Optimized air-ground data fusion method for mine slope modeling

Refined 3D modeling of mine slopes is pivotal for precise prediction of geological hazards.Aiming at the inadequacy of existing single modeling methods in comprehensively representing the overall and localized characteristics of mining slopes,this study introduces a new method that fuses model data from Unmanned aerial vehicles(UAV)tilt photogrammetry and 3D laser scanning through a data alignment algorithm based on control points.First,the mini batch K-Medoids algorithm is utilized to cluster the point cloud data from ground 3D laser scanning.Then,the elbow rule is applied to determine the optimal cluster number(K0),and the feature points are extracted.Next,the nearest neighbor point algorithm is employed to match the feature points obtained from UAV tilt photogrammetry,and the internal point coordinates are adjusted through the distanceweighted average to construct a 3D model.Finally,by integrating an engineering case study,the K0 value is determined to be 8,with a matching accuracy between the two model datasets ranging from 0.0669 to 1.0373 mm.Therefore,compared with the modeling method utilizing K-medoids clustering algorithm,the new modeling method significantly enhances the computational efficiency,the accuracy of selecting the optimal number of feature points in 3D laser scanning,and the precision of the 3D model derived from UAV tilt photogrammetry.This method provides a research foundation for constructing mine slope model.

Performances of conventional fusion methods evaluated for inland water body observation using GF-1 image

Satellite remote sensing of inland water body requires a high spatial resolution and a multiband narrow spectral resolution, which makes the fusion between panchromatic(PAN) and multi-spectral(MS) images particularly important. Taking the Daquekou section of the Qiantang River as an observation target, four conventional fusion methods widely accepted in satellite image processing, including pan sharpening(PS), principal component analysis(PCA), Gram-Schmidt(GS), and wavelet fusion(WF), are utilized to fuse MS and PAN images of GF-1.The results of subjective and objective evaluation methods application indicate that GS performs the best,followed by the PCA, the WF and the PS in the order of descending. The existence of a large area of the water body is a dominant factor impacting the fusion performance. Meanwhile, the ability of retaining spatial and spectral informations is an important factor affecting the fusion performance of different fusion methods. The fundamental difference of reflectivity information acquisition between water and land is the reason for the failure of conventional fusion methods for land observation such as the PS to be used in the presence of the large water body. It is suggested that the adoption of the conventional fusion methods in the observing water body as the main target should be taken with caution. The performances of the fusion methods need re-assessment when the large-scale water body is present in the remote sensing image or when the research aims for the water body observation.

High-resolution azimuth estimation algorithm based on data fusion method for the vector hydrophone vertical array

To aim at the problem that the horizontal directivity index of the vector hy- drophone vertical array is not higher than that of a vector hydrophone, the high-resolution azimuth estimation algorithm based on the data fusion method was presented. The proposed algorithnl first employs MUSIC algorithm to estimate the azimuth of each divided sub-band signal, and then the estimated azimuths of multiple hydrophones are processed by using the data fusion technique. The high-resolution estimated result is achieved finally by adopting the weighted histogram statistics method. The results of the simulation and sea trials indicated that the proposed algorithm has better azimuth estimation performance than MUSIC algorithm of a single vector hydrophone and the data fusion technique based on the acoustic energy flux method. The better performance is reflected in the aspects of the estimation precision, the probability of correct estimation, the capability to distinguish multi-objects and the inhibition of the noise sub-bands.

Parameter Estimation of a Valve-Controlled Cylinder System Model Based on Bench Test and Operating Data Fusion

The accurate estimation of parameters is the premise for establishing a high-fidelity simulation model of a valve-controlled cylinder system.Bench test data are easily obtained,but it is challenging to emulate actual loads in the research on parameter estimation of valve-controlled cylinder system.Despite the actual load information contained in the operating data of the control valve,its acquisition remains challenging.This paper proposes a method that fuses bench test and operating data for parameter estimation to address the aforementioned problems.The proposed method is based on Bayesian theory,and its core is a pool fusion of prior information from bench test and operating data.Firstly,a system model is established,and the parameters in the model are analysed.Secondly,the bench and operating data of the system are collected.Then,the model parameters and weight coefficients are estimated using the data fusion method.Finally,the estimated effects of the data fusion method,Bayesian method,and particle swarm optimisation(PSO)algorithm on system model parameters are compared.The research shows that the weight coefficient represents the contribution of different prior information to the parameter estimation result.The effect of parameter estimation based on the data fusion method is better than that of the Bayesian method and the PSO algorithm.Increasing load complexity leads to a decrease in model accuracy,highlighting the crucial role of the data fusion method in parameter estimation studies.

