The Experts below are selected from a list of 43347 Experts worldwide ranked by ideXlab platform
Anil K Chopra - One of the best experts on this subject based on the ideXlab platform.
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response spectrum analysis of concrete gravity dams including dam water foundation interaction
Journal of Structural Engineering-asce, 2015Co-Authors: Arnkjell Lokke, Anil K ChopraAbstract:AbstractA response spectrum analysis (RSA) procedure, which estimates the peak response directly from the earthquake design spectrum, is available for the preliminary phase of design and safety evaluation of concrete gravity dams. This analysis procedure includes the effects of dam-water foundation interaction, known to be important in the earthquake response of dams. This paper presents a comprehensive evaluation of the accuracy of this RSA procedure by comparing its Results with those obtained from response history analysis (RHA) of the dam modeled as a finite-element system, including dam-water-foundation interaction. The earthquake response of an actual dam to an ensemble of 58 ground motions, selected and scaled to be consistent with a target spectrum determined from a probabilistic seismic hazard analysis for the dam site, was determined by the RHA procedure. The median of the peak responses of the dam to 58 ground motions provided the Benchmark Result. The peak response was also estimated by the RS...
Hans Gersbach - One of the best experts on this subject based on the ideXlab platform.
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loanable funds vs money creation in banking a Benchmark Result
Social Science Research Network, 2017Co-Authors: Salomon Faure, Hans GersbachAbstract:We establish a Benchmark Result for the relationship between the loanablefunds and the money-creation approach to banking. In particular, we show that both processes yield the same allocations when there is no uncertainty and thus no bank default. In such cases, using the much simpler loanablefunds approach as a shortcut does not imply any loss of generality.
Daniel Roggen - One of the best experts on this subject based on the ideXlab platform.
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Human Activity Sensing - Benchmark Performance for the Sussex-Huawei Locomotion and Transportation Recognition Challenge 2018
Human Activity Sensing, 2019Co-Authors: Lin Wang, Mathias Ciliberto, Sami Mekki, Stefan Valentin, Hristijan Gjoreski, Daniel RoggenAbstract:The Sussex-Huawei Transportation-Locomotion (SHL) Recognition Challenge 2018 aims to recognize eight transportation activities (Still, Walk, Run, Bike, Bus, Car, Train, Subway) from the inertial and pressure sensor data of a smartphone. In this chapter, we, as part of competition organizing team, present reference recognition performance obtained by applying various classical and deep-learning classifiers to the testing dataset. The classical classifiers include naive Bayes, decision tree, random forest, K-nearest neighbours and support vector machine, while the deep-learning classifiers include fully-connected and convolutional deep neural networks. We feed different types of input to the classifier, including hand-crafted features, raw sensor data in the time domain, and in the frequency domain. We additionally employ a post-processing scheme, which smoothens the predictions in order and improves the recognition performance. Results show that convolutional neural network operating on frequency-domain raw data achieves the best performance among all the classifiers. Finally, we achieve a Benchmark Result with F1 score 92.9%, which is comparable to the best Result from the team that won the competition (achieving F1 score 93.9%). The competition dataset and the Benchmark implementation is made available online (http://www.shl-dataset.org/).
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Benchmark performance for the sussex huawei locomotion and transportation recognition challenge 2018
Human Activity Sensing, 2019Co-Authors: Lin Wang, Mathias Ciliberto, Sami Mekki, Stefan Valentin, Hristijan Gjoreski, Daniel RoggenAbstract:The Sussex-Huawei Transportation-Locomotion (SHL) Recognition Challenge 2018 aims to recognize eight transportation activities (Still, Walk, Run, Bike, Bus, Car, Train, Subway) from the inertial and pressure sensor data of a smartphone. In this chapter, we, as part of competition organizing team, present reference recognition performance obtained by applying various classical and deep-learning classifiers to the testing dataset. The classical classifiers include naive Bayes, decision tree, random forest, K-nearest neighbours and support vector machine, while the deep-learning classifiers include fully-connected and convolutional deep neural networks. We feed different types of input to the classifier, including hand-crafted features, raw sensor data in the time domain, and in the frequency domain. We additionally employ a post-processing scheme, which smoothens the predictions in order and improves the recognition performance. Results show that convolutional neural network operating on frequency-domain raw data achieves the best performance among all the classifiers. Finally, we achieve a Benchmark Result with F1 score 92.9%, which is comparable to the best Result from the team that won the competition (achieving F1 score 93.9%). The competition dataset and the Benchmark implementation is made available online (http://www.shl-dataset.org/).
Arnkjell Lokke - One of the best experts on this subject based on the ideXlab platform.
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response spectrum analysis of concrete gravity dams including dam water foundation interaction
Journal of Structural Engineering-asce, 2015Co-Authors: Arnkjell Lokke, Anil K ChopraAbstract:AbstractA response spectrum analysis (RSA) procedure, which estimates the peak response directly from the earthquake design spectrum, is available for the preliminary phase of design and safety evaluation of concrete gravity dams. This analysis procedure includes the effects of dam-water foundation interaction, known to be important in the earthquake response of dams. This paper presents a comprehensive evaluation of the accuracy of this RSA procedure by comparing its Results with those obtained from response history analysis (RHA) of the dam modeled as a finite-element system, including dam-water-foundation interaction. The earthquake response of an actual dam to an ensemble of 58 ground motions, selected and scaled to be consistent with a target spectrum determined from a probabilistic seismic hazard analysis for the dam site, was determined by the RHA procedure. The median of the peak responses of the dam to 58 ground motions provided the Benchmark Result. The peak response was also estimated by the RS...
Salomon Faure - One of the best experts on this subject based on the ideXlab platform.
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loanable funds vs money creation in banking a Benchmark Result
Social Science Research Network, 2017Co-Authors: Salomon Faure, Hans GersbachAbstract:We establish a Benchmark Result for the relationship between the loanablefunds and the money-creation approach to banking. In particular, we show that both processes yield the same allocations when there is no uncertainty and thus no bank default. In such cases, using the much simpler loanablefunds approach as a shortcut does not imply any loss of generality.