有翼飞行器外弹道测量数据融合处理研究

提高大气层内具有复杂弹道特性的飞行器外测弹道数据处理精度一直是困扰研究学者和数据处理人员的课题.本文应用已有的数据融合理论,结合大气层内机动飞行器的运动特性,提出了一种基于分段三次样条函数的外弹道数据融合处理算法.仿真和实测处理结果表明:该算法显著地提高了数据处理精度,在相关数据处理任务中具有一定的应用价值.     

An efficient peer-to-peer indexing tree structure for multidimensional data

As one of the most important technologies for implementing large-scale distributed systems, peer-to-peer (P2P) computing has attracted much attention in both research and industrial communities, for its advantages such as high availability, high performance, and high flexibility to the dynamics of networks. However, multidimensional data indexing remains as a big challenge to P2P computing, because of the inefficiency in search and network maintenance caused by the complicated existing index structures, which greatly limits the scalability of applications and dimensionality of the data to be indexed.We propose SDI (Swift tree structure for multidimensional Data Indexing), a swift index scheme with a simple tree structure for multidimensional data indexing in large-scale distributed systems. While keeping the query efficiency in O(logN) in terms of routing hops, SDI has extremely low maintenance costs which is proved through theoretical analysis. Furthermore, SDI overcomes the root-bottleneck problem existing in most other tree-based distributed indexing systems. Extensive empirical study verifies the superiority of SDI in both query and maintenance performance.     

An efficient parallel indexing structure for multi-dimensional big data using spark

The Journal of Supercomputing - With the increasing daily production of data in recent years, indexing, storing and retrieving huge amounts of data have become a common problem, especially for...     

A hyperplane based indexing technique for high-dimensional data

In this paper, we propose a novel hyperplane based indexing method to support efficient processing of similarity search queries in high-dimensional spaces. The main idea of the proposed index is to improve data partitioning efficiency in a high-dimensional space by using a hyperplane, which further partitions a subspace and can also take advantage of the twin node concept used in the key dimension based index. Compared with the key dimension concept, the hyperplane is more effective in data filtering. High space utilization is achieved by dynamically performing data reallocation between twin nodes. In addition, a post processing step is used after index building to ensure effective filtration. Extensive experiments based on two types of real data sets are conducted and the results illustrate a significantly improved filtering efficiency. Because of the feature of hyperplane, the proposed indexing method is only suitable to Euclidean spaces.     

A hybrid data-driven and mechanism-basedmethod for vehicle trajectory prediction

Ensuring the safe and efficient operation of self-driving vehicles relies heavily on accurately predicting their future trajectories.Existing approaches commonly employ an encoder–decoder neural network structure to enhance information extraction duringthe encoding phase. However, these methods often neglect the inclusion of road rule constraints during trajectory formulationin the decoding phase. This paper proposes a novel method that combines neural networks and rule-based constraints in thedecoder stage to improve trajectory prediction accuracy while ensuring compliance with vehicle kinematics and road rules.The approach separates vehicle trajectories into lateral and longitudinal routes and utilizes conditional variational autoencoder(CVAE) to capture trajectory uncertainty. The evaluation results demonstrate a reduction of 32.4% and 27.6% in the averagedisplacement error (ADE) for predicting the top five and top ten trajectories, respectively, compared to the baseline method.     

Random indexing of multidimensional data

Random indexing (RI) is a lightweight dimension reduction method, which is used, for example, to approximate vector semantic relationships in online natural language processing systems. Here we generalise RI to multidimensional arrays and therefore enable approximation of higher-order statistical relationships in data. The generalised method is a sparse implementation of random projections, which is the theoretical basis also for ordinary RI and other randomisation approaches to dimensionality reduction and data representation. We present numerical experiments which demonstrate that a multidimensional generalisation of RI is feasible, including comparisons with ordinary RI and principal component analysis. The RI method is well suited for online processing of data streams because relationship weights can be updated incrementally in a fixed-size distributed representation, and inner products can be approximated on the fly at low computational cost. An open source implementation of generalised RI is provided.     

