In conventional computed tomography （CT） reconstruction based on fixed voltage, the projective data often ap- pear overexposed or underexposed, as a result, the reconstructive results are poor. To solve this problem...In conventional computed tomography （CT） reconstruction based on fixed voltage, the projective data often ap- pear overexposed or underexposed, as a result, the reconstructive results are poor. To solve this problem, variable voltage CT reconstruction has been proposed. The effective projective sequences of a structural component are obtained through the variable voltage. The total variation is adjusted and minimized to optimize the reconstructive results on the basis of iterative image using algebraic reconstruction technique （ART）. In the process of reconstruction, the reconstructive image of low voltage is used as an initial value of the effective proiective reconstruction of the adjacent high voltage, and so on until to the highest voltage according to the gray weighted algorithm. Thereby the complete structural information is reconstructed. Simulation results show that the proposed algorithm can completely reflect the information of a complicated structural com- ponent, and the pixel values are more stable than those of the conventional.展开更多
Purpose–The deformation of the roadbed is easily influenced by the external environment to improve the accuracy of high-speed railway subgrade settlement prediction.Design/methodology/approach–A high-speed railway s...Purpose–The deformation of the roadbed is easily influenced by the external environment to improve the accuracy of high-speed railway subgrade settlement prediction.Design/methodology/approach–A high-speed railway subgrade settlement interval prediction method using the secretary bird optimization(SBOA)algorithm to optimize the BP neural network under the premise of gray relational analysis is proposed.Findings–Using the SBOA algorithm to optimize the BP neural network,the optimal weights and thresholds are obtained,and the best parameter prediction model is combined.The data were collected from the sensors deployed through the subgrade settlement monitoring system,and the gray relational analysis is used to verify that all four influencing factors had a great correlation to the subgrade settlement,and the collected data are verified using the model.Originality/value–The experimental results show that the SBOA-BP model has higher prediction accuracy than the BP model,and the SBOA-BP model has a wider range of prediction intervals for a given confidence level,which can provide higher guiding value for practical engineering applications.展开更多
A new improved algorithm of histogram equalization was discussed and actualized by analyzing the traditional algorithm. This improved algorithm has better effect than the traditional one, especially it is used to proc...A new improved algorithm of histogram equalization was discussed and actualized by analyzing the traditional algorithm. This improved algorithm has better effect than the traditional one, especially it is used to process poor quality images.展开更多
The widespread penetration of distributed energy sources and the use of load response programs,especially in a microgrid,have caused many power system issues,such as control and operation of these networks,to be affec...The widespread penetration of distributed energy sources and the use of load response programs,especially in a microgrid,have caused many power system issues,such as control and operation of these networks,to be affected.The control and operation of many small-distributed generation units with different performance characteristics create another challenge for the safe and efficient operation of the microgrid.In this paper,the optimum operation of distributed generation resources and heat and power storage in a microgrid,was performed based on real-time pricing through the proposed gray wolf optimization(GWO)algorithm to reduce the energy supply cost with the microgrid.Distributed generation resources such as solar panels,diesel generators with battery storage,and boiler thermal resources with thermal storage were used in the studied microgrid.Also,a combined heat and power(CHP)unit was used to produce thermal and electrical energy simultaneously.In the simulations,in addition to the gray wolf algorithm,some optimization algorithms have also been used.Then the results of 20 runs for each algorithm confirmed the high accuracy of the proposed GWO algorithm.The results of the simulations indicated that the CHP energy resources must be managed to have a minimum cost of energy supply in the microgrid,considering the demand response program.展开更多
