Multi-view ladar data registration in obscure environment

Multi-view laser radar （ladar） data registration in obscure environments is an important research field of obscured target detection from air to ground. There are few overlap regions of the observational data in different views because of the occluder, so the multi-view data registration is rather difficult. Through indepth analyses of the typical methods and problems, it is obtained that the sequence registration is more appropriate, but needs to improve the registration accuracy. On this basis, a multi-view data registration algorithm based on aggregating the adjacent frames, which are already registered, is proposed. It increases the overlap region between the pending registration frames by aggregation and further improves the registration accuracy. The experiment results show that the proposed algorithm can effectively register the multi-view ladar data in the obscure environment, and it also has a greater robustness and a higher registration accuracy compared with the sequence registration under the condition of equivalent operating efficiency.

Sensor Registration Based on Neural Network in Data Fusion

The contents of sensor registration in the multi-sensor data fusion system are introduced, and some existing methods are analyzed. Then, one approach to sensor registration based on BP neural network is proposed. Here the measurements from radar are transformed from the polar coordinate system to the Cartesian coordinate through a BP neural network. With this approach, the systematic errors are removed as well as the coordinate is transformed. The efficiency of this method is demonstrated by simulation, and the result show that this approach could remove the systematic errors effectively and the DAR are closer to real position than DBR.

A review of laser scanning for geological and geotechnical applications in underground mining

Laser scanning can provide timely assessments of mine sites despite adverse challenges in the operational environment.Although there are several published articles on laser scanning,there is a need to review them in the context of underground mining applications.To this end,a holistic review of laser scanning is presented including progress in 3D scanning systems,data capture/processing techniques and primary applications in underground mines.Laser scanning technology has advanced significantly in terms of mobility and mapping,but there are constraints in coherent and consistent data collection at certain mines due to feature deficiency,dynamics,and environmental influences such as dust and water.Studies suggest that laser scanning has matured over the years for change detection,clearance measurements and structure mapping applications.However,there is scope for improvements in lithology identification,surface parameter measurements,logistic tracking and autonomous navigation.Laser scanning has the potential to provide real-time solutions but the lack of infrastructure in underground mines for data transfer,geodetic networking and processing capacity remain limiting factors.Nevertheless,laser scanners are becoming an integral part of mine automation thanks to their affordability,accuracy and mobility,which should support their widespread usage in years to come.

Attention-Based and Time Series Models for Short-Term Forecasting of COVID-19 Spread

The growing number of COVID-19 cases puts pressure on healthcare services and public institutions worldwide.The pandemic has brought much uncertainty to the global economy and the situation in general.Forecasting methods and modeling techniques are important tools for governments to manage critical situations caused by pandemics,which have negative impact on public health.The main purpose of this study is to obtain short-term forecasts of disease epidemiology that could be useful for policymakers and public institutions to make necessary short-term decisions.To evaluate the effectiveness of the proposed attention-based method combining certain data mining algorithms and the classical ARIMA model for short-term forecasts,data on the spread of the COVID-19 virus in Lithuania is used,the forecasts of epidemic dynamics were examined,and the results were presented in the study.Nevertheless,the approach presented might be applied to any country and other pandemic situations.The COVID-19 outbreak started at different times in different countries,hence some countries have a longer history of the disease with more historical data than others.The paper proposes a novel approach to data registration and machine learning-based analysis using data from attention-based countries for forecast validation to predict trends of the spread of COVID-19 and assess risks.

A Rapid Method for Compensating Output Error of Electromagnetic Tracker

Some methods which use an optical tracker(OT) as a standard reference of electromagnetic tracker(EMT) were proposed in order to compensate the output error of electromagnetic tracker. Usually,they use a magneto-optic tool to collect the outputs of OT and EMT simultaneously,and then compare the output of OT with that of EMT to compensate the error of EMT. Although the outputs of EMT and OT can be matched each other by using a time stamp which denotes when the acquirement command is sent,but the accuracy will decrease if the tool moves faster for the errors of the time stamp itself. A rapid method for compensating EMT output error is proposed. A particular scan mode of the magneto-optic tool is designed for collecting EMT and OT outputs,and a data registration method is proposed to match the outputs of EMT and OT. The simulated results show that the output error of EMT can be decreased efficiently and the accuracy of the compensation can be improved by about 15% compared with that of the existing methods.

Research on the Realization Path and Application of a Data Governance System Based on Data Architecture

The construction and development of the digital economy,digital society and digital government are facing some common basic problems.Among them,the construction of the data governance system and the improvement of data governance capacity are short boards and weak links,which have seriously restricted the construction and development of the digital economy,digital society and digital government.At present,the broad concept of data governance goes beyond the scope of traditional data governance,which“involves at least four aspects:the establishment of data asset status,management system and mechanism,sharing and openness,security and privacy protection”.Traditional information technologies and methods are powerless to comprehensively solve these problems,so it is urgent to improve understanding and find another way to reconstruct the information technology architecture to provide a scientific and reasonable technical system for effectively solving the problems of data governance.This paper redefined the information technology architecture and proposed the data architecture as the connection link and application support system between the traditional hardware architecture and software architecture.The data registration system is the core composition of the data architecture,and the public key encryption and authentication system is the key component of the data architecture.This data governance system based on the data architecture supports complex,comprehensive,collaborative and cross-domain business application scenarios.It provides scientific and feasible basic support for the construction and development of the digital economy,digital society and digital government.

A volumetric change detection framework using UAV oblique photogrammetry–a case study of ultra-high-resolution monitoring of progressive building collapse

In this paper,we present a case study that performs an unmanned aerial vehicle(UAV)based fine-scale 3D change detection and monitoring of progressive collapse performance of a building during a demolition event.Multi-temporal oblique photogrammetry images are collected with 3D point clouds generated at different stages of the demolition.The geometric accuracy of the generated point clouds has been evaluated against both airborne and terrestrial LiDAR point clouds,achieving an average distance of 12 cm and 16 cm for roof and façade respectively.We propose a hierarchical volumetric change detection framework that unifies multi-temporal UAV images for pose estimation(free of ground control points),reconstruction,and a coarse-to-fine 3D density change analysis.This work has provided a solution capable of addressing change detection on full 3D time-series datasets where dramatic scene content changes are presented progressively.Our change detection results on the building demolition event have been evaluated against the manually marked ground-truth changes and have achieved an F-1 score varying from 0.78 to 0.92,with consistently high precision(0.92–0.99).Volumetric changes through the demolition progress are derived from change detection and have been shown to favorably reflect the qualitative and quantitative building demolition progression.