On the assessment of multivariable controllers using closed loop data. Part I: Identification of system models

Controller performance assessment of SISO and MIMO systems requires effective and systematic identification of the associated system models based on closed-loop data. In this work, a new methodology for the identification of the process, controller and disturbance models is presented for the purpose of enabling the evaluation of the performance of MIMO control systems. The methodology is based on subspace identification algorithms for the identification of the controller, process and disturbance models from closed-loop data. However, identification of the process model is enhanced by the estimation of the associated interactor matrix via the Variable Regression Estimation technique, the existence of which is mathematically proved. The proposed identification methodology is applied to two 2 × 2 systems utilizing both step-response and PRBS closed-loop data.     

基于.NET Remoting的电能质量监控数据的发布

本文提出了基于.NET Remoting技术的电能质量监测数据发布模型。采用该模型有利于提高监测数据的传输效率,使电能质量网络化监测的系统扩展性、兼容性、可移植性更好。最后采用某系统的电能质量数据作为数据源进行测试,证明了该模型的可行性和实用性。     

Tool wear condition monitoring based on a two-layer angle kernel extreme learning machine using sound sensor for milling process

Journal of Intelligent Manufacturing - Tool condition monitoring (TCM) in numerical control machines plays an essential role in ensuring high manufacturing quality. The TCM process is conducted...     

Remote sensing monitoring of the suspended particle size in Hongze Lake based on GF-1 data

In remote sensing analyses of water colour, suspended particle size is an important optical parameter that also plays an important role in inland and coastal biogeochemical processes. Knowledge of the suspended particle size and its changes in month and area can be used to assess the contributions by suspended particulate matter to backscatter coefficient, particle sinking, and carbon sequestration under lake water. In this study, in situ samples collected in the summer and winter from Hongze Lake (HZL), 2016, were used to develop an empirical model to estimate the median diameter (D_V⁵⁰) of suspended particle sizes. The spatial distributions of D_V⁵⁰ were derived using 37 WFV (Wide Field Viewer) images of GF-1 (GaoFen-1), China, and the fluctuational diversification and the potential influencing factors were discussed. Several crucial findings can be drawn: (1) the empirical band ratio algorithm R_rs,green: R_rs,red was suitable for D_V⁵⁰ estimation with a coefficient of determination (R²) of approximately 0.7 for the modelling data. In addition, the validation data showed that the MAPE (mean absolute percentage error) is below 34%, the RMSE (root mean square error) is less than 4.2 μm, and the Mean ratio is close to 1; (2) the average median particle size shows an increasing trend from the northeast of the lake (NE) to Chengzi Lake (CZL) and the wetland (WL) in HZL from 2015 to 2016; (3) the D_V⁵⁰ of HZL is higher in summer than in the other seasons during the study period; (4) the fluctuation in hydrological factors, especially the monthly water discharge and flow, might be the driving force behind the seasonal variations in D_V⁵⁰ of HZL; and (5) channel transportation reduced D_V⁵⁰, and the reduced amplitude might be more than 22%.     

Missing data analyses: a hybrid multiple imputation algorithm using Gray System Theory and entropy based on clustering

Researchers and practitioners who use databases usually feel that it is cumbersome in knowledge discovery or application development due to the issue of missing data. Though some approaches can work with a certain rate of incomplete data, a large portion of them demands high data quality with completeness. Therefore, a great number of strategies have been designed to process missingness particularly in the way of imputation. Single imputation methods initially succeeded in predicting the missing values for specific types of distributions. Yet, the multiple imputation algorithms have maintained prevalent because of the further promotion of validity by minimizing the bias iteratively and less requirement on prior knowledge to the distributions. This article carefully reviews the state of the art and proposes a hybrid missing data completion method named Multiple Imputation using Gray-system-theory and Entropy based on Clustering (MIGEC). Firstly, the non-missing data instances are separated into several clusters. Then, the imputed value is obtained after multiple calculations by utilizing the information entropy of the proximal category for each incomplete instance in terms of the similarity metric based on Gray System Theory (GST). Experimental results on University of California Irvine (UCI) datasets illustrate the superiority of MIGEC to other current achievements on accuracy for either numeric or categorical attributes under different missing mechanisms. Further discussion on real aerospace datasets states MIGEC is also applicable for the specific area with both more precise inference and faster convergence than other multiple imputation methods in general.     