Since the logarithmic form of Shannon entropy has the drawback of undefined value at zero points,and most existing threshold selection methods only depend on the probability information,ignoring the within-class unifo...Since the logarithmic form of Shannon entropy has the drawback of undefined value at zero points,and most existing threshold selection methods only depend on the probability information,ignoring the within-class uniformity of gray level,a method of reciprocal gray entropy threshold selection is proposed based on two-dimensional(2-D)histogram region oblique division and artificial bee colony(ABC)optimization.Firstly,the definition of reciprocal gray entropy is introduced.Then on the basis of one-dimensional(1-D)method,2-D threshold selection criterion function based on reciprocal gray entropy with histogram oblique division is derived.To accelerate the progress of searching the optimal threshold,the recently proposed ABC optimization algorithm is adopted.The proposed method not only avoids the undefined value points in Shannon entropy,but also achieves high accuracy and anti-noise performance due to reasonable 2-D histogram region division and the consideration of within-class uniformity of gray level.A large number of experimental results show that,compared with the maximum Shannon entropy method with 2-D histogram oblique division and the reciprocal entropy method with 2-D histogram oblique division based on niche chaotic mutation particle swarm optimization(NCPSO),the proposed method can achieve better segmentation results and can satisfy the requirement of real-time processing.展开更多
A real-time electronic image stabilization motion estimation method based on fast sub- block gray projection algorithm is proposed. In the method, each image is divided into a number of sub-blocks, and sub-blocks are ...A real-time electronic image stabilization motion estimation method based on fast sub- block gray projection algorithm is proposed. In the method, each image is divided into a number of sub-blocks, and sub-blocks are sifted with their gray gradients. After removing sub-blocks whose gray gradients are lower than the given threshold, the calculation amount of projection is reduced and the motion estimation accuracy is improved. Then gray projection is done in each remained sub- block, and global motion vector of the image is calculated according to the local motion vectors of sub-blocks and the affine motion model. The drawbacks as the local motions reducing the global mo- tion estimation accuracy and traditional gray projection algorithm could not deal with rotation are re- solved well by this algorithm. The experiment results show that the algorithm is more accurate and efficient than the gray projection algorithm.展开更多
A uniform experimental design(UED)is an extremely used powerful and efficient methodology for designing experiments with high-dimensional inputs,limited resources and unknown underlying models.A UED enjoys the followi...A uniform experimental design(UED)is an extremely used powerful and efficient methodology for designing experiments with high-dimensional inputs,limited resources and unknown underlying models.A UED enjoys the following two significant advantages:(i)It is a robust design,since it does not require to specify a model before experimenters conduct their experiments;and(ii)it provides uniformly scatter design points in the experimental domain,thus it gives a good representation of this domain with fewer experimental trials(runs).Many real-life experiments involve hundreds or thousands of active factors and thus large UEDs are needed.Constructing large UEDs using the existing techniques is an NP-hard problem,an extremely time-consuming heuristic search process and a satisfactory result is not guaranteed.This paper presents a new effective and easy technique,adjusted Gray map technique(AGMT),for constructing(nearly)UEDs with large numbers of four-level factors and runs by converting designs with s two-level factors and n runs to(nearly)UEDs with 2^(t−1)s four-level factors and 2tn runs for any t≥0 using two simple transformation functions.Theoretical justifications for the uniformity of the resulting four-level designs are given,which provide some necessary and/or sufficient conditions for obtaining(nearly)uniform four-level designs.The results show that the AGMT is much easier and better than the existing widely used techniques and it can be effectively used to simply generate new recommended large(nearly)UEDs with four-level factors.展开更多
An effective damage test method based on a marker-based watershed algorithm with gray control(MWGC) is proposed to study the properties of damage induced by near-field laser irradiation for large-aperture laser facili...An effective damage test method based on a marker-based watershed algorithm with gray control(MWGC) is proposed to study the properties of damage induced by near-field laser irradiation for large-aperture laser facilities.Damage tests were performed on fused silica samples and information on the size of damage sites was obtained by this new algorithm,which can effectively suppress the issue of over-segmentation of images resulting from non-uniform illumination in darkfield imaging.Experimental analysis and results show that the lateral damage growth on the exit surface is exponential,and the number of damage sites decreases sharply with damage site size in the damage site distribution statistics.The average damage growth coefficients fitted according to the experimental results for Corning-7980 and Heraeus-Suprasil312 samples at 351 nm are 1.10 ± 0.31 and 0.60 ± 0.09,respectively.展开更多
