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.

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.

UV,five wavelengths fusion and electrochemical fingerprints combined with antioxidant activity for quality control of antiviral mixture

Aiming to ensure the consistency of quality control of Traditional Chinese Medicines(TCMs),a combination method of high-performance liquid chromatography(HPLC),ultraviolet(UV),electrochemical(EC)was developed in this study to comprehensively evaluate the quality of Antiviral Mixture(AM),and Comprehensive Linear Quantification Fingerprint Method(CLQFM)was used to process the data.Quantitative analysis of three active substances in TCM was conducted.A fivewavelength fusion fingerprint(FWFF)was developed,using second-order derivatives of UV spectral data to differentiate sample levels effectively.The combination of HPLC and UV spectrophotometry,along with electrochemical fingerprinting(ECFP),successfully evaluated total active substances.Ultimately,a multidimensional profiling analytical system for TCM was developed.

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.

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.

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.

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.

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.

Phase-field simulation of lack-of-fusion defect and grain growth during laser powder bed fusion of Inconel 718

The anisotropy of the structure and properties caused by the strong epitaxial growth of grains during laser powder bed fusion(L-PBF)significantly affects the mechanical performance of Inconel 718 alloy components such as turbine disks.The defects(lack-of-fusion Lo F)in components processed via L-PBF are detrimental to the strength of the alloy.The purpose of this study is to investigate the effect of laser scanning parameters on the epitaxial grain growth and LoF formation in order to obtain the parameter space in which the microstructure is refined and LoF defect is suppressed.The temperature field of the molten pool and the epitaxial grain growth are simulated using a multiscale model combining the finite element method with the phase-field method.The LoF model is proposed to predict the formation of LoF defects resulting from insufficient melting during L-PBF.Defect mitigation and grain-structure control during L-PBF can be realized simultaneously in the model.The simulation shows the input laser energy density for the as-deposited structure with fine grains and without LoF defects varied from 55.0–62.5 J·mm^(-3)when the interlayer rotation angle was 0°–90°.The optimized process parameters(laser power of 280 W,scanning speed of 1160 mm·s^(-1),and rotation angle of 67°)were computationally screened.In these conditions,the average grain size was 7.0μm,and the ultimate tensile strength and yield strength at room temperature were(1111±3)MPa and(820±7)MPa,respectively,which is 8.8%and10.5%higher than those of reported.The results indicating the proposed multiscale computational approach for predicting grain growth and Lo F defects could allow simultaneous grain-structure control and defect mitigation during L-PBF.

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.

A classification method of building structures based on multi-feature fusion of UAV remote sensing images

In order to improve the accuracy of building structure identification using remote sensing images,a building structure classification method based on multi-feature fusion of UAV remote sensing image is proposed in this paper.Three identification approaches of remote sensing images are integrated in this method:object-oriented,texture feature,and digital elevation based on DSM and DEM.So RGB threshold classification method is used to classify the identification results.The accuracy of building structure classification based on each feature and the multi-feature fusion are compared and analyzed.The results show that the building structure classification method is feasible and can accurately identify the structures in large-area remote sensing images.

A Comparison of Weighing Methods Prior to Fusion Preparing Iron Samples for XRF Analysis

Sample preparation by fusion for XRF analysis is all about knowing the exact weights of the sample and the flux (sample-to-flux ratio). The whole analytical chain, including the weighing step in sample preparation prior to fusion, is of crucial importance to get precise and accurate x-ray fluorescence (XRF) results. Consequently, the weighing method will affect the quality of the analytical results given by the spectrometer. In this study, the effects of different weighing methods on the precision (RSD) of the obtained XRF results are compared to determine the best weighing method for sample preparation by fusion in terms of comparable precisions in the XRF results.

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.

A Single Image Derain Method Based on Residue Channel Decomposition in Edge Computing

The numerous photos captured by low-price Internet of Things(IoT)sensors are frequently affected by meteorological factors,especially rainfall.It causes varying sizes of white streaks on the image,destroying the image texture and ruining the performance of the outdoor computer vision system.Existing methods utilise training with pairs of images,which is difficult to cover all scenes and leads to domain gaps.In addition,the network structures adopt deep learning to map rain images to rain-free images,failing to use prior knowledge effectively.To solve these problems,we introduce a single image derain model in edge computing that combines prior knowledge of rain patterns with the learning capability of the neural network.Specifically,the algorithm first uses Residue Channel Prior to filter out the rainfall textural features then it uses the Feature Fusion Module to fuse the original image with the background feature information.This results in a pre-processed image which is fed into Half Instance Net(HINet)to recover a high-quality rain-free image with a clear and accurate structure,and the model does not rely on any rainfall assumptions.Experimental results on synthetic and real-world datasets show that the average peak signal-to-noise ratio of the model decreases by 0.37 dB on the synthetic dataset and increases by 0.43 dB on the real-world dataset,demonstrating that a combined model reduces the gap between synthetic data and natural rain scenes,improves the generalization ability of the derain network,and alleviates the overfitting problem.