S-MRST: a novel framework for indexing uncertain data

This paper studies the problem of probabilistic range query over uncertain data. Although existing solutions could support such query, it still has space for improvement. In this paper, we firstly propose a novel index called S-MRST for indexing uncertain data. For one thing, via using an irregular shape for bounding uncertain data, it has a stronger space pruning ability. For another, by taking the gradient of probability density function into consideration, S-MRST is also powerful in terms of probability pruning ability. More important, S-MRST is a general index which could support multiple types of probabilistic queries. Theoretical analysis and extensive experimental results demonstrate the effectiveness and efficiency of the proposed index.     

RRSi: indexing XML data for proximity twig queries

Twig query pattern matching is a core operation in XML query processing. Indexing XML documents for twig query processing is of fundamental importance to supporting effective information retrieval. In practice, many XML documents on the web are heterogeneous and have their own formats; documents describing relevant information can possess different structures. Therefore some “user-interesting” documents having similar but non-exact structures against a user query are often missed out. In this paper, we propose the RRSi, a novel structural index designed for structure-based query lookup on heterogeneous sources of XML documents supporting proximate query answers. The index avoids the unnecessary processing of structurally irrelevant candidates that might show good content relevance. An optimized version of the index, oRRSi, is also developed to further reduce both space requirements and computational complexity. To our knowledge, these structural indexes are the first to support proximity twig queries on XML documents. The results of our preliminary experiments show that RRSi and oRRSi based query processing significantly outperform previously proposed techniques in XML repositories with structural heterogeneity.

类自动车牌识别轨迹数据的伴随车辆组挖掘

自动车牌识别（ANPR）数据比私人全球定位系统（GPS）数据更易获得,且包含更有用的信息,但是相对成熟的针对GPS轨迹数据挖掘伴随车辆组方法并不适用于自动车牌识别数据,现有的少量自动车牌识别数据伴随车辆组挖掘算法存在重视轨迹相似而忽视时间因素的缺陷,因此提出一种基于轨迹特征的聚类方法挖掘伴随车辆组。针对自动车牌识别数据中采样点固定而采样时间不定的特点,通过轨迹中共现的次数判定两个对象构成伴随模式。该共现定义引入豪斯多夫距离,综合考虑轨迹的地点、方向和时间特征,旨在挖掘数据中采样点不同但采样点距离近且轨迹相似的伴随车辆组,以此提高伴随车辆组挖掘效率。实验结果表明,所提方法较现有方法更能有效挖掘伴随车辆组,识别非伴随模式数据,效率提升了近两倍。     

A MapReduce-based scalable discovery and indexing of structured big data

Various methods and techniques have been proposed in past for improving performance of queries on structured and unstructured data. The paper proposes a parallel B-Tree index in the MapReduce framework for improving efficiency of random reads over the existing approaches. The benefit of using the MapReduce framework is that it encapsulates the complexity of implementing parallelism and fault tolerance from users and presents these in a user friendly way. The proposed index reduces the number of data accesses for range queries and thus improves efficiency. The B-Tree index on MapReduce is implemented in a chained-MapReduce process that reduces intermediate data access time between successive map and reduce functions, and improves efficiency. Finally, five performance metrics have been used to validate the performance of proposed index for range search query in MapReduce, such as, varying cluster size and, size of range search query coverage on execution time, the number of map tasks and size of Input/Output (I/O) data. The effect of varying Hadoop Distributed File System (HDFS) block size and, analysis of the size of heap memory and intermediate data generated during map and reduce functions also shows the superiority of the proposed index. It is observed through experimental results that the parallel B-Tree index along with a chained-MapReduce environment performs better than default non-indexed dataset of the Hadoop and B-Tree like Global Index (Zhao et al., 2012) in MapReduce.     

基于交互车辆轨迹预测的自动驾驶车辆轨迹规划

针对城市道路等复杂行车场景,提出了一种基于交互车辆轨迹预测的自动驾驶车辆轨迹规划方法,将高维度的轨迹规划解耦为低维度的路径规划和速度规划;首先,采用五次多项式曲线和碰撞剩余时间规划车辆行驶路径;其次,在社会生成对抗网络Social-GAN的基础上结合车辆空间影响和注意力机制对交互车辆进行轨迹预测;然后,结合主车规划路径、交互车辆预测轨迹及碰撞判定模型得到主车S-T图,采用动态规划和数值优化方法求解S-T图,从而得到满足车辆动力学约束的安全、舒适最优速度曲线;最后,搭建PreScan-CarSim-Matlab&Simulink-Python联合仿真模型进行实验验证。仿真结果表明,提出的轨迹规划方法能够在对交互车辆有效避撞的前提下,保证车辆行驶的舒适性和高效性。     

