A RBPF Algorithm with Non-intact Particle Data for FastSLAM

RBPF( Rao-Blackwellized Particle Filter) is a popular PF( Particle Filter) in decreasing the dimension of estimation problems and FastSLAM( Fast Simultaneous Localization and Mapping) is a RBPFbased algorithm. In FastSLAM,each particle carries a large amount of data which results in low computing efficiency and large memory space occupancy. To solve this problem,a RBPF algorithm with non-intact particle data is studied. The key idea is to differentiate the particle data. Through the screening of particles,the number of particles carrying individual map data is limited to reduce the data occupied space and speed up the computational efficiency. The simulation and experiment results have verified the effectiveness and accuracy of the algorithm. Compared with the original one,this proposed algorithm reduces time consumption by 18%-34% and considerably saves memory space.

Graph Regularized L_p Smooth Non-negative Matrix Factorization for Data Representation

This paper proposes a Graph regularized Lpsmooth non-negative matrix factorization(GSNMF) method by incorporating graph regularization and L_p smoothing constraint, which considers the intrinsic geometric information of a data set and produces smooth and stable solutions. The main contributions are as follows: first, graph regularization is added into NMF to discover the hidden semantics and simultaneously respect the intrinsic geometric structure information of a data set. Second,the Lpsmoothing constraint is incorporated into NMF to combine the merits of isotropic(L_2-norm) and anisotropic(L_1-norm)diffusion smoothing, and produces a smooth and more accurate solution to the optimization problem. Finally, the update rules and proof of convergence of GSNMF are given. Experiments on several data sets show that the proposed method outperforms related state-of-the-art methods.

GENERALIZED VARIATIONAL DATA ASSIMILATION METHOD AND NUMERICAL EXPERIMENT FOR NON-DIFFERENTIAL SYSTEM

The generalized variational data assimilation for non-differential dynamical systems is studied.There is no tangent linear model for non-differential systems and thus the general adjoint model can not be derived in the traditional way.The weak form of the original system was introduced, and then the generalized adjoint model was derived. The generalized variational data assimilation methods were developed for non-differential low dimensional system and non-differential high dimensional system with global and local observations. Furthermore, ideas in inverse problems are introduced to 4DVAR (Four-dimensional variational) of non-differential partial differential system with local observations.

Data envelopment analysis procedure with two non-homogeneous DMU groups

The classic data envelopment analysis（DEA） model is used to evaluate decision-making units＇（DMUs） efficiency under the assumption that all DMUs are evaluated with the same criteria setting. Recently, new researches begin to focus on the efficiency analysis of non-homogeneous DMU arose by real practices such as the evaluation of departments in a university, where departments argue for the adoption of different criteria based on their disciplinary characteristics. A DEA procedure is proposed in this paper to address the efficiency analysis of two non-homogeneous DMU groups. Firstly, an analytical framework is established to compromise diversified input and output（IO） criteria from two nonhomogenous groups. Then, a criteria fusion operation is designed to obtain different DEA analysis strategies. Meanwhile, Friedman test is introduced to analyze the consistency of all efficiency results produced by different strategies. Next, ordered weighted averaging（OWA） operators are applied to integrate different information to reach final conclusions. Finally, a numerical example is used to illustrate the proposed method. The result indicates that the proposed method relaxes the restriction of the classical DEA model,and can provide more analytical flexibility to address different decision analysis scenarios arose from practical applications.

An Indexed Non-Equijoin Algorithm Based on Sliding Windows over Data Streams

Processing a join over unbounded input streams requires unbounded memory, since every tuple in one infinite stream must be compared with every tuple in the other. In fact, most join queries over unbounded input streams are restricted to finite memory due to sliding window constraints. So far, non-indexed and indexed stream equijoin algorithms based on sliding windows have been proposed in many literatures. However, none of them takes non-equijoin into consideration. In many eases, non-equijoin queries occur frequently. Hence, it is worth to discuss how to process non-equijoin queries effectively and efficiently. In this paper, we propose an indexed join algorithm for supporting non-equijoin queries. The experimental results show that our indexed non-equijoin techniques are more efficient than those without index.

