目的:评价玻璃体腔内注射康柏西普治疗黄斑部小分支视网膜静脉阻塞继发黄斑水肿的有效性及安全性。方法:回顾性分析2015-07/2016-09在我院确诊为黄斑小分支视网膜静脉阻塞继发黄斑囊样水肿的患者资料19例19眼,所有患者均按3+按需注射(pr...目的:评价玻璃体腔内注射康柏西普治疗黄斑部小分支视网膜静脉阻塞继发黄斑水肿的有效性及安全性。方法:回顾性分析2015-07/2016-09在我院确诊为黄斑小分支视网膜静脉阻塞继发黄斑囊样水肿的患者资料19例19眼,所有患者均按3+按需注射(pro re nata,PRN)的方法行玻璃体腔内注射康柏西普0.05mL(0.5mg),每月随诊观察最佳矫正视力、中央视网膜厚度、注射次数及眼部相关并发症等。结果:治疗后1、2、3、6mo的最佳矫正视力与治疗前相比均有改善,差异具有统计学意义(P<0.01);治疗后1、2、3、6mo的黄斑中心凹厚度与治疗前相比均下降,差异具有统计学意义(P<0.01);其中有3眼出现反复发作的黄斑水肿,FFA检查显示微血管瘤渗漏,给予局部光凝封闭血管瘤后水肿吸收;治疗及随诊期间所有患者均未出现玻璃体出血、视网膜脱离、持续高眼压和眼内炎等并发症。结论:玻璃体腔注射康柏西普治疗黄斑小分支静脉阻塞继发的黄斑水肿安全有效,可以明显改善视力,减轻黄斑水肿;顽固的黄斑水肿建议行FFA检查,如水肿为微血管瘤渗漏造成建议联合局部光凝治疗。展开更多
Improving the prediction accuracy of wind power is an effective means to reduce the impact of wind power on power grid.Therefore,we proposed an improved African vulture optimization algorithm(AVOA)to realize the predi...Improving the prediction accuracy of wind power is an effective means to reduce the impact of wind power on power grid.Therefore,we proposed an improved African vulture optimization algorithm(AVOA)to realize the prediction model of multi-objective optimization least squares support vector machine(LSSVM).Firstly,the original wind power time series was decomposed into a certain number of intrinsic modal components(IMFs)using variational modal decomposition(VMD).Secondly,random numbers in population initialization were replaced by Tent chaotic mapping,multi-objective LSSVM optimization was introduced by AVOA improved by elitist non-dominated sorting and crowding operator,and then each component was predicted.Finally,Tent multi-objective AVOA-LSSVM(TMOALSSVM)method was used to sum each component to obtain the final prediction result.The simulation results show that the improved AVOA based on Tent chaotic mapping,the improved non-dominated sorting algorithm with elite strategy,and the improved crowding operator are the optimal models for single-objective and multi-objective prediction.Among them,TMOALSSVM model has the smallest average error of stroke power values in four seasons,which are 0.0694,0.0545 and 0.0211,respectively.The average value of DS statistics in the four seasons is 0.9902,and the statistical value is the largest.The proposed model effectively predicts four seasons of wind power values on lateral and longitudinal precision,and faster and more accurately finds the optimal solution on the current solution space sets,which proves that the method has a certain scientific significance in the development of wind power prediction technology.展开更多
Support Vector Machines （SVM） is a new general machine-learning tool based on structural risk minimization principle. This characteristic is very signific ant for the fault diagnostics when the number of fault sampl...Support Vector Machines （SVM） is a new general machine-learning tool based on structural risk minimization principle. This characteristic is very signific ant for the fault diagnostics when the number of fault samples is limited. Considering that SVM theory is originally designed for a two-class classification, a hybrid SVM scheme is proposed for multi-fault classification of rotating machinery in our paper. Two SVM strategies, 1-v-1 （one versus one） and 1-v-r （one versus rest）, are respectively adopted at different classification levels. At the parallel classification level, using l-v-1 strategy, the fault features extracted by various signal analysis methods are transferred into the multiple parallel SVM and the local classification results are obtained. At the serial classification level, these local results values are fused by one serial SVM based on 1-v-r strategy. The hybrid SVM scheme introduced in our paper not only generalizes the performance of signal binary SVMs but improves the precision and reliability of the fault classification results. The actually testing results show the availability suitability of this new method.展开更多
