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27th International Conference on Automation and Computing, ICAC 2022

作者： Wang, Guoliang Wei, Hua-Liang University of Sheffield Department of Automatic Control and Systems Engineering Sheffield United Kingdom

ISBN: (数字)9781665498074

ISBN: (纸本)9781665498074

Rolling bearings play a significant role in the operation of rotating machinery. Finding bearing faults in the early stage can not only keep machinery running safely but also avoid economic loss. Traditional machine learning (ML) and deep learning (DL) are two important classes of fault diagnosis methods. The process of feature extraction is significant for ML based methods and many signal processing techniques have been used in this process. However, when dealing with massive and complex signals, features extracted by these techniques may not fully describe the characteristics of faults. Convolutional neural network (CNN) models are applied in many areas, including image identification and classification, pattern recognition, and speech processing. In this paper, a novel hybrid model by combining one dimensional CNN (1DCNN) and support vector machine (SVM) is proposed for bearing fault diagnosis and classification, which is called 1DCNN-SVM model or method. The first part of the model, 1DCNN, is used to extract features of raw data, whereas the second part, SVM, is employed to carry out the fault recognition and classification tasks. The evaluation of the proposed method is operated based on a real dataset. The experiment results show that the fault diagnosis accuracy of proposed method based on the hybrid model is highly improved compared to the traditional SVM based methods. © 2022 IEEE.

关键词： Failure analysis

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Machine Learning for Medical and Healthcare Data Analysis and Modelling: Case Studies and Performance Comparisons of Different Methods 27

Machine Learning for Medical and Healthcare Data Analysis an...

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27th International Conference on Automation and Computing, ICAC 2022

作者： Sun, Bo Wei, Hua-Liang University of Sheffield Department of Automatic Control and Systems Engineering Sheffield United Kingdom

ISBN: (数字)9781665498074

ISBN: (纸本)9781665498074

In smart healthcare, binary classification is one of the most important tasks for disease or clinical outcome prediction. Machine learning (ML) methods have great potential to discover knowledge from data. While there are a wide range of ML methods that are potentially useful, the selection, design and implementation of an appropriate method from a variety of ML models for specific prediction tasks is still quite challenging in many medical and healthcare data modelling tasks. This paper investigates, evaluate and compares nine typical representative ML methods using four clinical and healthcare datasets. The main attention is paid to the strengths and weaknesses of these methods and provide suggestions to researchers in related fields. Results demonstrate that ensemble learning (such as Adaboost and random forest) outperform the other approaches in prediction accuracy and interpretability. © 2022 IEEE.

关键词： Adaptive boosting

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Benchmarking Semantic Communications for Image Transmission Over MIMO Interference Channels 16

Benchmarking Semantic Communications for Image Transmission ...

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16th International Conference on Wireless Communications and Signal Processing, WCSP 2024

作者： Wang, Yanhu Guo, Shuaishuai Dong, Anming Zhao, Hui School of Control Science and Engineering Shandong University China School of Computer Science and Technology Qilu University of Technology China Eurecom Communication Systems Department France

ISBN: (纸本)9798350390643

Semantic communications offer promising prospects for enhancing data transmission efficiency. However, existing schemes have predominantly concentrated on point-to-point transmissions. In this paper, we aim to investigate the validity of this claim in interference scenarios compared to baseline approaches. Specifically, our focus is on general multiple-input multiple-output (MIMO) interference channels, where we propose an interference-robust semantic communication (IRSC) scheme. This scheme involves the development of transceivers based on neural networks (NNs), which integrate channel state information (CSI) either solely at the receiver or at both transmitter and receiver ends. Moreover, we establish a composite loss function for training IRSC transceivers, along with a dynamic mechanism for updating the weights of various components in the loss function to enhance system fairness among users. Experimental results demonstrate that the proposed IRSC scheme effectively learns to mitigate interference and outperforms baseline approaches, particularly in low signal-tonoise (SNR) regimes. © 2024 IEEE.

关键词： Radio transceivers

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Multi-Task Learning for Time Series Forecasting Using NARMAX-LSTM 27

Multi-Task Learning for Time Series Forecasting Using NARMAX...

