A multi-source information fusion layer counting method for penetration fuze based on TCN-LSTM

When employing penetration ammunition to strike multi-story buildings,the detection methods using acceleration sensors suffer from signal aliasing,while magnetic detection methods are susceptible to interference from ferromagnetic materials,thereby posing challenges in accurately determining the number of layers.To address this issue,this research proposes a layer counting method for penetration fuze that incorporates multi-source information fusion,utilizing both the temporal convolutional network(TCN)and the long short-term memory(LSTM)recurrent network.By leveraging the strengths of these two network structures,the method extracts temporal and high-dimensional features from the multi-source physical field during the penetration process,establishing a relationship between the multi-source physical field and the distance between the fuze and the target plate.A simulation model is developed to simulate the overload and magnetic field of a projectile penetrating multiple layers of target plates,capturing the multi-source physical field signals and their patterns during the penetration process.The analysis reveals that the proposed multi-source fusion layer counting method reduces errors by 60% and 50% compared to single overload layer counting and single magnetic anomaly signal layer counting,respectively.The model's predictive performance is evaluated under various operating conditions,including different ratios of added noise to random sample positions,penetration speeds,and spacing between target plates.The maximum errors in fuze penetration time predicted by the three modes are 0.08 ms,0.12 ms,and 0.16 ms,respectively,confirming the robustness of the proposed model.Moreover,the model's predictions indicate that the fitting degree for large interlayer spacings is superior to that for small interlayer spacings due to the influence of stress waves.

Research on Data Fusion of Adaptive Weighted Multi-Source Sensor

Data fusion can effectively process multi-sensor information to obtain more accurate and reliable results than a single sensor.The data of water quality in the environment comes from different sensors,thus the data must be fused.In our research,self-adaptive weighted data fusion method is used to respectively integrate the data from the PH value,temperature,oxygen dissolved and NH3 concentration of water quality environment.Based on the fusion,the Grubbs method is used to detect the abnormal data so as to provide data support for estimation,prediction and early warning of the water quality.

Locality preserving fusion of multi-source images for sea-ice classification

We present a novel sea-ice classification framework based on locality preserving fusion of multi-source images information.The locality preserving fusion arises from two-fold,i.e.,the local characterization in both spatial and feature domains.We commence by simultaneously learning a projection matrix,which preserves spatial localities,and a similarity matrix,which encodes feature similarities.We map the pixels of multi-source images by the projection matrix to a set fusion vectors that preserve spatial localities of the image.On the other hand,by applying the Laplacian eigen-decomposition to the similarity matrix,we obtain another set of fusion vectors that preserve the feature local similarities.We concatenate the fusion vectors for both spatial and feature locality preservation and obtain the fusion image.Finally,we classify the fusion image pixels by a novel sliding ensemble strategy,which enhances the locality preservation in classification.Our locality preserving fusion framework is effective in classifying multi-source sea-ice images(e.g.,multi-spectral and synthetic aperture radar(SAR)images)because it not only comprehensively captures the spatial neighboring relationships but also intrinsically characterizes the feature associations between different types of sea-ices.Experimental evaluations validate the effectiveness of our framework.

Multi-Source Adaptive Selection and Fusion for Pedestrian Dead Reckoning

Accurate multi-source fusion is based on the reliability, quantity, and fusion mode of the sources. The problem of selecting the optimal set for participating in the fusion process is nondeterministic-polynomial-time-hard and is neither sub-modular nor super-modular. Furthermore, in the case of the Kalman filter(KF) fusion algorithm, accurate statistical characteristics of noise are difficult to obtain, and this leads to an unsatisfactory fusion result. To settle the referred cases, a distributed and adaptive weighted fusion algorithm based on KF has been proposed in this paper. In this method, on the basis of the pseudo prior probability of the estimated state of each source, the reliability of the sources is evaluated and the optimal set is selected on a certain threshold. Experiments were performed on multi-source pedestrian dead reckoning for verifying the proposed algorithm. The results obtained from these experiments indicate that the optimal set can be selected accurately with minimal computation, and the fusion error is reduced by 16.6% as compared to the corresponding value resulting from the algorithm without improvements.The proposed adaptive source reliability and fusion weight evaluation is effective against the varied-noise multi-source fusion system, and the fusion error caused by inaccurate statistical characteristics of the noise is reduced by the adaptive weight evaluation.The proposed algorithm exhibits good robustness, adaptability,and value on applications.

