Optimal Combination Method Research Articles

Analysis and Application of the Spatio-Temporal Feature in Wind Power Prediction

The spatio-temporal feature with historical wind power information and spatial information can effectively improve the accuracy of wind power prediction, but the role of the spatio-temporal feature has not yet been fully discovered. This paper investigates the variance of the spatio-temporal feature. Based on this, a hybrid machine learning method for wind power prediction is designed. First, the training set is divided into several groups according to the variance of the input pattern, and then each group is used to train one or more predictors respectively. Multiple machine learning methods, such as the support vector machine regression and the decision tree, are used in the proposed method. Second, all the trained predictors are adopted to make predictions for a sample, and the results generated from these predictors will be combined by an optimized combination method based on the variance. The experimental results based on the NREL dataset show that the method adopted in this paper can achieve a better performance than the stage-of-the-art approaches.

Combined beef thawing using response surface methodology

Based on four thawing methods (still air, still water, ultrasonic wave, and microwave) and single-factor tests, we established a four-factor three-level response surface methodology for a regression model (four factors: pH, drip loss rate, cooking loss rate, protein content). The optimal combined thawing method for beef rib-eye is: microwave thawing (35 s work/10 s stop, totally 170 s) until beef surfaces soften, then air thawing at 15°C until the beef centre temperature reaches –8°C, and finally ultrasonic thawing at 220 W until the beef centre temperature rises to 0°C. With this method, the drip loss rate is 1.9003%, cooking loss rate is 33.3997%, and protein content is 229.603 μg, which are not significantly different from the model-predicted theoretical results (P ≥ 0.05).

Hierarchical Forecasts of Agronomy-Based Data

SYNOPTIC ABSTRACTIn this article we explore the hierarchical nature of time series of various agriculture crops in Pakistan at the subdistrict level and produce short-term forecasts for these time series. The data and forecasts are organized in a hierarchy based on disaggregating the data according to the division and subdistrict levels (geographical regions). Following Athanasopoulos, Ahmed, and Hyndman, we consider five approaches to hierarchical forecasting; two variations of the top-down approach; the bottom-up method; top-down approach where top-level forecasts are disaggregated according to the forecasted proportions of the lower level series; and the new optimal combination approach introduced by Hyndman, Ahmed, Athanasopoulos, and Shang. The forecasts are obtained from these five approaches using two well-known methods; Exponential Smoothing (ES) and an autoregressive integrated moving average (ARIMA). The forecasts are then compared across the two methods and across the various approaches by using various out-of-sample forecast evaluations.The forecast performance evaluation shows that in most cases either the top-down or the bottom-up approach performs best while the optimal combination method approach is the second best for the major crops production hierarchies we consider. By applying these methods, we produce detailed forecasts of the production of major crops in Pakistan for all levels of hierarchies and draw some useful conclusions for policy makers.

Spatial-temporal pattern and factor diagnoses of agroecosystem health in major grain producing areas of Northeast China: A case study in Jilin Province

It is of great significance to study agroecosystem health in major grain producing areas based on the theory and method of ecosystem health. This paper selected Jilin Province as the study area, and an evaluation index system of agroecosystem health was built based on the SSI-VOR conceptual framework model. Using the optimal combined weights method, comprehensive evaluation assessment, GIS spatial analysis and grey slope similarity incidence models, the spatial-temporal pattern of agroecosystem health and influence factors were analyzed from 2000 to 2011 in Jilin Pro-vince. The results indicated that, temporally, the composite index of agroecosystem health showed a rising trend in Jilin Province from 1995 to 2011, and the agroecosystem health level changed from not healthy to relatively healthy; spatially, the spatial discrepancy of agroecosystem health level tended to become larger, which remained unchanged in central area, while was gradually improved in southeast and west. The main contributors which improved the agroecosystem health level were economic driving force, environmental management and social development, while the main 'dragging' factors were ecological pressure, organization structure and input capacity. Finally, relevant measures were put forward to improve the agroecosystem health condition.

