Pub Date : 2011-10-27DOI: 10.1109/CIMSA.2011.6059937
Xin Wang, E. Petriu
This paper presents a methodology for using the high resolution three dimensional (3D) surface data of fabric samples to acquire their surface roughness parameter measurement. Firstly, we compute a parameter FDFFT, which is the fractal dimension estimated from the two-dimensional fast Fourier transform (2DFFT) of 3D surface scan. We validate the rotation-invariance and scale-invariance of FDFFT using fractal Brownian images. Secondly, in order to evaluate the correctness of FDFFT, we provide a method of calculating standard roughness parameters from 3D fabric surface. According to the test results, we demonstrated that FDFFT is a fast and reliable parameter for fabric roughness measurement based on 3D surface data. Finally, we attempt a neural network model using back propagation algorithm and FDFFT for predicting the standard roughness parameters. The proposed neural network model shows good performance to both training samples and test samples.
{"title":"Neural fractal prediction of three dimensional surface roughness","authors":"Xin Wang, E. Petriu","doi":"10.1109/CIMSA.2011.6059937","DOIUrl":"https://doi.org/10.1109/CIMSA.2011.6059937","url":null,"abstract":"This paper presents a methodology for using the high resolution three dimensional (3D) surface data of fabric samples to acquire their surface roughness parameter measurement. Firstly, we compute a parameter FDFFT, which is the fractal dimension estimated from the two-dimensional fast Fourier transform (2DFFT) of 3D surface scan. We validate the rotation-invariance and scale-invariance of FDFFT using fractal Brownian images. Secondly, in order to evaluate the correctness of FDFFT, we provide a method of calculating standard roughness parameters from 3D fabric surface. According to the test results, we demonstrated that FDFFT is a fast and reliable parameter for fabric roughness measurement based on 3D surface data. Finally, we attempt a neural network model using back propagation algorithm and FDFFT for predicting the standard roughness parameters. The proposed neural network model shows good performance to both training samples and test samples.","PeriodicalId":422972,"journal":{"name":"2011 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) Proceedings","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122154375","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 : 2011-10-27DOI: 10.1109/CIMSA.2011.6059917
Patrik Jonsson
It is important to correctly determine road condition as it contains essential information for improving traffic safety. Knowledge about the road condition is used by maintenance personnel as a trigger for snow removal and deicing tasks. The presence of severe road conditions is also communicated as warnings and speed reduction recommendations to road users. Previous research shows that road images and data from Road Weather information Systems (RWiS) give enough information to identify road conditions, such as dry, wet, snowy, icy and tracks. The hypothesis of the new model was that it should be possible to develop a model that could classify road conditions from existing RWiS road weather data and road images. This paper proposes a model that gives a correct classification of the road conditions dry, wet, snowy and icy at an accuracy rate of 91% to 100%.
{"title":"Classification of road conditions: From camera images and weather data","authors":"Patrik Jonsson","doi":"10.1109/CIMSA.2011.6059917","DOIUrl":"https://doi.org/10.1109/CIMSA.2011.6059917","url":null,"abstract":"It is important to correctly determine road condition as it contains essential information for improving traffic safety. Knowledge about the road condition is used by maintenance personnel as a trigger for snow removal and deicing tasks. The presence of severe road conditions is also communicated as warnings and speed reduction recommendations to road users. Previous research shows that road images and data from Road Weather information Systems (RWiS) give enough information to identify road conditions, such as dry, wet, snowy, icy and tracks. The hypothesis of the new model was that it should be possible to develop a model that could classify road conditions from existing RWiS road weather data and road images. This paper proposes a model that gives a correct classification of the road conditions dry, wet, snowy and icy at an accuracy rate of 91% to 100%.","PeriodicalId":422972,"journal":{"name":"2011 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) Proceedings","volume":"355 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127683758","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 : 2011-10-27DOI: 10.1109/CIMSA.2011.6059916
B. Karimi, I. Saboori, Mostafa Lotfi-Forushani
This paper presents an optimized controller based on multi-objective particle swarm optimization (MOPSO) for a longitudinal axes of multivariable system in one of the aircraft flight conditions. This controller has been developed to control the attack angle and pitch attitude angle independently. These angles are required for designing a set of direct force modes for longitudinal axes. A particle swarm optimization algorithm is used to find the global optimal solution for this problem. The autopilot system in military or civil aircrafts is an essential component and in this paper, the autopilot system via 3 degree of freedom model for the control and guidance of aircraft is considered. The effectiveness of the proposed controller is illustrated by considering the HIMAT military aircraft. The simulation results verify the merits of the proposed controller.
