Logic Journal of the IGPL最新文献_第6页

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

Predictive maintenance of vehicle fleets through hybrid deep learning-based ensemble methods for industrial IoT datasets 针对工业物联网数据集，通过基于混合深度学习的集合方法，实现车队的预测性维护

IF 1 4区数学

Logic Journal of the IGPL Pub Date : 2024-03-27 DOI: 10.1093/jigpal/jzae017

Arindam Chaudhuri, Soumya K Ghosh

{"title":"Predictive maintenance of vehicle fleets through hybrid deep learning-based ensemble methods for industrial IoT datasets","authors":"Arindam Chaudhuri, Soumya K Ghosh","doi":"10.1093/jigpal/jzae017","DOIUrl":"https://doi.org/10.1093/jigpal/jzae017","url":null,"abstract":"Connected vehicle fleets have formed significant component of industrial internet of things scenarios as part of Industry 4.0 worldwide. The number of vehicles in these fleets has grown at a steady pace. The vehicles monitoring with machine learning algorithms has significantly improved maintenance activities. Predictive maintenance potential has increased where machines are controlled through networked smart devices. Here, benefits are accrued considering uptimes optimization. This has resulted in reduction of associated time and labor costs. It has also provided significant increase in cost benefit ratios. Considering vehicle fault trends in this research predictive maintenance problem is addressed through hybrid deep learning-based ensemble method (HDLEM). The ensemble framework which acts as predictive analytics engine comprises of three deep learning algorithms viz modified cox proportional hazard deep learning (MCoxPHDL), modified deep learning embedded semi supervised learning (MDLeSSL) and merged LSTM (MLSTM) networks. Both sensor as well as historical maintenance data are collected and prepared using benchmarking methods for HDLEM training and testing. Here, times between failures (TBF) modeling and prediction on multi-source data are successfully achieved. The results obtained are compared with stated deep learning models. This ensemble framework offers great potential towards achieving more profitable, efficient and sustainable vehicle fleet management solutions. This helps better telematics data implementation which ensures preventative management towards desired solution. The ensemble method's superiority is highlighted through several experimental results.","PeriodicalId":51114,"journal":{"name":"Logic Journal of the IGPL","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140311576","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

Black widow optimization for reducing the target uncertainties in localization wireless sensor networks 减少定位无线传感器网络中目标不确定性的黑寡妇优化技术

IF 1 4区数学

Logic Journal of the IGPL Pub Date : 2024-03-27 DOI: 10.1093/jigpal/jzae032

Rubén Ferrero-Guillén, José-Manuel Alija-Pérez, Alberto Martínez-Gutiérrez, Rubén Álvarez, Paula Verde, Javier Díez-González

{"title":"Black widow optimization for reducing the target uncertainties in localization wireless sensor networks","authors":"Rubén Ferrero-Guillén, José-Manuel Alija-Pérez, Alberto Martínez-Gutiérrez, Rubén Álvarez, Paula Verde, Javier Díez-González","doi":"10.1093/jigpal/jzae032","DOIUrl":"https://doi.org/10.1093/jigpal/jzae032","url":null,"abstract":"Localization Wireless Sensor Networks (WSN) represent a research topic with increasing interest due to their numerous applications. However, the viability of these systems is compromised by the attained localization uncertainties once implemented, since the network performance is highly dependent on the sensors location. The Node Location Problem (NLP) aims to obtain the optimal distribution of sensors for a particular environment, a problem already categorized as NP-Hard. Furthermore, localization WSN usually perform a sensor selection for determining which nodes are to be utilized for maximizing the achieved accuracy. This problem, defined as the Sensor Selection Problem (SSP), has also been categorized as NP-Hard. While different metaheuristics have been proposed for attaining a near optimal solution in both problems, no approach has considered the two problems simultaneously, thus resulting in suboptimal solutions since the SSP is biased by the actual node distribution once deployed. In this paper, a combined approach of both problems simultaneously is proposed, thus considering the SSP within the NLP. Furthermore, a novel metaheuristic combining the Black Widow Optimization (BWO) algorithm and the Variable Neighbourhood Descent Chains (VND-Chains) local search, denominated as BWO-VND-Chains, is particularly devised for the first time in the author’s best knowledge for the NLP, resulting in a more efficient and robust optimization technique. Finally, a comparison of different metaheuristic algorithms is proposed over an actual urban scenario, considering different sensor selection criteria in order to attain the best methodology and selection technique. Results show that the newly devised algorithm with the SSP criteria optimization achieves mean localization uncertainties up to 19.66 % lower than traditional methodologies.","PeriodicalId":51114,"journal":{"name":"Logic Journal of the IGPL","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140311577","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 variable neighbourhood search for minimization of operation times through warehouse layout optimization 通过优化仓库布局实现操作时间最小化的可变邻域搜索

IF 1 4区数学

Logic Journal of the IGPL Pub Date : 2024-03-27 DOI: 10.1093/jigpal/jzae018

Jon Díaz, Haizea Rodriguez, Jenny Fajardo-Calderín, Ignacio Angulo, Enrique Onieva