Reconciling Forecasts of Infant Mortality Rates at National and Sub-National Levels: Grouped Time-Series Methods

Mortality rates are often disaggregated by different attributes, such as sex, state, education, religion, or ethnicity. Forecasting mortality rates at the national and sub-national levels plays an important role in making social policies associated with the national and sub-national levels. However, base forecasts at the sub-national levels may not add up to the forecasts at the national level. To address this issue, we consider the problem of reconciling mortality rate forecasts from the viewpoint of grouped time-series forecasting methods (Hyndman et al. in, Comput Stat Data Anal 55(9):2579–2589, 2011). A bottom-up method and an optimal combination method are applied to produce point forecasts of infant mortality rates that are aggregated appropriately across the different levels of a hierarchy. We extend these two methods by considering the reconciliation of interval forecasts through a bootstrap procedure. Using the regional infant mortality rates in Australia, we investigate the one-step-ahead to 20-step-ahead point and interval forecast accuracies among the independent and these two grouped time-series forecasting methods. The proposed methods are shown to be useful for reconciling point and interval forecasts of demographic rates at the national and sub-national levels, and would be beneficial for government policy decisions regarding the allocations of current and future resources at both the national and sub-national levels.

Optimal partner wavelength combination method with application to near-infrared spectroscopic analysis

Using binary linear regression, the optimal partner wavelength of each wavelength is selected in an initial wavelength screening region. On the basis of strategy above, a novel approach for selecting appropriate wavelengths combination, named optimal partner wavelength combination (OPWC) coupled with partial least squares (PLS), is proposed, and was successfully applied for reagent-free near-infrared spectroscopic analysis of organic matter in soil. Moving window PLS (MW-PLS), successive projections algorithm (SPA) and Monte Carlo uninformative variable elimination (MC-UVE), which are well-performed wavelength selection methods, were also conducted for comparison.The OPWC-PLS, MW-PLS, SPA-PLS and MC-UVE-PLS methods selected 14, 210, 63, 199 wavelengths, respectively. The root-mean-square error and correlation coefficients for leave-one-out cross validation were 0.165gkg−1 and 0.967 for OPWC-PLS, 0.163gkg−1 and 0.968 for MW-PLS, 0.198gkg−1 and 0.953 for SPA-PLS, and 0.190gkg−1 and 0.956 for MC-UVE-PLS, respectively. The results indicate that OPWC-PLS and MW-PLS methods were almost the same, and were obvious better than SPA-PLS and MC-UVE-PLS methods. But the OPWC only contained 14 wavelengths, which is a high efficient approach for extracting information wavelengths and mitigating redundant wavelengths. OPWC can be also provided valuable reference for designing small dedicated spectrometers with a high signal-to-noise ratio.OPWC can be programmed determined, which has small amount of calculation and high operating speed, and it is a deterministic search technique whose results are reproducible. We believe that OPWC has such applicability and can be applied to other fields of spectroscopic analysis.

Wearable-Sensor-Based Classification Models of Faller Status in Older Adults.

Wearable sensors have potential for quantitative, gait-based, point-of-care fall risk assessment that can be easily and quickly implemented in clinical-care and older-adult living environments. This investigation generated models for wearable-sensor based fall-risk classification in older adults and identified the optimal sensor type, location, combination, and modelling method; for walking with and without a cognitive load task. A convenience sample of 100 older individuals (75.5 ± 6.7 years; 76 non-fallers, 24 fallers based on 6 month retrospective fall occurrence) walked 7.62 m under single-task and dual-task conditions while wearing pressure-sensing insoles and tri-axial accelerometers at the head, pelvis, and left and right shanks. Participants also completed the Activities-specific Balance Confidence scale, Community Health Activities Model Program for Seniors questionnaire, six minute walk test, and ranked their fear of falling. Fall risk classification models were assessed for all sensor combinations and three model types: multi-layer perceptron neural network, naïve Bayesian, and support vector machine. The best performing model was a multi-layer perceptron neural network with input parameters from pressure-sensing insoles and head, pelvis, and left shank accelerometers (accuracy = 84%, F1 score = 0.600, MCC score = 0.521). Head sensor-based models had the best performance of the single-sensor models for single-task gait assessment. Single-task gait assessment models outperformed models based on dual-task walking or clinical assessment data. Support vector machines and neural networks were the best modelling technique for fall risk classification. Fall risk classification models developed for point-of-care environments should be developed using support vector machines and neural networks, with a multi-sensor single-task gait assessment.

