Logic Journal of the IGPL最新文献_第3页

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":"45 1","pages":""},"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":"37 1","pages":""},"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

Clustering techniques performance comparison for predicting the battery state of charge: A hybrid model approach 预测电池充电状态的聚类技术性能比较：混合模型方法

IF 1 4区数学

Logic Journal of the IGPL Pub Date : 2024-05-09 DOI: 10.1093/jigpal/jzae021

María Teresa Ordás, David Yeregui Marcos del Blanco, José Aveleira-Mata, Francisco Zayas-Gato, Esteban Jove, José-Luis Casteleiro-Roca, Héctor Quintián, José Luis Calvo-Rolle, Héctor Alaiz-Moreton

引用次数: 0

The application of artificial neural networks to forecast financial time series 人工神经网络在金融时间序列预测中的应用

IF 1 4区数学

Logic Journal of the IGPL Pub Date : 2024-05-09 DOI: 10.1093/jigpal/jzae050

D González-Cortés, E Onieva, I Pastor, J Wu

{"title":"The application of artificial neural networks to forecast financial time series","authors":"D González-Cortés, E Onieva, I Pastor, J Wu","doi":"10.1093/jigpal/jzae050","DOIUrl":"https://doi.org/10.1093/jigpal/jzae050","url":null,"abstract":"The amount of information that is produced on a daily basis in the financial markets is vast and complex; consequently, the development of systems that simplify decision-making is an essential endeavor. In this article, several intelligent systems are proposed and tested to predict the closing price of the IBEX 35 index using more than ten years of historical data and five distinct architectures for neural networks. A multi-layer perceptron was the first step, followed by a simple recurrent neural network, a gated recurrent unit network and two long-short-term memory (LSTM) networks. The results of the analyses performed on these models have demonstrated a powerful capacity for prediction. Additionally, the findings of this research point to the fact that the application of intelligent systems can simplify the decision-making process in financial markets, which is a substantial advantage. Furthermore, by comparing the predicted outcome errors between the models, the LSTM presents the lowest error with a higher computational time in the training phase. The LSTM was able to accurately forecast the closing price of the day as well as the price for the following one and two days in advance. In conclusion, the empirical results demonstrated that these models could accurately predict financial data for trading purposes and that the application of intelligent systems, such as the LSTM network, represents a promising advancement in financial technology.","PeriodicalId":51114,"journal":{"name":"Logic Journal of the IGPL","volume":"17 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140934387","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

Combining statistical dialog management and intent recognition for enhanced response selection 将统计对话管理和意图识别结合起来，增强应答选择功能

IF 1 4区数学

Logic Journal of the IGPL Pub Date : 2024-05-09 DOI: 10.1093/jigpal/jzae045

David Griol, Zoraida Callejas

{"title":"Combining statistical dialog management and intent recognition for enhanced response selection","authors":"David Griol, Zoraida Callejas","doi":"10.1093/jigpal/jzae045","DOIUrl":"https://doi.org/10.1093/jigpal/jzae045","url":null,"abstract":"Conversational interfaces are becoming ubiquitous in an increasing number of application domains as Artificial Intelligence, Natural Language Processing and Machine Learning methods associated with the recognition, understanding and generation of natural language advance by leaps and bounds. However, designing the dialog model of these systems is still a very demanding task requiring a great deal of effort given the number of information sources to be considered related to the analysis of user utterances, interaction context, information repositories, etc. In this paper, we present a general framework for increasing the quality of the system responses by combining a statistical dialog management technique and a deep learning-based intention recognizer that allow replacing the system responses initially selected by the statistical dialog model with other presumably better candidates. This approach is portable to different task-oriented domains, a diversity of methodologies for dialog management and intention estimation techniques. We have evaluated our two-step proposal using two conversational systems, assessed several intention recognition methodologies and used the developed modules to dynamically select the system responses. The results of the evaluation show that the proposed framework achieves satisfactory results by making it possible to reduce the number of non-coherent dialog responses by replacing them by more coherent alternatives.","PeriodicalId":51114,"journal":{"name":"Logic Journal of the IGPL","volume":"23 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140934257","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

Data dimensionality reduction for an optimal switching mode classification applied to a step-down power converter 应用于降压型电源转换器的优化开关模式分类的数据降维方法

IF 1 4区数学

Logic Journal of the IGPL Pub Date : 2024-04-06 DOI: 10.1093/jigpal/jzae036

Luis-Alfonso Fernandez-Serantes, José-Luis Casteleiro-Roca, Hubert Berger, Dragan Simić, José-Luis Calvo-Rolle

