A modified stochastic model for LS+AR hybrid method and its application in polar motion short-term prediction

Short-term(up to 30 days)predictions of Earth Rotation Parameters(ERPs)such as Polar Motion(PM:PMX and PMY)play an essential role in real-time applications related to high-precision reference frame conversion.Currently,least squares(LS)+auto-regressive(AR)hybrid method is one of the main techniques of PM prediction.Besides,the weighted LS+AR hybrid method performs well for PM short-term prediction.However,the corresponding covariance information of LS fitting residuals deserves further exploration in the AR model.In this study,we have derived a modified stochastic model for the LS+AR hybrid method,namely the weighted LS+weighted AR hybrid method.By using the PM data products of IERS EOP 14 C04,the numerical results indicate that for PM short-term forecasting,the proposed weighted LS+weighted AR hybrid method shows an advantage over both the LS+AR hybrid method and the weighted LS+AR hybrid method.Compared to the mean absolute errors(MAEs)of PMX/PMY sho rt-term prediction of the LS+AR hybrid method and the weighted LS+AR hybrid method,the weighted LS+weighted AR hybrid method shows average improvements of 6.61%/12.08%and 0.24%/11.65%,respectively.Besides,for the slopes of the linear regression lines fitted to the errors of each method,the growth of the prediction error of the proposed method is slower than that of the other two methods.

基于MS(2)-AR-TVTP模型的I_(BD)波动周期非对称性和持续性分析

国际干散货运输市场源于国际贸易的衍生需求,受世界经济的影响,是一个典型的周期性市场。选取1999年11月~2021年12月的波罗的海干散货运价指数(I_(BD))月度数据,在检验序列平稳性的基础上,确定最优滞后长度,构建两区制的时变转换概率马尔科夫转换自回归模型,分析I_(BD)波动周期的持续时间、转换拐点和非对称性等主要特征。研究结果表明:模型能有效拟合I_(BD)波动周期的主要特征,周期平均持续时间为33.7个月,自2008年9月之后呈缩短态势,上升期和下降期交互更频繁;I_(BD)波动周期具有非对称性,周期内上升期持续时间比下降期长,I_(BD)维持上升期更具有稳定性。周期性特征结果可为干散货航运业造船投资和市场经营提供决策依据。

Unified Degradation Model in Low Gate Voltage Range During Hot-Carrier Stressing of p-MOS Transistors

Hot carrier effects of p MOSFETs with different oxide thicknesses are studied in low gate voltage range.All electrical parameters follow a power law relationship with stress time,but degradation slope is dependent on gate voltage.For the devices with thicker oxides,saturated drain current degradation has a close relationship with the product of gate current and electron fluence.For small dimensional devices,saturated drain current degradation has a close relationship with the electron fluence.This degradation model is valid for p MOSFETs with 0 25μm channel length and different gate oxide thicknesses.

Perceptual video coding method based on JND and AR model

In order to achieve better perceptual coding quality while using fewer bits, a novel perceptual video coding method based on the just-noticeable-distortion （JND） model and the auto-regressive （AR） model is explored. First, a new texture segmentation method exploiting the JND profile is devised to detect and classify texture regions in video scenes. In this step, a spatial-temporal JND model is proposed and the JND energy of every micro-block unit is computed and compared with the threshold. Secondly, in order to effectively remove temporal redundancies while preserving high visual quality, an AR model is applied to synthesize the texture regions. All the parameters of the AR model are obtained by the least-squares method and each pixel in the texture region is generated as a linear combination of pixels taken from the closest forward and backward reference frames. Finally, the proposed method is compared with the H.264/AVC video coding system to demonstrate the performance. Various sequences with different types of texture regions are used in the experiment and the results show that the proposed method can reduce the bit-rate by 15% to 58% while maintaining good perceptual quality.