Rather than attempting to separate signal from noise in the spatial domain, it is often advantageous to work in a transform domain. Building on previous work, a novel denoising method based on local adaptive least squ...Rather than attempting to separate signal from noise in the spatial domain, it is often advantageous to work in a transform domain. Building on previous work, a novel denoising method based on local adaptive least squares support vector regression is proposed. Investigation on real images contaminated by Gaussian noise has demonstrated that the proposed method can achieve an acceptable trade off between the noise removal and smoothing of the edges and details.展开更多
Seizure detection is extremely essential for long-term monitoring of epileptic patients. This paper investigates the detection of epileptic seizures in multi-channel long-term intracranial electroencephalogram （iEEG...Seizure detection is extremely essential for long-term monitoring of epileptic patients. This paper investigates the detection of epileptic seizures in multi-channel long-term intracranial electroencephalogram （iEEG）. The algorithm conducts wavelet decomposition of iEEGs with five scales, and transforms the sum of the three frequency bands into histogram for computing the distance. The proposed method combines a novel feature called EMD-L1, which is an efficient algorithm of earth movers＇ distance （EMD）, with support vector machine （SVM） for binary classification between seizures and non-sei- zures. The EMD-LI used in this method is characterized by low time complexity and high processing speed by exploiting the L~ metric structure. The smoothing and collar technique are applied on the raw outputs of SVM classifier to obtain more ac- curate results. Several evaluation criteria are recommended to compare our algorithm with other conventional methods using the same dataset from the Freiburg EEG database. Experiment results show that the proposed method achieves a high sensi- tivity, specificity and low false detection rate, which are 95.73 %, 98.45 % and 0.33/h, respectively. This algorithm is char- acterized by its robustness and high accuracy with the possibility of performing real-time analysis of EEG data, and may serve as a seizure detection tool for monitoring long-term EEG.展开更多
Based on the engineering project on a small coal pillar of 12,521 working face roadway in Xieqiao Coalmine, data regarding surface displacements of the coal pillar, deep displacements and mining stress have been colle...Based on the engineering project on a small coal pillar of 12,521 working face roadway in Xieqiao Coalmine, data regarding surface displacements of the coal pillar, deep displacements and mining stress have been collected and analyzed. The results show that macroscopic transverse fractures of the inner coal pillar are developed within 2–4 m of the roadway surface, which is located outside the coal pillar anchorage zone. There is a displacement of 530 mm at the monitoring point in the 6 m deep zone of the pillar. Transfer of the fracture zone is found in a small coal pillar and the fractures within 3–4 m of the coal-rock zone from the roadway surface undergo propagation and closure of cracks which means this fracture zone is transferred from 3–4 m outside the roadway to only 2–3 m from the roadway surface. In the monitoring zone, vertical and horizontal stresses increase with a feature that shows that acceleration in the deep zone of the pillar is greater than that in the shallow zone. Furthermore, the acceleration of vertical stress is also greater than that of horizontal stress with a peak value in the 4 m zone.The research findings provide a reference for the regulation of a reasonable width of coal pillar in coalmines and optimal control design of surrounding rock.展开更多
文摘目的:评价玻璃体腔内注射康柏西普治疗黄斑部小分支视网膜静脉阻塞继发黄斑水肿的有效性及安全性。方法:回顾性分析2015-07/2016-09在我院确诊为黄斑小分支视网膜静脉阻塞继发黄斑囊样水肿的患者资料19例19眼,所有患者均按3+按需注射(pro re nata,PRN)的方法行玻璃体腔内注射康柏西普0.05mL(0.5mg),每月随诊观察最佳矫正视力、中央视网膜厚度、注射次数及眼部相关并发症等。结果:治疗后1、2、3、6mo的最佳矫正视力与治疗前相比均有改善,差异具有统计学意义(P<0.01);治疗后1、2、3、6mo的黄斑中心凹厚度与治疗前相比均下降,差异具有统计学意义(P<0.01);其中有3眼出现反复发作的黄斑水肿,FFA检查显示微血管瘤渗漏,给予局部光凝封闭血管瘤后水肿吸收;治疗及随诊期间所有患者均未出现玻璃体出血、视网膜脱离、持续高眼压和眼内炎等并发症。结论:玻璃体腔注射康柏西普治疗黄斑小分支静脉阻塞继发的黄斑水肿安全有效,可以明显改善视力,减轻黄斑水肿;顽固的黄斑水肿建议行FFA检查,如水肿为微血管瘤渗漏造成建议联合局部光凝治疗。
基金supported by National Natural Science Foundation of China(Nos.61662042,62062049)Science and Technology Plan of Gansu Province(Nos.21JR7RA288,21JR7RE174).
