光距离传感器驱动研究和数据优化算法

文章对Android平台下光距离传感器驱动层作研究和设计,同时针对目前光距离传感器存在的问题,提出一种数据优化算法,旨在提高数据的可靠性,改善目前存在的问题。通过实验数据对比数据优化前后光距离传感器上报到上层的数据,同时结合用户体验得出结论。

基于粒子群的数据协调优化算法

根据非规则低密度奇偶校验码(LDPC)的密度进化基本原理,结合高斯近似算法和粒子群优化(PSO)算法对校验矩阵H进行智能优化,以得到性能更好的H.应用基于相干态的连续变量量子密钥分发的数据协调,可得到更低的收敛信噪比,提高协调效率,从而延长了密钥分发的通信距离.用C语言对该算法进行仿真的结果表明,当连续变量分组数为2×105时,在收敛性噪比SNR=4.9dB以上实现可靠的数据协调,协调效率可达92.06%,传输距离最大可达35.5公里.

蓝牙微蜂窝系统MAC协议的数据传输优化算法

目前蓝牙微蜂窝无线系统MAC协议数据传输采用的循环法(RR)信道时隙浪费严重.为此,根据主、从设备数据队列中的数据包存在情况提出了两种数据传输优化算法(HOL优先权准则和HOLK-合理性准则).该算法由于有效利用了蓝牙信道结构,系统吞吐量得以提高,数据包延时得以降低,系统性能得以改善.

基于神经辐射场算法的混合现实三维重建技术

针对目前在混合现实(MR)环境中高效率建立高质量三维(3D)模型的需求,基于神经辐射场算法(NeRF)的三维重建技术,提出了一种基于Laplacian算子的数据集优化算法。首先,围绕某线切割设备录制了一段1 min 51 s的视频,并采取等距提取视频帧的方式,获取了训练数据集;然后,使用Laplacian算子对数据集进行了优化,同时保留了原始数据集作为对比,使用了基于NeRF算法的重建方式与传统的基于COLMAP的稠密点云重建方式,分别对两组数据集进行了三维重建;最后,在重建精度与重建速度方面,对不同重建方式、不同重建数据集的重建结果进行了比较。研究结果表明:COLMAP稠密点云重建耗时是基于NeRF重建耗时的9.98倍,而相较于COLMAP稠密点云重建,使用NeRF重建方式的模型表面缺陷较少;此外,使用Laplacian算子优化的数据集的NeRF重建在峰值信噪比(PSNR)和结构相似性(SSIM)指标上分别提升了2.43%、0.72%,有利于提升重建模型的质量。研究结果支持混合现实技术在制造业数字化转型中的应用,可为其提供有益的参考。

基于紧密中心度的智能电网瓦片地图节点数据加载优化算法

针对瓦片地图节点数据加载过程中精度低、所需时间较长和准确性较低的问题,为此提出基于紧密中心度的智能电网瓦片地图节点数据加载优化算法。在地图加载方面采用瓦片地图服务器加载技术,通过地图的缩放等级和显示的地理范围两个数据来计算得到地图切片的范围并导出图片。采用地图拼接算法,将离线地图进行拼接多次向服务器请求数据。加载具有较高影响力和脆弱性的节点和馈线,确定节点的影响力,并对节点数据目标加载优化。实验结果表明:本文算法可准确优化瓦片地图节点数据信息,可有效缩短实验所需时间,准确性较高,在实际应用中具有重要意义。

多维数据分析处理技术

本文研究了基于大数据下的多维数据分析处理方法:针对大数据的多维离散拘束高效分析处理方法。该方法采用空间重构方法对多维数据采取离散映射,找到最小嵌入维数和最合适的时延构造信息流模型,并采用模糊聚类算法求解初始聚类中心搜索目标函数,从而获取到多维数据的最优聚类中心,利用优化算法实现聚类优化完成多维数据的高效聚类,仿真实验结果证明了该方法的可行性,提高计算效率。

