Target Ship Identification Algorithm Based on Comprehensive Correlation Discriminant and Information Entropy

Ship type identification is an important part of electronic reconnaissance. However, in the existing methods, such as statistical-based methods and fuzzy-mathematics-based methods, the information acquired by the passive sensor is not fully utilized, and there is a certain ambiguity in the assignment relationship of the emitters-ship. They can’t conclude the accurate and reliable assignment relationship of the emitters-ship. Therefore, this paper proposes a comprehensive correlation discriminant method to obtain a more reliable and comprehensive emitters-ship assignment, and then uses information entropy method to identify the type of the target ship on the basis of this association and assign the credibility. The simulation results show that this algorithm can effectively solve the problem of target ship type identification using the information of multi-passive sensors.

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.

Structural Stress Identification Using Fuzzy Pattern Recognition and Information Fusion Technique

In order to ensure the service security of space structures under wind load, the stress identification method based on the combination of fuzzy pattern recognition and information fusion technique is proposed, in which the measurements of limited strain sensors arranged on the structure are used. Firstly, the structure is divided into several regions according to the similarity and the most unfavorable region is selected to be the key region for stress identification, while the different numbers of the strain sensors are located on the key region and the normal regions; secondly, the different stress distributions of the key region are obtained based on the measurements of the strain sensors located on the key region and the normal regions separately, in which the fuzzy pattern recognition is used to identify the different stress distributions; thirdly, the stress distributions obtained by the measurements of sensors in normal regions are selected to calculate the synthesized stress distribution of the key region by D-S evidence theory; fourthly, the weighted fusion algorithm is used to assign the different fusion coefficients to the selected stress distributions obtained by the measurements of the normal regions and the key region, while the synthesized stress distribution of the key region can be obtained. Numerical study on a lattice shell model is carried out to validate the reliability of the proposed stress identification method. The simulated results indicate that the method can improve identification accuracy and be effective by different noise disturbing.

New Individual Identification Method of Radiation Source Signal Based on Entropy Feature and SVM

In this paper,according to the defect of methods which have low identification rate in low SNR,a new individual identification method of radiation source based on information entropy feature and SVM is presented. Firstly,based on the theory of multi-resolution wavelet analysis,the wavelet power spectrum of noncooperative signal can be gotten. Secondly,according to the information entropy theory,the wavelet power spectrum entropy is defined in this paper. Therefore,the database of signal's wavelet power spectrum entropy can be built in different SNR and signal parameters. Finally,the sorting and identification model based on SVM is built for the individual identification of radiation source signal. The simulation result indicates that this method has a high individual's identification rate in low SNR,when the SNR is greater than 4 dB,the identification rate can reach 100%. Under unstable SNR conditions,when the range of SNR is between 0 dB and 24 dB,the average identification rate is more than 92. 67%. Therefore,this method has a great application value in the complex electromagnetic environment.

Comprehensive security risk factor identification for small reservoirs with heterogeneous data based on grey relational analysis model

Identification of security risk factors for small reservoirs is the basis for implementation of early warning systems.The manner of identification of the factors for small reservoirs is of practical significance when data are incomplete.The existing grey relational models have some disadvantages in measuring the correlation between categorical data sequences.To this end,this paper introduces a new grey relational model to analyze heterogeneous data.In this study,a set of security risk factors for small reservoirs was first constructed based on theoretical analysis,and heterogeneous data of these factors were recorded as sequences.The sequences were regarded as random variables,and the information entropy and conditional entropy between sequences were measured to analyze the relational degree between risk factors.Then,a new grey relational analysis model for heterogeneous data was constructed,and a comprehensive security risk factor identification method was developed.A case study of small reservoirs in Guangxi Zhuang Autonomous Region in China shows that the model constructed in this study is applicable to security risk factor identification for small reservoirs with heterogeneous and sparse data.

Developing a Novel Method for Road Hazardous Segment Identification Based on Fuzzy Reasoning and GIS

Roads are one of the most important infrastructures in any country. One problem on road based transportation networks is accident. Current methods to identify of high potential segments of roads for accidents are based on statistical approaches that need statistical data of accident occurrences over an extended period of time so this cannot be applied to newly-built roads. In this research a new approach for road hazardous segment identification (RHSI) is introduced using Geospatial Information System (GIS) and fuzzy reasoning. In this research among all factors that usually play critical roles in the occurrence of traffic accidents, environmental factors and roadway design are considered. Using incomplete data the consideration of uncertainty is herein investigated using fuzzy reasoning. This method is performed in part of Iran's transit roads (Kohin-Loshan) for less expensive means of analyzing the risks and road safety in Iran. Comparing the results of this approach with existing statistical methods shows advantages when data are uncertain and incomplete, specially for recently built transportation roadways where statistical data are limited. Results show in some instances accident locations are somewhat displaced from the segments of highest risk and in few sites hazardous segments are not determined using traditional statistical methods.

