检索结果-内蒙古大学图书馆

International Conference on Information Fusion

作者： Jingxuan Su Sean Anderson Lyudmila Mihaylova Department of Automatic Control & Systems Engineering University of Sheffield UK

Fine-grained plant pathology classification is an important task for precision agriculture, but at the same time, it is challenging due to the subtle difference in plant categories. Variances in the lighting conditions, position, and stages of disease symptoms usually lead to degradation of classification accuracy. Knowledge distillation is a popular method to improve the model performance to deal with the indistinguishable image classification problem. It aims to have a well-optimised small student network guided by a large teacher network. Existing knowledge distillation methods mainly consider training a teacher network that needs a high storage space and considerable computing resources. Self-knowledge distillation methods have been proposed to distil knowledge from the same network. Although self-knowledge distillation saves time and space compared with knowledge distillation, it only learns label knowledge. In this paper, we propose a novel self-distillation method to recognize the fine-grained plant category, which considers holistic knowledge based on the Squeeze and Excitation Network. We label this new method as holistic self-distillation because it captures knowledge through spatial features and labels. The performance validation of the proposed approach is performed on two public fine-grained plant datasets: Plant Pathology 2021 and Plant Pathology 2020 with the accuracy of 98.22% and 90.72% respectively. We also present experiments on the state-of-the-art algorithm (ResNet-50). The classification results demonstrate the effectiveness of the proposed approach with respect to accuracy.

关键词：

来源：评论

学校读者我要写书评

暂无评论

On the Gaussian Filtering for Nonlinear Dynamic systems Using Variational Inference 27

On the Gaussian Filtering for Nonlinear Dynamic Systems Usin...

引用

27th International Conference on Information Fusion, FUSION 2024

作者： Liu, Yi Li, Xi Yang, Le Mihaylova, Lyudmila Li, Ji University of Canterbury Department of Electrical and Computer Engineering Christchurch New Zealand National University of Defense Technology State Key Lab of Complex Electromagnetic Environment Effects on Electronics and Information Systems Changsha China University of Sheffield Department of Automatic Control and System Engineering Sheffield United Kingdom Locaris Electronic Technology Co. Ltd Zhengzhou China

ISBN: (纸本)9781737749769

This paper introduces a new variational Gaussian filtering approach for estimating the state of a nonlinear dynamic system. We first assume that the predictive distribution of the state is Gaussian and derive an iterative method for updating the state posterior in the natural parameter space through KullbackLeibler divergence minimization. The obtained update rule is the same as that of the conjugate-computation variational inference technique in Bayesian learning. The derivation here is simpler and more insightful. We then impose a Wishart prior on the inverse of the state prediction covariance to take into account the impact of approximating the state predictive distribution using a Gaussian density on the state posterior estimation. The prediction covariance is identified jointly with the state using variational inference and the established state posterior update rule to achieve the desired Gaussian filtering. Simulation study examines the performance of the proposed filtering framework in target tracking based on bearing and range measurements. © 2024 ISIF.

关键词： Wiener filtering

来源：评论

学校读者我要写书评

暂无评论

Hybrid Computational Framework for Fault Detection in Coil Winding Manufacturing Process Using Knowledge Distillation

Hybrid Computational Framework for Fault Detection in Coil W...

引用

IEEE International Conference on Industrial Informatics (INDIN)

作者： Izhar Oswaldo Escudero-Ornelas Divya Tiwari Michael Farnsworth Ze Zhang Ashutosh Tiwari Department of Automatic Control and Systems Engineering (ACSE) Sheffield UK

This paper proposes a hybrid computational framework for fault detection during the coil winding manufacturing process by using a combination of Discrete Event Simulation (DES) model with a Supervised Machine Learning (SML) algorithm. The conventional End of the Line (EoL) tests are insufficient in detecting faults during process resulting in increased manufacturing costs and lead times. The proposed methodology utilises a Knowledge Distillation (KD) approach to address the challenges associated with the technique and optimise the student model's performance by employing architecture search and data augmentation. Multiple SML algorithms were evaluated to determine their effectiveness in predicting faults during manufacturing. The random forest algorithm demonstrated superior performance due to its ability to handle complex data and identify the impact of interdependencies of process parameters on the final product quality. The method was validated by conducting physical experiments on a linear coil-winding machine, and the results indicated that the random forest algorithm has the potential to decrease simulation time from 2 minutes to less than a second. The proposed methodology has the potential to reduce manufacturing time, enhance stator quality, and ultimately improve their reliability and safety.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Tool Condition Monitoring Based on Nonlinear Output Frequency Response Functions and Multivariate control Chart

引用

Journal of Dynamics, Monitoring and Diagnostics 2023年第4期2卷 243-251页

作者： Yufei Gui Ziqiang Lang Zepeng Liu Hatim Laalej Department of Automatic Control and Systems Engineering University of SheffieldSheffield S102TNUK Advanced Manufacturing Research Centre University of SheffieldSheffield S102TNUK

