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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Zhu, YanZong Yang, Yuesong Li, Feng Zhou, Zhou School of Electrical and Information Engineering Jiangsu University of Technology Changzhou213001 China

ISBN: (纸本)9798350361674

In this article, an identification modelling method for the Hammerstein nonlinear systems utilizing adaptive neural fuzzy networks and state space model is proposed. The proposed Hammerstein system consists of a five-layer adaptive neural fuzzy network approximating the static nonlinear block and a state space model representing the linear dynamical block with output noises, and identification of the adaptive neural fuzzy networks and the state space model are implemented independently by using designed combined signals, which include binary and random signals. Due to binary signals do not excite nonlinear systems, the recursive extended least squares algorithm is employed to identify the parameters of the state-space model and the colored noises model, which improves identification accuracy. Secondly, based on random signals input-output measurements, the cluster method and the recursive extended least squares algorithm are used to identify the adaptive neural fuzzy networks. The simulation results demonstrate the effective identification of the Hammerstein nonlinear system using the proposed parameter identification technique and the adaptive neural fuzzy networks can model the nonlinearity of the Hammerstein system. © 2024 ieee.

关键词： State space methods

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A Learnable Competitive Swarm Operator driven by Reinforcement learning for Multi-Objective Optimization Problems 6

A Learnable Competitive Swarm Operator Driven by Reinforceme...

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6th International conference on data-driven Optimization of Complex systems (DOCS)

作者： Meng, Xiaoding Li, Hecheng Qinghai Normal Univ Sch Comp Sci & Technol Xining Peoples R China Qinghai Normal Univ Sch Math & Stat Xining Peoples R China

ISBN: (纸本)9798350377859;9798350377842

During the optimization process, producing high-quality individuals is always the goal of evolutionary algorithms. So, how can we generate such promising offspring individuals? The key lies in an optimizer with the potential to consistently produce superior individuals. However, designing the operator with learning ability in an optimizer has not been well explored in the existing research. To overcome this drawback, a learnable competition operator driven by reinforcement learning (RL) technique is proposed to generate high-quality individuals and further improve the performance of the algorithm. First, with the help of Q-learning, a classical RL technique, the size of elite particle set in the population is taken as the set of action, each elite particle corresponds to a action. The optimal action is considered to be the next state. Then, a three-dimensional Q-table is designed for the whole population, so that each particle can learn its elite particles, rather than randomly selecting elite particles to guide the particle's evolution process. Furthermore, a novel algorithm embedded with the learnable competition operator is proposed. Finally, compared with the other state-of-the-art algorithms, the experimental results demonstrate that the proposed algorithm significantly enhances the solution accuracy.

关键词： Learnable operator Improved Q-learning Three-dimensional Q-table Multi-objective optimization

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Interpretable data-driven Model Predictive control of Building Energy systems using SHAP 6

Interpretable Data-Driven Model Predictive Control of Buildi...

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6th Annual learning for Dynamics and control conference

作者： Henkel, Patrick Kasperski, Tobias Stoffel, Phillip Mueller, Dirk Rhein Westfal TH Aachen Aachen Germany

Advanced building energy system controls, such as model predictive control, rely on accurate system models. To reduce the modelling effort in the building sector, data-driven models are becoming increasingly popular in research. Despite their promising performance, data-driven models are considered black boxes. This black box nature is an obstacle to widespread application, as it is difficult for building operators to understand how predictions are made. Concepts known as Explainable Artificial Intelligence are being developed to improve the interpretability of black box models. This work combines the popular Explainable Artificial Intelligence method Shapley Additive Explanations (SHAP) with data-driven model predictive control to increase the interpretability of artificial neural networks used as process models during model creation. Using a standardised residual building energy system for controller testing, an in-depth analysis of how the models make predictions is carried out. In addition, the influence of different model setups on the control performance is evaluated. The results show that the different control performances can be justified by analysing the underlying models with SHAP. SHAP shows how the characteristics of a feature affect the prediction and reveals weaknesses in the model. In addition, the features can be sorted according to their influence on the prediction, which is utilized for feature selection.