Traditional distribution network planning relies on the professional knowledge of planners,especially when analyzing the correlations between the problems existing in the network and the crucial influencing factors.Th...Traditional distribution network planning relies on the professional knowledge of planners,especially when analyzing the correlations between the problems existing in the network and the crucial influencing factors.The inherent laws reflected by the historical data of the distribution network are ignored,which affects the objectivity of the planning scheme.In this study,to improve the efficiency and accuracy of distribution network planning,the characteristics of distribution network data were extracted using a data-mining technique,and correlation knowledge of existing problems in the network was obtained.A data-mining model based on correlation rules was established.The inputs of the model were the electrical characteristic indices screened using the gray correlation method.The Apriori algorithm was used to extract correlation knowledge from the operational data of the distribution network and obtain strong correlation rules.Degree of promotion and chi-square tests were used to verify the rationality of the strong correlation rules of the model output.In this study,the correlation relationship between heavy load or overload problems of distribution network feeders in different regions and related characteristic indices was determined,and the confidence of the correlation rules was obtained.These results can provide an effective basis for the formulation of a distribution network planning scheme.展开更多
The use of massive image databases has increased drastically over the few years due to evolution of multimedia technology.Image retrieval has become one of the vital tools in image processing applications.Content-Base...The use of massive image databases has increased drastically over the few years due to evolution of multimedia technology.Image retrieval has become one of the vital tools in image processing applications.Content-Based Image Retrieval(CBIR)has been widely used in varied applications.But,the results produced by the usage of a single image feature are not satisfactory.So,multiple image features are used very often for attaining better results.But,fast and effective searching for relevant images from a database becomes a challenging task.In the previous existing system,the CBIR has used the combined feature extraction technique using color auto-correlogram,Rotation-Invariant Uniform Local Binary Patterns(RULBP)and local energy.However,the existing system does not provide significant results in terms of recall and precision.Also,the computational complexity is higher for the existing CBIR systems.In order to handle the above mentioned issues,the Gray Level Co-occurrence Matrix(GLCM)with Deep Learning based Enhanced Convolution Neural Network(DLECNN)is proposed in this work.The proposed system framework includes noise reduction using histogram equalization,feature extraction using GLCM,similarity matching computation using Hierarchal and Fuzzy c-Means(HFCM)algorithm and the image retrieval using DLECNN algorithm.The histogram equalization has been used for computing the image enhancement.This enhanced image has a uniform histogram.Then,the GLCM method has been used to extract the features such as shape,texture,colour,annotations and keywords.The HFCM similarity measure is used for computing the query image vector's similarity index with every database images.For enhancing the performance of this image retrieval approach,the DLECNN algorithm is proposed to retrieve more accurate features of the image.The proposed GLCM+DLECNN algorithm provides better results associated with high accuracy,precision,recall,f-measure and lesser complexity.From the experimental results,it is clearly observed that the proposed system provides efficient image retrieval for the given query image.展开更多
Under ultra-high-speed and harsh conditions,conventional control methods struggle to ensure the path tracking accuracy and driving stability of unmanned vehicles during the turning process.Therefore,this study propose...Under ultra-high-speed and harsh conditions,conventional control methods struggle to ensure the path tracking accuracy and driving stability of unmanned vehicles during the turning process.Therefore,this study proposes a cascade control to solve this problem.Based on the new vehicle error model that considers vehicle tire sideslip and road curvature,the feedforward-parametric adaptive linear quadratic regulator(LQR)and proportional integral control-based speed-keeping controllers are used to compose the path-tracking cascade optimization controller for unmanned vehicles.To improve the adaptability of the unmanned vehicle path-tracking control under harsh driving conditions,the LQR controller parameters are automatically adjusted using a back-propagation neural network,in which the initial weights and thresholds are optimized using the improved grey wolf optimization algorithm according to the driving conditions.The speed-keeping controller reduces the impact on the curve-tracking accuracy under nonlinear vehicle speed variations.Finally,a joint model of MATLAB/Simulink and CarSim was established,and simulations show that the proposed control method can achieve stable entry and exit curves at ultra-high speeds for unmanned vehicles.Under strong wind and ice road conditions,the method exhibits a higher tracking accuracy and is more adaptive and robust to external interference in driving and variable curvature roads than methods such as the feedforward-LQR,preview and pure pursuit controls.展开更多
