The Experts below are selected from a list of 74583 Experts worldwide ranked by ideXlab platform

Hromada Martin - One of the best experts on this subject based on the ideXlab platform.

  • Comparsion of the resulting models of dispersion of hazardous substances created in the software aloha and terex
    International Multidisciplinary Scientific Geoconference, 2021
    Co-Authors: Kotková Barbora, Hromada Martin
    Abstract:

    The article deals with the issue of accidents associated with hazardous substances, their prevention. Modeling of these accidents was chosen as a form of prevention. The production of dangerous toxic substances is currently growing, especially in the industry. The response to this trend is to introduce safer technological processes and preventive measures. These measures serve to prevent or at least reduce the risk of accidents and thus the escape of dangerous substances. To implement prevention, effective measures, and mitigation of the consequences of a chemical accident, it is necessary to know or at least correctly estimate the course and subsequent impact of the accident. Modeling programs are one of the most modern and effective tools used to illustrate the effects of accidents. These include two special software programs, the ALOHA program, and the TerEx program. The article describes the basic theoretical aspects related to the dispersion of gaseous toxic substances in the Earth's atmosphere and the extent of their harmful effects. It also compares the resulting models of both programs when entering the same Input Data - Type and amount of hazardous substance, ambient temperature, wind speed, degree of turbidity, weather stability class, Type of terrain surface. Verification of the theoretical results would be possible experimentally, provided that a design of a field test is carried out, which would take place under the same meteorological conditions as were performed when entering the model Data by the mentioned programs. This would confirm the legitimacy of using special software to estimate the impact of the negative effects of chemical accidents. © 2020 International Multidisciplinary Scientific Geoconference. All rights reserved

  • Comparsion of the resulting models of dispersion of hazardous substances created in the software aloha and terex
    International Multidisciplinary Scientific Geoconference, 2021
    Co-Authors: Kotková Barbora, Hromada Martin
    Abstract:

    The article deals with the issue of accidents associated with hazardous substances, their prevention. Modeling of these accidents was chosen as a form of prevention. The production of dangerous toxic substances is currently growing, especially in the industry. The response to this trend is to introduce safer technological processes and preventive measures. These measures serve to prevent or at least reduce the risk of accidents and thus the escape of dangerous substances. To implement prevention, effective measures, and mitigation of the consequences of a chemical accident, it is necessary to know or at least correctly estimate the course and subsequent impact of the accident. Modeling programs are one of the most modern and effective tools used to illustrate the effects of accidents. These include two special software programs, the ALOHA program, and the TerEx program. The article describes the basic theoretical aspects related to the dispersion of gaseous toxic substances in the Earth's atmosphere and the extent of their harmful effects. It also compares the resulting models of both programs when entering the same Input Data - Type and amount of hazardous substance, ambient temperature, wind speed, degree of turbidity, weather stability class, Type of terrain surface. Verification of the theoretical results would be possible experimentally, provided that a design of a field test is carried out, which would take place under the same meteorological conditions as were performed when entering the model Data by the mentioned programs. This would confirm the legitimacy of using special software to estimate the impact of the negative effects of chemical accidents. © 2020 International Multidisciplinary Scientific Geoconference. All rights reserved.Univerzita Tomáše Bati ve Zlíně: IGA / FAI / 2020/00

Kotková Barbora - One of the best experts on this subject based on the ideXlab platform.

  • Comparsion of the resulting models of dispersion of hazardous substances created in the software aloha and terex
    International Multidisciplinary Scientific Geoconference, 2021
    Co-Authors: Kotková Barbora, Hromada Martin
    Abstract:

    The article deals with the issue of accidents associated with hazardous substances, their prevention. Modeling of these accidents was chosen as a form of prevention. The production of dangerous toxic substances is currently growing, especially in the industry. The response to this trend is to introduce safer technological processes and preventive measures. These measures serve to prevent or at least reduce the risk of accidents and thus the escape of dangerous substances. To implement prevention, effective measures, and mitigation of the consequences of a chemical accident, it is necessary to know or at least correctly estimate the course and subsequent impact of the accident. Modeling programs are one of the most modern and effective tools used to illustrate the effects of accidents. These include two special software programs, the ALOHA program, and the TerEx program. The article describes the basic theoretical aspects related to the dispersion of gaseous toxic substances in the Earth's atmosphere and the extent of their harmful effects. It also compares the resulting models of both programs when entering the same Input Data - Type and amount of hazardous substance, ambient temperature, wind speed, degree of turbidity, weather stability class, Type of terrain surface. Verification of the theoretical results would be possible experimentally, provided that a design of a field test is carried out, which would take place under the same meteorological conditions as were performed when entering the model Data by the mentioned programs. This would confirm the legitimacy of using special software to estimate the impact of the negative effects of chemical accidents. © 2020 International Multidisciplinary Scientific Geoconference. All rights reserved