Optimization of Well Position and Sampling Frequency for Groundwater Monitoring and Inverse Identification of Contamination Source Conditions Using Bayes’Theorem

Coupling Bayes’Theorem with a two-dimensional(2D)groundwater solute advection-diffusion transport equation allows an inverse model to be established to identify a set of contamination source parameters including source intensity(M),release location(0 X,0 Y)and release time(0 T),based on monitoring well data.To address the issues of insufficient monitoring wells or weak correlation between monitoring data and model parameters,a monitoring well design optimization approach was developed based on the Bayesian formula and information entropy.To demonstrate how the model works,an exemplar problem with an instantaneous release of a contaminant in a confined groundwater aquifer was employed.The information entropy of the model parameters posterior distribution was used as a criterion to evaluate the monitoring data quantity index.The optimal monitoring well position and monitoring frequency were solved by the two-step Monte Carlo method and differential evolution algorithm given a known well monitoring locations and monitoring events.Based on the optimized monitoring well position and sampling frequency,the contamination source was identified by an improved Metropolis algorithm using the Latin hypercube sampling approach.The case study results show that the following parameters were obtained:1)the optimal monitoring well position(D)is at(445,200);and 2)the optimal monitoring frequency(Δt)is 7,providing that the monitoring events is set as 5 times.Employing the optimized monitoring well position and frequency,the mean errors of inverse modeling results in source parameters(M,X0,Y0,T0)were 9.20%,0.25%,0.0061%,and 0.33%,respectively.The optimized monitoring well position and sampling frequency canIt was also learnt that the improved Metropolis-Hastings algorithm(a Markov chain Monte Carlo method)can make the inverse modeling result independent of the initial sampling points and achieves an overall optimization,which significantly improved the accuracy and numerical stability of the inverse modeling results.

Self-tuning Information Fusion Kalman Predictor Weighted by Diagonal Matrices and Its Convergence Analysis

为有未知噪音统计的 multisensor 系统，使用现代时间系列分析方法，基于革新建模的动人的一般水准(麻省)的联机鉴定，并且基于为关联功能的矩阵方程的解决方案，噪音变化的评估者被获得，并且在线性最小的变化下面由斜矩阵加权的最佳的信息熔化标准，一个自我调节的信息熔化 Kalman 预言者被介绍，它认识到自我调节的 dec 基于动态错误系统，一个新集中分析方法为自我调节的 fuser 被介绍。在一条认识的集中的一个新概念被介绍，它是比有概率一的集中弱的。如果 MA 革新模型的参数评价是一致的，那么，自我调节的熔化 Kalman 预言者将在一条认识收敛到最佳的熔化 Kalman 预言者，这严格地被证明，或与概率一，以便它有 asymptotic optimality。它能减少计算负担，并且对实时应用合适。为追踪系统的一个目标的一个模拟例子显示出它的有效性。

A Material Identification Approach Based on Wi-Fi Signal

Material identification is a technology that can help to identify the type of target material.Existing approaches depend on expensive instruments,complicated pre-treatments and professional users.It is difficult to find a substantial yet effective material identification method to meet the daily use demands.In this paper,we introduce a Wi-Fi-signal based material identification approach by measuring the amplitude ratio and phase difference as the key features in the material classifier,which can significantly reduce the cost and guarantee a high level accuracy.In practical measurement of WiFi based material identification,these two features are commonly interrupted by the software/hardware noise of the channel state information(CSI).To eliminate the inherent noise of CSI,we design a denoising method based on the antenna array of the commercial off-the-shelf(COTS)Wi-Fi device.After that,the amplitude ratios and phase differences can be more stably utilized to classify the materials.We implement our system and evaluate its ability to identify materials in indoor environment.The result shows that our system can identify 10 commonly seen liquids with an average accuracy of 98.8%.It can also identify similar liquids with an overall accuracy higher than 95%,such as various concentrations of salt water.

Generalized Nonlinear Irreducible Auto-Correlation and Its Applications in Nonlinear Prediction Models Identification

There is still an obstacle to prevent neural network from wider and more effective applications, i.e., the lack of effective theories of models identification. Based on information theory and its generalization, this paper introduces a universal method to achieve nonlinear models identification. Two key quantities, which are called nonlinear irreducible auto-correlation (NIAC) and generalized nonlinear irreducible auto-correlation (GNIAC), are defined and discussed. NIAC and GNIAC correspond with intrinstic irreducible auto-(dependency) (IAD) and generalized irreducible auto-(dependency) (GIAD) of time series respectively. By investigating the evolving trend of NIAC and GNIAC, the optimal auto-regressive order of nonlinear auto-regressive models could be determined naturally. Subsequently, an efficient algorithm computing NIAC and GNIAC is discussed. Experiments on simulating data sets and typical nonlinear prediction models indicate remarkable correlation between optimal auto-regressive order and the highest order that NIAC-GNIAC have a remarkable non-zero value, therefore demonstrate the validity of the proposal in this paper.

