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Annual Conference of Industrial Electronics Society

作者： Aurora Esteban Victor Cobilean Rashmika Nawaratne Dept. of Computer Science and Numerical Analysis University of Cordoba Cordoba Spain Dept. of Computer Science Virginia Commonwealth University Richmond VA USA Research Center for Data Analytics and Cognition La Trobe University Victoria Australia

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

Predictive maintenance involves proactive monitoring and prediction of equipment failures to optimize maintenance schedules to minimize downtime, reduce losses, and ensure optimal equipment performance. However, traditional methods often rely on classification or regression, overlooking the temporal aspect of failure prediction. Survival analysis is focused on modeling time-to-event data, making it well-suited for predicting the time until equipment failures. The primary goal of this paper is to evaluate the feasibility and effectiveness of Transformer-based model for survival analysis in the industrial domain. Additionally, this work aims to explore the integration of Large Language Models as virtual assistants to facilitate user interaction and decision-making in the predictive maintenance process. The proposed system was tested on a public predictive maintenance problem and the Transformer-based model obtained better survival predictions than the baseline statistical and machine learning models, with improvements of up to 20%. Furthermore, the virtual assistant based on the large language model demonstrated the ability to retrieve precise information based on natural language prompts.

关键词： Analytical models Schedules Large language models Virtual assistants Natural languages Predictive models Transformers Maintenance Monitoring Predictive maintenance

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Ying Bi, Bing Xue, Mengjie Zhang: Genetic programming for image classification—an automated approach to feature learning

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Genetic Programming and Evolvable Machines 2022年第4期23卷 589-590页

作者： Zafra, Amelia Department of Computer Science and Numerical Analysis Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Cordoba Cordoba Spain

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Marker-based Visual SLAM leveraging Hierarchical Representations

arXiv

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arXiv 2023年

作者： Tourani, Ali Bavle, Hriday Sanchez-Lopez, Jose Luis Salinas, Rafael Muñoz Voos, Holger University of Luxembourg Luxembourg The Faculty of Science Technology and Medicine University of Luxembourg Luxembourg The Department of Computer Science and Numerical Analysis University of Córdoba Rabanales Campus Spain

Fiducial markers can encode rich information about the environment and can aid Visual SLAM (VSLAM) approaches in reconstructing maps with practical semantic information. Current marker-based VSLAM approaches mainly utilize markers for improving feature detections in low-feature environments and/or for incorporating loop closure constraints, generating only low-level geometric maps of the environment prone to inaccuracies in complex environments. To bridge this gap, this paper presents a VSLAM approach utilizing a monocular camera along with fiducial markers to generate hierarchical representations of the environment while improving the camera pose estimate. The proposed approach detects semantic entities from the surroundings, including walls, corridors, and rooms encoded within markers, and appropriately adds topological constraints among them. Experimental results on a real-world dataset collected with a robot demonstrate that the proposed approach outperforms a traditional marker-based VSLAM baseline in terms of accuracy, given the addition of new constraints while creating enhanced map representations. Furthermore, it shows satisfactory results when comparing the reconstructed map quality to the one reconstructed using a LiDAR SLAM approach. © 2023, CC BY.

关键词： Cameras

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A Non-Parametric Identification Scheme of SISO-LTI Systems Using Blaschke-Products*

A Non-Parametric Identification Scheme of SISO-LTI Systems U...

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Mediterranean Conference on Control and Automation (MED)

作者： Gergő Ungvári Alexandros Soumelidis Tamás Dózsa Department of Numerical Analysis Faculty of Informatics Eötvös Loránd University Budapest Hungary Systems and Control Laboratory HUN-REN Institute for Computer Science and Control Budapest Hungary

ISBN: (数字)9798350395440

ISBN: (纸本)9798350395457

In this manuscript, a novel algorithm is presented for the identification of single input single output linear time invariant (SISO-LTI) systems. The proposed method is able to find poles of the transfer function describing the system without any assumptions on the system order. The results presented in this paper generalize earlier identification schemes based on expansions of the transfer function using generalized rational orthogonal basis functions (GOBFs). These previous methods depended on a convergent sequence constructed from the coefficients of the orthogonal GOBF expansion. The first contribution of this study is showing that a wider family of functions may be used to construct similar pole finding algorithms. The aforementioned previous approaches can be interpreted as special cases of the results presented here. In addition, using this generalization a new pole identification scheme is proposed using finite Blaschke-products. Through experiments, the practical utility of this scheme is verified when compared to earlier variations of the pole finding scheme.