  • Comparsion of the resulting models of dispersion of hazardous substances created in the software aloha and terex
    International Multidisciplinary Scientific Geoconference, 2021
    Co-Authors: Kotková Barbora, Hromada Martin
    Abstract:

    The article deals with the issue of accidents associated with hazardous substances, their prevention. Modeling of these accidents was chosen as a form of prevention. The production of dangerous toxic substances is currently growing, especially in the industry. The response to this trend is to introduce safer technological processes and preventive measures. These measures serve to prevent or at least reduce the risk of accidents and thus the escape of dangerous substances. To implement prevention, effective measures, and mitigation of the consequences of a chemical accident, it is necessary to know or at least correctly estimate the course and subsequent impact of the accident. Modeling programs are one of the most modern and effective tools used to illustrate the effects of accidents. These include two special software programs, the ALOHA program, and the TerEx program. The article describes the basic theoretical aspects related to the dispersion of gaseous toxic substances in the Earth's atmosphere and the extent of their harmful effects. It also compares the resulting models of both programs when entering the same Input Data - Type and amount of hazardous substance, ambient temperature, wind speed, degree of turbidity, weather stability class, Type of terrain surface. Verification of the theoretical results would be possible experimentally, provided that a design of a field test is carried out, which would take place under the same meteorological conditions as were performed when entering the model Data by the mentioned programs. This would confirm the legitimacy of using special software to estimate the impact of the negative effects of chemical accidents. © 2020 International Multidisciplinary Scientific Geoconference. All rights reserved.Univerzita Tomáše Bati ve Zlíně: IGA / FAI / 2020/00

David F Van Komen - One of the best experts on this subject based on the ideXlab platform.

  • seabed Type and source parameters predictions using ship spectrograms in convolutional neural networks
    Journal of the Acoustical Society of America, 2021
    Co-Authors: David F Van Komen, Tracianne B Neilsen, David P Knobles, Daniel B Mortenson, Mason C Acree, Mohsen Badiey, W S Hodgkiss
    Abstract:

    Broadband spectrograms from surface ships are employed in convolutional neural networks (CNNs) to predict the seabed Type, ship speed, and closest point of approach (CPA) range. Three CNN architectures of differing size and depth are trained on different representations of the spectrograms. Multitask learning is employed; the seabed Type prediction comes from classification, and the ship speed and CPA range are estimated via regression. Due to the lack of labeled field Data, the CNNs are trained on synthetic Data generated using measured sound speed profiles, four seabed Types, and a random distribution of source parameters. Additional synthetic Datasets are used to evaluate the ability of the trained CNNs to interpolate and extrapolate source parameters. The trained models are then applied to a measured Data sample from the 2017 Seabed Characterization Experiment (SBCEX 2017). While the largest network provides slightly more accurate predictions on tests with synthetic Data, the smallest network generalized better to the measured Data sample. With regard to the Input Data Type, complex pressure spectral values gave the most accurate and consistent results for the ship speed and CPA predictions with the smallest network, whereas using absolute values of the pressure provided more accurate results compared to the expected seabed Types.

  • optimal experimental design for machine learning using the fisher information matrix
    Journal of the Acoustical Society of America, 2018
    Co-Authors: Tracianne B Neilsen, Mark K Transtrum, David F Van Komen, David P Knobles
    Abstract:

    Optimal experimental design (OED) refers to a class of methods for selecting new Data collection conditions that minimize the statistical uncertainty in the inferred parameter values of a model. The Fisher information matrix (FIM) gives an estimate of the relative uncertainty in and correlation among the model parameters based on the local curvature of the cost function. FIM-based approaches to OED allow for rapid assessment of many different experimental conditions (e.g., Input Data Type, parameterizations, etc.). In machine learning models, accurate parameter estimates are often not a priority (nor even desirable) as they have no direct physical meaning. Instead, one would like to minimize the uncertainty in the model predictions for several quantities of interest. FIM approaches to OED can be generalized to minimize statistical variance, not in parameters, but in predictions of the quantities of interest. This approach has been applied, for example, to systems biology models of biochemical reaction networks [Transtrum and Qiu, BMC Bioinformatics 13(1), 181 (2012)]. Preliminary application of the FIM to optimize experimental design for source localization in an uncertain ocean environment is a first step towards an efficient machine learning algorithm that produces results with the least uncertainty in the quantities of interest.

