Set Of Prototypes Research Articles

Carbon sequestration through agroforestry in indigenous communities of Chiapas, Mexico

The importance of agroforestry systems as carbon sinks has recently been recognized due to the need of climate change mitigation. The objective of this study was to compare the carbon content in living biomass, soil (0–10, 10–20, 20–30 cm in depth), dead organic matter between a set of non-agroforestry and agroforestry prototypes in Chiapas, Mexico where the carbon sequestration programme called Scolel’te has been carried out. The prototypes compared were: traditional maize (rotational prototype with pioneer native trees evaluated in the crop period), Taungya (maize with timber trees), improved fallow, traditional fallow (the last three rotational prototypes in the crop-free period), Inga-shade-organic coffee, polyculture-shade organic coffee, polyculture-non-organic coffee, pasture without trees, pasture with live fences, and pasture with scattered trees. Taungya and improved fallow were designed agroforestry prototypes, while the others were reproduced traditional systems. Seventy-nine plots were selected in three agro-climatic zones. Carbon in living biomass, dead biomass, and soil organic matter was measured in each plot. Results showed that carbon in living biomass and dead organic matter were different according to prototype; while soil organic carbon and total carbon were influenced mostly by the agro-climatic zone (P < 0.01). Carbon density in the high tropical agro-climatic zone (1,000 m) was higher compared to the intermediate and low tropical agro-climatic zones (600 and 200 m, respectively, P < 0.01). All the systems contained more carbon than traditional maize and pastures without trees. Silvopastoral systems, improved fallow, Taungya and coffee systems (especially polyculture-shade coffee and organic coffee) have the potential to sequester carbon via growing trees. Agroforestry systems could also contribute to carbon sequestration and reducing emissions when burning is avoided. The potential of organic coffee to maintain carbon in soil and to reduce emissions from deforestation and ecosystem degradation (REDD) is discussed.

Toward a Ubiquitous Smart Space Design Framework

In this paper, a design framework is presented for integrating the various perspectives and major investigation aspects of ubiquitous smart spaces. A key problem in developing ubiquitous smart spaces is how to design a physical space that maps to the underlying computing infrastructure and the corresponding patterns of situated interactions in every life. This paper explores design methods and technological issues encountered in the development of ubiquitous smart spaces. Methods for mapping different aspects of ubiquitous smart spaces are illustrated by a set of system prototypes. The prototypes are finally integrated in an open living laboratory as a tested for smart living.

A methodological framework that accounts for farm diversity in the prototyping of crop management systems. Application to banana-based systems in Guadeloupe

Prototyping methods are useful for designing alternative crop management systems (CMS), but they are usually considered mainly at the field level and are poorly equipped to take into account farm diversity in terms of economic, social and natural constraints. This may limit the likelihood of adoption of alternative CMS. This paper proposes a two-step methodological framework that takes into account the diversity of farms in the prototyping of new CMS. The first step of the framework is to design a farm typology that is able to characterise the diversity of current CMS in terms of their technical nature, farming context, and performance. We define a farming context as a set of characteristics at the farm level that are likely to influence the structure and the biophysical and economic performances of CMS at the field level. The typology is designed from a statistical analysis of a set of descriptive variables collected for a sample of farms. The second step of the framework uses this farm typology in a specific work agenda with a panel of experts in order to design a set of alternative CMS prototypes. Prototypes, and the objectives they are meant to reach, are designed by analysing current CMS performances and by mobilising agro-ecological knowledge to identify technical ways to improve them. The typological characterisation of farm diversity then makes it possible to define a set of constraints (SOC) for each farm type. Once the prototypes are designed, the experts use a compatibility indicator (CI) ranging from zero to one to measure the level of compatibility between each prototype and the SOC of each farm type. We define a prototype as compatible with a farm type if the prototype is not likely to increase the constraints of a farm of that type (CI = 1). Using this indicator in a process of iterative improvement of prototype design makes it possible to define a set of a priori compatible prototypes for each farm type. The aim of this paper is to present our methodological framework and its application for prototyping new banana crop management systems in Guadeloupe. The six-class typology we developed corresponded to a great diversity of CMS with contrasting performances and farming contexts. The method led to the prototyping of 16 innovative CMS, involving different modalities of intercropping, patterns of pesticide use, choices of hybrid cultivars, and rotations with cover or cash crops. Finally, we discuss the limits of our methodological framework and its utility for improving prototyping approaches.

