2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES)最新文献_第2页

Determination of sugar content in whole Port Wine grape berries combining hyperspectral imaging with neural networks methodologies 结合高光谱成像和神经网络方法测定整个波特酒葡萄果实中的糖含量

2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES) Pub Date : 2014-12-01 DOI: 10.1109/CIES.2014.7011850

Véronique M. Gomes, A. Fernandes, A. Mendes-Faia, P. Melo-Pinto

{"title":"Determination of sugar content in whole Port Wine grape berries combining hyperspectral imaging with neural networks methodologies","authors":"Véronique M. Gomes, A. Fernandes, A. Mendes-Faia, P. Melo-Pinto","doi":"10.1109/CIES.2014.7011850","DOIUrl":"https://doi.org/10.1109/CIES.2014.7011850","url":null,"abstract":"The potential of hyperspectral imaging combined with machine learning algorithms to measure sugar content of whole grape berries is presented, as a starting point for developing generalized and flexible frameworks to estimate enological parameters in wine grape berries. In this context, to evaluate the generalization ability of the used machine learning procedure, two neural networks were trained with different training data to compare the performance of each one when tested with the same data set. Six whole grape berries were used for each sample to draw the hyperspectral spectrum in reflectance mode between 308 and 1028 nm. The sugar content was estimated from the spectra using feedforward multiplayer perceptrons in two different neural networks trained each one with a data set from a different year (2012 & 2013); the validation for both neural networks was done by n-fold cross-validation, and the test set used was from 2013. The test set revealed R2 values of 0.906 and RMSE of 1.165 °Brix for the neural network trained with 2012 data and R2 of 0.959 and RMSE of 1.026 °Brix for the 2013 training data neural network. The results obtained indicate that both neural networks present good results and that the 2012 training data neural network exhibits a good performance when compared with the other NN, suggesting that the approach is robust since a generalization (without further training) over years may be obtainable.","PeriodicalId":287779,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES)","volume":"60 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131453078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12

Artificial intelligence-based modelling and optimization of microdrilling processes 基于人工智能的微钻过程建模与优化

2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES) Pub Date : 2014-12-01 DOI: 10.1109/CIES.2014.7011830

Gerardo Beruvides, F. Castaño, R. Haber, Ramón Quiza Sardiñas, M. R. Santana

引用次数: 1

A survey on the application of Neural Networks in the safety assessment of oil and gas pipelines 神经网络在油气管道安全评价中的应用综述

2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES) Pub Date : 2014-12-01 DOI: 10.1109/CIES.2014.7011837

M. Layouni, S. Tahar, M. Hamdi

{"title":"A survey on the application of Neural Networks in the safety assessment of oil and gas pipelines","authors":"M. Layouni, S. Tahar, M. Hamdi","doi":"10.1109/CIES.2014.7011837","DOIUrl":"https://doi.org/10.1109/CIES.2014.7011837","url":null,"abstract":"Pipeline systems are an essential component of the oil and gas supply chain today. Although considered among the safest transportation methods, pipelines are still prone to failure due to corrosion and other types of defects. Such failures can lead to serious accidents resulting in big losses to life and the environment. It is therefore crucial for pipeline operators to reliably detect pipeline defects in an accurate and timely manner. Because of the size and complexity of pipeline systems, however, relying on human operators to perform the inspection is not possible. Automating the inspection process has been an important goal for the pipeline industry for a number of years. Significant progress has been made in that regard, and available techniques combine analytical modeling, numerical computations, and machine learning. This paper presents a survey of state-of-the-art methods used to assess the safety of the oil and gas pipelines. The paper explains the principles behind each method, highlights the setting where each method is most effective, and shows how several methods can be combined to achieve a higher level of accuracy.","PeriodicalId":287779,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES)","volume":"361 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124542167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

Energy price forecasting in the North Brazilian market using NN - ARIMA model and explanatory variables

2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES) Pub Date : 2014-12-01 DOI: 10.1109/CIES.2014.7011847

J. C. R. Filho, C. Affonso, R. C. L. Oliveira

引用次数: 4

Participatory learning in the neurofuzzy short-term load forecasting 参与式学习在神经模糊短期负荷预测中的应用

2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES) Pub Date : 2014-12-01 DOI: 10.1109/CIES.2014.7011848

M. Hell, P. Costa, F. Gomide

引用次数: 6

GA optimized time delayed feedback control of chaos in a memristor based chaotic circuit 遗传算法优化了基于忆阻器的混沌电路的混沌时滞反馈控制

2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES) Pub Date : 2014-12-01 DOI: 10.1109/CIES.2014.7011834

S. Saini, J. Saini

引用次数: 5

A Multi-Population Genetic Algorithm to solve multi-objective remote switches allocation problem in distribution networks 一种求解配电网多目标远程交换机分配问题的多种群遗传算法

2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES) Pub Date : 2014-12-01 DOI: 10.1109/CIES.2014.7011845

H. N. Alves, Railson Severiano de Sousa

引用次数: 7

Predicting the perforation capability of Kinetic Energy Projectiles using artificial neural networks 基于人工神经网络的动能弹丸射孔能力预测

2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES) Pub Date : 2014-12-01 DOI: 10.1109/CIES.2014.7011842

John R. Auten, R. Hammell

引用次数: 1

Video summarization based on Subclass Support Vector Data Description 基于子类支持向量数据描述的视频摘要

2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES) Pub Date : 2014-12-01 DOI: 10.1109/CIES.2014.7011849

V. Mygdalis, Alexandros Iosifidis, A. Tefas, I. Pitas

{"title":"Video summarization based on Subclass Support Vector Data Description","authors":"V. Mygdalis, Alexandros Iosifidis, A. Tefas, I. Pitas","doi":"10.1109/CIES.2014.7011849","DOIUrl":"https://doi.org/10.1109/CIES.2014.7011849","url":null,"abstract":"In this paper, we describe a method for video summarization that operates on a video segment level. We formulate this problem as the one of automatic video segment selection based on a learning process that employs salient video segment paradigms. We design an hierarchical learning scheme that consists of two steps. At the first step, an unsupervised process is performed in order to determine salient video segment types. The second step is a supervised learning process that is performed for each of the salient video segment type independently. For the latter case, since only salient training examples are available, the problem is stated as an one-class classification problem. In order to take into account subclass information that may appear in the video segment types, we introduce a novel formulation of the Support Vector Data Description method that exploits subclass information in its optimization process. We evaluate the proposed approach in three Hollywood movies, where the performance of the proposed Subclass SVDD (SSVDD) algorithm is compared with that of related methods. Experimental results show that the adoption of both hierarchical learning and the proposed SSVDD method contribute to the final classification performance.","PeriodicalId":287779,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130923523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 15

Application of hybrid incremental modeling for predicting surface roughness in micromachining processes 混合增量建模在微加工过程表面粗糙度预测中的应用

2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES) Pub Date : 2014-12-01 DOI: 10.1109/CIES.2014.7011831

F. Castaño, R. Haber, Raúl M. del Toro, Gerardo Beruvides

引用次数: 5