The research of polishing nozzle quality based on discrete element method

In order to get further study for the effect of abrasive grains to the wall of the workpiece during polishing process, a new method of discrete element that carry out the numerical simulation with DEM is put forward, and the visual calculation is performed for the abrasive grain movement in the nozzle. The interaction of particles-particles or particles-workpiece wall during the polishing process and the tracks of single grain in the workpiece are analyzed by observing the distribution of abrasive grain in the workpiece at different time. The surface removal mechanism of abrasive grains to the workpiece material is discussed by analyzing the collision process of particles to the workpiece wall. The wear level of the abrasive grains to the inner surface of the workpiece is studied through the force of abrasive grain to the workpiece wall consumption, and finally explore the cutting effect of particles to workpiece wall. As a consequence, the abrasive flow processing experiment is carried out. The surface roughness of the large hole and small hole of the nozzle are detected by stylus measurement. The conclusion shows that the surface roughness for the large hole and the small hole before the experiment is1.741 μm and 1.201 μm, its 0.801 μm, 0.651 μm after it. Further roughness tests are performed on the surface of the pores by means of a grating surface measuring instrument. The result indicates that the surface roughness reduces from 2.67 μm to 0.697 μm, 0.728 μm, 0.782 μm. Apparently, the surface roughness of the hole is sharply reduced, which has a smooth and flat inner surface, the effectiveness and reliability of the abrasive flow are verified

Human posture recognition based on multiple features and rule learning

The use of skeleton data for human posture recognition is a key research topic in the human-computer interaction field. To improve the accuracy of human posture recognition, a new algorithm based on multiple features and rule learning is proposed in this paper. Firstly, a 219-dimensional vector that includes angle features and distance features is defined. Specifically, the angle and distance features are defined in terms of the local relationship between joints and the global spatial location of joints. Then, during human posture classification, the rule learning method is used together with the Bagging and random sub-Weili Ding space methods to create different samples and features for improved classification of sub-classifiers for different samples. Finally, the performance of our proposed algorithm is evaluated on four human posture datasets. The experimental results show that our algorithm can recognize many kinds of human postures effectively, and the results obtained by the rule-based learning method are of higher interpretability than those by traditional machine learning methods and CNNs

Effects of different inlet velocity on the polishing quality of abrasive flow machining

In order to study the effect of different inlet velocity on the polishing quality of abrasive flow machining, this paper takes the variable diameter pipe as an example. The fluid dynamic pressure and total energy of abrasive particles under coupling field with different inlet velocities were carried out by using computational fluid dynamics software. The results of numerical analysis show that the polishing quality becomes better with the increase of the inlet velocity. At the same inlet velocity, the smaller the pipe diameter is, the higher the polishing quality will be. Therefore, the optimum inlet velocity can be selected by numerical simulation according to the size of the aperture of workpiece in the actual processing, which can provide technical support for the production

Multi-temporal Monitoring for Road Slope Collapse by Means of LUTAN-1 SAR Data and High Resolution Optical Data

Collapse is one of the most destructive natural disaster, being sudden, frequent, and highly concealed, causing large-scale damage. On August 10, 2023, the slope of 108 national highway in Weinan, Shaanxi Province collapsed. The lower edge of the collapse slope body is the Luohe river, and the collapse body rushes into the river to form a barrier lake. Remote sensing technique can provide multiple dimensional information for disaster emergency and management. Lutan-1 SAR satellites are the first group L-band SAR constellation for multiple applications in China. Owing to the precise orbit control ability and high revisit characteristics for Lutan-1 SAR satellites, surface deformation monitoring with centimeter even millimeter accuracy may be achieved. Based on the multi-temporal pre-disaster and post-disaster Lutan-1 SAR data and high resolution optical data, the collapse information including the pre-disaster and post-disaster were extracted and analysed. From July 11 to 27, 2023, the pre-collapse deformation was obtained with the maximum value of 6 cm, and obvious deformation occurred before the collapse. Lutan-1 monitored results pre-collapse can provide certain information for disaster early identification. From July 27 to August 24, 2023, due to the serious incoherence caused by large deformation and ground changes, effective deformation information cannot be obtained based on the InSAR technique. In addition, the collapse information was clearly extracted by the high resolution optical data acquired pre-collapse and post collapse. After the collapse, significant deformation was extracted from August 24 to September 21 with the maximum value of 6 cm, indicating that obvious deformation still occurred over the collapse area. Through the analysis for the series results obtained by SAR and optical data, it is favourable for disaster emergency and management

