The Experts below are selected from a list of 49941 Experts worldwide ranked by ideXlab platform

Eiji Kamioka - One of the best experts on this subject based on the ideXlab platform.

  • Synergistic approaches to mobile intelligent transportation systems considering low Penetration Rate
    Pervasive and Mobile Computing, 2014
    Co-Authors: T. M. Quang, Muhammad Ariff Baharudin, Eiji Kamioka
    Abstract:

    This paper investigates the effect of low Penetration Rate on mobile phone-based traffic state estimation (M-TES) models. Synergistic approaches, including an appropriate genetic algorithm (GA) based velocity-density estimation model and a notable artificial neural network (ANN) based prediction method for unacceptably low Penetration Rate, are proposed. The GA-based traffic state estimation model not only improves the effectiveness but also reduces the critical Penetration Rate required in the M-TES model. When the critical Penetration Rate is reduced the error-tolerance and the scalability of the estimation model can be significantly improved. The ANN-based prediction approach is introduced to overcome the weakness remaining in the GA-based traffic state estimation model when the Penetration Rate becomes unacceptably low or unknown. In addition, the effect of related road segments on the prediction effectiveness is thoroughly discussed. This work, therefore, provides practical instructions in narrowing the search space for finding prediction rules of the ANN model, thus improving the computational performance without compromising the prediction accuracy. The experimental evaluations confirm the effectiveness as well as the robustness of the proposed approaches. As a result, this research contributes to accelerating the realization of mobile phone-based intelligent transportation systems (M-ITS) or, of the M-TES systems in specific, since the essential issue of low Penetration Rate has been solved.

  • Uncertain Penetration Rate Issues in Mobile Intelligent Transportation Systems
    2012
    Co-Authors: Quang Tran Minh, Muhammad Ariff Baharudin, Eiji Kamioka
    Abstract:

    This paper thoroughly discusses the essential issues remaining in mobile phone technologies which impede the realization of mobile phone based traffic state estimation systems (M-TESs). Concretely, the inherent issues in mobile phone based applications, namely low and uncertain Penetration Rate issues, which affect the M-TES’s effectiveness, are resolved. A unique GA-based velocity-density estimation mechanism is proposed to improve the traffic state estimation accuracy when the Penetration Rate is low but still be relevant. A novel ANN-based prediction model is proposed to cope with unacceptably low and uncertain Penetration Rate issues. Moreover, a reasonable selection method is proposed aiming at selecting an appropriate traffic state estimation model without the actual Penetration Rate information. Experimental evaluations reveal the effectiveness as well as the robustness of the proposed solutions.

  • AINA - Uncertain Low Penetration Rate -- A Practical Issue in Mobile Intelligent Transportation Systems
    2012 IEEE 26th International Conference on Advanced Information Networking and Applications, 2012
    Co-Authors: Quang Tran Minh, Muhammad Ariff Baharudin, Eiji Kamioka
    Abstract:

    Low Penetration Rate is one of the essential issues in the mobile phone based traffic state estimation model. This paper proposes an appropriate genetic algorithm (GA) mechanism to optimize the traffic state estimation model even in cases of low Penetration Rate. This mechanism also reduces the critical Penetration Rate, thus improves the error-tolerance as well as the scalability of the traffic state estimation system. The paper also investigates the ANN-based prediction model to overcome the weakness of the GA-based traffic state estimation approach when the Penetration Rate becomes unacceptably low. In addition, the effect of different level related road segments on the prediction effectiveness is thoroughly discussed. Consequently, this study provides practically useful instructions in verifying the data missing Rate at different level related road segments to ensure the prediction accuracy. The experimental evaluations reveal the effectiveness and the robustness of the proposed solutions.

