Prediction of efficient outputs based on GM(1,N) model and weak DEA efficiency

This paper expresses the efficient outputs of decisionmaking unit（DMU） as the sum of ＂average outputs＂ forecasted by a GM（1,N） model and ＂increased outputs＂ which reflect the difficulty to realize efficient outputs.The increased outputs are solved by linear programming using data envelopment analysis efficiency theories,wherein a new sample is introduced whose inputs are equal to the budget in the issue No.n ＋ 1 and outputs are forecasted by the GM（1,N） model.The shortcoming in the existing methods that the forecasted efficient outputs may be less than the possible actual outputs according to developing trends of input-output rate in the periods of pre-n is overcome.The new prediction method provides decision-makers with more decisionmaking information,and the initial conditions are easy to be given.

Prediction Model of Data Envelopment Analysis with Undesirable Outputs

Data envelopment analysis (DEA) has become a standard non parametric approach to productivity analysis, especially to relative efficiency analysis of decision making units (DMUs). Extended to the prediction field, it can solve the prediction problem with multiple inputs and outputs which can not be solved easily by the regression analysis method.But the traditional DEA models can not solve the problem with undesirable outputs,so in this paper the inherent relationship between goal programming and the DEA method based on the relationship between multiple goal programming and goal programming is explored,and a mixed DEA model which can make all factors of inputs and undesirable outputs decrease in different proportions is built.And at the same time,all the factors of desirable outputs increase in different proportions.

Toward a More Accurate Web Service Selection Using Modified Interval DEA Models with Undesirable Outputs

With the growing number of Web services on the internet,there is a challenge to select the best Web service which can offer more quality-of-service(QoS)values at the lowest price.Another challenge is the uncertainty of QoS values over time due to the unpredictable nature of the internet.In this paper,we modify the interval data envelopment analysis(DEA)models[Wang,Greatbanks and Yang(2005)]for QoS-aware Web service selection considering the uncertainty of QoS attributes in the presence of desirable and undesirable factors.We conduct a set of experiments using a synthesized dataset to show the capabilities of the proposed models.The experimental results show that the correlation between the proposed models and the interval DEA models is significant.Also,the proposed models provide almost robust results and represent more stable behavior than the interval DEA models against QoS variations.Finally,we demonstrate the usefulness of the proposed models for QoS-aware Web service composition.Experimental results indicate that the proposed models significantly improve the fitness of the resultant compositions when they filter out unsatisfactory candidate services for each abstract service in the preprocessing phase.These models help users to select the best possible cloud service considering the dynamic internet environment and they help service providers to improve their Web services in the market.

Efficiency Valuation of DMUs with Undesirable Outputs

The researching on the DMUs with undesirable outputs gives the non-radial model FDEA of the relative efficiency evaluation,proves the efficiency theories, and calculates the example.The application of FDEA model shows its importance in the production to save the resources,improve the desirable outputs, and decrease the undesirable outputs,especially reduce the environmental pollution.

Efficient Graph-based Genetic Programming Representation with Multiple Outputs

In this work, we explore and study the implication of having more than one output on a genetic programming （GP） graph-representation. This approach, called multiple interactive outputs in a single tree （MIOST）, is based on two ideas. First, we defined an approach, called interactivity within an individual （IWI）, which is based on a graph-GP representation. Second, we add to the individuals created with the IWI approach multiple outputs in their structures and as a result of this, we have MIOST. As a first step, we analyze the effects of IWI by using only mutations and analyze its implications （i.e., presence of neutrality）. Then, we continue testing the effectiveness of IWI by allowing mutations and the standard GP crossover in the evolutionary process. Finally, we tested the effectiveness of MIOST by using mutations and crossover and conducted extensive empirical results on different evolvable problems of different complexity taken from the literature. The results reported in this paper indicate that the proposed approach has a better overall performance in terms of consistency reaching feasible solutions.

A Model for Retrieval of Dual Linear Polarization Radar Fields from Model Simulation Outputs

An algorithm for retrieving polarimetric variables from numerical model fields is developed. By using this technique, radar reflectivity at horizontal polarization~ differential reflectivity, specific differential phase shift and correlation coefficients between the horizontal and vertical polarization signals at zero lag can be derived from rain, snow and hail contents of numerical model outputs. Effects of environmental temperature and the melting process on polarimetric variables are considered in the algorithm. The algorithm is applied to the Advanced Regional Prediction System （ARPS） model simulation results for a hail storm. The spatial distributions of the derived parameters are reasonable when compared with observational knowledge. This work provides a forward model for assimilation of dual linear polarization radar data into a mesoscale model.