基于改进YOLOv5的红花目标检测算法研究

为实现农业非结构环境下采摘机器人对红花的准确识别,提出了一种基于改进YOLOv5的红花目标检测算法。将CBAM注意力机制嵌入到YOLOv5网络,提高了小尺寸目标物在高层次特征中的表现力;建立一种Alpha-IoU目标位置损失函数对原损失函数GIOU存在的梯度消失问题进行改进,提高了被遮挡红花的预测率,并通过在目标检测网络中增加分割检测模块,提高宽和高小于最低像素的小目标物检测精度,利用图像扩增数据集对改进后的YOLOv5算法进行训练,再分别与改进前后YOLOv5网络和Faster R-CNN网络在不同红花品种、不同自然光照情况、不同天气条件和不同遮挡情况下进行对比。试验结果表明:改进后的YOLOv5算法P值、R值分别为90.45%和0.90,对非结构环境下盛开期的未采摘红花mAP值达到94.48%,在不同影响因素下都可以准确识别出红花且置信度较高,可为红花采摘机器人自动化作业中的红花识别提供技术支持。

基于MongoDB的CNONIX数据存储方法研究

大量根据CNONIX标准编写的XML格式的数据如何在数据库中存储是目前亟需研究并解决的问题.本文首先介绍非关系型数据库的特点,之后分析CNONIX数据的格式特点,将XML与BSON格式进行比对,提出了基于MongoDB的CNONIX数据存储方案,将XML格式转换为BSON数据后存入MongoDB数据库中.运用Apache JMeter对此方案进行性能测试,并与传统关系型数据库进行对比.结果表明,CNONIX数据在MongoDB数据库中存储简便,且效率较关系型数据库有明显提高,能够解决其在关系型数据库中不易存储的问题.

NON-PARAMETRIC ESTIMATION IN CONTAMINATED LINEAR MODEL

In this paper, the following contaminated linear model is considered:y i=(1-ε)x τ iβ+z i, 1≤i≤n,where r.v.'s ｛ y i ｝ are contaminated with errors ｛ z i ｝. To assume that the errors have the finite moment of order 2 only. The non parametric estimation of contaminated coefficient ε and regression parameter β are established, and the strong consistency and convergence rate almost surely of the estimators are obtained. A simulated example is also given to show the visual performance of the estimations.

Analysis of long non-coding RNA expression profiles in gastric cancer

AIM: To investigate the expression patterns of long non-coding RNAs (lncRNAs) in gastric cancer. METHODS: Two publicly available human exon arrays for gastric cancer and data for the corresponding normal tissue were downloaded from the Gene Expression Omnibus (GEO). We re-annotated the probes of the human exon arrays and retained the probes uniquely mapping to lncRNAs at the gene level. LncRNA expression profiles were generated by using robust multi-array average method in affymetrix power tools. The normalized data were then analyzed with a Bioconductor package linear models for microarray data and genes with adjusted P -values below 0.01 were considered differentially expressed. An independent data set was used to validate the results. RESULTS: With the computational pipeline established to re-annotate over 6.5 million probes of the Affymetrix Human Exon 1.0 ST array, we identified 136053 probes uniquely mapping to lncRNAs at the gene level. These probes correspond to 9294 lncRNAs, covering nearly 76% of the GENCODE lncRNA data set. By analyzing GSE27342 consisting of 80 paired gastric cancer and normal adjacent tissue samples, we identified 88 lncRNAs that were differentially expressed in gastric cancer, some of which have been reported to play a role in cancer, such as LINC00152, taurine upregulated 1, urothelial cancer associated 1, Pvt1 oncogene, small nucleolar RNA host gene 1 and LINC00261. In the validation data set GSE33335, 59% of these differentially expressed lncRNAs showed significant expression changes (adjusted P -value < 0.01) with the same direction. CONCLUSION: We identified a set of lncRNAs differentially expressed in gastric cancer, providing useful information for discovery of new biomarkers and therapeutic targets in gastric cancer.

Fault detection and diagnosis for data incomplete industrial systems with new Bayesian network approach

For the fault detection and diagnosis problem in largescale industrial systems, there are two important issues： the missing data samples and the non-Gaussian property of the data. However, most of the existing data-driven methods cannot be able to handle both of them. Thus, a new Bayesian network classifier based fault detection and diagnosis method is proposed. At first, a non-imputation method is presented to handle the data incomplete samples, with the property of the proposed Bayesian network classifier, and the missing values can be marginalized in an elegant manner. Furthermore, the Gaussian mixture model is used to approximate the non-Gaussian data with a linear combination of finite Gaussian mixtures, so that the Bayesian network can process the non-Gaussian data in an effective way. Therefore, the entire fault detection and diagnosis method can deal with the high-dimensional incomplete process samples in an efficient and robust way. The diagnosis results are expressed in the manner of probability with the reliability scores. The proposed approach is evaluated with a benchmark problem called the Tennessee Eastman process. The simulation results show the effectiveness and robustness of the proposed method in fault detection and diagnosis for large-scale systems with missing measurements.