引用次数: 0

Gaussian process-based analysis of the nitrogen dioxide at Madrid Central Low Emission Zone 基于高斯过程的马德里中央低排放区二氧化氮分析

IF 1 4区数学

Logic Journal of the IGPL Pub Date : 2024-04-06 DOI: 10.1093/jigpal/jzae041

Juan Luis Gómez-González, Miguel Cárdenas-Montes

{"title":"Gaussian process-based analysis of the nitrogen dioxide at Madrid Central Low Emission Zone","authors":"Juan Luis Gómez-González, Miguel Cárdenas-Montes","doi":"10.1093/jigpal/jzae041","DOIUrl":"https://doi.org/10.1093/jigpal/jzae041","url":null,"abstract":"Concern about air-quality in urban areas has led to the implementation of Low Emission Zones as one of many other initiatives to control it. Recently in Spain, the enactment of a law made this mandatory for cities with a population larger than 50k inhabitants. The delimitation of these areas is not without controversy because of possible negative economic and social impacts. Therefore, clear assessments of how these initiatives decrease pollutant concentrations are to be provided. Madrid Central is a major initiative of Madrid city council for reducing motor traffic and the associated air pollution in the city centre. This Low Emission Zone starts at the end of 2018, but the first fully-operational period corresponds to the second quarter of 2019. In this work, a methodology based on the Gaussian Process to analyse the evolution of Nitrogen Dioxide inside Madrid Central is undertaken. A Gaussian Process is a stochastic process suitable for interpretable model selection and predictions. Due to its probabilistic nature it provides error estimation at predictions. After the activation of Madrid Central, a relevant reduction of Nitrogen Dioxide has been observed. However, the role of the meteorology during this period must be ascertained to correctly evaluate the role of the activation of the Low Emission Zone against a prone weather. In this work, a model based on the Gaussian Process is trained with meteorological information to predict the concentration of Nitrogen Dioxide at Madrid Central, $[NO_{2}]$. This probabilistic description allows extracting statistical information on the reduction affected by the meteorological scenario and separately by the Madrid Central activation.","PeriodicalId":51114,"journal":{"name":"Logic Journal of the IGPL","volume":"37 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140601117","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 learning for electric energy consumption forecasting: Application to the Paraguayan system 电能消耗预测的机器学习：在巴拉圭系统中的应用

IF 1 4区数学

Logic Journal of the IGPL Pub Date : 2024-04-06 DOI: 10.1093/jigpal/jzae035

Félix Morales-Mareco, Miguel García-Torres, Federico Divina, Diego H Stalder, Carlos Sauer

{"title":"Machine learning for electric energy consumption forecasting: Application to the Paraguayan system","authors":"Félix Morales-Mareco, Miguel García-Torres, Federico Divina, Diego H Stalder, Carlos Sauer","doi":"10.1093/jigpal/jzae035","DOIUrl":"https://doi.org/10.1093/jigpal/jzae035","url":null,"abstract":"In this paper we address the problem of short-term electric energy prediction using a time series forecasting approach applied to data generated by a Paraguayan electricity distribution provider. The dataset used in this work contains data collected over a three-year period. This is the first time that these data have been used; therefore, a preprocessing phase of the data was also performed. In particular, we propose a comparative study of various machine learning and statistical strategies with the objective of predicting the electric energy consumption for a given prediction horizon, in our case seven days, using historical data. In this paper we have tested the effectiveness of the techniques with different historical window sizes. Specifically, we considered two ensemble strategies, a neural network, a deep learning technique and linear regression. Moreover, in this study, we tested whether the inclusion of meteorological data can help achieve better predictions. In particular, we considered data regarding temperature, humidity, wind speed and atmospheric pressure registered during the three-year period of data collection. The results show that, in general, the deep learning approach obtains the best results and that such results are obtained when meteorological data are also considered. Moreover, when meteorological data is used, a smaller historical window size is required to obtain precise predictions.","PeriodicalId":51114,"journal":{"name":"Logic Journal of the IGPL","volume":"97 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140601009","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

PhrasIS: Phrase Inference and Similarity benchmark PhrasIS：短语推理和相似性基准

IF 1 4区数学

Logic Journal of the IGPL Pub Date : 2024-04-06 DOI: 10.1093/jigpal/jzae037

I Lopez-Gazpio, J Gaviria, P García, H Sanjurjo-González, B Sanz, A Zarranz, M Maritxalar, E Agirre

引用次数: 0

On split-octonionic curves 关于分裂八离子曲线

IF 1 4区数学

Logic Journal of the IGPL Pub Date : 2024-03-28 DOI: 10.1093/jigpal/jzae039

Jeta Alo, MÜcahit Akbiyik

引用次数: 0