基于时间序列AR(P)模型的边坡变形预测与应用

获取边坡的监测数据进行分析,并预测其接下来的变化趋势,具有重要的意义。本文以贵州省福泉市高坪矿区英坪矿段内边坡工程项目为研究对象,对监测数据采用时间序列AR(P)模型方法进行了分析与预测。研究结果表明,模型拟合的结果和预测精度较好地反映了监测点的变化趋势,可为矿区边坡模型建立和监测数据的预测提供一定的参考。

Empirical Likelihood Inference for AR（p） Model

In this article we study the empirical likelihood inference for AR（p） model. We propose the moment restrictions, by which we get the empirical likelihood estimator of the model parametric, and we also propose an empirical log-likelihood ratio base on this estimator. Our result shows that the EL estimator is asymptotically normal, and the empirical log-likelihood ratio is proved to be asymptotically standard chi-squared.

oncausal spatial prediction filtering based on an ARMA model

Conventional f-x prediction filtering methods are based on an autoregressive model. The error section is first computed as a source noise but is removed as additive noise to obtain the signal, which results in an assumption inconsistency before and after filtering. In this paper, an autoregressive, moving-average model is employed to avoid the model inconsistency. Based on the ARMA model, a noncasual prediction filter is computed and a self-deconvolved projection filter is used for estimating additive noise in order to suppress random noise. The 1-D ARMA model is also extended to the 2-D spatial domain, which is the basis for noncasual spatial prediction filtering for random noise attenuation on 3-D seismic data. Synthetic and field data processing indicate this method can suppress random noise more effectively and preserve the signal simultaneously and does much better than other conventional prediction filtering methods.

A Preliminary Study on Dynamics and Models of N,P,K Absorption for High-Yield Cotton

The field experiments were carried out to investigate the dynamics and models of N, P and K absorption for the cotton plants with a lint of 3 000 kg ha-1 in Xinjiang. The main results were as follows: The contents of N, P2O5, K2O in cotton leaves, stems, squares and bolls decreased obviously with the time over the whole growth duration and the falling extent was greater in high-yield cotton than in CK. Contents of N in leaves, squares and bolls, in particular in the leaves of fruit-bearing shoot was higher in high-yield cotton than in CK. Contents of P2O5 in squares and bolls and that of K2O in stems were higher in high-yield cotton than in CK during the whole growing period. The accumulations of N, P2O5 and K2O in the cotton plants could be described with a logistic curve equation. There was the fastest nutrient uptake at about 90 d for N, 92 d for P2O5 and 85 d for K2O after emergence, respectively. Total nutrient accumulation of N, P2O5 and K2O was 385.8, 244. 7 and 340.3 kg ha-1, respectively. Approximately 12. 5 kg N, 8. 0 kg P2O5 and 11.1 kg K2O were needed for producing 100 kg lint with the leaves and stems under the super high yield condition of 3 000 kg ha-1 in Xinjiang.

AUTOREGRESSIVE MODEL AND POWER SPECTRUM CHARATERISTICS OF CURRENT SIGNAL IN HIGH FREQUENCY GROUP PULSE MICRO-ELECTROCHEMICAL MACHINING

The identification of the inter-electrode gap size in the high frequency group pulse micro-electrochemical machining （HGPECM） is mainly discussed. The auto-regressive（AR） model of group pulse current flowing across the cathode and the anode are created under different situations with different processing parameters and inter-electrode gap size. The AR model based on the current signals indicates that the order of the AR model is obviously different relating to the different processing conditions and the inter-electrode gap size; Moreover, it is different about the stability of the dynamic system, i.e. the white noise response of the Green＇s function of the dynamic system is diverse. In addition, power spectrum method is used in the analysis of the dynamic time series about the current signals with different inter-electrode gap size, the results show that there exists a strongest power spectrum peak, characteristic power spectrum（CPS）, to the current signals related to the different inter-electrode gap size in the range of 0～5 kHz. Therefore, the CPS of current signals can implement the identification of the inter-electrode gap.

