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ieee World Engineering Education conference (EDUNINE)

作者： Queiros, Ricardo Pinto, Carla M. A. Cruz, Mario Polytech Inst Porto Sch Media Arts & Design CRACS INESCTEC Porto Portugal Polytech Inst Porto Sch Engn CMUP Porto Portugal Polytech Inst Porto Sch Educ inED Porto Portugal

ISBN: (纸本)9798350348729;9798350348736

This paper explores the integration of virtual escape rooms as innovative educational tools in the realm of computer programming. Recognizing the need to engage and motivate learners in this complex domain, we investigate the use of virtual escape rooms in a typical educational setting where learning Management systems play a pivotal role. The paper starts by surveying existing escape rooms designed for teaching programming and related domains, considering factors such as interactivity, educational efficacy, and learner engagement. Additionally, it is emphasized the role of standards in creating interoperable learning environments, introducing IMS LTI for seamless integration with learning management systems and xAPI for tracking learner activities within escape rooms. By leveraging these standards and a learning Record Store (LRS) as a central repository, an architectural framework is presented which enables personalized learning experiences and data-driven insights, catering to the diverse needs and preferences of the new generation of learners.

关键词： escape rooms computer programming interoperability analytics

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data-driven Feedforward control for Trade-Off Optimization in Machine Stand Vibration systems

Data-Driven Feedforward Control for Trade-Off Optimization i...

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2025 ieee International conference on Mechatronics, ICM 2025

作者： Yamaguchi, Daigo Teramoto, Shota Shiohara, Takuya Maeda, Yoshihiro Nagoya Institute of Technology Dept. of Engineering Nagoya Japan

ISBN: (纸本)9798331533892

In various industrial mechatronic equipment for processing and assembling electronic components, high-acceleration drives in the internal positioning mechanisms cause machine stand vibrations, degrading their production accuracy and throughput. Generally, a trade-off exists between the fast and precise control of positioning mechanisms and suppressing machine stand vibrations, which complicates control design. This study presents an extended data-driven vibration suppression feedforward (FF) control method that addresses both positioning performance and machine stand vibrations. The proposed method designs FF controllers to pursue the trade-off through a multi-objective optimization problem using predicted responses of both the positioning device and the machine stand. Experiments with a laboratory table positioning system demonstrate the effectiveness of the proposed method, compared to an existing data-driven vibration suppression FF control method. © 2025 ieee.

关键词： Multiobjective optimization

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Intrusion Detection Mechanism Using Deep learning 5

Intrusion Detection Mechanism Using Deep Learning

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5th International conference on IoT Based control Networks and Intelligent systems, ICICNIS 2024

作者： Ananthi, P. Nirmaladevi, K. Naveen Kumar, S. Kongu Engineering College Department of Computer Technology-PG Tamilnadu Erode638060 India Madanapalle Institute of Technology & Science Department of Computer Applications India M.Sc Software Systems Kongu Engineering College Erode India

ISBN: (纸本)9798331518097

In today's connected and data-driven world, networks and digital systems need to be protected from malicious attacks. The effectiveness of conventional Intrusion Detection systems (IDS) in recognizing and impeding novel threats and intricate methodologies has its limits. This study introduces a new method for improving network security by using deep learning techniques in the design and development of an intrusion detection system (IDS). The suggested Intrusion Detection System uses deep learning techniques, specifically Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNNs), to identify and react to known and new security threats. data can be taught to be encoded into a lower-dimensional representation and then decoded back into its original form using autoencoders (AEs). Autoencoders are a cutting-edge intrusion detection system that can lower false positives and raise overall detection accuracy because of their capacity to automatically learn from and adjust to the ever-changing threat landscape. They enhance computer networks capacity to spot irregularities and possible security threats. With the deep learning model obtaining a high accuracy of 99% in real-world threat detection, the results show that deep learning is effective in developing intrusion detection systems. © 2024 ieee.

关键词： Network intrusion

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An Optimal Synchronization control Method of PLL Utilizing Adaptive Dynamic Programming to Synchronize Inverter-Based Resources With Unbalanced, Low-Inertia, and Very Weak Grids

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ieee TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 24-42页

作者： Davari, Masoud Gao, Weinan Aghazadeh, Amir Blaabjerg, Frede Lewis, Frank L. Georgia Southern Univ Dept Elect & Comp Engn Statesboro Campus Statesboro GA 30460 USA Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Univ Leeds Sch Elect & Elect Engn Leeds LS2 9JT W Yorkshire England Aalborg Univ AAU Energy DK-9220 Aalborg Denmark Univ Texas Arlington UTA Res Inst Ft Worth TX 76118 USA

