Kazuo Nose

Novel 3D image analysis and particle matching techniques for the use in the volumetric particle tracking velocimetry have been developed and tested by using synthetic images and experimental images of unsteady 3D flows. A tomography based particle reconstruction scheme along with the subsequent process of individual particle detection and validation was used. The detected particles in the two time differential samples are matched by using Self Organising Map (SOM) neural network scheme. SOM neural network tracking algorithm is highly adaptive to time differential tracking even with loss-of-pair particles. The particle location and velocity results of the present new approach turned out accurate, reliable and robust in comparison to the conventional 3D PTV approaches.

Novel 3D image analysis and particle matching techniques for the use in the volumetric particle tracking velocimetry have been developed and tested by using synthetic images and experimental images of unsteady 3D flows. A tomography based particle reconstruction scheme along with the subsequent process of individual particle detection and validation was used. The detected particles in the two time differential samples are matched by using Self Organising Map (SOM) neural network scheme. SOM neural network tracking algorithm is highly adaptive to time differential tracking even with loss-of-pair particles. The particle location and velocity results of the present new approach turned out accurate, reliable and robust in comparison to the conventional 3D PTV approaches.

New algorithms of 3D particle tracking velocimetry (3D PTV) based on a tomographic reconstruction approach have been developed and tested by using synthetic images of unsteady 3D flows. The new algorithms are considered not only in the tomographic reconstruction process of the fluid volume with particles but also in the subsequent process of individual particle detection and validation. In particular, the tomographic reconstruction accuracy is boosted up by using a new recursive validation scheme through which many of ghost particles can be removed effectively. The particle detection process includes the particle mask correlation operator and the dynamic threshold scheme to extract individual particle centroids from the reconstructed intensity clusters of the fluid volume. The overall reconstruction accuracy is checked by the synthetic image data sets with different particle density and different volume thickness.

New algorithms of 3D particle tracking velocimetry (3D PTV) based on a tomographic reconstruction approach have been developed and tested by using synthetic images of unsteady 3D flows. The new algorithms are considered not only in the tomographic reconstruction process of the fluid volume with particles but also in the subsequent process of individual particle detection and validation. In particular, the tomographic reconstruction accuracy is boosted up by using a new recursive validation scheme through which many of ghost particles can be removed effectively. The particle detection process includes the particle mask correlation operator and the dynamic threshold scheme to extract individual particle centroids from the reconstructed intensity clusters of the fluid volume. The overall reconstruction accuracy is checked by the synthetic image data sets with different particle density and different volume thickness.

An ant colony optimization (ACO) based stereoscopic particle matching algorithm has been developed for three-dimensional (3-D) particle tracking velocimetry (PTV). In a stereoscopic particle pairing process, each individual particle in the left camera frame should be uniquely paired with the most probable correct partner in the right camera frame or vice-versa for evaluating the exact 3-D coordinate of the particles. In the present work, a new algorithm based on an ant colony optimization has been proposed for this stereoscopic particle matching. The algorithm is tested with various standard 3-D particle image velocimetry (PIV) images of the Visualization Society of Japan (VSJ) and the matching results show that the performance of the stereoscopic particle pairing is improved by applying proposed ACO techniques in comparison to the conventional nearest-neighbor particle pairing method of 3-D stereoscopic PTV. (C) 2011 Elsevier Ltd. All rights reserved.

An ant colony optimization (ACO) based stereoscopic particle matching algorithm has been developed for three-dimensional (3-D) particle tracking velocimetry (PTV). In a stereoscopic particle pairing process, each individual particle in the left camera frame should be uniquely paired with the most probable correct partner in the right camera frame or vice-versa for evaluating the exact 3-D coordinate of the particles. In the present work, a new algorithm based on an ant colony optimization has been proposed for this stereoscopic particle matching. The algorithm is tested with various standard 3-D particle image velocimetry (PIV) images of the Visualization Society of Japan (VSJ) and the matching results show that the performance of the stereoscopic particle pairing is improved by applying proposed ACO techniques in comparison to the conventional nearest-neighbor particle pairing method of 3-D stereoscopic PTV. (C) 2011 Elsevier Ltd. All rights reserved.

A new technique for the particle depth and size determination is implemented and tested by using synthetic as well as experimental hologram images. The particle depth and size are detected by geometric measures of the particle signal peak in the reconstructed images. Performance tests are carried out with different holograms patterns consisting of different sizes particles and overlapping interference fringes. The results obtained in both synthetic and in experimental holograms demonstrate the potential of the present method to the flow measurements.

A new technique for the particle depth and size determination is implemented and tested by using synthetic as well as experimental hologram images. The particle depth and size are detected by geometric measures of the particle signal peak in the reconstructed images. Performance tests are carried out with different holograms patterns consisting of different sizes particles and overlapping interference fringes. The results obtained in both synthetic and in experimental holograms demonstrate the potential of the present method to the flow measurements.

A new improved ant colony optimization (ACO) based algorithm has been developed for temporal particle matching in 2-D and 3-D particle tracking velocimetry (PTV). Two of the present authors have already applied the ant colony optimization (ACO) based algorithm effectively and successfully to the time differential particle pairing process of particle tracking velocimetry (PTV). In the present study, the algorithm has been further improved for the reduced com putation time as well as for the same or slightly better particle pairing results than that of the authors' previous ACO algorithm. This improvement is mainly achieved due to the revision of the selection probability and pheromone update formulae devised specially for the purpose of accurate and fast computation. In addition, the new algorithm also provides better matching results when dealing with the loss-of-pair particles (i.e., those particles which exist in one frame but do not have their matching pair in the other frame), a typical problem in the real image particle tracking velocimetry. The performance of the new improved algorithm is tested with 2-D and 3-D standard particle images with successful results. © 2010 Springer-Verlag Berlin Heidelberg.