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27th International Conference on Automation and Computing, ICAC 2022

作者： Nikentari, Nerfita Wei, Hua-Liang University of Sheffield Department of Automatic Control and Systems Engineering Sheffield United Kingdom

ISBN: (数字)9781665498074

ISBN: (纸本)9781665498074

Traditional time series forecasting models based on neural networks are usually designed for a single task, which ignores the source of knowledge or information from other related tasks. On the other hand, multitask learning (MTL) offers a new perspective where one model is not only for one forecasting case or task but several tasks at the same time. The MTL model proposed in this study is based on the Nonlinear AutoRegressive Moving Average with eXogenous input (NARMAX) model and Long-Short Term Memory (LSTM) Neural Networks. The NARMAX-LSTM model is tested and validated using three different time horizons and different cases/tasks. The model forecasting performance is compared with that of three other linear and nonlinear models. The comparison results show that the multi-task NARMAX-LSTM model does not only have better prediction performance but also avoid suffering from negative transfer and overfitting. © 2022 IEEE.

关键词： Forecasting

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Efficient Mask Attention-Based NARMAX (MAB-NARMAX) Model Identification 27

Efficient Mask Attention-Based NARMAX (MAB-NARMAX) Model Ide...

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27th International Conference on Automation and Computing, ICAC 2022

作者： Sun, Yiming Wei, Hua-Liang University of Sheffield Department of Automatic Control and Systems Engineering Sheffield United Kingdom

ISBN: (数字)9781665498074

ISBN: (纸本)9781665498074

Model structure selection is crucial in system identification and data-driven modelling. Many spurious candidate variables can influence the determination of model structures due to the lack of prior knowledge of the system of interest. The commonly used method is to test as many possible models as possible and select a set of best models. This study proposes a novel mask attention-based NARMRAX (MAB-NARMAX) modelling method for nonlinear dynamic system identification. The mask attention mechanism comes from the widely used neural network Transformer to reduce the dependency of the features and neurons. The performance of the proposed method is tested on three simulation datasets. Results show that the proposed MAB-NARMAX modelling framework has convincing multistep-ahead prediction performance for nonlinear system identification in that it can produce the precious model structure. Even when the data are polluted with high noise (resulting in low SNR), the proposed method can still generate reliable system models compared with the state-of-the-art machine learning methods, e.g., LASSO and LSTM. © 2022 IEEE.

关键词： Signal to noise ratio

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Design of an Optimally Tuned Fractional Order PI-PD controller for DC Motor Speed via a Henry Gas Solubility Algorithm 6

Design of an Optimally Tuned Fractional Order PI-PD Controll...

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6th International Conference on Futuristic Trends in Networks and Computing Technologies, FTNCT 2024

作者： Taha, Ezzulddin Yaseen Oglah, Ahmed Alaa Department of Control and Systems Engineering University of Technology Baghdad Iraq

This work, a high-performance fractional-order PI-PD (FOPI/FOPD) controller is proposed for speed control of a DC motor tuned through Henry Gas Solubility Optimization (HGSO) algorithm. This work shows that the HGSO is successful in fine-tuning FOPI/FOPD controller parameters to enhance transient and steady-state characteristics. The suggested method surpasses alternatives in overshoot minimization, decrease of rise and settling times, enhancement of abnormal disturbance rejection amplitude (1-12), and robustness, as evaluated by Root Mean Square Error (RMSE) as a performance metric. Comparative analysis against other controllers optimized by HGSO, GWO, SCA, PSO, and IWO reveals that the HGSO-FOPI/FOPD outperforms traditional PID and PI-PD, and the results show the FOPI-FOPD controller better than PI-PD and the other controllers in the settling time (9%), rise time (4%) second and Maximum overshoot zero. This highlights its potential as an effective method for DC motor control applications requiring precision and robustness. © 2025 The Author(s). Published by Elsevier B.V.

关键词： Three term control systems

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A Real-time Local Planner Design for Smart Wheelchairs Based on Koopman Bilinear Model Predictive control

A Real-time Local Planner Design for Smart Wheelchairs Based...

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2024 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2024

作者： Yu, Guanfeng Zhang, Han Wang, Zehao Wang, Jingchuan Shanghai Jiao Tong University Institute of Medical Robotics Department of Automation China Ministry of Education of China Key Laboratory of System Control and Information Processing China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

ISBN: (纸本)9798350372601

The paper introduces a Koopman bilinear model predictive control (KBMPC) as a local planner for smart wheelchair systems. This approach leverages the Koopman Operator to streamline the nonlinear terms in MPC, aiming to achieve superior real-time performance. In particular, we employ the Extended Dynamic Mode Decomposition (EDMD) method to approximate the infinite dimensional Koopman operator. To tackle the challenge of designing lifting functions, we systematically utilize the derivatives of known nonlinear structures to methodically derive lifting functions. This systematic approach facilitates the identification of a precise bilinear model. Next, we formulate the corresponding Koopman bilinear MPC problem. Finally, an iterative algorithm is employed to efficiently solve the KBMPC problem. This method linearizes the bilinear model around estimations and iteratively solves the resulting quadratic programming problem to refine estimations. This allows KBMPC to function as a real-time local planner for smart wheelchairs. The prediction of nonlinear terms shows that the identified Koopman bilinear model increases the prediction accuracy. Furthermore, experiments demonstrate the advantages of the designed KBMPC local planner, highlighting its superior real-time performance and predictive capabilities. © 2024 IEEE.