Image Processing on Geological Data in Vector Format and Multi-Source Spatial Data Fusion

The geological data are constructed in vector format in geographical information system (GIS) while other data such as remote sensing images, geographical data and geochemical data are saved in raster ones. This paper converts the vector data into 8 bit images according to their importance to mineralization each by programming. We can communicate the geological meaning with the raster images by this method. The paper also fuses geographical data and geochemical data with the programmed strata data. The result shows that image fusion can express different intensities effectively and visualize the structure characters in 2 dimensions. Furthermore, it also can produce optimized information from multi-source data and express them more directly.

A multi-source information fusion method for tool life prediction based on CNN-SVM

For milling tool life prediction and health management,accurate extraction and dimensionality reduction of its tool wear features are the key to reduce prediction errors.In this paper,we adopt multi-source information fusion technology to extract and fuse the features of cutting vibration signal,cutting force signal and acoustic emission signal in time domain,frequency domain and time-frequency domain,and downscale the sample features by Pearson correlation coefficient to construct a sample data set;then we propose a tool life prediction model based on CNN-SVM optimized by genetic algorithm(GA),which uses CNN convolutional neural network as the feature learner and SVM support vector machine as the trainer for regression prediction.The results show that the improved model in this paper can effectively predict the tool life with better generalization ability,faster network fitting,and 99.85%prediction accuracy.And compared with the BP model,CNN model,SVM model and CNN-SVM model,the performance of the coefficient of determination R2 metric improved by 4.88%,2.96%,2.53%and 1.34%,respectively.

Recent trends of machine learning applied to multi-source data of medicinal plants

In traditional medicine and ethnomedicine,medicinal plants have long been recognized as the basis for materials in therapeutic applications worldwide.In particular,the remarkable curative effect of traditional Chinese medicine during corona virus disease 2019(COVID-19)pandemic has attracted extensive attention globally.Medicinal plants have,therefore,become increasingly popular among the public.However,with increasing demand for and profit with medicinal plants,commercial fraudulent events such as adulteration or counterfeits sometimes occur,which poses a serious threat to the clinical outcomes and interests of consumers.With rapid advances in artificial intelligence,machine learning can be used to mine information on various medicinal plants to establish an ideal resource database.We herein present a review that mainly introduces common machine learning algorithms and discusses their application in multi-source data analysis of medicinal plants.The combination of machine learning algorithms and multi-source data analysis facilitates a comprehensive analysis and aids in the effective evaluation of the quality of medicinal plants.The findings of this review provide new possibilities for promoting the development and utilization of medicinal plants.

Unequal-interval data fusion algorithm for inertial/gravity matching integrated navigation system

Inertial/gravity matching integrated navigation system can effectively improve the longendurance navigation ability of underwater vehicles.Through the analysis of the matching process,the problem of unequal-interval in matching trajectory is addressed by an unequal-interval data fusion algorithm which is based on the unequal-interval characteristics analysis of the matching trajectory.Compared with previously available methods,the proposed algorithm improves the location precision.In conclusion,simulations of the integrated navigation system demonstrated the effectiveness and superiority of the proposed algorithm.

Threat Modeling and Application Research Based on Multi-Source Attack and Defense Knowledge

Cyber Threat Intelligence(CTI)is a valuable resource for cybersecurity defense,but it also poses challenges due to its multi-source and heterogeneous nature.Security personnel may be unable to use CTI effectively to understand the condition and trend of a cyberattack and respond promptly.To address these challenges,we propose a novel approach that consists of three steps.First,we construct the attack and defense analysis of the cybersecurity ontology(ADACO)model by integrating multiple cybersecurity databases.Second,we develop the threat evolution prediction algorithm(TEPA),which can automatically detect threats at device nodes,correlate and map multisource threat information,and dynamically infer the threat evolution process.TEPA leverages knowledge graphs to represent comprehensive threat scenarios and achieves better performance in simulated experiments by combining structural and textual features of entities.Third,we design the intelligent defense decision algorithm(IDDA),which can provide intelligent recommendations for security personnel regarding the most suitable defense techniques.IDDA outperforms the baseline methods in the comparative experiment.

