Pub Date : 2014-12-01DOI: 10.1109/CIES.2014.7011850
Véronique M. Gomes, A. Fernandes, A. Mendes-Faia, P. Melo-Pinto
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.
{"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}
Pub Date : 2014-12-01DOI: 10.1109/CIES.2014.7011830
Gerardo Beruvides, F. Castaño, R. Haber, Ramón Quiza Sardiñas, M. R. Santana
This paper presents one strategy for modeling and optimization of a microdilling process. Experimental work has been carried out for measuring the thrust force for five different commonly used alloys, under several cutting conditions. An artificial neural network-based model was implemented for modelling the thrust force. Neural model showed a high goodness of fit and appropriate generalization capability. The optimization process was executed by considered two different and conflicting objectives: the unit machining time and the thrust force (based on the previously obtained model). A multiobjective genetic algorithm was used for solving the optimization problem and a set of non-dominated solutions was obtained. The Pareto's front representation was depicted and used for assisting the decision making process.
{"title":"Artificial intelligence-based modelling and optimization of microdrilling processes","authors":"Gerardo Beruvides, F. Castaño, R. Haber, Ramón Quiza Sardiñas, M. R. Santana","doi":"10.1109/CIES.2014.7011830","DOIUrl":"https://doi.org/10.1109/CIES.2014.7011830","url":null,"abstract":"This paper presents one strategy for modeling and optimization of a microdilling process. Experimental work has been carried out for measuring the thrust force for five different commonly used alloys, under several cutting conditions. An artificial neural network-based model was implemented for modelling the thrust force. Neural model showed a high goodness of fit and appropriate generalization capability. The optimization process was executed by considered two different and conflicting objectives: the unit machining time and the thrust force (based on the previously obtained model). A multiobjective genetic algorithm was used for solving the optimization problem and a set of non-dominated solutions was obtained. The Pareto's front representation was depicted and used for assisting the decision making process.","PeriodicalId":287779,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES)","volume":"90 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":"126063396","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}
Pub Date : 2014-12-01DOI: 10.1109/CIES.2014.7011837
M. Layouni, S. Tahar, M. Hamdi
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.
{"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}
Pub Date : 2014-12-01DOI: 10.1109/CIES.2014.7011847
J. C. R. Filho, C. Affonso, R. C. L. Oliveira
This paper proposes a new hybrid approach for short-term energy price prediction. This approach combines ARIMA and NN models in a cascaded structure and uses explanatory variables. A two step procedure is applied. In the first step, the explanatory variables are predicted. In the second one, the energy prices are forecasted by using the explanatory variables prediction. The prediction time horizon is 12 weeks-ahead and is applied to the North Brazilian submarket, which adopts a cost-based model with unique characteristics of price behavior. The proposed strategy is compared with traditional techniques like ARIMA and NN and the results show satisfactory accuracy and good ability to predict spikes. Thus, the model can be an attractive tool to mitigate risks in purchasing power.
{"title":"Energy price forecasting in the North Brazilian market using NN - ARIMA model and explanatory variables","authors":"J. C. R. Filho, C. Affonso, R. C. L. Oliveira","doi":"10.1109/CIES.2014.7011847","DOIUrl":"https://doi.org/10.1109/CIES.2014.7011847","url":null,"abstract":"This paper proposes a new hybrid approach for short-term energy price prediction. This approach combines ARIMA and NN models in a cascaded structure and uses explanatory variables. A two step procedure is applied. In the first step, the explanatory variables are predicted. In the second one, the energy prices are forecasted by using the explanatory variables prediction. The prediction time horizon is 12 weeks-ahead and is applied to the North Brazilian submarket, which adopts a cost-based model with unique characteristics of price behavior. The proposed strategy is compared with traditional techniques like ARIMA and NN and the results show satisfactory accuracy and good ability to predict spikes. Thus, the model can be an attractive tool to mitigate risks in purchasing power.","PeriodicalId":287779,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES)","volume":"28 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":"131558469","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}
Pub Date : 2014-12-01DOI: 10.1109/CIES.2014.7011848
M. Hell, P. Costa, F. Gomide
This paper presents a new approach for short-term load forecasting using the participatory learning paradigm. Participatory learning paradigm is a new training procedure that follows the human learning mechanism adopting an acceptance mechanism to determine which observation is used based upon its compatibility with the current beliefs. Here, participatory learning is used to train a class of hybrid neuro-fuzzy network to forecast 24-h daily energy consumption series of an electrical operation unit located at the Southeast region of Brazil. Experimental results show that the neurofuzzy approach with participatory learning requires less computational effort, is more robust, and more efficient than alternative neural methods. The approach is particularly efficient when training data reflects anomalous load conditions or contains spurious measurements. Comparisons with alternative approaches suggested in the literature are also included to show the effectiveness of participatory learning.
