The Experts below are selected from a list of 39729 Experts worldwide ranked by ideXlab platform

Xiangyu Wang - One of the best experts on this subject based on the ideXlab platform.

  • a critical review of the use of virtual reality in construction engineering education and training
    International Journal of Environmental Research and Public Health, 2018
    Co-Authors: Peng Wang, Jun Wang, Hunglin Chi, Xiangyu Wang
    Abstract:

    Virtual Reality (VR) has been rapidly recognized and implemented in construction engineering education and training (CEET) in recent years due to its benefits of providing an engaging and immersive environment. The objective of this review is to critically collect and analyze the VR applications in CEET, aiming at all VR-related journal papers published from 1997 to 2017. The review follows a three-stage analysis on VR technologies, applications and future directions through a systematic analysis. It is found that the VR technologies adopted for CEET evolve over time, from desktop-based VR, immersive VR, 3D game-based VR, to Building Information Modelling (BIM)-enabled VR. A sibling technology, Augmented Reality (AR), for CEET adoptions has also emerged in recent years. These technologies have been applied in architecture and design visualization, construction health and safety training, equipment and Operational Task training, as well as structural analysis. Future research directions, including the integration of VR with emerging education paradigms and visualization technologies, have also been provided. The findings are useful for both researchers and educators to usefully integrate VR in their education and training programs to improve the training performance.

Erkan Zergeroglu - One of the best experts on this subject based on the ideXlab platform.

  • Operational Task space learning control of robot manipulators with dynamical uncertainties
    International Conference on Control Applications, 2015
    Co-Authors: Merve K Dogan, Enver Tatlicioglu, Erkan Zergeroglu
    Abstract:

    In this work, we consider the problem of Operational/Task space tracking control of a robot manipulator where a periodic desired end-effector pose is to be tracked. Specifically, we designed a repetitive learning controller that guarantees asymptotic end-effector tracking of periodic trajectories (with known period) while “learning” the overall uncertainties in the system dynamics. The proposed controller does not make use of the inverse kinematic formulation on the position level and the stability of the closed-loop system is guaranteed via Lyapunov based arguments. Numerical studies are conducted on a two link planar robot are presented to illustrate the performance and viability of the proposed method.

  • CCA - Operational/Task space learning control of robot manipulators with dynamical uncertainties
    2015 IEEE Conference on Control Applications (CCA), 2015
    Co-Authors: K. Merve Dogan, Enver Tatlicioglu, Erkan Zergeroglu
    Abstract:

    In this work, we consider the problem of Operational/Task space tracking control of a robot manipulator where a periodic desired end-effector pose is to be tracked. Specifically, we designed a repetitive learning controller that guarantees asymptotic end-effector tracking of periodic trajectories (with known period) while “learning” the overall uncertainties in the system dynamics. The proposed controller does not make use of the inverse kinematic formulation on the position level and the stability of the closed-loop system is guaranteed via Lyapunov based arguments. Numerical studies are conducted on a two link planar robot are presented to illustrate the performance and viability of the proposed method.

  • Operational/Task Space Learning Control of Robot Manipulators with
    2015
    Co-Authors: K. Merve Dogan, Enver Tatlicioglu, Erkan Zergeroglu
    Abstract:

    In this work, we consider the problem of op- erational/Task space tracking control of a robot manipulator where a periodic desired end-effector pose is to be tracked. Specifically, we designed a repetitive learning controller that guarantees asymptotic end-effector tracking of periodic tra- jectories (with known period) while "learning" the overall uncertainties in the system dynamics. The proposed controller does not make use of the inverse kinematic formulation on the position level and the stability of the closed-loop system is guaranteed via Lyapunov based arguments. Numerical studies are conducted on a two link planar robot are presented to illustrate the performance and viability of the proposed method. I. INTRODUCTION Most industrial robotic applications require the robot to perform periodic Tasks repetitively in its Operational/Task space. Given the highly nonlinear nature of robot dynamics and existence of several uncertainties, use of model based controllers seems imperative to increase the tracking per- formance. Due to the repetitive nature of the desired end- effector pose and also their capabilities to compensate time- varying uncertainties without requiring of high gain or high frequency feedback terms, learning type controllers loom

