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RISS 인기검색어
- 검색키워드
- 저자 : Gao Lewen (검색결과 3 건)
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A Regional Forest Tree Layer Biomass Estimation Method Based on Clustering Analysis
Wang Nihong,Gao Meng,Liu Lichen,Gao Lewen 보안공학연구지원센터 2016 International Journal of u- and e- Service, Scienc Vol.9 No.1
As an area always contains varies of tree spices or forest types, therefore, when using biomass estimation model based on single tree or forest stand to estimate regional biomass, the modeling workload is big, and the existing models do not adequately reflect the factors that influence the biomass. Aiming at the problems above, this paper proposes a regional forest tree layer biomass estimation method based on clustering analysis, using the forest resources survey data of the study area as the research object, using principal component analysis to extract characteristic factors from 17 indexes, using the improved K-means algorithm to clustering the forest subcompartment, and using support vector regression algorithm to separately build the biomass estimation model based on clusters. The results show that 8 principal components can reflect over 80% information of the original data; the subcompartment of the study area can be divided into 6 classes, the coefficients of each model are ranging from 0.7 to 0.92, the average relative error absolute values of each model are ranging from 11.173% to 23.583%, this method has got a satisfactory accuracy, which can provide a new way for regional biomass estimation.
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Anomaly Driving Speed Detection and Correction Algorithm based on Quantiles and KNN
Guo Yanling,Liu Lichen,Gao Meng,Gao Lewen 보안공학연구지원센터 2016 International Journal of Hybrid Information Techno Vol.9 No.3
Driving speed is a key parameter for building the traffic state identification model, its precision directly affects the model reliability and the traffic state identification accuracy. Aiming at the standard normal deviation method’s defects in dealing with the extreme noise data, an anomaly driving speed detection algorithm based on quantiles is proposed, use historical data to establish the exception borders which are used to detect whether an unknown data is abnormal; on the basis of the abnormal data detection, a driving speed prediction algorithm based on improved KNN is proposed, use K-means algorithm to clustering the historical data, and predict the next moment’s speed according to the distance between the data to be predicted and the clusters, the predicted speed can be used to correct the abnormal speed. Experimental results show that the detection rate of the proposed anomaly detection algorithm has improved about 4.25% compared with the standard normal deviation method, and the false detection rate has reduced about 25.51%; the mean relative error of the proposed speed prediction algorithm is 13.69%, it can predict the driving speed well, namely, the anomaly driving speed detection and correction algorithm based on quantiles and KNN is feasible and effective.
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Urban Road Traffic State Identification Algorithm Based On Particle Filter and Fuzzy Discrimination
Guo Yanling,Liu Lichen,Gao Meng,Gao Lewen 보안공학연구지원센터 2015 International Journal of Smart Home Vol.9 No.8
Urban road traffic state identification is a key link to realize the intelligent transportation based on the Internet of Vehicles, and accurately positioning vehicles is the foundation to realize the traffic state identification. Aiming at the problem that GPS has signal blind area in positioning vehicles, a vehicle positioning algorithm based on particle filter was proposed, it could improve the traditional algorithm on degradation and large amount of calculations; Based on vehicle positioning, an urban road traffic state identification algorithm based on fuzzy discrimination was proposed, it could comprehensively consider multiple factors’ influence on traffic state. The experiment results show that the improved particle filter algorithm’s mean squared error has increased about 55.437% compared with GPS method, and the traffic state identification algorithm can accurately identify the traffic state of the study area, it can prove that the urban road traffic state identification algorithm based on particle filter and fuzzy discrimination is feasible and effective.
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