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Deep & Attention: A Self-Attention based Neural Network for Remaining Useful Lifetime Predictions

Deep & Attention: A Self-Attention based Neural Network for ...

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Mechatronics and robotics Engineering (ICMRE), international conference on

作者： Yuanjun Liu Xingang Wang Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9781665414890

ISBN: (纸本)9781665430531

the remaining useful lifetime (RUL) of assets plays a critical role in machine prognostics and health management (PHM). Accurate RUL predictions can reduce losses caused by equipment faults. Most existing data-driven PHM methods rely on long short-term memory (LSTM) networks to model the relationship of time series data and RUL. However, because of the sequential nature of LSTM, it is not conducive to parallel computing. Herein, we propose the Deep & Attention Network, which uses a combination of convolutional neural networks and Attention methodologies instead of LSTM. In the proposed Deep & Attention Network, the Attention component models the temporal property, while the Deep component learns the effect of noise data. Experiments on NASA's Commercial Modular Aero- Propulsion System Simulation datasets demonstrate that the proposed network achieves a level of performance similar to that of other state-of-the-art RUL prediction models. Moreover, compared with LSTM-based methods, our Self-Attention-based method is conducive to parallel computing.

关键词： Computational modeling Time series analysis Parallel processing Data models Timing Systems simulation Prognostics and health management

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Interval type-2 non-singleton type-2 Takagi-Sugeno-Kang fuzzy logic systems using the hybrid learning mechanism recursive-least-square and back-propagation methods

Interval type-2 non-singleton type-2 Takagi-Sugeno-Kang fuzz...

引用

international conference on Control, automation, robotics and Vision (ICARCV)

作者： Gerardo M. Mendez Maria de los Angeles Hernandez Electrical and Electronics Engineering Department Instituto Tecnologico de Nuevo Leon-ITNL Nuevo Leon Mexico Business Administration Department Instituto Tecnologico de Nuevo Leon-ITNL Nuevo Leon Mexico

this article presents a novel learning methodology based on the hybrid mechanism for training an interval type-2 non-singleton type-2 Takagi-Sugeno-Kang fuzzy logic systems (FLS). Using input-output data pairs during the forward pass of the training and prediction processes, the interval type-2 non-singleton type-2 TSK FLS the consequent parameters were tuned by using the recursive least squares (RLS) method. In the backward pass, the antecedent parameters were tuned by using the back-propagation (BP) method. As reported in the literature, the performance indexes of these hybrid models have proved to be better than the individual training mechanism when used alone. the proposed hybrid methodology was tested thru the modeling and prediction of the steel strip temperature at the descaler box entry as rolled in an industrial hot strip mill. Results show that the proposed method compensates better for uncertain measurements than previous type-2 Takagi-Sugeno-Kang using non-hybrid or only back propagation learning mechanisms.

关键词： Training Temperature measurement Fuzzy systems Learning systems Fuzzy logic Strips Measurement uncertainty

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Adaptive Obstacle Avoidance of UAV Leader/Followers with Nonlinear Logarithm Quaternion Control

Adaptive Obstacle Avoidance of UAV Leader/Followers with Non...

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international conference on methods and models in automation and robotics (MMAR)

作者： Huda Naji Al-Sudany Béla Lantos Department of Control Engineering and Information Technology Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary

ISBN: (数字)9798350362343

ISBN: (纸本)9798350362350

this paper addresses a significant challenge within the realm of fixed wing Unmanned Aerial Vehicles (UAVs), namely obstacle avoidance of the Leader-Follower system. the proposed approach involves formulating the Leader-Follower problem, consisting of one leader and three followers, with control strategies utilizing the logarithm exponential Quaternion method. Subsequently, the designed control law employing quaternions is applied to both the leader and the follower UAVs. Moreover, a notable contribution of this research lies in the incorporation of adaptive obstacle avoidance. the idea based on potential field as obstacle avoidance algorithm, with adaptive techniques for determining obstacle avoidance parameters. these parameters are dynamically adjusted based on the distance between the UAV and the obstacle through a non-linear adaptation function. the comprehensive implementation of these methods is rigorously evaluated by employing a predefined reference path for the leader and conducting numerous scenario-based tests. Random directions of followers were used in case of stuck in local minimum to avoid it; this was applied in case when potential filed force is less than small value. Experiments to test some scenarios of obstacle avoidance were conducted. the final results conclusively demonstrate the stability of the control law even in the presence of obstacles. the effectiveness of the adaptive obstacle avoidance approach is clearly evident from the trajectories of the UAV followers.

关键词： Adaptation models automation Quaternions Heuristic algorithms Force Autonomous aerial vehicles Stability analysis

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Application of Model-Based System Engineering to a Planetary Rover Design

Application of Model-Based System Engineering to a Planetary...

