The generation of a perceptual map via three-way multidimensional scaling allows analysts to see the separation of objects in Euclidean space. The MDSvarext method incorporates the objects＇ confidence regions in this...The generation of a perceptual map via three-way multidimensional scaling allows analysts to see the separation of objects in Euclidean space. The MDSvarext method incorporates the objects＇ confidence regions in this analysis, allowing for statistical inference in the difference between objects, but the confidence regions that are generated are very large because of the inherent variability among the evaluators. One solution to this problem is cluster generation prior to the application of the MDSvarext method in order to obtain homogeneous subgroups and to achieve greater control of the variance. This work is relevant to studies of perception which usually evaluate the difference between objects or stimuli in the point of view of different people that judge this difference using several dimensions. This study investigated the possibility of using a K-means algorithm to generate subgroups before the MDSvarext method was applied, evaluating the process with two quality indicators, one Ex-Ante and one Ex-Post. The experiments were conducted based on simulation of judgment matrix of different objects in multiple dimensions being evaluated by several judges. In this experiment, the matrix used was a 10 objects, in 10 features, judged by 10 people. The results are promising as possible interpretations of the perceptual map and the indicators generated.展开更多
In the present study, a physically-based hydraulic modeling tool and a data-driven approach using artificial neural networks （ANNs） were evaluated for their ability to simulate the fate and transport of microorganis...In the present study, a physically-based hydraulic modeling tool and a data-driven approach using artificial neural networks （ANNs） were evaluated for their ability to simulate the fate and transport of microorganisms in a water system. To produce reliable data, a pipe network was constructed and a series of experiments using a fecal coliform indicator （Escherichia coli 15597） was conducted. For the physically-based model, morphological （pipe size, link length, slope, etc.） and hydraulic （flow rate） conditions were used as input variables, and for ANNs, water quality parameters （conductivity, pH, and turbidity） were used. Both approaches accurately described the fate and transport of microorganisms （physically-based model： correlation coefficient （R） in the range of 0.914 - 0.977 and ANNs： R in the range of 0.949 - 0.980）, with the exception of one case at a low flow rate （q = 31.56 cm^3/sec）. This study also indicated that these approaches could be complementarily utilized to assess the vulnerability of water facilities and to establish emergency plans based on hypothetical scenarios.展开更多
文摘The generation of a perceptual map via three-way multidimensional scaling allows analysts to see the separation of objects in Euclidean space. The MDSvarext method incorporates the objects＇ confidence regions in this analysis, allowing for statistical inference in the difference between objects, but the confidence regions that are generated are very large because of the inherent variability among the evaluators. One solution to this problem is cluster generation prior to the application of the MDSvarext method in order to obtain homogeneous subgroups and to achieve greater control of the variance. This work is relevant to studies of perception which usually evaluate the difference between objects or stimuli in the point of view of different people that judge this difference using several dimensions. This study investigated the possibility of using a K-means algorithm to generate subgroups before the MDSvarext method was applied, evaluating the process with two quality indicators, one Ex-Ante and one Ex-Post. The experiments were conducted based on simulation of judgment matrix of different objects in multiple dimensions being evaluated by several judges. In this experiment, the matrix used was a 10 objects, in 10 features, judged by 10 people. The results are promising as possible interpretations of the perceptual map and the indicators generated.
文摘In the present study, a physically-based hydraulic modeling tool and a data-driven approach using artificial neural networks （ANNs） were evaluated for their ability to simulate the fate and transport of microorganisms in a water system. To produce reliable data, a pipe network was constructed and a series of experiments using a fecal coliform indicator （Escherichia coli 15597） was conducted. For the physically-based model, morphological （pipe size, link length, slope, etc.） and hydraulic （flow rate） conditions were used as input variables, and for ANNs, water quality parameters （conductivity, pH, and turbidity） were used. Both approaches accurately described the fate and transport of microorganisms （physically-based model： correlation coefficient （R） in the range of 0.914 - 0.977 and ANNs： R in the range of 0.949 - 0.980）, with the exception of one case at a low flow rate （q = 31.56 cm^3/sec）. This study also indicated that these approaches could be complementarily utilized to assess the vulnerability of water facilities and to establish emergency plans based on hypothetical scenarios.