Fusion of Convolutional Self-Attention and Cross-Dimensional Feature Transformationfor Human Posture Estimation

Human posture estimation is a prominent research topic in the fields of human-com-puter interaction,motion recognition,and other intelligent applications.However,achieving highaccuracy in key point localization,which is crucial for intelligent applications,contradicts the lowdetection accuracy of human posture detection models in practical scenarios.To address this issue,a human pose estimation network called AT-HRNet has been proposed,which combines convolu-tional self-attention and cross-dimensional feature transformation.AT-HRNet captures significantfeature information from various regions in an adaptive manner,aggregating them through convolu-tional operations within the local receptive domain.The residual structures TripNeck and Trip-Block of the high-resolution network are designed to further refine the key point locations,wherethe attention weight is adjusted by a cross-dimensional interaction to obtain more features.To vali-date the effectiveness of this network,AT-HRNet was evaluated using the COCO2017 dataset.Theresults show that AT-HRNet outperforms HRNet by improving 3.2%in mAP,4.0%in AP75,and3.9%in AP^(M).This suggests that AT-HRNet can offer more beneficial solutions for human posture estimation.

ADAPTIVE FUSION ALGORITHMS BASED ON WEIGHTED LEAST SQUARE METHOD

Weighted fusion algorithms, which can be applied in the area of multi-sensor data fusion, are advanced based on weighted least square method. A weighted fusion algorithm, in which the relationship between weight coefficients and measurement noise is established, is proposed by giving attention to the correlation of measurement noise. Then a simplified weighted fusion algorithm is deduced on the assumption that measurement noise is uncorrelated. In addition, an algorithm, which can adjust the weight coefficients in the simplified algorithm by making estimations of measurement noise from measurements, is presented. It is proved by emulation and experiment that the precision performance of the multi-sensor system based on these algorithms is better than that of the multi-sensor system based on other algorithms.

Flow field fusion simulation method based on model features and its application in CRDM

The control rod drive mechanism(CRDM)is an essential part of the control and safety protection system of pressurized water reactors.Current CRDM simulations are mostly performed collectively using a single method,ignoring the influence of multiple motion units and the differences in various features among them,which strongly affect the efficiency and accuracy of the simulations.In this study,we constructed a flow field fusion simulation method based on model features by combining key motion unit analysis and various simulation methods and then applied the method to the CRDM simulation process.CRDM performs motion unit decomposition through the structural hierarchy of function-movement-action method,and the key meta-actions are identified as the nodes in the flow field simulation.We established a fused feature-based multimethod simulation process and processed the simulation methods and data according to the features of the fluid domain space and the structural complexity to obtain the fusion simulation results.Compared to traditional simulation methods and real measurements,the simulation method provides advantages in terms of simulation efficiency and accuracy.

Performance Validation and Analysis for Multi-Method Fusion Based Image Quality Metrics in A New Image Database

Considering that there is no single full reference image quality assessment method that could give the best performance in all situations, some multi-method fusion metrics were proposed. Machine learning techniques are often involved in such multi-method fusion metrics so that its output would be more consistent with human visual perceptions. On the other hand, the robustness and generalization ability of these multi-method fusion metrics are questioned because of the scarce of images with mean opinion scores. In order to comprehensively validate whether or not the generalization ability of such multi-method fusion IQA metrics are satisfying, we construct a new image database which contains up to 60 reference images. The newly built image database is then used to test the generalization ability of different multi-method fusion IQA metrics. Cross database validation experiment indicates that in our new image database, the performances of all the multi-method fusion IQA metrics have no statistical significant different with some single-method IQA metrics such as FSIM and MAD. In the end, a thorough analysis is given to explain why the performance of multi-method fusion IQA framework drop significantly in cross database validation.

Multi-Behavior Fusion Based Potential Field Method for Path Planning of Unmanned Surface Vessel

The problem of the unmanned surface vessel (USV) path planning in static and dynamic obstacle environments is addressed in this paper. Multi-behavior fusion based potential field method is proposed, which contains three behaviors: goal-seeking, boundary-memory following and dynamic-obstacle avoidance. Then, different activation conditions are designed to determine the current behavior. Meanwhile, information on the positions, velocities and the equation of motion for obstacles are detected and calculated by sensor data. Besides, memory information is introduced into the boundary following behavior to enhance cognition capability for the obstacles, and avoid local minima problem caused by the potential field method. Finally, the results of theoretical analysis and simulation show that the collision-free path can be generated for USV within different obstacle environments, and further validated the performance and effectiveness of the presented strategy.