Identification of dynamic errors-in-variables models: Approaches based on two-dimensional ARMA modeling of the data

In this paper we propose a parametric and a non-parametric identification algorithm for dynamic errors-in-variables model. We show that the two-dimensional process composed of the input-output data admits a finite order ARMA representation. The non-parametric method uses the ARMA structure to compute a consistent estimate of the joint spectrum of the input and the output. A Frisch scheme is then employed to extract an estimate of the joint spectrum of the noise free input-output data, which in turn is used to estimate the transfer function of the system. The parametric method exploits the ARMA structure to give estimates of the system parameters. The performances of the algorithms are illustrated using the results obtained from a numerical simulation study.     

HJ-1B卫星遥感数据在重庆市城市热环境监测的应用分析

本文以MODIS反演大气透射率,以HJ-1B/CCD分类结果反演地表比辐射率,并基于单窗算法,利用HJ-1B/IRS4数据反演地表温度.在此基础上,提取研究区的热场变异指数来分析重庆热岛空间分布特征,并就NDVI与NDBI对热岛效应的影响进行了分析.其结果如下:1)重庆城市热岛大致位于中梁山、铜锣山之间,呈东北、西南走向分布;2)热岛中心不在市中心,而是集中在大渡口工业园区、江北机场这些能耗大、人口密集区域,热岛强度范围在5?C-10?C之间;3)接近长江、嘉陵江水域的建筑用地密集区域,其热岛效应并不明显;4)NDVI与热岛强度呈负相关关系,NDBI与热岛强度呈现较为明显的正相关关系,二者对热岛都有重要影响,而NDBI的影响更大.因此,利用HJ-1B数据监测城市热环境,能较好地揭示重庆城市热岛空间分布特征,为城市环境监测与改善提供参考.     

数据均匀分布和正态分布的数据总线低功耗研究

低功耗设计已成为数字系统设计中必须考虑的问题,而总线低功耗设计是其中的重要分支。由于CMOS电路功耗的特性,降低总线上相邻两个传输数据状态的电压变化能有效降低总线功耗,降低总线翻转技术已成为降低总线功耗的重要研究领域之一,而Bus-Invert编码、ShiftInv编码是这一研究领域中的重要研究成果。以往的研究主要集中在遵从随机均匀分布的数据总线上,对该状态下的总线编码进行研究。由于总线上数据的相关性,现实中的总线数据往往服从正态分布的规律。通过对两种总线翻转编码技术的研究,得出对于服从均匀分布和正态分布的数据总线,ShiftInv编码技术降低功耗的能力均优于Bus-Invert编码技术。     

Detecting end-effectors on 2.5D data using geometric deformable models: Application to human pose estimation

End-effectors are usually related to the location of limbs, and their reliable detection enables robust body tracking as well as accurate pose estimation. Recent innovation in depth cameras has re-stated the pose estimation problem. We focus on the information provided by these sensors, for which we borrow the name 2.5D data from the Graphics community. In this paper we propose a human pose estimation algorithm based on topological propagation. Geometric Deformable Models are used to carry out such propagation, implemented according to the Narrow Band Level Set approach. A variant of the latter method is proposed, including a density restriction which helps preserving the topological properties of the object under analysis. Principal end-effectors are extracted from a directed graph weighted with geodesic distances, also providing a skeletal-like structure describing human pose. An evaluation against reference methods is performed with promising results. The proposed solution allows a frame-wise end-effector detection, with no temporal tracking involved, which may be generalized to the tracking of other objects beyond human body.     

Generating vegetation leaf area index earth system data record from multiple sensors. Part 1: Theory

The generation of multi-decade long Earth System Data Records (ESDRs) of Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) from remote sensing measurements of multiple sensors is key to monitoring long-term changes in vegetation due to natural and anthropogenic influences. Challenges in developing such ESDRs include problems in remote sensing science (modeling of variability in global vegetation, scaling, atmospheric correction) and sensor hardware (differences in spatial resolution, spectral bands, calibration, and information content). In this paper, we develop a physically based approach for deriving LAI and FPAR products from the Advanced Very High Resolution Radiometer (AVHRR) data that are of comparable quality to the Moderate resolution Imaging Spectroradiometer (MODIS) LAI and FPAR products, thus realizing the objective of producing a long (multi-decadal) time series of these products. The approach is based on the radiative transfer theory of canopy spectral invariants which facilitates parameterization of the canopy spectral bidirectional reflectance factor (BRF). The methodology permits decoupling of the structural and radiometric components and obeys the energy conservation law. The approach is applicable to any optical sensor, however, it requires selection of sensor-specific values of configurable parameters, namely, the single scattering albedo and data uncertainty. According to the theory of spectral invariants, the single scattering albedo is a function of the spatial scale, and thus, accounts for the variation in BRF with sensor spatial resolution. Likewise, the single scattering albedo accounts for the variation in spectral BRF with sensor bandwidths. The second adjustable parameter is data uncertainty, which accounts for varying information content of the remote sensing measurements, i.e., Normalized Difference Vegetation Index (NDVI, low information content), vs. spectral BRF (higher information content). Implementation of this approach indicates good consistency in LAI values retrieved from NDVI (AVHRR-mode) and spectral BRF (MODIS-mode). Specific details of the implementation and evaluation of the derived products are detailed in the second part of this two-paper series.     

