Ellipsoidal bounding set-membership identification approach for robust fault diagnosis with application to mobile robots

A robust fault diagnosis approach is developed by incorporating a set-membership identification (SMI) method. A class of systems with linear models in the form of fault related parameters is investigated, with model uncertainties and parameter variations taken into account explicitly and treated as bounded errors. An ellipsoid bounding set-membership identification algorithm is proposed to propagate bounded uncertainties rigorously and the guaranteed feasible set of faults parameters enveloping true parameter values is given. Faults arised from abrupt parameter variations can be detected and isolated on-line by consistency check between predicted and observed parameter sets obtained in the identification procedure. The proposed approach provides the improved robustness with its ability to distinguish real faults from model uncertainties, which comes with the inherent guaranteed robustness of the set-membership framework. Efforts are also made in this work to balance between conservativeness and computation complexity of the overall algorithm. Simulation results for the mobile robot with several slipping faults scenarios demonstrate the correctness of the proposed approach for faults detection and isolation (FDI).

Fuzzy Set Identification of Bone Marrow Cells

Using rubricytes and lymphocytes as examples,this paper presents a fuzzy set theory and method to identify human bone marrow hematopoiesis system cells (BMCs).On the basis of the Cauchy’s distribution function,this paper sets up a series of membership function formulae of the BMC feature fuzzy subsets,general identification formulae of fuzzy sets for the BMCs,as well as identification formulae of fuzzy sets for rubricytes and lymphocytes.These formulae will assist with the quantitation of unknown cells compared to standard cells.

A new identification control for generalized Julia sets

In this paper, we propose a new method to realize drive-response system synchronization control and parameter identification for a class of generalized Julia sets. By means of this method, the zero asymptotic sliding variables are applied to control the fractal identification. Furthermore, the problems of synchronization control are solved in the case of a drive system with unknown parameters, and the unknown parameters of the drive system can be identified in the asymptotic synchronization process. The results of simulation examples demonstrate the effectiveness of this new method. Particularly, the basic Julia set is also discussed.

Identification of the Quaternary low gas-saturation reservoirs in the Sanhu area of the Qaidam Basin,China

Low gas-saturation reservoirs are gas bearing intervals whose gas saturation is less than 47%. They are common in the Quaternary of the Sanhu area in the Qaidam Basin.Due to the complex genesis mechanisms and special geological characteristics,the logging curves of low gas-saturation reservoirs are characterized by ambiguity and diversity,namely without significant log response characteristics. Therefore,it is particularly difficult to identify the low gas-saturation reservoirs in the study area.In addition,the traditional methods such as using the relations among lithology,electrical property,physical property and gas bearing property,as well as their threshold values,can not effectively identify low gas-saturation reservoirs.To solve this problem,we adopt the decision tree,support vector machine and rough set methods to establish a predictive model of low gas-saturation reservoirs,which is capable of classifying a mass of multi-dimensional and fuzzy data.According to the transparency of learning processes and the understandability of learning results,the predictive model was also revised by absorbing the actual reservoir characteristics.Practical applications indicate that the predictive model is effective in identifying low gas-saturation reservoirs in the study area.

Identification and Categorization of Climate Change Risks

The scientific evidence that climate is changing due to greenhouse gas emission is now incontestable, which may put many social, biological, and geophysical systems in the world at risk. In this paper, we first identified main risks induced from or aggravated by climate change. Then we categorized them applying a new risk categorization system brought forward by Renn in a framework of International Risk Governance Council. We proposed that ＂uncertainty＂ could be treated as the classification criteria. Based on this, we established a quantitative method with fuzzy set theory, in which ＂confidence＂ and ＂likelihood＂, the main quantitative terms for expressing uncertainties in IPCC, were used as the feature parameters to construct the fuzzy membership functions of four risk types. According to the maximum principle, most climate change risks identified were classified into the appropriate risk types. In the mean time, given that not all the quantitative terms are available, a qualitative approach was also adopted as a complementary classification method. Finally, we get the preliminary results of climate change risk categorization, which might iay the foundation for the future integrated risk management of climate change.

Non-probabilistic information fusion technique for structural damage identification based on measured dynamic data with uncertainty

Based on measured natural frequencies and acceleration responses,a non-probabilistic information fusion technique is proposed for the structural damage detection by adopting the set-membership identification（SMI） and twostep model updating procedure.Due to the insufficiency and uncertainty of information obtained from measurements,the uncertain problem of damage identification is addressed with interval variables in this paper.Based on the first-order Taylor series expansion,the interval bounds of the elemental stiffness parameters in undamaged and damaged models are estimated,respectively.The possibility of damage existence（PoDE） in elements is proposed as the quantitative measure of structural damage probability,which is more reasonable in the condition of insufficient measurement data.In comparison with the identification method based on a single kind of information,the SMI method will improve the accuracy in damage identification,which reflects the information fusion concept based on the non-probabilistic set.A numerical example is performed to demonstrate the feasibility and effectiveness of the proposed technique.

