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A Rapid Fitting Method for the Estimation of Kinematic Errors in 3-RPR Robots Using Small Sample Sizes

A Rapid Fitting Method for the Estimation of Kinematic Error...

引用

IEEE international conference on Industrial Informatics (IndIN)

作者： Likang Song Yuqing Chen Min Chen Hongbin Zhang School of Advanced Technology Xi’ an Jiaotong-Liverpool University Suzhou China Maxwell Technologies Suzhou China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Since the inverse kinematics of the 3-RPR mecha-nism involves a 6th-degree equation, it does not have an analytical solution. However, when calculating kinematic precision down to the micrometer level in practical applications, the error becomes significant. Traditional approaches using neural networks or deep learning algorithms to compensate these errors often need a lot of data and tends to over-fitting. In this article, we focus on a platform based on the 3-RPR parallel mechanism, proposing a systematic method to reduce the inverse kinematic error to micrometer level. This article analyzed the components of system error. A method is proposed that allows for the rapid estimation of error model parameters using a subset of sampling points in practical applications. This provides a practical solution for quickly re-calibrating the machinery during disassembly and maintenance. A potential application of this method to forward kinematics is also discussed.

关键词： Micrometers Systematics Fitting Neural networks Estimation Kinematics Maintenance engineering Mathematical models Maintenance Robots

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Clinician-Assisted Exploratory data Analysis Framework for Early Diagnosis of Keratoconus

Clinician-Assisted Exploratory Data Analysis Framework for E...

引用

IEEE SSD international Multi-conference on Systems, Signals and Devices

作者： Zahra J. Muhsin Rami Qahwaji Ibrahim Ghafir Muawyah Al Bdour Saif AlRyalat Mo'ath AlShawabkeh Majid Al-Taee Faculty of Eng. and Digital Technologies University of Bradford UK Scool of Medicine The University of Jordan Amman Jordan Department of Ophthalmology The Hashemite University Zarqa Jordan Independent Consultant of Computing and Systems Engineering Liverpool UK

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Advances in corneal imaging have generated vast datasets, making it challenging to extract clinically relevant insights. Machine learning (ML) provides ophthalmologists with essential tools for keratoconus (KC) detection and clinical decision-making. However, effective Exploratory data Analysis (EDA) remains crucial for identifying trends, forming hypotheses, and selecting key parameters to build robust ML models. This paper presents a comprehensive EDA framework that fosters collaboration between AI specialists and clinicians to enable early KC detection. Using statistical and visual techniques alongside expert input, a clinical dataset with 79 features from 2,491 cases is pre-processed, analyzed, and effectively prepared for ML modelling. The analysis identified a key subset of features–just 6.3% of the original dataset–which was used to develop and evaluate the classification performance of several ML models. Among them, the Random Forest model achieved 99.6% accuracy, surpassing previous studies and demonstrating the effectiveness of the proposed EDA framework.

关键词： Training Analytical models Visualization data analysis Accuracy Redundancy Tomography Feature extraction data models Random forests

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Automatic intelligent Chronic Kidney Disease Detection in Healthcare 5.0

Automatic Intelligent Chronic Kidney Disease Detection in He...

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IEEE international conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Geng Tian Amir Rehman Huanlai Xing Li Feng Nighat Gulzar Abid Hussain 081 Electronic Group Co. Ltd Chengdu China School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China School of Geosciences and Environmental Engineering Southwest Jiaotong University Chengdu China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Health systems worldwide have an unprecedented opportunity to enhance healthcare service delivery due to the rapid development of emerging digital technologies. Many advancements have been made in the medical field, with deep learning proving particularly useful when applied to a large enough number of well-defined samples. Although, this aspect may make deep learning harder to implement in settings with limited-size datasets. In this study, we present a new method of chronic kidney disease detection (CKDD) by combining Generative Adversarial Networks (GAN) with Convolutional Neural Networks (CNN). Afterward, synthetic sample data was created using GAN, which enlarged the dataset. Subsequently, processing these synthetic samples, the CNN classifier was applied. According to experimental assessments, the suggested CKDD-GAN methodology accuracy is superior to without the GAN technique. Moreover, the proposed CKDD-GAN-based model outperformed with an accuracy of 98.10%. Even though standard synthetic data samples seemed to improve classification performance, GAN-based enhancements resulted in a 2.91% improvement. GAN implementations for detecting chronic kidney disease are highly beneficial since they also increase awareness about its possible uses in various other diseases.