{"title":"Participatory learning in the neurofuzzy short-term load forecasting","authors":"M. Hell, P. Costa, F. Gomide","doi":"10.1109/CIES.2014.7011848","DOIUrl":"https://doi.org/10.1109/CIES.2014.7011848","url":null,"abstract":"This paper presents a new approach for short-term load forecasting using the participatory learning paradigm. Participatory learning paradigm is a new training procedure that follows the human learning mechanism adopting an acceptance mechanism to determine which observation is used based upon its compatibility with the current beliefs. Here, participatory learning is used to train a class of hybrid neuro-fuzzy network to forecast 24-h daily energy consumption series of an electrical operation unit located at the Southeast region of Brazil. Experimental results show that the neurofuzzy approach with participatory learning requires less computational effort, is more robust, and more efficient than alternative neural methods. The approach is particularly efficient when training data reflects anomalous load conditions or contains spurious measurements. Comparisons with alternative approaches suggested in the literature are also included to show the effectiveness of participatory learning.","PeriodicalId":287779,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES)","volume":"65 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":"121589169","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}
Pub Date : 2014-12-01DOI: 10.1109/CIES.2014.7011834
S. Saini, J. Saini
Chaotic state of a nonlinear system may be harmful due to its extreme sensitivity to initial conditions and irregularity in behavior. This paper addresses the problem of controlling chaos in a memristor based chaotic circuit using time delayed feedback method. Genetic algorithm has been used as a search tool to optimize the feedback path gain. Extensive computer simulations demonstrate that successful chaos control can be achieved by using this scheme, leading the system's chaotic state towards a fixed point or sustained oscillations depending on the range of feedback gain values.
{"title":"GA optimized time delayed feedback control of chaos in a memristor based chaotic circuit","authors":"S. Saini, J. Saini","doi":"10.1109/CIES.2014.7011834","DOIUrl":"https://doi.org/10.1109/CIES.2014.7011834","url":null,"abstract":"Chaotic state of a nonlinear system may be harmful due to its extreme sensitivity to initial conditions and irregularity in behavior. This paper addresses the problem of controlling chaos in a memristor based chaotic circuit using time delayed feedback method. Genetic algorithm has been used as a search tool to optimize the feedback path gain. Extensive computer simulations demonstrate that successful chaos control can be achieved by using this scheme, leading the system's chaotic state towards a fixed point or sustained oscillations depending on the range of feedback gain values.","PeriodicalId":287779,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES)","volume":"2 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":"115190847","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}
Pub Date : 2014-12-01DOI: 10.1109/CIES.2014.7011845
H. N. Alves, Railson Severiano de Sousa
This paper presents a Multi-Population Genetic Algorithm to solve the switches allocation problem in electric distribution networks considering remote and manual switches. In the procedure, reliability index, remote or manual controlled switch and investments costs are considered. The problem is formulated as a multi-objective optimization problem to be solved trough of weighted sum method. This method obtains the optimal solution considering a priori articulation of preferences established by the decision maker in terms of an aggregating function which combines individual objective values into a single utility value. A 282-bus test system is presented. The results confirm the efficiency of the proposed method which makes it promising to solve complex problems of switches placement in electric distribution feeders.