In recent years,with the rapid development of Internet of things(IoT)technology,radio frequency identification(RFID)technology as the core of IoT technology has been paid more and more attention,and RFID network plann...In recent years,with the rapid development of Internet of things(IoT)technology,radio frequency identification(RFID)technology as the core of IoT technology has been paid more and more attention,and RFID network planning(RNP)has become the primary concern.Compared with the traditional methods,meta-heuristic method is widely used in RNP.Aiming at the target requirements of RFID,such as fewer readers,covering more tags,reducing the interference between readers and saving costs,this paper proposes a hybrid gray wolf optimization-cuckoo search(GWO-CS)algorithm.This method uses the input representation based on random gray wolf search and evaluates the tag density and location to determine the combination performance of the reader's propagation area.Compared with particle swarm optimization(PSO)algorithm,cuckoo search(CS)algorithm and gray wolf optimization(GWO)algorithm under the same experimental conditions,the coverage of GWO-CS is 9.306%higher than that of PSO algorithm,6.963%higher than that of CS algorithm,and 3.488%higher than that of GWO algorithm.The results show that the GWO-CS algorithm cannot only improve the global search range,but also improve the local search depth.展开更多
This paper delves into the parameter tuning of fractional-order PID(FOPID)controllers.FOPID controllers,with additional integral and derivative orders compared to traditional PID controllers,possess enhanced capabilit...This paper delves into the parameter tuning of fractional-order PID(FOPID)controllers.FOPID controllers,with additional integral and derivative orders compared to traditional PID controllers,possess enhanced capabilities in handling complex systems.However,effective tuning of its five parameters is challenging.To address this,multiple intelligent algorithms are investigated.The improved sparrow search algorithm(ISSA)utilizes Chebyshev chaotic mapping initialization,adaptive t-distribution,and the firefly algorithm to overcome the limitations of the basic algorithm,showing high accuracy,speed,and robustness in multi-modal problems.The grey wolf optimizer(GWO),inspired by the hunting behavior of grey wolves,has procedures for encircling,hunting,and attacking but may encounter local optima,and several improvement methods have been proposed.The genetic algorithm,based on the survival of the fittest principle,involves encoding,decoding,and other operations.Taking vehicle ABS control as an example,the genetic algorithm-based FOPID controller outperforms the traditional PID controller.In conclusion,different algorithms have their own advantages in FOPID parameter tuning,and the selection depends on system characteristics and control requirements.Future research can focus on further algorithm improvement and hybrid methods to achieve better control performance,providing a valuable reference for FOPID applications in industry.展开更多
An accurate extraction of vibration signal characteristics of an on-load tap changer(OLTC)during contact switching can effectively help detect its abnormal state.Therefore,an improved fuzzy C-means clustering method f...An accurate extraction of vibration signal characteristics of an on-load tap changer(OLTC)during contact switching can effectively help detect its abnormal state.Therefore,an improved fuzzy C-means clustering method for abnormal state detection of the OLTC contact is proposed.First,the wavelet packet and singular spectrum analysis are used to denoise the vibration signal generated by the moving and static contacts of the OLTC.Then,the Hilbert-Huang transform that is optimized by the ensemble empirical mode decomposition(EEMD)is used to decompose the vibration signal and extract the boundary spectrum features.Finally,the gray wolf algorithm-based fuzzy C-means clustering is used to denoise the signal and determine the abnormal states of the OLTC contact.An analysis of the experimental data shows that the proposed secondary denoising method has a better denoising effect compared to the single denoising method.The EEMD can improve the modal aliasing effect,and the improved fuzzy C-means clustering can effectively identify the abnormal state of the OLTC contacts.The analysis results of field measured data further verify the effectiveness of the proposed method and provide a reference for the abnormal state detection of the OLTC.展开更多
文摘In conventional computed tomography （CT） reconstruction based on fixed voltage, the projective data often ap- pear overexposed or underexposed, as a result, the reconstructive results are poor. To solve this problem, variable voltage CT reconstruction has been proposed. The effective projective sequences of a structural component are obtained through the variable voltage. The total variation is adjusted and minimized to optimize the reconstructive results on the basis of iterative image using algebraic reconstruction technique （ART）. In the process of reconstruction, the reconstructive image of low voltage is used as an initial value of the effective proiective reconstruction of the adjacent high voltage, and so on until to the highest voltage according to the gray weighted algorithm. Thereby the complete structural information is reconstructed. Simulation results show that the proposed algorithm can completely reflect the information of a complicated structural com- ponent, and the pixel values are more stable than those of the conventional.