Using object and trajectory analysis to facilitate indexing and retrieval of video

This paper aims to show that by using low level feature extraction, motion and object identifying and tracking methods, features can be extracted and indexed for efficient and effective retrieval for video; such as an awards ceremony video. Video scene/shot analysis and key frame extraction are used as a foundation to identify objects in video and be able to find spatial relationships within the video. The compounding of low level features such as colour, texture and abstract object identification lead into higher level real object identification and tracking and scene detection. The main focus is on using a video style that is different to the heavily used sports and news genres. Using different video styles can open the door to creating methods that could encompass all video types instead of specialized methods for each specific style of video.     

Airborne laser scanning data storage and indexing: state-of-the-art review

While the rapid growth in the availability and quality of airborne laser scanning data offers unprecedented information, it challenges the existing data management and dissemination solutions. Data management has become a bottleneck to effective laser scanning data exploration. This paper documents the state-of-the-art techniques for aerial laser scanning (ALS) data storage and indexing during the post-processing stage. A brief history of laser scanning technology is presented with the intention to offer an overview about the evolution of the technology from the data point of view inclusive of full waveform data management. The key strategies for data handling, including data modelling and indexing, are extensively discussed.     

Advances in trajectory optimization for space vehicle control

Space mission design places a premium on cost and operational efficiency. The search for new science and life beyond Earth calls for spacecraft that can deliver scientific payloads to geologically rich yet hazardous landing sites. At the same time, the last four decades of optimization research have put a suite of powerful optimization tools at the fingertips of the controls engineer. As we enter the new decade, optimization theory, algorithms, and software tooling have reached a critical mass to start seeing serious application in space vehicle guidance and control systems. This survey paper provides a detailed overview of recent advances, successes, and promising directions for optimization-based space vehicle control. The considered applications include planetary landing, rendezvous and proximity operations, small body landing, constrained attitude reorientation, endo-atmospheric flight including ascent and reentry, and orbit transfer and injection. The primary focus is on the last ten years of progress, which have seen a veritable rise in the number of applications using three core technologies: lossless convexification, sequential convex programming, and model predictive control. The reader will come away with a well-rounded understanding of the state-of-the-art in each space vehicle control application, and will be well positioned to tackle important current open problems using convex optimization as a core technology.     

多无人机自适应编队协同航迹规划

针对多无人机(UAV)协同航迹规划中因编队队形约束而忽略部分较窄通道的问题,提出了一种基于自适应分布式模型预测控制的快速粒子群优化(ADMPC-FPSO)方法。该方法利用领航跟随法和虚拟结构法相结合的编队策略构造出虚拟编队引导点,以完成自适应编队协同控制任务。根据模型预测控制的思想,结合分布式控制方法,将协同航迹规划转化为滚动在线优化问题,且以最小距离等性能指标为代价函数。通过设计评价函数准则,使用变权重快速粒子群优化算法对问题进行求解。仿真结果表明,通过所提算法能够有效实现多无人机协同航迹规划,并可根据环境变化快速完成自适应编队变换,同时较传统编队策略代价更低。     

Nonlinear feedback control and trajectory tracking of vehicle

This paper mainly studies nonlinear feedback control applied to the nonlinear vehicle dynamics with varying velocity. The main objective of this study is the stabilisation of longitudinal, lateral and yaw angular vehicle velocities. To this end, a nonlinear vehicle model is developed which takes both the lateral and longitudinal vehicle dynamics into account. Based on this model, a method to build a nonlinear state feedback control is first designed by which the complexity of system structure can be simplified. The obtained system is then synthesised by the combined Lyapunov–LaSalle method. The simulation results show that the proposed control can improve stability and comfort of vehicle driving. Moreover, this paper presents a lemma which ensures the trajectory tracking and path-following problem for vehicle. It can also be exploited simultaneously to solve both the tracking and path-following control problems of the vehicle ride and driving stability. We also show how the results of the lemma can be applied to solve the path-following problem, in which the vehicle converges and follows a designed path. The effectiveness of the proposed lemma for trajectory tracking is clearly demonstrated by simulation results.     