Ocean Data Assimilation with Background Error Covariance Derived from OGCM Outputs

The background error covariance plays an important role in modern data assimilation and analysis systems by determining the spatial spreading of information in the data. A novel method based on model output is proposed to estimate background error covariance for use in Optimum Interpolation. At every model level, anisotropic correlation scales are obtained that give a more detailed description of the spatial correlation structure. Furthermore, the impact of the background field itself is included in the background error covariance. The methodology of the estimation is presented and the structure of the covariance is examined. The results of 20-year assimilation experiments are compared with observations from TOGA-TAO (The Tropical Ocean-Global Atmosphere-Tropical Atmosphere Ocean) array and other analysis data.

Performability of Research Infrastructure Comsidering Costs and Values for Different Outputs

The research infrastructure is very crucial for the development of some fundamental research. However,it is costly to buy,install and operate the infrastructure. This paper analyzes the costs and benefits of research infrastructure using a concept termed as investment reliability. Both the costs and the outputs are represented by a cash flow diagram,and the investment reliability is modeled. Different types of outputs are taken into concern in the model formulation. Illustrative examples are proposed to show how to apply the model.

Variations in Tomotherapy Beam Outputs: A Multiple-Institutional Investigation

This study aimed to determine variations in tomotherapy beam outputs at multiple institutions. Measurements were obtained at 22 radiotherapy institutions. The first parameter was the absolute dose to water (Dfmsrw, Qmsr) in the machine-specific reference field (fmsr), which indicated a static field in the tomotherapy reference conditions defined by the International Atomic Energy Agency (IAEA) study group. The second measured parameter was the difference between the measured and the planed doses in the intensity modulated radiotherapy (IMRT) verification plans, which were created using a solid phantom by the vendor during tomotherapy apparatus installation to adjust the beam output. The IMRT verification plan error at each institution was defined as the systematic error of the beam output;Dfmsrw, Qmsr was subsequently modified. The Dfmsrw, Qmsr values of four institutions with a modified energy fluence per ideal open time (EFIOT) were lower than the values at other institutions. The mean value of all institutions except those four was 0.994 ± 0.013 Gy (range: 0.974 Gy, 1.017 Gy). When the Dfmsrw, Qmsr value was corrected by the IMRT verification error, this variation decreased. In addition, the mean IMRT verification errors in the TomoDirectTM and TomoHelicalTM modes with the TomoEDGETM mode were 1.2% ± 0.8% (range: -0.6%, 1.8%) and 0.2% ± 0.5% (range: -0.6%, 0.9%), respectively (p

A 3D convolutional neural network model with multiple outputs for simultaneously estimating the reactive transport parameters of sandstone from its CT images

Porosity,tortuosity,specific surface area(SSA),and permeability are four key parameters of reactive transport modeling in sandstone,which are important for understanding solute transport and geochemical reaction pro-cesses in sandstone aquifers.These four parameters reflect the characteristics of pore structure of sandstone from different perspectives,and the traditional empirical formulas cannot make accurate predictions of them due to their complexity and heterogeneity.In this paper,eleven types of sandstone CT images were firstly segmented into numerous subsample images,the porosity,tortuosity,SSA,and permeability of the subsamples were calculated,and the dataset was established.The 3D convolutional neural network(CNN)models were subse-quently established and trained to predict the key reactive transport parameters based on subsample CT images of sandstones.The results demonstrated that the 3D CNN model with multiple outputs exhibited excellent prediction ability for the four parameters compared to the traditional empirical formulas.In particular,for the prediction of tortuosity and permeability,the 3D CNN model with multiple outputs even showed slightly better prediction ability than its single-output variant model.Additionally,it demonstrated good generalization per-formance on sandstone CT images not included in the training dataset.The study showed that the 3D CNN model with multiple outputs has the advantages of simplifying operation and saving computational resources,which has the prospect of popularization and application.

Nonparametric Probabilistic Prediction of Regional PV Outputs Based on Granule-based Clustering and Direct Optimization Programming

Regional photovoltaic(PV) power prediction plays an important role in power system planning and operation. To effectively improve the performance of prediction intervals(PIs) for very short-term regional PV outputs, an efficient nonparametric probabilistic prediction method based on granulebased clustering(GC) and direct optimization programming(DOP) is proposed. First, GC is proposed to formulate and cluster the sample granules consisting of numerical weather prediction(NWP) and historical regional output data, for the enhanced hierarchical clustering performance. Then, to improve the accuracy of samples' utilization, an unbalanced extension is used to reconstruct the training samples consisting of power time series. After that, DOP is applied to quantify the output weights based on the optimal overall performance. Meanwhile, a balance coefficient is studied for the enhanced reliability of PIs. Finally, the proposed method is validated through multistep PIs based on the numerical comparison of real PV generation data.