关键词： Iterative methods

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Improving incentive policies to salespeople cross-sells: a cost-sensitive uplift modeling approach

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Neural Computing and Applications 2024年第28期36卷 17541-17558页

作者： Vairetti, Carla Vargas, Raimundo Sánchez, Catalina García, Andrés Armelini, Guillermo Maldonado, Sebastián Facultad de Ingeniería y Ciencias Aplicadas Universidad de Los Andes Santiago Chile ESE Universidad de los Andes Santiago Chile Department of Management Control and Information Systems School of Economics and Business University of Chile Santiago Chile Santiago Chile

In this study, we present a novel cost-sensitive approach for uplift modeling in the context of cross-selling and workforce analytics. We leverage referrals from sales agents across business units to estimate the individual treatment effects of incentives on the cross-selling outcomes within a company. Uplift modeling is employed to predict relationships between salespeople that should be encouraged based on the probability of successful cross-selling - defined when a customer accepts the product suggested by sales agents. We conducted experiments on data from a Chilean financial group, evaluating both statistical and profit metrics. Exploring various machine learning classifiers for predictive purposes, we observed a significant improvement over the current approach, which exhibits an uplift below 0.01. Finally, we show that selecting the best classifier with profit metrics results in a 31.6% improvement in terms of average customer profit. This emphasizes the importance of defining an adequate compensation scheme and integrating it into the modeling process. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Profitability

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Safe Motion Along Harmonic Potential Surfaces with Cardinal Spline Dirichlet Boundary Conditions 63

Safe Motion Along Harmonic Potential Surfaces with Cardinal ...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Nicu, Theodor-Gabriel Stoican, Florin Ioan, Daniel Prodan, Ionela Department of Automatic Control and Systems Engineering Romania 313 Splaiul Independenţei Sector 6 Bucharest060042 Romania Lcis ValenceF-26000 France

ISBN: (纸本)9798350316339

This work pertains to the generation and use of harmonic functions for obstacle avoidance and target tracking. For each cell of a polyhedral complex, we generate a harmonic potential surface from a Dirichlet-type condition on its boundary. The condition is expressed as a smooth cardinal B-spline curve, thus allowing an efficient computation of the potential which avoids the large gradients characteristic to the piecewise constant boundary conditions usually employed in the literature. Enumerating all pairs of 'in' and 'out' facets for each feasible cell (one not part of the obstacles) and attaching a relevant cost to each such pair (e.g., proportional to the path length inside the cell), we arrive at a directed graph where, by construction, all possible paths linking the source and the target avoid the obstacles. The optimal path results from a variation of Dijkstra's algorithm. © 2024 IEEE.

关键词： Harmonic analysis

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Enhancing Grid Reliability: The Effects of Integrating Renewable Energy Sources 11

Enhancing Grid Reliability: The Effects of Integrating Renew...

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11th IEEE International Conference on systems and control, ICSC 2023

作者： Nathan, Ngada Haran Ammar, Mohsen Ben Zdiri, Mohamed Ali Djemel, Mohamed University of Sfax National Engineering School of Sfax Enis CEMLab Control and Energy Management Laboratory Department of Electrical Engineering Sfax3038 Tunisia

ISBN: (纸本)9798350304886

The ultimate goal of the electrical grid is to maintain a balance of production and consumption. Unfortunately, in the event of overproduction, the electrical grid is in danger of collapsing. Indeed, this paper presents a study on how to improve the electrical grid by incorporating renewable sources (photovoltaic solar and wind). These are referred to as decentralized productions. Renewable energy sources, particularly photovoltaic solar systems (PV), have grown exponentially in recent years and offer numerous benefits. They are limitless and non-polluting to the environment;they are also known as green energy. Their integration into the electrical grid, however, is not without consequences. It alters the voltage profile, power flows, energy quality, and so on. The PSAT tool in Matlab was used in this context to calculate power flow and study the electrical grid's stability. The 'Newton-Raphson' algorithm and continuous power flow technique were used to evaluate various electrical quantities of the IEEE 30 Bus system, both with and without decentralized sources. This allows us to track the progress of these electrical grid sources (voltages, power loss, etc.). © 2023 IEEE.

关键词： Renewable energy

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