The Experts below are selected from a list of 324 Experts worldwide ranked by ideXlab platform

William J. Kettinger - One of the best experts on this subject based on the ideXlab platform.

  • System sourcing and information processing Capability in supply chains: a study of small suppliers
    Information Technology and Management, 2016
    Co-Authors: Liwen Hou, Ling Xue, Son Ngoc Bui, William J. Kettinger
    Abstract:

    Although the adoption of inter-organizational systems (IOS) has been studied extensively in the existing literature, relatively little research attention has been paid to how small firms in supply chains are influenced by their larger partners in making their system sourcing decisions. In this study, we examine how small suppliers may choose their system sourcing approaches to better serve and collaborate with their large customers in supply chains. Our empirical findings suggest that information processing requirements and collaboration needs drive small suppliers to adopt more commercial off-the-shelf software package (i.e., external sourcing) to build their IOSs. Moreover, we find that small suppliers take such external sourcing as a way to achieve system effectiveness and enhance their information processing capabilities. The findings of this study generate important theoretical and managerial implications for IOS adoption and organizational information processing.

Kohei Nakajima - One of the best experts on this subject based on the ideXlab platform.

  • Effect of recurrent infomax on the information processing Capability of input-driven recurrent neural networks.
    Neuroscience research, 2020
    Co-Authors: Takuma Tanaka, Kohei Nakajima, Toshio Aoyagi
    Abstract:

    Abstract Reservoir computing is a framework for exploiting the inherent transient dynamics of recurrent neural networks (RNNs) as a computational resource. On the basis of this framework, much research has been conducted to evaluate the relationship between the dynamics of RNNs and the RNNs’ information processing Capability. In this study, we present a detailed analysis of the information processing Capability of an RNN optimized by recurrent infomax (RI), an unsupervised learning method that maximizes the mutual information of RNNs by adjusting the connection weights of the network. The results indicate that RI leads to the emergence of a delay-line structure and that the network optimized by the RI possesses a superior short-term memory, which is the ability to store the temporal information of the input stream in its transient dynamics.

  • Computing with vortices: Bridging fluid dynamics and its Information-Processing Capability
    arXiv: Fluid Dynamics, 2020
    Co-Authors: Ken Goto, Kohei Nakajima, Hirofumi Notsu
    Abstract:

    Herein, the Karman vortex system is considered to be a large recurrent neural network, and the computational Capability is numerically evaluated by emulating nonlinear dynamical systems and the memory capacity. Therefore, the Reynolds number dependence of the Karman vortex system computational performance is revealed and the optimal computational performance is achieved near the critical Reynolds number at the onset of Karman vortex shedding, which is associated with a Hopf bifurcation. Our finding advances the understanding of the relationship between the physical properties of fluid dynamics and its computational Capability as well as provides an alternative to the widely believed viewpoint that the information processing Capability becomes optimal at the edge of chaos.

  • RoboSoft - Information Processing Capability of Soft Continuum Arms
    2019 2nd IEEE International Conference on Soft Robotics (RoboSoft), 2019
    Co-Authors: Estefany A. Torres, Kohei Nakajima, Isuru S. Godage
    Abstract:

    Soft Continuum arms, such as trunk and tentacle robots, can be considered as the “dual” of traditional rigid-bodied robots in terms of manipulability, degrees of freedom, and compliance. Introduced two decades ago, continuum arms have not yet realized their full potential, and largely remain as laboratory curiosities. The reasons for this lag rest upon their inherent physical features such as high compliance which contribute to their complex control problems that no research has yet managed to surmount. Recently, reservoir computing has been suggested as a way to employ the body dynamics as a computational resource toward implementing compliant body control. In this paper, as a first step, we investigate the information processing Capability of soft continuum arms. We apply input signals of varying amplitude and bandwidth to a soft continuum arm and generate the dynamic response for a large number of trials. These data is aggregated and used to train the readout weights to implement a reservoir computing scheme. Results demonstrate that the information processing Capability varies across input signal bandwidth and amplitude. These preliminary results demonstrate that soft continuum arms have optimal bandwidth and amplitude where one can implement reservoir computing.

  • Information Processing Capability of Soft Continuum Arms.
    arXiv: Robotics, 2018
    Co-Authors: Estefany A. Torres, Kohei Nakajima, Isuru S. Godage
    Abstract:

    Soft Continuum arms, such as trunk and tentacle robots, can be considered as the "dual" of traditional rigid-bodied robots in terms of manipulability, degrees of freedom, and compliance. Introduced two decades ago, continuum arms have not yet realized their full potential, and largely remain as laboratory curiosities. The reasons for this lag rest upon their inherent physical features such as high compliance which contribute to their complex control problems that no research has yet managed to surmount. Recently, reservoir computing has been suggested as a way to employ the body dynamics as a computational resource toward implementing compliant body control. In this paper, as a first step, we investigate the information processing Capability of soft continuum arms. We apply input signals of varying amplitude and bandwidth to a soft continuum arm and generate the dynamic response for a large number of trials. These data is aggregated and used to train the readout weights to implement a reservoir computing scheme. Results demonstrate that the information processing Capability varies across input signal bandwidth and amplitude. These preliminary results demonstrate that soft continuum arms have optimal bandwidth and amplitude where one can implement reservoir computing.

