This study examined the potential of adapting the software Capability Maturity Model as a process improvement paradigm within the context of industrial process improvement. Traditional methods of process improvement incorporate some facets of Total Quality Management (TQM), business process improvement (BPI), business process reengineering (BPR), business process management (BPM), benchmarking, regulation, legislation, Six Sigma, and standards. Hypothesis testing showed two statistically significant outcomes regarding the first and the fifth maturity levels reflecting ad hoc processes and optimized processes, respectively.
The control of thermostats of a heating, ventilation, and air-conditioning (HVAC) system installed in commercial and residential buildings remains a pertinent problem in building energy efficiency and thermal comfort research. The ability to determine the number of people at a particular time in an area is imperative for energy efficiency in order to condition only occupied regions and thermally deficient regions. In this study of the best features comparison for detecting the number of people in an area, feature extraction techniques including wavelet scattering, wavelet decomposition, grey-level co-occurrence matrix (GLCM) and feature maps convolution neural network (CNN) layers were explored using thermal camera imagery. Specifically, the pretrained CNN networks explored are the deep residual (Resnet-50) and visual geometry group (VGG-16) networks. The discriminating potential of Haar, Daubechies and Symlets wavelet statistics on different distributions of data were investigated. The performance of VGG-16 and ResNet-50 in an end-to-end manner utilizing transfer learning approach was investigated. Experimental results showed the classification and regression trees (CART) model trained on only GLCM and Haar wavelet statistic features, individually achieved accuracies of approximately 80% and 84%, respectively, in the detection problem. Moreover, k-nearest neighbors (KNN) trained on the combined features of GLCM and Haar wavelet statistics achieved an accuracy of approximately 86%. In addition, the performance accuracy of the multi classification support vector machine (SVM) trained on deep features obtained from layers of pretrained ResNet-50 and VGG-16 was between 96% and 97%. Furthermore, ResNet-50 transfer learning outperformed the VGG-16 transfer learning model for occupancy detection using thermal imagery. Overall, the SVM model trained on features extracted from wavelet scattering emerged as the best performing classifier with an accuracy of 100%. A principal component analysis (PCA) on the wavelet scattering features proved that the first twenty (20) principal components achieved a similar accuracy level instead of training on the whole feature set to reduce the execution time. The occupancy detection models can be integrated into HVAC control systems for energy efficiency and security systems, and aid in the distribution of resources to people in an area.
Abstract-This research examined quantitatively in-port grain loading levels during the periods preceding and succeeding selected human-made and natural disasters among U.S. Gulf Coast ports. The array of selected disasters consisted of the 2010 British Petroleum oil spill, the 2011 Mississippi River flood, Hurricane Katrina, Hurricane Gustav, and Hurricane Isaac. The outcomes of the analyses showed that the examined in-port Gulf Coast grain loading activities have not fully recovered and achieved the level of normalcy that existed before the examined cataclysms.
This study uses multiple regressions to examine campus safety and campus security from the perspective of societal crime that occurs external to an institution of higher education versus institutional enrollment. The findings herein showed one statistically significant outcome involving the crime of aggravated assault. Student affairs and other institutional leaders may find this study useful when contemplating enrollment issues and Clery Act reporting requirements.
This article examined a variant of the capability maturity model integrated (CMMi) through the lens of market engineering process improvement. The population and sample represented a national array of U.S. marketing organizations. Using ANOVA, a 0.05 significance level, and a stratification of urban marketing organizations versus rural marketing organizations, the study showed three statistically significant differences representing the second (p = 0.00; M = 2.90), fourth (p = 0.01; M = 3.22), and sixth hypotheses (p = 0.04; M = 3.15). The second hypothesis corresponded to the first maturity level (ad hoc, random processes), the fourth hypothesis corresponded to the third maturity level (characterized and expressed processes), and the sixth hypothesis corresponded to the fifth maturity level (optimized processes).
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