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international conference on Control, Mechatronics and automation (ICCMA)

作者： Hugo Castaing Gilbert Tang Marco Chacin Fabien Bonnefoi SATM Cranfield University Cranfield UK Automation Robotics and Assembly Technology R&D AIRBUS Stevenage UK Computer Science IPSA Paris France

Traditional system engineering methods have shown their limits when applied to highly complex systems such as rovers. In parallel, the digitalisation of the industry and the democratisation of the use of models in engineering force the system engineering field to adapt and change its practices by adopting a new approach: Model-Based System Engineering. this project aimed to provide a comprehensive example of an MBSE approach applied to a planetary rover design case study. the MathWorks MBSE toolchain is used to reverse engineer the NASA Sojourner rover. As a result, the defined requirements, and functional and logical architectures are implemented and dynamically linked together. Additionally, a multi-physical simulation model of the rover's power and mobility system is implemented. Requirements, architectures, and physical models are linked together to form a unique and comprehensive knowledge base, providing a vertical model-centric approach. the results of the simulation are used to conduct a trade study and deduct a design change for the system. the results presented in this study form the basis for a discussion aimed at evaluating the benefits and limitations of the MBSE approach mentioned in the scientific literature.

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Warehouse automation solution through a goods-to-person AMR

Warehouse automation solution through a goods-to-person AMR

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international conference on methods and models in automation and robotics (MMAR)

作者： Dimitra Zotou Stefanos Papadam Christina theodoridou Andreas Kargakos Kosmas Tsiakas Dimitrios Alexiou Ioannis Kostavelis Dimitrios Giakoumis Nikolaos Morenos Dimosthenis Ioannidis Dimitrios Tzovaras Centre for Research and Technology Hellas Information Technologies Institute (CERTH / ITI) Thessaloniki Greece Department of Supply Chain Management International Hellenic University Katerini Greece TETTIX - N.MORENOS SINGLE MEMBER PC Thessaloniki Greece

ISBN: (数字)9798350362343

ISBN: (纸本)9798350362350

the use of goods-to-person robotic systems in a warehouse enhances storage density by devoting specific areas for Automated Mobile Robots (AMRs). Nevertheless, operation in narrow aisles and confined spaces presents notable challenges. this study outlines a comprehensive approach for AMRs to autonomously navigate within narrow aisles of a warehouse, ensuring safety and streamlining order completion. the goal is to independently collect and transport bins with products referred to the order, to the workstation area, where workers finalize order packing. Precise positioning of fiducial markers on the floor, initiates a fusion-based localization system that merges information from wheel odometry and fiducial detection. the markers are organized as an undirected graph to facilitate navigation purposes and Dijkstra algorithm is employed to successfully generate both safe and optimal paths. A pipeline has been established for the retrieval and positioning of bins from shelves or workstations to optimize the order picking process. the proposed method has been tested on a simulation environment with realistic constraints stem from the development of an autonomous warehouse environment.

关键词： Location awareness automation Navigation Pipelines Wheels Workstations Safety

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Drive Modeling and Dynamic Control in Mössbauer Spectroscopy for Space & Terrestrial Applications

Drive Modeling and Dynamic Control in Mössbauer Spectroscop...

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international conference on methods and models in automation and robotics (MMAR)

作者： Mohammad Beyki Justus Pawlak Martin Mutz Robert Patzke Franz Renz Department of Computer Engineering and Applied Mathematics University of Applied Sciences and Arts Hannover Germany Department of Integrated Circuits and Embedded Systems University of Applied Sciences and Arts Hannover Germany Institute of Inorganic Chemistry Leibniz University Hannover Germany

ISBN: (数字)9798350362343

ISBN: (纸本)9798350362350

An important component of the Mössbauer spectrometer is the Mössbauer drive, which allows, utilizing the Doppler effect, to vary the amount of energy of the emitted gamma quanta so that it corresponds to the resonance energy and energetically excites the irradiated nuclei. this paper deals with drive modelling and control in Mössbauer spectroscopy. the equations of motion of the linear drive are derived and compared in different versions. In this context, the single-mass representation proves to be a sufficiently accurate model, which shows almost identical results compared to multibody models. this leads to a reduction of the system order from n=6 to n=2, which greatly simplifies the evaluation steps in this context and the further controller design modalities. Based on this, a heuristic control concept is developed that fundamentally revises common design approaches and opens up a broad spectrum of research. the controller design provides for a combination of signal-smoothing and signal amplifying low-pass filtering in combination with dynamic input vector normalisation. this achieves the desired frequency and amplitude response of $1.1485 \cdot 10^{3} \frac{\mathrm{rad}}{\mathrm{s}}$ and 0.3 mm and optimises the dynamics for real laboratory use.

关键词： Spectroscopy automation Filtering Aerospace electronics Mathematical models Vectors Resonance

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Proposal of an Automated Feature Engineering Pipeline for High-Dimensional In-Vehicle Bus Data Using Reinforcement Learning

Proposal of an Automated Feature Engineering Pipeline for Hi...

