In this paper, we present a hybrid position/force controller for operating joint robots. The hybrid controller has two goals—motion tracking and force regulating. As long as these two goals are not mutually exclusive, they can be decoupled in some way. In this work, we make use of the smooth and invertible mapping from the joint space to the task space to decouple the two control goals and design controllers separately. The traditional motion controller in the task space is used for motion control, while the force controller is designed through manipulating the desired trajectory to regulate the force indirectly. Two case studies—contour tracking/polishing surfaces and grabbing boxes with two robotic arms—are presented to show the efficacy of the hybrid controller, and simulations with physics engines are carried out to validate the efficacy of the proposed method.

Landslides are common natural disasters that often cause serious impact and damage to human society. Since landslide disasters threaten people's production and life all the time, it is particularly important to predict the risk of landslides and to control landslide disasters. When studying landslide risk and deciding whether to treat the landslide, it is meaningful to classify and compare the risk of landslides so as to select those landslides with a higher degree of danger for priority treatment. The target of this paper is to extract factors related to landslide risk, and train a classification models for landslide risk. It employs ensemble machine learning algorithms to classify landslide hazards. Because the landslide feature has a large number of dimensions, this paper uses the PCA method to reduce the dimension. Due to the imbalance of the samples, this paper uses the SMOTE method to handle the imbalanced learning. The results of study show that the selected factors are highly related to landslide risk, the classification model in this paper has good accuracy.

In recent years, the problem of land subsidence in urban areas has attracted more attention. differential interferometric synthetic aperture radar (D-InSAR) is a common surface deformation measurement technology. About our research, first of all, the processing effects of the ascending and descending images, VV polarization and VH polarization of Sentinel-1A data in the study area are compared. The ascending image with better coverage in the study area and VV polarization with better interference processing effect are selected. Second, the filtering algorithm and unwrapping algorithm in D-InSAR are contrasted. In terms of filtering algorithms, the improved Goldstein method with the coherence coefficient to adjust the power exponent of the weighting function in the frequency domain had the best filtering effect. In terms of unwrapping algorithms, there are fewer unwrapping islands in the minimum cost network flow method. Finally, D-InSAR is used to process the Sentinel-1A data to obtain the surface deformation results of downtown Chengdu. Therefore, the deformation of downtown Chengdu based on Sentinel-1A data is abtained.

In addition to GPS and leveling, there are also synthetic aperture radar (SAR) measurements in the field of remote sensing. Differential interferometric SAR (D-InSAR) is a surface deformation measurement technology developed from synthetic aperture radar interferometry (InSAR). In the research of D-InSAR, although the processing flow is certain, the selection of data and process method is not universal. In the process of InSAR interferometric data processing, phase unwrapping is the key link, which directly affects the accuracy of digital elevation model (DEM). In this paper, the phase unwrapping methods of dual pass D-InSAR are compared. There will be islands in the process of unwrapping, and the less the islanding, the better. Through the situation of islanding, the unwrapping effect of region growth method and minimum network cost flow method is compared. The results show that the best method is the minimum cost network flow method.

Drought is one of the main natural hazards affecting the environment and economy of countries all over the world. Fusing weather data with satellite images therefore becomes a superior method of identifying and monitoring drought in a given region. We established the relationship between land surface temperature (LST), the normalized differential vegetation index (NDVI) and rainfall data to derive areas of drought. Then, we obtained the indexes from the rainfall anomaly and NDVI anomaly as indicators which confirm the drought indicative claims of the maps produced. Our further examination of the NDVI, LST and rainfall maps indicate that the western, central and Volta Regions of the study area are the least prone to drought, with Axim (one of the most southern towns) in Ghana recording the highest rainfall in the country each year.

Hyperspectral image is usually composed of hundreds of bands rich of spatial and spectral information. And this is an advantage for the common remotely sensed data. Thus, the classification of hyperspectral image could be of great value. However, the dimensionality of hyperspectral image may lead to the curse of dimensionality phenomenon when it is directly used for land use classification or other applications, making it difficult to be utilized effectively. In this paper, we presented a novel classification framework with capsule network based on the spectral and spatial information of hyperspectral images. At first, we use principal components analysis (PCA) to reduce the dimensionalities of hyperspectral image. Then, we use the capsule network to classify hyperspectral image. Our experimental result showed the novel classification framework is more efficient than other six popular methods. Therefore, the capsule network method is robust for hyperspectral image classification.