Vehicle type recognition(VTR)is an important research topic due to its significance in intelligent transportation systems.However,recognizing vehicle type on the real-world images is challenging due to the illuminatio...Vehicle type recognition(VTR)is an important research topic due to its significance in intelligent transportation systems.However,recognizing vehicle type on the real-world images is challenging due to the illumination change,partial occlusion under real traffic environment.These difficulties limit the performance of current state-of-art methods,which are typically based on single-stage classification without considering feature availability.To address such difficulties,this paper proposes a two-stage vehicle type recognition method combining the most effective Gabor features.The first stage leverages edge features to classify vehicles by size into big or small via a similarity k-nearest neighbor classifier(SKNNC).Further the more specific vehicle type such as bus,truck,sedan or van is recognized by the second stage classification,which leverages the most effective Gabor features extracted by a set of Gabor wavelet kernels on the partitioned key patches via a kernel sparse representation-based classifier(KSRC).A verification and correction step based on minimum residual analysis is proposed to enhance the reliability of the VTR.To improve VTR efficiency,the most effective Gabor features are selected through gray relational analysis that leverages the correlation between Gabor feature image and the original image.Experimental results demonstrate that the proposed method not only improves the accuracy of VTR but also enhances the recognition robustness to illumination change and partial occlusion.展开更多
Glaucoma is a progressive eye disease that can lead to blindness if left untreated.Early detection is crucial to prevent vision loss,but current manual scanning methods are expensive,time-consuming,and require special...Glaucoma is a progressive eye disease that can lead to blindness if left untreated.Early detection is crucial to prevent vision loss,but current manual scanning methods are expensive,time-consuming,and require specialized expertise.This study presents a novel approach to Glaucoma detection using the Enhanced Grey Wolf Optimized Support Vector Machine(EGWO-SVM)method.The proposed method involves preprocessing steps such as removing image noise using the adaptive median filter(AMF)and feature extraction using the previously processed speeded-up robust feature(SURF),histogram of oriented gradients(HOG),and Global features.The enhanced Grey Wolf Optimization(GWO)technique is then employed with SVM for classification.To evaluate the proposed method,we used the online retinal images for glaucoma analysis(ORIGA)database,and it achieved high accuracy,sensitivity,and specificity rates of 94%,92%,and 92%,respectively.The results demonstrate that the proposed method outperforms other current algorithms in detecting the presence or absence of Glaucoma.This study provides a novel and effective approach to Glaucoma detection that can potentially improve the detection process and outcomes.展开更多
The process of selecting features or reducing dimensionality can be viewed as a multi-objective minimization problem in which both the number of features and error rate must be minimized.While it is a multi-objective ...The process of selecting features or reducing dimensionality can be viewed as a multi-objective minimization problem in which both the number of features and error rate must be minimized.While it is a multi-objective problem,current methods tend to treat feature selection as a single-objective optimization task.This paper presents enhanced multi-objective grey wolf optimizer with Lévy flight and mutation phase(LMuMOGWO)for tackling feature selection problems.The proposed approach integrates two effective operators into the existing Multi-objective Grey Wolf optimizer(MOGWO):a Lévy flight and a mutation operator.The Lévy flight,a type of random walk with jump size determined by the Lévy distribution,enhances the global search capability of MOGWO,with the objective of maximizing classification accuracy while minimizing the number of selected features.The mutation operator is integrated to add more informative features that can assist in enhancing classification accuracy.As feature selection is a binary problem,the continuous search space is converted into a binary space using the sigmoid function.To evaluate the classification performance of the selected feature subset,the proposed approach employs a wrapper-based Artificial Neural Network(ANN).The effectiveness of the LMuMOGWO is validated on 12 conventional UCI benchmark datasets and compared with two existing variants of MOGWO,BMOGWO-S(based sigmoid),BMOGWO-V(based tanh)as well as Non-dominated Sorting Genetic Algorithm II(NSGA-II)and Multi-objective Particle Swarm Optimization(BMOPSO).The results demonstrate that the proposed LMuMOGWO approach is capable of successfully evolving and improving a set of randomly generated solutions for a given optimization problem.Moreover,the proposed approach outperforms existing approaches in most cases in terms of classification error rate,feature reduction,and computational cost.展开更多
