Logic Journal of the IGPL最新文献_第3页

Past five years on strategies and applications in hybrid brain storm optimization algorithms: a review 过去五年混合式头脑风暴优化算法的策略与应用：综述

IF 1 4区数学

Logic Journal of the IGPL Pub Date : 2024-06-03 DOI: 10.1093/jigpal/jzae051

Dragan Simić, Z. Bankovic, José R. Villar, J. Calvo-Rolle, V. Ilin, S. Simic, Svetlana Simić

引用次数: 0

The d-elements of precoherent preidempotent quantales and their applications 前相干先验量子的 d 元素及其应用

IF 1 4区数学

Logic Journal of the IGPL Pub Date : 2024-06-03 DOI: 10.1093/jigpal/jzae063

Xianglong Ruan

引用次数: 0

Structural analysis of code-based algorithms of the NIST post-quantum call NIST 后量子调用基于代码算法的结构分析

IF 1 4区数学

Logic Journal of the IGPL Pub Date : 2024-06-03 DOI: 10.1093/jigpal/jzae071

M. Á. González de la Torre, L. H. Encinas, J. I. S. GarcÍa

{"title":"Structural analysis of code-based algorithms of the NIST post-quantum call","authors":"M. Á. González de la Torre, L. H. Encinas, J. I. S. GarcÍa","doi":"10.1093/jigpal/jzae071","DOIUrl":"https://doi.org/10.1093/jigpal/jzae071","url":null,"abstract":"\u0000 Code-based cryptography is currently the second most promising post-quantum mathematical tool for quantum-resistant algorithms. Since in 2022 the first post-quantum standard Key Encapsulation Mechanism, Kyber (a latticed-based algorithm), was selected to be established as standard, and after that the National Institute of Standards and Technology post-quantum standardization call focused in code-based cryptosystems. Three of the four candidates that remain in the fourth round are code-based algorithms. In fact, the only non-code-based algorithm (SIKE) is now considered vulnerable. Due to this landscape, it is crucial to update previous results about these algorithms and their functioning. The Fujisaki-Okamoto transformation is a key part of the study of post-quantum algorithms and in this work we focus our analysis on Classic McEliece, BIKE and HQC proposals, and how they apply this transformation to obtain IND-CCA semantic security. Since after security the most important parameter in the evaluation of the algorithms is performance, we have compared the performance of the code-based algorithms of the NIST call considering the same architecture for all of them.","PeriodicalId":51114,"journal":{"name":"Logic Journal of the IGPL","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141269301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine overstrain prediction for early detection and effective maintenance: A machine learning algorithm comparison 用于早期检测和有效维护的机器过度应变预测：机器学习算法比较

IF 1 4区数学

Logic Journal of the IGPL Pub Date : 2024-05-27 DOI: 10.1093/jigpal/jzae055

Bruno Mota, Pedro Faria, Carlos Ramos

{"title":"Machine overstrain prediction for early detection and effective maintenance: A machine learning algorithm comparison","authors":"Bruno Mota, Pedro Faria, Carlos Ramos","doi":"10.1093/jigpal/jzae055","DOIUrl":"https://doi.org/10.1093/jigpal/jzae055","url":null,"abstract":"Machine stability and energy efficiency have become major issues in the manufacturing industry, primarily during the COVID-19 pandemic where fluctuations in supply and demand were common. As a result, Predictive Maintenance (PdM) has become more desirable, since predicting failures ahead of time allows to avoid downtime and improves stability and energy efficiency in machines. One type of machine failure stands out due to its impact, machine overstrain, which can occur when machines are used beyond their tolerable limit. From the current literature, there are little to no relevant works that focus on machine overstrain failure detection or prediction. Accordingly, the purpose of this paper is to implement and compare four Machine Learning (ML) algorithms for PdM applied to machine overstrain failures: Artificial Neural Network (ANN), Gradient Boosting, Random Forest and Support Vector Machine (SVM). Moreover, it proposes a training methodology for imbalanced data and the automatic optimization of hyperparameters, which aims to improve performance in the ML models. To evaluate the performance of the ML models, a synthetic dataset that simulates industrial machine data is used. The obtained results show the robustness of the proposed methodology, with the ANN and SVM models achieving a perfect recall score, with 98.95% and 98.85% in accuracy, respectively.","PeriodicalId":51114,"journal":{"name":"Logic Journal of the IGPL","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141167699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Płonka adjunction 膜连接