文摘Purpose–The deformation of the roadbed is easily influenced by the external environment to improve the accuracy of high-speed railway subgrade settlement prediction.Design/methodology/approach–A high-speed railway subgrade settlement interval prediction method using the secretary bird optimization(SBOA)algorithm to optimize the BP neural network under the premise of gray relational analysis is proposed.Findings–Using the SBOA algorithm to optimize the BP neural network,the optimal weights and thresholds are obtained,and the best parameter prediction model is combined.The data were collected from the sensors deployed through the subgrade settlement monitoring system,and the gray relational analysis is used to verify that all four influencing factors had a great correlation to the subgrade settlement,and the collected data are verified using the model.Originality/value–The experimental results show that the SBOA-BP model has higher prediction accuracy than the BP model,and the SBOA-BP model has a wider range of prediction intervals for a given confidence level,which can provide higher guiding value for practical engineering applications.
文摘A new improved algorithm of histogram equalization was discussed and actualized by analyzing the traditional algorithm. This improved algorithm has better effect than the traditional one, especially it is used to process poor quality images.
基金This work was supported in part by an International Research Partnership“Electrical Engineering—Thai French Research Center(EE-TFRC)”under the project framework of the Lorraine Universitéd’Excellence(LUE)in cooperation between Universitéde Lorraine and King Mongkut’s University of Technology North Bangkok and in part by the National Research Council of Thailand(NRCT)under Senior Research Scholar Program under Grant No.N42A640328.
文摘The widespread penetration of distributed energy sources and the use of load response programs,especially in a microgrid,have caused many power system issues,such as control and operation of these networks,to be affected.The control and operation of many small-distributed generation units with different performance characteristics create another challenge for the safe and efficient operation of the microgrid.In this paper,the optimum operation of distributed generation resources and heat and power storage in a microgrid,was performed based on real-time pricing through the proposed gray wolf optimization(GWO)algorithm to reduce the energy supply cost with the microgrid.Distributed generation resources such as solar panels,diesel generators with battery storage,and boiler thermal resources with thermal storage were used in the studied microgrid.Also,a combined heat and power(CHP)unit was used to produce thermal and electrical energy simultaneously.In the simulations,in addition to the gray wolf algorithm,some optimization algorithms have also been used.Then the results of 20 runs for each algorithm confirmed the high accuracy of the proposed GWO algorithm.The results of the simulations indicated that the CHP energy resources must be managed to have a minimum cost of energy supply in the microgrid,considering the demand response program.
