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28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Ţiru, Andreea Elena Vasiliev, Iulian Diaconu, Larisa Vilanova, Ramon Voipan, Daniel Ratnaweera, Harsha Dunǎrea de Jos University of Galaţi Department of Automatic Control and Electrical Engineering Galaţi Romania Autonomous University of Barcelona Department of Automation Systems and Advanced Control Research Barcelona Spain Dunǎrea de Jos University of Galaţi Department of Computer Science and Information Technology Galaţi Romania Ås Norway

ISBN: (纸本)9798350339918

The management of wastewater is a significant global concern that calls for innovative solutions to lessen its negative effects on the environment. Conventional techniques of treating wastewater need improvement in order to deal with newly discovered contaminants, which highlights the importance of providing precise estimates of process performance and resource requirements. The worsening water shortage situation requires a paradigm shift in which wastewater is viewed as a useful resource. It is possible to create an economy that is both sustainable and circular by treating and recycling wastewater, putting less pressure on freshwater supplies, and leaving as little of an environmental footprint as possible. This study investigates the use of Artificial Neural Networks (ANNs) as software estimators in the treatment of wastewater, with a particular emphasis on predicting ammonium concentrations in effluent. In order to deal with imbalanced time-series data, the research introduces innovative data pretreatment strategies. These techniques include a Sliding Window protocol, Data Normalization, and a K-Fold training scheme. This illustrates the potential of ANNs to revolutionize wastewater treatment procedures and drive developments in this field. The suggested method demonstrates higher performance when estimating pollutant concentrations, showing the ability of ANNs to do so. © 2023 IEEE.

关键词： Neural networks

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Condition Monitoring with Incomplete Data: An Integrated Variational Autoencoder and Distance Metric Framework

Condition Monitoring with Incomplete Data: An Integrated Var...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Maryam Ahang Mostafa Abbasi Todd Charter Homayoun Najjaran Department of Electrical and Computer Engineering University of Victoria Victoria Canada Advanced Control and Intelligent Systems Laboratory (ACIS) University of Victoria Victoria Canada Department of Mechanical Engineering University of Victoria Victoria Canada

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Condition monitoring of industrial systems is crucial for ensuring safety and maintenance planning, yet notable challenges arise in real-world settings due to the limited or non-existent availability of fault samples. This paper introduces an innovative solution to this problem by proposing a new method for fault detection and condition monitoring for unseen data. Adopting an approach inspired by zero-shot learning, our method can identify faults and assign a relative health index to various operational conditions. Typically, we have plenty of data on normal operations, some data on compromised conditions, and very few (if any) samples of severe faults. We use a variational autoencoder to capture the probabilistic distribution of previously seen and new unseen conditions. The health status is determined by comparing each sample’s deviation from a normal operation reference distribution in the latent space. Faults are detected by establishing a threshold for the health indexes, allowing the model to identify severe, unseen faults with high accuracy, even amidst noise. We validate our approach using the run-to-failure IMS-bearing dataset and compare it with other methods. The health indexes generated by our model closely match the established descriptive model of bearing wear, attesting to the robustness and reliability of our method. These findings highlight the potential of our methodology in augmenting fault detection capabilities within industrial domains, thereby contributing to heightened safety protocols and optimized maintenance practices.

关键词： Condition monitoring Fault diagnosis Protocols Fault detection Zero shot learning Probabilistic logic Robustness Safety Maintenance Indexes

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Geometric Parameterized Macromodel of Uniaxial Capacitance Accelerometer Based on Modal Superposition 16

Geometric Parameterized Macromodel of Uniaxial Capacitance A...