Structural damage detection method based on information fusion technique

Multi-source information fusion （MSIF） is imported into structural damage diagnosis methods to improve the validity of damage detection. After the introduction of the basic theory, the function model, classifications and mathematical methods of MSIF, a structural damage detection method based on MSIF is presented, which is to fuse two or more damage character vectors from different structural damage diagnosis methods on the character-level. In an experiment of concrete plates, modal information is measured and analyzed. The structural damage detection method based on MSIF is taken to localize cracks of concrete plates and it is proved to be effective. Results of damage detection by the method based on MSIF are compared with those from the modal strain energy method and the flexibility method. Damage, which can hardly be detected by using the single damage identification method, can be diagnosed by the damage detection method based on the character-level MSIF technique. Meanwhile multi-location damage can be identified by the method based on MSIF. This method is sensitive to structural damage and different mathematical methods for MSIF have different preconditions and applicabilities for diversified structures. How to choose mathematical methods for MSIF should be discussed in detail in health monitoring systems of actual structures.

基于地球物理数据融合的大兴安岭中段东缘地壳结构研究

位于中亚造山带东段的兴安地块和松辽锡林浩特地块被晚古生代贺根山-黑河缝合带分开。随后,大兴安岭与松辽盆地被中新生代嫩江-八里罕断裂分开。在大兴安岭中段,这两构造带的位置及板块拼合时间尚不明确。为研究大兴安岭中段东缘重要构造断裂、构造单元及结晶基底属性特征,在扎兰屯-阿荣旗地区采集处理一条长60 km的重力、磁法和大地电磁数据,重新处理了1:20万扎兰屯幅、阿荣旗幅区域布格重力异常数据。通过多种地球物理数据融合,解译了7条重要断裂,其中庞家街断裂和红星断裂是控制贺根山-黑河缝合带的深大断裂。北西向阿伦河断裂为深大断裂,至少切割到中地壳。发现了两个中地壳低阻带,其中C2异常解释为贺根山-黑河缝合带。通过1:25万阿荣旗幅地质填图资料,证实了地球物理数据融合的推断结果,为大兴安岭中段东缘构造带演化研究提供了重要支撑。

基于迭代卡尔曼滤波器的GPS-激光-IMU融合建图算法

在当前机器人导航和环境感知领域,室外大尺度场景下的三维激光SLAM一直是一个挑战性问题。由于GPS信号在某些环境下的不稳定性和激光SLAM的误差累积特性,传统算法在大尺度场景下表现不佳。针对室外大尺度场景下三维激光SLAM(同步定位和地图构建)存在的误差累积严重问题,本文提出了一种基于迭代卡尔曼滤波器的GPS-激光-IMU融合建图算法。该算法通过利用惯性测量单元(IMU)数据对机器人状态进行预测,同时以激光和全球定位系统(GPS)数据作为观测,更新机器人状态,推导出观测方程和雅可比矩阵,显著提高了建图的精度和鲁棒性。里程计中融合GPS数据的绝对位置信息以解决长时间运行中的误差累积问题。在特征稀疏的环境中,由于约束不足可能导致算法崩溃,GPS数据的引入可以提高系统的鲁棒性。此外,重力对于IMU数据预测机器人状态起到关键的作用。虽然重力是三维向量,但在不发生区域变化的情况下,其模长是不变的,因此被视为二自由度向量。通过将重力的优化转化为旋转矩阵群上的优化,成功避免了重力过参数化的问题,提高了算法的精度。在室外场景下与其他算法进行了性能测试对比并且验证了在大尺度场景下的鲁棒性和精度,结果表明:本文算法的均方根误差为0.089 m,与其他算法相比降低了54%。