恒星大气中Li的non-LTE效应研究进展

Li元素是性质非常活跃且易发生核反应的轻核元素。其精确的丰度可以检验宇宙大爆炸学说。Li元素在宇宙早期化学演化、元素核合成理论和恒星的结构与演化研究中都有着非常重要的地位。近几年来,人们逐渐认识到在局部热动平衡(LTE)的假设下不能准确得到Li丰度,特别在冷的矮星中非局部热动平衡(non-LTE)效应的影响不容忽视。随着理论的发展,人们对原子参数有了更深入的了解,对non-LTE效应的研究也更趋成熟。确定元素丰度时考虑non-LTE影响已成为主要趋势。该文分析了影响Li元素non-LTE效应的各种机制,介绍了确定Li元素non-LTE效应的重要参数及Li原子模型的发展历程,最后对Li在各类恒星中的non-LTE效应进行了综合分析。

MANAGEMENT OF SPATIO-TEMPORAL DATA OF CADASTRAL INFORMATION SYSTEM IN CHINA

Cadastral Information System (CIS) is designed for the office automation of cadastral management. With the development of the market economics in China, cadastral management is facing many new problems. The most crucial one is the temporal problem in cadastral management. That is, CIS must consider both spatial data and temporal data. This paper reviews the situation of the current CIS and provides a method to manage the spatiotemporal data of CIS, and takes the CIS for Guangdong Province as an example to explain how to realize it in practice.

Empirical data decomposition and its applications in image compression

A nonlinear data analysis algorithm, namely empirical data decomposition （EDD） is proposed, which can perform adaptive analysis of observed data. Analysis filter, which is not a linear constant coefficient filter, is automatically determined by observed data, and is able to implement multi-resolution analysis as wavelet transform. The algorithm is suitable for analyzing non-stationary data and can effectively wipe off the relevance of observed data. Then through discussing the applications of EDD in image compression, the paper presents a 2-dimension data decomposition framework and makes some modifications of contexts used by Embedded Block Coding with Optimized Truncation （EBCOT） . Simulation results show that EDD is more suitable for non-stationary image data compression.

剖析DataGrid在ASP．Net中的数据处理机制

基于ADO．Net以非连接环境方式访问数据库的Asp．Net应用程序中，DataGrid是比较常用的数据处理控件。本文主要对DataGrid如何实现数据库的更新、删除和插入操作进行了论述．

Long-term survival of early-stage non-small cell lung cancer patients who underwent robotic procedure:a propensity score-matched study

Background:In the past decade,many researchers focused on to robot-assisted surgery.However,on long-term outcomes for patients with early-stage non-small cell lung cancer(NSCLC),whether the robotic procedure is superior to video-assisted thoracic surgery(VATS) and thoracotomy is unclear.Nonetheless,in the article titled "Long-term survival based on the surgical approach to lobectomy for clinical stage I non-small cell lung cancer:comparison of robotic,video assisted thoracic surgery,and thoracotomy lobectomy" by Yang et al.that was recently published in Annals of Surgery,the authors provided convincing evidence that the robotic procedure results in similar long-term survival as compared with VATS and thoracotomy.Minimally invasive procedures typically result in shorter lengths of hospital stay,and the robotic procedure in particular results in superior lymph node assessment.Main body:Our propensity score-matched study generated high-quality data.Based on our findings,we see promise in expanding patient access to robotic lung resections.In this study,propensity score matching minimized the bias involved between groups.Nevertheless,due to its retrospective nature,bias may still exist.Currently,the concept of rapid rehabilitation is widely accepted,and it is very difficult to set up a randomized controlled trial to compare robotic,VATS,and thoracotomy procedures for the treatment of NSCLC.Therefore,to overcome this limitation and to minimize bias,the best approach is to use a registry and prospectively collected,propensity score-matched data.Conclusions:Robotic lung resections result in similar long-term survival as compared with VATS and thoracotomy.Robot-assisted and VATS procedures are associated with short lengths of hospital stay,and the robotic procedure in particular results in superior lymph node assessment.Considering the alarming increase in the incidence of lung cancer in China,a nationwide database of prospectively collected data available for clinical research would be especially important.