Prescribed Performance Tracking Control of Time-Delay Nonlinear Systems With Output Constraints

The problem of prescribed performance tracking control for unknown time-delay nonlinear systems subject to output constraints is dealt with in this paper. In contrast with related works, only the most fundamental requirements, i.e., boundedness and the local Lipschitz condition, are assumed for the allowable time delays. Moreover, we focus on the case where the reference is unknown beforehand, which renders the standard prescribed performance control designs under output constraints infeasible. To conquer these challenges, a novel robust prescribed performance control approach is put forward in this paper.Herein, a reverse tuning function is skillfully constructed and automatically generates a performance envelop for the tracking error. In addition, a unified performance analysis framework based on proof by contradiction and the barrier function is established to reveal the inherent robustness of the control system against the time delays. It turns out that the system output tracks the reference with a preassigned settling time and good accuracy,without constraint violations. A comparative simulation on a two-stage chemical reactor is carried out to illustrate the above theoretical findings.

A frequency servo SoC with output power stabilization loop technology for miniaturized atomic clocks

A frequency servo system-on-chip(FS-SoC)featuring output power stabilization technology is introduced in this study for high-precision and miniaturized cesium(Cs)atomic clocks.The proposed power stabilization loop(PSL)technique,incorporating an off-chip power detector(PD),ensures that the output power of the FS-SoC remains stable,mitigating the impact of power fluctuations on the atomic clock's stability.Additionally,a one-pulse-per-second(1PPS)is employed to syn-chronize the clock with GPS.Fabricated using 65 nm CMOS technology,the measured phase noise of the FS-SoC stands at-69.5 dBc/Hz@100 Hz offset and-83.9 dBc/Hz@1 kHz offset,accompanied by a power dissipation of 19.7 mW.The Cs atomic clock employing the proposed FS-SoC and PSL obtains an Allan deviation of 1.7×10^(-11) with 1-s averaging time.

Adaptive Optimal Discrete-Time Output-Feedback Using an Internal Model Principle and Adaptive Dynamic Programming

In order to address the output feedback issue for linear discrete-time systems, this work suggests a brand-new adaptive dynamic programming(ADP) technique based on the internal model principle(IMP). The proposed method, termed as IMP-ADP, does not require complete state feedback-merely the measurement of input and output data. More specifically, based on the IMP, the output control problem can first be converted into a stabilization problem. We then design an observer to reproduce the full state of the system by measuring the inputs and outputs. Moreover, this technique includes both a policy iteration algorithm and a value iteration algorithm to determine the optimal feedback gain without using a dynamic system model. It is important that with this concept one does not need to solve the regulator equation. Finally, this control method was tested on an inverter system of grid-connected LCLs to demonstrate that the proposed method provides the desired performance in terms of both tracking and disturbance rejection.

基于降维变换的低复杂度双基地EMVS-MIMO雷达高分辨多维参数估计

针对当前算法在实现双基地电磁矢量传感器多输入多输出(electromagnetic vector sensors multiple input multiple output,EMVS-MIMO)雷达的多维参数估计时计算代价较高的问题,通过利用降维变换技术来实现低复杂度的角度参数和极化参数求解。针对阵列接收数据维度较大问题,通过设计相应的波束空间变换矩阵来实现对阵列接收数据的降维处理。针对算法本身的较高计算复杂度问题,采用低计算复杂度的平行因子分解算法。所提算法能够精确地实现对发射因子矩阵和接收因子矩阵的求解。同时,通过新的旋转不变关系构建新的估计信号参数,可以实现对发射/接收俯仰角的求解。进一步,发射/接收方位角、发射/接收极化角和发射/接收极化相位差的估计可以通过发射/接收空间响应矩阵的重构来实现。仿真实验结果表明,所提算法在降低计算复杂度的同时能够保持优越的多维参数估计性能。

Graph Theory Methods for Decomposition w.r.t. Outputs of Boolean Control Networks

This paper focuses graph theory method for the problem of decomposition w.r.t. outputs for Boolean control networks(BCNs). First, by resorting to the semi-tensor product of matrices and the matrix expression of BCNs, the definition of decomposition w.r.t. outputs is introduced. Second, by referring to the graphical structure of BCNs, a necessary and sufficient condition for the decomposition w.r.t. outputs is obtained based on graph theory method. Third, an effective algorithm to realize the maximum decomposition w.r.t. outputs is proposed. Finally, some examples are addressed to validate the theoretical results.