  • Adaptive Approaches in Mobile Phone Based Traffic State Estimation with Low Penetration Rate
    Journal of Information Processing, 2012
    Co-Authors: Quang Tran Minh, Eiji Kamioka
    Abstract:

    The Penetration Rate is one of the most important factors that affects the effectiveness of the mobile phone-based traffic state estimation. This article thoroughly investigates the influence of the Penetration Rate on the traffic state estimation using mobile phones as traffic probes and proposes reasonable solutions to minimize such influence. In this research, the so-called “acceptable” Penetration Rate, at which the estimation accuracy is kept as an “acceptable” level, is identified. This recognition is important to bring the mobile phone-based traffic state estimation systems into realization. In addition, two novel “velocity-density inference” models, namely the “adaptive” and the “adaptive feedback” velocity-density inference circuits, are proposed to improve the effectiveness of the traffic state estimation. Furthermore, an artificial neural network-based prediction approach is introduced to a the effectiveness of the velocity and the density estimation when the Penetration Rate degrades to 0%. These improvements are practically meaningful since they help to guarantee a high accuRate traffic state estimation, even in cases of very low Penetration Rate. The experimental evaluations reveal the effectiveness as well as the robustness of the proposed solutions.

  • VTC Fall - Assuring Accuracy on Low Penetration Rate Mobile Phone-Based Traffic State Estimation System
    2011 IEEE Vehicular Technology Conference (VTC Fall), 2011
    Co-Authors: Quang Tran Minh, Eiji Kamioka
    Abstract:

    This paper investigates the effect of the Penetration Rate on the effectiveness of the mobile phone-based traffic state estimation. As a result, the acceptable Penetration Rate is identified. This recognition is useful for the investigating to bring the traffic state estimation using mobile phones as traffic probes into the real world application. In addition, an adaptive velocity-density estimation model, namely the velocity-density inference circuit, is proposed to improve the accuracy of the average velocity and the density estimations in cases of low Penetration Rate. Furthermore, a neural network-based prediction model is introduced to assure the effectiveness of the velocity/density estimation when the Penetration Rate degrades to zero. The experimental evaluations reveal the effectiveness as well as the robustness of the proposed solutions.

Quang Tran Minh - One of the best experts on this subject based on the ideXlab platform.

  • Uncertain Penetration Rate Issues in Mobile Intelligent Transportation Systems
    2012
    Co-Authors: Quang Tran Minh, Muhammad Ariff Baharudin, Eiji Kamioka
    Abstract:

    This paper thoroughly discusses the essential issues remaining in mobile phone technologies which impede the realization of mobile phone based traffic state estimation systems (M-TESs). Concretely, the inherent issues in mobile phone based applications, namely low and uncertain Penetration Rate issues, which affect the M-TES’s effectiveness, are resolved. A unique GA-based velocity-density estimation mechanism is proposed to improve the traffic state estimation accuracy when the Penetration Rate is low but still be relevant. A novel ANN-based prediction model is proposed to cope with unacceptably low and uncertain Penetration Rate issues. Moreover, a reasonable selection method is proposed aiming at selecting an appropriate traffic state estimation model without the actual Penetration Rate information. Experimental evaluations reveal the effectiveness as well as the robustness of the proposed solutions.

  • AINA - Uncertain Low Penetration Rate -- A Practical Issue in Mobile Intelligent Transportation Systems
    2012 IEEE 26th International Conference on Advanced Information Networking and Applications, 2012
    Co-Authors: Quang Tran Minh, Muhammad Ariff Baharudin, Eiji Kamioka
    Abstract:

    Low Penetration Rate is one of the essential issues in the mobile phone based traffic state estimation model. This paper proposes an appropriate genetic algorithm (GA) mechanism to optimize the traffic state estimation model even in cases of low Penetration Rate. This mechanism also reduces the critical Penetration Rate, thus improves the error-tolerance as well as the scalability of the traffic state estimation system. The paper also investigates the ANN-based prediction model to overcome the weakness of the GA-based traffic state estimation approach when the Penetration Rate becomes unacceptably low. In addition, the effect of different level related road segments on the prediction effectiveness is thoroughly discussed. Consequently, this study provides practically useful instructions in verifying the data missing Rate at different level related road segments to ensure the prediction accuracy. The experimental evaluations reveal the effectiveness and the robustness of the proposed solutions.

