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7th International Conference on Biomedical and Bioinformatics engineering, ICBBE 2020

作者： Zhang, Manyu Zhang, Yifei Zhao, Alice Guo, Chun Guo, Lingzhong University of Sheffield United Kingdom The University of Sheffield Department of Automatic Control and Systems Engineering United Kingdom The University of Sheffield Department of Biomedical Science United Kingdom The University of Sheffield Department of Automatic Control and Systems Engineering INSIGNEO Institute for in Silico Medicine United Kingdom

ISBN: (纸本)9781450388221

Understanding how cell fate is determined when exposed to extreme stresses such as heat shock is critical in biomedical systems. It has long been understood that exposure of cells to high temperature typically protect themselves with a heat shock response (HSR), where accumulation of denatured or unfolded proteins triggers the synthesis of heat shock proteins (HSPs) through the heat shock transcription factor, e.g., heat shock factor 1 (HSF1). Recent experimental work has also shown that protein posttranslational modifications (PTMs) such as SUMOylation play crucial roles in cellular responses to heat shock. As a complementary approach to the current experimental methodologies, in this study we aim to develop a mathematical model of SUMOylation-development synergism of HSR for the purpose of studying the dynamical behaviour of HSR quantitatively. The structure of our dynamical model is derived mostly from mass action kinetics while the model parameters are optimized by using a genetic algorithm (GA) based data-driven approach. The preliminary results show GA based data-driven approach has potentials for our modelling purpose. © 2020 ACM.

关键词： Genetic algorithms

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On-The-Fly CNC interpolation using frequency-domain FFT-based filtering

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Procedia CIRP 2022年 107卷 1571-1576页

作者： Rob Ward Burak Sencer George Panoutsos Erdem Ozturk Department of Automatic Control and Systems Engineering University of Sheffield Sheffield S1 3JD UK Advanced Manufacturing Research Centre University of Sheffield Rotherham S60 5TZ UK Mechanical Engineering Department Oregon State University Corvallis OR USA

Finite Impulse Response filtering is increasingly becoming the interpolation method of choice in modern computer numerically controlled (CNC) machining centres. Recently, On-The-Fly (OTF) interpolation was introduced using direct convolution to adaptively change the FIR flter time constant throughout the toolpath. In this paper, it is shown that computational advantages can be gained by using frequency domain methods instead. This paper introduces a novel on-the-fly CNC interpolation method using Fast Fourier Transforms (FFTs). The presented OTF FFT method demonstrates up to 10 times increase in computational speed than the direct convolution OTF method. The effectiveness of the proposed method is validated in simulation based case studies.

关键词： CNC Interpolation FFT Convolution Motion control

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Towards Data-Driven Modelling of Sumoylation Following Heat Shock 20

Towards Data-Driven Modelling of Sumoylation Following Heat ...

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Proceedings of the 2020 7th International Conference on Biomedical and Bioinformatics engineering

作者： Manyu Zhang Yifei Zhang Alice Zhao Chun Guo Lingzhong Guo University of Sheffield UK The University of Sheffield and Department of Automatic Control and Systems Engineering UK The University of Sheffield and Department of Biomedical Science UK The University of Sheffield and Department of Automatic Control and Systems Engineering and INSIGNEO institute for In Silico Medicine UK

ISBN: (纸本)9781450388221

关键词： quantitative refinement data-driven modelling parameter optimization Heat shock response (HSR) genetic algorithm (GA)

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Multi-Target Tracking Using a Swarm of UAVs by Q-learning Algorithm

Multi-Target Tracking Using a Swarm of UAVs by Q-learning Al...

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Sensor Signal Processing for Defence (SSPD)

作者： Seyed Ahmad Soleymani Shidrokh Goudarzi Xingchi Liu Lyudmila Mihaylova Wenwu Wang Pei Xiao Centre for Vision Speech and Signal Processing (CVSSP) The University of Surrey UK School of Computing and Engineering University of West London UK Department of Automatic Control and Systems Engineering The University of Sheffield UK Institute for Communication Systems (5GIC) The University of Surrey UK

This paper proposes a scheme for multiple un-manned aerial vehicles (UAVs) to track multiple targets in challenging 3-D environments while avoiding obstacle collisions. The scheme relies on Received-Signal-Strength-Indicator (RSSI) measurements to estimate and track target positions and uses a Q-Learning (QL) algorithm to enhance the intelligence of UAVs for autonomous navigation and obstacle avoidance. Considering the limitation of UAVs in their power and computing capacity, a global reward function is used to determine the optimal actions for the joint control of energy consumption, computation time, and tracking accuracy. Extensive simulations demonstrate the effectiveness of the proposed scheme, achieving accurate and efficient target tracking with low energy consumption.