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16th IEEE International Scientific and Technical Conference Actual Problems of Electronic Instrument engineering, APEIE 2023

作者： Gridchin, Aleksandr V. Koleda, Aleksei N. Barbin, Evgenii S. Nesterenko, Tamara G. Kolchuzhin, Vladimir A. Department of Semiconductor Devices and Microelectronics Novosibirsk State Technical University Novosibirsk Russia Department of Electronic Engineering National Research Tomsk Polytechnic University Tomsk Russia Laboratory of Intelligent Computer Systems Tomsk State University of Control Systems and Radioelectronics Tomsk Russia Advanced Cellular Group Qorvo Munich GmbH Munich Germany

ISBN: (纸本)9798350330885

The paper presents a piecewise geometric parameterization method and a parametric analysis of uniaxial capacitance accelerometer using a reduced model formulated on the modal superposition method. The extraction of parameters for the macromodel such as modal frequencies, modal masses, modal stiffnesses, strain energy, and capacitances are described. The reduced model consists of the two modes: torsion mode, and transversal mode, three electrodes in the electrostatic domain, and eight master nodes in the geometrical domain located at the edges of the plate. The generation of the discrete macromodels is executed using commercial software. The thickness of the plate has been selected as a design parameter. Linear piecewise interpolation is used for macromodel parameterization which have been done inside VHDL-AMS code. The accelerometer's dynamic response simulated using of parametrized macromodel considering the mechanical contact phenomena and the nonlinear electrostatic interaction are presented and discussed. The developed approach can be applied for fast system level simulations in view of the variation of the geometric parameters. © 2023 IEEE.

关键词： Capacitance

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Data Preprocessing with IRM for skill extraction using NMF 26

Data Preprocessing with IRM for skill extraction using NMF

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26th International Conference on Knowledge-Based and Intelligent Information and engineering systems, KES 2022

作者： Oeda, Shinichi Iizumi, Shunsuke Department of Information and Computer Engineering National Institute of Technology Kisarazu College 11-1 Kiyomidaihigashi 2-chome Chiba Kisarazu Japan Advanced Course of Control and Information Engineering National Institute of Technology Kisarazu College 11-1 Kiyomidaihigashi 2-chome Chiba Kisarazu Japan

To provide examinees with appropriate question items, identifying and evaluating users' latent and acquired skills is important in e-learning systems. These latent skills are defined in terms of a Q-matrix. Constructing Q-matrices manually is relatively labor-intensive, because doing so requires expert insight as well as verbal examination to identify which skills students have mastered. Methods to extract a Q-matrix from examination results automatically using nonnegative matrix factorization (NMF) have been explored, and improved methods such as online NMF have been proposed, which extracts an immutable Q-matrix from time-series data. In this study, we propose a data preprocessing method to improve the accuracy of NMF using a co-clustering method called an infinite relational model (IRM). We also describe experiments conducted using synthetic data, in which learners'skills and the bias in the number of their responses were evaluated, and show that the results demonstrate the efficacy of the proposed approach. © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 26th International Conference on Knowledge-Based and Intelligent Information & engineering systems (KES 2022)

关键词： Non-negative matrix factorization

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Deep Learning-based Source Code Classification and Visualization of Decision Rationales

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Procedia Computer Science 2024年 246卷 2753-2761页

作者： Shinichi Oeda Seiya Mitsumori Department of Information and Computer Engineering National Institute of Technology Kisarazu College 11-1 Kiyomidaihigashi 2-chome Kisarazu City Chiba Japan Advanced Course of Control and Information Engineering National Institute of Technology Kisarazu College 11-1 Kiyomidaihigashi 2-chome Kisarazu City Chiba Japan

In programming education, it is crucial to provide instruction that is tailored to students’ proficiency levels. For this purpose, an objective evaluation of each student’s coding ability is essential. Additionally, it is necessary to understand the characteristics of source code written by both beginners and advanced students. Previous research has successfully converted the structural information of source code into graphs and assessed coding skills using deep learning, achieving high accuracy. However, it remains unclear which specific structural elements significantly influence these assessments. This study addresses this gap by transforming source code into abstract syntax trees and developing a model that uses Graph Convolutional Networks to categorize code as either beginner or advanced users based on learned structural information. Furthermore, we apply Integrated Gradients to visualize the decision-making basis of our model and elucidate the structural characteristics distinguishing source code written by beginner and advanced users.