关键词： interpretable machine learning explainable AI XAI MPC DDMPC ANN

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2024 ieee International conference on Communications, control, and Computing Technologies for Smart Grids, SmartGridComm 2024

2024 IEEE International Conference on Communications, Contro...

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2024 ieee International conference on Communications, control, and Computing Technologies for Smart Grids, SmartGridComm 2024

ISBN: (纸本)9798350318555

The proceedings contain 93 papers. The topics discussed include: zero-day attack detection in digital substations using in-context learning;fast grid emissions sensitivities using parallel decentralized implicit differentiation;electricity substation supply area based data collation and visualization techniques for local area energy planning: perspectives from UK distribution network datasets;graphical learning based fault detection and classification in distribution systems;MANTRA: a multi-appliance transformer for non-intrusive load monitoring;REC to ReCert: introducing re-certification to empower prosumer-driven certificate aggregators;machine learning-based feature selection for intrusion detection systems in IEC 61850-based digital substations;and on the relation between the phase and amplitude response of indoor PLC channels.

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Intelligent Drug Recognition System Based on Improved YOLOv7 and PP-OCRv3 13

Intelligent Drug Recognition System Based on Improved YOLOv7...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Chen, Zhenhao Chen, Yilin Yang, Zongjin Qin, Na Huang, Deqing Institute of Systems Science and Technology Southwest Jiaotong University Chengdu610031 China

ISBN: (纸本)9798350361674

The manual dispensing of drugs by pharmacists is time-consuming, labor-intensive, and has a low accuracy rate. In order to address this issue, this paper proposes an automated drug dispensing method based on improved object detection and text recognition. This method first embeds a Simple Attention Module (SimAM) into the network structure of YOLOv7, named YOLOv7_Sim, to achieve the localization and recognition of medicine bottles. Additionally, a lightweight PP-OCRv3 is employed for text recognition on liquid bags, and the similarity between the recognition results and the names of drugs on the prescription is used to determine a match. The novelty of this method lies in the combination of YOLOv7 and SimAM, which improves the accuracy of medicine bottle recognition and enhances the algorithm's robustness. Experimental results demonstrate that compared to YOLOv7, YOLOv7_Sim shows significant performance improvements, with a 3.12% increase in mAP, 0.69% increase in precision, and 2.83% increase in recall for the same type of medicine bottle. The proportion of fully recognized text on liquid bags is 90.6%. The drug recognition system, which combines YOLOv7_Sim and lightweight PP-OCRv3, has practical engineering significance for automating the drug dispensing process. © 2024 ieee.

关键词： Bottles

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COVID-19 Adaptive E-learning: data-driven Student Engagement Analysis 2

COVID-19 Adaptive E-Learning: Data-Driven Student Engagement...

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2nd ieee International conference on Integrated Circuits and Communication systems, ICICACS 2024

作者： Rani, L. Lalli Thirunirai Senthil, S. Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350317558

The rapid growth in online learning during the COVID-19 epidemic demanded an evaluation of adaptive e-learning's impact on student performance and engagement. Using data-driven methodologies, the research examines the effectiveness of adaptive e-learning tactics during the pandemic. The data gathered from various educational institutions, including the xAPI-Educational Mining dataset, yields intriguing results. Adaptive e-learning improves student engagement, as demonstrated by an average 10% increase in online discussion participation, a 15% increase in assignment submissions, and a 20% increase in LMS usage among students who use adaptive platforms. The regression analysis confirms a substantial link (p © 2024 ieee.

关键词： Academic performance Adaptive E-learning COVID Online learning Pandemic Student engagement

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WIP: Study on a data-driven Adaptive learning Support System Design for Individualized Optimal learning 54

WIP: Study on a Data-Driven Adaptive Learning Support System...

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54th ieee Frontiers in Education conference, FIE 2024

作者： Horinouchi, Takahito Wakitani, Shin Hayashida, Tomohiro Kinoshita, Takuya Tsutsumi, Kento Hiroshima University Graduate School of Advanced Science and Engineering Higashihiroshima Japan Yamaguchi University Faculty of Education Yamaguchi Japan

ISBN: (纸本)9798350351507

In current Japanese education, realizing individualized and optimal learning through the effective use of ICT terminals has been required. Adaptive learning support systems are expected to help solve the above issues. This study proposes an adaptive learning support system that maximizes the learner's characteristics such as the ability and motivation to learn using an extremum-seeking control method. The proposed system is validated by using a mathematical learner model that includes the forgetting factor of human short-term memory. The simulation result shows that the system maximized the learner's characteristics by tuning the assist ratio. © 2024 ieee.

