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methods of identification of data transmitted in the in-vehicle CAN-BUS networks

Methods of identification of data transmitted in the in-vehi...

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international conference on methods and models in automation and robotics

作者： Krzysztof Rokicki Krzysztof Szczurowski Institute of Vehicles Warsaw University of Technology Warsaw Poland

ISBN: (纸本)9781479987023

In the first part of this paper the operation mechanism of exchanging the data used in vehicles and equipment as well as the applied security measures have been presented In the further part of the work, the identification method for the data in the network, for which the encoding system is not known, has been discussed Subsequently, a suggestion of the decoding method for the network-derived data has been presented.

关键词： CAN bus data sniffer car hacking

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PRBJE: A Novel Pre-Relation Bidirectional Joint Extraction Model

PRBJE: A Novel Pre-Relation Bidirectional Joint Extraction M...

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robotics, Intelligent Control and Artificial Intelligence (RICAI), international conference on

作者： Qiang Yu Wenhui Zhou Shuai Dong Dongyuan Li School of Automation Engineering University of Electronic Science and Technology of China Chengdu China Zhongshan Institute University of Electronic Science and Technology of China Zhongshan China

ISBN: (数字)9798331541699

ISBN: (纸本)9798331541705

Joint extraction of entities and their semantic relationships to form factual triples from unstructured text is a fundamental task in information extraction. However, existing methods either ignore the interactions between relationships and entities or extract the subjects and objects in a fixed sequential order, which makes these approaches too sensitive to the extraction results of subjects. To address the two issues, we propose a pre-relation bidirectional joint extraction model (PRBJE). Specifically, we first detect potential relationships from the sentence to assist in the subsequent extraction of head and tail entities. then, we extract all possible entity pairs maintained in the text using two complementary directions: extracting the tail entity based on the head entity and vice versa. Finally, we assign relationships to all entity pairs by a biaffine model, thereby forming complete relational triples. Extensive experimental results demonstrate that our model outperforms state-of-the-art methods, with improved F1 scores across four public datasets, particularly achieving gains of 0.9 and 0.7 on NYT and WebNLG, respectively.

关键词： Semantics Refining Tail Predictive models Information retrieval Natural language processing Data mining Artificial intelligence Robots Intelligent control

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Probabilistic Modeling of Human Locomotion for Biped Robot Trajectory Generation

Probabilistic Modeling of Human Locomotion for Biped Robot T...

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IEEE UP Section conference on Electrical Computer and Electronics (UPCON)

作者： Bharat Singh Vishu Gupta Rajesh Kumar Malaviya National Institute of Technology Jaipur

ISBN: (纸本)9781665409636

the wheel-type robot has found numerous applications in hospitals, restaurants, entertainment, the automation industry, etc., and shows its applicability in solving the tasks efficiently. However, it failed to achieve the same efficiency in an unstructured environment that is mostly found in the real world. thus, a biped robot can replace the wheel-type robot for better performance. the biped robot has many joints which make it a complex higher degree of freedom system. Hence, the designing of the controller, reference trajectory generation, state estimation and, filter design for feedback signal is a very cumbersome task. this paper focuses on the generation of the reference trajectories. Since human locomotion is optimal naturally, therefore, the human data is used for this study, which is collected at robotics and Machine ANalytics (RAMAN) Lab, MNIT, Jaipur, India. In the literature, various authors have implemented model-based learning methods to develop a model based on data. However, these models suffer from model bias i.e., it is assumed that learned model accurately define the real system. therefore, in this paper, the authors have proposed probabilistic models to model the human locomotion data. the reference trajectory is generated using the Bayesian ridge regression, Automatic relevance determination regression, and Gaussian process regression. the performance evaluation of developed models are based on average error, maximum error, root mean square error, and percentage normalized root mean square error.

关键词： Service robots Computational modeling Gaussian processes Predictive models Probabilistic logic Data models Trajectory

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Bearing Fault Diagnosis with Multi-source Information Fusion and Deep Transfer Learning

Bearing Fault Diagnosis with Multi-source Information Fusion...