关键词： Automation Transfer functions

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Existence of solutions to the generalized periodic fractional boundary value problem

Existence of solutions to the generalized periodic fractiona...

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作者： Fečkan, Michal Marynets, Kateryna Wang, JinRong Department of Mathematical Analysis and Numerical Mathematics Comenius University in Bratislava Bratislava Slovakia Mathematical Institute of Slovak Academy of Sciences Bratislava Slovakia Delft Institute of Applied Mathematics Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Department of Mathematics Guizhou University Guiyang China

We study a boundary value problem for a Caputo-type fractional differential equation subjected to periodic boundary conditions. For an auxiliary problem with the simplified right-hand side, we explicitly construct its unique solution. In addition, based on the theory of the topological index, we prove existence of at least one solution to the original problem. © 2023 The Authors. Mathematical Methods in the Applied sciences published by John Wiley & Sons Ltd.

关键词： Boundary value problems

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A two-stage algorithm in evolutionary product unit neural networks for classification

arXiv

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arXiv 2024年

作者： Tallón-Ballesteros, Antonio J. Hervás-Martínez, César Department of Languages and Computer Systems University of Seville Reina Mercedes Avenue Seville41012 Spain Department of Computer Science and Numerical Analysis University of Córdoba Campus of Rabanales Albert Einstein Building Córdoba14071 Spain

This paper presents a procedure to add broader diversity at the beginning of the evolutionary process. It consists of creating two initial populations with different parameter settings, evolving them for a small number of generations, selecting the best individuals from each population in the same proportion and combining them to constitute a new initial population. At this point the main loop of an evolutionary algorithm is applied to the new population. The results show that our proposal considerably improves both the efficiency of previous methodologies and also, significantly, their efficacy in most of the data sets. We have carried out our experimentation on twelve data sets from the UCI repository and two complex real-world problems which differ in their number of instances, features and classes. Copyright © 2024, The Authors. All rights reserved.

关键词： Evolutionary algorithms

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Convolutional and Deep Learning based techniques for Time Series Ordinal Classification

arXiv

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arXiv 2023年

作者： Ayllón-Gavilán, Rafael Guijo-Rubio, David Gutiérrez, Pedro Antonio Bagnall, Anthony Hervás-Martínez, César Dept. of Clinical-Epidemiological Research in Primary Care IMIBIC Spain Dept. of Computer Science and Numerical Analysis University of Cordoba Spain Dept. of Electronics and Computer Science University of Southampton United Kingdom

Time Series Classification (TSC) covers the supervised learning problem where input data is provided in the form of series of values observed through repeated measurements over time, and whose objective is to predict the category to which they belong. When the class values are ordinal, classifiers that take this into account can perform better than nominal classifiers. Time Series Ordinal Classification (TSOC) is the field bridging this gap, yet unexplored in the literature. There are a wide range of time series problems showing an ordered label structure, and TSC techniques that ignore the order relationship discard useful information. Hence, this paper presents the first benchmarking of TSOC methodologies, exploiting the ordering of the target labels to boost the performance of current TSC state-of-the-art. Both convolutional- and deep learning-based methodologies (among the best performing alternatives for nominal TSC) are adapted for TSOC. For the experiments, a selection of 29 ordinal problems has been made. In this way, this paper contributes to the establishment of the state-of-the-art in TSOC. The results obtained by ordinal versions are found to be significantly better than current nominal TSC techniques in terms of ordinal performance metrics, outlining the importance of considering the ordering of the labels when dealing with this kind of problems. © 2023, CC BY-NC-ND.

关键词： Convolution

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Towards Explainable Test Case Prioritisation with Learning-to-Rank Models

Towards Explainable Test Case Prioritisation with Learning-t...