  • Adaptive Approaches in Mobile Phone Based Traffic State Estimation with Low Penetration Rate
    Journal of Information Processing, 2012
    Co-Authors: Quang Tran Minh, Eiji Kamioka
    Abstract:

    The Penetration Rate is one of the most important factors that affects the effectiveness of the mobile phone-based traffic state estimation. This article thoroughly investigates the influence of the Penetration Rate on the traffic state estimation using mobile phones as traffic probes and proposes reasonable solutions to minimize such influence. In this research, the so-called “acceptable” Penetration Rate, at which the estimation accuracy is kept as an “acceptable” level, is identified. This recognition is important to bring the mobile phone-based traffic state estimation systems into realization. In addition, two novel “velocity-density inference” models, namely the “adaptive” and the “adaptive feedback” velocity-density inference circuits, are proposed to improve the effectiveness of the traffic state estimation. Furthermore, an artificial neural network-based prediction approach is introduced to a the effectiveness of the velocity and the density estimation when the Penetration Rate degrades to 0%. These improvements are practically meaningful since they help to guarantee a high accuRate traffic state estimation, even in cases of very low Penetration Rate. The experimental evaluations reveal the effectiveness as well as the robustness of the proposed solutions.

  • VTC Fall - Assuring Accuracy on Low Penetration Rate Mobile Phone-Based Traffic State Estimation System
    2011 IEEE Vehicular Technology Conference (VTC Fall), 2011
    Co-Authors: Quang Tran Minh, Eiji Kamioka
    Abstract:

    This paper investigates the effect of the Penetration Rate on the effectiveness of the mobile phone-based traffic state estimation. As a result, the acceptable Penetration Rate is identified. This recognition is useful for the investigating to bring the traffic state estimation using mobile phones as traffic probes into the real world application. In addition, an adaptive velocity-density estimation model, namely the velocity-density inference circuit, is proposed to improve the accuracy of the average velocity and the density estimations in cases of low Penetration Rate. Furthermore, a neural network-based prediction model is introduced to assure the effectiveness of the velocity/density estimation when the Penetration Rate degrades to zero. The experimental evaluations reveal the effectiveness as well as the robustness of the proposed solutions.

Lakhdar Khochemane - One of the best experts on this subject based on the ideXlab platform.

  • Optimization of Penetration Rate in rotary percussive drilling using two techniques: Taguchi analysis and response surface methodology (RMS)
    Powder Technology, 2018
    Co-Authors: Fatima Zohra Derdour, Mohamed Kezzar, Lakhdar Khochemane
    Abstract:

    Abstract The efficient use of drilling machines is one of the key factors that are considered in the economic evaluation of mining operations. A prediction of Penetration Rate is necessary to work in a cost effective way. In this work, drilling parameters such as air pressure, specific advance pressure, rotation speed, and bit diameter were taken into account to optimize the Penetration Rate of a rotary percussive drilling in the Hadjer Soud quarry using Taguchi and Surface Response Methodology. The experiments were carried out on the basis of a mixed experimental array Taguchi L18 and were analyzed using signal/noise ratio (S/N), variance analysis and regression analysis. From the optimization and the experimental analyses carried out, the results show that air pressure is statistically the most dominant factor in the rotary -percussive drilling in the quarry of Hadjer Soud with a contribution of 59.90%. The Taguchi method was used to determine the optimal rational operating values a drill bit diameter of 115 mm (level 1), the specific advance pressure of 50 Kgf/cm2 (level 2), at a rotation speed of 55 Rpm (level 3) and an air pressure of 17 Bars (level 3). A mathematical model was developed for the Penetration Rate to understand the effect of the control factors on the response. The predicted values are compared with the experimental data and are seen to be in good agreement. The optimal values obtained during the optimization of the study by the Taguchi method and the response surface model (RSM) were then validated by confirmatory experiments.

Muhammad Ariff Baharudin - One of the best experts on this subject based on the ideXlab platform.