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

作者： Shikai Lu Jun Fei Bin Nie Qilei Zhou Yi Le Ruixue Li Guohong Lai Yu Liao Dongping Hydropower Company Limited Xuanen China College of Intelligent Systems Science And Enginerring Hubei Minzu University Enshi China

ISBN: (数字)9798350355642

ISBN: (纸本)9798350355659

In the fault diagnosis of bearings in hydropower station units, the information provided by a single sensor is limited, and the dynamic characteristics of the operating conditions vary under different load conditions. To address these issues, this paper proposes a bearing fault diagnosis method that integrates multi-source information with deep transfer learning. Firstly, multi-scale features of the input signals are extracted through a fusion convolution module to ensure the completeness and effectiveness of fault information. Subsequently, channel attention and spatial attention mechanisms are used to retain effective information with minimal data, enhancing the efficiency and accuracy of information fusion. Finally, the fault diagnosis method based on transfer learning effectively copes with detection under different loads. To more comprehensively mine and utilize the hidden features in fault signals, this paper combines vibration and temperature signals to build a bearing fault diagnosis experimental platform, collecting vibration and temperature signals at different speeds to generate a dual-channel dataset. Compared to common domain adaptation methods such as deep correlation alignment and domain adversarial neural networks, the proposed model's fault diagnosis performance under different working conditions is validated. Experimental results show that the proposed multi-source information fusion method performs with higher diagnostic accuracy both under the same and varying working conditions, significantly outperforming traditional methods.

关键词： Fault diagnosis Vibrations Employee welfare Adaptation models Accuracy Attention mechanisms Transfer learning Training data Feature extraction Motors

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Compact Convolutional Neural Networks for Multi-Class, Personalised, Closed-Loop EEG-BCI

Compact Convolutional Neural Networks for Multi-Class, Perso...

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IEEE/RAS-EMBS international conference on Biomedical robotics and Biomechatronics (BioRob)

作者： Pablo Ortega Cédric Colas A. Aldo Faisal French Institute for Research in Computer science and Automation (INRIA) Bordeaux France

ISBN: (纸本)9781538681848

Ahstract- For many people suffering from motor disabilities, assistive devices controlled with only brain activity are the only way to interact with their environment [1]. Natural tasks often require different kinds of interactions, involving different controllers the user should be able to select in a self-paced way. We developed a Brain-Computer Interface (BCI) allowing users to switch between four control modes in a self-paced way in real-time. Since the system is devised to be used in domestic environments in a user-friendly way, we selected non-invasive electroencephalographic (EEG) signals and convolutional neural networks (CNNs), known for their ability to find the optimal features in classification tasks. We tested our system using the Cybathlon BCI computer game, which embodies all the challenges inherent to real-time control. Our preliminary results show that an efficient architecture (SmallNet), with only one convolutional layer, can classify 4 mental activities chosen by the user. the BCI system is run and validated online. It is kept up-to-date through the use of newly collected signals along playing, reaching an online accuracy of 47.6% where most approaches only report results obtained offline. We found that models trained with data collected online better predicted the behaviour of the system in real-time. this suggests that similar (CNN based) offline classifying methods found in the literature might experience a drop in performance when applied online. Compared to our previous decoder of physiological signals relying on blinks, we increased by a factor 2 the amount of states among which the user can transit, bringing the opportunity for finer control of specific subtasks composing natural grasping in a self-paced way. Our results are comparable to those showed at the Cybathlon's BCI Race but further improvements on accuracy are required.

关键词： Electroencephalography Training Task analysis Decoding Games Computer architecture Brain modeling

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Continuous Recurrent Neural Network Identifier Based On Exponential Barrier Lyapunov Functions

Continuous Recurrent Neural Network Identifier Based On Expo...