{"title":"A Multi-Population Genetic Algorithm to solve multi-objective remote switches allocation problem in distribution networks","authors":"H. N. Alves, Railson Severiano de Sousa","doi":"10.1109/CIES.2014.7011845","DOIUrl":"https://doi.org/10.1109/CIES.2014.7011845","url":null,"abstract":"This paper presents a Multi-Population Genetic Algorithm to solve the switches allocation problem in electric distribution networks considering remote and manual switches. In the procedure, reliability index, remote or manual controlled switch and investments costs are considered. The problem is formulated as a multi-objective optimization problem to be solved trough of weighted sum method. This method obtains the optimal solution considering a priori articulation of preferences established by the decision maker in terms of an aggregating function which combines individual objective values into a single utility value. A 282-bus test system is presented. The results confirm the efficiency of the proposed method which makes it promising to solve complex problems of switches placement in electric distribution feeders.","PeriodicalId":287779,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES)","volume":"49 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":"116728852","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}
Pub Date : 2014-12-01DOI: 10.1109/CIES.2014.7011842
John R. Auten, R. Hammell
The U.S. Army requires the evaluation of new weapon and vehicle systems through the use of experimental testing and Vulnerability/Lethality (V/L) modeling & simulation (M&S). The current M&S methods being utilized often require significant amounts of time and subject matter expertise. This typically means that quick results cannot be provided when needed to address new threats encountered in theater. Recently there has been an increased focus on rapid results for M&S efforts that can also provide accurate results. Accurately modeling the penetration and residual properties of a ballistic threat as it progresses through a target is an extremely important part of determining the effectiveness of the threat against that target. This paper presents preliminary results from the training of an artificial neural network for the prediction of perforation of a monolithic metallic target plate.
{"title":"Predicting the perforation capability of Kinetic Energy Projectiles using artificial neural networks","authors":"John R. Auten, R. Hammell","doi":"10.1109/CIES.2014.7011842","DOIUrl":"https://doi.org/10.1109/CIES.2014.7011842","url":null,"abstract":"The U.S. Army requires the evaluation of new weapon and vehicle systems through the use of experimental testing and Vulnerability/Lethality (V/L) modeling & simulation (M&S). The current M&S methods being utilized often require significant amounts of time and subject matter expertise. This typically means that quick results cannot be provided when needed to address new threats encountered in theater. Recently there has been an increased focus on rapid results for M&S efforts that can also provide accurate results. Accurately modeling the penetration and residual properties of a ballistic threat as it progresses through a target is an extremely important part of determining the effectiveness of the threat against that target. This paper presents preliminary results from the training of an artificial neural network for the prediction of perforation of a monolithic metallic target plate.","PeriodicalId":287779,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES)","volume":"103 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":"130735406","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}
Pub Date : 2014-12-01DOI: 10.1109/CIES.2014.7011849
V. Mygdalis, Alexandros Iosifidis, A. Tefas, I. Pitas
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.
{"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}
Pub Date : 2014-12-01DOI: 10.1109/CIES.2014.7011831
F. Castaño, R. Haber, Raúl M. del Toro, Gerardo Beruvides
This paper presents the application of a hybrid incremental modeling strategy (HIM) for real-time estimation of surface roughness in micromachining processes. This strategy essentially consists of two steps. First, a representative hybrid incremental model of micromachining process is obtained. The final result of this model describes output as a function of two inputs (feed per tooth quadratic and vibration mean quadratic (rms) in the Z axis) and output (surface roughness Ra). Second, the hybrid incremental model is evaluated in real time for predicting the surface roughness. The model is experimentally tested by embedding the computational procedure in a real-time monitoring system of surface roughness. The prototype evaluation shows a success rate in the estimate of surface roughness about 80%. These results are the basement for developing a new generation of embedded systems for monitoring surface roughness of micro components in real time and the further exploitation of the monitoring system at industrial level.
{"title":"Application of hybrid incremental modeling for predicting surface roughness in micromachining processes","authors":"F. Castaño, R. Haber, Raúl M. del Toro, Gerardo Beruvides","doi":"10.1109/CIES.2014.7011831","DOIUrl":"https://doi.org/10.1109/CIES.2014.7011831","url":null,"abstract":"This paper presents the application of a hybrid incremental modeling strategy (HIM) for real-time estimation of surface roughness in micromachining processes. This strategy essentially consists of two steps. First, a representative hybrid incremental model of micromachining process is obtained. The final result of this model describes output as a function of two inputs (feed per tooth quadratic and vibration mean quadratic (rms) in the Z axis) and output (surface roughness Ra). Second, the hybrid incremental model is evaluated in real time for predicting the surface roughness. The model is experimentally tested by embedding the computational procedure in a real-time monitoring system of surface roughness. The prototype evaluation shows a success rate in the estimate of surface roughness about 80%. These results are the basement for developing a new generation of embedded systems for monitoring surface roughness of micro components in real time and the further exploitation of the monitoring system at industrial level.","PeriodicalId":287779,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES)","volume":"61 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":"126583522","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：