无线传感器网络中基于压缩感知和GM(1,1)的异常检测方案

针对现有的异常事件检测算法准确率低和能量开销较大等问题,该文提出一种基于压缩感知(CS)和GM(1,1)的异常事件检测方案。首先,基于分簇的思想将传感器节点的数据进行压缩采样后传输至Sink,针对传感器网络中数据稀疏度未知的特点,提出一种基于步长自适应的块稀疏信号重构算法。然后,Sink基于GM(1,1)对节点发生的异常进行预测,并对节点的工作状态进行自适应调整。仿真实验结果表明,相比于其它异常检测算法,该算法的误警率和漏检率较低,在保证异常事件检测可靠性的同时,有效地节省了节点能量。

基于改进的GM(1,1)模型的上海市垃圾产量的预测

GM（1,1）模型是上海市垃圾产量预测的一种有效的方法,但序列的随机波动性难以在GM（1,1）模型得到反映。利用灰色震荡序列GM{1,1}模型,对上海市2000年~2008年垃圾产量进行预测,预测结果表明,此方法能够反映出上海市垃圾产量所具有的波动性特性,得到更高的预测精度。

Improvement and application of GM(1,1) model based on multivariable dynamic optimization

For the classical GM(1,1)model,the prediction accuracy is not high,and the optimization of the initial and background values is one-sided.In this paper,the Lagrange mean value theorem is used to construct the background value as a variable related to k.At the same time,the initial value is set as a variable,and the corresponding optimal parameter and the time response formula are determined according to the minimum value of mean relative error(MRE).Combined with the domestic natural gas annual consumption data,the classical model and the improved GM(1,1)model are applied to the calculation and error comparison respectively.It proves that the improved model is better than any other models.

The GM Models That x(n) Be Taken as Initial Value

As a kind of mathematical model, grey systems predi ct ion model has been widely applied to economy, management and engineering technol ogy. In 1982, Professor Deng Ju-long presented GM prediction model. Then some o ther scholars made improvements on GM model. Of course, much still should be don e to develop it. What the scholars have done is to take the first component of X (1) as the starting conditions of the grey differential model. It occ urs that the new information can not be used enough. This paper is addressed to choose the nth component of X (1) as the starting conditions to improv e the models. The main results of the paper is given in Theorem 2: The time response function of the grey differential equation x (0)(k)+az (1)(k)=b is given by x (1)(k)=x (1)(n)-ba e -a(k-n )+ba. and Theorem4: The time response of the grey Verhulst model is given by (1)(k) =ax (1)(n)bx (1)(n)+(a-bx (1)(n))ae a(k-n). As the new information is fully used, the accuracy of prediction is improved gre atly. Therefore, the new model with a certain theoretical and practical value.

Nonlinear total least-squares variance component estimation for GM(1,1)model

The solution of the grey model(GM(1,1)model)generally involves equal-precision observations,and the(co)variance matrix is established from the prior information.However,the data are generally available with unequal-precision measurements in reality.To deal with the errors of all observations for GM(1,1)model with errors-in-variables(EIV)structure,we exploit the total least-squares(TLS)algorithm to estimate the parameters of GM(1,1)model in this paper.Ignoring that the effect of the improper prior stochastic model and the homologous observations may degrade the accuracy of parameter estimation,we further present a nonlinear total least-squares variance component estimation approach for GM(1,1)model,which resorts to the minimum norm quadratic unbiased estimation(MINQUE).The practical and simulative experiments indicate that the presented approach has significant merits in improving the predictive accuracy in comparison with control methods.

