Speed prediction for triggering vehicle activated signs

Accurate speed prediction is a crucial step in the development of a dynamic vehcile activated sign (VAS). A previous study showed that the optimal trigger speed of such signs will need to be pre-determined according to the nature of the site and to the traffic conditions. The objective of this paper is to find an accurate predictive model based on historical traffic speed data to derive the optimal trigger speed for such signs. Adaptive neuro fuzzy (ANFIS), classification and regression tree (CART) and random forest (RF) were developed to predict one step ahead speed during all times of the day. The developed models were evaluated and compared to the results obtained from artificial neural network (ANN), multiple linear regression (MLR) and naïve prediction using traffic speed data collected at four sites located in Sweden. The data were aggregated into two periods, a short term period (5-min) and a long term period (1-hour). The results of this study showed that using RF is a promising method for predicting mean speed in the two proposed periods.. It is concluded that in terms of performance and computational complexity, a simplistic input features to the predicitive model gave a marked increase in the response time of the model whilse still delivering a low prediction error.

Whale distribution in relation to prey abundance and oceanographic processes in the shelf waters of the Western Antarctic Peninsula

The Western Antarctic Peninsula (WAP) is a biologically rich area supporting large standing stocks of krill and top predators (including whales, seals and seabirds). Physical forcing greatly affects productivity, recruitment, survival and distribution of krill in this area. In turn, such interactions are likely to affect the distribution of baleen whales. The Southern Ocean GLOBEC research program aims to explore the relationships and interactions between the environment, krill and predators around Marguerite Bay (WAP) in autumn 2001 and 2002. Bathymetric and environmental variables including acoustic backscattering as an indicator of prey abundance were used to model whale distribution patterns. We used an iterative approach employing (1) classification and regression tree (CART) models to identify oceanographic and ecological variables contributing to variability in humpback Megaptera novaeangliae and minke Balaenoptera acutorstrata whale distribution, and (2) generalized additive models (GAMs) to elucidate functional ecological relationships between these variables and whale distribution. The CART models indicated that the cetacean distribution was tightly coupled with zooplankton acoustic volume backscatter in the upper (25 to 100 m), and middle (100 to 300 m) portions of the water column. Whale distribution was also related to distance from the ice edge and bathymetric slope. The GAMs indicated a persistent, strong, positive relationship between increasing zooplankton volume and whale relative abundance. Furthermore, there was a lower limit for averaged acoustic volume backscatter of zooplankton below which the relationship between whales and prey was not significant. The GAMs also supported an annual relationship between whale distribution, distance from the ice edge and bathymetric slope, suggesting that these are important features for aggregating prey. Our results demonstrate that during the 2 yr study, whales were consistently and predictably associated with the distribution of zooplankton. Thus, humpback and minke whales may be able to locate physical features and oceanographic processes that enhance prey aggregation.

Lymphedema after complete axillary node dissection for melanoma: assessment using a new, objective definition

Objectives: The objectives of this study were to define appropriate criteria for assessing the presence of lymphedema, and to report the prevalence and risk factors for development of upper limb lymphedema after level I-III axillary dissection for melanoma.
Summary Background Data: The lack of a consistent and reliable objective definition for lymphedema remains a significant barrier to appreciating its prevalence after axillary dissection for melanoma (or breast carcinoma).
Methods: Lymphedema was assessed in 107 patients (82 male, 25 female) who had previously undergone complete level I-III axillary dissection. Of the 107 patients, 17 had also received postoperative axillary radiotherapy. Arm volume was measured using a water displacement technique. Change in volume of the arm on the side of the dissection was referenced to the volume of the other (control) arm. Volume measurements were corrected for the effect of handedness using corrections derived from a control group. Classification and regression tree (CART) analysis was used to determine a threshold fractional arm volume increase above which volume changes were considered to indicate lymphedema.
Results: Based on the CART analysis results, lymphedema was defined as an increase in arm volume greater than 16% of the volume of the control arm. Using this definition, lymphedema prevalence for patients in the present study was 10% after complete level I-III axillary dissection for melanoma and 53% after additional axillary radiotherapy. Radiotherapy and wound complications were independent risk factors for the development of lymphedema.
Conclusions: A suggested objective definition for arm lymphedema after axillary dissection is an arm volume increase of greater than 16% of the volume of the control arm.

