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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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Output Tracking for Uncertain Time-Delay systems via Robust Reinforcement Learning control

Output Tracking for Uncertain Time-Delay Systems via Robust ...

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第43届中国控制会议

作者： Zezhi Tang J Anthony Rossiter Xin Jin Benyuan Zhang George Panoutsos Department of Automatic Control and Systems Engineering University of Sheffield

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

The composite optimal output tracking control problem of time-delay uncertain systems is investigated in this paper. To improve the robustness of the uncertain systems, a disturbance observer-based(DOB) compensation scheme is proposed to reduce the influence of the *** further enhance the control efficiency, we incorporate a Smith predictor into the control framework to precisely predict future states of the delayed system. Subsequently, a composite actor-critic reinforcement learning(RL) scheme is built for approximating both the ideal value function associated with the compensated system and the optimal control policy in real-time, by augmenting the system states with the output tracking error. To counteract the impact of the probing signal, concurrent learning is employed in this paper with the data collected from the known system *** results based on an experimental platform of slot die coating machines demonstrate the effectiveness of the proposed scheme.

关键词： Smith Output Tracking for Uncertain Time-Delay systems via Robust Reinforcement Learning control

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Tide Level Prediction Using NARX-based Recurrent Neural Networks 27

Tide Level Prediction Using NARX-based Recurrent Neural Netw...

引用

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

Tide level forecasting is one of major components in oceanography and marine research. The future prediction of tides is an important issue for human and environment. This study proposes new tide level prediction methods with multiple input multiple output (MIMO) approach based on nonlinear autoregressive with exogenous inputs (NARX) model and three types of recurrent neural networks (RNNs), namely, long short-term memory (LSTM), bi-directional long short-term memory (biLSTM) and gated recurrent unit (GRU) networks. We develop three types of NARX models: NARX-LSTM, NARX-biLSTM and NARX-GRU. We evaluate the three models and compare their performances with LSTM, biLSTM and GRU network models. It turns out that the proposed MIMO NARX based RNN models, especially NARX-LSTM and NARX-GRU have better performance in comparison with their RNN counterparts (i.e., LSTM, biLSTM, and GRU). The NARX-GRU model shows the best performance for one step and three step ahead prediction in terms of RMSE in each location and average RMSE, while NARX-LSTM performs better for two steps ahead prediction in terms average RMSE. The results also show that the mixture of RNN and NARX improves the performance of RNN model for all the four tasks at four locations in the UK (Wick, Workington, Ullapol and New Heaven) and for the three time horizons (i.e., 1, 2 and 3 steps ahead). © 2022 IEEE.

关键词： Forecasting

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A Hybrid 1DCNN-SVM Model for Bearing Fault Recognition and Classification 27

A Hybrid 1DCNN-SVM Model for Bearing Fault Recognition and C...

引用

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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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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Analysing and Modelling Human Trust to a Navigation Robot 2

Analysing and Modelling Human Trust to a Navigation Robot

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2nd Edition IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural engineering, MetroXRAINE 2023

作者： Onyeoru, Happy Chidi Wirth, Christopher Giles, Joshua Arvaneh, Mahnaz University of Sheffield Department of Automatic Control and Systems Engineering United Kingdom School of Medical Sciences University of Manchester United Kingdom

ISBN: (纸本)9798350300802

Human trust plays a crucial role in Human-Machine Interactions (HMIs) within autonomous systems. This paper delves into the factors that influence human trust in machines, including varying error rates and types made by the machine, as well as the human's ability to intervene and rectify errors. To explore these factors, we conducted three scenarios involving a simulated claw robot navigating through multiple objects to detect and locate a target object. The first scenario examined the effect of changing error rates on human trust in the machine. In the second scenario, we investigated how variability in speed and accuracy of reaching the target impacted human trust. Lastly, we explored whether human trust in the machine changed when individuals had the capability to intervene and correct severe errors made by the machine. We then proposed a regression model to estimate human trust. Our results showed that human trust is significantly affected by changes in error rates. Our participants reported a higher trust on robot with low speed but higher accuracy in performing the task than the robot with high speed but lower accuracy. Interestingly, the ability to intervene and correct the robot's errors improved the participants' trust in the robot. Our regression result showed that we can estimate trust using different type of errors committed by machine which can be applied in real world scenarios. © 2023 IEEE.

关键词： computational modelling error rates human-machine interactions (HMIs) machine or robot action perceptions target identification trust

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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 ...

引用

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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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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Sector-Based Incremental Clustering and Scalable Deletion for Real-Time Big Data Streaming Application 2nd

Sector-Based Incremental Clustering and Scalable Deletion fo...

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2nd International Conference on Deep Sciences for Computing and Communications, IconDeepCom 2023

作者： Ananthi, M. Mangayarkarasi, T. Department of Computer Science and Business Systems Sri Sairam Engineering College Chennai India Department of Instrumentation and Control Engineering Sri Sairam Engineering College Chennai India

ISBN: (纸本)9783031689079

Massive streaming data is the most current technology for storing and manipulating large quantities of data. Processing the substantial amount of streaming data is still a challenging issue. The speed and throughput can be improved while extracting this huge amount of data by using a dynamic query processing technique. The sector-based incremental clustering indexing and scalable deletion approach is used in this paper to improve the system performance. Stream processing can be more effective and flexible by applying dynamic query processing technique. Incremental clustering using sector-based method and scalable deletion which removes unwanted, spurious data is proposed in this work in order to process incoming streaming data effectively. Incremental clustering is one of the clustering methods used for processing dynamic information. It is not necessary to plan the entire dataset ahead of time in this method which is suitable for live streaming data, because it processes one instance at a time. So, less space and time is used. Various queries with different types of datasets executed using sector based incremental clustering and scalable deletion methods. The proposed system achieves the highest results for more than 90% accuracy with less space and more speed when the execution time and data retrieval are empirically increased compared with conventional approaches. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Data streams

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Deep Reinforcement Learning Method for control of Mixed Autonomy Traffic systems

Deep Reinforcement Learning Method for Control of Mixed Auto...

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2024 IEEE International Conference on Multisensor Fusion and Integration for Intelligent systems, MFI 2024

作者： Liu, Xingyu Apriaskar, Esa Mihaylova, Lyudmila University of Sheffield Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom Universitas Negeri Semarang Faculty of Engineering Department of Electrical Engineering Semarang50229 Indonesia

ISBN: (纸本)9798350368031

The introduction of autonomous vehicles (AVs) presents a novel approach to regulating and optimising traffic flow through the automated control of AVs. In this context, the AV is defined as the actuator and an optimal control policy is desired to make control decisions. Deep Reinforcement Learning (DRL) is a novel method which aims to maximize the cumulative rewards given by the predefined reward function by making sequential decisions in a stochastic environment. In light of the above, we propose a DRL-based vehicular control method to train an optimal policy for the control of AV in a model-free fashion, and consequently improve the traffic efficiency with the obtained control policy. A single-lane circular road environment with both AV and human-driven vehicles is selected to serve as the mixed autonomy traffic system in the Simulation of Urban MObility (SUMO) [1] traffic simulator, and the Proximal Policy Optimization (PPO) algorithm is applied for the policy improvement. Simulation results demonstrate that our strategy is effective in mitigating the unstable stop-and-go waves, increasing 67.7% of the average driving speed and reducing 19.3% of the average energy consumption in a closed-ring road environment. © 2024 IEEE.

关键词： Deep reinforcement learning

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