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.

Enhanced Feature Fusion Segmentation for Tumor Detection Using Intelligent Techniques

In thefield of diagnosis of medical images the challenge lies in tracking and identifying the defective cells and the extent of the defective region within the complex structure of a brain cavity.Locating the defective cells precisely during the diagnosis phase helps tofight the greatest exterminator of mankind.Early detec-tion of these defective cells requires an accurate computer-aided diagnostic system(CAD)that supports early treatment and promotes survival rates of patients.An ear-lier version of CAD systems relies greatly on the expertise of radiologist and it con-sumed more time to identify the defective region.The manuscript takes the efficacy of coalescing features like intensity,shape,and texture of the magnetic resonance image(MRI).In the Enhanced Feature Fusion Segmentation based classification method(EEFS)the image is enhanced and segmented to extract the prominent fea-tures.To bring out the desired effect the EEFS method uses Enhanced Local Binary Pattern(EnLBP),Partisan Gray Level Co-occurrence Matrix Histogram of Oriented Gradients(PGLCMHOG),and iGrab cut method to segment image.These prominent features along with deep features are coalesced to provide a single-dimensional fea-ture vector that is effectively used for prediction.The coalesced vector is used with the existing classifiers to compare the results of these classifiers with that of the gen-erated vector.The generated vector provides promising results with commendably less computatio nal time for pre-processing and classification of MR medical images.

基于动态视觉传感器的无人机目标检测与避障

针对无人机在动态环境中感知动态目标与躲避高速动态障碍物,提出了基于动态视觉传感器的目标检测与避障算法。设计了滤波方法和运动补偿算法,滤除事件流中背景噪声、热点噪声及由相机自身运动产生的冗余事件;设计了一种融合事件图像和RGB图像的动态目标融合检测算法,保证检测的可靠性。根据检测结果对目标运动轨迹进行估计,结合障碍物运动特点和无人机动力学约束改进速度障碍法躲避动态障碍物。大量仿真试验、手持试验及飞行试验验证了所提算法的可行性。

农科特色有机化学课程的教学探索与实践

立足高等农林教育,从教学案例、教学方法、教师团队建设等方面对有机化学课程教学进行了探索和实践。教学实践表明,激发了学生的学习兴趣,拓展了学生的视野,助力学生领略了有机化学研究前沿(尤其是与农科交叉融合应用前沿)魅力,培养了学生的文化素养、职业素养和科学素养,塑造了学生“知农爱农”的情怀,厚植了学生的家国情怀;实现了知识传授、能力培养与价值引领的同向同行和交相辉映,有效解决了公共基础课与专业教育需求不能完全匹配的问题,充分发挥了课程在农科人才培养中的作用。

典型场景彩色夜视融合图像质量主观评价实验

评价特定场景下红外和可见光(微光)彩色融合图像质量的研究对彩色夜视融合技术和嵌入式系统开发具有重要作用。由于主观评价实验能够得到与人眼感受一致的评价结果因而成为研究的热点。因此,该文针对海天、森林、城镇、乡村和特殊条件五个典型应用场景,提出基于动态参考的多刺激比较主观评价法,开展了大规模典型场景彩色融合图像主观评价实验,建立了包含1648幅彩色融合图像的质量主观评价数据库;此外,提出基于评价实验特性的模糊综合评判方法用于分析不同融合算法的场景适用性。实验数据一致性分析中,该主观评价法具有较高的皮尔逊相关系数,表明本文构建的数据集具有良好的有效性与可靠性。该文结果可为进一步开展夜视融合图像质量评价研究提供实验基础和理论依据。

硅基OLED微显示器的集中式融合扫描策略

本研究针对数字驱动型硅基OLED(Organic Light-emitting Diode,OLED)微显示器在显示动态图像时引发的视觉感知问题,尤其是动态假轮廓和闪烁现象,提出了一种新的扫描策略——集中式融合扫描。集中式融合扫描策略采用灰度权值重分配和融合子场概念,通过对整数子场数目和权值的重新分配,以及将融合子场固定于调制周期中间位置,改善显示器图像质量。实验结果表明,集中式融合扫描在峰值信噪比方面较传统扫描方法平均提高约13%,均方误差降低了约10%,并且结构相似度评分接近1,显著高于现有扫描方法。集中式融合扫描在JEITA闪烁评估中的表现优于19子场扫描法,闪烁量化值降低了约22%。集中式融合扫描策略在改善数字驱动型硅基OLED微显示器的图像显示质量方面提供了一种有效解决方案,为未来显示技术的研究和创新提供了新的方向。