The Experts below are selected from a list of 66816 Experts worldwide ranked by ideXlab platform

Bao Rong Chang - One of the best experts on this subject based on the ideXlab platform.

  • Timing of Resources Exploration in the behavior of firm-Empirical simulation by intelligent hybrid model
    Applied Soft Computing, 2011
    Co-Authors: Bao Rong Chang, Hsiu Fen Tsai
    Abstract:

    We have insight into the importance of resource Exploration derived from the quest for sustaining competitive advantage as well as the growth of the firm, which are well-explicated in the Resources point of view. However, we really do not know when the firm will seriously commit to this kind of activities. Therefore, this study proposes an intelligent hybrid model using quantum minimization (QM) to tune a composite model adaptive support vector regression (ASVR) and nonlinear generalized autoregressive conditional heteroscedasticity (NGARCH) such that it constitutes the relationship among five indicators, the growth rate of long-term investment, the firm size, the return on total asset, the return on common equity, and the return on sales. In particular, this proposed approach outperforms several typical methods such as auto-regressive moving-average regression (ARMAX), back-propagation neural network (BPNN), or adaptive neuron-fuzzy inference system (ANFIS) for this timing problem in term of comparing their achievement and the goodness of fit. Consequently, the preceding methods involved in this problem truly explain the timing of Resources Exploration in the behavior of firm. Meanwhile, the performance summary among methods is compared quantitatively.

  • Timing of Resources Exploration in the behavior of firm - Innovative approach and empirical simulation
    Expert Systems with Applications, 2008
    Co-Authors: Hsiu Fen Tsai, Bao Rong Chang
    Abstract:

    We have insight into the importance of resource Exploration derived from the quest for sustaining competitive advantage as well as the growth of the firm, which are well-explicated in the Resources point of view. However, we really do not know when the firm will seriously commit to this kind of activities. Therefore, this study proposes an innovative approach using quantum minimization (QM) to tune a composite model comprising adaptive neuron-fuzzy inference system (ANFIS) and nonlinear generalized autoregressive conditional heteroscedasticity (NGARCH) such that it constitutes the relationship among five indicators, the growth rate of long-term investment, the firm size, the return on total asset, the return on common equity, and the return on sales. In particularly, this proposed approach outperforms several typical methods such as auto-regressive moving-average regression (ARMAX), back-propagation neural network (BPNN), or adaptive support vector regression (ASVR) for this timing problem in term of comparing their achievement and the goodness-of-fit. Consequently, the preceding methods involved in this problem truly explain the timing of Resources Exploration in the behavior of firm. Meanwhile, the performance summary among methods is compared quantitatively.

  • Intelligence-Based Model to Timing Problem of Resources Exploration in the Behavior of Firm
    Lecture Notes in Computer Science, 2006
    Co-Authors: Hsiu Fen Tsai, Bao Rong Chang
    Abstract:

    We have insight into the importance of resource Exploration derived from the quest for sustaining competitive advantage as well as the growth of the firm, which are well-explicated in the Resources-based view. However, we really do not know when the firm will seriously commit to this kind of activities. Therefore, this study proposes an intelligence-based model using quantum minimization (QM) to tune a composite model of adaptive neuron-fuzzy inference system (ANFIS) and nonlinear generalized autoregressive conditional heteroscedasticity (NGARCH) such that it constitutes the relationship among five indicators, the growth rate of long-term investment, the firm size, the return on total asset, the return on common equity, and the return on sales. In particularly, this proposed approach outperforms several typical methods such as autoregressive moving-average regression (ARMAX), back-propagation neural network (BPNN), or adaptive support vector regression (ASVR) for this timing problem in term of comparing their achievement and the goodness of fit. Consequently, the preceding methods involved in this problem truly explain the timing of Resources Exploration in the behavior of firm. Meanwhile, the performance summary among methods is compared quantitatively.

  • ICONIP (3) - Intelligence-based model to timing problem of Resources Exploration in the behavior of firm
    Neural Information Processing, 2006
    Co-Authors: Hsiu Fen Tsai, Bao Rong Chang
    Abstract:

    We have insight into the importance of resource Exploration derived from the quest for sustaining competitive advantage as well as the growth of the firm, which are well-explicated in the Resources-based view. However, we really do not know when the firm will seriously commit to this kind of activities. Therefore, this study proposes an intelligence-based model using quantum minimization (QM) to tune a composite model of adaptive neuron-fuzzy inference system (ANFIS) and nonlinear generalized autoregressive conditional heteroscedasticity (NGARCH) such that it constitutes the relationship among five indicators, the growth rate of long-term investment, the firm size, the return on total asset, the return on common equity, and the return on sales. In particularly, this proposed approach outperforms several typical methods such as autoregressive moving-average regression (ARMAX), back-propagation neural network (BPNN), or adaptive support vector regression (ASVR) for this timing problem in term of comparing their achievement and the goodness of fit. Consequently, the preceding methods involved in this problem truly explain the timing of Resources Exploration in the behavior of firm. Meanwhile, the performance summary among methods is compared quantitatively.