When it comes to integrating inverter-based resources (IBRs) into modern grids with varying characteristics like unbalanced systems, low-inertia networks, or very weak grids, synthesizing the synchronization control method (SCM) of the IBR's phase-locked loop can be a challenging task. This paper provides a unique solution to enhance the three-phase IBR's SCM using the adaptive dynamic programming (ADP) method based on reinforcement learning. By making the SCM more intelligent and self-learning, IBRs can be easily integrated into diverse grids. To this end, this article investigates the synchronization process's detailed dynamics, including all incorporating disturbances and parameters required for the first step in designing the ADP method. Afterward, this research synthesizes an optimal controller using an ADP method. It is a data-driven and practically sound approach to the problem under investigation. The new methodology is based on the adaptive optimal control employing measurement feedback to control the output regulation problem of uncertain synchronization process dynamics via the internal model principle. The proposed SCM design deploys an ADP learning methodology to tackle uncertain parameters and unknown disturbance signals to synchronize IBRs during transients, thereby enhancing IBRs' synchronization in challenging conditions of modern power systems with unbalanced, low-inertia, and very weak grids. For comparison purposes, this paper applies a robust controller based on the well-established mu synthesis approach (benefiting from the well-known D-K iteration process). Comparative simulations are performed;experiments are conducted to reveal the effectiveness and practicality of the ADP-based optimal SCM proposed in this paper. Note to Practitioners-As different nations strive to combat global warming and accelerate decarbonization, power and energy systems are undergoing a significant shift. Inverter-based resources are being used as an essential compone

关键词： Adaptive dynamic programming (ADP) D-K iteration process grid synchronization inverter-based resources (IBRs) mu-synthesis-based robust controller phase-locked loop (PLL) reinforcement learning synchronization control method (SCM) voltage-source (or voltage-sourced) converter

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A Methodical Approach to Hybrid Modelling for Contextual Anomaly Detection on Time-Series data 21

A Methodical Approach to Hybrid Modelling for Contextual Ano...

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ieee 21st International conference on Industrial Informatics (INDIN)

作者： Lenz, Cederic Henke, Christian Traechtler, Ansgar Fraunhofer Inst Mechatron Syst Design Paderborn Germany

ISBN: (纸本)9781665493130

In this Paper a methodical approach to hybrid modelling for contextual anomaly detection on time-series data is presented. It enhances widely used proximity- or distribution-based anomaly detection approaches for industrial processes by a hybrid model. This hybrid model consists of a physical model using a priori process knowledge and a data driven model constructed through a machine learning method. The main advantage of the novel approach is that it is capable of detecting contextual anomalies that remain otherwise undiscovered.

关键词： contextual anomaly detection hybrid modelling process control

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AI-powered Thermal Comfort: Predicting Indoor Air Temperature for Efficient HVAC systems 29

AI-powered Thermal Comfort: Predicting Indoor Air Temperatur...

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29th International conference on Automation and Computing (ICAC)

作者： Zubair, Syed Hamayat, Faizan Nazir, Ahsen Mil Technol Coll Syst Engn Dept Muscat Oman Univ Engn & Technol Elect Engn Dept Lahore Pakistan Univ Engn & Technol Taxila Elect Engn Dept Taxila Pakistan Pakistan Inst Engn & Appl Sci Dept Comp & Informat Sci Islamabad Pakistan

ISBN: (纸本)9798350360882;9798350360899

The building sector is a major contributor to global energy consumption and greenhouse gas (GHG) emissions. HVAC systems are responsible for a significant portion of this energy usage, making their optimization crucial for achieving sustainability goals. Conventional rule-based control methods for HVAC systems have limitations, prompting the exploration of advanced data-driven approaches. This paper introduces two different data-driven black-box models for indoor air temperature (IAT) prediction in modern buildings. The first proposed hybrid model utilizes a convolutional neural network (CNN) for feature extraction and employs bidirectional long short-term memory (Bi-LSTM) networks to capture temporal dependencies in IAT time-series data. The second proposed model is based on transformers and employs a vision transformer (ViT) for IAT prediction. A comprehensive comparative analysis of the prediction performance of proposed models and baseline models is conducted. Additionally, a computational cost analysis is performed to assess the suitability of the models for real-time applications. The results demonstrate the superior performance of both proposed models over conventional IAT prediction methods and their suitability for real-time applications.

关键词： HVAC System Deep-learning IAT Modelling Predictive control

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Nonlinear data-driven Predictive control using Deep Subspace Prediction Networks 62

Nonlinear Data-Driven Predictive Control using Deep Subspace...

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62nd ieee conference on Decision and control (CDC)

作者： Lazar, Mircea Popescu, Mihai-Serban Schoukens, Maarten Eindhoven Univ Technol Dept Elect Engn Eindhoven Netherlands

ISBN: (纸本)9798350301243

Indirect data-driven predictive control (DPC) algorithms for nonlinear systems typically employ multi-step predictors, which are identified from input-output data using neural networks. In this paper we put forward a unifying multi-step prediction network architecture, i.e., the deep subspace prediction network (DSPN). We then prove that the DSPN architecture specialized to multi-layer-perceptron neural networks recovers the linear predictor corresponding to subspace predictive control for a sufficient number of hidden layer neurons. Hence, we establish a well-posed generalization of subspace predictive control for nonlinear systems. Moreover, we develop a regularized DSPN architecture that embeds a linear subspace predictor to improve extrapolation properties for non-training data. Simulation results on a benchmark inverted pendulum show that nonlinear DPC based on DSPN achieves high control performance for both noiseless and noisy data.

