A method of controllable internal perturbation inside the chaotic map is proposed to solve the problem in chaotic systems caused by finite precision.A chaotic system can produce large amounts of initial-sensitive,non-...A method of controllable internal perturbation inside the chaotic map is proposed to solve the problem in chaotic systems caused by finite precision.A chaotic system can produce large amounts of initial-sensitive,non-cyclical pseudo-random sequences.However,the finite precision brings short period and odd points which obstruct application of chaos theory seriously in digital communication systems.Perturbation in chaotic systems is a possible efficient method for solving finite precision problems,but former researches are limited in uniform distribution maps.The proposed internal perturbation can work on both uniform and non-uniform distribution chaotic maps like Chebyshev map and Logistic map.By simulations,results show that the proposed internal perturbation extends sequence periods and eliminates the odd points,so as to improve chaotic performances of perturbed chaotic sequences.展开更多
In order to classify the alertness status, 19 channels of electroencephalogram(EEG) signals from 5 subjects were acquired during daytime nap. Ten different types of features(including time domain features, frequency d...In order to classify the alertness status, 19 channels of electroencephalogram(EEG) signals from 5 subjects were acquired during daytime nap. Ten different types of features(including time domain features, frequency domain features and nonlinear features) were extracted from EEG signals, and an improved self-organizing map(ISOM) neuron network was proposed, which successfully identify three different brain status of the subjects: awareness, drowsiness and sleep. Compared with traditional SOM, the experiment results show that the ISOM generates much better classification accuracy, reaching as high as 89.59%.展开更多
This paper presents the comprehensive results of landing site topographic mapping and rover localization in Chang’e-3 mission.High-precision topographic products of the landing site with extremely high resolutions(up...This paper presents the comprehensive results of landing site topographic mapping and rover localization in Chang’e-3 mission.High-precision topographic products of the landing site with extremely high resolutions(up to 0.05 m)were generated from descent images and registered to CE-2 DOM.Local DEM and DOM with 0.02 m resolution were produced routinely at each waypoint along the rover traverse.The lander location was determined to be(19.51256°W,44.11884°N,-2615.451 m)using a method of DOM matching.In order to reduce error accumulation caused by wheel slippage and IMU drift in dead reckoning,cross-site visual localization and DOM matching localization methods were developed to localize the rover at waypoints;the overall traveled distance from the lander is 114.8 m from cross-site visual localization and 111.2 m from DOM matching localization.The latter is of highest accuracy and has been verified using a LRO NAC image where the rover trajeactory is directly identifiable.During CE-3 mission operations,landing site mapping and rover localization products including DEMs and DOMs,traverse maps,vertical traverse profiles were generated timely to support teleoperation tasks such as obstacle avoidance and rover path planning.展开更多
We report the world's first all-season training and validation sample sets for global land cover classification with Landsat-8 data.Prior to this,such samples were only available at a single date primarily from th...We report the world's first all-season training and validation sample sets for global land cover classification with Landsat-8 data.Prior to this,such samples were only available at a single date primarily from the growing season.It is unknown how much limitation such a single-date sample has to mapping global land cover in other seasons of the year.To answer this question,we selected available Landsat-8 images from four seasons and collected training and validation samples from them.We compared the performances of training samples in different seasons using Random Forest algorithm.We found that the use of training samples from any individual season would result in the best overall classification accuracy when validated by samples in the same season.The global overall accuracy from combined best seasonal results was 67.2% when classifying the 11 Level-1 classes in the Finer Resolution Observation and Monitoring of Global Land Cover(FROM-GLC) classification system.The use of training samples from all seasons(named all-season training sample set hereafter) produced an overall accuracy of 67.0%.We also tested classification within 10° latitude 60° longitude zones using all-season training subsample within each zone and obtained an overall accuracy of 70.2%.This indicates that properly grouped subsamples in space can help improve classification accuracies.All the results in this study seem to suggest that it is possible to use an all-season training sample set to reach global optimality with universal applicability in classifying images acquired at any time of a year for global land cover mapping.展开更多
基金Supported by the National Basic Research Program of China(No.2007CB310606)
文摘A method of controllable internal perturbation inside the chaotic map is proposed to solve the problem in chaotic systems caused by finite precision.A chaotic system can produce large amounts of initial-sensitive,non-cyclical pseudo-random sequences.However,the finite precision brings short period and odd points which obstruct application of chaos theory seriously in digital communication systems.Perturbation in chaotic systems is a possible efficient method for solving finite precision problems,but former researches are limited in uniform distribution maps.The proposed internal perturbation can work on both uniform and non-uniform distribution chaotic maps like Chebyshev map and Logistic map.By simulations,results show that the proposed internal perturbation extends sequence periods and eliminates the odd points,so as to improve chaotic performances of perturbed chaotic sequences.
