For the existing support vector machine, when recognizing more questions, the shortcomings of high computational complexity and low recognition rate under the low SNR are emerged. The characteristic parameter of the s...For the existing support vector machine, when recognizing more questions, the shortcomings of high computational complexity and low recognition rate under the low SNR are emerged. The characteristic parameter of the signal is extracted and optimized by using a clustering algorithm, support vector machine is trained by grading algorithm so as to enhance the rate of convergence, improve the performance of recognition under the low SNR and realize modulation recognition of the signal based on the modulation system of the constellation diagram in this paper. Simulation results show that the average recognition rate based on this algorithm is enhanced over 30% compared with methods that adopting clustering algorithm or support vector machine respectively under the low SNR. The average recognition rate can reach 90% when the SNR is 5 dB, and the method is easy to be achieved so that it has broad application prospect in the modulating recognition.展开更多
Tarq geochemical 1:100,000 Sheet is located in Isfahan province which is investigated by Iran’s Geological and Explorations Organization using stream sediment analyzes. This area has stratigraphy of Precambrian to Qu...Tarq geochemical 1:100,000 Sheet is located in Isfahan province which is investigated by Iran’s Geological and Explorations Organization using stream sediment analyzes. This area has stratigraphy of Precambrian to Quaternary rocks and is located in the Central Iran zone. According to the presence of signs of gold mineralization in this area, it is necessary to identify important mineral areas in this area. Therefore, finding information is necessary about the relationship and monitoring the elements of gold, arsenic, and antimony relative to each other in this area to determine the extent of geochemical halos and to estimate the grade. Therefore, a well-known and useful K-means method is used for monitoring the elements in the present study, this is a clustering method based on minimizing the total Euclidean distances of each sample from the center of the classes which are assigned to them. In this research, the clustering quality function and the utility rate of the sample have been used in the desired cluster (S(i)) to determine the optimum number of clusters. Finally, with regard to the cluster centers and the results, the equations were used to predict the amount of the gold element based on four parameters of arsenic and antimony grade, length and width of sampling points.展开更多
The increasing trend towards independent fruit packaging demands a high appearance quality of individually packed fruits.In this paper,we propose an improved YOLOv5-based model,YOLO-Banana,to effectively grade banana ...The increasing trend towards independent fruit packaging demands a high appearance quality of individually packed fruits.In this paper,we propose an improved YOLOv5-based model,YOLO-Banana,to effectively grade banana appearance quality based on the number of banana defect points.Due to the minor and dense defects on the surface of bananas,existing detection algorithms have poor detection results and high missing rates.To address this,we propose a densitybased spatial clustering of applications with noise(DBSCAN)and K-means fusion clustering method that utilizes refined anchor points to obtain better initial anchor values,thereby enhancing the network’s recognition accuracy.Moreover,the optimized progressive aggregated network(PANet)enables better multi-level feature fusion.Additionally,the non-maximum suppression function is replaced with a weighted non-maximum suppression(weighted NMS)function based on distance intersection over union(DIoU).Experimental results show that the model’s accuracy is improved by 2.3%compared to the original YOLOv5 network model,thereby effectively grading the banana appearance quality.展开更多
The rolling force learning function is an important part of the hot strip parameter calculation process, and the steel classification is the basis of the model parameter calculation,which has a direct impact on the mo...The rolling force learning function is an important part of the hot strip parameter calculation process, and the steel classification is the basis of the model parameter calculation,which has a direct impact on the model calculation precision,and the classification of steel quality will directly affect the accuracy of the model calculation.This paper is for the classification of a certain type of steel,in order to find the optimal classification of steel model to achieve greater precision of the rolling force set.Cluster analysis is a method for the study of affinities between samples or variables,in which Distance Coefficient method can be used to demonstrate the differences between the equivalent measured values of the steel deformation resistance in hot strip rolling.Based on the characteristics of the steel components the composition calculation formula CEQ is created by the trial and error method,so as to achieve the consistency between the CEQ and the equivalent deformation resistance by cluster analysis,that is the composition calculation formula CEQ can be used to characterize the equivalent deformation resistance of the steel in hot rolling.In the actual production process,through the composition formula CEQ,the classification of the steel is made,by which the steel rolling force learning calculation is classified and the rolling force setup deviation is reduced,the rolling stability is improved.展开更多
基金supported in part by the National Natural Science Foundation of China under Grand No.61871129 and No.61301179Projects of Science and Technology Plan Guangdong Province under Grand No.2014A010101284
文摘For the existing support vector machine, when recognizing more questions, the shortcomings of high computational complexity and low recognition rate under the low SNR are emerged. The characteristic parameter of the signal is extracted and optimized by using a clustering algorithm, support vector machine is trained by grading algorithm so as to enhance the rate of convergence, improve the performance of recognition under the low SNR and realize modulation recognition of the signal based on the modulation system of the constellation diagram in this paper. Simulation results show that the average recognition rate based on this algorithm is enhanced over 30% compared with methods that adopting clustering algorithm or support vector machine respectively under the low SNR. The average recognition rate can reach 90% when the SNR is 5 dB, and the method is easy to be achieved so that it has broad application prospect in the modulating recognition.