RETRACTED ARTICLE: An effective stadium monitoring control algorithm based on big data in emerging mobile networks

Multimedia Tools and Applications - In the process of monitoring the gymnasium by the traditional radio frequency technology, the parallel computing problem of the large data environment in the...     

From data to diagnosis and control using generalized orthonormal basis filters. Part I: Development of state observers

This work is aimed at the development of a state observer (steady state Kalman filter) for a multivariable system with unknown time delays, which is subjected to unmeasured disturbances. To design such a filter, we explore the feasibility of capturing system dynamics using generalized orthonormal basis filters (GOBF). A two step identification procedure is proposed by exploiting the fact that the GOBF based models have output error structure. The deterministic component of the model is identified in the first step and used to compute a residual signal. In the second step, a filter that whitens the residuals is estimated using GOBF and combined with the deterministic component. A minimal order state realization of the innovation form of the state model is then generated from this high order model using realization based sub-space based state space (4SID) identification algorithm. When time delays are not known a-priori, the similarity between GOBF and Pade approximation is used to estimates time delay matrix directly from multivariate data. The efficacy of the proposed modeling technique is demonstrated by carrying out simulation studies on the benchmark Shell control problem and experimental evaluation on a stirred tank heater (STH) system. From the analysis of simulation and experimental results, it can be inferred that the proposed approach produces fairly accurate estimates of the time delay matrix and the deterministic and stochastic components of the dynamic model.     

A family of goal directed theorem provers based on conjunction and implication: Part 1

This paper presents a family of theorem provers for classical, intuitionistic and neighbouring logics. The theorem provers are based on a formulation of these logics in terms of conjunction, implication and falsity. Computation is goal directed, and, for the special case of Horn clauses, coincides with pure Prolog computation. Our approach is generic, and, as we shall show in a subsequent paper, can be adapted to other logics, such as modal and temporal systems.The research described in this paper has been funded partially by Esprit project No. 393, ACORD, of the European Commission.     

Growth profile based crop yield models: A case study of large area wheat yield modelling and its extendibility using atmospheric corrected NOAA AVHRR data

Wheat growth profile based yield models for 12 districts of Punjab State and 16 districts of Haryana State have been developed using the normalised difference vegetation index (NDVI) derived from NOAA-11 AVHRR data of the 1993-94 cropping season. Atmospheric normalisation of AVHRR data was performed prior to deriving district-level area weighted average NDVI (AWANDVI). The invariant growth profile model suggested by Badhwar was fitted and spectral emergence date, maximum vegetative vigour, peak day value of profile, growth rate and senescence rate, area under the curve, etc. were derived. These parameters were related to the reported district-level wheat yields using multiple regression analysis. A field study was also conducted using a handheld spectro-radiometer at the research station of Punjab Agricultural University (PAU), Ludhiana. From this field experimental data, wheat growth profile parameters were derived which were compared with satellite based parameters. Inversion of the models was carried out to evaluate the results by comparing the reported and predicted wheat yields. The results indicate highly significant fitting of the NDVI profile to the Badhwar model as indicated by multiple linear correlation coefficients and Fisher test. A significant relationship between district-level wheat yields and fractional area under the curve was also observed. The overall correlation of 0.82 for Punjab and Haryana states was obtained between reported yield and growth profile derived parameters. Atmospheric normalisation resulted in improvement of prediction model statistics ( R increased from 0.42 to 0.86). Evaluation of the models indicated that 10 out of 16 districts of Haryana State and 9 out of 12 districts of Punjab State showed relative deviations within 10% between reported and model predicted wheat yields.     

Adaptive sensor arrays for acoustic monitoring of bird behavior and diversity: preliminary results on source identification using support vector machines

This article summarizes the work in our laboratories toward developing adaptive sensor arrays for monitoring bird vocalizations. We have focused on four species of antbird in a tropical rainforest in Mexico. Preliminary results of individual identifications using support vector machines are presented. We also describe our initial attempts at higher-order processing of information about the identification and localization of each source.