Remaining useful life prediction of lithium-ion batteries using a fusion method based on Wasserstein GAN

Lithium-ion batteries are the main power supply equipment in many fields due to their advantages of no memory, high energy density, long cycle life and no pollution to the environment. Accurate prediction for the remaining useful life(RUL) of lithium-ion batteries can avoid serious economic and safety problems such as spontaneous combustion. At present, most of the RUL prediction studies ignore the lithium-ion battery capacity recovery phenomenon caused by the rest time between the charge and discharge cycles. In this paper, a fusion method based on Wasserstein generative adversarial network(GAN) is proposed. This method achieves a more reliable and accurate RUL prediction of lithium-ion batteries by combining the artificial neural network(ANN) model which takes the rest time between battery charging cycles into account and the empirical degradation models which provide the correct degradation trend. The weight of each model is calculated by the discriminator in the Wasserstein GAN model. Four data sets of lithium-ion battery provided by the National Aeronautics and Space Administration(NASA) Ames Research Center are used to prove the feasibility and accuracy of the proposed method.

A Content-Based Medical Image Retrieval Method Using Relative Difference-Based Similarity Measure

Content-based medical image retrieval(CBMIR)is a technique for retrieving medical images based on automatically derived image features.There are many applications of CBMIR,such as teaching,research,diagnosis and electronic patient records.Several methods are applied to enhance the retrieval performance of CBMIR systems.Developing new and effective similarity measure and features fusion methods are two of the most powerful and effective strategies for improving these systems.This study proposes the relative difference-based similarity measure(RDBSM)for CBMIR.The new measure was first used in the similarity calculation stage for the CBMIR using an unweighted fusion method of traditional color and texture features.Furthermore,the study also proposes a weighted fusion method for medical image features extracted using pre-trained convolutional neural networks(CNNs)models.Our proposed RDBSM has outperformed the standard well-known similarity and distance measures using two popular medical image datasets,Kvasir and PH2,in terms of recall and precision retrieval measures.The effectiveness and quality of our proposed similarity measure are also proved using a significant test and statistical confidence bound.

基于多传感器数据融合的区域水环境污染程度监测方法研究

水环境污染程度监测方法对保护水资源,维护生态平衡具有重要作用。传统的水环境监测方法存在时间性、精度和覆盖范围的限制,因此研究提出基于多传感器数据融合的区域水环境污染程度监测方法,并验证有效性与优越性。实验结果表明,温度平均精确率为98.84%,pH值平均精确率达到96.76%,溶解氧浓度最高误差率为4.83%,总氮浓度数据采集为0,总磷浓度平均精确率为97.69%,浊度误差率最高为6.57%。L-AWDF算法平均精确率达到了94.23%,L-AWDF算法平均能耗为278.43 J/h。证明研究提出的方法能够准确评估水质污染程度,对于水环境保护具有重要意义。

VirtualLab Fusion虚拟仿真辅助的菲涅尔波带法教学与探索

在教学实验中,由于受到衍射物加工精度和相机灵敏度的限制,常常不能明显地观察到与理论相匹配的菲涅耳衍射图样.本文先利用VirtualLab Fusion虚拟仿真平台计算出不同尺寸圆孔与圆屏在不同位置的菲涅耳衍射图样,再进一步针对特定尺寸圆孔与圆屏分别进行了光学实验、仿真计算以及理论预测,并对结果进行了对比分析,这将有助于增强教学效果.

Effect of Fusion Neutron Source Numerical Models on Neutron Wall Loading in a D-D Tokamak Device

Effect of various spatial and energy distributions of fusion neutron sourceon the calculation of neutron wall loading of Tokamak D-D fusion device has been investigated bymeans of the 3-D Monte Carlo code MCNP. A realistic Monte Carlo source model was developed based onthe accurate representation of the spatial distribution and energy spectrum of fusion neutrons tosolve the complicated problem of tokamak fusion neutron source modelling. The results show thatthose simplified source models will introduce significant uncertainties. For accurate estimation ofthe key nuclear responses of the tokamak design and analyses, the use of the realistic source isrecommended. In addition, the accumulation of tritium produced during D-D plasma operation should becarefully considered.

Crossed Intralaminar Screws for Fusion of the Cervicothoracic Junction and the Thoracic Spine: The Experience in an Iberic Service with Case Series and Review of the Current Literature with Technique Description

The treatment of pathologies in the thoracic spine is a challenge. The periodic failure of pedicle screw insertion and anatomical variations make the search for an alternative to pedicle screws in thoracic spine surgery necessary. The interlaminar crossed screws is a well-known and secure method for fusion in cervical spine, and in thoracic spine there used to be insufficient clinical data to support this technique, until now. We demonstrate in an initial series of 10 cases treated with interlaminar fusion in association of other fusion techniques in the thoracic spine with good results. Objective: Intralaminar screws have been shown to be a biomechanical salvage technique in the thoracic spine, especially in long cervicothoracic, thoracic and thoracolumbar fixation. The goals of this article are to demonstrate our initial experience and the range of indications for thoracic crossed intralaminar screws. Methods: In this article we describe our initial series performed at S&#227o Teot&#243nio Hospital in Viseu, Portugal, and our results, and also provide a comprehensive review of the recent literature in the use of intralaminar crossed fixation.