关键词： Abstract Syntax Tree Deep Learning Explainable AI Graph Convolutional Networks Integrated Gradients Source Code Classification

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Latent State Estimation Method for Students Using Knowledge Tracing Combining with IRT 26

Latent State Estimation Method for Students Using Knowledge ...

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26th International Conference on Knowledge-Based and Intelligent Information and engineering systems, KES 2022

作者： Oeda, Shinichi Kakizaki, Toma Department of Information and Computer Engineering National Institute of Technology Kisarazu College 11-1 Kiyomidaihigashi 2-chome Chiba Kisarazu Japan Advanced Course of Control and Information Engineering National Institute of Technology Kisarazu College 11-1 Kiyomidaihigashi 2-chome Chiba Kisarazu Japan

The Intelligent Tutoring systems (ITSs) are a system that provides an efficient learning environment by assigning questions that are suitable to the learner's skill state. To improve the performance of ITSs, conventional studies have proposed student models that can estimate skill states with high accuracy. However, these models were based on the assumption that the log data was correct. In other words, they do not take into account the possibility of students cheating or memorizing answers, assuming that the test was conducted fairly and impartially. In recent years, many examinations have been conducted online as a countermeasure against COVID-19 (coronavirus) infections, and students may be able to obtain hints for the examinations using the internet or books. In this way, it is important to know how students approach learning in order to estimate their skill state. In this study, we propose a method for estimating the latent state of learners using a model that combines Knowledge Tracing, the de-facto standard for student modeling method, and Item Response Theory. © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 26th International Conference on Knowledge-Based and Intelligent Information & engineering systems (KES 2022)

关键词： Students

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Improving Collision-Free Success Rate For Object Goal Visual Navigation Via Two-Stage Training With Collision Prediction

arXiv

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arXiv 2025年

作者： Lian, Shiwei Zhang, Feitian Robotics and Control Laboratory Department of Advanced Manufacturing and Robotics College of Engineering State Key Laboratory of Turbulence and Complex Systems Peking University Beijing100871 China

The object goal visual navigation is the task of navigating to a specific target object using egocentric visual observations. Recent end-to-end navigation models based on deep reinforcement learning have achieved remarkable performance in finding and reaching target objects. However, the collision problem of these models during navigation remains unresolved, since the collision is typically neglected when evaluating the success. Although incorporating a negative reward for collision during training appears straightforward, it results in a more conservative policy, thereby limiting the agent’s ability to reach targets. In addition, many of these models utilize only RGB observations, further increasing the difficulty of collision avoidance without depth information. To address these limitations, a new concept—collision-free success is introduced to evaluate the ability of navigation models to find a collision-free path towards the target object. A two-stage training method with collision prediction is proposed to improve the collision-free success rate of the existing navigation models using RGB observations. In the first training stage, the collision prediction module supervises the agent’s collision states during exploration to learn to predict the possible collision. In the second stage, leveraging the trained collision prediction, the agent learns to navigate to the target without collision. The experimental results in the AI2-THOR environment demonstrate that the proposed method greatly improves the collision-free success rate of different navigation models and outperforms other comparable collision-avoidance methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Nonlinear Model Predictive control for Autonomous Sailboat with Optimization of Sail Angle

Nonlinear Model Predictive Control for Autonomous Sailboat w...

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OCEANS 2024 - Halifax, OCEANS 2024

作者： Wu, Junzhuo Pan, Ya-Jun Shen, Chao Smith, Sean Witrant, Emmanuel Dalhousie University Advanced Control and Mechatronics Lab Department of Mechanical Engineering HalifaxB3H 4R2 Canada Carleton University Department of Systems and Computer Engineering OttawaK1S-5B6 Canada Université Grenoble Alpes-CNRS GIPSA-lab GrenobleF-38000 France Dalhousie University Departement of Mechanical Engineering HalifaxNSB3H 4R2 Canada

ISBN: (纸本)9798331540081

This paper studies trajectory tracking control problems of sailing vessels using a nonlinear model predictive control (NMPC) approach with a novel sail angle optimization approach. The proposed sail angle optimization method accounts for physical constraints during practical implementation such as operational sail angle bound and rate of change in sail angles. This technique also emphasizes the potential to use the sail to plan a safe trajectory, while a simple proportional-derivative (PD) controller is used for rudder angle regulation. The NMPC is implemented for trajectory tracking control in the simulation. Results show that the proposed controller can achieve excellent tracking performance in the presence of environmental disturbances in terms of ocean waves. © 2024 IEEE.