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IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW

作者： Aurora Ramírez Mario Berrios José Raúl Romero Robert Feldt Dept. of Computer Science and Numerical Analysis University of Córdoba Córdoba Spain Dept. of Computer Science and Engineering Softw. Eng. Division Chalmers University of Technology Goteborg Sweden

Test case prioritisation (TCP) is a critical task in regression testing to ensure quality as software evolves. Machine learning has become a common way to achieve it. In particular, learning-to-rank (LTR) algorithms provide an effective method of ordering and prioritising test cases. However, their use poses a challenge in terms of explainability, both globally at the model level and locally for particular results. Here, we present and discuss scenarios that require different explanations and how the particularities of TCP (multiple builds over time, test case and test suite variations, etc.) could influence them. We include a preliminary experiment to analyse the similarity of explanations, showing that they do not only vary depending on test case-specific predictions, but also on the relative ranks.

关键词： Software testing Machine learning algorithms Conferences Software algorithms Machine learning Software Task analysis

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Evaluating Automatic Annotation Techniques for Fine-Tuning Large Language Models in Financial Sentiment analysis

Evaluating Automatic Annotation Techniques for Fine-Tuning L...

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Sentiment analysis and Deep Learning (ICSADL), International Conference on

作者： Ibtissam Youb Mohamed Hamlich Sebastian Ventura Feras Al-Obeidat Department of Computer Science and Numerical Analysis University of Cordoba Cordoba Spain Department of Electronic CCPS Laboratory ENSAM University of Hassan II Casablanca Morocco College of Technological Innovation Zayed University Abu Dhabi UAE

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Accurate sentiment analysis in financial contexts is crucial for market analysis and investment decisions. However, manually annotating large datasets for training models is time-consuming and costly. This study evaluates two automatic an-notation tools, VADER and TextBlob, for their effectiveness in generating labels for training large language models (LLMs) like BERT and GPT-2 in financial sentiment analysis. We used a large dataset of stock market-related tweets, including a manually annotated subset for benchmarking. The remaining tweets were automatically labeled using VADER and TextBlob. We trained BERT and GPT-2 models with these sentiment labels and compared their performance against the benchmark dataset. Our findings show that models trained with VADER annotations had a higher correlation with human-labeled data (62% accuracy) compared to those trained with TextBlob annotations (48% accuracy). These results suggest that VADER is more suitable for automatic annotation in financial sentiment analysis, providing more accurate and reliable sentiment labels, which can significantly improve the efficiency of training large language models for financial applications.

关键词： Training Sentiment analysis Accuracy Annotations Social networking (online) Large language models Benchmark testing Data models Reliability Investment

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Cross-Modality Gait Recognition: Bridging LiDAR and Camera Modalities for Human Identification

Cross-Modality Gait Recognition: Bridging LiDAR and Camera M...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Rui Wang Chuanfu Shen Manuel J. Marin-Jimenez George Q. Huang Shiqi Yu Department of Computer Science and Engineering Southern University of Science and Technology Department of Industrial and Manufacturing Systems Engineering The University of Hong Kong Department of Computing and Numerical Analysis University of Cordoba Department of Industrial and Systems Engineering The Hong Kong Polytechnic University

ISBN: (数字)9798350364132

ISBN: (纸本)9798350364149

Current gait recognition research mainly focuses on identifying pedestrians captured by the same type of sensor, neglecting the fact that individuals may be captured by different sensors in order to adapt to various environments. A more practical approach should involve cross-modality matching across different sensors. Hence, this paper focuses on investigating the problem of cross-modality gait recognition, with the objective of accurately identifying pedestrians across diverse vision sensors. We present CrossGait inspired by the feature alignment strategy, capable of cross retrieving diverse data modalities. Specifically, we investigate the cross-modality recognition task by initially extracting features within each modality and subsequently aligning these features across modalities. To further enhance the cross-modality performance, we propose a Prototypical Modality-shared Attention Module that learns modality-shared features from two modality-specific features. Additionally, we design a Cross-modality Feature Adapter that transforms the learned modality-specific features into a unified feature space. Extensive experiments conducted on the SUSTech1K dataset demonstrate the effectiveness of CrossGait: (1) it exhibits promising cross-modality ability in retrieving pedestrians across various modalities from different sensors in diverse scenes, and (2) CrossGait not only learns modality-shared features for cross-modality gait recognition but also maintains modality-specific features for single-modality recognition.

关键词： Performance evaluation Visualization Pedestrians Laser radar Transforms Vision sensors Feature extraction Cameras Gait recognition Wireless fidelity

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