基金Supported by National Natural Science Foundation of China(No.51007063)
文摘In order to classify the alertness status, 19 channels of electroencephalogram(EEG) signals from 5 subjects were acquired during daytime nap. Ten different types of features(including time domain features, frequency domain features and nonlinear features) were extracted from EEG signals, and an improved self-organizing map(ISOM) neuron network was proposed, which successfully identify three different brain status of the subjects: awareness, drowsiness and sleep. Compared with traditional SOM, the experiment results show that the ISOM generates much better classification accuracy, reaching as high as 89.59%.
基金supported by the National Natural Science Foundation of China(Grant Nos.41201480,41171355 and 41301528)the Key Research Program of the Chinese Academy of Sciences(Grant No.KGZD-EW-603)
文摘This paper presents the comprehensive results of landing site topographic mapping and rover localization in Chang’e-3 mission.High-precision topographic products of the landing site with extremely high resolutions(up to 0.05 m)were generated from descent images and registered to CE-2 DOM.Local DEM and DOM with 0.02 m resolution were produced routinely at each waypoint along the rover traverse.The lander location was determined to be(19.51256°W,44.11884°N,-2615.451 m)using a method of DOM matching.In order to reduce error accumulation caused by wheel slippage and IMU drift in dead reckoning,cross-site visual localization and DOM matching localization methods were developed to localize the rover at waypoints;the overall traveled distance from the lander is 114.8 m from cross-site visual localization and 111.2 m from DOM matching localization.The latter is of highest accuracy and has been verified using a LRO NAC image where the rover trajeactory is directly identifiable.During CE-3 mission operations,landing site mapping and rover localization products including DEMs and DOMs,traverse maps,vertical traverse profiles were generated timely to support teleoperation tasks such as obstacle avoidance and rover path planning.
基金partially supported by the National High Technology Program(2013AA122804)the Special Fund for Meteorology Scientific Research in the Public Welfare(GYHY201506023)of ChinaOpen Fund of State Key Laboratory of Remote Sensing Science(OFSLRSS201514)
文摘We report the world's first all-season training and validation sample sets for global land cover classification with Landsat-8 data.Prior to this,such samples were only available at a single date primarily from the growing season.It is unknown how much limitation such a single-date sample has to mapping global land cover in other seasons of the year.To answer this question,we selected available Landsat-8 images from four seasons and collected training and validation samples from them.We compared the performances of training samples in different seasons using Random Forest algorithm.We found that the use of training samples from any individual season would result in the best overall classification accuracy when validated by samples in the same season.The global overall accuracy from combined best seasonal results was 67.2% when classifying the 11 Level-1 classes in the Finer Resolution Observation and Monitoring of Global Land Cover(FROM-GLC) classification system.The use of training samples from all seasons(named all-season training sample set hereafter) produced an overall accuracy of 67.0%.We also tested classification within 10° latitude 60° longitude zones using all-season training subsample within each zone and obtained an overall accuracy of 70.2%.This indicates that properly grouped subsamples in space can help improve classification accuracies.All the results in this study seem to suggest that it is possible to use an all-season training sample set to reach global optimality with universal applicability in classifying images acquired at any time of a year for global land cover mapping.