基金Supported by the CRSRI Open Research Program(CKWV2013225/KY)the Priority Academic Program Development of Jiangsu Higher Education Institution+2 种基金the Open Project Foundation of Key Laboratory of the Yellow River Sediment of Ministry of Water Resource(2014006)the State Key Lab of Urban Water Resource and Environment(HIT)(ES201409)the Open Project Program of State Key Laboratory of Food Science and Technology,Jiangnan University(SKLF-KF-201310)
文摘Since the logarithmic form of Shannon entropy has the drawback of undefined value at zero points,and most existing threshold selection methods only depend on the probability information,ignoring the within-class uniformity of gray level,a method of reciprocal gray entropy threshold selection is proposed based on two-dimensional(2-D)histogram region oblique division and artificial bee colony(ABC)optimization.Firstly,the definition of reciprocal gray entropy is introduced.Then on the basis of one-dimensional(1-D)method,2-D threshold selection criterion function based on reciprocal gray entropy with histogram oblique division is derived.To accelerate the progress of searching the optimal threshold,the recently proposed ABC optimization algorithm is adopted.The proposed method not only avoids the undefined value points in Shannon entropy,but also achieves high accuracy and anti-noise performance due to reasonable 2-D histogram region division and the consideration of within-class uniformity of gray level.A large number of experimental results show that,compared with the maximum Shannon entropy method with 2-D histogram oblique division and the reciprocal entropy method with 2-D histogram oblique division based on niche chaotic mutation particle swarm optimization(NCPSO),the proposed method can achieve better segmentation results and can satisfy the requirement of real-time processing.
基金Supported by the National Defense Scientific Research Project （ B2220132013 ）
文摘A real-time electronic image stabilization motion estimation method based on fast sub- block gray projection algorithm is proposed. In the method, each image is divided into a number of sub-blocks, and sub-blocks are sifted with their gray gradients. After removing sub-blocks whose gray gradients are lower than the given threshold, the calculation amount of projection is reduced and the motion estimation accuracy is improved. Then gray projection is done in each remained sub- block, and global motion vector of the image is calculated according to the local motion vectors of sub-blocks and the affine motion model. The drawbacks as the local motions reducing the global mo- tion estimation accuracy and traditional gray projection algorithm could not deal with rotation are re- solved well by this algorithm. The experiment results show that the algorithm is more accurate and efficient than the gray projection algorithm.
基金supported by the UIC Research Grants with No.of(R201912 and R202010)the Curriculum Development and Teaching Enhancement with No.of(UICR0400046-21CTL)+1 种基金the Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science,BNU-HKBU United International College with No.of(2022B1212010006)Guangdong Higher Education Upgrading Plan(2021-2025)with No.of(UICR0400001-22).
文摘A uniform experimental design(UED)is an extremely used powerful and efficient methodology for designing experiments with high-dimensional inputs,limited resources and unknown underlying models.A UED enjoys the following two significant advantages:(i)It is a robust design,since it does not require to specify a model before experimenters conduct their experiments;and(ii)it provides uniformly scatter design points in the experimental domain,thus it gives a good representation of this domain with fewer experimental trials(runs).Many real-life experiments involve hundreds or thousands of active factors and thus large UEDs are needed.Constructing large UEDs using the existing techniques is an NP-hard problem,an extremely time-consuming heuristic search process and a satisfactory result is not guaranteed.This paper presents a new effective and easy technique,adjusted Gray map technique(AGMT),for constructing(nearly)UEDs with large numbers of four-level factors and runs by converting designs with s two-level factors and n runs to(nearly)UEDs with 2^(t−1)s four-level factors and 2tn runs for any t≥0 using two simple transformation functions.Theoretical justifications for the uniformity of the resulting four-level designs are given,which provide some necessary and/or sufficient conditions for obtaining(nearly)uniform four-level designs.The results show that the AGMT is much easier and better than the existing widely used techniques and it can be effectively used to simply generate new recommended large(nearly)UEDs with four-level factors.
文摘An effective damage test method based on a marker-based watershed algorithm with gray control(MWGC) is proposed to study the properties of damage induced by near-field laser irradiation for large-aperture laser facilities.Damage tests were performed on fused silica samples and information on the size of damage sites was obtained by this new algorithm,which can effectively suppress the issue of over-segmentation of images resulting from non-uniform illumination in darkfield imaging.Experimental analysis and results show that the lateral damage growth on the exit surface is exponential,and the number of damage sites decreases sharply with damage site size in the damage site distribution statistics.The average damage growth coefficients fitted according to the experimental results for Corning-7980 and Heraeus-Suprasil312 samples at 351 nm are 1.10 ± 0.31 and 0.60 ± 0.09,respectively.