  • Operational Task space learning control of robot manipulators with
    2015
    Co-Authors: Merve K Dogan, Enver Tatlicioglu, Erkan Zergeroglu
    Abstract:

    In this work, we consider the problem of op- erational/Task space tracking control of a robot manipulator where a periodic desired end-effector pose is to be tracked. Specifically, we designed a repetitive learning controller that guarantees asymptotic end-effector tracking of periodic tra- jectories (with known period) while "learning" the overall uncertainties in the system dynamics. The proposed controller does not make use of the inverse kinematic formulation on the position level and the stability of the closed-loop system is guaranteed via Lyapunov based arguments. Numerical studies are conducted on a two link planar robot are presented to illustrate the performance and viability of the proposed method. I. INTRODUCTION Most industrial robotic applications require the robot to perform periodic Tasks repetitively in its Operational/Task space. Given the highly nonlinear nature of robot dynamics and existence of several uncertainties, use of model based controllers seems imperative to increase the tracking per- formance. Due to the repetitive nature of the desired end- effector pose and also their capabilities to compensate time- varying uncertainties without requiring of high gain or high frequency feedback terms, learning type controllers loom

Michael C. Caramanis - One of the best experts on this subject based on the ideXlab platform.

  • Demand-Side Management for Regulation Service Provisioning Through Internal Pricing
    IEEE Transactions on Power Systems, 2012
    Co-Authors: Ioannis Ch Paschalidis, Binbin Li, Michael C. Caramanis
    Abstract:

    We develop a market-based mechanism that enables a building smart microgrid operator (SMO) to offer regulation service reserves and meet the associated obligation of fast response to commands issued by the wholesale market independent system operator (ISO) who provides energy and purchases reserves. The proposed market-based mechanism allows the SMO to control the behavior of internal loads through price signals and to provide feedback to the ISO. A regulation service reserves quantity is transacted between the SMO and the ISO for a relatively long period of time (e.g., a one-hour-long time-scale). During this period the ISO follows shorter time-scale stochastic dynamics to repeatedly request from the SMO to decrease or increase its consumption. We model the Operational Task of selecting an optimal short time-scale dynamic pricing policy as a stochastic dynamic program that maximizes average SMO and ISO utility. We then formulate an associated nonlinear programming static problem that provides an upper bound on the optimal utility. We study an asymptotic regime in which this upper bound is shown to be tight and the static policy provides an efficient approximation of the dynamic pricing policy. Equally importantly, this framework allows us to optimize the long time-scale decision of determining the optimal regulation service reserve quantity. We demonstrate, verify and validate the proposed approach through a series of Monte Carlo simulations of the controlled system time trajectories.

  • CDC-ECE - A market-based mechanism for providing demand-side regulation service reserves
    IEEE Conference on Decision and Control and European Control Conference, 2011
    Co-Authors: Ioannis Ch Paschalidis, Binbin Li, Michael C. Caramanis
    Abstract:

    We develop a market-based mechanism that enables a building Smart Microgrid Operator (SMO) to offer regulation service reserves and meet the associated obligation of fast response to commands issued by the wholesale market Independent System Operator (ISO) who provides energy and purchases reserves. The proposed market-based mechanism allows the SMO to control the behavior of internal loads through price signals and to provide feedback to the ISO. A regulation service reserves quantity is transacted between the SMO and the ISO for a relatively long period of time (e.g., a one hour long time-scale). During this period the ISO repeatedly requests from the SMO to decrease or increase its consumption. We model the Operational Task of selecting an optimal short time-scale dynamic pricing policy as a stochastic dynamic program that maximizes average SMO and ISO utility. We then formulate an associated non-linear programming static problem that provides an upper bound on the optimal utility. We study an asymptotic regime in which this upper bound is tight and the static policy provides an efficient approximation of the dynamic pricing policy. We demonstrate, verify and validate the proposed approach through a series of Monte Carlo simulations of the controlled system time trajectories.

Roope Raisamo - One of the best experts on this subject based on the ideXlab platform.