Using conditional FCM to mine event-related brain dynamics

We introduce a framework for mining event related dynamics based on conditional FCM (CFCM). For a given set of responses, the variation in the data is summarized by means of a small set of meaningful prototypes accompanied with a low-dimensional graph capturing their relative relationships. CFCM enables prototyping in a principled manner. User-defined constraints, which are imposed by the nature of experimental data and/or dictated by the neuroscientist's intuition, direct the process of knowledge extraction and can robustify single-trial analysis. The method is introduced using simulated data and demonstrated using actual encephalographic data.

Linguistic modelling and information coarsening based on prototype theory and label semantics

The theory of prototypes provides a new semantic interpretation of vague concepts. In particular, the calculus derived from this interpretation results in the same calculus as label semantics proposed by Lawry. In the theory of prototypes, each basic linguistic label L has the form ‘ about P’, where P is a set of prototypes of L and the neighborhood size of the underlying concept is described by the word ‘ about’ which represents a probability density function δ on [ 0 , + ∞ ) . In this paper we propose an approach to vague information coarsening based on the theory of prototypes. Moreover, we propose a framework for linguistic modelling within the theory of prototypes, in which the rules are concise and transparent. We then present a linguistic rule induction method from training data based on information coarsening and data clustering. Finally, we apply this linguistic modelling method to some benchmark time series prediction problems, which show that our linguistic modelling and information coarsening methods are potentially powerful tools for linguistic modelling and uncertain reasoning.

Improved Cross-Polarization Characteristics of Circular Microstrip Antenna Employing Arc-Shaped Defected Ground Structure (DGS)

Application of defected ground structure (DGS) to suppress cross-polarized (XP) radiation from a microstrip patch antenna has been reinvestigated using a new DGS geometry for much improved characteristics. Arc-shaped defect has been used in pair, symmetrically located under a circular patch. A number of optimization parameters have been examined using simulated results, leading to a design indicating improved XP behavior. A set of identical prototypes, with and without DGS, have been experimentally studied. The presence of the DGS shows as much as 30 dB isolation of the XP level from its peak radiation, and that compared to an identical patch without DGS indicates an improvement by as much as 12 dB. The relative suppression in XP values are found to be around 7-12 dB over ±75° elevation around the boresight of the patch.

Three-Level Current-Source PWM Inverter with No Isolated Switching Devices for Photovoltaic Conditioner

This paper presents a new configuration of a three-level current-source PWM inverter (CSI) with a full common-source or common-emitter topology in terms of all transistor switching devices. Using this new configuration, the number of gate or base drive power supplies can dramatically be reduced to only a single instead of use of several isolated power supplies or conventional bootstrap circuits. As a result, bulky, heavy and expensive transformers and capacitors in the drive circuits are eliminated. In addition, the proposed configuration is capable to be operated at a higher switching frequency and a higher voltage without violent potential changes because of its common-source or common-emitter topology. Design and operation principle of the new circuit are investigated and analyzed using computer simulations. Finally, effectiveness of the circuit is experimentally verified by using a laboratory prototype set up. The simulation and the experimental results show that the circuit works properly to generate a three-level output current waveform, which proves feasibility of the proposed approach.