基金supported by the Science and Technology Project of China Southern Power Grid(GZHKJXM20210043-080041KK52210002).
文摘Traditional distribution network planning relies on the professional knowledge of planners,especially when analyzing the correlations between the problems existing in the network and the crucial influencing factors.The inherent laws reflected by the historical data of the distribution network are ignored,which affects the objectivity of the planning scheme.In this study,to improve the efficiency and accuracy of distribution network planning,the characteristics of distribution network data were extracted using a data-mining technique,and correlation knowledge of existing problems in the network was obtained.A data-mining model based on correlation rules was established.The inputs of the model were the electrical characteristic indices screened using the gray correlation method.The Apriori algorithm was used to extract correlation knowledge from the operational data of the distribution network and obtain strong correlation rules.Degree of promotion and chi-square tests were used to verify the rationality of the strong correlation rules of the model output.In this study,the correlation relationship between heavy load or overload problems of distribution network feeders in different regions and related characteristic indices was determined,and the confidence of the correlation rules was obtained.These results can provide an effective basis for the formulation of a distribution network planning scheme.
文摘The use of massive image databases has increased drastically over the few years due to evolution of multimedia technology.Image retrieval has become one of the vital tools in image processing applications.Content-Based Image Retrieval(CBIR)has been widely used in varied applications.But,the results produced by the usage of a single image feature are not satisfactory.So,multiple image features are used very often for attaining better results.But,fast and effective searching for relevant images from a database becomes a challenging task.In the previous existing system,the CBIR has used the combined feature extraction technique using color auto-correlogram,Rotation-Invariant Uniform Local Binary Patterns(RULBP)and local energy.However,the existing system does not provide significant results in terms of recall and precision.Also,the computational complexity is higher for the existing CBIR systems.In order to handle the above mentioned issues,the Gray Level Co-occurrence Matrix(GLCM)with Deep Learning based Enhanced Convolution Neural Network(DLECNN)is proposed in this work.The proposed system framework includes noise reduction using histogram equalization,feature extraction using GLCM,similarity matching computation using Hierarchal and Fuzzy c-Means(HFCM)algorithm and the image retrieval using DLECNN algorithm.The histogram equalization has been used for computing the image enhancement.This enhanced image has a uniform histogram.Then,the GLCM method has been used to extract the features such as shape,texture,colour,annotations and keywords.The HFCM similarity measure is used for computing the query image vector's similarity index with every database images.For enhancing the performance of this image retrieval approach,the DLECNN algorithm is proposed to retrieve more accurate features of the image.The proposed GLCM+DLECNN algorithm provides better results associated with high accuracy,precision,recall,f-measure and lesser complexity.From the experimental results,it is clearly observed that the proposed system provides efficient image retrieval for the given query image.
基金the Natural Science Foundation of Guangxi(No.2020GXNSFDA238011)the Open Fund Project of Guangxi Key Laboratory of Automation Detection Technology and Instrument(No.YQ21203)the Independent Research Project of Guangxi Key Laboratory of Auto Parts and Vehicle Technology(No.2020GKLACVTZZ02)。
文摘Under ultra-high-speed and harsh conditions,conventional control methods struggle to ensure the path tracking accuracy and driving stability of unmanned vehicles during the turning process.Therefore,this study proposes a cascade control to solve this problem.Based on the new vehicle error model that considers vehicle tire sideslip and road curvature,the feedforward-parametric adaptive linear quadratic regulator(LQR)and proportional integral control-based speed-keeping controllers are used to compose the path-tracking cascade optimization controller for unmanned vehicles.To improve the adaptability of the unmanned vehicle path-tracking control under harsh driving conditions,the LQR controller parameters are automatically adjusted using a back-propagation neural network,in which the initial weights and thresholds are optimized using the improved grey wolf optimization algorithm according to the driving conditions.The speed-keeping controller reduces the impact on the curve-tracking accuracy under nonlinear vehicle speed variations.Finally,a joint model of MATLAB/Simulink and CarSim was established,and simulations show that the proposed control method can achieve stable entry and exit curves at ultra-high speeds for unmanned vehicles.Under strong wind and ice road conditions,the method exhibits a higher tracking accuracy and is more adaptive and robust to external interference in driving and variable curvature roads than methods such as the feedforward-LQR,preview and pure pursuit controls.