基于AR-LSTM-BP的CPI组合预测模型

针对居民消费价格指数(CPI)预测准确性的问题,提出一种AR-LSTM-BP组合预测模型.首先分别用回归(AR)、长短时记忆网络(LSTM)和BP神经网络这三种模型对CPI预测,并对预测结果进行比较分析;随后引入诱导有序加权调和平均算子(IOWHA)的概念,构建AR-LSTM-BP组合预测模型.结果表明,IOWHA组合预测模型的误差均小于单项预测模型,预测结果准确性较高,能够更好地反映CPI的波动走势.

A COMPARISON OF FORECASTING MODELS OF THE VOLATILITY IN SHENZHEN STOCK MARKET

Based on the weekly closing price of Shenzhen Integrated Index, this article studies the volatility of Shenzhen Stock Market using three different models： Logistic, AR（1） and AR（2）. The time-variable parameters of Logistic regression model is estimated by using both the index smoothing method and the time-variable parameter estimation method. And both the AR（1） model and the AR（2） model of zero-mean series of the weekly dosing price and its zero-mean series of volatility rate are established based on the analysis results of zero-mean series of the weekly closing price, Six common statistical methods for error prediction are used to test the predicting results. These methods are： mean error （ME）, mean absolute error （MAE）, root mean squared error （RMSE）, mean absolute percentage error （MAPE）, Akaike＇s information criterion （AIC）, and Bayesian information criterion （BIC）. The investigation shows that AR（1） model exhibits the best predicting result, whereas AR（2） model exhibits predicting results that is intermediate between AR（1） model and the Logistic regression model.

耦合优化蚁群算法与P-Median model的选址模型设计

为节省城建部门对于公共体育设施的投入成本以及提高城市人民生活质量,以运动场所优化选址为例,提出一种新型设施选址模型。该模型主要基于P-Median model(最小化阻抗模型)根据需求点数量从全部候选设施选址中选择设施空间位置,让用户达到离自己最近设施距离成本总和最小的目的,对选址的基本原则和实际情况提出要求,构造目标函数用于优化后蚁群算法求解进行选址工作。优化蚁群算法实现基于Python语言模块,通过改进蚁群原始信息素,提升原有算法的收敛速度,求出目标函数最优解,可以很好地模拟对于运动场所的选址。用二者耦合进行优势互补所设计的选址模型来搜寻研究区蚁群信息素浓度残留最大的栅格像元,从而确定未被已有设施点服务半径覆盖的最佳设施点建立位置。实验结果表明,该新型选址模型相较于最小化阻抗模型与最大化覆盖模型,新增优化设施点使整体服务半径覆盖率分别高出10.42%和6.95%,适合求解较为精确且小规模空间下的选址问题。

Identification of Denatured Biological Tissues Based on Improved Variational Mode Decomposition and Autoregressive Model during HIFU Treatment

During high-intensity focused ultrasound(HIFU)treatment,the accurate identification of denatured biological tissue is an important practical problem.In this paper,a novel method based on the improved variational mode decomposition(IVMD)and autoregressive(AR)model was proposed,which identified denatured biological tissue according to the characteristics of ultrasonic scattered echo signals during HIFU treatment.Firstly,the IVMD method was proposed to solve the problem that the VMD reconstruction signal still has noise due to the limited number of intrinsic mode functions(IMF).The ultrasonic scattered echo signals were reconstructed by the IVMD to achieve denoising.Then,the AR model was introduced to improve the recognition rate of denatured biological tissues.The AR model order parameter was determined by the Akaike information criterion(AIC)and the characteristics of the AR coefficients were extracted.Finally,the optimal characteristics of the AR coefficients were selected according to the results of receiver operating characteristic(ROC).The experiments showed that the signal-to-noise ratio(SNR)and root mean square error(RMSE)of the reconstructed signal obtained by IVMD was better than those obtained by variational mode decomposition(VMD).The IVMD-AR method was applied to the actual ultrasonic scattered echo signals during HIFU treatment,and the support vectormachine(SVM)was used to identify the denatured biological tissue.The results show that compared with sample entropy,information entropy,and energy methods,the proposed IVMD-AR method can more effectively identify denatured biological tissue.The recognition rate of denatured biological tissue was higher,up to 93.0%.