关键词： Deep learning Privacy Medical services Generative adversarial networks Chronic kidney disease Convolutional neural networks Security

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Robustness and Privacy for Green learning under Noisy Labels

Robustness and Privacy for Green Learning under Noisy Labels

引用

IEEE international conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： De Li Tiange Xia Qiyu Li Xianxian Li Jinyan Wang Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Guangxi Key Lab of Multi-Source Information Mining & Security Guangxi Normal University Guilin China School of Computer Science and Engineering Guangxi Normal University Guilin China Faculty of Education Guangxi Normal University Guilin China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

As a new paradigm of machine learning, green learning has achieved performance comparable to deep learning in vision tasks, knowledge graph learning, and modeling graph structures. Compared with deep learning, green learning has the advantages of a low carbon footprint, lightweight model and logical transparency. In this paper, to enhance the robustness of green learning, we study the performance of the green model when the training dataset labels are noisy. Through theoretical analysis and experimental verification, we found that noisy labels will not only reduce the performance of green learning, but also amplify the risk of privacy leaks of training dataset members. Therefore, we propose a robust green learning to approach the above two threats. Specifically, we design a multi-stage label-consistency sample selection method to filter out noisy labels in the training dataset by taking advantage of the characteristics of unsupervised representation learning in green learning. In order to further improve the stable classification performance of the model, we propose a feature-level class-balanced data augmentation method in the feature decision learning stage to solve the class imbalance problem caused by sample selection. Finally, a large number of experiments on multiple noisy datasets show that the robust green learning method proposed in this paper can not only enhance the generalization of the model under noisy training labels, but also alleviate the risk of member privacy information leakage.

关键词： Training Deep learning Representation learning data privacy Privacy Robustness data models

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External Climate data Extraction Using the Forward Feature Selection Method in the Context of Occupational Safety 22nd

External Climate Data Extraction Using the Forward Feature ...

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22nd international conference on Computational Science and Its Applications , ICCSA 2022

作者： Silva, Felipe G. Sena, Inês Lima, Laires A. Fernandes, Florbela P. Pacheco, Maria F. Vaz, Clara B. Lima, José Pereira, Ana I. Instituto Politécnico de Bragança Campus de Santa Apolónia Bragança5300-253 Portugal

ISBN: (纸本)9783031105616

Global climate changes and the increase in average temperatures are some of the major contemporary problems that have not been considered in the context of external factors to increase accident risk. Studies that include climate information as a safety parameter in machine learning models designed to predict the occurrence of accidents are not usual. This study aims to create a dataset with the most relevant climatic elements, to get better predictions. The results will be applied in future studies to correlate with the accident history in a retail sector company to understand its impact on accident risk. The information was collected from the National Oceanic and Atmospheric Administration (NOAA) climate database and computed by a wrapper method to ensure the selection of the most features. The main goal is to retain all the features in the dataset without causing significant negative impacts on the prediction score. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： data mining

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NOCOL - Nonnegative Orthogonal Constraint Outlier learning 22nd

NOCOL - Nonnegative Orthogonal Constraint Outlier Learning

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22nd international conference on Web Information Systems engineering (WISE)

作者： Balasubramaniam, Thirunavukarasu Mohotti, Wathsala Anupama Nayak, Richi Yuen, Chau Queensland Univ Technol Brisbane Qld Australia Univ Ruhuna Matara Sri Lanka Singapore Univ Technol & Design Singapore Singapore

ISBN: (纸本)9783030915605;9783030915599

Identifying anomalous documents in a text corpus is an important problem that has wide applications. Due to the high dimensional and sparse nature of text data, traditional outlier detection methods fail to identify features that distinguish outliers. Inspired by the capability of Nonnegative Matrix Factorization (NMF) for text clustering, we explore it for text outlier detection. In this paper, a novel NMF-based method called Nonnegative Orthogonal Constraint Outlier learning (NOCOL) is introduced that learns the outliers effectively during the factorization process. Experimental results show the higher accuracy of NOCOL in identifying text outliers in comparison to the state-of-the-art methods.

关键词： Nonnegative matrix factorization Outlier detection Selective learning Text outliers Orthogonal constraint

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Hybrid Fine-Tuning in Large Language Model learning for Machinery Fault Diagnosis

Hybrid Fine-Tuning in Large Language Model Learning for Mach...

引用

IEEE international conference on Industrial Informatics (IndIN)

作者： Zhendong Pang Hao Zhang Teng Li School of Control Science and Engineering Shandong University Jinan China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Large Language Models (LLMs) have already made significant innovations and breakthroughs in the field of natural language processing (NLP). Considering the advances in addressing large-volume sequential data, LLMs can be implemented to deal with the problem of machinery fault diagnosis, which is still at a relatively early stage of exploration. Recently, LLMs have also been explored and applied in the field of time series analysis with excellent performance. Inspired by the use of LLMs in handling time series data, this paper investigates the effectiveness of LLMs in addressing fault diagnosis tasks. A novel hybrid fine-tuning for an LLM-based framework, called LLaMA-HFT, is proposed. The framework integrates the LLaMA model as a network backbone for feature extraction. Moreover, a hybrid fine-tuning (HFT) strategy is proposed for efficient fine-tuning of the model parameters. Experiments are conducted systematically and evaluated using a real-world fault diagnosis dataset. The experimental results show that the proposed method outperforms the other baseline methods in machinery fault diagnosis.