{"title":"Multivariable controller design for aircraft longitudinal autopilot based on particle swarm optimization algorithm","authors":"B. Karimi, I. Saboori, Mostafa Lotfi-Forushani","doi":"10.1109/CIMSA.2011.6059916","DOIUrl":"https://doi.org/10.1109/CIMSA.2011.6059916","url":null,"abstract":"This paper presents an optimized controller based on multi-objective particle swarm optimization (MOPSO) for a longitudinal axes of multivariable system in one of the aircraft flight conditions. This controller has been developed to control the attack angle and pitch attitude angle independently. These angles are required for designing a set of direct force modes for longitudinal axes. A particle swarm optimization algorithm is used to find the global optimal solution for this problem. The autopilot system in military or civil aircrafts is an essential component and in this paper, the autopilot system via 3 degree of freedom model for the control and guidance of aircraft is considered. The effectiveness of the proposed controller is illustrated by considering the HIMAT military aircraft. The simulation results verify the merits of the proposed controller.","PeriodicalId":422972,"journal":{"name":"2011 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) Proceedings","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131249696","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 : 2011-10-27DOI: 10.1109/CIMSA.2011.6059938
D. Megherbi, B. Soper
Predicting the clinical outcome prior to minimally invasive treatments for Benign Prostatic Hperlasia (BPH) cases would be very useful. However, clinical prediction has not been reliable in spite of multiple assessment parameters, such as symptom indices and flow rates. In our prior study, Artificial Intelligence (AI) algorithms were used to train computers to predict the surgical outcome in BPH patients treated by TURP or VLAP. Our aim was to investigate whether, based on eleven clinical biomarker features, AI can reproduce the clinical outcome of known cases and assist the urologist in predicting surgical outcomes. In this paper, the objective is to perform data analysis to investigate if specific features have a greater impact on predicting whether the patients had the desired outcome after a surgical procedure is done. Finally, how the number of significant features ought to be weighted to predict the outcome after surgery, is determined to create the most accurate prediction method. Here both the Decision Tree and Naïve Bayse machine learning methods are used and compared.
{"title":"Effect of feature selection on machine learning algorithms for more accurate predictor of surgical outcomes in Benign Pro Static Hyperplasia cases (BPH)","authors":"D. Megherbi, B. Soper","doi":"10.1109/CIMSA.2011.6059938","DOIUrl":"https://doi.org/10.1109/CIMSA.2011.6059938","url":null,"abstract":"Predicting the clinical outcome prior to minimally invasive treatments for Benign Prostatic Hperlasia (BPH) cases would be very useful. However, clinical prediction has not been reliable in spite of multiple assessment parameters, such as symptom indices and flow rates. In our prior study, Artificial Intelligence (AI) algorithms were used to train computers to predict the surgical outcome in BPH patients treated by TURP or VLAP. Our aim was to investigate whether, based on eleven clinical biomarker features, AI can reproduce the clinical outcome of known cases and assist the urologist in predicting surgical outcomes. In this paper, the objective is to perform data analysis to investigate if specific features have a greater impact on predicting whether the patients had the desired outcome after a surgical procedure is done. Finally, how the number of significant features ought to be weighted to predict the outcome after surgery, is determined to create the most accurate prediction method. Here both the Decision Tree and Naïve Bayse machine learning methods are used and compared.","PeriodicalId":422972,"journal":{"name":"2011 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) Proceedings","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134278997","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 : 2011-10-27DOI: 10.1109/CIMSA.2011.6059930
A. Genovese, R. D. Labati, V. Piuri, F. Scotti
In this paper, we propose an image processing system for the detection of wildfire smoke based on computational intelligence techniques and capable of adapting to different applicative environments. The proposed system is designed for processing with limited computational complexity. The detection process focuses on the extraction of specific features of wildfire smoke. A computational intelligence classifier is adopted to identify the presence of smoke. In order to test its effectiveness, the proposed system has been tested with low quality frame sequences, providing the capability to deal also with low cost cameras. The results indicate that the proposed approach is accurate and can be effectively applied in different environmental conditions.
{"title":"Wildfire smoke detection using computational intelligence techniques","authors":"A. Genovese, R. D. Labati, V. Piuri, F. Scotti","doi":"10.1109/CIMSA.2011.6059930","DOIUrl":"https://doi.org/10.1109/CIMSA.2011.6059930","url":null,"abstract":"In this paper, we propose an image processing system for the detection of wildfire smoke based on computational intelligence techniques and capable of adapting to different applicative environments. The proposed system is designed for processing with limited computational complexity. The detection process focuses on the extraction of specific features of wildfire smoke. A computational intelligence classifier is adopted to identify the presence of smoke. In order to test its effectiveness, the proposed system has been tested with low quality frame sequences, providing the capability to deal also with low cost cameras. The results indicate that the proposed approach is accurate and can be effectively applied in different environmental conditions.","PeriodicalId":422972,"journal":{"name":"2011 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) Proceedings","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115758131","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 : 2011-10-27DOI: 10.1109/CIMSA.2011.6059935
Nasser H. Dardas, E. Petriu
This paper presents a real time system, which includes detecting and tracking bare hand in cluttered background using skin detection and hand postures contours comparison algorithm after face subtraction, and recognizing hand gestures using Principle Components Analysis (PCA). In the training stage, a set of hand postures images with different scales, rotation and lighting conditions are trained. Then, the most eigenvectors of training images are determined, and the training weights are calculated by projecting each training image onto the most eigenvectors. In the testing stage, for every frame captured from a webcam, the hand gesture is detected using our algorithm, then the small image that contains the detected hand gesture is projected onto the most eigenvectors of training images to form its test weights. Finally, the minimum Euclidean distance is determined between the test weights and the training weights of each training image to recognize the hand gesture.