数据压缩在实时测井网络传输中的应用

针对EILog成套装备应用中大数据量越来越广泛，讨论了ACME采集软件平台在面对大数据量测井数据网络传输过程中存在的缺陷，从数据压缩和优化发送方式2个方面对实时网络传输进行了优化；测试了不同压缩方法在不同测试条件下的优缺点，对压缩方案进行了应用测试和数据分析，选择了LZW压缩算法作为测井实时传输的优化方法。通过改善网络传输方式，优化数据压缩算法可以提升25％02E右的效率，软件运行期间CPU和内存运行平稳，特别对数据量较小的仪器效果更好，减少了软件崩溃的发生。

结合形态学梯度互信息和多分辨率寻优的图像配准新方法

对互信息配准法进行算法改进.在互信息基础上结合形态学梯度作为新的图像配准测度,不仅考虑所有体素信息,而且有效结合像素在空间位置的相互关系.将粒子群优化(Particle swarm optimization,PSO)算法这种全局寻优算法和Powell这一局部寻优算法相结合,前者的配准结果为后者的算法优化提供了非常有效的初始点,优化时间大为减少.借鉴小波变换中多分辨率的思想,在低分辨率图像中粗略配准后,上升到高分辨率图像上进一步细化配准结果,增加算法鲁棒性.实验结果证明,本文算法效果良好,寻优过程在数分钟内完成,能够满足诊断和科研的实时性要求.

Molecular reconstruction model based on structure oriented lumping and group contribution methods

Molecular management is a promising technology to face challenges in the refining industry, such as more stringent requirements for product oil and heavier crude oil, and to maximize the value of every molecule in petroleum fractions. To achieve molecular management in refining processes, a novel model that is based on structure oriented lumping(SOL) and group contribution(GC) methods was proposed in this study. SOL method was applied to describe a petroleum fraction with structural increments, and GC method aimed to estimate molecular properties. The latter was achieved by associating rules between SOL structural increments and GC structures. A three-step reconstruction algorithm was developed to build a representative set of molecules from partial analytical data. First, structural distribution parameters were optimized with several properties. Then, a molecular library was created by using the optimized parameters. In the final step, maximum information entropy(MIE) method was applied to obtain a molecular fraction. Two industrial samples were used to validate the method, and the simulation results of the feedstock properties agreed well with the experimental data.

A Fairness Resource Allocation Algorithm for Coverage and Capacity Optimization in Wireless Self-Organized Network

To achieve the higher resource efficiency, Coverage and Capacity Optimization(CCO) as an important role of the network self-healing and self-optimization, has become a focus topic in wireless Self-Organized Network(SON). In this paper, a novel CCO scheme is proposed to maximize utility function of the integrated coverage and capacity. It starts with the analysis on the throughput proportional fairness(PF) algorithm and then proposes the novel Coverage and Capacity Proportional Fairness(CCPF) allocation algorithm along with a proof of the algorithms convergence. This proposed algorithm is applied in a coverage capacity optimization scheme which can guarantee the reasonable network capacity by the coverage range accommodation. Next, we simulate the proposed CCO scheme based on telecom operators' real network data and compare with three typical resource allocation algorithms: round robin(RR), proportional fairness(PF) and max C/I. In comparison of the PF algorithm, the numerical results show that our algorithm increases the average throughput by 1.54 and 1.96 times with constructed theoretical data and derived real network data respectively.

EXPERIMENTS TO TRACK STORMS USING MODERN OPTIMIZATION ALGORITHMS

Storm identification and tracking based on weather radar data are essential to nowcasting and severe weather warning. A new two-dimensional storm identification method simultaneously seeking in two directions is proposed, and identification results are used to discuss storm tracking algorithms. Three modern optimization algorithms （simulated annealing algorithm, genetic algorithm and ant colony algorithm） are tested to match storms in successive time intervals. Preliminary results indicate that the simulated annealing algorithm and ant colony algorithm are effective and have intuitionally adjustable parameters, whereas the genetic algorithm is unsatisfaetorily constrained by the mode of genetic operations Experiments provide not only the feasibility and characteristics of storm tracking with modern optimization algorithms, but also references for studies and applications in relevant fields.

