![]() ![]() Examined with an on-site experiment, the results suggest that the ANN-based model with fused data has the best performance, while the SVM model is more suitable with Wi-Fi data. Three error measurement metrics, the mean average error (MAE), mean average percentage error (MAPE), and root mean squared error (RMSE), have been employed to compare the models’ accuracies. This study compared three popular machine learning algorithms, including k-nearest neighbors (kNN), support vector machine (SVM), and artificial neural network (ANN), combined more » with three data sources, including environmental data, Wi-Fi data, and fused data, to optimize the occupancy models’ performance in various scenarios. As many methods have been developed and a variety of sensory data sources are available, establishing a proper selection of model and data source is critical to the successful implementation of occupancy prediction systems. Many studies propose the use of environmental sensors (such as carbon dioxide, air temperature, and relative humidity sensors) and radio-frequency sensors (Wi-Fi networks) to monitor, assess, and predict occupancy information for buildings. ![]() Occupancy information is crucial to building facility design, operation, and energy efficiency. An overview from this evaluation is summarized in this paper. #Fireworks problem visual logic portableof Utah, Salt Lake City, UT (United States) Sponsoring Org.: USDOE National Nuclear Security Administration (NNSA), Office of Defense Nuclear Nonproliferation OSTI Identifier: 1440899 Grant/Contract Number: NA0002576 Resource Type: Accepted Manuscript Journal Name: Critical Developments and Applications of Swarm Intelligence Additional Journal Information: Journal Volume: 2018 Journal ID: ISSN 2327-0411 Publisher: IRMA International Country of Publication: United States Language: English Subject: 98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL PROTECTION 97 MATHEMATICS AND COMPUTING fireworks algorithm spectrum fitting gamma-ray analysis nuclear nonproliferation error = portable system and brought it to ORNL to evaluate the capability of the system. Publication Date: Mon Jan 01 00:00: Research Org.: Purdue Univ., West Lafayette, IN (United States) Univ. Purdue Univ., West Lafayette, IN (United States).of Utah, Salt Lake City, UT (United States) Purdue Univ., West Lafayette, IN (United States). Finally and furthermore, FWA is benchmarked against genetic algorithms and multiple linear regression, showing its superiority over those algorithms regarding precision with respect to MAE, MAPE, and MAP measures. FWA is tested on a set of experimentally obtained measurements optimizing various objective functions-MSE, RMSE, Theil-2, MAE, MAPE, MAP-with results exhibiting its potential in providing highly accurate and precise signature detection. In particular, FWA is utilized to fit a set of known signatures to a measured spectrum by optimizing an objective function, where non-zero coefficients express the detected signatures. In this paper, a method that employs the fireworks algorithm (FWA) for analyzing gamma-ray spectra aiming at detecting gamma signatures is presented. Among various types of measurements, gamma-ray spectra is the widest utilized type of data in nonproliferation applications. The analysis of measured data plays a significant role in enhancing nuclear nonproliferation mainly by inferring the presence of patterns associated with special nuclear materials. ![]()
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