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| 1 | GIS-based landslide susceptibility mapping using numerical risk factor bivariate model and its ensemble with linear multivariate regression and boosted regression tree algorithms显示文摘In this study, a novel approach of the landslide numerical risk factor(LNRF) bivariate model was used in ensemble with linear multivariate regression(LMR) and boosted regression tree(BRT) models, coupled with radar remote sensing data and geographic information system(GIS), for landslide susceptibility mapping(LSM) in the Gorganroud watershed, Iran. Fifteen topographic, hydrological, geological and environmental conditioning factors and a landslide inventory(70%, or 298 landslides) were used in mapping. Phased array-type L-band synthetic aperture radar data were used to extract topographic parameters. Coefficients of tolerance and variance inflation factor were used to determine the coherence among conditioning factors. Data for the landslide inventory map were obtained from various resources, such as Iranian Landslide Working Party(ILWP), Forestry, Rangeland and Watershed Organisation(FRWO), extensive field surveys, interpretation of aerial photos and satellite images, and radar data. Of the total data, 30% were used to validate LSMs, using area under the curve(AUC), frequency ratio(FR) and seed cell area index(SCAI).Normalised difference vegetation index, land use/land cover and slope degree in BRT model elevation, rainfall and distance from stream were found to be important factors and were given the highest weightage in modelling. Validation results using AUC showed that the ensemble LNRF-BRT and LNRFLMR models(AUC = 0.912(91.2%) and 0.907(90.7%), respectively) had high predictive accuracy than the LNRF model alone(AUC = 0.855(85.5%)). The FR and SCAI analyses showed that all models divided the parameter classes with high precision. Overall, our novel approach of combining multivariate and machine learning methods with bivariate models, radar remote sensing data and GIS proved to be a powerful tool for landslide susceptibility mapping. | Alireza ARABAMERI Biswajeet PRADHAN Khalil REZAE Masoud SOHRABI Zahra KALANTARI | 2019 | Journal of Mountain Science2019，16，3： | 11 |
| 2 | 基于距离误差的机器人参数辨识模型与冗余性分析显示文摘为避免机器人运动学参数辨识过程中,测量坐标系与机器人基坐标系之间繁琐的坐标变换,首先利用关节旋量的空间几何特性,提出了基于伴随变换的距离误差模型。其次,针对距离误差模型中可辨识参数的冗余性,通过辨识雅可比矩阵的零空间分析,确定了可辨识参数的数目与误差测量方式之间的关系。确定了绕对应关节旋转的测量方式和相对初始位形的测量方式下可辨识参数的数目。最后,对KUKA you Bot机器人的运动学参数辨识进行了实验研究,实验结果验证了距离误差模型的有效性和参数冗余性分析的正确性。 | 申景金 郭家桢 MASOUD Kalantari | 2018 | 农业机械学报2018，49，11： | 5 |
| 3 | A Decision Support System for Order Acceptance/ rejection in Hybrid MTS/MTO Production Systems显示文摘 | MAHDOKHT Kalantari MASOUD Rabbani MAHMOOD Ebadian | 2011 | Applied Mathematical Modelling2011，35，： | 1 |
| 4 | Nondestructive Testing of Bridge Stay Cable Surface Defects Based on Computer Vision显示文摘The automatically defect detection method using vision inspectionis a promising direction. In this paper, an efficient defect detection method fordetecting surface damage to cables on a cable-stayed bridge automatically isdeveloped. A mechanism design method for the protective layer of cables of abridge based on vision inspection and diameter measurement is proposed bycombining computer vision and diameter measurement techniques. A detectionsystem for the surface damages of cables is de-signed. Images of cablesurfaces are then enhanced and subjected to threshold segmentation by utilizingthe improved local grey contrast enhancement method and the improvedmaximum correlation method. Afterwards, the data obtained through diametermeasurement are mined by employing the moving average method. Imageenhancement, threshold segmentation, and diameter measurement methodsare separately validated experimentally. The experimental test results showthat the system delivers recall ratios for type-I and II surface defects of cablesreaching 80.4% and 85.2% respectively, which accurately detects bulges oncable surfaces. | Fengyu Xu Masoud Kalantari Bangjian Li Xingsong Wang | 2023 | Computers, Materials & Continua2023，，4： | 0 |
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