A new improved ant colony optimization (ACO) based algorithm has been developed for temporal particle matching in 2-D and 3-D particle tracking velocimetry (PTV). Two of the present authors have already applied the ant colony optimization (ACO) based algorithm effectively and successfully to the time differential particle pairing process of particle tracking velocimetry (PTV). In the present study, the algorithm has been further improved for the reduced com putation time as well as for the same or slightly better particle pairing results than that of the authors' previous ACO algorithm. This improvement is mainly achieved due to the revision of the selection probability and pheromone update formulae devised specially for the purpose of accurate and fast computation. In addition, the new algorithm also provides better matching results when dealing with the loss-of-pair particles (i.e., those particles which exist in one frame but do not have their matching pair in the other frame), a typical problem in the real image particle tracking velocimetry. The performance of the new improved algorithm is tested with 2-D and 3-D standard particle images with successful results. © 2010 Springer-Verlag Berlin Heidelberg.

This paper is a survey report about why students use online video services. Based on the Technology Acceptance Model (TAM), a hypothesis model for online video services users was designed. The hypothesis model incorporates social influence, flow experience, comfortable communication, and advertisement interference into the basic TAM. With a questionnaire given to about 350 students, behaviors of online video service users were analyzed.

This paper is a survey report about why students use online video services. Based on the Technology Acceptance Model (TAM), a hypothesis model for online video services users was designed. The hypothesis model incorporates social influence, flow experience, comfortable communication, and advertisement interference into the basic TAM. With a questionnaire given to about 350 students, behaviors of online video service users were analyzed.

The presentation of the travel time information to drivers is one of the important roles of the information system concerned with road traffic. This paper proposes a prediction method of the travel time in urban district using the travel time data of the small section which constitutes a route. The forecasting model consists of past time series data about the travel time of the small section. The travel time and accuracy of a near future of the route are estimated using State Equation of the forecasting model. The validity of the method is verified using real data. As the results of examinations, the prediction error became almost 2.5 minutes. Generally, to fifty minutes of travel time, it is desirable that prediction error is within five minutes. Therefore, it is conceivable that this result is fit to sufficient practical use. © 2008 The Institute of Electrical Engineers of Japan.

In this paper, the numerical solution method of an on-ramp traffic control problem is discussed from the viewpoint of the application of evolutionary computation. The problem is formulated as a discrete-time nonlinear optimal control problem. The optimal control problem is then transferred to an optimization problem with successive constraints. A parallel-adaptive genetic algorithm is proposed to obtain the numerical solution of the problem. A real-time practical control method is also proposed. Numerical simulations for assessing the performance of the proposed methods are performed using observed data of Hanshin Expressway. As a result, the proposed method obtains the solution at comparatively small generations. This means shortening the calculation time, and is useful for the real-time control. Moreover, it is shown that the proposed real-time control is effective to reduce heavy congestion on the expressway.

In this paper, the numerical solution method of an on-ramp traffic control problem is discussed from the viewpoint of the application of evolutionary computation. The problem is formulated as a discrete-time nonlinear optimal control problem. The optimal control problem is then transferred to an optimization problem with successive constraints. A parallel-adaptive genetic algorithm is proposed to obtain the numerical solution of the problem. A real-time practical control method is also proposed. Numerical simulations for assessing the performance of the proposed methods are performed using observed data of Hanshin Expressway. As a result, the proposed method obtains the solution at comparatively small generations. This means shortening the calculation time, and is useful for the real-time control. Moreover, it is shown that the proposed real-time control is effective to reduce heavy congestion on the expressway.

This paper provides a method that calculates the optimal control of on-ramp traffic density from the basis of the dynamic estimate of traffic density. The on-ramp traffic control problem is formulated as follows. The problem is being able to find the optimal inflow of each on-ramp, based on the evaluation of an on-ramp and main line traffic density. We consider the on-ramp traffic density as the decision variable, and treat this control problem as the nonlinear optimization problem that maximizes the evaluation function under restrictions. To maximize the evaluation function under the restrictions, we propose an adaptive Genetic Algorithm. The solution candidates for on-ramp traffic density have inequality restrictions. Therefore, we consider gene values as ratio of the inflow traffic density within the limit of inequality restrictions. Moreover, in the proposed method, genetic operations are not performed in series, but they are performed in parallel and are adaptive. As numerical examples, we adapt statistical data (the on-ramp traffic density) and examine whether the proposed method can control the traffic jam.

This paper provides a method that calculates the optimal control of on-ramp traffic density from the basis of the dynamic estimate of traffic density. The on-ramp traffic control problem is formulated as follows. The problem is being able to find the optimal inflow of each on-ramp, based on the evaluation of an on-ramp and main line traffic density. We consider the on-ramp traffic density as the decision variable, and treat this control problem as the nonlinear optimization problem that maximizes the evaluation function under restrictions. To maximize the evaluation function under the restrictions, we propose an adaptive Genetic Algorithm. The solution candidates for on-ramp traffic density have inequality restrictions. Therefore, we consider gene values as ratio of the inflow traffic density within the limit of inequality restrictions. Moreover, in the proposed method, genetic operations are not performed in series, but they are performed in parallel and are adaptive. As numerical examples, we adapt statistical data (the on-ramp traffic density) and examine whether the proposed method can control the traffic jam.