  • Use of recurrent infomax to improve the memory Capability of input-driven recurrent neural networks.
    arXiv: Neural and Evolutionary Computing, 2018
    Co-Authors: Hisashi Iwade, Kohei Nakajima, Takuma Tanaka, Toshio Aoyagi
    Abstract:

    The inherent transient dynamics of recurrent neural networks (RNNs) have been exploited as a computational resource in input-driven RNNs. However, the information processing Capability varies from RNN to RNN, depending on their properties. Many authors have investigated the dynamics of RNNs and their relevance to the information processing Capability. In this study, we present a detailed analysis of the information processing Capability of an RNN optimized by recurrent infomax (RI), which is an unsupervised learning scheme that maximizes the mutual information of RNNs by adjusting the connection strengths of the network. Thus, we observe that a delay-line structure emerges from the RI and the network optimized by the RI possesses superior short-term memory, which is the ability to store the temporal information of the input stream in its transient dynamics.

Sebastian Jilke - One of the best experts on this subject based on the ideXlab platform.

  • Impact of technological uncertainty and technological complexity on organizational information processing Capability: the moderating role of work experience
    European Journal of Innovation Management, 2020
    Co-Authors: Sebastian Jilke
    Abstract:

    PurposeTechnological uncertainty and technological complexity are key characteristics of new product development (NPD) projects that impose significant information processing requirements on organizations. This paper examines the direct influence of technological uncertainty and technological complexity as well as the indirect influence of work experience on organizational information processing capabilities.Design/methodology/approachThe author used a sample of 166 respondents from the German automotive industry and applied linear hierarchical regression analysis.FindingsThe results confirm a negative influence of technological uncertainty and technological complexity on organizational information processing Capability. This research also supports a moderating influence of work experience on these relationships.Originality/valueThis research helps to understand the relationship between technological uncertainty, technological complexity and OIPC. It represents a first and different approach to measure these constructs for further empirical studies and provides interesting managerial implications.

Liwen Hou - One of the best experts on this subject based on the ideXlab platform.

  • System sourcing and information processing Capability in supply chains: a study of small suppliers
    Information Technology and Management, 2016
    Co-Authors: Liwen Hou, Ling Xue, Son Ngoc Bui, William J. Kettinger
    Abstract:

    Although the adoption of inter-organizational systems (IOS) has been studied extensively in the existing literature, relatively little research attention has been paid to how small firms in supply chains are influenced by their larger partners in making their system sourcing decisions. In this study, we examine how small suppliers may choose their system sourcing approaches to better serve and collaborate with their large customers in supply chains. Our empirical findings suggest that information processing requirements and collaboration needs drive small suppliers to adopt more commercial off-the-shelf software package (i.e., external sourcing) to build their IOSs. Moreover, we find that small suppliers take such external sourcing as a way to achieve system effectiveness and enhance their information processing capabilities. The findings of this study generate important theoretical and managerial implications for IOS adoption and organizational information processing.

Isuru S. Godage - One of the best experts on this subject based on the ideXlab platform.

  • RoboSoft - Information Processing Capability of Soft Continuum Arms
    2019 2nd IEEE International Conference on Soft Robotics (RoboSoft), 2019
    Co-Authors: Estefany A. Torres, Kohei Nakajima, Isuru S. Godage
    Abstract:

    Soft Continuum arms, such as trunk and tentacle robots, can be considered as the “dual” of traditional rigid-bodied robots in terms of manipulability, degrees of freedom, and compliance. Introduced two decades ago, continuum arms have not yet realized their full potential, and largely remain as laboratory curiosities. The reasons for this lag rest upon their inherent physical features such as high compliance which contribute to their complex control problems that no research has yet managed to surmount. Recently, reservoir computing has been suggested as a way to employ the body dynamics as a computational resource toward implementing compliant body control. In this paper, as a first step, we investigate the information processing Capability of soft continuum arms. We apply input signals of varying amplitude and bandwidth to a soft continuum arm and generate the dynamic response for a large number of trials. These data is aggregated and used to train the readout weights to implement a reservoir computing scheme. Results demonstrate that the information processing Capability varies across input signal bandwidth and amplitude. These preliminary results demonstrate that soft continuum arms have optimal bandwidth and amplitude where one can implement reservoir computing.

  • Information Processing Capability of Soft Continuum Arms.
    arXiv: Robotics, 2018
    Co-Authors: Estefany A. Torres, Kohei Nakajima, Isuru S. Godage
    Abstract:

    Soft Continuum arms, such as trunk and tentacle robots, can be considered as the "dual" of traditional rigid-bodied robots in terms of manipulability, degrees of freedom, and compliance. Introduced two decades ago, continuum arms have not yet realized their full potential, and largely remain as laboratory curiosities. The reasons for this lag rest upon their inherent physical features such as high compliance which contribute to their complex control problems that no research has yet managed to surmount. Recently, reservoir computing has been suggested as a way to employ the body dynamics as a computational resource toward implementing compliant body control. In this paper, as a first step, we investigate the information processing Capability of soft continuum arms. We apply input signals of varying amplitude and bandwidth to a soft continuum arm and generate the dynamic response for a large number of trials. These data is aggregated and used to train the readout weights to implement a reservoir computing scheme. Results demonstrate that the information processing Capability varies across input signal bandwidth and amplitude. These preliminary results demonstrate that soft continuum arms have optimal bandwidth and amplitude where one can implement reservoir computing.