Function S-rough sets and law identification

By introducing element equivalence class that proposes dynamic characteristic into Pawlak Z rough sets theory, the first author of this paper improved Pawlak Z rough sets and put forward S-rough sets （singular rough sets）. S-rough sets are defined by element equivalence class that proposes dynamic characteristic. S-rough sets have dynamic characteristic. By introducing the function equivalence class （law equivalence class） that proposes dynamic characteristic into S-rough sets, the first author improved S-rough sets and put forward function S-rough sets （function singular rough sets）. Function S-rough sets have dynamic characteristic and law characteristic, and a function is a law. By using function S-rough sets, this paper presents law identification, law identification theorem, and law identification criterion and applications. Function S-rough sets are a new research direction of rough sets theory, and it is also a new tool to the research of system law identification.

Identification of Critical Components of Complex Product Based on Hybrid Intuitionistic Fuzzy Set and Improved Mahalanobis-Taguchi System

To avoid the decrease of system reliability due to insufficient component maintenance and the resource waste caused by excessive component maintenance,identifying the critical components of complex products is an effective way to improve the efficiency of maintenance activities.Existing studies on identifying critical components of complex products are mainly from two aspects i.e.,topological properties and functional properties,respectively.In this paper,we combine these two aspects to establish a hybrid intuitionistic fuzzy set to incorporate the different types of attribute values.Considering the mutual correlation between attributes,a combination of AHP(Analytic Hierarchy Process)and Improved Mahalanobis-Taguchi System(MTS)is used to determine the λ-Shapley fuzzy measures for attributes.Then,the λ-Shapley Choquet integral intuitionistic fuzzy TOPSIS(Technique for Order Preference by Similarity to an Ideal Solution)method is proposed for calculating the closeness degrees of components to generate their ranking order.Finally,a case study which is about the right gear of airbus 320 is taken as an example to verify the feasibility and effectiveness of this method.This novel methodology can effectively solve the critical components identification problem with different types of evaluation information and completely unknown weight information of attributes,which provides the implementation of protection measures for the system reliability of complex products.

兔抗小鼠含SET结构域(赖氨酸甲基转移酶)8(Setd8)多克隆抗体制备和应用

目的在大肠杆菌表达小鼠含SET结构域(赖氨酸甲基转移酶)8(Setd8)蛋白并制备兔抗Setd8多克隆抗体。方法将p GADT7-Setd8质粒和p ET-30a载体分别双酶切后连接、转化和鉴定,构建p ET-30a-Setd8原核表达质粒。将p ET-30a-Setd8质粒转化到E.coli BL21中,用异丙基-β-D-硫代吡喃半乳糖苷(IPTG)诱导蛋白表达,通过镍亲和层析柱进行蛋白纯化。应用纯化的Setd8蛋白免疫新西兰大白兔,获得Setd8多克隆抗体。利用ELISA、Western blot法及免疫组织化学染色法对所得多克隆抗体进行效价测定及特异性鉴定。结果应用限制性内切酶双酶切后电泳及序列测定法,成功构建的p ET-30a-Setd8重组质粒在大肠杆菌中IPTG诱导下能够高效表达重组Setd8蛋白。免疫获得的多克隆抗体经ELISA检测其抗体效价为1∶1 000 000,Western blot法结果显示制备的多克隆抗体能特异性识别细胞和组织中的Setd8蛋白,免疫组织化学染色法显示多克隆抗体识别的Setd8主要表达于成年小鼠睾丸组织中的精原细胞。结论成功获得较高纯度Setd8蛋白,制备的Setd8多克隆抗体具有较高反应性和特异性。

基于Rough Set理论的油层识别方法

本文简介了智能信息处理中新出现的ＲｏｕｇｈＳｅｔ（ＲＳ）理论，讨论了ＲＳ理论进行决策分析的方法，提出了将连续属性离散化的最优化思想，并探讨了ＲＳ理论用于测井数据判别油水层的问题。判别结果表明：本方法具有速度快、易实现、可优选属性等特点，且判别符合率优于手工方法与ＢＰ网络方法，具有实用价值。

基于SET数字证书的在线支付身份认证方案

文章对基于SET协议的电子商务系统中的身份认证进行了研究,将SET规范与RSA算法以及第三方证书认证相结合,提出了一种SET在线支付体系中对交易各方进行身份认证的方案,为电子商务的信息安全、支付安全的解决方案提供了充分的依据。

Solvability identification and feasibility restoring of divergent optimal power flow problems