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international conference on Control, Mechatronics and automation (ICCMA)

作者： Manuela Gomez-Correa Mariana Ballesteros Rita Q. Fuentes-Aguilar Isaac Chairez David Cruz-Ortiz CIDETEC Instituto Politécnico Nacional Mexico City Mexico School of Engineering and Sciences Tecnologico de Monterrey Mexico City Mexico Medical Robotics and Biosignals Lab Instituto Politécnico Nacional Mexico City Mexico

ISBN: (数字)9798331517519

ISBN: (纸本)9798331517526

this paper describes an identifier for a class of nonlinear systems based on continuous recurrent neural networks (CRNN). the identifier is proposed considering the approximation properties of artificial neural networks. the learning or adaptive laws are obtained using Barrier Lyapunov functions with an exponentially decreasing barrier. the application of such a function results in a bounded identification error with an exponential convergence and predefined decay. Additionally, it ensures the convergence of the weights for the activation functions to the fitting values. the proposed identifier was used to identify a virtual Cartesian robot with two degrees of freedom. the results showed the performance of the identification error, which does not violate the imposed exponential barrier. Moreover, the effect of the predefined convergence parameter was observed in the identification error evolution without the need for the change of any other parameter in the CRNN.

关键词： Recurrent neural networks Mechatronics Artificial neural networks Stability analysis Mathematical models Nonlinear systems MATLAB Robots Convergence Lyapunov methods

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Anomaly Detection System for Semiconductor Deposition Process using Transformer

Anomaly Detection System for Semiconductor Deposition Proces...

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international conference on Control, automation and Systems ( ICCAS)

作者： Min-Ho Ha Saba Arshad Min-Seon Chae Sung-Chul Yun Seung-Hwan Kim Tae-Hyoung Park Department of Intelligent Systems and Robotics Chungbuk National University Cheongju Korea Industrial AI Research Center Chungbuk National University Cheongju Korea GSF Solution Seongnam Korea

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

As the artificial intelligence market develops, demand for semiconductors also increases. In order to increase the efficiency of the semiconductor production process, anomaly detection technology is needed based on sensor values attached to process machines. In this paper, we propose an anomaly detection system that determines whether there are abnormalities in the values obtained by attaching a sensor to the actual deposition process. the attached sensors include gas, temperature, and pressure sensors. A communication module is designed and made into a database, and the presence or absence of anomalies is determined using the characteristics of multivariate time series data through transformer-based anomaly detection network. An attention module-based transformer network is used in the AI-based anomaly detection network. through evaluation, we confirmed that the transformer-based anomaly detection network achieves the best performance in terms of accuracy relative to other anomaly detection methods.

关键词： Semiconductor device modeling Training Temperature sensors Accuracy Temperature Time series analysis Semiconductor device manufacture Transformers Data models Anomaly detection

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Motion Planning of a Rehabilitation Robotic Arm using Brain data & Deep Learning

Motion Planning of a Rehabilitation Robotic Arm using Brain ...

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RSI/ISM international conference on robotics and Mechatronics (ICROM)

作者： Ali Soltani S. Ali A. Moosavian Advanced Robotics and Automated Systems (ARAS) Lab K. N Toosi University of Technology Tehran Iran

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

the Brain-Computer Interface (BCI) technology has emerged as an innovative tool in the field of rehabilitation, aimed at restoring and improving patient motor and sensory functions. the motivation for using BCI in rehabilitation is primarily based on the ability of these systems to establish a direct correlation between brain activity and external devices, such as motor prostheses or assistive robots. this technology aids patients with spinal cord injuries, strokes, or neurological diseases in regaining lost functionality by directly controlling assistive devices. the primary objective of research in this area is to enhance the quality of life for individuals with motor impairments and to improve the efficiency of rehabilitation methods, potentially leading to the development of models for automation in clinics and rehabilitation centers. In this study, to utilize the BCI as an automated system for controlling rehabilitation devices or functional prostheses, a series of experiments were designed and conducted to record electroencephalography (EEG) data from healthy individuals during a variety of hand motions, while simultaneously the motion is captured with two webcams. the initial data was collected and analyzed, and the results indicate that increasing the number of test subjects and employing more advanced equipment for signal recording could lead to more accurate results and the development of a more comprehensive model. Obtained model can then be used to command a rehabilitation arm according to the patient brain signals.