文摘Tarq geochemical 1:100,000 Sheet is located in Isfahan province which is investigated by Iran’s Geological and Explorations Organization using stream sediment analyzes. This area has stratigraphy of Precambrian to Quaternary rocks and is located in the Central Iran zone. According to the presence of signs of gold mineralization in this area, it is necessary to identify important mineral areas in this area. Therefore, finding information is necessary about the relationship and monitoring the elements of gold, arsenic, and antimony relative to each other in this area to determine the extent of geochemical halos and to estimate the grade. Therefore, a well-known and useful K-means method is used for monitoring the elements in the present study, this is a clustering method based on minimizing the total Euclidean distances of each sample from the center of the classes which are assigned to them. In this research, the clustering quality function and the utility rate of the sample have been used in the desired cluster (S(i)) to determine the optimum number of clusters. Finally, with regard to the cluster centers and the results, the equations were used to predict the amount of the gold element based on four parameters of arsenic and antimony grade, length and width of sampling points.
基金supported by the Beijing Science Foundation(No.9232005)the Beijing Municipal Philosophy and Social Science Foundation of China(No.19GLB036)the Beijing Science and Technology Project(No.Z221100005822014)。
文摘The increasing trend towards independent fruit packaging demands a high appearance quality of individually packed fruits.In this paper,we propose an improved YOLOv5-based model,YOLO-Banana,to effectively grade banana appearance quality based on the number of banana defect points.Due to the minor and dense defects on the surface of bananas,existing detection algorithms have poor detection results and high missing rates.To address this,we propose a densitybased spatial clustering of applications with noise(DBSCAN)and K-means fusion clustering method that utilizes refined anchor points to obtain better initial anchor values,thereby enhancing the network’s recognition accuracy.Moreover,the optimized progressive aggregated network(PANet)enables better multi-level feature fusion.Additionally,the non-maximum suppression function is replaced with a weighted non-maximum suppression(weighted NMS)function based on distance intersection over union(DIoU).Experimental results show that the model’s accuracy is improved by 2.3%compared to the original YOLOv5 network model,thereby effectively grading the banana appearance quality.
文摘目的:探讨基于集束化理念的气道分级管理在新生儿呼吸窘迫综合征(neonatal respiratory distress syndrome,NRDS)机械通气患儿中的应用。方法:选取2018年10月—2022年10月鄂州市中心医院收治的96例NRDS机械通气患儿作为研究对象。根据随机数表法将其分为研究组与常规组,各48例。常规组实施常规气道管理护理,研究组实施基于集束化理念的气道分级管理。比较两组时间指标,护理前、出院时血气分析指标,不良事件。结果:研究组机械通气时间与住院时间均短于常规组,差异有统计学意义(P<0.05)。出院时,两组动脉血氧分压(partial pressure of oxygen in arterial blood,PaO_(2))、氧合指数(oxygenation index,OI)水平均升高,动脉血二氧化碳分压(partial pressure of carbon dioxide in arterial blood,PaCO_(2))降低,研究组PaO_(2)、OI水平均高于常规组,PaCO_(2)低于常规组,差异有统计学意义(P<0.05)。研究组呼吸机相关性肺炎、痰液堵塞发生率均低于常规组,差异有统计学意义(P<0.05)。结论:基于集束化理念的气道分级管理应用于NRDS机械通气患儿中,可缩短其机械通气时间与住院时间、改善血气指标、降低呼吸机相关性肺炎与痰液堵塞发生率。
文摘The rolling force learning function is an important part of the hot strip parameter calculation process, and the steel classification is the basis of the model parameter calculation,which has a direct impact on the model calculation precision,and the classification of steel quality will directly affect the accuracy of the model calculation.This paper is for the classification of a certain type of steel,in order to find the optimal classification of steel model to achieve greater precision of the rolling force set.Cluster analysis is a method for the study of affinities between samples or variables,in which Distance Coefficient method can be used to demonstrate the differences between the equivalent measured values of the steel deformation resistance in hot strip rolling.Based on the characteristics of the steel components the composition calculation formula CEQ is created by the trial and error method,so as to achieve the consistency between the CEQ and the equivalent deformation resistance by cluster analysis,that is the composition calculation formula CEQ can be used to characterize the equivalent deformation resistance of the steel in hot rolling.In the actual production process,through the composition formula CEQ,the classification of the steel is made,by which the steel rolling force learning calculation is classified and the rolling force setup deviation is reduced,the rolling stability is improved.