Pre‐Actuation and Optimal State Transition Based Precise Tracking for Maximum Phase System

AbstractPrecise trajectory tracking for the maximum phase system can be achieved by the stable inversion method. However, its side effect is requiring the sufficient extended zero trajectory. To overcome this drawback, firstly, this paper introduces the concept of stable initial state which is of vital importance to achieve precise output tracking. And then several methods have been presented to obtain the stable initial state. Both the standard method and the pre‐actuation method can be seen as the simplified stable inversion method under certain conditions, but the two methods only perform approximate output tracking. To achieve precise trajectory tracking, the optimal state transition method and the combination method consisting of the pre‐actuation and the optimal state transition are introduced. Finally, the optimal combination method is proposed to obtain the best overall tracking effect. In brief, different methods are fit for different situation but the optimal combination method is the finest method in practical situation. The effectiveness of the proposed methods are verified through numerical simulations for the maximum phase system.

Adaptively optimized combination (AOC) of magnetic resonance spectroscopy data from phased array coils.

MR spectroscopy (MRS) can benefit from multi-element coil arrays with enhanced signal-to-noise ratio (SNR). However, how to combine the MRS data in an optimized way from a multi-element coil array has been studied much less than MRI. A recently published method and routine combination methods have detrimental effects on SNR. We present herein a new method for optimal combination of multi-coil MRS data. Based on an analytical solution for maximizing the SNR of the combined spectrum, a new method called "adaptively optimized combination (AOC)" of MRS data from phased array coils was developed in which the inversion of the full noise correlation matrix was incorporated into the coil weighting coefficients. Simulations were carried out to demonstrate the superior performance of the proposed AOC method in various noise scenarios. Validation experiments on human subjects were performed with different voxel locations and sizes on a 3T MRI scanner using an eight-element phased array head coil. Compared with a recently published method (i.e., weighting with the ratio of signal to the square of noise) and routine methods, our proposed AOC method adaptively and robustly produced significant SNR improvement in the combined spectra. The simulation and human experiments demonstrate that the proposed AOC method represents the theoretical optimal combination of MR spectroscopic data from multi-element coil arrays. Magn Reson Med 75:2235-2244, 2016. © 2015 Wiley Periodicals, Inc.

Inventory Evaluation Model and Application of Shipbuilding Enterprise Based on the Method of Optimal Combination

As there is no scientific norm in China in managing the inventory of manufacturing enterprises in the ship industry, the managers always have more than one inventory evaluation index systems. Based on the theoretical research and field investigation, we construct an inventory evaluation index system consisting of 20 tertiary indicators of ship industry. We adopt the optimal combination method which composes of G1, fuzzy Borda number analytical method, entropy value method and deviation to investigate the case study. We propose that income index, operation index and security index are the most significant indicators to evaluate the inventory of the enterprise. The shipbuilding enterprise of empirical analysis is used to verify the validity of the conclusions.

Estimating Leaf Nitrogen Concentration In Barley By Coupling Hyperspectral Measurements With Optimal Combination Principle

Leaf nitrogen concentration (LNC), as a key indicator of nitrogen (N) status, can be used to evaluate N nutrient levels and improve fertilizer regulation in fields. Due to the non-destructive and quick detection, hyperspectral remote sensing with hundreds of very narrow bands plays an unique role in monitoring LNC in crop, but most of the current methods using hyperspectral techniques are still based on spectral univariate analyses, which often bring about the unstability of the models for LNC estimates. By introducing the optimal combination principle to conduct multivariate analyses and form the combination model, this paper proposes a new method with hyperspectral measurments to estimate LNC in barley. First, this study analyzed the relationships between LNC in barley and three types of spectral parameters including spectral position, area features, vegetation indices, and established the quantitative models of determining LNC with the key spectral variables, then using the optimal combination method w...