Predicting future ecological degradation based on modelled thresholds

Threshold models are becoming important in determining the ecological consequences of our actions within the environment and have a key role in setting bounds on targets used by natural resource managers. We have been using thresholds and related concepts adapted from the multiple stable-states literature to model ecosystem response in the Coorong, the estuary for Australia’s largest river. Our modelling approach is based upon developing a state-and-transition model, with the states defined by the biota and the transitions defined by a classification and regression tree (CART) analysis of the environmental data for the region. Here we explore the behaviour of thresholds within that model. Managers tend to plan for a set of often arbitrarily-derived thresholds in their natural resource management. We attempt to assess how the precision afforded by analyses such as CART translates into ecological outcomes, and explicitly trial several approaches to understanding thresholds and transitions in our model and how they might be relevant for management. We conclude that the most promising approach would be a mixture of further modelling (using past behaviour to predict future degradation) in conjunction with targeted experiments to confirm the results. Our case study of the Coorong is further developed, particularly for the modelling stages of the protocol, to provide recommendations to improve natural resource management strategies that are currently in use.

Online motor fault detection and diagnosis using a hybrid FMM-CART model

In this brief, a hybrid model combining the fuzzy min-max (FMM) neural network and the classification and regression tree (CART) for online motor detection and diagnosis tasks is described. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. To evaluate the applicability of the proposed FMM-CART model, an evaluation with a benchmark data set pertaining to electrical motor bearing faults is first conducted. The results obtained are equivalent to those reported in the literature. Then, a laboratory experiment for detecting and diagnosing eccentricity faults in an induction motor is performed. In addition to producing accurate results, useful rules in the form of a decision tree are extracted to provide explanation and justification for the predictions from FMM-CART. The experimental outcome positively shows the potential of FMM-CART in undertaking online motor fault detection and diagnosis tasks.

Motor fault detection and diagnosis using a hybrid FMM-CART model with online learning

In this paper, a hybrid online learning model that combines the fuzzy min-max (FMM) neural network and the Classification and Regression Tree (CART) for motor fault detection and diagnosis tasks is described. The hybrid model, known as FMM-CART, incorporates the advantages of both FMM and CART for undertaking data classification (with FMM) and rule extraction (with CART) problems. In particular, the CART model is enhanced with an importance predictor-based feature selection measure. To evaluate the effectiveness of the proposed online FMM-CART model, a series of experiments using publicly available data sets containing motor bearing faults is first conducted. The results (primarily prediction accuracy and model complexity) are analyzed and compared with those reported in the literature. Then, an experimental study on detecting imbalanced voltage supply of an induction motor using a laboratory-scale test rig is performed. In addition to producing accurate results, a set of rules in the form of a decision tree is extracted from FMM-CART to provide explanations for its predictions. The results positively demonstrate the usefulness of FMM-CART with online learning capabilities in tackling real-world motor fault detection and diagnosis tasks. © 2014 Springer Science+Business Media New York.

Geospatial analysis of groundwater depth for manual well drilling suitability in the Zinder Region, Niger

Manual and low-tech well drilling techniques have potential to assist in reaching the United Nations' millennium development goal for water in sub-Saharan Africa. This study used publicly available geospatial data in a regression tree analysis to predict groundwater depth in the Zinder region of Niger to identify suitable areas for manual well drilling. Regression trees were developed and tested on a database for 3681 wells in the Zinder region. A tree with 17 terminal leaves provided a range of ground water depth estimates that were appropriate for manual drilling, though much of the tree's complexity was associated with depths that were beyond manual methods. A natural log transformation of groundwater depth was tested to see if rescaling dataset variance would result in finer distinctions for regions of shallow groundwater. The RMSE for a log-transformed tree with only 10 terminal leaves was almost half that of the untransformed 17 leaf tree for groundwater depths less than 10 m. This analysis indicated important groundwater relationships for commonly available maps of geology, soils, elevation, and enhanced vegetation index from the MODIS satellite imaging system.