  • remote expert for assistance in a physical Operational Task
    Human Factors in Computing Systems, 2018
    Co-Authors: Jari Kangas, Antti Sand, Tero Jokela, Petri Piippo, Peter Eskolin, Marja Salmimaa, Roope Raisamo
    Abstract:

    We created a mobile asymmetric collaboration system that allows a remote expert to assist a nomadic operative in a maintenance Task. We explored the use of a virtual reality headset and several cameras and controls that (1) are easy to setup by the operative on the site, (2) enable the expert to better understand the situation on the site (be more immersed) and (3) offer the expert various flexible interface options to study the environment and to act on the devices on the site independent of the operative. The preliminary results indicate that the expert appreciates the opportunity for 360 degreeview and the flexibility of using detailed controls on a close-up camera view.

  • CHI Extended Abstracts - Remote Expert for Assistance in a Physical Operational Task
    Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems, 2018
    Co-Authors: Jari Kangas, Antti Sand, Tero Jokela, Petri Piippo, Peter Eskolin, Marja Salmimaa, Roope Raisamo
    Abstract:

    We created a mobile asymmetric collaboration system that allows a remote expert to assist a nomadic operative in a maintenance Task. We explored the use of a virtual reality headset and several cameras and controls that (1) are easy to setup by the operative on the site, (2) enable the expert to better understand the situation on the site (be more immersed) and (3) offer the expert various flexible interface options to study the environment and to act on the devices on the site independent of the operative. The preliminary results indicate that the expert appreciates the opportunity for 360 degreeview and the flexibility of using detailed controls on a close-up camera view.

Ioannis Ch Paschalidis - One of the best experts on this subject based on the ideXlab platform.

  • Demand-Side Management for Regulation Service Provisioning Through Internal Pricing
    IEEE Transactions on Power Systems, 2012
    Co-Authors: Ioannis Ch Paschalidis, Binbin Li, Michael C. Caramanis
    Abstract:

    We develop a market-based mechanism that enables a building smart microgrid operator (SMO) to offer regulation service reserves and meet the associated obligation of fast response to commands issued by the wholesale market independent system operator (ISO) who provides energy and purchases reserves. The proposed market-based mechanism allows the SMO to control the behavior of internal loads through price signals and to provide feedback to the ISO. A regulation service reserves quantity is transacted between the SMO and the ISO for a relatively long period of time (e.g., a one-hour-long time-scale). During this period the ISO follows shorter time-scale stochastic dynamics to repeatedly request from the SMO to decrease or increase its consumption. We model the Operational Task of selecting an optimal short time-scale dynamic pricing policy as a stochastic dynamic program that maximizes average SMO and ISO utility. We then formulate an associated nonlinear programming static problem that provides an upper bound on the optimal utility. We study an asymptotic regime in which this upper bound is shown to be tight and the static policy provides an efficient approximation of the dynamic pricing policy. Equally importantly, this framework allows us to optimize the long time-scale decision of determining the optimal regulation service reserve quantity. We demonstrate, verify and validate the proposed approach through a series of Monte Carlo simulations of the controlled system time trajectories.

  • CDC-ECE - A market-based mechanism for providing demand-side regulation service reserves
    IEEE Conference on Decision and Control and European Control Conference, 2011
    Co-Authors: Ioannis Ch Paschalidis, Binbin Li, Michael C. Caramanis
    Abstract:

    We develop a market-based mechanism that enables a building Smart Microgrid Operator (SMO) to offer regulation service reserves and meet the associated obligation of fast response to commands issued by the wholesale market Independent System Operator (ISO) who provides energy and purchases reserves. The proposed market-based mechanism allows the SMO to control the behavior of internal loads through price signals and to provide feedback to the ISO. A regulation service reserves quantity is transacted between the SMO and the ISO for a relatively long period of time (e.g., a one hour long time-scale). During this period the ISO repeatedly requests from the SMO to decrease or increase its consumption. We model the Operational Task of selecting an optimal short time-scale dynamic pricing policy as a stochastic dynamic program that maximizes average SMO and ISO utility. We then formulate an associated non-linear programming static problem that provides an upper bound on the optimal utility. We study an asymptotic regime in which this upper bound is tight and the static policy provides an efficient approximation of the dynamic pricing policy. We demonstrate, verify and validate the proposed approach through a series of Monte Carlo simulations of the controlled system time trajectories.