  • Synergistic approaches to mobile intelligent transportation systems considering low Penetration Rate
    Pervasive and Mobile Computing, 2014
    Co-Authors: T. M. Quang, Muhammad Ariff Baharudin, Eiji Kamioka
    Abstract:

    This paper investigates the effect of low Penetration Rate on mobile phone-based traffic state estimation (M-TES) models. Synergistic approaches, including an appropriate genetic algorithm (GA) based velocity-density estimation model and a notable artificial neural network (ANN) based prediction method for unacceptably low Penetration Rate, are proposed. The GA-based traffic state estimation model not only improves the effectiveness but also reduces the critical Penetration Rate required in the M-TES model. When the critical Penetration Rate is reduced the error-tolerance and the scalability of the estimation model can be significantly improved. The ANN-based prediction approach is introduced to overcome the weakness remaining in the GA-based traffic state estimation model when the Penetration Rate becomes unacceptably low or unknown. In addition, the effect of related road segments on the prediction effectiveness is thoroughly discussed. This work, therefore, provides practical instructions in narrowing the search space for finding prediction rules of the ANN model, thus improving the computational performance without compromising the prediction accuracy. The experimental evaluations confirm the effectiveness as well as the robustness of the proposed approaches. As a result, this research contributes to accelerating the realization of mobile phone-based intelligent transportation systems (M-ITS) or, of the M-TES systems in specific, since the essential issue of low Penetration Rate has been solved.

  • Uncertain Penetration Rate Issues in Mobile Intelligent Transportation Systems
    2012
    Co-Authors: Quang Tran Minh, Muhammad Ariff Baharudin, Eiji Kamioka
    Abstract:

    This paper thoroughly discusses the essential issues remaining in mobile phone technologies which impede the realization of mobile phone based traffic state estimation systems (M-TESs). Concretely, the inherent issues in mobile phone based applications, namely low and uncertain Penetration Rate issues, which affect the M-TES’s effectiveness, are resolved. A unique GA-based velocity-density estimation mechanism is proposed to improve the traffic state estimation accuracy when the Penetration Rate is low but still be relevant. A novel ANN-based prediction model is proposed to cope with unacceptably low and uncertain Penetration Rate issues. Moreover, a reasonable selection method is proposed aiming at selecting an appropriate traffic state estimation model without the actual Penetration Rate information. Experimental evaluations reveal the effectiveness as well as the robustness of the proposed solutions.

  • AINA - Uncertain Low Penetration Rate -- A Practical Issue in Mobile Intelligent Transportation Systems
    2012 IEEE 26th International Conference on Advanced Information Networking and Applications, 2012
    Co-Authors: Quang Tran Minh, Muhammad Ariff Baharudin, Eiji Kamioka
    Abstract:

    Low Penetration Rate is one of the essential issues in the mobile phone based traffic state estimation model. This paper proposes an appropriate genetic algorithm (GA) mechanism to optimize the traffic state estimation model even in cases of low Penetration Rate. This mechanism also reduces the critical Penetration Rate, thus improves the error-tolerance as well as the scalability of the traffic state estimation system. The paper also investigates the ANN-based prediction model to overcome the weakness of the GA-based traffic state estimation approach when the Penetration Rate becomes unacceptably low. In addition, the effect of different level related road segments on the prediction effectiveness is thoroughly discussed. Consequently, this study provides practically useful instructions in verifying the data missing Rate at different level related road segments to ensure the prediction accuracy. The experimental evaluations reveal the effectiveness and the robustness of the proposed solutions.

Peter Kolapo - One of the best experts on this subject based on the ideXlab platform.

  • Investigating the Effects of Mechanical Properties of Rocks on Specific Energy and Penetration Rate of Borehole Drilling
    Geotechnical and Geological Engineering, 2020
    Co-Authors: Peter Kolapo
    Abstract:

    This paper deals with the analysis of effects of mechanical properties of rock on Penetration Rate and specific energy. Five specimens were collected from the four locations in Federal capital territory, Abuja. These samples were subjected to laboratory point load strength index using point load testing machine. Results of the point load tests were used to estimate the uniaxial compressive strength of the rocks. The Penetration Rate and specific energy were determined using empirical equations. The results from the investigation showed that the Rate of Penetration decreases with increase in rock strength and the specific energy increases with increase in rock strength. Based on the investigation, a number of linear relationships that described the effects of mechanical properties on Penetration Rate in the drilling process were obtained with strong correlation coefficients.