PARTICLE FILTERING BASED AUTOREGRESSIVE CHANNEL PREDICTION MODEL

A particle filtering based AutoRegressive (AR) channel prediction model is presented for cognitive radio systems. Firstly, this paper introduces the particle filtering and the system model. Secondly, the AR model of order p is used to approximate the flat Rayleigh fading channels; its stability is discussed, and an algorithm for solving the AR model parameters is also given. Finally, an AR channel prediction model based on particle filtering and second-order AR model is presented. Simulation results show that the performance of the proposed AR channel prediction model based on particle filtering is better than that of Kalman filtering.

BIM Improved with RV and AR Technologies

The study addresses the integration of the Building Information Modelling (BIM) methodology with Virtual Reality (VR) and Augmented Reality (AR) technologies in the context of the development of a multidisciplinary project, involving architecture, structures, water network and electrical system components. In order to cover in detail the various design features, the case study was limited to a specific area of a house, the sanitary rooms, as it presents sufficient complexity in modeling and the application of VR and AR software. The VR/AR functionalities applied over the BIM model increase the potential of BIM in the construction sector, contributing to the achievement of a high level of collaboration and control of the project based on an immersive and interactive environment. The elaboration of the different phases of a BIM design requires the transfer of models between BIM and VR/AR systems, allowing us to analyze the main advantages that BIM/VR/AR integration can introduce in the construction industry. The study contributes positively to achieving new knowledge in BIM, being disseminated in an academic research work and illustrated in a practical context.

Differential Evolution of High-Pressure and Ultrahigh-Pressure Metapelites from Habutengsu, Chinese Western Tianshan: Phase Equilibria Modelling and ^(40)Ar/^(39)Ar Geochronology

Metapelite is one of the predominant rock types in the high-pressure–ultrahigh-pressure（HP–UHP） metamorphic belt of western Tianshan, NW China; however, the spatial and temporal variations of this belt during metamorphism are poorly understood. In this study, we present comparative petrological studies and 40^Ar/39 ^Ar geochronology of HP and UHP pelitic schist exposed along the Habutengsu valley. The schist mainly comprises quartz, white mica, garnet, albite and bluish amphibole. In the Mn O–Na2O–Ca O–K2O–Fe O–Mg O–Al2O3–Si O2–H2O（Mn NCKFMASH） system, P–T pseudosections were constructed using THERMOCALC 333 for two representative pelitic schists. The results demonstrate that there was a break in the peak metamorphic pressures in the Habutengsu area. The northern schist has experienced UHP metamorphism, consistent with the presence of coesite in the same section, while the southern one formed at lower pressures that stabilized the quartz. This result supports the previous finding of a metamorphic gradient through the HP–UHP metamorphic belt of the Chinese western Tianshan by the authors. Additionally, phengite in the northern schist was modelled as having a Si content of 3.55–3.70（a.p.f.u.） at the peak stage, a value much higher than that of oriented matrix phengite（Si content 3.32–3.38 a.p.f.u.）. This indicates that the phengite flakes in the UHP schist were subjected to recrystallization during exhumation, which is consistent with the presence of phengite aggregates surrounding garnet porphyroblast. The 40^Ar/39^ Ar age spectra of white mica（dominantly phengite） from the two schists exhibit similar plateau ages of ca. 315 Ma, which is interpreted as the timing of a tectonometamorphic event that occurred during the exhumation of the HP–UHP metamorphic belt of the Chinese western Tianshan.

