Precision Evaluation and Fusion of Topographic Data Based on UAVs and TLS Surveys of a Loess Landslide

In recent years, low-cost unmanned aerial vehicles (UAVs) photogrammetry and terrestrial laser scanner (TLS) techniques have become very important non-contact measurement methods for obtaining topographic data about landslides. However, owing to the differences in the types of UAVs and whether the ground control points (GCPs) are set in the measurement, the obtained topographic data for landslides often have large precision differences. In this study, two types of UAVs (DJI Mavic Pro and DJI Phantom 4 RTK) with and without GCPs were used to survey a loess landslide. UAVs point clouds and digital surface model (DSM) data for the landslide were obtained. Based on this, we used the Geomorphic Change Detection software (GCD 7.0) and the Multiscale Model-To-Model Cloud Comparison (M3C2) algorithm in the Cloud Compare software for comparative analysis and accuracy evaluation of the different point clouds and DSM data obtained using the same and different UAVs. The experimental results show that the DJI Phantom 4 RTK obtained the highest accuracy landslide terrain data when the GCPs were set. In addition, we also used the Maptek I-Site 8,820 terrestrial laser scanner to obtain higher precision topographic point cloud data for the Beiguo landslide. However, owing to the terrain limitations, some of the point cloud data were missing in the blind area of the TLS measurement. To make up for the scanning defect of the TLS, we used the iterative closest point (ICP) algorithm in the Cloud Compare software to conduct data fusion between the point clouds obtained using the DJI Phantom 4 RTK with GCPs and the point clouds obtained using TLS. The results demonstrate that after the data fusion, the point clouds not only retained the high-precision characteristics of the original point clouds of the TLS, but also filled in the blind area of the TLS data. This study introduces a novel perspective and technical scheme for the precision evaluation of UAVs surveys and the fusion of point clouds data based on different sensors in geological hazard surveys.

The precision analysis of the Chinese VLBI Network in probe delay measurement

We propose a VLBI precision evaluation method for probe delay measurement, so as to investigate the error contributions from different components in the Chinese VLBI Network (CVN). This method takes the idea of traditional closure delay analysis for distant radio sources. It focuses on the VLBI closure delay only and therefore excludes the influence of probe orbit determination, which makes it very suitable to evaluate the capability of VLBI probe delay measurement. In this paper, we first introduce the principles of closure delay analysis. Then the statistical results of typical CE5 (Chinese Chang'e 5 lunar exploration mission) and HX1 (Chinese Mars exploration mission) observations are presented, including the comparison of the closure delay precisions between CE5 and HX1 for four closed baseline triangles in CVN. According to the result, we realize that, the precision discrepancy between CE5 and HX1 in the closure delay analysis is less than that of residual delay after orbit determination, which reflects the precision level of the VLBI delay measurement.

High Precision Mammography Lesion Identification From Imprecise Medical Annotations

Breast cancer screening using Mammography serves as the earliest defense against breast cancer, revealing anomalous tissue years before it can be detected through physical screening. Despite the use of high resolution radiography, the presence of densely overlapping patterns challenges the consistency of human-driven diagnosis and drives interest in leveraging state-of-art localization ability of deep convolutional neural networks (DCNN). The growing availability of digitized clinical archives enables the training of deep segmentation models, but training using the most widely available form of coarse hand-drawn annotations works against learning the precise boundary of cancerous tissue in evaluation, while producing results that are more aligned with the annotations rather than the underlying lesions. The expense of collecting high quality pixel-level data in the field of medical science makes this even more difficult. To surmount this fundamental challenge, we propose LatentCADx, a deep learning segmentation model capable of precisely annotating cancer lesions underlying hand-drawn annotations, which we procedurally obtain using joint classification training and a strict segmentation penalty. We demonstrate the capability of LatentCADx on a publicly available dataset of 2,620 Mammogram case files, where LatentCADx obtains classification ROC of 0.97, AP of 0.87, and segmentation AP of 0.75 (IOU = 0.5), giving comparable or better performance than other models. Qualitative and precision evaluation of LatentCADx annotations on validation samples reveals that LatentCADx increases the specificity of segmentations beyond that of existing models trained on hand-drawn annotations, with pixel level specificity reaching a staggering value of 0.90. It also obtains sharp boundary around lesions unlike other methods, reducing the confused pixels in the output by more than 60%.

Precision Evaluation of the DC Decay Method for Accurate Characterization of Wound-Field Synchronous Machines and Comparison with the Sudden Short-Circuit

The sudden short-circuit is considered the gold-standard parameter measurement method for wound-field synchronous machines (WFSMs) as it enables the recording of the characteristic quantities in near-to-real conditions. However, the test needs huge pieces of equipment, but even worse, it reduces the lifetime of the electrical components by up to 10 years due to the high winding overhang mechanical forces. The DC-Decay tests are low-power alternatives to obtain the characteristic quantities without damaging the machinery. To allow wider use of this method, there are a couple of challenges left that are tackled by this paper. The two main open challenges are, firstly, the number of measurements needed to reach a particular precision, and secondly, a comparison of the DC-Decay with the sudden short-circuit test to allow the validation against the gold standard. More detailed, this paper explores the main challenges associated with the practical use of the DC decay method, which is a non-conventional and detailed-level approach to characterize WFSMs. We provide replies and recommendations regarding the number of measurements, suggesting the minimum number of recorded tests needed to obtain the equivalent diagram with a given accuracy, which has been further validated with an experimental case study. Moreover, the potential enhancement and precision of the parameter identification algorithm are studied in detail. Finally, the equivalent parameters of the DC decay method are compared to the gold standard, which concludes on what the characterization means in terms of predicting accurate transient short-circuit currents for WFSMs.