关键词： Contrastive learning

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Intelligent Fault Diagnosis of Bearings Using a Flexible Residual Neural Network Tuned by Seagull Optimization Algorithm 13

Intelligent Fault Diagnosis of Bearings Using a Flexible Res...

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13th ieee data driven control and learning systems conference, DDCLS 2024

作者： Li, Xianfeng Chen, Chuang Shi, Jiantao College of Electrical Engineering and Control Science Nanjing Tech University Nanjing211816 China

ISBN: (纸本)9798350361674

As an important part of most machinery, the normal operation of bearings greatly influences the lifespan of machine. Aimed at improving the accuracy of fault diagnosis for rolling bearings, a seagull optimization algorithm (SOA)-tuned flexible residual neural network (FResNet) is proposed. Concretely, a CNN based flexible residual module is designed to form the FResNet, which allows changing the numbers of convolution layers and convolution kernels as an optimizer iterates, to enhance the fault feature extraction ability while reducing network degradation. The SOA is used to configure the optimal model structure and parameters of FResN et. Thus, the integration of SOA and FResN et forms an efficacious model for bearing fault diagnosis, named SOA-FResNet. Finally, the performance of the proposed SOA-FResNet model is verified using the bearing fault dataset from Case Western Reserve University, and the higher rate of accuracy of the proposed model compared to other diagnostic models is demonstrated under different noise environments. © 2024 ieee.

关键词： Roller bearings

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data-driven control and Transfer learning Using Neural Canonical control Structures 9

Data-Driven Control and Transfer Learning Using Neural Canon...

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9th International conference on control, Decision and Information Technologies, CoDIT 2023

作者： Ecker, Lukas Schöberl, Markus Institute of Automatic Control and Control Systems Technology Johannes Kepler University Linz Altenberger Str. 69 LinzA-4040 Austria

ISBN: (纸本)9798350311402

An indirect data-driven control and transfer learning approach based on a data-driven feedback linearization with neural canonical control structures is proposed. An artificial neural network auto-encoder structure trained on recorded sensor data is used to approximate state and input transformations for the identification of the sampled-data system in Brunovsky canonical form. The identified transformations, together with a designed trajectory controller, can be transferred to a system with varied parameters, where the neural network weights are adapted using newly collected recordings. The proposed approach is demonstrated using an academic and an industrially motivated example. © 2023 ieee.

关键词： Feedback linearization

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Meta-learning Augmented MPC for Disturbance-Aware Motion Planning and control of Quadrotors

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ieee control systems LETTERS 2024年 8卷 3045-3050页

作者： Lapandic, Dzenan Xie, Fengze Verginis, Christos K. Chung, Soon-Jo Dimarogonas, Dimos V. Wahlberg, Bo KTH Royal Inst Technol Div Decis & Control Syst S-11428 Stockholm Sweden CALTECH Div Engn & Appl Sci Pasadena CA 91125 USA Uppsala Univ Dept Elect Engn Div Signals & Syst S-75237 Uppsala Sweden

A major challenge in autonomous flights is unknown disturbances, which can jeopardize safety and cause collisions, especially in obstacle-rich environments. This letter presents a disturbance-aware motion planning and control framework for autonomous aerial flights. The framework is composed of two key components: a disturbance-aware motion planner and a tracking controller. The motion planner consists of a predictive control scheme and an online-adapted learned disturbance model. The tracking controller, developed using contraction control methods, ensures safety bounds on the quadrotor's behavior near obstacles with respect to the motion plan. The algorithm is tested in simulations with a quadrotor facing strong crosswind and ground-induced disturbances.

关键词： Adaptation models Predictive models Metalearning Quadrotors Planning Trajectory Autonomous aerial vehicles Safety Artificial neural networks Prediction algorithms Nonlinear dynamical systems robust control adaptive control metalearning multi-layer neural network data-driven modeling predictive control motion planning real-time systems robots autonomous systems autonomous aerial vehicles

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