Optimal power flow (OPF) has been considered as an important problem in power systems. Although several excellent algorithms, such as Newton method and interior point method, have been developed to solve the OPF problem, divergences still often occur. Till now, few works have focused on the solv- ability identification and feasibility restoring of divergent OPF problems. In this paper, we propose a systematic approach to identify the solvability of divergent OPF problems, and restore a feasible solu- tion for unsolvable OPF cases. The proposed approach consists of two phases: solvability identifica- tion phase (SIP) and feasibility restoring phase (FRP). In SIP, a novel methodology based on problem transformation and active set is adopted to identify the solvability of divergent OPF problem. If a fea- sible solution can be obtained in SIP, then this divergent OPF problem is solvable, otherwise, FRP is used to restore a feasible or optimal solution by relaxing soft constraints and load shedding. In FRP, a feasibility restoring model is presented, and a priority-listing strategy of restoring actions is proposed to restore the unsolvable OPF problems. Numerical studies indicate that the proposed SIP and FRP are reliable to diagnose the solvability of the divergent OPF problems, give an index to measure the un- solvability, and restore an unsolvable OPF case.

The research and the development of the wide area relaying protection based on fault element identification

The urgent problem of the relaying protection in the modern AC/DC hybrid connected grid and the development of the wide area communication,the information process and the intelligent technology powerfully promotes the development of the technology of the wide area relaying protection(WARP),which has become a research hotspot that attracts extensive attention.Originated from the basic concept of the wide area relaying protection,this paper analyses the advantages,the effects and the functions of the wide area relaying protection.The two main approaches to realize the wide area protection,which are on-line adaptive setting(OAS)principle and fault element identification(FEI),are introduced in this paper.Aimed at improving the performance of the backup protection,the research content and the technology demand of the wide area protection are proposed,meanwhile,the basic principle and the algorithm of the fault element identification are introduced.At last,the scheme of the limited wide area relaying protection based on the existing pilot channel of the main protection is discussed.

基于level set方法的含水层参数识别

简单介绍了level set方法的基本原理,并将其应用到含水层参数识别问题。将含水层中两种介质的界面看作level set函数的零水平集。首先给定一个初始界面,然后根据观测水头与计算水头之差,使lcvel set函数以一定方式运动,从而使计算出来的界面逐步逼近真实界面。文中给出了两个数值算例,结果表明该方法可以有效地处理含水层参数识别问题。

P-sets and Its Dependence Characteristic

P-sets is a set pair, it is composed of internal P-set and outer P-set, it has dynamic characteristic. By using structure of P-sets, dependence theorem and identification theorem are proposed in this paper.

基于量子衍生涡流算法和T⁃S模糊推理模型的储层岩性识别

鉴于梯度下降法易陷入局部极值、普通群智能优化算法易早熟收敛,提出一种基于量子衍生涡流算法(Quantum Vortex Search Algorithm,QVSA)和T⁃S模糊推理模型的岩性识别方法,QVSA具有操作简单、收敛速度快、寻优能力强等优点,有助于T⁃S模糊推理模型获得最优参数配置,从而实现储层岩性的准确识别。首先利用具有全局搜索能力的QVSA优化T⁃S模糊推理模型的各种参数;然后利用主成分分析方法降低获取的地震属性维度;再利用优化的T⁃S模糊推理模型识别储层岩性。实验结果表明,利用反映储层特征的8个地震属性识别储层岩性时,所提方法的识别正确率达到92%,比普通BP网络方法高5.1%,同时查准率、查全率、F1分数等指标也较BP网络方法提升明显。

组织文化认同对铁路工程企业员工建言行为的影响研究

为激发铁路工程企业员工建言行为的发生,研究以组织文化认同为切入点,在文献理论分析基础上,基于社会认同理论和自我决定理论,引入情感投入和感知组织支持作为组织文化认同和建言行为间关系的中介变量和调节变量,探究组织文化认同对铁路工程企业员工建言行为影响机理。使用组织文化认同和建言行为等成熟量表,以中国铁道建筑总公司、中国铁路工程总公司,中国交通建设集团有限公司企业员工为研究对象进行问卷调查;运用Mplus8.0和SPSS27.0等分析软件对收集数据进行验证性因子分析、相关性分析;在验证性因子分析和相关性分析的基础上,将建立的结构方程模型通过运用Mplus8.0进行路径假设检验;运用fsQCA3.0进行前因变量对建言行为模糊定性比较分析。结构方程模型检验结果表明,组织文化认同显著正向影响情感投入;情感投入正向影响建言行为;即情感投入在组织文化认同和建言行为影响关系中起中介作用;组织文化认同显著正向影响建言行为;感知组织支持显著正向调节情感投入对建言行为的影响。fsQCA分析结果表明,有5种组态可以发生建言行为,其中组织文化认同3个维度为核心存在条件,与结构方程模型检验结果相符合。以上结果丰富了建言行为边界机制和作用条件,基于“想做”和“能做”二元视角,揭示了组织工作氛围中,组织文化认同对员工产生“想建言”的心理动机产生重要影响,感知组织支持(“能建言”)正向促进了员工将建言的动机转化为实际的建言行为,为铁路工程企业管理者在实践中提供建议和启示。