Assessing e-book model sustainability

The project described in this article explores the transition from a paper-based book, which deals with higher education (HE) ranking tables, namely The Times Good University Guide (GUG), to a fully customizable e-book. This article sets the background of the project, identifies design research as the general methodological approach, examines the existing e-book models and describes the current state of the research. It concludes, based on a set of real-life full prototypes that have now been live on the web for three years, that current e-book model propositions are too limited in terms of implementing a fully customizable and dynamic league table web application. The article's originality lies in the specific nature of the book being digitized and the nature of that digitization. The GUG is an entire book based around a university league table. What is envisaged is not just the translation of this league table, and corresponding information into a digital format, but a fully customizable and dynamic web application that fully explores this table in ways that would not be possible in a paper-based form.

Beyond Traditional Kernels: Classification in Two Dissimilarity-Based Representation Spaces

Proximity captures the degree of similarity between examples and is thereby fundamental in learning. Learning from pairwise proximity data usually relies on either kernel methods for specifically designed kernels or the nearest neighbor (NN) rule. Kernel methods are powerful, but often cannot handle arbitrary proximities without necessary corrections. The NN rule can work well in such cases, but suffers from local decisions. The aim of this paper is to provide an indispensable explanation and insights about two simple yet powerful alternatives when neither conventional kernel methods nor the NN rule can perform best. These strategies use two proximity-based representation spaces (RSs) in which accurate classifiers are trained on all training objects and demand comparisons to a small set of prototypes. They can handle all meaningful dissimilarity measures, including non-Euclidean and nonmetric ones. Practical examples illustrate that these RSs can be highly advantageous in supervised learning. Simple classifiers built there tend to outperform the NN rule. Moreover, computational complexity may be controlled. Consequently, these approaches offer an appealing alternative to learn from proximity data for which kernel methods cannot directly be applied, are too costly or impractical, while the NN rule leads to noisy results.

A generalization of dissimilarity representations using feature lines and feature planes

Even though, under representational restrictions, the nearest feature rules and the dissimilarity-based classifiers are feasible alternatives to the nearest neighbor method; individually, they may not be sufficiently powerful if a very small set of prototypes is required, e.g. when it is computationally expensive to deal with larger sets of prototypes. In this paper, we show that combining both strategies, taking advantage of their individual properties, provides an improvement, particularly for correlated data sets. The combined strategy consists in deriving an enriched (generalized) dissimilarity representation by using the nearest feature rules, namely feature lines and feature planes. On top of that enriched representation, Bayesian classifiers can be constructed in order to obtain a good generalization.

Alignment of the Pixel and SCT Modules for the 2004 ATLAS Combined Test Beam

A small set of final prototypes of the ATLAS Inner Detector silicon tracking system (Pixel Detector and SemiConductor Tracker), were used to take data during the 2004 Combined Test Beam. Data were collected from runs with beams of different flavour (electrons, pions, muons and photons) with a momentum range of 2 to 180 GeV/c. Four independent methods were used to align the silicon modules. The corrections obtained were validated using the known momenta of the beam particles and were shown to yield consistent results among the different alignment approaches. From the residual distributions, it is concluded that the precision attained in the alignment of the silicon modules is of the order of 5 μm in their most precise coordinate.

FLEXIBLE 3D OBJECT RECOGNITION FRAMEWORK USING 2D VIEWS VIA A SIMILARITY-BASED ASPECT-GRAPH APPROACH

This work presents a flexible framework for recognizing 3D objects from 2D views. Similarity-based aspect-graph, which contains a set of aspects and prototypes for these aspects, is employed to represent the database of 3D objects. An incremental database construction method that maximizes the similarity of views in the same aspect and minimizes the similarity of prototypes is proposed as the core of the framework to build and update the aspect-graph using 2D views randomly sampled from a viewing sphere. The proposed framework is evaluated on various object recognition problems, including 3D object recognition, human posture recognition and scene recognition. Shape and color features are employed in different applications with the proposed framework and the top three matching rates show the efficiency of the proposed method.

