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Bhavik R. Bakshi
Researcher at Ohio State University
Publications - 226
Citations - 8440
Bhavik R. Bakshi is an academic researcher from Ohio State University. The author has contributed to research in topics: Sustainability & Life-cycle assessment. The author has an hindex of 43, co-authored 216 publications receiving 7569 citations. Previous affiliations of Bhavik R. Bakshi include TERI University & Massachusetts Institute of Technology.
Papers
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Multiscale PCA with application to multivariate statistical process monitoring
TL;DR: Multiscale Principal Component Analysis (MSPCA) as mentioned in this paper combines the ability of PCA to decorrelate the variables by extracting a linear relationship with that of wavelet analysis to extract deterministic features and approximately decorrelation of autocorrelated measurements.
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Promise and problems of emergy analysis
Jorge L. Hau,Bhavik R. Bakshi +1 more
TL;DR: The authors discusses the main features and criticisms of solar emergy and provides insight into the relationship between emergent and concepts from engineering thermodynamics, such as exergy and cumulative exergy consumption.
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Particle filtering and moving horizon estimation
TL;DR: An overview of currently available methods for state estimation of linear, constrained and nonlinear systems is provided, and the following methods are discussed: Kalman filtering, extended KalMan filtering, unscented Kalman filters, particle filtering, and moving horizon estimation.
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Exergy: Its Potential and Limitations in Environmental Science and Technology
Jo Dewulf,Herman Van Langenhove,Bart Muys,Stijn Bruers,Bhavik R. Bakshi,Geoffrey F. Grubb,David M. Paulus,Enrico Sciubba +7 more
TL;DR: It proves that exergy as a tool in environmental impact analysis may be the most mature field of application, particularly with respect to resource and efficiency accounting, one of the major challenges in the development of sustainable technology.
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Wave‐net: a multiresolution, hierarchical neural network with localized learning
TL;DR: This article presents the mathematical framework for the development of Wave-Nets and discusses the various aspects of their practical implementation and presents two examples on the application; the prediction of a chaotic time-series, representing population dynamics, and the classification of experimental data for process fault diagnosis.