Tool condition monitoring(TCM)is a key technology for intelligent *** objective is to monitor the tool operation status and detect tool breakage so that the tool can be changed in time to avoid significant damage to workpieces and reduce manufacturing ***,an innovative TCM approach based on sensor data modelling and model frequency analysis has been *** from traditional signal feature-based monitoring,the data from sensors are utilized to build a dynamic process ***,the nonlinear output frequency response functions,a concept which extends the linear system frequency response function to the nonlinear case,over the frequency range of the tooth passing frequency of the machining process are extracted to reveal tool health *** order to extend the novel sensor data modelling and model frequency analysis to unsupervised condition monitoring of cutting tools,in the present study,a multivariate control chart is proposed for TCM based on the frequency domain properties of machining processes derived from the innovative sensor data modelling and model frequency *** feature dimension is reduced by principal component analysis *** the moving average strategy is exploited to generate monitoring variables and overcome the effects of *** milling experiments of titanium alloys are conducted to verify the effectiveness of the proposed approach in detecting excessive flank wear of solid carbide end *** results demonstrate the advantages of the new approach over conventional TCM techniques and its potential in industrial applications.

关键词： intelligent manufacturing multivariate control chart Nonlinear Autoregressive with eXogenous Input modelling Nonlinear Output Frequency Response Functions tool condition monitoring

来源：评论

学校读者我要写书评

暂无评论

Designing Quantum-Resilient Blockchain Frameworks: Enhancing Transactional Security with Quantum Algorithms in Decentralized Ledgers

引用

International Journal of Advanced computer Science and Applications 2025年第4期16卷 618-628页

作者： Kale, Meenal R. Baker El-Ebiary, Yousef A. Sathiya, L. Burugari, Vijay Kumar Yulduz, Erkiniy Muniyandy, Elangovan Alanazi, Rakan Department of Humanities Yeshwantrao Chavan College of Engineering Hingna Nagpur India Faculty of Informatics and Computing UniSZA University Malaysia Department of CSE Panimalar Engineering College Chennai India Dept of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Automatic Control and Computer Engineering Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Department of Information Technology Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia

Quantum computing is progressing at a fast rate and there is a real threat that classical cryptographic methods can be compromised and therefore impact the security of blockchain networks. All of the ways used to secure blockchain like Rivest–Shamir–Adleman (RSA), Elliptic Curve Cryptography (ECC) and Secure Hash Algorithm 256-bit (SHA256) are the characteristic of the traditional cryptographic techniques vulnerable to attack by quantum algorithms: Shor’s and Grover’s algorithms: can efficiently break asymmetric encryption and speed up brute force attacks. Because of this vulnerability, there exists a need to develop an advance quantum resilient blockchain framework to protect the decentralized ledgers from the future threats of the quantum. This research proposes Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD) and Quantum Random Number Generation (QRNG) as a formidable architectural integration, to fortify security of blockchain. Classical encryption is replaced with PQC, QKD with secure key exchange by detecting eavesdropping, and QRNG with improving cryptographic randomness to remove the predictable key vulnerability. Only with a small loss of transaction efficiency, we increase transaction encryption accuracy, key exchange security, and resistance to quantum attacks. In this quantum enhanced blockchain design, the idea is to preserve the decentralization, transparency and security and at the same time overcome the future quantum threat. By going through rigorous analysis and comparative evaluation, we demonstrate that the approach saves blockchain networks from the emerging quantum risks to make sure that the decentralized finance, smart contracts and cross chain transactions. © (2025), (Science and Information Organization). All rights reserved.

关键词： Quantum cryptography

来源：评论

学校读者我要写书评

暂无评论

Management of the Traffic Networks Transportation in Supplier-Customers Services by Strategic Games

引用

Procedia computer Science 2024年 242卷 25-34页

作者： Radu Bîlbîie Dumitru Popescu Catalin Dimon Department of Automatic Control and Systems Engineering UPB Bucharest Romania Academy of Romanian Scientists Ilfov 3 050044 Bucharest Romania

This paper presents an innovative approach to optimize traffic networks in a supplier-customer system based on specific strategies of game theory. The traffic network is represented as a routing configuration in which the arcs represent various possible roads between supplier and customer entities, each route having an associated transport cost for the product unit. The game structure is defined by a matrix of transport costs between the players, supplier and customer, with their corresponding strategies. Based on these costs, the game matrix is built to which the existing quantities in warehouses are added, respectively the quantities requested by the final customers. By solving the game associated to the transportation traffic network, the different strategies are proposed for choosing the optimal solution. The simulation results confirm the effectiveness of the proposed approach and recommend some performant procedures for solving the game, such as Hurwicz, Laplace and Savage, close to optimal solution. This research opens new directions for management optimization in other domains such as comunication, electrical and administrativ networks.