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Generalized Multiscale RBF Networks and the DCT for Breast Cancer Detection

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International Journal of Automation and computing 2020年第1期17卷 55-70页

作者： Carlos Beltran-Perez Hua-Liang Wei Adrian Rubio-Solis Department of Automatic Control and Systems Engineering University of SheffieldSheffield S13JDUK Tecnologico de Monterrey Campus TolucaEduardo Monroy Cardenas 2000San Antonio BuenavistaToluca 50110Mexico

The use of the multiscale generalized radial basis function(MSRBF)neural networks for image feature extraction and medical image analysis and classification is proposed for the first time in this *** MSRBF networks hold a simple and flexible architecture that has been successfully used in forecasting and model structure detection of input-output nonlinear *** this work instead,MSRBF networks are part of an integrated computer-aided diagnosis(CAD)framework for breast cancer detection,which holds three stages:an input-output model is obtained from the image,followed by a high-level image feature extraction from the model and a classification module aimed at predicting breast *** the first stage,the image data is rendered into a multiple-input-single-output(MISO)*** order to improve the characterisation,the nonlinear autoregressive with exogenous inputs(NARX)model is introduced to rearrange the available input-output data in a nonlinear *** forward regression orthogonal least squares(FROLS)algorithm is then used to take advantage of the previous arrangement by solving the system as a model structure detection problem and finding the output layer weights of the NARX-MSRBF *** the second stage,once the network model is available,the feature extraction takes place by stimulating the input to produce output signals to be compressed by the discrete cosine transform(DCT).In the third stage,we leverage the extracted features by using a clustering algorithm for classification to integrate a CAD system for breast cancer *** test the method performance,three different and well-known public image repositories were used:the mini-MIAS and the MMSD for mammography,and the BreaKHis for histopathology images.A comparison exercise was also made between different database partitions to understand the mammogram breast density effect in the performance since there are few remarks in the literature on this *** results show that

关键词： Nonlinear system identification image processing discrete cosine transform radial basis functions computer-aided diagnosis neural networks

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Stress Matrix-Based Formation control of Multi-Agent systems with Discrete-Time Communication

Stress Matrix-Based Formation Control of Multi-Agent Systems...

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International Conference on systems and control (ICSC)

作者： Okechi Onuoha Brendan Chijioke Ubochi Suleiman Kurawa Zezhi Tang Changshun Wu Yi Dong School of Physics Engineering and Computer Science (SPECS) University of Hertfordshire UK The Department of Electrical and Electronic Engineering Federal University of Technology Akure Nigeria The Department of Electrical Engineering University of Manchester UK The Department of Automatic Control and Systems Engineering University of Sheffield UK University Grenoble Alpes Grenoble France The School of Electronics & Computer Science University of Southampton UK

ISBN: (数字)9798331531812

ISBN: (纸本)9798331531829

This study addresses the affine formation maneuver control of cooperative multi-agent systems (MAS) having periodic inter-agent communication for both static and dynamic leader cases. Here, we focus on the leader-follower MASs. The primary aim of the control system is to steer the entire collection of agents to produce required patterns (geometric) along with any required maneuver through the direct control of only few a selected agents referred to as leaders. Most of the existing works are constrained to either the individual agents communicate with each other in continuous-time or the sample-data scenario where the leaders are stationary or have constant acceleration or velocities. Here, we consider the scenarios where the velocities of the leaders can be time-varying or constant. Here, different cases are addressed and some control laws are proposed. Conditions are established to help guarantee the overall stability of the systems. A simulation study is employed for the illustration of our proposed laws.

关键词： Target tracking Simulation Accelerated aging control systems Formation control Stability analysis Stress Time-varying systems Multi-agent systems

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Orthogonal least squares based fast feature selection for linear classification

arXiv

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arXiv 2021年

作者： Zhang, Sikai Lang, Zi-Qiang Department of Mechanical Engineering The University of Sheffield Sheffield United Kingdom Department of Automatic Control and Systems Engineering The University of Sheffield Sheffield United Kingdom

An Orthogonal Least Squares (OLS) based feature selection method is proposed for both binomial and multinomial classification. The novel Squared Orthogonal Correlation Coefficient (SOCC) is defined based on Error Reduction Ratio (ERR) in OLS and used as the feature ranking criterion. The equivalence between the canonical correlation coefficient, Fisher's criterion, and the sum of the SOCCs is revealed, which unveils the statistical implication of ERR in OLS for the first time. It is also shown that the OLS based feature selection method has speed advantages when applied for greedy search. The proposed method is comprehensively compared with the mutual information based feature selection methods and the embedded methods using both synthetic and real world datasets. The results show that the proposed method is always in the top 5 among the 12 candidate methods. Besides, the proposed method can be directly applied to continuous features without discretisation, which is another significant advantage over mutual information based methods. © 2021, CC BY-NC-ND.

关键词： Feature extraction

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On the Approximation of the Quotient of Two Gaussian Densities for Multiple-Model Smoothing

On the Approximation of the Quotient of Two Gaussian Densiti...