Saad Bin Qaisar - One of the best experts on this subject based on the ideXlab platform.

  • One-class support vector machines: analysis of outlier detection for wireless sensor networks in harsh environments
    Artificial Intelligence Review, 2015
    Co-Authors: Nauman Shahid, Ijaz Haider Naqvi, Saad Bin Qaisar
    Abstract:

    Machine learning, like its various applications, has received a great interest in outlier detection in Wireless Sensor Networks. Support Vector Machines (SVM) are a special Type of Machine learning techniques which are computationally inexpensive and provide a sparse solution. This work presents a detailed analysis of various formulations of one-class SVMs, like, hyper-plane, hyper-sphere, quarter-sphere and hyper-ellipsoidal. These formulations are used to separate the normal Data from anomalous Data. Various techniques based on these formulations have been analyzed in terms of a number of characteristics for harsh environments. These characteristics include Input Data Type, spatio-temporal and attribute correlations, user specified thresholds, outlier Types, outlier identification(event/error), outlier degree, susceptibility to dynamic topology, non-stationarity and inhomogeneity. A tabular description of improvement and feasibility of various techniques for deployment in the harsh environments has also been presented.

David P Knobles - One of the best experts on this subject based on the ideXlab platform.

  • seabed Type and source parameters predictions using ship spectrograms in convolutional neural networks
    Journal of the Acoustical Society of America, 2021
    Co-Authors: David F Van Komen, Tracianne B Neilsen, David P Knobles, Daniel B Mortenson, Mason C Acree, Mohsen Badiey, W S Hodgkiss
    Abstract:

    Broadband spectrograms from surface ships are employed in convolutional neural networks (CNNs) to predict the seabed Type, ship speed, and closest point of approach (CPA) range. Three CNN architectures of differing size and depth are trained on different representations of the spectrograms. Multitask learning is employed; the seabed Type prediction comes from classification, and the ship speed and CPA range are estimated via regression. Due to the lack of labeled field Data, the CNNs are trained on synthetic Data generated using measured sound speed profiles, four seabed Types, and a random distribution of source parameters. Additional synthetic Datasets are used to evaluate the ability of the trained CNNs to interpolate and extrapolate source parameters. The trained models are then applied to a measured Data sample from the 2017 Seabed Characterization Experiment (SBCEX 2017). While the largest network provides slightly more accurate predictions on tests with synthetic Data, the smallest network generalized better to the measured Data sample. With regard to the Input Data Type, complex pressure spectral values gave the most accurate and consistent results for the ship speed and CPA predictions with the smallest network, whereas using absolute values of the pressure provided more accurate results compared to the expected seabed Types.

  • optimal experimental design for machine learning using the fisher information matrix
    Journal of the Acoustical Society of America, 2018
    Co-Authors: Tracianne B Neilsen, Mark K Transtrum, David F Van Komen, David P Knobles
    Abstract:

    Optimal experimental design (OED) refers to a class of methods for selecting new Data collection conditions that minimize the statistical uncertainty in the inferred parameter values of a model. The Fisher information matrix (FIM) gives an estimate of the relative uncertainty in and correlation among the model parameters based on the local curvature of the cost function. FIM-based approaches to OED allow for rapid assessment of many different experimental conditions (e.g., Input Data Type, parameterizations, etc.). In machine learning models, accurate parameter estimates are often not a priority (nor even desirable) as they have no direct physical meaning. Instead, one would like to minimize the uncertainty in the model predictions for several quantities of interest. FIM approaches to OED can be generalized to minimize statistical variance, not in parameters, but in predictions of the quantities of interest. This approach has been applied, for example, to systems biology models of biochemical reaction networks [Transtrum and Qiu, BMC Bioinformatics 13(1), 181 (2012)]. Preliminary application of the FIM to optimize experimental design for source localization in an uncertain ocean environment is a first step towards an efficient machine learning algorithm that produces results with the least uncertainty in the quantities of interest.