A Statistical Comparison Method of the Differences among Single Points for Linear Dynamic Experimental Data

The experimental random error and desired valuse of non observed points in dynamic indexes were estimated by establishing the linear regression equations about variety regulations of dynamic indexes.The methods for difference significant test among different treatments using dynamic point as indexes were presented without setting the replication on each dynamic point observed.

A Multilevel Secure Relation-Hierarchical Data Model for a Secure DBMS

A multilevel secure relation hierarchical data model for multilevel secure database is extended from the relation hierarchical data model in single level environment in this paper. Based on the model, an upper lower layer relationalintegrity is presented after we analyze and eliminate the covert channels caused by the database integrity.Two SQL statements are extended to process polyinstantiation in the multilevel secure environment.The system based on the multilevel secure relation hierarchical data model is capable of integratively storing and manipulating complicated objects ( e.g. , multilevel spatial data) and conventional data ( e.g. , integer, real number and character string) in multilevel secure database.

Study on Mandatory Access Control in a Secure Database Management System

This paper proposes a security policy model for mandatory access control in class B1 database management system whose level of labeling is tuple. The relation hierarchical data model is extended to multilevel relation hierarchical data model. Based on the multilevel relation hierarchical data model, the concept of upper lower layer relational integrity is presented after we analyze and eliminate the covert channels caused by the database integrity. Two SQL statements are extended to process polyinstantiation in the multilevel secure environment. The system is based on the multilevel relation hierarchical data model and is capable of integratively storing and manipulating multilevel complicated objects ( e.g., multilevel spatial data) and multilevel conventional data ( e.g., integer, real number and character string).

基于non-local先验的贝叶斯变量选择方法及其在高维数据分析中的应用

目的对高维数据进行变量筛选并构建预测模型是组学数据分析的研究热点之一。本研究旨在为结局为二分类变量的高维组学数据筛选自变量并构建预测结局的稀疏统计模型。方法本研究通过模拟研究和实例分析阐释基于non-local先验的贝叶斯变量选择方法--乘积逆矩先验(product inverse moment,piMOM)相较于惩罚类方法ISIS-光滑平切绝对偏差(iterative sure independence screening-smoothly clipped absolute deviation,ISIS-SCAD)和ISIS-最小最大凹惩罚(iterative sure independence screening-minimax concave penalty,ISIS-MCP)在高维数据中变量筛选及其预测效果的性能优劣。结果模拟研究发现:在高维的情况下,经piMOM、ISIS-SCAD和ISIS-MCP方法筛选所得变量的平均真阳性数和受试者工作特征曲线下面积(AUC,area under curve)基本相等,ISIS-SCAD、ISIS-MCP的平均假阳性数、回归系数均方误差以及预测均方误差明显高于基于non-local先验的贝叶斯变量方法所获得的对应值。piMOM方法分析弥漫大B细胞淋巴瘤实例数据共识别5个有意义的基因,AUC为0.996;ISIS-SCAD识别7个基因,AUC为0.975;ISIS-MCP识别7个基因,AUC为0.968。结论在模型选择相合性和预测准确性方面,piMOM方法与ISIS-SCAD和ISIS-MCP相比,具有优势,在一定意义上可有效控制假阳性率。

Unconventional phase transition of phase-change-memory materials for optical data storage

Recent years, optically controlled phase-change memory draws intensive attention owing to some advanced applications including integrated all-optical nonvolatile memory, in-memory computing, and neuromorphic computing. The light-induced phase transition is the key for this technology. Traditional understanding on the role of light is the heating effect. Generally, the RESET operation of phase-change memory is believed to be a melt-quenching-amorphization process. However, some recent experimental and theoretical investigations have revealed that ultrafast laser can manipulate the structures of phase-change materials by non-thermal effects and induces unconventional phase transitions including solid-to-solid amorphization and order-to-order phase transitions. Compared with the conventional thermal amorphization,these transitions have potential superiors such as faster speed, better endurance, and low power consumption. This article summarizes some recent progress of experimental observations and theoretical analyses on these unconventional phase transitions. The discussions mainly focus on the physical mechanism at atomic scale to provide guidance to control the phase transitions for optical storage. Outlook on some possible applications of the non-thermal phase transition is also presented to develop new types of devices.