  • Intelligent approach to timing of Resources Exploration in the behavior of firm using ARMAX, BPNN, OR SASVR
    2005 International Symposium on Intelligent Signal Processing and Communication Systems, 2005
    Co-Authors: Bao Rong Chang, Hsiu Fen Tsai
    Abstract:

    We have insight into the importance of resource Exploration derived from the quest for sustaining competitive advantage as well as the growth of the firm, which are well-explicated in the Resources-based view. However, we really do not know when the firm will seriously commit to this kind of activities. Therefore, this study proposes intelligent approach using auto-regressive moving-average regression (ARMAX), back-propagation neural network (BPNN), or segmented adaptive support vector regression (SASVR) to constitute the relationship among five indicators, the growth rate of long-term investment, the firm size, the return on total asset, the return on common equity, and the return on sales. In such a way, the methods we build can explain the timing of Resources Exploration in the behavior of firm. Meanwhile, the performance between these methods is compared quantitatively.

Hsiu Fen Tsai - One of the best experts on this subject based on the ideXlab platform.

  • Timing of Resources Exploration in the behavior of firm-Empirical simulation by intelligent hybrid model
    Applied Soft Computing, 2011
    Co-Authors: Bao Rong Chang, Hsiu Fen Tsai
    Abstract:

    We have insight into the importance of resource Exploration derived from the quest for sustaining competitive advantage as well as the growth of the firm, which are well-explicated in the Resources point of view. However, we really do not know when the firm will seriously commit to this kind of activities. Therefore, this study proposes an intelligent hybrid model using quantum minimization (QM) to tune a composite model adaptive support vector regression (ASVR) and nonlinear generalized autoregressive conditional heteroscedasticity (NGARCH) such that it constitutes the relationship among five indicators, the growth rate of long-term investment, the firm size, the return on total asset, the return on common equity, and the return on sales. In particular, this proposed approach outperforms several typical methods such as auto-regressive moving-average regression (ARMAX), back-propagation neural network (BPNN), or adaptive neuron-fuzzy inference system (ANFIS) for this timing problem in term of comparing their achievement and the goodness of fit. Consequently, the preceding methods involved in this problem truly explain the timing of Resources Exploration in the behavior of firm. Meanwhile, the performance summary among methods is compared quantitatively.

  • Timing of Resources Exploration in the behavior of firm - Innovative approach and empirical simulation
    Expert Systems with Applications, 2008
    Co-Authors: Hsiu Fen Tsai, Bao Rong Chang
    Abstract:

    We have insight into the importance of resource Exploration derived from the quest for sustaining competitive advantage as well as the growth of the firm, which are well-explicated in the Resources point of view. However, we really do not know when the firm will seriously commit to this kind of activities. Therefore, this study proposes an innovative approach using quantum minimization (QM) to tune a composite model comprising adaptive neuron-fuzzy inference system (ANFIS) and nonlinear generalized autoregressive conditional heteroscedasticity (NGARCH) such that it constitutes the relationship among five indicators, the growth rate of long-term investment, the firm size, the return on total asset, the return on common equity, and the return on sales. In particularly, this proposed approach outperforms several typical methods such as auto-regressive moving-average regression (ARMAX), back-propagation neural network (BPNN), or adaptive support vector regression (ASVR) for this timing problem in term of comparing their achievement and the goodness-of-fit. Consequently, the preceding methods involved in this problem truly explain the timing of Resources Exploration in the behavior of firm. Meanwhile, the performance summary among methods is compared quantitatively.

  • Intelligence-Based Model to Timing Problem of Resources Exploration in the Behavior of Firm
    Lecture Notes in Computer Science, 2006
    Co-Authors: Hsiu Fen Tsai, Bao Rong Chang
    Abstract:

    We have insight into the importance of resource Exploration derived from the quest for sustaining competitive advantage as well as the growth of the firm, which are well-explicated in the Resources-based view. However, we really do not know when the firm will seriously commit to this kind of activities. Therefore, this study proposes an intelligence-based model using quantum minimization (QM) to tune a composite model of adaptive neuron-fuzzy inference system (ANFIS) and nonlinear generalized autoregressive conditional heteroscedasticity (NGARCH) such that it constitutes the relationship among five indicators, the growth rate of long-term investment, the firm size, the return on total asset, the return on common equity, and the return on sales. In particularly, this proposed approach outperforms several typical methods such as autoregressive moving-average regression (ARMAX), back-propagation neural network (BPNN), or adaptive support vector regression (ASVR) for this timing problem in term of comparing their achievement and the goodness of fit. Consequently, the preceding methods involved in this problem truly explain the timing of Resources Exploration in the behavior of firm. Meanwhile, the performance summary among methods is compared quantitatively.