Prospectivity modeling of porphyry copper deposits: recognition of efficient mono-and multi-element geochemical signatures in the Varzaghan district, NW Iran

The Varzaghan district at the northwestern margin of the Urumieh–Dokhtar magmatic arc, is considered a promising area for the exploration of porphyry Cu deposits in Iran. In this study we identified mono-and multi-element geochemical anomalies associated with Cu–Au–Mo–Bi mineralization in the central parts of the Varzaghan district by applying the concentration–area fractal method. After mono-element geochemical investigations, principal component analysis was applied to ten selected elements in order to acquire a multi-element geochemical signature based on the mineralization-related component. Quantitative comparisons of the obtained fractal-based populations were carried out in accordance with known Cu occurrences using Student's t-values. Then,significant mono-and multi-element geochemical layers were separately combined with related geologic and structural layers to generate prospectivity models, using the fuzzy GAMMA approach. For quantitative evaluation of the effectiveness of different geochemical signatures in final prospectivity models, a prediction-area plot was adapted. The results show that the multi-element geochemical signature of principal component one(PC1) is more effective than mono-element layers in delimiting exploration targets related to porphyry Cu deposits.

Mathematical Model Validation of Search Protocols in MP2P Networks

Broadcasting is a basic technique in Mobile ad-hoc network(MANET),and it refers to sending a packet from one node to every other node within the transmission range.Flooding is a type of broadcast where the received packet is retransmitted once by every node.The naive flooding technique,floods the network with query messages,while the random walk technique operates by contacting the subsets of every node’s neighbors at each step,thereby restricting the search space.One of the key challenges in an ad-hoc network is the resource or content discovery problem which is about locating the queried resource.Many earlier works have mainly focused on the simulation-based analysis of flooding,and its variants under a wired network.Although,there have been some empirical studies in peer-to-peer(P2P)networks,the analytical results are still lacking,especially in the context of P2P systems running over MANET.In this paper,we describe how P2P resource discovery protocols perform badly over MANETs.To address the limitations,we propose a new protocol named ABRW(Address Broadcast Random Walk),which is a lightweight search approach,designed considering the underlay topology aimed to better suit the unstructured architecture.We provide the mathematical model,measuring the performance of our proposed search scheme with different widely popular benchmarked search techniques.Further,we also derive three relevant search performance metrics,i.e.,mean no.of steps needed to find a resource,the probability of finding a resource,and the mean no.of message overhead.We validated the analytical expressions through simulations.The simulation results closely matched with our analyticalmodel,justifying our findings.Our proposed search algorithm under such highly dynamic self-evolving networks performed better,as it reduced the search latency,decreased the overall message overhead,and still equally had a good success rate.

Cluster based hierarchical resource searching model in P2P network

For the problem of large network load generated by the Gnutella resource-searching model in Peer to Peer （P2P） network, a improved model to decrease the network expense is proposed, which establishes a duster in P2P network, auto-organizes logical layers, and applies a hybrid mechanism of directional searching and flooding. The performance analysis and simulation results show that the proposed hierarchical searching model has availably reduced the generated message load and that its searching-response time performance is as fairly good as that of the Gnutella model.

A Simple Predictive Performance Model of Solar Cell under Very Hot and Humide Climate

In the scope of solar energy-based electrical needs in rural tropical regions, the present article develops and confronts experimental power models from the using of manufacturer data and a new model made with the meteorological and electrical data acquired. These data are registered through an acquisition station around a monocrystalline photovoltaic panel, designed and realized in the scope of this work. After the acquisition of meteorological data, a choice of the most relevant meteorological variable as input vectors to express the output powers obtained was carried out. Around the Single-Diode model, seven models are performed with analytics equations, iterative methods and an optimization method with a multi-objective function to get internal parameters. The proposed experimental model is made by a combination of the solution got at STC of an iterative method, with the value of nameplate and the use of an open circuit voltage equation with experimental coefficient to predict power output in operating conditions, and it’s demonstrated more efficient. The optimization of a multi-objective function using Nonlinear Squares (NLS) through the Leveng-Marqued method to solve the parameter estimation of a PV panel has been well done and the results are useful, like classic iterative method and less time-consuming.