The Experts below are selected from a list of 12432 Experts worldwide ranked by ideXlab platform
Ronald E Mcroberts - One of the best experts on this subject based on the ideXlab platform.
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predicting categorical forest Variables using an improved k nearest neighbour estimator and landsat imagery
Remote Sensing of Environment, 2009Co-Authors: Erkki Tomppo, Caterina Gagliano, Flora De Natale, Matti Katila, Ronald E McrobertsAbstract:The k-Nearest Neighbour (k-NN) estimation and prediction technique is widely used to produce pixel-level predictions and areal estimates of continuous forest Variables such as area and volume, often by subcategories such as species. An advantage of k-NN is that the same parameters (e.g., k-value, distance metric, weight vector for the feature space Variables) can be used for all Variables, whether continuous or categorical. An obvious question is the degree to which accuracy can be improved if the k-NN estimation parameters are tailored for specific Variable groups such as volumes by tree species or categorical Variables. We investigated prediction of categorical forest Attribute Variables from satellite image spectral data using k-NN with optimisation of the weight vector for the ancillary Variables obtained using a genetic algorithm. We tested several genetic algorithm fitness functions, all derived from well-known accuracy measures. For a Finnish test site, the categorical forest Attribute Variables were site fertility and tree species dominance, and for an Italian test site, the Variables were forest type and conifer/broad-leaved dominance. The results for both test sites were validated using independent data sets. Our results indicate that use of the genetic algorithm to optimize the weight vector for prediction of a single forest Attribute Variable had a slight positive effect on the prediction accuracies for other Variables. Errors can be further decreased if the optimisation is done by Variable groups.
Reide D Corbett - One of the best experts on this subject based on the ideXlab platform.
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spatial and temporal variability of streambed hydraulic conductivity in west bear creek north carolina usa
Journal of Hydrology, 2008Co-Authors: David P Genereux, Casey D Kennedy, Helena Mitasova, Scott Leahy, Reide D CorbettAbstract:Summary The hydraulic conductivity ( K ) of the streambed is an important Variable influencing water and solute exchange between streams and surrounding groundwater systems. However, there are few detailed data on spatial variability in streambed K and almost none on temporal variability. The spatial and temporal variability of streambed K in a North Carolina stream were investigated with 487 field measurements of K over a 1-year period. Measurements were made bimonthly from December 2005 to December 2006 at 46 measurement locations in a 262.5 m reach (the “large reach”). To give a more detailed picture of spatial variability, closely-spaced one-time measurements were made in two 62.5 m reaches (the “small reaches”, one investigated in July 2006 and the other in August 2006) that were part of the large reach. Arithmetic mean K for the large reach was ∼16 m/day (range was 0.01 to 66 m/day). Neither K nor ln K was normally distributed, and K distributions appeared somewhat bimodal. There was significant spatial variability over horizontal length scales of a few m. Perhaps the clearest feature within this variability was the generally higher K in the center of the channel. This feature may be an important control on water and chemical fluxes through the streambed (e.g., other measurements show generally higher water seepage velocity, but lower porewater nitrate concentration, in the center of the streambed). Grain size analysis of streambed cores showed that layers of elevated fines (silt + clay) content were less common in the center of the channel (overall, the streambed was about 94% sand). Results also suggest a modest but discernable difference in average streambed K upstream and downstream of a small beaver dam: K was about 23% lower upstream, when the dam was present during the first few months of the study. This upstream/downstream difference in K disappeared after the dam collapsed, perhaps in response to re-mobilization of fine sediments or leaf matter that had accumulated in quiet waters ponded on the upstream side of the dam. Temporal variability was significant and followed a variety of different patterns at the 46 measurement locations in the large reach. Temperature data show that variation in streambed and groundwater temperature was not an important cause of the observed temporal variability in K . Measurements of changes in the elevation of the streambed surface suggest erosion and deposition played an important role in causing the observed temporal variability in streambed K (of which the change described above following collapse of the beaver dam was a special case), though other potentially time-varying factors (e.g., gas content, bioturbation, or biofilms in the streambed) were not explicitly addressed and cannot be ruled out as contributors to the temporal variability in streambed K . Temporal variability in streambed K merits additional study as a potentially important control on temporal variability in the magnitudes and spatial patterns of water and solute fluxes between groundwater and surface water. From the data available it seems appropriate to view streambed K as a dynamic Attribute, Variable in both space and time.