{"title":"Hand gesture detection and recognition using principal component analysis","authors":"Nasser H. Dardas, E. Petriu","doi":"10.1109/CIMSA.2011.6059935","DOIUrl":"https://doi.org/10.1109/CIMSA.2011.6059935","url":null,"abstract":"This paper presents a real time system, which includes detecting and tracking bare hand in cluttered background using skin detection and hand postures contours comparison algorithm after face subtraction, and recognizing hand gestures using Principle Components Analysis (PCA). In the training stage, a set of hand postures images with different scales, rotation and lighting conditions are trained. Then, the most eigenvectors of training images are determined, and the training weights are calculated by projecting each training image onto the most eigenvectors. In the testing stage, for every frame captured from a webcam, the hand gesture is detected using our algorithm, then the small image that contains the detected hand gesture is projected onto the most eigenvectors of training images to form its test weights. Finally, the minimum Euclidean distance is determined between the test weights and the training weights of each training image to recognize the hand gesture.","PeriodicalId":422972,"journal":{"name":"2011 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) Proceedings","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127668787","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 : 2011-10-27DOI: 10.1109/CIMSA.2011.6059913
Zhiliang Liu, M. Zuo, J. Qu, Hongbing Xu
Linear discriminant analysis (LDA) is a method of feature extraction that has demonstrated successful applications. The selection of the number of discriminant directions (r) is important to LDA, yet little attention is paid in the reported literature. In this paper a method is proposed for determining the optimal r in terms of the classification accuracy of support vector machine. The method is applied to identify gear damage levels in a planetary gearbox. Planet gears with four damage levels labeled as baseline, slight, moderate, and severe were used in lab experiments for data collection. Results demonstrate that the proposed method outperforms two reported methods and is effective to address the given problem.
{"title":"Classification of gear damage levels in planetary gearboxes","authors":"Zhiliang Liu, M. Zuo, J. Qu, Hongbing Xu","doi":"10.1109/CIMSA.2011.6059913","DOIUrl":"https://doi.org/10.1109/CIMSA.2011.6059913","url":null,"abstract":"Linear discriminant analysis (LDA) is a method of feature extraction that has demonstrated successful applications. The selection of the number of discriminant directions (r) is important to LDA, yet little attention is paid in the reported literature. In this paper a method is proposed for determining the optimal r in terms of the classification accuracy of support vector machine. The method is applied to identify gear damage levels in a planetary gearbox. Planet gears with four damage levels labeled as baseline, slight, moderate, and severe were used in lab experiments for data collection. Results demonstrate that the proposed method outperforms two reported methods and is effective to address the given problem.","PeriodicalId":422972,"journal":{"name":"2011 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) Proceedings","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129260123","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 : 2011-10-27DOI: 10.1109/CIMSA.2011.6059925
B. Priego, Daniel Souto, F. Peña, R. Duro
In this paper we report on some of the current advances in the development of Hywacoss (Hyperspectral waterway control and security system). The objective of the Hywacoss project is to produce a real time small, light and easy to transport visible and near infrared hyperspectral detection and recognition system that autonomously monitors waterways, especially port and bay areas, and detects and classifies all the traffic, producing alerts when previously unknown objects or behavior patterns arise. Obviously, Hywacoss involves dedicated hardware and software modules, some of them based on computational intelligence methods. Here we will provide a global description of the system and a detailed analysis of some of its modules, in particular those related to hyperspectral image segmentation and the Artificial Neural Network based spectral-geometrical identification and profiling subsystems.
{"title":"An ANN based hyperspectral waterway control and security system","authors":"B. Priego, Daniel Souto, F. Peña, R. Duro","doi":"10.1109/CIMSA.2011.6059925","DOIUrl":"https://doi.org/10.1109/CIMSA.2011.6059925","url":null,"abstract":"In this paper we report on some of the current advances in the development of Hywacoss (Hyperspectral waterway control and security system). The objective of the Hywacoss project is to produce a real time small, light and easy to transport visible and near infrared hyperspectral detection and recognition system that autonomously monitors waterways, especially port and bay areas, and detects and classifies all the traffic, producing alerts when previously unknown objects or behavior patterns arise. Obviously, Hywacoss involves dedicated hardware and software modules, some of them based on computational intelligence methods. Here we will provide a global description of the system and a detailed analysis of some of its modules, in particular those related to hyperspectral image segmentation and the Artificial Neural Network based spectral-geometrical identification and profiling subsystems.","PeriodicalId":422972,"journal":{"name":"2011 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) Proceedings","volume":"13 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132968074","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：