Prefectural University of Hiroshima

www.pu-hiroshima.ac.jp
Minami-rinkan, Japan

Prefectural University of Hiroshima is a public university in Hiroshima Prefecture, Japan, established in 2005.It has three campuses, located in these cities: Hiroshima Shōbara Mihara Wikipedia.

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Kamada S.,Hiroshima City University | Ichimura T.,Prefectural University of Hiroshima
IEEE Region 10 Annual International Conference, Proceedings/TENCON | Year: 2017

Deep Belief Network (DBN) has a deep architecture that represents multiple features of input patterns hierarchically with the pre-trained Restricted Boltzmann Machines (RBM). A traditional RBM or DBN model cannot change its network structure during the learning phase. Our proposed adaptive learning method can discover the optimal number of hidden neurons and weights and/or layers according to the input space. The model is an important method to take account of the computational cost and the model stability. The regularities to hold the sparse structure of network is considerable problem, since the extraction of explicit knowledge from the trained network should be required. In our previous research, we have developed the hybrid method of adaptive structural learning method of RBM and Learning Forgetting method to the trained RBM. In this paper, we propose the adaptive learning method of DBN that can determine the optimal number of layers during the learning. We evaluated our proposed model on some benchmark data sets. © 2016 IEEE.


Kamada S.,Hiroshima City University | Ichimura T.,Prefectural University of Hiroshima
2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings | Year: 2016

Restricted Boltzmann Machine (RBM) is a generative stochastic energy-based model of artificial neural network for unsupervised learning. Recently, RBM is well known to be a pre-training method of Deep Learning. In addition to visible and hidden neurons, the structure of RBM has a number of parameters such as the weights between neurons and the coefficients for them. Therefore, we may meet some difficulties to determine an optimal network structure to analyze big data. In order to evade the problem, we investigated the variance of parameters to find an optimal structure during learning. For the reason, we should check the variance of parameters to cause the fluctuation for energy function in RBM model. In this paper, we propose the adaptive learning method of RBM that can discover an optimal number of hidden neurons according to the training situation by applying the neuron generation and annihilation algorithm. In this method, a new hidden neuron is generated if the energy function is not still converged and the variance of the parameters is large. Moreover, the inactivated hidden neuron will be annihilated if the neuron does not affect the learning situation. The experimental results for some benchmark data sets were discussed in this paper. © 2016 IEEE.


Wu H.,Prefectural University of Hiroshima
IET Control Theory and Applications | Year: 2010

The problem of adaptive robust stabilisation is considered for a class of dynamical systems with multiple time-varying delayed state perturbations, time-varying uncertain parameters, and external disturbances. It is assumed that the upper bounds of the delayed state perturbations, uncertainties and external disturbances are unknown, and that the time-varying delays are any non-negative continuous and bounded functions. In particular, it is not required that the derivatives of time-varying delays have to be less than one. For such a class of uncertain time-delay systems, a new method is presented whereby a class of memoryless continuous adaptive robust state feedback controllers is proposed. By employing a quasi-Lyapunov function, it is shown that the solutions of uncertain time-delay systems can be guaranteed to be uniformly exponentially convergent towards a ball which can be as small as desired. In addition, since the proposed adaptive robust state feedback controllers are completely independent of time delays, the results obtained in the study may be also applicable to a class of dynamical systems with uncertain time delays. Finally, a numerical example is given to demonstrate the validity of the results. © 2010 The Institution of Engineering and Technology.


Wu H.,Prefectural University of Hiroshima
International Journal of Systems Science | Year: 2012

The problem of decentralised adaptive robust stabilisation is considered for a class of uncertain large-scale time-delay interconnected dynamical systems. It is assumed that the upper bounds of the uncertainties, interconnection terms and external disturbances are unknown, and that the time-varying delays are any nonnegative continuous and bounded functions, and do not require that their derivatives have to be less than one. For such a class of uncertain large-scale time-delay interconnected systems, a new method is presented whereby a class of continuous memoryless decentralised local adaptive robust state feedback controllers is proposed. It is also shown that the solutions of uncertain large-scale time-delay interconnected systems can be guaranteed to be uniformly exponentially convergent towards a ball which can be as small as desired. In addition, since the proposed decentralised local adaptive robust state feedback controllers are completely independent of time delays, the results obtained in this article may also be applicable to a class of large-scale interconnected dynamical systems with uncertain time delays. Finally, a numerical example is given to demonstrate the validity of the results. © 2012 Copyright Taylor and Francis Group, LLC.