  • ICONIP (3) - Intelligence-based model to timing problem of Resources Exploration in the behavior of firm
    Neural Information Processing, 2006
    Co-Authors: Hsiu Fen Tsai, Bao Rong Chang
    Abstract:

    We have insight into the importance of resource Exploration derived from the quest for sustaining competitive advantage as well as the growth of the firm, which are well-explicated in the Resources-based view. However, we really do not know when the firm will seriously commit to this kind of activities. Therefore, this study proposes an intelligence-based model using quantum minimization (QM) to tune a composite model of adaptive neuron-fuzzy inference system (ANFIS) and nonlinear generalized autoregressive conditional heteroscedasticity (NGARCH) such that it constitutes the relationship among five indicators, the growth rate of long-term investment, the firm size, the return on total asset, the return on common equity, and the return on sales. In particularly, this proposed approach outperforms several typical methods such as autoregressive moving-average regression (ARMAX), back-propagation neural network (BPNN), or adaptive support vector regression (ASVR) for this timing problem in term of comparing their achievement and the goodness of fit. Consequently, the preceding methods involved in this problem truly explain the timing of Resources Exploration in the behavior of firm. Meanwhile, the performance summary among methods is compared quantitatively.

  • Intelligent approach to timing of Resources Exploration in the behavior of firm using ARMAX, BPNN, OR SASVR
    2005 International Symposium on Intelligent Signal Processing and Communication Systems, 2005
    Co-Authors: Bao Rong Chang, Hsiu Fen Tsai
    Abstract:

    We have insight into the importance of resource Exploration derived from the quest for sustaining competitive advantage as well as the growth of the firm, which are well-explicated in the Resources-based view. However, we really do not know when the firm will seriously commit to this kind of activities. Therefore, this study proposes intelligent approach using auto-regressive moving-average regression (ARMAX), back-propagation neural network (BPNN), or segmented adaptive support vector regression (SASVR) to constitute the relationship among five indicators, the growth rate of long-term investment, the firm size, the return on total asset, the return on common equity, and the return on sales. In such a way, the methods we build can explain the timing of Resources Exploration in the behavior of firm. Meanwhile, the performance between these methods is compared quantitatively.

Koji Takahashi - One of the best experts on this subject based on the ideXlab platform.

  • Reconstructed real-time 3D image of chimneys using underwater acoustic video camera in the experimental field
    OCEANS 2016 MTS IEEE Monterey, 2016
    Co-Authors: Kazuki Abukawa, Sayuri Matsumoto, Taketsugu Hirabayashi, Tomoo Sato, Hiroshi Iida, Mitsuhiko Nanri, Muneo Yoshie, Kageyoshi Katakura, Koji Takahashi
    Abstract:

    Ocean Resources Exploration and underwater works using Remotely Operated Vehicle and Autonomous Underwater Vehicle increase interest, and a techniques of underwater image with acoustic has become a significant concern. Therefore, a development of new underwater acoustic device adapted to ocean Resources Exploration and underwater works. In this study, we reconstructed real-time 3D image of chimneys using underwater acoustic video camera in the experimental field. The experiment was performed by means of an acoustic video camera, underwater crawler, and chimneys. High spatial imaging and software were developed for this experiment and they were used to generate 3D views, to display an operation views and measurement distance of targets. Our results will be necessary for the ocean Resources Exploration and underwater works.

  • Experimentation for development of underwater acoustic video camera: In experiment dock
    OCEANS 2015 - MTS IEEE Washington, 2015
    Co-Authors: Kazuki Abukawa, Sayuri Matsumoto, Taketsugu Hirabayashi, Kazuhiro Shirai, Tomoo Sato, Hiroshi Iida, Mitsuhiko Nanri, Muneo Yoshie, Kageyoshi Katakura, Koji Takahashi
    Abstract:

    Ocean Resources Exploration and underwater works using Remotely Operated Vehicle and Autonomous Underwater Vehicle increase interest, and a techniques of underwater image with acoustic has become a significant concern. Therefore, a development of new underwater acoustic device adapted to ocean Resources Exploration and underwater works. In this study, we experiment with the use of acoustic video camera for development of a new acoustic video camera and software to meet ocean Resources Exploration and underwater works needs. The experiment was performed by means of an acoustic video camera, underwater teleoperated excavator, underwater crawler, and simulated chimney. High spatial imaging and software were developed for this experiment and they were used to generate 3D views, to display an operation views and measurement distance of targets. Our results will be necessary for the ocean Resources Exploration and underwater works.

Kazuki Abukawa - One of the best experts on this subject based on the ideXlab platform.