Grey GM(1,1) Model with Function-Transfer Method for Wear Trend Prediction and its Application

Trend forecasting is an important aspect in fault diagnosis and work state supervision. The principle, where Grey theory is applied in fault forecasting, is that the forecast system is considered as a Grey system; the existing known information is used to infer the unknown information's character, state and development trend in a fault pattern, and to make possible forecasting and decisions for future development. It involves the whitenization of a Grey process. But the traditional equal time interval Grey GM (1,1) model requires equal interval data and needs to bring about accumulating addition generation and reversion calculations. Its calculation is very complex. However, the non equal interval Grey GM (1,1) model decreases the condition of the primitive data when establishing a model, but its requirement is still higher and the data were pre processed. The abrasion primitive data of plant could not always satisfy these modeling requirements. Therefore, it establishes a division method suited for general data modeling and estimating parameters of GM (1,1), the standard error coefficient that was applied to judge accuracy height of the model was put forward; further, the function transform to forecast plant abrasion trend and assess GM (1,1) parameter was established. These two models need not pre process the primitive data. It is not only suited for equal interval data modeling, but also for non equal interval data modeling. Its calculation is simple and convenient to use. The oil spectrum analysis acted as an example. The two GM (1,1) models put forward in this paper and the new information model and its comprehensive usage were investigated. The example shows that the two models are simple and practical, and worth expanding and applying in plant fault diagnosis.

Grey System Judgment on Reliability of Mechanical Equipment

he Grey system theory -was applied in reliability analysis of mechanical equip-ment. It is a new theory and method in reliability engineering of mechanical engineering of mechanical equipment. Through the Grey forecast of reliability parameters and the reliability forecast of parts and systems, decisions were made in the real operative state of e-quipment in real time. It replaced the old method that required mathematics and physical statistics in a large base of test data to obtain a pre-check , and it was used in a practical problem. Because of applying the data of practical operation state in real time, it could much more approach the real condition of equipment; it-was applied to guide the procedure and had rather considerable economic and social benefits.

Improvement on GM models

Since grey system theory was established by prof. Deng, GM models and their improvements have all taken the first vector of the original sequence as the initialization, which resulted to deficiency in making use of the latest information. Based on the principle, which new information should be used fully, we think it is scientific to pay more attention to the new information or endow them a more weigh. So, this paper deals with the GM improvement by taking the n-th vector as the initialization, and gets great improvement in forecasting precision. Last, we validate the practicability and reliability of the models with examples.

ARMA-GM combined forewarning model for the quality control

Three forecasting models are set up: the auto\|regressive moving average model, the grey forecasting model for the rate of qualified products P t, and the grey forecasting model for time intervals of the quality catastrophes. Then a combined forewarning system for the quality of products is established, which contains three models, judgment rules and forewarning state illustration. Finally with an example of the practical production, this modeling system is proved fairly effective.

Composites Based on GM(1,1)-Markov in the Salt Spray Corrosion Environment

This study was designed to solve the problem of magnesium hazards due to potash extraction in the salt lake region.Using basalt fiber(BF)as the reinforcement material and magnesium oxychloride cement(MOC)as the gelling material,a BF/MOC composite material was prepared.Firstly,the effect of BF addition content on the basic mechanical properties of the composites was investigated.Then,through the salt spray corrosion test,the durability damage deterioration evaluation analysis was carried out from both macroscopic and microscopic aspects using mass change,relative dynamic modulus of elasticity(RDME)change,SEM analysis and FT-IR analysis.Finally,a GM(1,1)-Markov model was established to predict the durability life of composite materials by using durability evaluation indicators.The results show that:when the BF content is 0.10%(by volumetric content),the composites have the best mechanical properties and resistance to salt spray corrosion.However,when the volume of BF content exceeds 0.10%,a large number of magnesium salt crystallization products are observed from the microscopic point of view,and the corrosion of the main strength phase of MOC is more serious.The prediction results of the GM(1,1)-Markov model are highly identical with the raw data.In addition,using the change of RDME as a predictor,RDME is more sensitive to environmental factor compared to the change of mass.Predictions using the change of RDME as a threshold indicate that MOC-BF0.10 has the longest durability life,which is 836 days.The model is important to promote the application of MOC composites in the salt lake region and to promote the healthy development of green building materials.

