A modified support vector regression: Integrated selection of training subset and model

In recent years, support vector regression (SVR) has become an emerging and popular forecasting technique in the field of machine learning. However, it is subjected to the model selection and learning complexity O(K * N³), especially for a massive data set (N is the size of training dataset, and K is the number of search). How to simultaneously reduce K and N can give us insight and inspiration on designing an effective and accurate selection algorithm. To this end, this paper tries to integrate the selection of training subset and model for SVR, and proposes a nested particle swarm optimization (NPSO) by inheriting the model selection of the existing training subset based SVR (TS-SVR). This nested algorithm is achieved by adaptively and periodically estimating the search region of the optimal parameter setting for TS-SVR. Complex SVR, involving large-scale training data, can be seen as extensions of TS-SVRs, yielding a nested sequence of TS-SVRs with increasing sample size. The uniform design idea is transplanted to the above modeling process, and the convergence for the proposed model is proofed. By using two artificial regression problems, Boston housing and electric load in New South Wales as empirical data, the proposed approach is compared with the standard ones, the APSO-OTS-SVR, and other existing approaches. Empirical results show that the proposed approach not only can select proper training subset and parameter, but also has better generalization performance and fewer processing time.     

Effects of prestressing the reinforcement on the behavior of reinforced granular beds overlying weak soil

The effects of prestressing the reinforcement on the strength improvement and settlement reduction of a reinforced granular bed overlying weak soil are being investigated through a series of laboratory scale bearing capacity tests. The influences of parameters such as strength of underlying weak soil, thickness of granular bed, magnitude of prestressing force, direction of prestressing forces and number of layers of reinforcement are being examined. Finite element analyses are carried out using the FE program PLAXIS to study the effect of prestressing the reinforcement. Results obtained from finite element analyses are found to be in reasonably good agreement with the experimental results.     

Determination of porosity change from shrinkage curves during drying of food material

Based on a very simple model of mass conservation, three experimental properties (solid density, liquid density and initial bulk density) and the simultaneous acquisition of the reduced moisture content and the volume shrinkage during drying, a simple method is proposed to calculate the bulk porosity of a material during drying. This model allows a graphical interpretation to visualize the porosity change by comparing the experimental shrinkage curve with an ideal shrinkage curve. In the present work, several examples were taken from the literature to illustrate the application of this method to foodstuffs (apple, banana, carrot, garlic, pear, potato and sweet potato) with two different processes (convective drying, freeze-drying) and different drying conditions. Porosity calculations including error estimations showed a good agreement with experimental values reported in the literature.     

基于聚类分析的宇航元器件模糊综合评价

为确保宇航元器件应用的准确性，提出采用模糊综合评价的方法来实现对宇航元器件的有效评价。在调研数据的基础上，首次选用聚类分析方法对原始数据进行区分与摒除，保证了数据的可靠性，并结合宇航元器件评价的特点选用模糊综合评价作为主要的评价方法，最后以BXRA元器件为例进行了实证分析，结果表明了模糊综合评价方法在宇航元器件评价中的实用性。     

The MILESTONES modeling framework: An integrated analysis of national bioenergy strategies and their global environmental impacts

Bioenergy policies affect both the environment and biomass availability for food, feed, and fiber on a national and international scale. To support policy makers, knowledge and methods from different scientific disciplines in the form of integrated assessments is necessary. Therefore we developed the MILESTONES framework which models the links between the national bioenergy system and the global land-use system as an integrated modeling approach. It builds on a set of three well-tested models (MAGNET, LandSHIFT and BENSIM). The prototype's functionality was demonstrated by assessing the environmental impacts of future German bioenergy strategies on a global level and along the entire biomass provision chain. The results from the case study show that, on the one hand, German bioenergy strategies have little effect on international market prices, but on the other hand land-use policies on an international level strongly influence the environmental performance of any German bioenergy strategy.     

Two-phase flow component fraction measurement using gamma-ray attenuation technique

Multiphase flow meters as the potential alternatives to separation and metering techniques have been in rapid development since 1980 s.Before its field operation,the instrument should be calibrated in a standard test-facility.In spite of the known medium and large scale facilities all over the world,we developed a laboratory scale instrument for component fraction measurements.It has a two-phase flow homogenizer loop with the clamp-on potential of the meters to provide a regime independent measurement.It is capable of delivering a complete homogenization by γ-ray densitometer.With an error of±5%in component fraction measurements,this instrument is appropriate for testing and calibrating other meters.     

A taxonomy-based approach to shed light on the babel of mathematical models for rice simulation

For most biophysical domains, differences in model structures are seldom quantified. Here, we used a taxonomy-based approach to characterise thirteen rice models. Classification keys and binary attributes for each key were identified, and models were categorised into five clusters using a binary similarity measure and the unweighted pair-group method with arithmetic mean. Principal component analysis was performed on model outputs at four sites. Results indicated that (i) differences in structure often resulted in similar predictions and (ii) similar structures can lead to large differences in model outputs. User subjectivity during calibration may have hidden expected relationships between model structure and behaviour. This explanation, if confirmed, highlights the need for shared protocols to reduce the degrees of freedom during calibration, and to limit, in turn, the risk that user subjectivity influences model performance.     

Approximative model reconstruction of cascade systems

This letter considers how to approximately reconstruct a cascade system from a given unstructured system estimate. Many system identification methods, including subspace methods, provide reliable but generally unstructured black-box models. The problem we consider is how to find cascade systems that are close to such black-box models. For this, we use model matching techniques and optimal weighted Hankel-norm approximation to obtain accurate low-order cascade systems. We show that it is possible to bound the reconstruction error in terms of an error tolerance parameter and weighted Hankel singular values. The suggested methods are illustrated on both a numerical example and a real double tank system with experimental data.     

Synthesizing interpreted domain-specific models to manage smart microgrids

The increase in prominence of model-driven software development (MDSD) has placed emphasis on the use of domain-specific modeling languages (DSMLs) during the development process. DSMLs allow for domain concepts to be conceptualized and represented at a high level of abstraction. Currently, most DSML models are converted into high-level languages (HLLs) through a series of model-to-model and/or model-to-text transformations before they are executed. An alternative approach for model execution is the interpretation of models directly without converting them into an HLL. These models are created using interpreted DSMLs (i-DSMLs) and realized using a semantic-rich execution engine or domain-specific virtual machine (DSVM).In this article we present an approach for model synthesis, the first stage of model interpretation, that separates the domain-specific knowledge (DSK) from the model of execution (MoE). Previous work on model synthesis tightly couples the DSK and MoE reducing the ability for implementations of the DSVM to be easily reused in other domains. To illustrate how our approach to model synthesis works for i-DSMLs, we have created MGridML, an i-DSML for energy management in smart microgrids, and an MGridVM prototype, the DSVM for MGridML. We evaluated our approach by performing experiments on the model synthesis aspect of MGridVM and comparing the results to a DSVM from the user-centric communication domain.