Ubiquitous data computing and information using in a smart factory with wireless manufacturing

This study proposesan over all framework for applying wireless manufacturing(WM)technologies in a smart factory and establishes a smart factory data computing and information using system (dc-IUS). Several plug-and-play (PnP) application modules of the dc-IUS are presented in the fields of machining process and quality control,material flow and inventory control,and factory resource tracking. Different schemes are discussed about how and where to apply these functions. Then some running examples are studied to demonstrate the feasibility and reliability of dc-IUS. At last,the challenges of applying WM are discussed and a conclusion is given.

Automatic Horizontal Road Design Information Extraction from Georeferenced Polygonals： A Brazilian Federal Highway Network Study

Road geometric design data are a vital input for diverse transportation studies. This information is usually obtained from the road design project. However, these are not always available and the as-built course of the road may diverge considerably from its projected one, rendering subsequent studies inaccurate or impossible. Moreover, the systematic acquisition of this data for the entire road network of a country or even a state represents a very challenging and laborious task. This study＇s goal was the extraction of geometric design data for the paved segments of the Brazilian federal highway network, containing more than 47,000 km of highways. It presents the details of the method＇s adoption process, the particularities of its application to the dataset and the obtained geometric design information. Additionally, it provides a first overview of the Brazilian federal highway network composition （curves and tangents） and geometry.

Stability-mutation feature identification of Web search keywords based on keyword concentration change ratio

Purpose: The aim of this paper is to discuss how the keyword concentration change ratio(KCCR) is used while identifying the stability-mutation feature of Web search keywords during information analyses and predictions.Design/methodology/approach: By introducing the stability-mutation feature of keywords and its significance, the paper describes the function of the KCCR in identifying keyword stability-mutation features. By using Ginsberg's influenza keywords, the paper shows how the KCCR can be used to identify the keyword stability-mutation feature effectively.Findings: Keyword concentration ratio has close positive correlation with the change rate of research objects retrieved by users, so from the characteristic of the 'stability-mutation' of keywords, we can understand the relationship between these keywords and certain information. In general, keywords representing for mutation fit for the objects changing in short-term, while those representing for stability are suitable for long-term changing objects. Research limitations: It is difficult to acquire the frequency of keywords, so indexes or parameters which are closely related to the true search volume are chosen for this study.Practical implications: The stability-mutation feature identification of Web search keywords can be applied to predict and analyze the information of unknown public events through observing trends of keyword concentration ratio.Originality/value: The stability-mutation feature of Web search could be quantitatively described by the keyword concentration change ratio(KCCR). Through KCCR, the authors took advantage of Ginsberg's influenza epidemic data accordingly and demonstrated how accurate and effective the method proposed in this paper was while it was used in information analyses and predictions.

基于分形维数的高速铁路地震预警系统地震P波震相识别方法

为更好保障高速铁路的运营安全,高速铁路地震预警系统的实施十分必要。5级以下的小震事件占据地震事件的95%以上,提高高速铁路预警系统在小震震相识别的速度与精度能有效保障高铁安全运营。提出一种基于分形维数的地震震相识别算法,优化传统分形维数的计算方法,提高分形维数的计算速度。引入分形斜率曲线并划分为4个阶段,分析临界点的分形斜率特征,通过识别分形斜率极值时刻判断地震波精准到达时刻。提出的算法平均误差达到0.006 3 s,标准差达到0.043 8 s,满足高速铁路地震预警系统的时效性和准确度要求。

基于相机感知的域自适应行人重识别模型

针对行人重识别在损坏场景下训练集和测试集分布差距过大、背景复杂度过高和噪声种类过多导致识别性能过低的问题,提出基于相机感知的域自适应行人重识别模型,引入并充分利用相机信息,在训练阶段对齐不同摄像机的图像分布,在测试阶段利用时序信息进行排序优化,减少训练集和测试集分布差异带来的影响,有效应对背景复杂度和噪声种类的问题.模型不仅从数据集处理角度有效减轻损坏图像的影响,还对排序优化进行二次加权,显著提高其在损坏场景中的性能.在Market-1501、DukeMTMC-reID、CUHK03数据集上的实验表明文中模型的有效性.