文摘Improving the prediction accuracy of wind power is an effective means to reduce the impact of wind power on power grid.Therefore,we proposed an improved African vulture optimization algorithm(AVOA)to realize the prediction model of multi-objective optimization least squares support vector machine(LSSVM).Firstly,the original wind power time series was decomposed into a certain number of intrinsic modal components(IMFs)using variational modal decomposition(VMD).Secondly,random numbers in population initialization were replaced by Tent chaotic mapping,multi-objective LSSVM optimization was introduced by AVOA improved by elitist non-dominated sorting and crowding operator,and then each component was predicted.Finally,Tent multi-objective AVOA-LSSVM(TMOALSSVM)method was used to sum each component to obtain the final prediction result.The simulation results show that the improved AVOA based on Tent chaotic mapping,the improved non-dominated sorting algorithm with elite strategy,and the improved crowding operator are the optimal models for single-objective and multi-objective prediction.Among them,TMOALSSVM model has the smallest average error of stroke power values in four seasons,which are 0.0694,0.0545 and 0.0211,respectively.The average value of DS statistics in the four seasons is 0.9902,and the statistical value is the largest.The proposed model effectively predicts four seasons of wind power values on lateral and longitudinal precision,and faster and more accurately finds the optimal solution on the current solution space sets,which proves that the method has a certain scientific significance in the development of wind power prediction technology.
文摘Support Vector Machines （SVM） is a new general machine-learning tool based on structural risk minimization principle. This characteristic is very signific ant for the fault diagnostics when the number of fault samples is limited. Considering that SVM theory is originally designed for a two-class classification, a hybrid SVM scheme is proposed for multi-fault classification of rotating machinery in our paper. Two SVM strategies, 1-v-1 （one versus one） and 1-v-r （one versus rest）, are respectively adopted at different classification levels. At the parallel classification level, using l-v-1 strategy, the fault features extracted by various signal analysis methods are transferred into the multiple parallel SVM and the local classification results are obtained. At the serial classification level, these local results values are fused by one serial SVM based on 1-v-r strategy. The hybrid SVM scheme introduced in our paper not only generalizes the performance of signal binary SVMs but improves the precision and reliability of the fault classification results. The actually testing results show the availability suitability of this new method.
基金Supported by the Foundation of Hubei Provincial Department of Education(No.2003EB0018).
文摘Rather than attempting to separate signal from noise in the spatial domain, it is often advantageous to work in a transform domain. Building on previous work, a novel denoising method based on local adaptive least squares support vector regression is proposed. Investigation on real images contaminated by Gaussian noise has demonstrated that the proposed method can achieve an acceptable trade off between the noise removal and smoothing of the edges and details.
基金Key Program of Natural Science Foundation of Shandong Province(No.ZR2013FZ002)Program of Science and Technology of Suzhou(No.ZXY2013030)Independent Innovation Foundation of Shandong University(No.2012DX008)
文摘Seizure detection is extremely essential for long-term monitoring of epileptic patients. This paper investigates the detection of epileptic seizures in multi-channel long-term intracranial electroencephalogram （iEEG）. The algorithm conducts wavelet decomposition of iEEGs with five scales, and transforms the sum of the three frequency bands into histogram for computing the distance. The proposed method combines a novel feature called EMD-L1, which is an efficient algorithm of earth movers＇ distance （EMD）, with support vector machine （SVM） for binary classification between seizures and non-sei- zures. The EMD-LI used in this method is characterized by low time complexity and high processing speed by exploiting the L~ metric structure. The smoothing and collar technique are applied on the raw outputs of SVM classifier to obtain more ac- curate results. Several evaluation criteria are recommended to compare our algorithm with other conventional methods using the same dataset from the Freiburg EEG database. Experiment results show that the proposed method achieves a high sensi- tivity, specificity and low false detection rate, which are 95.73 %, 98.45 % and 0.33/h, respectively. This algorithm is char- acterized by its robustness and high accuracy with the possibility of performing real-time analysis of EEG data, and may serve as a seizure detection tool for monitoring long-term EEG.
基金the Program for Changjiang Scholars and Innovative Research Team in University (No. IRT1084)the Open Fund of Hunan provincial Key Laboratory for Safe Mining Technology of Coal Mine (No. 201103)the National Natural Science Foundation of China (No. 51274193)
文摘Based on the engineering project on a small coal pillar of 12,521 working face roadway in Xieqiao Coalmine, data regarding surface displacements of the coal pillar, deep displacements and mining stress have been collected and analyzed. The results show that macroscopic transverse fractures of the inner coal pillar are developed within 2–4 m of the roadway surface, which is located outside the coal pillar anchorage zone. There is a displacement of 530 mm at the monitoring point in the 6 m deep zone of the pillar. Transfer of the fracture zone is found in a small coal pillar and the fractures within 3–4 m of the coal-rock zone from the roadway surface undergo propagation and closure of cracks which means this fracture zone is transferred from 3–4 m outside the roadway to only 2–3 m from the roadway surface. In the monitoring zone, vertical and horizontal stresses increase with a feature that shows that acceleration in the deep zone of the pillar is greater than that in the shallow zone. Furthermore, the acceleration of vertical stress is also greater than that of horizontal stress with a peak value in the 4 m zone.The research findings provide a reference for the regulation of a reasonable width of coal pillar in coalmines and optimal control design of surrounding rock.