Review of the field environmental sensing methods based on multi-sensor information fusion technology

Field environmental sensing can acquire real-time environmental information,which will be applied to field operation,through the fusion of multiple sensors.Multi-sensor fusion refers to the fusion of information obtained from multiple sensors using more advanced data processing methods.The main objective of applying this technology in field environment perception is to acquire real-time environmental information,making agricultural mechanical devices operate better in complex farmland environment with stronger sensing ability and operational accuracy.In this paper,the characteristics of sensors are studied to clarify the advantages and existing problems of each type of sensors and point out that multiple sensors can be introduced to compensate for the information loss.Secondly,the mainstream information fusion types at present are outlined.The characteristics,advantages and disadvantages of different fusion methods are analyzed.The important studies and applications related to multi-sensor information fusion technology published at home and abroad are listed.Eventually,the existing problems in the field environment sensing at present are summarized and the prospect for future of sensors precise sensing,multi-dimensional fusion strategies,discrepancies in sensor fusion and agricultural information processing are proposed in hope of providing reference for the deeper development of smart agriculture.

Identification of Pitch Dynamics of An Autonomous Underwater Vehicle Using Sensor Fusion

This paper presents a method for identification of the hydrodynamic coefficients of the dive plane of an autonomous underwater vehicle. The proposed identification method uses the governing equations of motion to estimate the coefficients of the linear damping, added mass and inertia, cross flow drag and control. Parts of data required by the proposed identification method are not measured by the onboard instruments. Hence, an optimal fusion algorithm is devised which estimates the required data accurately with a high sampling rate. To excite the dive plane dynamics and obtain the required measurements, diving maneuvers should be performed. Hence, a reliable controller with satisfactory performance and stability is needed. A cascaded controller is designed based on the coefficients obtained using a semi-empirical method and its robustness to the uncertainties is verified by the μ-analysis method. The performance and accuracy of the identification and fusion algorithms are investigated through 6-DOF numerical simulations of a realistic autonomous underwater vehicle.

Multi-voltage X-ray image sequence fusion based on selection of valid region

A key step is to extract valid information region in the fusion of multi-voltage X-ray image sequence for complicated components. To improve the self-adaption of extraction, a method is presented in this paper. In this paper, the valid informa-tion region is selected by the grey level interval, which is computed by the optimization of image quality evaluation model. The model is based on the histogram equalization and the grey level interval. Then, every valid region of images at different voltages is extracted and they are fused according their grey level transformation function. The fusion image contains completed struc-ture information of the component. The fusion experiment of a cylinder head shows the effectiveness of the presented method.

Data Fusion with Optimized Block Kernels in LS-SVM for Protein Classification

In this work, we developed a method to efficiently optimize the kernel function for combined data of various different sources with their corresponding kernels being already available. The vectorization of the combined data is achieved by a weighted concatenation of the existing data vectors. This induces a kernel matrix composed of the existing kernels as blocks along the main diagonal, weighted according to the corresponding the subspaces span by the data. The induced block kernel matrix is optimized in the platform of least-squares support vector machines simultaneously as the LS-SVM is being trained, by solving an extended set of linear equations, other than a quadratically constrained quadratic programming as in a previous method. The method is tested on a benchmark dataset, and the performance is significantly improved from the highest ROC score 0.84 using individual data source to ROC score 0.92 with data fusion.

An electrochemical method for evaluating the resistance to cathodic disbondment of anti-corrosion coatings on buried pipelines

Methods for evaluating the resistance to cathodic disbondment （RCD） of anti-corrosion coatings on buried pipelines were reviewed. It is obvious that these traditional cathodic disbondment tests （CDT） have some disadvantages and the evaluated results are only simple figures and always rely on the subjective experience of the operator. A new electrochemical method for evaluating the RCD of coatings, that is, the potentiostatic evaluation method （PEM）, was developed and studied. During potentiostatic anodic polarization testing, the changes of stable polarization current of specimens before and after cathodic disbonding （CD） were measured, and the degree of cathodic disbondment of the coating was quantitatively evaluated, among which the equivalent cathodic disbonded distance AD was suggested as a parameter for evaluating the RCD. A series of testing parameters of the PEM were determined in these experiments.