  • Reconstructed real-time 3D image of chimneys using underwater acoustic video camera in the experimental field
    OCEANS 2016 MTS IEEE Monterey, 2016
    Co-Authors: Kazuki Abukawa, Sayuri Matsumoto, Taketsugu Hirabayashi, Tomoo Sato, Hiroshi Iida, Mitsuhiko Nanri, Muneo Yoshie, Kageyoshi Katakura, Koji Takahashi
    Abstract:

    Ocean Resources Exploration and underwater works using Remotely Operated Vehicle and Autonomous Underwater Vehicle increase interest, and a techniques of underwater image with acoustic has become a significant concern. Therefore, a development of new underwater acoustic device adapted to ocean Resources Exploration and underwater works. In this study, we reconstructed real-time 3D image of chimneys using underwater acoustic video camera in the experimental field. The experiment was performed by means of an acoustic video camera, underwater crawler, and chimneys. High spatial imaging and software were developed for this experiment and they were used to generate 3D views, to display an operation views and measurement distance of targets. Our results will be necessary for the ocean Resources Exploration and underwater works.

  • Experimentation for development of underwater acoustic video camera: In experiment dock
    OCEANS 2015 - MTS IEEE Washington, 2015
    Co-Authors: Kazuki Abukawa, Sayuri Matsumoto, Taketsugu Hirabayashi, Kazuhiro Shirai, Tomoo Sato, Hiroshi Iida, Mitsuhiko Nanri, Muneo Yoshie, Kageyoshi Katakura, Koji Takahashi
    Abstract:

    Ocean Resources Exploration and underwater works using Remotely Operated Vehicle and Autonomous Underwater Vehicle increase interest, and a techniques of underwater image with acoustic has become a significant concern. Therefore, a development of new underwater acoustic device adapted to ocean Resources Exploration and underwater works. In this study, we experiment with the use of acoustic video camera for development of a new acoustic video camera and software to meet ocean Resources Exploration and underwater works needs. The experiment was performed by means of an acoustic video camera, underwater teleoperated excavator, underwater crawler, and simulated chimney. High spatial imaging and software were developed for this experiment and they were used to generate 3D views, to display an operation views and measurement distance of targets. Our results will be necessary for the ocean Resources Exploration and underwater works.

Maxence Rioblanc - One of the best experts on this subject based on the ideXlab platform.

  • High productivity multi-sensor seabed mapping sonar for Marine Mineral Resources Exploration
    2013 IEEE OES Acoustics in Underwater Geosciences Symposium, 2013
    Co-Authors: Maxence Rioblanc
    Abstract:

    Marine Mineral Resources Exploration is an emerging activity requiring suitable equipment that will deliver on a timely manner both geo-referenced seabed maps and sub-bottom profiles. The race for deep sea mining has globally started while costs involved have to be minimized at all stages from Exploration to exploitation. Building on its 20-year long experience in designing and manufacturing active transducers and passive sensors, the Sonar Systems Division of iXBlue is also at the cutting edge of the synthetic aperture sonar (SAS) development while mastering the long range underwater acoustic communication transducer technology (used for instance by JAMSTEC) and the Wide-Band, Flat Spectrum Sub-Bottom Profiler Transducers. The SAMS is a modular Synthetic Aperture Mapping Sonar product-line delivering real-time geo-referenced maps at a high productivity rate (>133km2/day). Several solutions with diverse resolutions and ranges are available while host platforms are multiple (Towed Fish, AllV). The SAMS-DT6000 in particular will be presented as it has been especially designed for Marine Mineral Resource Exploration as deep as -6000m.

  • High productivity multi-sensor seabed Mapping Sonar for Marine Mineral Resources Exploration
    2013 IEEE International Underwater Technology Symposium (UT), 2013
    Co-Authors: Maxence Rioblanc
    Abstract:

    Marine Mineral Resources Exploration is an emerging activity requiring suitable equipment that will deliver on a timely manner both geo-referenced seabed maps and sub-bottom profiles. The race for deep sea mining has globally started while costs involved have to be minimized at all stages from Exploration to exploitation. Building on its 20-year long experience in designing and manufacturing active transducers and passive sensors, the Sonar Systems Division of iXBlue is also at the cutting edge of the synthetic aperture sonar (SAS) development while mastering the long range underwater acoustic communication transducer technology (used for instance by JAMSTEC) and the Wide-Band, Flat Spectrum Sub-Bottom Profiler Transducers. The SAMS is a modular Synthetic Aperture Mapping Sonar product-line delivering real-time geo-referenced maps at a high productivity rate (>133km/day). Several solutions with diverse resolutions and ranges are available while host platforms are multiple (Towed Fish, AUV). The SAMS-DT6000 in particular will be presented as it has been especially designed for Marine Mineral Resource Exploration as deep as -6000m. This paper describes more specifically the time synchronization and references to manage accurate data positioning.