Wu H.,Prefectural University of Hiroshima
IET Control Theory and Applications | Year: 2012

The problem of decentralised adaptive robust stabilisation is considered for a class of uncertain large-scale interconnected non-linear systems with time-varying delays. It is assumed that the upper bounds of the uncertainties and interconnection terms are unknown, and that the time-varying delays are any non-negative continuous and bounded functions, and do not require their derivatives to be less than one. In particular, it is only required that the non-linear interconnection terms, which can also include time-varying delays, are bounded in any non-negative non-linear functions, which are not required to be known for the system designer. For such a class of uncertain large-scale time-delay interconnected non-linear systems, a new method is presented whereby a class of continuous memoryless decentralised local adaptive robust state feedback controllers with a rather simpler structure is proposed. It is also shown that the solutions of uncertain large-scale time-delay interconnected systems can be guaranteed as uniformly exponentially convergent towards a ball that can be as small as desired. Finally, a numerical example is given to demonstrate the validity of the results. © 2012 The Institution of Engineering and Technology.


Uchida K.,Prefectural University of Hiroshima | Aizawa S.-I.,Prefectural University of Hiroshima
Journal of Bacteriology | Year: 2014

The length of the flagellar hook is controlled by the soluble protein FliK. FliK is structurally divided into two halves with distinct functions; the N-terminal half determines hook length, while the C-terminal half switches the secretion substrate specificity, consequently terminating hook elongation. FliK properly achieves both functions only when it is secreted. In a previous paper, we showed that a temperature-sensitive flgE mutant of Salmonella enterica serovar Typhimurium, SJW2219, produced basal bodies with short hooks (average length, 25 nm) at 37°C. In this study, we show that the mutant cells grown at 37°C secrete FliK but not flagellin (FliC), indicating that FliK is abortively secreted into the medium when the hook is shorter than 30 nm. In contrast, FliK unfailingly switches the gate modes when the hook is longer than 30 nm. Taking the FliC, FliK, and FlgM secretion patterns into account, we conclude that FliK determines the minimal length of the hook. We will discuss how FliK detects the critical switching point of the secretion gate. © 2014, American Society for Microbiology.


Xiao Y.,Prefectural University of Hiroshima
IEEE Transactions on Audio, Speech and Language Processing | Year: 2011

A new narrowband ANC system structure is proposed which requires only two reference signal filtering (x-filtering) blocks regardless of the number of targeted frequencies. The reference cosine or sine waves are combined, respectively, to form an input to an x-filtering block. The output of each x-filtering block is decomposed into filtered-x cosine or sine waves by a special bandpass filter bank. In this way, the computational cost of the system may be significantly reduced. Analysis of the new system is then provided and discussed in some detail. Analytical results reveal that the proposed system performs quite the same as its counterpart does while requiring considerably fewer multiplications. Modification to the proposed structure is also made to cope with the frequency mismatch (FM) in real-life applications. Extensive simulations are conducted to demonstrate the effectiveness of the proposed system and its modified version, and as well as to confirm the validity of analysis. © 2010 IEEE.


Han H.,Prefectural University of Hiroshima
International Journal of Innovative Computing, Information and Control | Year: 2010

This paper deals with control design and stability analysis for a class of nonlinear systems with system uncertainties as well as input constraint when using the T-S fuzzy model. As a result, it achieves an adaptive fuzzy controller, which consists of two components: one is corresponding with the regular state feedback controller based on the LMI approach in order to deal with the known part in the T-S fuzzy model; and another one is obtained based on adaptive law in order to deal with the unknown part in the T-S fuzzy model. In the end, the problem of relaxing LMI conservatism is considered. © 2010 ISSN.


Wu H.,Prefectural University of Hiroshima
IET Control Theory and Applications | Year: 2013

The problem of adaptive robust state observer design is considered for a class of uncertain non-linear dynamical systems with multiple time-varying delays. It is assumed that the upper bounds of the non-linear delayed state perturbations are unknown and that the time-varying delays are any non-negative continuous and bounded functions, which do not require that their derivatives have to be less than one. In particular, it is only required that the non-linear uncertainties, which can also include time-varying delays, are bounded in any non-negative non-linear functions, which are not required to be known for the system designer. For such a class of uncertain non-linear time-delay systems, a new method is presented whereby a class of memoryless adaptive robust state observers with a rather simpler structure is proposed. It is also shown that by employing the proposed adaptive robust state observer, the observation error between the observer state estimate and the true state can be guaranteed to be uniformly exponentially convergent towards a ball, which can be as small as desired, in the presence of significant uncertainties and time delays. Finally, a numerical example is given to demonstrate the validity of the results. © The Institution of Engineering and Technology 2013.


Xiao Y.,Prefectural University of Hiroshima
IEEE Signal Processing Letters | Year: 2014

In this reply, we first provide a MATLAB program used in our simulations and clarify our claim regarding the occurrence of the so-called 'firework' noise in the broadband active noise control (BANC) system. Next, we present some simulation results of the BANC using the FXLMS and the normalized FXLMS (NFXLMS) algorithms in the presence of a real noise signal generated by a large-scale rotating machine. The real noise is a mixture of several low-frequency sinusoids and a wideband noise component. The existence of 'firework' noise is again confirmed in the BANC system when it is made to converge relatively fast. © 2014 IEEE.

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