基于广义柔度曲率信息熵的板式轨道脱空损伤识别

板式轨道填充层作为轨道结构关键部位,在高频列车荷载和环境共同作用下出现脱空损伤,引起脱空位置轨道结构刚度改变。为有效检测板式轨道的轨道板脱空情况,采用数值仿真分析得到无砟轨道模态信息,利用轨道脱空区域广义柔度曲率局部峰值进行轨道脱空损伤识别。结合广义柔度、均匀荷载面(Uniform load surface, ULS)、曲率和局部信息熵,提出可定位损伤的ULS曲率信息熵,并在CRTS III板式轨道上进行验证。研究结果表明:广义柔度曲率利用轨道脱空前后模态信息计算轨道脱空损伤曲率差,能够有效定位脱空位置;ULS曲率信息熵表征值只需要轨道的一阶模态信息便能够有效地反映轨道脱空位置及面积,且克服了广义柔度曲率需要健康模态信息的不足;轨道对称位置上相同面积脱空的ULS曲率信息熵值相同;ULS曲率信息熵值与脱空面积和厚度成正相关关系;ULS曲率信息熵表征值具有较好的损伤识别敏感性,能够识别小于单个测点布置面积的0.1 m×0.1 m小面积脱空,并且对轨道板边脱空识别敏感性高于轨道板中脱空识别敏感性。

健康信息甄别中任务难度对大学生APP使用行为的影响

研究查询页、搜索引擎结果页和详情页面中的用户行为,有助于揭示用户健康信息搜索行为机理,优化健康信息甄别服务策略。研究招募了32名用户完成两种不同难度的健康信息甄别任务,基于开放性编码获取用户使用的APP类型,以甄别过程中的查询页、搜索引擎结果页和详情页及用户主观感知有用并进行截图的页面为信息载体,从信息源选择和认知资源分配策略两个维度深入剖析用户APP使用行为。结果表明,任务难度对社区类APP的信息源选择影响显著,表现为高难度任务中用户访问的各类页面数量均更多,截图采用的信息也更多。同时,任务难度对健康类APP的认知资源分配策略影响显著,表现为高难度任务中用户会付出更多的认知资源在详情页上,减少其在查询页上认知资源分配比率,且此时的信息采用率也更高。据此,本研究揭示了健康信息甄别中任务难度对用户APP使用行为具有内外智力资源的“同步唤醒”作用,验证了健康信息甄别中的媒介依赖和权威依赖现象。

基于类型矩阵转移的汉越事件因果关系识别

针对汉越跨语言新闻事件因果关系识别中,汉越跨语言的文本语义空间难以统一、新闻之间的因果关联特征捕获困难的问题,提出了基于类型矩阵转移的汉越跨语言新闻事件因果关系识别方法。通过跨语言预训练统一汉越跨语言的文本语义空间,使用树形长短期记忆循环神经网络提取汉越文本中的句法结构化特征,融入汉越句法特征并结合基于事件类型转移的注意力机制,对汉越事件句对的因果关系进行识别。实验结果表明,该方法在汉越跨语言新闻事件因果关系的识别上较基线模型准确率有所提升。

无人机场景下基于Transformer的轻量化行人重识别

针对无人机场景下行人重识别所呈现的多视角多尺度特点,以及传统的基于卷积神经网络的行人重识别算法受限于局部感受野结构和下采样操作,很难对行人图像的全局特征进行提取且图像空间特征分辨率不高。提出一种无人机场景下基于Transformer的轻量化行人重识别(Lightweight Transformer-based Person Re-Identification,LTReID)算法,利用多头多注意力机制从全局角度提取人体不同部分特征,使用Circle损失和边界样本挖掘损失,以提高图像特征提取和细粒度图像检索性能,并利用快速掩码搜索剪枝算法对Transformer模型进行训练后轻量化,以提高模型的无人机平台部署能力。更进一步,提出一种可学习的面向无人机场景的空间信息嵌入,在训练过程中通过学习获得优化的非视觉信息,以提取无人机多视角下行人的不变特征,提升行人特征识别的鲁棒性。最后,在实际的无人机行人重识别数据库中,讨论了在不同量级主干网和不同剪枝率情况下所提LTReID算法的行人重识别性能,并与多种行人重识别算法进行了性能对比,结果表明了所提算法的有效性和优越性。

基于多维分割和特征融合的车辆重识别算法

针对车辆重识别算法受类内差异性和类间相似性干扰,细粒度特征提取不充分的问题,本文提出一种融合全局与局部信息,基于卷积和自注意力机制的车辆重识别算法。采用双分支架构对车辆语义进行编码,更好地捕捉车辆信息,为了增强局部语义信息的提取,针对不同维度上的注意力模块,通过设计全局局部交互模块,将全局信息更好地融入局部特征。在公开数据集VeRi 776与VehicleID数据集进行实验验证,实验结果证明所提方法的优越性。