Precision Evaluation of the DC Decay Method for Accurate Characterization of Wound-Field Synchronous Machines and Comparison with the Sudden Short-Circuit

The sudden short-circuit is considered the gold-standard parameter measurement method for wound-field synchronous machines (WFSMs) as it enables the recording of the characteristic quantities in near-to-real conditions. However, the test needs huge pieces of equipment, but even worse, it reduces the lifetime of the electrical components by up to 10 years due to the high winding overhang mechanical forces. The DC-Decay tests are low-power alternatives to obtain the characteristic quantities without damaging the machinery. To allow wider use of this method, there are a couple of challenges left that are tackled by this paper. The two main open challenges are, firstly, the number of measurements needed to reach a particular precision, and secondly, a comparison of the DC-Decay with the sudden short-circuit test to allow the validation against the gold standard. More detailed, this paper explores the main challenges associated with the practical use of the DC decay method, which is a non-conventional and detailed-level approach to characterize WFSMs. We provide replies and recommendations regarding the number of measurements, suggesting the minimum number of recorded tests needed to obtain the equivalent diagram with a given accuracy, which has been further validated with an experimental case study. Moreover, the potential enhancement and precision of the parameter identification algorithm are studied in detail. Finally, the equivalent parameters of the DC decay method are compared to the gold standard, which concludes on what the characterization means in terms of predicting accurate transient short-circuit currents for WFSMs.

High precision evaluation of the combustion enthalpy by ab-intio computations

Accurate evaluation of combustion enthalpy is of high scientific and industrial importance. Although via ab-initio computation of heat of reactions, as one of the promising and well-established approaches in computational chemistry, this goal should in principle be achievable, examples of reliable and precise evaluation of heat of combustion by ab-initio methods has surprisingly not yet been reported. A handful of works carried out for this purpose report significant inconsistencies between the ab-initio evaluated and experimentally determined combustion enthalpies and suggest empirical corrections to improve the accuracy of predicted data. With this background, the main aims of the present study is to investigate the reasons behind those reported inconsistencies and propose guidelines for highly accurate evaluation of combustion enthalpy via ab-initio computations. Through the provided guidelines, the most accurate results ever reported, with average absolute deviation, mean unsigned error and correlation coefficient of 1.556 kJ/mole, 0.072% and 0.99999, respectively, is achieved for theoretically computed combustion enthalpies of 40 studied hydrocarbons.

Precision Evaluation of NeXus, a Custom Multi-Robot System for Microsystem Integration

Industrial robots, as mature and high-efficient equipment, have been applied to various fields, such as vehicle manufacturing, product packaging, painting, welding, and medical surgery. Most industrial robots are only operating in their own workspace, in other words, they are floor-mounted at the fixed locations. Just some industrial robots are wall-mounted on one linear rail based on the applications. Sometimes, industrial robots are ceiling-mounted on an X-Y gantry to perform upside-down manipulation tasks. The main objective of this paper is to describe the NeXus, a custom robotic system that has been designed for precision microsystem integration tasks with such a gantry. The system tasks include assembly, bonding, and 3D printing of sensor arrays, solar cells, and microrobotic prototypes. The NeXus consists of a custom designed frame, providing structural rigidity, a large overhead X-Y gantry carrying a 6 degrees of freedom industrial robot, and several other precision positioners and processes. We focus here on the design and precision evaluation of the overhead ceiling-mounted industrial robot of NeXus and its supporting frame. We first simulated the behavior of the frame using Finite Element Analysis (FEA), then experimentally evaluated the pose repeatability of the robot end-effector using three different types of sensors. Results verify that the performance objectives of the design are achieved.

The Share Distribution and Precision Evaluation in Underground Topographic Routes and Networks

The underground topographic works of elevating and drawing are necessary for the execution of underground works (mining, hydrotechnics, communications, etc.) Fulfilling these works efficiently and safely requires an advance study for the precisions in topographic support routes and networks. This paper aims to establish the share distribution correlated with the underground works distribution.

Semantic Segmentation of Aerial Imagery via Split-Attention Networks with Disentangled Nonlocal and Edge Supervision

In this work, we propose a new deep convolution neural network (DCNN) architecture for semantic segmentation of aerial imagery. Taking advantage of recent research, we use split-attention networks (ResNeSt) as the backbone for high-quality feature expression. Additionally, a disentangled nonlocal (DNL) block is integrated into our pipeline to express the inter-pixel long-distance dependence and highlight the edge pixels simultaneously. Moreover, the depth-wise separable convolution and atrous spatial pyramid pooling (ASPP) modules are combined to extract and fuse multiscale contextual features. Finally, an auxiliary edge detection task is designed to provide edge constraints for semantic segmentation. Evaluation of algorithms is conducted on two benchmarks provided by the International Society for Photogrammetry and Remote Sensing (ISPRS). Extensive experiments demonstrate the effectiveness of each module of our architecture. Precision evaluation based on the Potsdam benchmark shows that the proposed DCNN achieves competitive performance over the state-of-the-art methods.