基于GANomaly的水声通信信号调制方式开集识别

针对水声通信信号调制方式的开集识别问题,提出了一种基于GANomaly的新型开集识别方法。GANomaly网络模型结合了生成对抗网络(GAN)的生成能力和异常检测技术的判别能力,通过对抗训练,重构调制信号的时频特征并确定误差阈值,进而有效地区分已知和未知的调制方式。在信噪比>8 dB的条件下进行的仿真实验显示,该方法对已知调制方式信号的识别率均超过了86.00%,对未知调制方式信号的识别率超过了80.00%。仿真实验结果验证了所提方法对未知调制方式的有效识别能力,为水声通信领域开集识别问题提供了新的解决思路。

用于玉米品种真实性鉴定的最优核心SNP位点集的研发

品种真实性是种子质量监测的一个重要指标。为建立准确可靠、快速简便、高通量、低成本的玉米品种真实性鉴定技术,本文利用200个核心SNP位点构建的5816个玉米杂交品种,3274个自交系的指纹数据,基于遗传算法、品种识别率评估确定了一套高鉴别力的核心SNP位点集,包含96个SNP位点。这96个SNPs全部位于基因内区域,相对均匀分布在10对染色体上。采用上述杂交品种和自交系的指纹数据评估显示这96个位点具有较高多态性和品种区分能力,PIC、MAF、DP平均值分别为0.36、0.40、0.60和0.36、0.39、0.48,对杂交品种、自交系的品种识别率达到99.14%和99.24%。两两样品成对比较结果显示,99.99%的品种间差异位点数目≥3个,杂交品种和自交系中96.74%和95.67%的成对比较差异位点数目集中在30~65个和30~60个。基于221个主推杂交品种的40个SSR位点、96个SNP位点的基因型数据分析结果显示,这2组标记集的鉴定结果具有较高的一致性。综上所述,本研究报道了一套具有位点数量最少、区分能力最强,兼容多平台、适于自动化分型等优点的最优核心SNP集。期望位点集将在玉米品种真实性监测、种子质量控制中得到广泛应用,进而维护玉米种子市场秩序、保障育种者权利以及保护农民利益。

野生稻单片段代换系苗期耐旱性评价及QTL鉴定

【目的】干旱是影响水稻生产力的主要非生物因素之一,筛选抗旱水稻可有效保障水稻产量。【方法】选取以南方野生稻和展颖野生稻为供体、‘华粳籼74’(HJX74)为受体构建的93份单片段代换系(Single-segment substitution lines,SSSLs)为供试材料,以HJX74为对照,采用20%PEG-6000模拟干旱条件进行苗期耐旱性试验,以4个生长发育性状指标(相对苗高、相对苗干质量、相对根干质量、相对根长)作为评价指标,采用隶属函数法对SSSLs进行排序、筛选相关性状,并筛选与HJX74差异显著的SSSLs,进行QTL鉴定与加性效应分析,初步鉴定与苗期耐旱性状相关的QTL。【结果】4个生长发育性状指标与平均隶属函数值的相关性分析结果均为极显著正相关,可作为SSSLs苗期耐旱性鉴定和评价指标。隶属函数法分析结果显示,93份SSSLs平均隶属函数值范围为0.20~0.71,HJX74平均隶属函数值为0.54;有29个SSSLs的平均隶属函数值> HJX74的平均隶属函数值,其中,M124的隶属函数值最大,表明M124具有较强的耐旱性。通过单因素方差分析,从8个SSSLs(M78-1、M78-2、M124、M107、M151、M103、M130、M115)鉴定出7个相对苗高QTLs(qRSH1-1、qRSH1-2、qRSH2-1、qRSH3-1、qRSH3-2、qRSH5-1、qRSH6-1),分别分布在1、2、3、5、6号染色体,其加性效应为0.05~0.06,表型贡献率为6.97%~9.01%;从3个SSSLs(M79、M145、M148)中鉴定出3个相对根干质量QTLs(qRRDW10-1、qRRDW11-1、qRRDW11-2),分别分布在10、11号染色体,其加性效应为0.12~0.20,表型贡献率为12.51%~19.95%;从2个耐旱SSSLs(M80、X149)中鉴定出1个相对苗干质量QTL(qRSDW5-1),分布在5号染色体,加性效应为0.07,表型贡献率为10.5%。【结论】筛选出13份SSSLs携带苗期耐旱QTLs,为后续苗期耐旱QTLs精细定位与克隆研究奠定基础。