关键词： Supplier – Customer Services System management Transportation Trafic network Game theory Optimization Decision

来源：评论

学校读者我要写书评

暂无评论

A Retractable Soft Growing Robot with a Flexible Backbone

A Retractable Soft Growing Robot with a Flexible Backbone

引用

IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Xinyi Pi Isabella A. Szczech Lin Cao Department of Automatic Control and Systems Engineering University of Sheffield UK

Soft-growing robots are emerging with numerous potential applications because of their superior capability of frictionless navigation. However, their success is hindered by their tendency to buckle under the tension required to retract them via inversion. In this paper, we propose a simple and scalable tubular backbone to facilitate retracting the robot body without buckling. With this backbone, compressive forces at the robot's tip are mitigated and a limit is placed on the effective length for retraction during the application of tension. We first present the selection of the backbone and the development of such a retractable soft-growing robot. Along with the characterization of the working principles behind this buckling-free mechanism, success was observed with the use of the backbone in retraction tests. The effects of different parameters such as robot body lengths, air pressures, curvatures, and retraction modes on the performance were also investigated. This backbone approach requires no bulky or in-situ mechatronic components inside the robot body and thus may be used in medical applications which appreciate simple, compact, and in-situ electronic-free designs.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Uncertainty-Informed Model Selection Method for Nonlinear System Identification and Interpretable Machine Learning

Uncertainty-Informed Model Selection Method for Nonlinear Sy...

引用

Mediterranean Conference on control and Automation (MED)

作者： Yuanlin Gu Hua-Liang Wei Division of Computer Science and Maths University of Stirling Stirling United Kingdom Department of Automatic Control and Systems Engineering University of Sheffield Sheffield

ISBN: (数字)9798350395440

ISBN: (纸本)9798350395457

Modeling uncertainty has been an active and important topic in the fields of data-driven modeling and machine learning. Uncertainty ubiquitously exists in any data modeling process, making it challenging to identify the optimal models among many potential candidates. This article proposes an uncertainty-informed method to address the model selection problem. The performance of the proposed method is evaluated on a dataset generated from a complex system model. The experimental results demonstrate the effectiveness of the proposed method and its superiority over conventional approaches. This method has minimal requirements for the length of training data and model types, making it applicable for various modeling frameworks.

关键词： Data-driven modeling Adaptation models Uncertainty Recurrent neural networks Training data Machine learning Data models

来源：评论

学校读者我要写书评

暂无评论

LEARNING CONTINUOUS NORMALIZING FLOWS FOR FASTER CONVERGENCE TO TARGET DISTRIBUTION VIA ASCENT REGULARIZATIONS 11

LEARNING CONTINUOUS NORMALIZING FLOWS FOR FASTER CONVERGENCE...

引用

11th International Conference on Learning Representations, ICLR 2023

作者： Chen, Shuangshuang Ding, Sihao Karayiannidis, Yiannis Björkman, Mårten School of Electrical Engineering and Computer Science Royal Institute of Technology Sweden AI Lab Volvo Car Corporation Sweden Department of Automatic Control Lund University Sweden

Normalizing flows (NFs) have been shown to be advantageous in modeling complex distributions and improving sampling efficiency for unbiased *** this work, we propose a new class of continuous NFs, ascent continuous normalizing flows (ACNFs), that makes a base distribution converge faster to a target *** solving such a flow is non-trivial and barely possible, we propose a practical implementation to learn flexibly parametric ACNFs via ascent regularization and apply it in two learning cases: maximum likelihood learning for density estimation and minimizing reverse KL divergence for unbiased sampling and variational *** learned ACNFs demonstrate faster convergence towards the target distributions, therefore, achieving better density estimations, unbiased sampling and variational approximation at lower computational ***, the flows show to stabilize themselves to mitigate performance deterioration and are less sensitive to the choice of training flow length T. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Maximum likelihood estimation

来源：评论

学校读者我要写书评

暂无评论

Developing a Novel MATLAB control Toolbox for Encouraging Student Engagement

Developing a Novel MATLAB Control Toolbox for Encouraging St...

引用

Experiment@ International Conference (exp.at)

作者： John Anthony Rossiter Department of Automatic Control and Systems Engineering University of Sheffield UK

ISBN: (数字)9798350314403

ISBN: (纸本)9798350314410

The numerous toolboxes within MATLAB [12] to aid student engagement and learning largely require deep understanding of coding, as well as the associated engineering. This paper discusses some work in progress which is focussing on developing files and resources for a control 101 course [17] which are both engaging and also have much lower pedestals of understanding before use. The intention is focus on attracting student interest and communicating core concepts before students need to begin the longer process of deep learning. Thus this toolbox fills a large gap in the current provision.

关键词： Deep learning Codes Collaboration Encoding Matlab

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：