关键词： Fault diagnosis Adaptation models Technological innovation Analytical models Large language models Time series analysis Feature extraction Natural language processing Machinery Informatics

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Selective data Augmentation for Improving the Performance of Offline Reinforcement learning

Selective Data Augmentation for Improving the Performance of...

引用

international conference on Control, Automation and Systems ( ICCAS)

作者： Jungwoo Han Jinwhan Kim Department of Mechanical Engineering Korea Advanced Institute of Science and Technology Daejeon Korea

This study proposes a new data augmentation technique for offline reinforcement learning (RL). Rather than randomly choosing data points to carry out the data augmentation, our methodology selectively chooses data from sparse subspaces of the dataset to effectively augment the data region that is insufficient in the original dataset. For the augmentation, the subspaces of the dataset would be represented in the latent space created by the variational autoencoder (VAE). data is then sampled from the latent space and converted back to the original space by using the decoder of the VAE so that the augmented data can be added to the original dataset. By using the VAE, virtual data that does not severely deviate from the original data could be generated because the VAE creates new data points by using the latent space that captures the original data distribution. We evaluate the performance of our methodology using several offline RL datasets generated from OpenAI Gym benchmark control simulations which mainly use state-based inputs.

关键词： Automation Training data Reinforcement learning Benchmark testing Aerospace electronics Control systems Decoding

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A Novel Cascading Artificial Neural Networks for Enhanced Distribution Network State Estimation

A Novel Cascading Artificial Neural Networks for Enhanced Di...

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Mediterranean Electrotechnical conference (MELECON)

作者： Mohamad El Iaali Reza Razi Antoine Bruyere Bruno Francois Joao Soares Centrale Lille Junia ULR 2697 - L2EP Univ. Lille Arts et Metiers Institute of Technology Lille France GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI - Intelligent Systems Associate Laboratory Polytechnic of Porto Porto Portugal

ISBN: (数字)9798350387025

ISBN: (纸本)9798350387032

The growing integration of renewable energy sources and the widespread adoption of electric vehicles add complexity to power flows in distribution systems. Understanding the system dynamics and implementing control actions are imperative for effective grid management. While measurements in specific nodes are facilitated by smart meters, accurate sensors, communication networks, and electric vehicles, it is costly and, in some cases, impossible to obtain measurements at all nodes. In response to these challenges, this paper introduces a machine learning-based state estimation method, utilizing available measurements and estimating others. By harnessing data-driven techniques, the proposed method aims to provide accurate estimations of voltages and currents in distribution networks, offering valuable insights into flexibilities management and control processes. Specific case study is considered, confirming the efficiency of the suggested state estimation approach and providing insights into its flexibility across a range of scenarios. In addition to the mentioned benefits, the results validate the proposed method, demonstrating a small margin of error. This approach holds significant promises in improving situational awareness for power system operators and enhancing their understanding of the evolving grid dynamics.

关键词： Accuracy System dynamics Current measurement Power system dynamics Distribution networks Electric vehicles Smart meters

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Comparative Analysis of Advanced Optimization Algorithms for Highway Photovoltaic System Layout

Comparative Analysis of Advanced Optimization Algorithms for...

引用

IEEE international conference on Industrial Informatics (IndIN)

作者： Yanmeng Tao Zhengke Liu Wenxin Ma Xiaolei Ma School of Transportation Science and Engineering Beihang University Beijing China Key Laboratory of Intelligent Transportation Technology and System Ministry of Education Beijing China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

In the context of the current transition in energy structures and the development of renewable energy, optimizing the layout of solar energy systems along highways has become a significant research topic. This study employs five different advanced optimization algorithms, namely NSGA-II, Particle Swarm Optimization (PSO), Simulated Annealing (SA), Differential Evolution (DE), and a combination of NSGA-II and DE (NSGA-II-DE), to minimize total supply cost and maximize energy self-sufficiency. The comparative analysis reveals that the combination of NSGA-II and DE exhibits the best optimization performance. Through detailed experiment and data analysis, the performance and advantages of each algorithm in solving the layout problem of highway solar energy system are demonstrated, and specific implementation strategies are proposed for the optimal solution. In addition, sensitivity analysis is used to understand how changes in parameters such as module efficiency, energy demand fluctuations, and electricity price changes affect the optimization results, adding depth to the findings and making them more applicable to a variety of application scenarios.

关键词： Road transportation Energy consumption Costs Uncertainty Sensitivity analysis Electricity Layout Solar energy Robustness Optimization

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