A Reliable Routing Algorithm with Network Coding in Internet of Vehicles

The intelligent vehicle network uses advanced information technology to establish an efficient integrated vehicle transport system, which has received great attention in industry and academia, lnternet of Vehicles （loV） in an urban environment is operated in a wireless environment with high bit error rate and interference. In addition, the wireless link between vehicles is likely to be lost. All of this makes it an important challenge to provide reliable mobile routing in an urban traffic environment. In this paper, a reliable routing algorithm with network coding （RR_ NC） is proposed to solve the above problems. A routing node sequence is discovered in IoV from source to destination by multi-metric ant colony optimization algorithm （MACO）, and then clusters are formed around every node in the sequence. By adding linear encoding into the transmission of data between vehicle＇s clusters, the RR_NC provides much more reliable transmission and can recover the original message in the event of disorder and loss of message. Simulations are taken under different scenarios, and the results prove that this novel algorithm can deliver the information more reliably between vehicles in real-time with lower data loss and communication overhead.

A combination algorithm of Chaos optimization and genetic algorithm and its application in maneuvering multiple targets data association

The most important problem in targets tracking is data association which may be represented as a sort of constraint combinational optimization problem. Chaos optimization and adaptive genetic algorithm were used to deal with the problem of multi-targets data association separately. Based on the analysis of the limitation of chaos optimization and genetic algorithm, a new chaos genetic optimization combination algorithm was presented. This new algorithm first applied the "rough" search of chaos optimization to initialize the population of GA, then optimized the population by real-coded adaptive GA. In this way, GA can not only jump out of the "trap" of local optimal results easily but also increase the rate of convergence. And the new method can also avoid the complexity and time-consumed limitation of conventional way. The simulation results show that the combination algorithm can obtain higher correct association percent and the effect of association is obviously superior to chaos optimization or genetic algorithm separately. This method has better convergence property as well as time property than the conventional ones.

Optimization in Allocating Goods to Shop Shelves Utilizing Genetic Algorithm for Yogurt Sales Data

How to allocate goods to shop shelves makes great influence to sales amount. Searching the best fit allocation of goods to the shelves is a kind of combinatorial problem. This becomes a problem of integer programming and utilizing genetic algorithm （GA） being an effective method. Reviewing past researches, there are few researches made on this. Formerly, the paper concerning optimization in allocating goods to shop shelves utilizing the GA was presented by us. In those papers, the problem that goods were allowed to allocate in multiple shelves was pursued. In this paper, the problem that does not allow goods to be allocated in multiple shelves is examined and the concept of sales profits and sales probabilities is introduced. Optimization in allocating goods to shop shelves is investigated. An application to the shop shelves for yogurt is executed. Utilizing GA, the optimum solution is pursued and verified by a numerical example. Various patterns of problems must be examined hereafter.

Research on the Multimedia Data Mining and Classification Algorithm based on the Database Optimization Techniques

In this research article, we analyze the multimedia data mining and classification algorithm based on database optimization techniques. Of high performance application requirements of various kinds are springing up constantly makes parallel computer system structure is valued by more and more common but the corresponding software system development lags far behind the development of the hardware system, it is more obvious in the field of database technology application. Multimedia mining is different from the low level of computer multimedia processing technology and the former focuses on the extracted from huge multimedia collection mode which focused on specific features of understanding or extraction from a single multimedia objects. Our research provides new paradigm for the methodology which will be meaningful and necessary.

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Iterative algorithms for solving the data assimilation problems are considered，based on the main and adjoint equations.Spectral properties of the control operators of the problem are studied, the iterative algorithms are justified.