Champ, Charles W - One of the best experts on this subject based on the ideXlab platform.
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Using Runs Rules to Monitor an Attribute Chart for a Markov Process
'Informa UK Limited', 2012Co-Authors: Shepherd, Deborah K., Rigdon, Steve E., Champ, Charles WAbstract:For some repetitive production processes, the quality measure taken on the output is an Attribute Variable. An Attribute Variable classifies each output item into one of a countable set of categories. One of the simplest and most commonly used Attribute Variables is the one that classifies an item as either conforming or nonconforming. A tool used with a considerable amount of success in industry for monitoring the quality of a production process is the quality control chart. In this paper, a sequence of random Variables, Xi , i = 1,2,… , is used to classify an item as conforming or nonconforming under a stationary Markov chain model and under 100% sequential sampling. The sequence of random Variables to be plotted on the control chart is Yi , i = 1,2,… , where Y1 counts the number of conforming items before the first nonconforming item and Yi , i = 2,3,… counts the number of conforming items between the (i-1)th and the ith nonconforming items. In the literature, Yi is called the CRL (Conforming Run Length). The contribution of this paper to the literature is to present runs rules for an Attribute control chart of this type. The efficiency of these charts is evaluated using the average run length (ARL) of the charts. The supplemental runs rules that are presented are two out of three values of Yi , i 1,2, falling below determined lower limits, four out of five values of Yi , i =1,2,…falling below determined lower limits, and eight out of eight values of Yi , i =1,2,… falling below determined lower limits
Guangxing Wei - One of the best experts on this subject based on the ideXlab platform.
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product configuration flow from obtaining customer requirement to providing the final customized product
Journal of Software, 2012Co-Authors: Yanhong Qin, Guangxing WeiAbstract:The paper analyzes the m odular structure of product family by decompos ing the product family into generic modules. Then, the module model denoted by Attribute Variable is established for each generic module. Based on the classification of customer requirement and erection of product decision tree, the description and explanation type of customer requirement can be used to restrict the sub branch of decision tree in order to find out the certain product family satisfying customer. In approach of quality function deployment , the customer requirement i s mapped to the module Attribute, w hich can determinate the value and weight of module Attribute Variable. After retrieving the candidate set of modules which are nearest to the customer requirement on the module accordingly, the candidate modules are combined efficiently under the constraints relative to modules and Attributes, In this way, many invalidate module combinations can be avoided and hence the efficiency of product configuration is promoted.
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customer requirement translation and product configuration based on modular product family
2008Co-Authors: Guangxing Wei, Yanhong QinAbstract:Firstly, this paper establishes the modular structure of product family and then decomposes it into generic modules. Secondly, the module model represented by Attribute Variable is formated for each generic module. According to quality function deployment, the mapping of customer requirements to module Attributes is constructed, which can determinate the Attribute value and weight of module model. Thirdly, by searching the candidate set of modules which best satisfy customer requirements, the candidate modules are combined efficiently subjecting to some constraints in the structure of modular product family from down to top.
Charles W Champ - One of the best experts on this subject based on the ideXlab platform.
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using runs rules to monitor an Attribute chart for a markov process
Quality Technology and Quantitative Management, 2012Co-Authors: Deborah K Shepherd, Steven E Rigdon, Charles W ChampAbstract:AbstractFor some repetitive production processes, the quality measure taken on the output is an Attribute Variable. An Attribute Variable classifies each output item into one of a countable set of categories. One of the simplest and most commonly used Attribute Variables is the one that classifies an item as either “conforming” or “nonconforming.” A tool used with a considerable amount of success in industry for monitoring the quality of a production process is the quality control chart. In this paper, a sequence of random Variables, Xi, i = 1, 2, … , is used to classify an item as conforming or nonconforming under a stationary Markov chain model and under 100% sequential sampling. The sequence of random Variables to be plotted on the control chart is Y, i = 1, 2, … , where Y1 counts the number of conforming items before the first nonconforming item and Yi, i = 2, 3,. counts the number of conforming items between the (i-l)th and the ih nonconforming items. In the literature, Yi is called the CRL (Conformi...