重磁测量在海岛礁及其邻域中的应用与展望

重磁测量在海岛礁及其邻域的应用与研究中不可或缺。由于海岛礁及邻域重磁场空间分布的梯度和幅度变化较大,又受地理环境与技术的限制,获取该区域高分辨率、高精度的重磁测量信息较为困难,导致测量成果质量不高且产品单一,亟需科技创新来扭转此局面。本文系统分析了海岛礁及其邻域地理环境、重磁特征和相关测量技术方法的发展现状,针对存在的问题,深入探讨了该领域的新技术、新方法及应用研究,并对未来发展方向作出展望:(1)加强重磁测量技术方法的研究,构建高水平的立体观测系统;(2)深化多源数据融合处理技术方法研究,丰富测量成果的输出方式;(3)充分利用海岛礁作为定点的中长期观测平台的天然优势,开展地球物理场的融合研究,拓展其应用与研究领域。

四川长宁及周缘重力数据融合及深浅构造特征研究

高精度地球物理数据是获取地壳内部物质变化的基础,2019年6月17日四川长宁发生MS6.0级地震,近期该区域多次发生5级左右地震,且区域内地震的发震诱因存在争议。本研究以实测重力剖面数据为基准,首先寻找重力场模型数据与实测重力剖面数据之间的映射关系式;然后对重力场模型数据进行修正,采用双线性插值法获取研究区内高精度网格数据;以长宁MS6.0地震震中为中心点,提取4条重力剖面并采用密度反演获取跨长宁MS6.0地震震中的地壳密度差变化特征。研究结果表明:通过重力场模型数据与实测重力数据间的映射关系式计算所获取的融合数据精度小于5%;长宁及周边震群发震区域对应三叠纪地层且震群多沿背斜、向斜发生;长宁MS6.0地震位于剩余布格重力异常0~30 mGal的正低值区域和密度反演高低转换带上0.048~0.058 g/cm^(3)的正低值区域,该区域位于长宁背斜上的大地湾断层与双河背斜轴部隐伏断层的交汇处。本研究可为多源重力数据融合提供技术基础,服务于以动力学为基础的地震物理预测。

基于精化预积分的GNSS/IMU/视觉多源融合定位方法

针对传统预积分算法固定地球重力值和忽略地球自转的问题,提出一种考虑地球自转和重力变化的惯性测量单元(inertial measurement unit, IMU)预积分算法。参照高精度捷联惯性导航解算的动力学模型,在IMU预积分动力学模型的姿态更新中引入地球自转角速率,速度和位置更新中引入由地球自转引起的科里奥利加速度,同时将由载体位置引起的地球重力变化及时反馈至预积分算法中,详细推导了引入地球自转和重力变化后预积分算法的具体过程,实现对传统预积分模型的精化。并将精化的预积分算法应用于基于紧耦合全球导航卫星系统(global navigation satellite system, GNSS)/IMU/视觉多源融合系统中,实测实验结果表明:利用精化的预积分模型可使系统预积分的模型误差有效减小,显著提升多源融合系统整体的定位定姿精度,其中系统定位精度提升32.41%,航向角精度提升4.23%。

Multi-source Remote Sensing Image Registration Based on Contourlet Transform and Multiple Feature Fusion

Image registration is an indispensable component in multi-source remote sensing image processing. In this paper, we put forward a remote sensing image registration method by including an improved multi-scale and multi-direction Harris algorithm and a novel compound feature. Multi-scale circle Gaussian combined invariant moments and multi-direction gray level co-occurrence matrix are extracted as features for image matching. The proposed algorithm is evaluated on numerous multi-source remote sensor images with noise and illumination changes. Extensive experimental studies prove that our proposed method is capable of receiving stable and even distribution of key points as well as obtaining robust and accurate correspondence matches. It is a promising scheme in multi-source remote sensing image registration.

Enhancing train position perception through Al-driven multi-source information fusion

This paper addresses the challenge of accurately and timely determining the position of a train,with specific consideration given to the integration of the global navigation satellite system(GNSS)and inertial navigation system(INS).To overcome the increasing errors in the INS during interruptions in GNSS signals,as well as the uncertainty associated with process and measurement noise,a deep learning-based method for train positioning is proposed.This method combines convolutional neural networks(CNN),long short-term memory(LSTM),and the invariant extended Kalman filter(IEKF)to enhance the perception of train positions.It effectively handles GNSS signal interruptions and mitigates the impact of noise.Experimental evaluation and comparisons with existing approaches are provided to illustrate the effectiveness and robustness of the proposed method.