山东省中医类医院卫生人力资源需求预测

目的了解山东省中医类医院卫生人力资源的现状,预测卫生人力资源未来的需求量并提出合理建议,以期为相关部门制定中医药人力资源规划提供依据和数据支持。方法运用差分自回归移动平均(auto-regressive moving average,ARIMA)模型、灰色系统预测模型(grey system forecasting model,GM)中的GM(1,1)模型以及两者的线性组合模型预测2021—2025年山东省中医类医院卫生人力资源需求量,比较不同模型预测的精准度。结果组合模型的系统误差小,预测效果最好;卫生技术人员、执业(助理)医师、中医类别执业(助理)医师、注册护士、药师(士)及中药师(士)2025年对应的人力资源预测值分别是107457人、43304人、22807人、51372人、5718人、3242人。结论山东省中医类别执业(助理)医师数量储备充足,但中药师(士)相对短缺,人才结构不合理,医护比有待优化。建议政府适当地增加中药师(士)的编制,促进执业(助理)医师与中药师(士)平衡发展;增加对中医类医院的财政拨款,加强人才引进力度,创新人才培养机制,优化山东省中医药人才结构;制定科学合理的排班制度,提高护士的社会地位,进一步优化医护比。

中国重症床位资源配置现状及预测

目的预测2022—2030年我国重症床位资源配置情况,为合理配置重症床位资源提供参考依据。方法运用灰色系统GM(1,1)模型、趋势外推法对重症床位数进行预测,结合现场访谈分析重症床位配置情况。结果2022—2030年我国重症床位资源呈平稳增长趋势。2030年医疗卫生机构重症床位数将达到132643张;省级、市级、县级三级医院重症床位数占比将达到3.36%、3.07%、3.15%。结论政府须增加省级、市级三级医院重症床位的配置,根据实际情况合理配置县级重症床位数量和规模,重点关注偏远地区以及重症床位资源人均拥有量或拥有重症床位县域占比较低地区的重症资源配置与需求情况。

人工干预压采条件下东风湖泉域地下水环境响应研究

为掌握中国北方典型岩溶泉域在人工干预条件下地下水环境的动态响应规律,以涉县东风湖泉域为研究对象,结合区内地质、水文地质、2011—2021年地下水位动态监测和地下水压采资料,在分析该地区地下水动态变化特征的基础上,运用GMS(groundwater modeling system)软件构建了在人工压采干预作用下的地下水流数值模拟模型,对地下水位环境变化趋势进行了预测。结果表明:研究区地下水动态变化明显,具有周期性、滞后性,具体表现为4个变化阶段;研究区内岩溶地下水整体呈现回升的状态,但是空间分布特征不同,上升区主要分布于下游地区;地下水类型为降水入渗-开采型,低水位期基本在5—6月,高水位期在1—2月;在干预条件下,区内地下水位有着不同程度的回升,南部(下游)浅层水位响应特征值超过15 m,中游地下水位响应特征值达到了5~10 m,北部(上游)地下水位响应特征值为3~5 m;上游和中游地下水位影响整体小于下游,下游水位响应尤为突出,中下游地下水位上升后,包气带厚度变薄,地下水环境逐渐改善。该研究对于泉域内泉的保护以及地下水开发利用和保护具有重要的理论和实际意义。

基于无偏灰色马尔科夫链的稻瘟病预测研究(英文)

To create a new prediction model, the unbiased GM （1,1） model is optimized by the five-point slide method in this paper. Then, based on the occurrence areas of dce blast in Enshi District during 1995 -2004, the new model and unbiased GM （1, 1 ） model are applied to predict the occurrence areas of rice blast during 2005 -2010. Predicting outcomes show that the prediction accuracy of five-point unbiased sliding optimized GM （1, 1 ） model is higher than the unbiased GM （1,1） model. Finally, combined with the prediction results, the author provides some suggestion for Enshi District in the prevention and control of rice blast in 2010.

Predicting changes in Bitcoin price using grey system theory

Bitcoin is currently the leading global provider of cryptocurrency.Cryptocurrency allows users to safely and anonymously use the Internet to perform digital currency transfers and storage.In recent years,the Bitcoin network has attracted investors,businesses,and corporations while facilitating services and product deals.Moreover,Bitcoin has made itself the dominant source of decentralized cryptocurrency.While considerable research has been done concerning Bitcoin network analysis,limited research has been conducted on predicting the Bitcoin price.The purpose of this study is to predict the price of Bitcoin and changes therein using the grey system theory.The first order grey model(GM(1,1))is used for this purpose.It uses a firstorder differential equation to model the trend of time series.The results show that the GM(1,1)model predicts Bitcoin’s price accurately and that one can earn a maximum profit confidence level of approximately 98%by choosing the appropriate time frame and by managing investment assets.