IF 1 4区数学

Logic Journal of the IGPL Pub Date : 2024-05-25 DOI: 10.1093/jigpal/jzae064

J Climent Vidal, E Cosme Llópez

引用次数: 0

Combination of fuzzy control and reinforcement learning for wind turbine pitch control 风力涡轮机变桨控制的模糊控制与强化学习相结合

IF 1 4区数学

Logic Journal of the IGPL Pub Date : 2024-05-25 DOI: 10.1093/jigpal/jzae054

J Enrique Sierra-Garcia, Matilde Santos

{"title":"Combination of fuzzy control and reinforcement learning for wind turbine pitch control","authors":"J Enrique Sierra-Garcia, Matilde Santos","doi":"10.1093/jigpal/jzae054","DOIUrl":"https://doi.org/10.1093/jigpal/jzae054","url":null,"abstract":"The generation of the pitch control signal in a wind turbine (WT) is not straightforward due to the nonlinear dynamics of the system and the coupling of its internal variables; in addition, they are subjected to the uncertainty that comes from the random nature of the wind. Fuzzy logic has proved useful in applications with changing system parameters or where uncertainty is relevant as in this one, but the tuning of the fuzzy logic controller (FLC) parameters is neither straightforward nor an easy task. On the other hand, reinforcement learning (RL) allows systems to automatically learn, and this capability can be exploited to tune the FLC. In this work, a WT pitch control architecture that uses RL to tune the membership functions and scale the output of a fuzzy controller is proposed. The RL strategy calculates the fuzzy controller gains in order to reduce the output power error of the WT according to the wind speed. Different reward mechanisms based on the output power error have been considered. Simulation results with different wind profiles show that this architecture performs better (123.7 W) in terms of power errors than an FLC without RL (133.2 W) or a simpler PID (208.8 W). Even more, it provides a smooth response and outperforms other hybrid controllers such as RL-PID and radial basis function neural network control.","PeriodicalId":51114,"journal":{"name":"Logic Journal of the IGPL","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141151280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Pseudo-Deterministic Noisy Extremal Optimization algorithm for the pairwise connectivity Critical Node Detection Problem 针对成对连接关键节点检测问题的伪确定性噪声极值优化算法

IF 1 4区数学

Logic Journal of the IGPL Pub Date : 2024-05-20 DOI: 10.1093/jigpal/jzae056

Noémi Gaskó, M. Suciu, Rodica Ioana Lung, Tamás Képes

引用次数: 0

A non-stressful vision-based method for weighing live lambs 基于视觉的无压力活羔羊称重法

IF 1 4区数学

Logic Journal of the IGPL Pub Date : 2024-05-20 DOI: 10.1093/jigpal/jzae059

Virginia Riego del Castillo, Lidia Sánchez-González, Laura Fernández, Ruben Rebollar, E. Samperio

{"title":"A non-stressful vision-based method for weighing live lambs","authors":"Virginia Riego del Castillo, Lidia Sánchez-González, Laura Fernández, Ruben Rebollar, E. Samperio","doi":"10.1093/jigpal/jzae059","DOIUrl":"https://doi.org/10.1093/jigpal/jzae059","url":null,"abstract":"\u0000 Accurate measurement of livestock weight is a primary indicator in the meat industry to increase the economic gain. In lambs, the weight of a live animal is still usually estimated manually using traditional scales, resulting in a tedious process for the experienced assessor and stressful for the animal. In this paper, we propose a solution to this problem using computer vision techniques; thus, the proposed procedure estimates the weight of a lamb by analysing its zenithal image without interacting with the animal, which speeds up the process and reduces weighing costs. It is based on a data-driven decision support system that uses RGB-D machine vision techniques and regression models. Unlike existing methods, it does not require walk-over-weighing platforms or special and expensive infrastructures. The proposed method includes a decision support system that automatically rejects those images that are not appropriate to estimate the lamb weight. After determining the body contour of the lamb, we compute several features that feed different regression models. Best results were achieved with Extra Tree Regression ($R^{2}$=91.94%), outperforming the existing techniques. Using only an image, the proposed approach can identify with a minimum error the optimal weight of a lamb to be slaughtered, so as to maximise the economic profit.","PeriodicalId":51114,"journal":{"name":"Logic Journal of the IGPL","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141119789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Chlorophyll-α forecasting using LSTM, bidirectional LSTM and GRU networks in El Mar Menor (Spain) 利用 LSTM、双向 LSTM 和 GRU 网络对 El Mar Menor（西班牙）进行叶绿素-α 预报