关键词： Water waves

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Attitude Takeover control for Noncooperative Space Targets Based on Gaussian Processes with Online Model Learning

arXiv

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arXiv 2023年

作者： Liu, Yuhan Wang, Pengyu Lee, Chang-Hun Tóth, Roland The Control Systems Group Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands The Flight Dynamics and Control Laboratory Department of Aerospace Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of The Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

One major challenge for autonomous attitude takeover control for on-orbit servicing of spacecraft is that an accurate dynamic motion model of the combined vehicles is highly nonlinear, complex and often costly to identify online, which makes traditional model-based control impractical for this task. To address this issue, a recursive online sparse Gaussian Process (GP)-based learning strategy for attitude takeover control of noncooperative targets with maneuverability is proposed, where the unknown dynamics are online compensated based on the learnt GP model in a semi-feedforward manner. The method enables the continuous use of on-orbit data to successively improve the learnt model during online operation and has reduced computational load compared to standard GP regression. Next to the GP-based feedforward, a feedback controller is proposed that varies its gains based on the predicted model confidence, ensuring robustness of the overall scheme. Moreover, rigorous theoretical proofs of Lyapunov stability and boundedness guarantees of the proposed method-driven closed-loop system are provided in the probabilistic sense. A simulation study based on a high-fidelity simulator is used to show the effectiveness of the proposed strategy and demonstrate its high performance. © 2023, CC BY-NC-SA.

关键词： Maneuverability

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Dynamic Tip-Over Avoidance Method for Mobile Manipulators Based on the Extended Zero-Moment Point Algorithm for Human–Robot Collaboration

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Journal of Computing and Information Science in engineering 2025年第5期25卷 051003页

作者： Tao, Yong Liu, Haitao Song, Yian Deng, Changyi Wang, Baicun Zheng, Pai School of Mechanical Engineering and Automation Beihang University Beijing 100191 China Institute of Software China Industrial Control Systems Cyber Emergency Response Team Beijing 100040 China School of Mechanical Engineering Zhejiang University Hangzhou 310030 China Research Institute for Advanced Manufacturing Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Hung Hom Hong Kong

Human–robot collaboration (HRC), as an important role in intelligent manufacturing, is widely utilized in modern factories. Mobile manipulators offer greater flexibility due to the ability to simultaneously move their bases and arms. This provides them an advantage in facing challenging tasks such as flexible production and intelligent manufacturing. The environment of modern factories is complex, with slopes and thresholds at the entrance. Stability plays a fundamental role in complex environments. Therefore, for the future deployment and application of mobile manipulator in HRC, a novel dynamic tip-over avoidance method is proposed for mobile manipulators based on the extended zero-moment point (ZMP) algorithm. This method extends the traditional ZMP stability criterion into three dimensions, deriving a system-wide three-dimensional (3D) zero-moment point. The integration of the extended ZMP with redundant features facilitates adaptive weight matrix adjustments, enabling the robot to adapt its motion based on environmental constraints, thus preventing instability such as tip-over. Simulation verification is carried out withcoppeliasim. Experimental verification is performed using the MR2000 + FR3 mobile manipulator. The results confirm that the extended ZMP algorithm effectively prevents tipping in complex environments.

关键词： tip-over avoidance null-space projection Weight (Mass) mobile manipulator Stability Manipulators Robots human–robot interfaces/interactions velocity controlled manufacturing automation extended ZMP Algorithms

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