Aiming to enable robust large-scale fault diagnostics and optimized control for supermarket refrigerationsystems, a data-driven grey box model for an evaporator and its surrounding cooling cabinet (or room) ispresente...Aiming to enable robust large-scale fault diagnostics and optimized control for supermarket refrigerationsystems, a data-driven grey box model for an evaporator and its surrounding cooling cabinet (or room) ispresented. It is a non-linear model with two states: the cabinet temperature and the refrigerant mass in theevaporator. To demonstrate its applicability, data with one-minute sampling resolution from ten evaporators ina supermarket in Otterup (Denmark) was used. The model parameters were estimated using a Kalman filter andthe maximum likelihood method. Since the dynamical properties of the cabinets constantly change as goodsare added and removed, the parameters were re-estimated for each night, over a period of approximately 2.5years. The model is validated through a statistical analysis of the residuals and the importance of the ongoingre-estimation of parameters is highlighted. Furthermore, the physical meaning of the estimated parameters isdiscussed and potential applications for characterization and classification of cabinets are demonstrated, byshowing how they can be differentiated as either open- or closed cabinets or rooms, using only the estimatedheat transfer coefficients and heat capacities. For a selected case it is shown that the estimated parametervalues are close to physics derived values, and that the accuracy measured by the standard errors of theestimates is approximately ±10% relative to the estimated values. The analysis demonstrates that the modelis robust, accurate and reliable in terms of estimating physically meaningful parameters and it is thereforeappropriate for large-scale implementation.展开更多
基金supported in part by the National Natural Science Foundation of China(Nos.61304205 and 61502240)the Natural Science Foundation of Jiangsu Province(BK20191401)the Innovation and Entrepreneurship Training Project of College Students(202010300290,202010300211,202010300116E).
文摘Vehicle type recognition(VTR)is an important research topic due to its significance in intelligent transportation systems.However,recognizing vehicle type on the real-world images is challenging due to the illumination change,partial occlusion under real traffic environment.These difficulties limit the performance of current state-of-art methods,which are typically based on single-stage classification without considering feature availability.To address such difficulties,this paper proposes a two-stage vehicle type recognition method combining the most effective Gabor features.The first stage leverages edge features to classify vehicles by size into big or small via a similarity k-nearest neighbor classifier(SKNNC).Further the more specific vehicle type such as bus,truck,sedan or van is recognized by the second stage classification,which leverages the most effective Gabor features extracted by a set of Gabor wavelet kernels on the partitioned key patches via a kernel sparse representation-based classifier(KSRC).A verification and correction step based on minimum residual analysis is proposed to enhance the reliability of the VTR.To improve VTR efficiency,the most effective Gabor features are selected through gray relational analysis that leverages the correlation between Gabor feature image and the original image.Experimental results demonstrate that the proposed method not only improves the accuracy of VTR but also enhances the recognition robustness to illumination change and partial occlusion.
基金supported in part by the Beijing Natural Science Foundation(No.4212015)China Ministry of Education-China Mobile Scientific Research Foundation(No.MCM20200102).
文摘Glaucoma is a progressive eye disease that can lead to blindness if left untreated.Early detection is crucial to prevent vision loss,but current manual scanning methods are expensive,time-consuming,and require specialized expertise.This study presents a novel approach to Glaucoma detection using the Enhanced Grey Wolf Optimized Support Vector Machine(EGWO-SVM)method.The proposed method involves preprocessing steps such as removing image noise using the adaptive median filter(AMF)and feature extraction using the previously processed speeded-up robust feature(SURF),histogram of oriented gradients(HOG),and Global features.The enhanced Grey Wolf Optimization(GWO)technique is then employed with SVM for classification.To evaluate the proposed method,we used the online retinal images for glaucoma analysis(ORIGA)database,and it achieved high accuracy,sensitivity,and specificity rates of 94%,92%,and 92%,respectively.The results demonstrate that the proposed method outperforms other current algorithms in detecting the presence or absence of Glaucoma.This study provides a novel and effective approach to Glaucoma detection that can potentially improve the detection process and outcomes.