IF 1 4区数学

Logic Journal of the IGPL Pub Date : 2024-05-19 DOI: 10.1093/jigpal/jzae046

Javier González-Enrique, María Inmaculada RodrÍguez-GarcÍa, Juan Jesús Ruiz-Aguilar, MarÍa Gema Carrasco-GarcÍa, Ivan Felis Enguix, Ignacio J Turias

{"title":"Chlorophyll-α forecasting using LSTM, bidirectional LSTM and GRU networks in El Mar Menor (Spain)","authors":"Javier González-Enrique, María Inmaculada RodrÍguez-GarcÍa, Juan Jesús Ruiz-Aguilar, MarÍa Gema Carrasco-GarcÍa, Ivan Felis Enguix, Ignacio J Turias","doi":"10.1093/jigpal/jzae046","DOIUrl":"https://doi.org/10.1093/jigpal/jzae046","url":null,"abstract":"The objective of this research is to develop accurate forecasting models for chlorophyll-α concentrations at various depths in El Mar Menor, Spain. Chlorophyll-α plays a crucial role in assessing eutrophication in this vulnerable ecosystem. To achieve this objective, various deep learning forecasting techniques, including long short-term memory, bidirectional long short-term memory and gated recurrent uni networks, were utilized. The models were designed to forecast the chlorophyll-α levels with a 2-week prediction horizon. To enhance the models’ accuracy, a sliding window method combined with a blocked cross-validation procedure for time series was also applied to these techniques. Two input strategies were also tested in this approach: using only chlorophyll-α time series and incorporating exogenous variables. The proposed approach significantly improved the accuracy of the predictive models, no matter the forecasting technique employed. Results were remarkable, with $overline{sigma}$ values reaching approximately 0.90 for the 0.5-m depth level and 0.80 for deeper levels. The proposed forecasting models and methodologies have great potential for predicting eutrophication episodes and acting as decision-making tools for environmental agencies. Accurate prediction of eutrophication episodes through these models could allow for proactive measures to be implemented, resulting in improved environmental management and the preservation of the ecosystem.","PeriodicalId":51114,"journal":{"name":"Logic Journal of the IGPL","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141062326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Behaviour of Machine Learning algorithms in the classification of energy consumption in school buildings 机器学习算法在学校建筑能耗分类中的行为

IF 1 4区数学

Logic Journal of the IGPL Pub Date : 2024-05-19 DOI: 10.1093/jigpal/jzae058

José Machado, António Chaves, Larissa Montenegro, Carlos Alves, Dalila Durães, Ricardo Machado, Paulo Novais

{"title":"Behaviour of Machine Learning algorithms in the classification of energy consumption in school buildings","authors":"José Machado, António Chaves, Larissa Montenegro, Carlos Alves, Dalila Durães, Ricardo Machado, Paulo Novais","doi":"10.1093/jigpal/jzae058","DOIUrl":"https://doi.org/10.1093/jigpal/jzae058","url":null,"abstract":"The significance of energy efficiency in the development of smart cities cannot be overstated. It is essential to have a clear understanding of the current energy consumption (EC) patterns in both public and private buildings. One way to achieve this is by employing machine learning classification algorithms, which offer a broader perspective on the factors influencing EC. These algorithms can be applied to real data from databases, making them valuable tools for smart city applications. In this paper, our focus is specifically on the EC of public schools in a Portuguese city, as this plays a crucial role in designing a Smart City. By utilizing a comprehensive dataset on school EC, we thoroughly evaluate multiple ML algorithms. The objective is to identify the most effective algorithm for classifying average EC patterns. The outcomes of this study hold significant value for school administrators and facility managers. By leveraging the predictions generated from the selected algorithm, they can optimize energy usage and, consequently, reduce costs. The use of a comprehensive dataset ensures the reliability and accuracy of our evaluations of various ML algorithms for EC classification.","PeriodicalId":51114,"journal":{"name":"Logic Journal of the IGPL","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141062351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0