Mapping and characterising subtropical estuarine landscapes using aerial photography and GIS for potential application in wildlife conservation and management

Coarse-resolution thematic maps derived from remotely sensed data and implemented in GIS play an important role in coastal and marine conservation, research and management. Here, we describe an approach for fine-resolution mapping of land-cover types using aerial photography and ancillary GIs and ground data in a large (100 x 35 km) subtropical estuarine system (Moreton Bay, Queensland, Australia). We have developed and implemented a classification scheme representing 24 coastal (subtidal, intertidal. mangrove, supratidal and terrestrial) cover types relevant to the ecology of estuarine animals, nekton and shorebirds. The accuracy of classifications of the intertidal and subtidal cover types, as indicated by the agreement between the mapped (predicted) and reference (ground) data, was 77-88%, depending on the zone and level of generalization required. The variability and spatial distribution of habitat mosaics (landscape types) across the mapped environment were assessed using K-means clustering and validated with Classification and Regression Tree models. Seven broad landscape types could be distinguished and ways of incorporating the information on landscape composition into site-specific conservation and field research are discussed. This research illustrates the importance and potential applications of fine-resolution mapping for conservation and management of estuarine habitats and their terrestrial and aquatic wildlife. (c) 2005 Elsevier Ltd. All rights reserved.

Project risk management:a combined analytic hierarchy process and decision tree approach

Time, cost and quality achievements on large-scale construction projects are uncertain because of technological constraints, involvement of many stakeholders, long durations, large capital requirements and improper scope definitions. Projects that are exposed to such an uncertain environment can effectively be managed with the application of risk management throughout the project life cycle. Risk is by nature subjective. However, managing risk subjectively poses the danger of non-achievement of project goals. Moreover, risk analysis of the overall project also poses the danger of developing inappropriate responses. This article demonstrates a quantitative approach to construction risk management through an analytic hierarchy process (AHP) and decision tree analysis. The entire project is classified to form a few work packages. With the involvement of project stakeholders, risky work packages are identified. As all the risk factors are identified, their effects are quantified by determining probability (using AHP) and severity (guess estimate). Various alternative responses are generated, listing the cost implications of mitigating the quantified risks. The expected monetary values are derived for each alternative in a decision tree framework and subsequent probability analysis helps to make the right decision in managing risks. In this article, the entire methodology is explained by using a case application of a cross-country petroleum pipeline project in India. The case study demonstrates the project management effectiveness of using AHP and DTA.

A fuzzy decision tree-based duration model for Standard Yorùbá text-to-speech synthesis

In this paper, we present syllable-based duration modelling in the context of a prosody model for Standard Yorùbá (SY) text-to-speech (TTS) synthesis applications. Our prosody model is conceptualised around a modular holistic framework. This framework is implemented using the Relational Tree (R-Tree) techniques. An important feature of our R-Tree framework is its flexibility in that it facilitates the independent implementation of the different dimensions of prosody, i.e. duration, intonation, and intensity, using different techniques and their subsequent integration. We applied the Fuzzy Decision Tree (FDT) technique to model the duration dimension. In order to evaluate the effectiveness of FDT in duration modelling, we have also developed a Classification And Regression Tree (CART) based duration model using the same speech data. Each of these models was integrated into our R-Tree based prosody model. We performed both quantitative (i.e. Root Mean Square Error (RMSE) and Correlation (Corr)) and qualitative (i.e. intelligibility and naturalness) evaluations on the two duration models. The results show that CART models the training data more accurately than FDT. The FDT model, however, shows a better ability to extrapolate from the training data since it achieved a better accuracy for the test data set. Our qualitative evaluation results show that our FDT model produces synthesised speech that is perceived to be more natural than our CART model. In addition, we also observed that the expressiveness of FDT is much better than that of CART. That is because the representation in FDT is not restricted to a set of piece-wise or discrete constant approximation. We, therefore, conclude that the FDT approach is a practical approach for duration modelling in SY TTS applications. © 2006 Elsevier Ltd. All rights reserved.

Calibration of the maximum carboxylation velocity (Vcmax) using data mining techniques and ecophysiological data from the Brazilian Semiarid region, for use in Dynamic Global Vegetation Models.

The semiarid region of northeastern Brazil, the Caatinga, is extremely important due to its biodiversity and endemism. Measurements of plant physiology are crucial to the calibration of Dynamic Global Vegetation Models (DGVMs) that are currently used to simulate the responses of vegetation in face of global changes. In a field work realized in an area of preserved Caatinga forest located in Petrolina, Pernambuco, measurements of carbon assimilation (in response to light and CO2) were performed on 11 individuals of Poincianella microphylla, a native species that is abundant in this region. These data were used to calibrate the maximum carboxylation velocity (Vcmax) used in the INLAND model. The calibration techniques used were Multiple Linear Regression (MLR), and data mining techniques as the Classification And Regression Tree (CART) and K-MEANS. The results were compared to the UNCALIBRATED model. It was found that simulated Gross Primary Productivity (GPP) reached 72% of observed GPP when using the calibrated Vcmax values, whereas the UNCALIBRATED approach accounted for 42% of observed GPP. Thus, this work shows the benefits of calibrating DGVMs using field ecophysiological measurements, especially in areas where field data is scarce or non-existent, such as in the Caatinga