引用次数: 0

A novel memetic algorithm for solving the generalized traveling salesman problem 解决广义旅行推销员问题的新型记忆算法

IF 1 4区数学

Logic Journal of the IGPL Pub Date : 2024-03-27 DOI: 10.1093/jigpal/jzae019

Ovidiu Cosma, Petrică C Pop, Laura Cosma

引用次数: 0

Recurrent variational autoencoder approach for remaining useful life estimation 用于估算剩余使用寿命的递归变异自动编码器方法

IF 1 4区数学

Logic Journal of the IGPL Pub Date : 2024-03-27 DOI: 10.1093/jigpal/jzae023

Nahuel Costa, Luciano Sánchez

{"title":"Recurrent variational autoencoder approach for remaining useful life estimation","authors":"Nahuel Costa, Luciano Sánchez","doi":"10.1093/jigpal/jzae023","DOIUrl":"https://doi.org/10.1093/jigpal/jzae023","url":null,"abstract":"A new method for evaluating aircraft engine monitoring data is proposed. Commonly, prognostics and health management systems use knowledge of the degradation processes of certain engine components together with professional expert opinion to predict the Remaining Useful Life (RUL). New data-driven approaches have emerged to provide accurate diagnostics without relying on such costly processes. However, most of them lack an explanatory component to understand model learning and/or the nature of the data. A solution based on a novel recurrent version of a VAE is proposed in this paper to overcome this gap. The latent space learned by the model, trained with data from sensors placed in different parts of these engines, is exploited to build a self-explanatory map that can visually evaluate the rate of deterioration of the engines. Besides, a simple regressor model is built on top of the learned features of the encoder in order to numerically predict the RUL. As a result, remarkable prognostic accuracy is achieved, outperforming most of the novel and state-of-the-art approaches on the available modular aero-propulsion system simulation data (C-MAPSS dataset) from NASA. In addition, a practical real-world application is included for Turbofan engine data. This study shows that the proposed prognostic and explainable framework presents a promising new approach.","PeriodicalId":51114,"journal":{"name":"Logic Journal of the IGPL","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140316943","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

LSTM vs CNN in real ship trajectory classification LSTM 与 CNN 在真实船舶轨迹分类中的对比

IF 1 4区数学

Logic Journal of the IGPL Pub Date : 2024-03-26 DOI: 10.1093/jigpal/jzae027

Juan Pedro Llerena, Jesús García, José Manuel Molina

{"title":"LSTM vs CNN in real ship trajectory classification","authors":"Juan Pedro Llerena, Jesús García, José Manuel Molina","doi":"10.1093/jigpal/jzae027","DOIUrl":"https://doi.org/10.1093/jigpal/jzae027","url":null,"abstract":"Ship-type identification in a maritime context can be critical to the authorities to control the activities being carried out. Although Automatic Identification Systems has been mandatory for certain vessels, if a vessel does not have them voluntarily or not, it can lead to a whole set of problems, which is why the use of tracking alternatives such as radar is fully complementary for a vessel monitoring systems. However, radars provide positions, but not what they are detecting. Having systems capable of adding categorical information to radar detections of vessels makes it possible to increase control of the activities being carried out, improve safety in maritime traffic, and optimize on-site inspection resources on the part of the authorities. This paper addresses the binary classification problem (fishing ships versus all other vessels) using unbalanced data from real vessel trajectories. It is performed from a deep learning approach comparing two of the main trends, Convolutional Neural Networks and Long Short-Term Memory. In this paper, it is proposed the weighted cross-entropy methodology and compared with classical data balancing strategies. Both networks show high performance when applying weighted cross-entropy compared with the classical machine learning approaches and classical balancing techniques. This work is shown to be a novel approach to the international problem of identifying fishing ships without context.","PeriodicalId":51114,"journal":{"name":"Logic Journal of the IGPL","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140316876","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

Tabu search and genetic algorithm in rims production process assignment 轮辋生产工艺分配中的塔布搜索和遗传算法

IF 1 4区数学

Logic Journal of the IGPL Pub Date : 2024-03-26 DOI: 10.1093/jigpal/jzae031

Anna Burduk, Grzegorz Bocewicz, Łukasz Łampika, Dagmara Łapczyńska, Kamil Musiał

引用次数: 0

An innovative framework for supporting content-based authorship identification and analysis in social media networks 支持社交媒体网络中基于内容的作者身份识别和分析的创新框架

IF 1 4区数学

Logic Journal of the IGPL Pub Date : 2024-03-26 DOI: 10.1093/jigpal/jzae020

José Gaviria de la Puerta, Iker Pastor-López, Alberto Tellaeche, Borja Sanz, Hugo Sanjurjo-González, Alfredo Cuzzocrea, Pablo G Bringas

引用次数: 0

AGV fuzzy control optimized by genetic algorithms 利用遗传算法优化 AGV 模糊控制

IF 1 4区数学

Logic Journal of the IGPL Pub Date : 2024-03-24 DOI: 10.1093/jigpal/jzae033

J Enrique Sierra-Garcia, Matilde Santos

引用次数: 0