基金supported by Universiti Teknologi PETRONAS,under the Yayasan Universiti Teknologi PETRONAS (YUTP)Fundamental Research Grant Scheme (YUTPFRG/015LC0-274)support by Researchers Supporting Project Number (RSP-2023/309),King Saud University,Riyadh,Saudi Arabia.
文摘The process of selecting features or reducing dimensionality can be viewed as a multi-objective minimization problem in which both the number of features and error rate must be minimized.While it is a multi-objective problem,current methods tend to treat feature selection as a single-objective optimization task.This paper presents enhanced multi-objective grey wolf optimizer with Lévy flight and mutation phase(LMuMOGWO)for tackling feature selection problems.The proposed approach integrates two effective operators into the existing Multi-objective Grey Wolf optimizer(MOGWO):a Lévy flight and a mutation operator.The Lévy flight,a type of random walk with jump size determined by the Lévy distribution,enhances the global search capability of MOGWO,with the objective of maximizing classification accuracy while minimizing the number of selected features.The mutation operator is integrated to add more informative features that can assist in enhancing classification accuracy.As feature selection is a binary problem,the continuous search space is converted into a binary space using the sigmoid function.To evaluate the classification performance of the selected feature subset,the proposed approach employs a wrapper-based Artificial Neural Network(ANN).The effectiveness of the LMuMOGWO is validated on 12 conventional UCI benchmark datasets and compared with two existing variants of MOGWO,BMOGWO-S(based sigmoid),BMOGWO-V(based tanh)as well as Non-dominated Sorting Genetic Algorithm II(NSGA-II)and Multi-objective Particle Swarm Optimization(BMOPSO).The results demonstrate that the proposed LMuMOGWO approach is capable of successfully evolving and improving a set of randomly generated solutions for a given optimization problem.Moreover,the proposed approach outperforms existing approaches in most cases in terms of classification error rate,feature reduction,and computational cost.
基金This document is the results of the research projects Digital twins for large-scale heat pumps and refrigeration systems(EUDP 64019-0570)Flexible Energy Denmark(FED)(IFD 8090-00069B).
文摘Aiming to enable robust large-scale fault diagnostics and optimized control for supermarket refrigerationsystems, a data-driven grey box model for an evaporator and its surrounding cooling cabinet (or room) ispresented. It is a non-linear model with two states: the cabinet temperature and the refrigerant mass in theevaporator. To demonstrate its applicability, data with one-minute sampling resolution from ten evaporators ina supermarket in Otterup (Denmark) was used. The model parameters were estimated using a Kalman filter andthe maximum likelihood method. Since the dynamical properties of the cabinets constantly change as goodsare added and removed, the parameters were re-estimated for each night, over a period of approximately 2.5years. The model is validated through a statistical analysis of the residuals and the importance of the ongoingre-estimation of parameters is highlighted. Furthermore, the physical meaning of the estimated parameters isdiscussed and potential applications for characterization and classification of cabinets are demonstrated, byshowing how they can be differentiated as either open- or closed cabinets or rooms, using only the estimatedheat transfer coefficients and heat capacities. For a selected case it is shown that the estimated parametervalues are close to physics derived values, and that the accuracy measured by the standard errors of theestimates is approximately ±10% relative to the estimated values. The analysis demonstrates that the modelis robust, accurate and reliable in terms of estimating physically meaningful parameters and it is thereforeappropriate for large-scale implementation.
