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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

基于时空数据融合的塔吉克斯坦中高时空分辨率NDVI数据集(2010-2020)

归一化植被指数(Normalized Difference Vegetation Index,NDVI)是研究植被最常用的遥感指数之一。NDVI长时间序列数据对于植被变化研究有着重要的意义。然而由于传感器的限制,遥感数据的时间分辨率与空间分辨率不能兼顾,因此在目前广泛使用的NDVI数据产品中,高时空分辨率的数据还较为缺乏。本产品基于Cubist模型对MODIS数据与Landsat及哨兵等遥感数据进行时空数据融合,得到了塔吉克斯坦2010–2020年中高时空分辨率Landsat-MODIS融合数据,以及2020年中高时空分辨率Sentinel-MODIS融合数据。为保证数据的准确性和可靠性,本数据集从数据源的质控,模型训练优化,以及模型独立验证三个方面对数据产品进行质量控制,且取得了较好的验证效果。本数据集可反映塔吉克斯坦2010–2020年NDVI时空变化情况,可为该地区植被变化分析、生态环境监测等提供长时间序列数据支撑。

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。

多尺度融合图像去雾方法

图像去雾能够使视觉系统适应不同的天气状况.为克服传统暗通道先验方法会在物体边界区域形成光晕效应的问题,提出一种用于估计有雾图像透射率的多尺度融合算法.应用不同大小的最小值半径得到多尺度的透射率估计值,再根据局部区域像素具有类似的透射率值这一现象,对透射率图进行多尺度融合,选择小透射图区域中最亮的像素来计算大气光值,最后使用大气散射模型恢复清晰图像.分别从视觉效果和量化指标两个方面,对比所提方法与传统的基于先验和基于深度学习的去雾方法在进行图像去雾后的效果.结果发现,针对4种典型场景,采用本研究算法去雾后的重构图像能够保留更多的结构、细节和颜色信息,避免了过分增强和边缘部分的雾残留问题,视觉效果均优于对比方法;量化指标峰值信噪比和结构相似性均高于对比方法,分别为15.65和0.78.

Scientific Advances and Weather Services of the China Meteorological Administration’s National Forecasting Systems during the Beijing 2022 Winter Olympics

Since the Beijing 2022 Winter Olympics was the first Winter Olympics in history held in continental winter monsoon climate conditions across complex terrain areas,there is a deficiency of relevant research,operational techniques,and experience.This made providing meteorological services for this event particularly challenging.The China Meteorological Administration(CMA)Earth System Modeling and Prediction Centre,achieved breakthroughs in research on short-and medium-term deterministic and ensemble numerical predictions.Several key technologies crucial for precise winter weather services during the Winter Olympics were developed.A comprehensive framework,known as the Operational System for High-Precision Weather Forecasting for the Winter Olympics,was established.Some of these advancements represent the highest level of capabilities currently available in China.The meteorological service provided to the Beijing 2022 Games also exceeded previous Winter Olympic Games in both variety and quality.This included achievements such as the“100-meter level,minute level”downscaled spatiotemporal resolution and forecasts spanning 1 to 15 days.Around 30 new technologies and over 60 kinds of products that align with the requirements of the Winter Olympics Organizing Committee were developed,and many of these techniques have since been integrated into the CMA’s operational national forecasting systems.These accomplishments were facilitated by a dedicated weather forecasting and research initiative,in conjunction with the preexisting real-time operational forecasting systems of the CMA.This program represents one of the five subprograms of the WMO’s high-impact weather forecasting demonstration project(SMART2022),and continues to play an important role in their Regional Association(RA)II Research Development Project(Hangzhou RDP).Therefore,the research accomplishments and meteorological service experiences from this program will be carried forward into forthcoming highimpact weather forecasting activities.This article provides an overview and assessment of this program and the operational national forecasting systems.

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.

证据方法视阈下大数据证据诉讼应用及正当程序保障

证据方法的作用在于通过从客观上揭示证据内含的信息内容,联接证据与刑事证明。大数据证据表现出独特的建构性与质素多样性特征,使得现有证据的证据方法无所适从,需要依托融合性证据方法为其诉讼应用提供可操作性指引。而在刑事诉讼中运用融合性证据方法,可能对现有诉讼构造构成挑战。对此,可以从证据方法的工具与程序二维属性出发,建构正当程序保障:在工具属性维度,发挥法官庭外调查的能动、兜底性作用;在程序属性维度,构造“对等”性权利保障,弱化微观权力。

基于幅度和相位融合的微波两相流测量系统设计

针对油井开采过程中需要对各相含量进行精确预测以调整开采策略的实际问题,设计了一种基于微波法的油水两相流检测系统。该系统利用微波在不同介质中透射能力的差异,构建了一套包含微波信号源、功率放大器、功率分配器、检波器和STM32F103ZET6核心板的硬件电路系统,通过编写AD采集和串口通信的软件代码,来接收检波器端幅度和相位数据。在数据处理方面,分别对幅度数据、相位数据和幅度-相位融合数据采用BP神经网络的方法预测含水率。实验表明:在使用融合数据时,预测准确度可以提高至96.33%,取得了较理想的效果。