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Spring-IMU Fusion-Based Proprioception for Feedback control of Soft Manipulators

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ieee-ASME TRANSACTIONS ON MECHATRONICS 2024年第2期29卷 832-842页

作者： Meng, Yinan Fang, Guoxin Yang, Jiong Guo, Yuhu Wang, Charlie C. L. Univ Manchester Fac Sci & Engn Manchester M13 9PL England

This article presents a novel framework to realize proprioception and closed-loop control for soft manipulators. Deformations with large elongation and large bending can be precisely predicted using geometry-based sensor signals obtained from the inductive springs and the inertial measurement units (IMUs) with the help of machine learning techniques. Multiple geometric signals are fused into robust pose estimations, and a data-efficient training process is achieved after applying the strategy of sim-to-real transfer. As a result, we can achieve proprioception that is robust to the variation of external loading and has an average error of 0.7% across the workspace on a pneumatic-driven soft manipulator. The realized proprioception on soft manipulator is then contributed to building a sensor-space-based algorithm for closed-loop control. A gradient-descent solver is developed to drive the end-effector to achieve the required poses by iteratively computing a sequence of reference sensor signals. A conventional controller is employed in the inner loop of our algorithm to update actuators (i.e., the pressures in chambers) for approaching a reference signal in the sensor-space. The systematic function of closed-loop control has been demonstrated in tasks like path following and pick-and-place under different external loads.

关键词： Springs Robot sensing systems Bending Task analysis Inductance Training End effectors Feedback control proprioception sensor fusion soft robotics

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Online Voltage control Strategy: Multi-Mode Based data-driven Approach for Active Distribution Networks

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ieee TRANSACTIONS ON INDUSTRY APPLICATIONS 2025年第1期61卷 1569-1580页

作者： Wei, Xiang Zhang, Xian Wang, Guibin Hu, Ze Zhu, Ziqing Chan, Ka Wing Hong Kong Polytech Univ Dept Elect & Elect Engn Hong Kong Peoples R China Harbin Inst Technol Sch Mech Engn & Automat Shenzhen 518055 Peoples R China Shenzhen Univ Coll Mechatron & Control Engn Shenzhen 518060 Peoples R China

Active distribution network (ADN) is faced with significant challenges, including frequent and fast voltage violations, due to the increased integration of intermittent renewable energy resources. This paper proposes a two-stage multi-mode voltage control strategy based on a deep reinforcement learning (DRL) algorithm, designed to alleviate voltage violations in ADN and minimize network power loss. In the first stage, a DRL algorithm, the soft actor-critic (SAC), is introduced to determine the hourly dispatch of on-load tap changers and capacitor banks, ensuring voltage security during the day-ahead stage. A multi-mode voltage regulation strategy is then proposed to obtain real-time dispatch of PV inverters, aiming to save energy and enforce voltage constraints under various conditions. The real-time voltage regulation problem is formulated as a Markov decision process and solved using a multi-agent SAC integrated with an attention mechanism. All agents undergo centralized offline training to learn the optimal coordinated voltage control strategy, then make decentralized online decisions based on locally available information only. The effectiveness of the proposed approach is confirmed through extensive testing on the ieee 33-bus distribution system, with simulation results conclusively demonstrating its ability to address voltage violation challenges.

关键词： Voltage control Real-time systems Regulation Inverters Distribution networks Training Optimization Active distribution network attention mechanism deep reinforcement learning multi-mode PV inverters voltage regulation

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data-driven H∞ control for Unknown Linear Time-Invariant systems with Bounded Disturbances 61

Data-Driven H∞ Control for Unknown Linear Time-Invariant Sy...

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ieee 61st conference on Decision and control (CDC)

作者： Hu, Kaijian Liu, Tao Univ Hong Kong Dept Elect & Elect Engn Hong Kong Peoples R China Univ Hong Kong Shenzhen Inst Res & Innovat Hong Kong Peoples R China

ISBN: (纸本)9781665467612

Motivated by learning controllers directly from data, this paper studies the problem of designing a data-driven controller for unknown linear time-invariant (LTI) systems with bounded disturbances. An H-infinity controller is designed using a group of input/state/output data without any model information. The obtained results are further applied to the ARX systems using only input/output data. The main advantage of the proposed controller is that it can balance the trade-off between the robustness and control performance of the closed-loop system by choosing the number of data sequences and the length of each sequence. The effectiveness of the proposed method is shown using simulation results of an outdoor antenna servo system.

关键词： Linear time-invariant system

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