基于广义模型和分类回归树的中国常见树种地理分布模拟研究

研究植被、物种分布与环境的关系一直是生态学中的重点。长期以来，在全球变化与陆地生态系统的研究中，主要研究重点是对大尺度植被分布的模拟和预测，并建立了大量的气候－植被分布关系模型。而对于物种潜在分布的模拟和预测，国内外相关的研究较少。近年来，随着统计技术和地理信息系统的发展，用于预测物种分布的统计模型技术得到了迅速的发展。统计模型技术已被广泛应用于生物地理分布、植物群落、生物多样性、气候变化影响评估等方面。 本论文基于当前在物种分布研究中应用广泛的广义线性模型、广义加法模型及分类回归树3种统计模型技术，对我国常见树种的地理分布进行模拟分析，并比较不同模型模拟精度的优劣，将模拟精度较高的模型应用于预测未来气候情景下我国几种主要树种的未来潜在地理分布。 基于建立的广义线性模型（GLM）、二次项逐步回归广义线性模型（SGLM）、广义加法模型（GAM）和分类回归树（CART）4个模型对我国20种常见树种地理分布进行模拟，结果表明，4个模型均有较高的模拟精度。GAM的模拟精度最高;添加二次项并进行逐步回归有效的提高了GLM的模拟精度;CART是一种基于规则的模型技术，模拟结果比GLM稍好，比GAM略差。 对不同树种的模拟分析表明，4个模型对于主要分布在暖温带落叶阔叶林区域的油松、辽东栎分布的模拟结果较差;GLM对分布在温带针阔混交林中红松、蒙古栎、胡桃楸和糠椴的模拟结果不太理想;4个模型对分布在中国亚热带常绿阔叶林区域的树种均表现出较高的模拟精度;对广布种也表现出很高的模拟精度。 结合地理信息系统，以地图形式将青冈、油松的模拟结果表示出来。结果表明：地理信息系统直观的反映出了模型模拟结果差异。4个模型均能很好模拟青冈的分布，且模拟结果接近;而对油松分布模拟结果4个模型均不甚理想，以GLM最差。这些结果与模型模拟评估结果相吻合。 在未来气候变化情景下，基于4个模型模拟结果优劣，以我国三种主要造林树种马尾松、油松、红松和两种常见树种青冈、蒙古栎为研究对象，分析其未来变化趋势。结果表明，未来气候变化情景下，对于马尾松而言，4个模型均预测马尾松在基本保持原有分布的基础上，其未来潜在分布区域均有所扩大，且有向西和向北扩展的趋势;对于油松而言，基于GLM、SGLM和GAM3个模型，油松的未来潜在分布除有北移的趋势外，其分布区还将向东北和西南两个方向扩展;对于红松而言，基于SGLM、GAM和CART3个模型的预测结果较为接近，即红松的未来潜在分布区域将有所减少;对蒙古栎而言，4个模型预测蒙古栎未来分布均将向西扩展;对青冈而言，4个模型预测青冈能基本保持其原有分布区，并向西和向北扩展，其中CART预测结果还表明，青冈在广东南部及广西南部的分布区域将消失。

Predicting forest attributes In southeast Alaska using artificial neural networks

Artificial neural network (ANN) methods are used to predict forest characteristics. The data source is the Southeast Alaska (SEAK) Grid Inventory, a ground survey compiled by the USDA Forest Service at several thousand sites. The main objective of this article is to predict characteristics at unsurveyed locations between grid sites. A secondary objective is to evaluate the relative performance of different ANNs. Data from the grid sites are used to train six ANNs: multilayer perceptron, fuzzy ARTMAP, probabilistic, generalized regression, radial basis function, and learning vector quantization. A classification and regression tree method is used for comparison. Topographic variables are used to construct models: latitude and longitude coordinates, elevation, slope, and aspect. The models classify three forest characteristics: crown closure, species land cover, and tree size/structure. Models are constructed using n-fold cross-validation. Predictive accuracy is calculated using a method that accounts for the influence of misclassification as well as measuring correct classifications. The probabilistic and generalized regression networks are found to be the most accurate. The predictions of the ANN models are compared with a classification of the Tongass national forest in southeast Alaska based on the interpretation of satellite imagery and are found to be of similar accuracy.