mode in electrical automatic control engineering have better effects than traditional electrical control. Therefore, the extensive use of intelligent technology in electrical automatic control engineering can greatly improve the production efficiency and control efficiency. In order to prove the effectiveness of the methods proposed in this paper, digital simulations are carried out. From the simulation results, it can be seen that they have a good effect on the generator resynchronization and can meet the requirements of actual operation. © 2022 ACM.

关键词： Production efficiency

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A Multi-strategy Improved Osprey Optimization Algorithm for UAV path Planning

A Multi-strategy Improved Osprey Optimization Algorithm for ...

引用

robotics, Control and automation Engineering (RCAE), international conference on

作者： Long Yang Weimin Wang Binzi Xu School of Computer Jiangsu University of Science and Technology Zhenjiang China School of Electrical Engineering Anhui Polytechnic University Wuhu China

ISBN: (数字)9798350355642

ISBN: (纸本)9798350355659

Addressing the challenges of reduced population diversity and susceptibility to local optima in the later iterations of the Osprey algorithm, this paper presents a Multi-Strategy Improved Osprey Algorithm (IOOA). Firstly, Piecewise mapping is employed to initialize the population, providing a robust foundation for global search. Secondly, during the exploration stage, we design an adaptive parameter Levy exploration method based on the fishhawk leader. Additionally, we integrate a random disturbance strategy inspired by trigonometric functions to enhance population diversity and improve global exploration capacity. During the exploring phase, we propose nonlinear control parameters to effectively balance exploration and exploitation, thus decreasing the risk of the algorithm stagnating to local optimum. Additionally, we introduce an update formula based on the positions of elite leaders to expedite convergence. Finally, we conduct simulation experiments and statistical tests on 13 benchmark test functions, comparing the performance of IOOA with seven popular algorithms. Simultaneously, we apply IOOA to the UAV path planning problem to validate its optimization performance in practical scenarios. The results indicate that IOOA exhibits faster convergence speed and higher effectiveness compared to other optimizers.

关键词： automation Perturbation methods Benchmark testing Autonomous aerial vehicles Path planning Optimization Robots Convergence

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Control and Planning of Rotary Wing Unmanned Aerial Vehicles Applied to High-Altitude Cableway Detection

Control and Planning of Rotary Wing Unmanned Aerial Vehicles...

引用

IEEE international conference on Unmanned Systems (ICUS)

作者： Xuan Zhang Liying Yang Siliang Li Zhaoxiong Huang Yuqing He State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798350384185

ISBN: (纸本)9798350384192

A novel detection method is to use unmanned aerial vehicles for the detection of high-altitude passenger ropeways. This paper posed a challenging problem for detection system modeling and control. This article proposed a method of detecting high-altitude passenger ropeways by connecting two unmanned aerial vehicles. Firstly, the structural composition of the detection system was designed according to the testing scenario. Secondly, the dynamic models of the two drones are established. Besides this, a geometric controller is used to ensure the exactly tracking of the planed trajectory. Finally, the feasibility and validity are verified in the experiment.

关键词： Autonomous systems Systems modeling Autonomous aerial vehicles Control systems Trajectory Planning Vehicle dynamics Testing Drones

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Applying Pre-trained Multilingual BERT in Embeddings for Improved Malicious Prompt Injection Attacks Detection 2

Applying Pre-trained Multilingual BERT in Embeddings for Imp...

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2nd international conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2024

作者： Rahman, Md Abdur Shahriar, Hossain Wu, Fan Cuzzocrea, Alfredo University of West Florida Dept. of Intelligent Systems and Robotics PensacolaFL United States University of West Florida Center for Cybersecurity PensacolaFL United States Tuskegee University Department of Computer Science TuskegeeAL United States University of Calabria Idea Lab Rende Italy

ISBN: (纸本)9798331529529

Large language models (LLMs) are renowned for their exceptional capabilities, and applying to a wide range of applications. However, this widespread use brings significant vulnerabilities. Also, it is well observed that there are huge gap which lies in the need for effective detection and mitigation strategies against malicious prompt injection attacks in large language models, as current approaches may not adequately address the complexity and evolving nature of these vulnerabilities in real-world applications. Therefore, this work focuses the impact of malicious prompt injection attacks which is one of most dangerous vulnerability on real LLMs applications. It examines to apply various BERT (Bidirectional Encoder Representations from Transformers) like multilingual BERT, DistilBert for classifying malicious prompts from legitimate prompts. Also, we observed how tokenizing the prompt texts and generating embeddings using multilingual BERT contributes to improve the performance of various machine learning methods: Gaussian Naive Bayes, Random Forest, Support Vector Machine, and Logistic Regression. The performance of each model is rigorously analyzed with various parameters to improve the binary classification to discover malicious prompts. Multilingual BERT approach to embed the prompts significantly improved and outperformed the existing works and achieves an outstanding accuracy of 96.55% by Logistic regression. Additionally, we investigated the incorrect predictions of the model to gain insights into its limitations. The findings can guide researchers in tuning various BERT for finding the most suitable model for diverse LLMs vulnerabilities. © 2024 IEEE.

关键词： Embeddings

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