基金supported by the National Natural Science Foundation of China (61761004)the Natural Science Foundation of Guangxi Province,China (2019GXNSFAA245045)。
文摘In recent years,with the rapid development of Internet of things(IoT)technology,radio frequency identification(RFID)technology as the core of IoT technology has been paid more and more attention,and RFID network planning(RNP)has become the primary concern.Compared with the traditional methods,meta-heuristic method is widely used in RNP.Aiming at the target requirements of RFID,such as fewer readers,covering more tags,reducing the interference between readers and saving costs,this paper proposes a hybrid gray wolf optimization-cuckoo search(GWO-CS)algorithm.This method uses the input representation based on random gray wolf search and evaluates the tag density and location to determine the combination performance of the reader's propagation area.Compared with particle swarm optimization(PSO)algorithm,cuckoo search(CS)algorithm and gray wolf optimization(GWO)algorithm under the same experimental conditions,the coverage of GWO-CS is 9.306%higher than that of PSO algorithm,6.963%higher than that of CS algorithm,and 3.488%higher than that of GWO algorithm.The results show that the GWO-CS algorithm cannot only improve the global search range,but also improve the local search depth.
文摘This paper delves into the parameter tuning of fractional-order PID(FOPID)controllers.FOPID controllers,with additional integral and derivative orders compared to traditional PID controllers,possess enhanced capabilities in handling complex systems.However,effective tuning of its five parameters is challenging.To address this,multiple intelligent algorithms are investigated.The improved sparrow search algorithm(ISSA)utilizes Chebyshev chaotic mapping initialization,adaptive t-distribution,and the firefly algorithm to overcome the limitations of the basic algorithm,showing high accuracy,speed,and robustness in multi-modal problems.The grey wolf optimizer(GWO),inspired by the hunting behavior of grey wolves,has procedures for encircling,hunting,and attacking but may encounter local optima,and several improvement methods have been proposed.The genetic algorithm,based on the survival of the fittest principle,involves encoding,decoding,and other operations.Taking vehicle ABS control as an example,the genetic algorithm-based FOPID controller outperforms the traditional PID controller.In conclusion,different algorithms have their own advantages in FOPID parameter tuning,and the selection depends on system characteristics and control requirements.Future research can focus on further algorithm improvement and hybrid methods to achieve better control performance,providing a valuable reference for FOPID applications in industry.
文摘An accurate extraction of vibration signal characteristics of an on-load tap changer(OLTC)during contact switching can effectively help detect its abnormal state.Therefore,an improved fuzzy C-means clustering method for abnormal state detection of the OLTC contact is proposed.First,the wavelet packet and singular spectrum analysis are used to denoise the vibration signal generated by the moving and static contacts of the OLTC.Then,the Hilbert-Huang transform that is optimized by the ensemble empirical mode decomposition(EEMD)is used to decompose the vibration signal and extract the boundary spectrum features.Finally,the gray wolf algorithm-based fuzzy C-means clustering is used to denoise the signal and determine the abnormal states of the OLTC contact.An analysis of the experimental data shows that the proposed secondary denoising method has a better denoising effect compared to the single denoising method.The EEMD can improve the modal aliasing effect,and the improved fuzzy C-means clustering can effectively identify the abnormal state of the OLTC contacts.The analysis results of field measured data further verify the effectiveness of the proposed method and provide a reference for the abnormal state detection of the OLTC.