A New Method for Grey Forecasting Model Group

In order to describe the characteristics of some systems, such as the process of economic and product forecasting, a lot of discrete data may be used. Although they are discrete, the inside law can be founded by some methods. For a series that the discrete degree is large and the integrated tendency is ascending, a new method for grey forecasting model group is given by the grey system theory. The method is that it firstly transforms original data, chooses some clique values and divides original data into groups by different clique values; then, it establishes non-equigap GM(1,1) model for different groups and searches forecasting area of original data by the solution of model. At the end of the paper, the result of reliability of forecasting value is obtained. It is shown that the method is feasible.

A NEW DATA ASSOCIATION ALGORITHM USING PROBABILITY HYPOTHESIS DENSITY FILTER

Probability Hypothesis Density (PHD) filtering approach has shown its advantages in tracking time varying number of targets even when there are noise,clutter and misdetection. For linear Gaussian Mixture (GM) system,PHD filter has a closed form recursion (GMPHD). But PHD filter cannot estimate the trajectories of multi-target because it only provides identity-free estimate of target states. Existing data association methods still remain a big challenge mostly because they are com-putationally expensive. In this paper,we proposed a new data association algorithm using GMPHD filter,which significantly alleviated the heavy computing load and performed multi-target trajectory tracking effectively in the meantime.

Modified Grey Model Predictor Design Using Optimal Fractional-order Accumulation Calculus

The major advantage of grey system theory is that both incomplete information and unclear problems can be processed precisely. Considering that the modeling of grey model(GM) depends on the preprocessing of the original data,the fractional-order accumulation calculus could be used to do preprocessing. In this paper, the residual sequence represented by Fourier series is used to ameliorate performance of the fractionalorder accumulation GM(1,1) and improve the accuracy of predictor. The state space model of optimally modified GM(1,1)predictor is given and genetic algorithm(GA) is used to find the smallest relative error during the modeling step. Furthermore,the fractional form of continuous GM(1,1) is given to enlarge the content of prediction model. The simulation results illustrated that the fractional-order calculus could be used to depict the GM precisely with more degrees of freedom. Meanwhile, the ranges of the parameters and model application could be enlarged with better performance. The method of modified GM predictor using optimal fractional-order accumulation calculus is expected to be widely used in data processing, model theory, prediction control and related fields.

Dynamic Evaluation of Land Ecological Security in Anhui Province Based on PSR Model

Based on PSR framework method, the land ecological security evaluation index system of 16 cities of Anhui Province was constructed. The land ecological security value of subsystem in Anhui Province from 2000 to 2011 was calculated using the index weight which was determined by the entropy weight method, and land ecological security trend from 2012 to 2017 was forecasted using GM （1,1） model. The results indicated that, the land ecological security index in Anhui Province from 2000 to 2017 was rising on the whole, with the average value increasing from 0.442 in 2000 to 0.450 in 2017, and there was a huge difference among cities; at the same time, the state index and response index of each subsystem of land ecological security also rose. GM （ 1, 1 ） model had high simulation precision and was able to predict the land ecological security level and the de- velopment trend of each subsystem of Anhui Province from 2012 to 2017. The main factors that influenced the land ecological security of Anhui Prov- ince included per capita farmland area, population density, natural growth rate of population, urbanization level, soil coordination degree, agricultur- al mechanization degree, and the area proportion of nature reserve, which are the focus of land ecological security regulation in the future.

Application of Gray Metabolic Model in the Prediction of the Cotton Output in China

In order to forecast the cotton output of China in the year 2011, Gray Metabolic Forecast Model is established based on both the Gray Forecast Model and the Metabolic Theory. According to the actual situation, forecast results of conventional GM (1, 1) Model and Metabolism GM (1, 1) Model are analyzed, showing that Metabolic Forecast Model has higher precision than the conventional forecast model. Therefore, Metabolism GM (1, 1) Model is used to forecast the cotton output of China in the year 2011, which is 614 968.3 thousand tons.