Thickness dependence of the levitation performance of double-layer high-temperature superconductor bulks above a magnetic rail

A double-layer high-temperature superconductor (HTSC) arrangement was proposed and proved to be able to bring improvements to both levitation force and guidance force compared with present single-layer HTSC arrangement. To fully exploit the applied magnetic field by a magnetic rail, the thickness dependence of a double-layer HTSC arrangement on the levitation performance was further investigated in the paper. In this study, the lower-layer bulk was polished step by step to different thicknesses, and the upper-layer bulk with constant thickness was directly superimposed on the lower-layer one. The levitation force and the force relaxation of the double-layer HTSC arrangement were measured above a Halbach magnetic rail. Experimental result shows that a bigger levitation force and a less levitation force decay could be achieved by optimizing the thickness of the lower-layer bulk HTSC. This thickness optimization method could be applied together with former reported double-layer HTSC arrangement method with aligned growth sector boundaries pattern. This series of study on the optimized combination method do bring a significant improvement on the levitation performance of present HTS maglev systems.

A Combined Membership Function and its Application on Fuzzy Evaluation of Power Quality

In the fuzzy evaluation process of the power quality, different membership function scan lead to different results, which affect the credibility of the evaluation. In order to avoid subjectivity of choosing membership function, a combined membership function based on the Variance-covariance optimized combination method is proposed. Firstly, five typical member-ship function is selected after the deeply analysis of the power quality data. Secondly, weights of each function are calculated by the variance-covariance optimized combination method, and then a combined membership function of general applicability is constructed according to the weights. Thirdly, the fuzzy evaluation is performed by using this combined membership function. Finally, an application of this combined membership function in the fuzzy evaluation of power quality of a power supply bureau shows that this function can improve credibility of three valuation result.

Optimization approach of background value and initial item for improving prediction precision of GM(1,1) model

A combination method of optimization of the background value and optimization of the initial item is proposed. The sequences of the unbiased exponential distribution are simulated and predicted through the optimization of the background value in grey differential equations. The principle of the new information priority in the grey system theory and the rationality of the initial item in the original GM(1,1) model are fully expressed through the improvement of the initial item in the proposed time response function. A numerical example is employed to illustrate that the proposed method is able to simulate and predict sequences of raw data with the unbiased exponential distribution and has better simulation performance and prediction precision than the original GM(1,1) model relatively.

Efficient Combination of Topology and Parameter Optimization

This paper presents a combination method of Particle Swarm Optimization (PSO) and topology optimization. With this method a better result can be achieved compared with the sequential application of these two optimization methods. It inherits the ability in finding global optimum from PSO and also suits for discretized design domain. Some special schemes are used in order to provide higher computation efficiency. This method has only been tested with a convex optimization problem. The application in case of a concave problem will be a future study.

Using optimal combination method and in situ hyperspectral measurements to estimate leaf nitrogen concentration in barley

Leaf nitrogen concentration (LNC), a good indicator of nitrogen (N) status in crops, is of special significance to diagnose nutrient stress and guide N fertilization in fields. Due to non-destructive and quick detectability, hyperspectral remote sensing plays a unique role in detecting LNC in crops. Barley, especially malting barley, is very demanding for N nutrition and requires timely monitoring and accurate estimation of N concentration in barley leaves. Hyperspectral techniques can help make effective diagnosis and facilitate dynamic regulation of plant N status. In this study, canopy reflectance spectra (between 350 and 1 050 nm) from 38 typical barley fields were measured as well as the corresponding LNC in Hailar Nongken, China’s Inner Mongolia Autonomous Region in July, 2010. Existing spectral indices that are considered to be good indicators for assessing N status in crops were selected to estimate LNC in barley. In addition, the optimal combination (OC) method was tested to extract the sensitive indices and first-order spectral derivative wavebands that are responsible for variation of leaf N in barley, and expected to develop some combination models for improving the accuracy of LNC estimates. The results showed that most of the selected indices (such as NPCI, PRI and DCNI) could adequately describe the dynamic changes of LNC in barley. The combined models based on OC performed better in comparison with the individual models using either spectral indices or first-order derivatives and the other methods (such as PCA). A combined model that integrated the first-order derivatives from five wavebands with OC performed well with R 2 of 0.82 and RMSE of 0.50 for LNC in barley. This good correlation with ground measurements indicates that hyperspectral reflectance and the OC method have good potential for assessing N status in barley.