An empirical study of shape recognition in ensemble learning context

Shape recognition has been a popular application of machine learning, where each shape is defined as a class for training classifiers that recognize the shapes of new instances. Since training of classifiers is essentially achieved through learning from features, it is crucial to extract and select a set of relevant features that can effectively distinguish one class from other classes. However, different instances could present features which are highly dissimilar, even if these instances belong to the same class. The above difference in feature representation can also result in high diversity among classifiers trained by using different algorithms or data samples. In this paper, we investigate the impact of multi-classifier fusion on shape recognition by using six features extracted from a 2D shape data set. In particular, popular single learning algorithms, such as Decision Trees, Support Vector Machine and K Nearest Neighbours, are adopted to train base classifiers on features selected by using a wrapper approach. Furthermore, two popular ensemble learning algorithms (Random Forests and Gradient Boosted Trees) are adopted to train decision tree ensembles on the same feature sets. The outputs of the two ensemble classifiers are finally combined with the outputs of all the other base classifiers The experimental results show the effectiveness of the above setting of multi-classifier fusion for advancing the performance in comparison with using each single (non-ensemble) learning algorithm

Updating Active Deformation Inventory Maps in Mining Areas by Integrating InSAR and LiDAR Datasets

Slope failures, subsidence, earthworks, consolidation of waste dumps, and erosion are typical active deformation processes that pose a significant hazard in current and abandoned mining areas, given their considerable potential to produce damage and affect the population at large. This work proves the potential of exploiting space-borne InSAR and airborne LiDAR techniques, combined with data inferred through a simple slope stability geotechnical model, to obtain and update inventory maps of active deformations in mining areas. The proposed approach is illustrated by analyzing the region of Sierra de Cartagena-La Union (Murcia), a mountainous mining area in southeast Spain. Firstly, we processed Sentinel-1 InSAR imagery acquired both in ascending and descending orbits covering the period from October 2016 to November 2021. The obtained ascending and descending deformation velocities were then separately post-processed to semi-automatically generate two active deformation areas (ADA) maps by using ADATool. Subsequently, the PS-InSAR LOS displacements of the ascending and descending tracks were decomposed into vertical and east-west components. Complementarily, open-access, and non-customized LiDAR point clouds were used to analyze surface changes and movements. Furthermore, a slope stability safety factor (SF) map was obtained over the study area adopting a simple infinite slope stability model. Finally, the InSAR-derived maps, the LiDAR-derived map, and the SF map were integrated to update a previously published landslides’ inventory map and to perform a preliminary classification of the different active deformation areas with the support of optical images and a geological map. Complementarily, a level of activity index is defined to state the reliability of the detected ADA. A total of 28, 19, 5, and 12 ADAs were identified through ascending, descending, horizontal, and vertical InSAR datasets, respectively, and 58 ADAs from the LiDAR change detection map. The subsequent preliminary classification of the ADA enabled the identification of eight areas of consolidation of waste dumps, 11 zones in which earthworks were performed, three areas affected by erosion processes, 17 landslides, two mining subsidence zone, seven areas affected by compound processes, and 23 possible false positive ADAs. The results highlight the effectiveness of these two remote sensing techniques (i.e., InSAR and LiDAR) in conjunction with simple geotechnical models and with the support of orthophotos and geological information to update inventory maps of active deformation areas in mining zones.This research was funded by the ESA-MOST China DRAGON-5 project (ref. 59339) and funded by a Chinese Scholarship Council studentship awarded to Liuru Hu (Ref. 202004180062)

An empirical study of shape recognition in ensemble learning context

Shape recognition has been a popular application of machine learning, where each shape is defined as a class for training classifiers that recognize the shapes of new instances. Since training of classifiers is essentially achieved through learning from features, it is crucial to extract and select a set of relevant features that can effectively distinguish one class from other classes. However, different instances could present features which are highly dissimilar, even if these instances belong to the same class. The above difference in feature representation can also result in high diversity among classifiers trained by using different algorithms or data samples. In this paper, we investigate the impact of multi-classifier fusion on shape recognition by using six features extracted from a 2D shape data set. In particular, popular single learning algorithms, such as Decision Trees, Support Vector Machine and K Nearest Neighbours, are adopted to train base classifiers on features selected by using a wrapper approach. Furthermore, two popular ensemble learning algorithms (Random Forests and Gradient Boosted Trees) are adopted to train decision tree ensembles on the same feature sets. The outputs of the two ensemble classifiers are finally combined with the outputs of all the other base classifiers The experimental results show the effectiveness of the above setting of multi-classifier fusion for advancing the performance in comparison with using each single (non-ensemble) learning algorithm

Quality analysis of T-tube with solid-liquid two-phase abrasive flow polished

For the problem affected by speed and uneven grinding in abrasive flow with non-linear pipe, the T-tube is regarded as the research object, the numerical simulation of the flow state of the abrasive flow under different inlet velocities is carried out by using the large eddy simulation (LES). The dynamic pressure, turbulent kinetic energy, turbulence intensity and wall shear force under different inlet conditions are compared and analyzed. We can see from the numerical analysis that with the increase of inlet velocity, the dynamic pressure, turbulent kinetic energy, turbulence intensity and wall shear force also increase, and the polished effect is improved. The surface roughness and surface morphology of the T-tube workpiece before and after polished of abrasive flow are measured, the surface roughness decreased from 3.633 μm to 1.201 μm. Therefore, the effectiveness of the abrasive flow polished inner channel structure is confirmed, Also the credibility of numerical analysis is verified and provides theoretical support for the development of abrasive flow polished technology