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International Conference on Information Fusion

作者： Yi Liu Xi Li Le Yang Lyudmila Mihaylova Yanbo Xue Department of Electrical and Computer Engineering University of Canterbury Christchurch NZ State Key Lab of Complex Electromagnetic Environment Effects on Electronics and Information Systems National University of Defense Technology Changsha China Department of Automatic Control and System Engineering University of Sheffield Sheffield UK Career Science Lab Beijing China

The quotient of two multivariate Gaussian densities can be written as an unnormalized Gaussian density, which has been applied in some recently developed multiple-model fixed-interval smoothing algorithms. However, this expression is invalid if instead of being positive definite, the covariance of the unnormalized Gaussian density is indefinite (i.e., it has both positive and negative eigenvalues) or undefined (i.e., computing it requires inverting a singular matrix). This paper considers approximating the quotient of two Gaussian densities in this case using two different approaches to mitigate the caused numerical problems. The first approach directly replaces the indefinite covariance of the unnormalized Gaussian density with a positive definite matrix nearest to it. The second approach computes the approximation through solving, using the natural gradient, an optimization problem with a Kullback-Leibler divergence-based cost function. This paper illustrates the application of the theoretical results by incorporating them into an existing smoothing method for jump Markov systems and utilizing the obtained smoothers to track a maneuvering target.

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On the formation height of low-corona and chromospheric channels of the Atmospheric Imaging Assembly (AIA) on board the Solar Dynamics Observatory (SDO)

arXiv

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arXiv 2024年

作者： Sanjay, Y. Prasad, S. Krishna Erdélyi, R. Korsós, M.B. Banerjee, D. Rawat, P.S. Department of Physics School of Advanced Engineering University of Petroleum and Energy Studies Uttarakhand Dehradun248007 India Manora Peak Uttarakhand Nainital263001 India School of Mathematics and Statistics University of Sheffield Hounsfield Road SheffieldS3 7RH United Kingdom University of Sheffield Department of Automatic Control and Systems Engineering Amy Johnson Building Portabello Street SheffieldS1 3JD United Kingdom

The multi-wavelength data from the Solar Dynamics Observatory (SDO) is extensively used in studying the physics of the Sun and its atmosphere. In this study, we estimate the formation heights of low-corona and chromospheric channels of the Atmospheric Imaging Assembly (AIA) over the atmospheres of sunspot umbrae during the quiet condition period within 20 different active regions. The upward propagating slow magnetoacoustic waves (slow MAWs) of 3-min period, which are perpetually present in sunspots, are utilized for this purpose. Employing a cross-correlation technique, the most frequent time lag between different channel pairs is measured. By combining this information with the local sound speed obtained from the characteristic formation temperatures of individual channels, we estimate the respective formation heights. The median values of formation heights obtained across all active regions in our sample are 356, 368, 858, 1180, and 1470 km, respectively, for the AIA 1600 Å, 1700 Å, 304 Å, 131 Å, and 171 Å channels. The corresponding ranges in the formation heights are 247 − 453, 260 − 468, 575 − 1155, 709 − 1937, and 909 − 2585 km, respectively. These values are measured with respect to the HMI continuum. We find the formation height of UV channels is quite stable (between 250 − 500 km) and displays only a marginal difference between the AIA 1600 Å and 1700 Å during quiet conditions. On the other hand, the formation height of coronal channels is quite variable. © 2024, CC BY.

关键词： Aspherics

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Scalable learning with a structural recurrent neural network for short-term traffic prediction

arXiv

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arXiv 2021年

作者： Kim, Youngjoo Wang, Peng Mihaylova, Lyudmila Sevendof AS Trondheim Norway The Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom

This paper presents a scalable deep learning approach for short-term traffic prediction based on historical traffic data in a vehicular road network. Capturing the spatio-temporal relationship of the big data often requires a significant amount of computational burden or an ad-hoc design aiming for a specific type of road network. To tackle the problem, we combine a road network graph with recurrent neural networks (RNNs) to construct a structural RNN (SRNN). The SRNN employs a spatio-temporal graph to infer the interaction between adjacent road segments as well as the temporal dynamics of the time series data. The model is scalable thanks to two key aspects. First, the proposed SRNN architecture is built by using the semantic similarity of the spatio-temporal dynamic interactions of all segments. Second, we design the architecture to deal with fixed-length tensors regardless of the graph topology. With the real traffic speed data measured in the city of Santander, we demonstrate the proposed SRNN outperforms the image-based approaches using the capsule network (CapsNet) by 14.1% and the convolutional neural network (CNN) by 5.87%, respectively, in terms of root mean squared error (RMSE). Moreover, we show that the proposed model is scalable. The SRNN model trained with data of a road network is able to predict traffic speed of different road networks, with the fixed number of parameters to train. © 2021, CC BY-NC-ND.

关键词： Time series analysis

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