Is there an optimal forecast combination?

We consider several geometric approaches for combining forecasts in large samples—a simple eigenvector approach, a mean corrected eigenvector and trimmed eigenvector approach. We give conditions where geometric approach yields identical result as the regression approach. We also consider a mean and scale corrected simple average of all predictive models for finite sample and give conditions where simple average is an optimal combination. Monte Carlos are conducted to compare the finite sample performance of these and some popular forecast combination and information combination methods and to shed light on the issues of “forecast combination” vs “information combination”. We also try to shed light on whether there exists an optimal forecast combination method by comparing various forecast combination methods to predict US real output growth rate and excess equity premium.

Combined Forecasting for Short-Term Output Power of Wind Farm

Wind power forecast is of great significance for power grid operation and scheduling. The effection of historical time series of output power and weather factors to wind power are considered in this paper. By use of BP neural network, an iterative forecasting model about output power time series is built. An Elman neural network forecasting model is established between numerical weather prediction data and output power. Then combining the above two forecasting models using covariance optimal combination method, a combined forecasting model for wind power is achieved so as to use all effective information of different data. The simulation experiment shows that the prediction accuracy has been improved by the combination forecast.

Optimized combination model and algorithm of parking guidance information configuration

Operators of parking guidance and information (PGI) systems often have difficulty in providing the best car park availability information to drivers in periods of high demand. A new PGI configuration model based on the optimized combination method was proposed by analyzing of parking choice behavior. This article first describes a parking choice behavioral model incorporating drivers perceptions of waiting times at car parks based on PGI signs. This model was used to predict the influence of PGI signs on the overall performance of the traffic system. Then relationships were developed for estimating the arrival rates at car parks based on driver characteristics, car park attributes as well as the car park availability information displayed on PGI signs. A mathematical program was formulated to determine the optimal display PGI sign configuration to minimize total travel time. A genetic algorithm was used to identify solutions that significantly reduced queue lengths and total travel time compared with existing practices. These procedures were applied to an existing PGI system operating in Deqing Town and Xiuning City. Significant reductions in total travel time of parking vehicles with PGI being configured. This would reduce traffic congestion and lead to various environmental benefits.

Improved R2* measurement accuracy with absolute SNR truncation and optimal coil combination

Accurate R2* measurements are critical for many abdominal imaging applications. Conventionally, R2* maps are derived via the monoexponential fitting of signal decay within a series of gradient-echo (GRE) images reconstructed from multichannel datasets combined using a root sum-of-squares (RSS) approach. However, the noise bias at low-SNR TEs from RSS-reconstructed data often causes the underestimation of R2* values. In phantom, ex vivo animal model and normal volunteer studies, we investigated the accuracy of low-SNR R2* measurement when combining truncation and coil combination methods. The accuracy for R2* estimations was shown to be affected by the intrinsic R2* value, SNR level and the chosen reconstruction method. The R2* estimation error was found to decrease with increasing SNR level, decreasing R2* value and the use of the optimal B1-weighted combined (OBC) image reconstruction method. Data truncation based on rigorous voxel-wise SNR estimates can reduce R2* measurement error in the setting of low SNR with fast signal decay. When optimal SNR truncation thresholds are unknown, the OBC method can provide optimal R2* measurements given the minimal truncation requirements.