Model transformation by example using inductive logic programming

Model transformation by example is a novel approach in model-driven software engineering to derive model transformation rules from an initial prototypical set of interrelated source and target models, which describe critical cases of the model transformation problem in a purely declarative way. In the current paper, we automate this approach using inductive logic programming (Muggleton and Raedt in J Logic Program 19-20:629–679, 1994) which aims at the inductive construction of first-order clausal theories from examples and background knowledge.

Cluster-Based Nearest-Neighbour Classifier and Its Application on the Lightning Classification

The problem addressed in this paper concerns the prototype generation for a cluster-based nearest-neighbour classifier. It considers, to classify a test pattern, the lines that link the patterns of the training set and a set of prototypes. An efficient method based on clustering is here used for finding subgroups of similar patterns with centroid being used as prototype. A learning method is used for iteratively adjusting both position and local-metric of the prototypes. Finally, we show that a simple adaptive distance measure improves the performance of our nearest-neighbour-based classifier. The performance improvement with respect to other nearest-neighbour-based classifiers is validated by testing our method on a lightning classification task using data acquired from the Fast On-orbit Recording of Transient Events (FORTE) satellite, moreover the performance improvement is validated through experiments with several benchmark datasets. The performance of the proposed methods are also validated using the Wilcoxon Signed-Rank test.

Discretization error-free estimate of low temperature statistical dissociation rates in gas phase: Applications to Lennard-Jones clusters X13−nYn (n=–3)

In this work, an improved approach for computing cluster dissociation rates using Monte Carlo (MC) simulations is proposed and a discussion is provided on its applicability as a function of environmental variables (e.g., temperature). With an analytical transformation of the integrals required to compute variational transition state theory (vTST) dissociation rates, MC estimates of the expectation value for the Dirac delta delta(q(rc)-q(c)) have been made free of the discretization error that is present when a prelimit form for delta is used. As a by-product of this transformation, the statistical error associated with delta(q(rc)-q(c)) is reduced making this step in the calculation of vTST rates substantially more efficient (by a factor of 4-2500, roughly). The improved MC procedure is subsequently employed to compute the dissociation rate for Lennard-Jones clusters X(13-n)Y(n) (n=0-3) as a function of temperature (T), composition, and X-Y interaction strength. The X(13-n)Y(n) family has been previously studied as prototypical set of systems for which it may be possible to select and stabilize structures different from the icosahedral global minimum of X(13). It was found that both the dissociation rate and the dissociation mechanism, as suggested by the statistical simulations, present a marked dependence on n, T, and the nature of Y. In particular, it was found that a vacancy is preferentially formed close to a surface impurity when the X-Y interaction is weaker than the X-X one whatever the temperature. Differently, the mechanism was found to depend on T for stronger X-Y interactions, with vacancies being formed opposite to surface impurities at higher temperature. These behaviors are a reflex of the important role played by the surface fluctuations in defining the properties of clusters.

An unsupervised method of classifying remotely sensed images using Kohonen self‐organizing maps and agglomerative hierarchical clustering methods

Unlike conventional unsupervised classification methods, such as K‐means and ISODATA, which are based on partitional clustering techniques, the methodology proposed in this work attempts to take advantage of the properties of Kohonen's self‐organizing map (SOM) together with agglomerative hierarchical clustering methods to perform the automatic classification of remotely sensed images. The key point of the proposed method is to execute the cluster analysis process by means of a set of SOM prototypes, instead of working directly with the original patterns of the image. This strategy significantly reduces the complexity of the data analysis, making it possible to use techniques that have not normally been considered viable in the processing of remotely sensed images, such as hierarchical clustering methods and cluster validation indices. Through the use of the SOM, the proposed method maps the original patterns of the image to a two‐dimensional neural grid, attempting to preserve the probability distribution and topology of the input space. Afterwards, an agglomerative hierarchical clustering method with restricted connectivity is applied to the trained neural grid, generating a simplified dendrogram for the image data. Utilizing SOM statistic properties, the method employs modified versions of cluster validation indices to automatically determine the ideal number of clusters for the image. The experimental results show examples of the application of the proposed methodology and compare its performance to the K‐means algorithm.

