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Auto-Detection of Non-Isolated Pulmonary Nodules Connected to The Chest Walls in X-ray CT images
Satoshi Shimoyama,Noriyasu Homma,Masao Sakai,Tadashi Ishibashi,Makoto Yoshizawa 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
In this paper, we develop an auto-detection method of non-isolated pulmonary nodules for computer-aided diagnosis (CAD) of lung cancers using X-ray CT images. An essential core of the method is to transform the non-isolated nodules connected to the walls of the chest into isolated ones that can be detected more easily by CAD systems developed previously. To this end, an active contour model is proposed to extract the lung area from the original CT image. The proposed model can solve the local optimum problem of the contour model by using anatomical features of the lung in X-ray CT slices. Some experimental results demonstrate the usefulness of the proposed method by using clinical CT images.
Complexity Control Method of Chaos Dynamics in Recurrent Neural Networks
Sakai, Masao,Homma, Noriyasu,Abe, Kenichi Institute of Control 2002 Transaction on control, automation and systems eng Vol.4 No.2
This paper demonstrates that the largest Lyapunov exponent λ of recurrent neural networks can be controlled efficiently by a stochastic gradient method. An essential core of the proposed method is a novel stochastic approximate formulation of the Lyapunov exponent λ as a function of the network parameters such as connection weights and thresholds of neural activation functions. By a gradient method, a direct calculation to minimize a square error (λ - λ$\^$obj/)$^2$, where λ$\^$obj/ is a desired exponent value, needs gradients collection through time which are given by a recursive calculation from past to present values. The collection is computationally expensive and causes unstable control of the exponent for networks with chaotic dynamics because of chaotic instability. The stochastic formulation derived in this paper gives us an approximation of the gradients collection in a fashion without the recursive calculation. This approximation can realize not only a faster calculation of the gradient, but also stable control for chaotic dynamics. Due to the non-recursive calculation. without respect to the time evolutions, the running times of this approximation grow only about as N$^2$ compared to as N$\^$5/T that is of the direct calculation method. It is also shown by simulation studies that the approximation is a robust formulation for the network size and that proposed method can control the chaos dynamics in recurrent neural networks efficiently.
Complexity Control Method of Chaos Dynamics in Recurrent Neural Networks
Masao Sakai,Noriyasu Homma,Kenichi Abe 제어·로봇·시스템학회 2002 International Journal of Control, Automation, and Vol.4 No.2
This paper demonstrates that the largest Lyapunov exponent λ of recurrent neural networks can be controlled efficiently by a stochastic gradient method. An essential core of the proposed method is a novel stochastic approximate formulation of the Lyapunov exponent λ as a function of the network parameters such as connection weights and thresholds of neural activation functions. By a gradient method, a direct calculation to minimize a square error (λ-λ^obj)^2, where λ^obj is a desired exponent value, needs gradients collection through time which are given by a recursive calculation from past to present values. The collection is computationally expensive and caused unstable control for the exponent for networks with chaotic dynamics because of chaotic instability. The stochastic formulation derived in this paper gives us an approximation of the gradients collection in a fashion without the recursive calculation. This approximation can realize not only a faster calculation of the gradient, but also stable control for chaotic dynamics. Due to the non-recursive calculation, without respect to the time evolutions, the running times of this approximation grow only about is N^2 compared to as N^5 T that is of the direct calculation method. It is also shown by simulation studies that the approximation is a robust formulation for the network size and that proposed method can control the chaos dynamics in recurrent neural networks efficiently.
Manual control of a nonholonomic system by multiple predictor-controller pair architecture
Shinpei Kato,Takakuni Goto,Noriyasu Homma,Makoto Yoshizawa 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
Humans can often conduct both linear and nonlinear control tasks after a sufficient number of trials, even ifthey initially do not have sufficient knowledge about the system’s dynamics and the way to control it. Theoretically, it is well known that some nonlinear systems cannot be stabilized asymptotically by any linear controllers. However,there is a possibility that human may use a linear control scheme for nonlinear control tasks. The hypothesis proposedin this paper is that humans switch the linear controllers with a virtual constraint. Each controller is responsible for a small region where the linear approximation can work well for nonlinear tasks. It is suggested that the proposed virtual constraint can play an important role to overcome a limitation of the linear controllers.