关键词： Webcams Stroke (medical condition) Motors Brain modeling Assistive robots Robot sensing systems Electroencephalography Brain-computer interfaces Recording Spinal cord injury

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Novel Approach for Intelligent Fault Diagnosis in Electromechanical Drive Systems Using an Attention-Based Residual 1D Convolutional Neural Network

Novel Approach for Intelligent Fault Diagnosis in Electromec...

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Artificial Intelligence, robotics, and Communication (ICAIRC), IEEE international conference on

作者： Jonathan Okonda Danping Xu Bovic Kilundu School of Mechatronic Engineering and Automation Shanghai University Shanghai China Department of Mechanical Engineering Brussels Engineering School (ISIB) Haute Ecole de Bruxelles Brabant (HE2B) Brussels Belgium

ISBN: (数字)9798331531225

ISBN: (纸本)9798331531232

Numerous existing deep learning-based fault diagnosis models, have been implemented for intelligent fault diagnosis in electromechanical drive systems (EMDS). However, these models often are designed to detect and diagnose single fault types at specific operating conditions. these models are frequently susceptible to overfitting, suffer from the vanishing gradient problem, and struggle with long-range feature dependencies. Such limitations significantly hinder their accuracy and reliability in diagnosing complex fault scenarios for EMDS under different operating conditions using noisy data. To address these limitations, we propose a novel deep learning-based fault diagnosis model named Attention-based Residual 1D Convolutional Neural Network (AR1D-CNN). this model is designed to effectively diagnose complex faults in EMDS across various load conditions using noisy and imbalanced vibration data. Experimental results demonstrate exceptional classification accuracies of 99.98% and 100.00% on the KAIST dataset for 0 and 2 Nm load conditions, respectively, and 99.31% on the AETWPSU broken rotor bar dataset. Notably, the AR1D-CNN outperforms existing state-of-the-art methods by approximately 4% on the AETWPSU dataset, highlighting its robustness and reliability. this research significantly enhances predictive maintenance strategies by enabling early and precise fault detection, ultimately improving the operational efficiency of EMDS.

关键词： Fault diagnosis Vibrations Accuracy Fault detection Rotors Data models Noise measurement Convolutional neural networks Load modeling Predictive maintenance

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Discrete-time Adaptive Dynamic Surface Control Method with Performance-prescribed for Dielectric Elastomer Actuator

Discrete-time Adaptive Dynamic Surface Control Method with P...

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IEEE international conference of Intelligent Robotic and Control Engineering (IRCE)

作者： Yue Wang Yuan Liu Ming Che School of Automation Engineering Northeast Electric Power University China Northeast Development and Construction Corporation State Grid XinYuan Company Limited Shenyang China

ISBN: (数字)9798350352399

ISBN: (纸本)9798350352405

this paper proposes a discrete control scheme based on performance-prescribed dynamic surface technology for adaptive neural network with implicit inverse compensation to address the control problem of dielectric elastomer actuators (DEAs). Compared to continuous control, discrete control is more in line with the working rules of computers. Innovatively introducing performance-prescribed functions in discrete control systems, enabling tracking errors to converge within a preset range in advance, utilizing implicit inverse compensation for hysteresis nonlinearity, and integrating it into adaptive dynamic surface control methods to achieve precise control of DEAs. Finally, the effectiveness of the proposed scheme was verified based on the constructed dielectric elastomer driven motion experimental platform.

关键词： Adaptive systems Control engineering Tracking Inverse problems Dynamics Neural networks Dielectric elastomers Mathematical models Hysteresis Robots

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