A combined approach for segment-specific market basket analysis

Market baskets arise from consumers’ shopping trips and include items from multiple categories that are frequently chosen interdependently from each other. Explanatory models of multicategory choice behavior explicitly allow for such category purchase dependencies. They typically estimate own and across-category effects of marketing-mix variables on purchase incidences for a predefined set of product categories. Because of analytical restrictions, however, multicategory choice models can only handle a small number of categories. Hence, for large retail assortments, the issue emerges of how to determine the composition of shopping baskets with a meaningful selection of categories. Traditionally, this is resolved by managerial intuition. In this article, we combine multicategory choice models with a data-driven approach for basket selection. The proposed procedure also accounts for customer heterogeneity and thus can serve as a viable tool for designing target marketing programs. A data compression step first derives a set of basket prototypes which are representative for classes of market baskets with internally more distinctive (complementary) cross-category interdependencies and are responsible for the segmentation of households. In a second step, segment-specific cross-category effects are estimated for suitably selected categories using a multivariate logistic modeling framework. In an empirical illustration, significant differences in cross-effects and price elasticities can be shown both across segments and compared to the aggregate model.

Application of the synergetic algorithm on the classification of lymph tissue cells

A synergetic classification algorithm for lymphocyte discrimination is developed. By improving the ISODATA algorithm, it is possible to cluster the cell types in the training set with respect to the cluster center samples which plays a role as the cell prototype set types. Based on synergetic theory, a two-step synergetic competition mechanism was established: (1) the order parameter competition was introduced between the test cell sample and the prototypes of each prototype set, by setting the prototype of the maximum order parameter as the best prototype of each set; (2) similar matching competition was performed between the test cell sample and the best prototypes. After that the classification is achieved by selecting the most similar result as the final result. This classification method was applied on four cell groups, i.e. mantle cells, follicular centrocytes, follicular centroblastic cells and centroblastic lymphoma cells. The results show that the averaged lymphocyte classification accuracy is up to 94.1%

Particle swarm optimization for prototype reduction

The problem addressed in this paper concerns the prototype reduction for a nearest-neighbor classifier. An efficient method based on particle swarm optimization is proposed here for finding a good set of prototypes. Starting from an initial random selection of a small number of training patterns, we generate a set of prototypes, using the particle swarm optimization, which minimizes the error rate on the training set. To improve the classification performance, during the training phase the prototype generation is repeated N times, then each of the resulting N sets of prototypes is used to classify each test pattern, and finally these N classification results are combined by the “vote rule”. The performance improvement with respect to the state-of-the-art approaches is validated through experiments with several benchmark datasets.

Prototype reduction using an artificial immune model

Artificial immune system (AIS)-based pattern classification approach is relatively new in the field of pattern recognition. The study explores the potentiality of this paradigm in the context of prototype selection task that is primarily effective in improving the classification performance of nearest-neighbor (NN) classifier and also partially in reducing its storage and computing time requirement. The clonal selection model of immunology has been incorporated to condense the original prototype set, and performance is verified by employing the proposed technique in a practical optical character recognition (OCR) system as well as for training and testing of a set of benchmark databases available in the public domain. The effect of control parameters is analyzed and the efficiency of the method is compared with another existing techniques often used for prototype selection. In the case of the OCR system, empirical study shows that the proposed approach exhibits very good generalization ability in generating a smaller prototype library from a larger one and at the same time giving a substantial improvement in the classification accuracy of the underlying NN classifier. The improvement in performance has been statistically verified. Consideration of both OCR data and public domain datasets demonstrate that the proposed method gives results better than or at least comparable to that of some existing techniques.