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robotics, automation and Artificial Intelligence (RAAI), IEEE international conference on

作者： Julian Mueller Jonas Wagenknecht Florian Schuchter Georg Frey Mercedes-Benz AG Sindelfingen Germany Reutlingen University Reutlingen Germany Chair of Automation and Energy Systems Saarland University Saarbrücken Germany

ISBN: (数字)9798331520038

ISBN: (纸本)9798331520045

the complexity of the communication between electronic control units in contemporary vehicles is increasing with the rising demand of customer functions such as automated driving and infotainment resulting in up to several thousand data elements (signals) per data bus. Consequently, engineers have no choice but to apply data science methods to extract meaningful insights from this high-dimensional communication data, particularly before training machine learning models. A critical step in this process is identifying the bus signals relevant for a specific data science task. Extracting them manually is not only time-consuming but it also demands a high degree of in-vehicle communication knowledge which is hard to obtain. To address this challenge, we propose an automated feature engineering approach that leverages reinforcement learning combined with state-of-the-art feature selection and engineering methods and a tunable sampling rate for the bus data. Due to the high degree of automation it requires little prior knowledge of how in-vehicle communication works. Our method enhances important key performance indicators for machine learning model quality for the task of energy consumption prognosis and significantly reduces the number of bus signals required for accurate model predictions. this approach does not only improve the understanding of relevant data but also reduces the model's size as less features are used for the prediction, leading to more efficient and interpretable machine learning solutions.

关键词： Training Energy consumption automation Pipelines Reinforcement learning Predictive models Data science Feature extraction Data models Robots

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Multi-Sensor-based Predictive Control for Autonomous Parking in Presence of Pedestrians

Multi-Sensor-based Predictive Control for Autonomous Parking...

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international conference on Control, automation, robotics and Vision (ICARCV)

作者： David Pérez-Morales Olivier Kermorgant Salvador Domínguez-Quijada Philippe Martinet LS2N Laboratoire des Sciences du Numérique de Nantes École Centrale de Nantes Nantes France INRIA Sophia Antipolis Valbonne France

ISBN: (数字)9781728177090

ISBN: (纸本)9781728177106

this paper explores the feasibility of a Multi-Sensor-Based Predictive Control (MSBPC) approach in order to have constraint-based backward non-parallel (perpendicular and diagonal) parking maneuvers capable of dealing with moving pedestrians and, if necessary, performing multiple maneuvers. Our technique relies solely in sensor data expressed relative to the vehicle and therefore no localization is inherently required. Since the proposed approach does not plan any path and instead the controller maneuvers the vehicle directly, the classical path planning related issues are avoided. Real experimentation validates the effectiveness of our approach.

关键词： Robot sensing systems Robots Path planning Kinematics Data models Automobiles Transmission line matrix methods

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methods for Gastrointestinal Examination and Motion Simulation of a Roundworm-like Robot for Gastrointestinal Inspection

Methods for Gastrointestinal Examination and Motion Simulati...

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international conference on robotics and automation Engineering (ICRAE)

作者： Chang Xu Mechanical Engineering Hebei University of Technology Tianjin China

ISBN: (数字)9798331518301

ISBN: (纸本)9798331518318

this article investigates the application of robotic systems, particularly a novel roundworm-like robot for gastrointestinal examinations. Unlike traditional endoscopic methods, this biomimetic approach leverages the movement patterns of roundworm-like to develop a non-invasive, efficient robotic system for navigating the human gastrointestinal tract. the proposed robot aims to enhance diagnostic accuracy and patient comfort by minimizing physical trauma and eliminating the need for anesthesia. this article utilizes a robotic model consisting of six rods and five joints to simulate the movement of a roundworm. the roundworm-like robot performs linear motion within a simulated model of the small intestine. By altering the length of each segment, the study aims to determine an appropriate size for the roundworm robot and analyze its feasibility. Initial simulations demonstrate the robot’s potential to adapt its movement through the simple environment of the gastrointestinal tract, offering a promising alternative to current diagnostic techniques.

关键词： Adaptation models Biological system modeling Heuristic algorithms Biomimetics Prototypes Robot sensing systems Gastrointestinal tract Stability analysis Real-time systems Reliability

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A Hybrid Dynamic Modeling Method for External Torque Estimation of Robot Manipulators

A Hybrid Dynamic Modeling Method for External Torque Estimat...

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

作者： Hanqing Yuan Shanrong Ren Jun Yao Jianliang Mao College of Automation Engineering Shanghai University of Electric Power Shanghai China School of Automation Southeast University Nanjing China

this paper introduces a hybrid modeling approach designed to estimate external forces in robot manipulators, with a focus on achieving high-precision force estimation while minimizing reliance on force/torque sensors. Traditional methods used for robot dynamics modeling and identification often grapple with unmodeled dynamics, system measurement noise, filtering delays, and equipment vibrations, all of which contribute to significant errors in the resulting dynamic models. To address these challenges, this paper leverages neural networks to mitigate the discrepancies in the physical model. the process commences with the application of the Newton-Euler method to perform dynamic modeling of the robot manipulators. Subsequent parameter identification is conducted to establish the physical model governing the robot manipulators' dynamics. Building on this physical model, a backpropagation (BP) neural network is employed to create a data-driven model of the model errors, thereby further enhancing model accuracy. To validate the effectiveness of this approach, external torque estimation experiments were carried out on a six-axis robot manipulators platform utilizing a Beckhoff controller. the experimental results affirm the method's capability to significantly improve the accuracy of external torque estimation in scenarios where force/torque sensors are unavailable.

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