Searching web data: An entity retrieval and high-performance indexing model

More and more (semi) structured information is becoming available on the web in the form of documents embedding metadata (e.g., RDF, RDFa, Microformats and others). There are already hundreds of millions of such documents accessible and their number is growing rapidly. This calls for large scale systems providing effective means of searching and retrieving this semi-structured information with the ultimate goal of making it exploitable by humans and machines alike.This article examines the shift from the traditional web document model to a web data object (entity) model and studies the challenges faced in implementing a scalable and high performance system for searching semi-structured data objects over a large heterogeneous and decentralised infrastructure. Towards this goal, we define an entity retrieval model, develop novel methodologies for supporting this model and show how to achieve a high-performance entity retrieval system. We introduce an indexing methodology for semi-structured data which offers a good compromise between query expressiveness, query processing and index maintenance compared to other approaches. We address high-performance by optimisation of the index data structure using appropriate compression techniques. Finally, we demonstrate that the resulting system can index billions of data objects and provides keyword-based as well as more advanced search interfaces for retrieving relevant data objects in sub-second time.This work has been part of the Sindice search engine project at the Digital Enterprise Research Institute (DERI), NUI Galway. The Sindice system currently maintains more than 200 million pages downloaded from the web and is being used actively by many researchers within and outside of DERI.     

Large-scale indexing of spatial data in distributed repositories: the SD-Rtree

We propose a scalable distributed data structure (SDDS) called SD-Rtree. We intend our structure for point, window and kNN queries over large spatial datasets distributed on clusters of interconnected servers. The structure balances the storage and processing load over the available resources, and aims at minimizing the size of the cluster. SD-Rtree generalizes the well-known Rtree structure. It uses a distributed balanced binary tree that scales with insertions to potentially any number of storage servers through splits of the overloaded ones. A user/application manipulates the structure from a client node. The client addresses the tree through its image that can be possibly outdated due to later split. This may generate addressing errors, solved by the forwarding among the servers. Specific messages towards the clients incrementally correct the outdated images. We present the building of an SD-Rtree through insertions, focusing on the split and rotation algorithms. We follow with the query algorithms. We describe then a flexible allocation protocol which allows to cope with a temporary shortage of storage resources through data storage balancing. Experiments show additional aspects of SD-Rtree and compare its behavior with a distributed quadtree. The results justify our various design choices and the overall utility of the structure.     

An indexing method for wireless broadcast XML data

The paper considers a wireless information system, wherein various pieces of information represented in XML are broadcast via wireless channels, and mobile clients access the broadcast stream using energy-restricted portable devices.In this paper, we propose a wireless XML streaming method designed to provide energy-efficient access to a wireless stream. We construct two hierarchical structures to represent the XML data and their index information, called the XML data tree and XML index tree, respectively. The wireless XML stream is generated by traversing these two structures with some replications. We design three data/index replication strategies (PP, TT, and TP) in the streaming method. We compare the proposed streaming method with a naı¨ve method called the (1, X) method both analytically and experimentally. Also, based on our analysis results, we determine the optimal method of replication.     

Mining consumer product data via latent semantic indexing

One important focus of data mining research is in the development of algorithms for extracting valuable information from large databases in order to facilitate business decisions. This study explores a new technique for data mining – latent semantic indexing (LSI). LSI is an efficient information retrieval method for textual documents. By determining the singular value decomposition (SVD) of a large sparse term-by-document matrix, LSI constructs an approximate vector space model which represents important associative relationships between terms and documents that are not evident in individual documents. This paper explores the applicability of the LSI model to numerical databases, namely consumer product data. By properly choosing attributes of data records as terms or documents, a term-by-document frequency matrix is built from which a distribution-based indexing scheme is employed to construct a correlated distribution matrix (CDM). An LSI-like vector space model is then used to detect useful or hidden patterns in the numerical data. The extracted information can then be validated using statistical hypotheses testing or resampling. LSI is an automatic yet intelligent indexing method. Its application to numerical data introduces a promising way to discover knowledge in important commercial application areas such as retail and consumer banking.