EFFICIENCY DECOMPOSITION WITH SHARED INPUTS AND OUTPUTS IN TWO-STAGE DEA

Data envelopment analysis （DEA） is an effective non-parametric method for measuring the relative efficiencies of decision making units （DMUs） with multiple inputs and outputs. In many real situations, the internal structure of DMUs is a two-stage network process with shared inputs used in both stages and common outputs produced by the both stages. For example, hospitals have a two-stage network structure. Stage 1 consumes resources such as information technology system, plant, equipment and admin personnel to generate outputs such as medical records, laundry and housekeeping. Stage 2 consumes the same set of resources used by stage 1 （named shared inputs） and the outputs generated by stage 1 （named intermediate measures） to provide patient services. Besides, some of outputs, for instance, patient satisfaction degrees, are generated by the two individual stages together （named shared outputs）. Since some of shared inputs and outputs are hard split up and allocated to each individual stage, it needs to develop two-stage DEA methods for evaluating the performance of two-stage network processes in such problems. This paper extends the centralized model to measure the DEA efficiency of the two-stage process with non split-table shared inputs and outputs. A weighted additive approach is used to combine the two individual stages. Moreover, additive efficiency decomposition models are developed to simultaneously evaluate the maximal and the minimal achievable efficiencies for the individual stages. Finally, an example of 17 city branches of China Construction Bank in Anhui Province is employed to illustrate the proposed approach.

A Range Adjusted Measure of Super-Efficiency in Integer-Valued Data Envelopment Analysis with Undesirable Outputs

DEA(data envelopment analysis) models can be divided into two groups: Radial DEA and non-radial DEA, and the latter has higher discriminatory power than the former. The range adjusted measure(RAM) is an effective and widely used non-radial DEA approach. However, to the best of our knowledge, there is no literature on the integer-valued super-efficiency RAM-DEA model,especially when undesirable outputs are included. We first propose an integer-valued RAM-DEA mode with undesirable outputs and then extend this model to an integer-valued super-efficiency RAM-DEA model with undesirable outputs. Compared with other DEA models, the two novel models have many advantages: 1) They are non-oriented and non-radial DEA models, which enable decision makers to simultaneously and non-proportionally improve inputs and outputs;2) They can handle integer-valued variables and undesirable outputs, so the results obtained are more reliable;3) The results can be easily obtained as it is based on linear programming;4) The integer-valued super-efficiency RAM-DEA model with undesirable outputs can be used to accurately rank efficient DMUs. The proposed models are applied to evaluate the efficiency of China’s regional transportation systems(RTSs) considering the number of transport accidents(an undesirable output). The results help decision makers improve the performance of inefficient RTSs and analyze the strengths of efficient RTSs.

Time-delay signature concealment of polarizationresolved chaos outputs in vertical-cavity surface-emitting lasers with variable-polarization filtered optical feedback

Through employing permutation entropy and the self-correlation function, the time-delay signature （TDS） of a vertical-cavity surface-emitting laser （VCSEL） with variable-polarization filtered optical feedback （VPFOF） is evaluated theoretically. The work shows that the feedback rate η, polarizer angle Op, and filter bandwidth A have an obvious influence on the TDS. The evolution maps of the TDS in parameter space （η, A） and （ηθp） are simulated for searching the chaos with weak TDS. Furthermore, compared with a VCSEL with polarization-preserved filtered optical feedback and a VCSEL with variable-polarization mirror optical feedback, this VPFOF-VCSEL shows superiority in TDS suppression.

Plant life form determines spatiotemporal variability and climate response of plant seed rain in subtropical forests

Spatiotemporal variation of seed rain reflects the response of plants in terms of their reproductive strategy to environmental gradients.In this study,we collected seeds from four sites in the Dalaoling Nature Reserve,Hubei Province,China,between 2011 and 2014,measured seed output and seed mass as seed rain traits,and compared their interannual and elevational variation.Then,we ran phylogenetic generalized mixed linear models(PGLMMs) to explore the effects of temperature and precipitation as well as interspecific differences on seed rain,and fitted the best regression models for seed rain vs.weather of canopy and understory species.The results showed no correlation between values of seed output and seed mass.However,the variation of the two traits showed significantly positive correlation.Seed output of canopy species generally decreased with increasing elevation,and showed significant interannual difference;however,seed output of understory species and seed mass for both canopy and understory species did not show consistency tends along elevational or in interannual variation.Seed output was significantly affected by temperature and precipitation,while seed mass mainly varied due to interspecific differences.Weather explained more the variation of the seed output of canopy species than that of understory species,with R^(2) values of 43.0%and 29.9%,respectively.These results suggested that canopy plants contributed more to the reproductive dynamics of the whole communities,and the canopy's buffer effect on the underground weakened the response of understory plants to weather variation in terms of their reproductive strategy.