Carbon Emission Evaluation in Jinan Western New District based on Multi-source Data Fusion

Carbon emissions caused by human activities are closely related to the process of urbanization,and urban land utilization,function vitality and traffic systems are three important factors that may influence the emission levels.For clarifying the space structure of a low-carbon eco-city,and combining the concept of"Combining Assessment with Construction"to track and contrast the construction of the low-carbon eco-city,this research selects quantifiable low-carbon eco-city spatial characteristics as indicators,and evaluates and analyzes the potential carbon emissions.Taking the Jinan Western New District as an example,diversity of construction land,travel carbon emission potential,and density and accessibility of adjacent road networks in the overall urban planning were measured.After the completion of the new urban area,the evaluation mainly reflected certain factors,such as the mixed degree of urban functions,the density of urban functions,the walking distance to bus stops and the density and number of bus stops.Dividing the levels and adding equal weights after index normalization,the carbon emission potential is evaluated at the two levels of the overall and fragmented areas.The results show that:(1)The low-carbon emission potential areas in the planning scheme basically reached the planned goals.(2)There is inconsistency between districts and indicators in the planning scheme.The diversity of construction land and the accessibility of the adjacent road network are relatively small;however,there is a large difference between the travel carbon emission potential and the road network accessibility.(3)Carbon emission potential after completion did not reach the planned expectation,and the low-carbon emission potential plots were concentrated in the Changqing Old City Area and Central Area of Dangjia Town Area.(4)The carbon emission indicators varied greatly in different areas,and there were serious imbalances in the density of public transportation lines and the mixed degree of urban functions.

A heuristic cabin-type component alignment method based on multi-source data fusion

In cabin-type component alignment, digital measurement technology is usually adopted to provide guidance for assembly. Depending on the system of measurement, the alignment process can be divided into measurement-assisted assembly(MAA) and force-driven assembly. In MAA,relative pose between components is directly measured to guide assembly, while in force-driven assembly, only contact state can be recognized according to measured six-dimensional force and torque(6 D F/T) and the process is completed based on preset assembly strategy. Aiming to improve the efficiency of force-driven cabin-type component alignment, this paper proposed a heuristic alignment method based on multi-source data fusion. In this method, measured 6 D F/T, pose data and geometric information of components are fused to calculate the relative pose between components and guide the movement of pose adjustment platform. Among these data types, pose data and measured 6 D F/T are combined as data set. To collect the data sets needed for data fusion, dynamic gravity compensation method and hybrid motion control method are designed. Then the relative pose calculation method is elaborated, which transforms collected data sets into discrete geometric elements and calculates the relative poses based on the geometric information of components.Finally, experiments are conducted in simulation environment and the results show that the proposed alignment method is feasible and effective.

卫星测高与船载重力测量数据融合的点质量拟合法

以拟合方差最小为准则,通过点质量法拟合船载重力测量数据,得到点质量大小、埋深等参数。回避点质量法数值求解的不稳定性问题,借鉴移去-恢复技术的思路,利用该参数计算船载重力测量点上的重力异常,并将其在测线上的重力异常中扣除,计算出船载重力残差值。以点质量大小、埋深等参数计算卫星测高重力格网点上重力异常,同样得到测高重力残差值。采用加权最小曲率格网化方法,将船载重力残差值与测高重力残差值格网化,进而恢复由点质量大小、埋深等参数计算格网点处的重力异常,实现卫星测高与船载重力测量数据融合。经国际船载重力测量数据检核,融合后的模型较国际船载重力测量数据的平均偏差在1~2mGal(1Gal=1×10^(-2) m/s^2),标准差约为4mGal。本文的研究方法可为陆地、海岸带区域的多种重力数据的融合、航空重力及卫星重力的向下延拓等问题提供参考。