Kenya Jinno | Nippon Institute of Technology (original) (raw)
Papers by Kenya Jinno
Given an undirected weighted graph G = (V, E, c) and a set T , where V is the set of nodes, E is ... more Given an undirected weighted graph G = (V, E, c) and a set T , where V is the set of nodes, E is the set of edges, c is a cost function, and T is a subset of nodes called terminals, the Steiner tree problem in graphs is that of finding the subgraph of the minimum weight that connects all of terminals. The Steiner tree problem is an example of an NP-complete combinatorial optimization problem [1]. Thus, approximate methods are usually employed for constructing the Steiner tree. In this study, the KMB algorithm [2], which is an efficient construction method for Steiner tree problems, is enhanced by considering edge betweenness [3]. The results of numerical simulations indicate that our improved KMB algorithm shows good performances for various types of benchmark Steiner tree problems.
SUMMARY In this study we investigate the synchronization of relaxation oscillators having individ... more SUMMARY In this study we investigate the synchronization of relaxation oscillators having individual differences by using non-periodic signal injection. When a common non-periodic signal is injected into the relaxation oscillators, the oscillators exhibit synchronization phenomena. Such synchronization phenomena can be classified as injection locking. We also consider the relation between the synchronization state and the individual difference. Further, we pay attention to the effect of the fluctuation range of the non-periodic injected signal. When the fluctuation range is wide, we confirm that the synchronization range increases if the individual difference is small. key words: injection locking, relaxation oscillator, non-periodic injected signal, piecewise linear system
The purpose of this paper is to improve the output quality of three-phase PWM DC-AC inverter. The... more The purpose of this paper is to improve the output quality of three-phase PWM DC-AC inverter. The improvement in output quality is required to reduce the harmonic components. The PWM control can reduce harmonic components by adjusting the width of each pulse. We design the switching phase to improve output quality, also propose an evaluation function for evaluating the frequency components. In order to optimize the switching phase, particle swarm optimization is applied.
We confirm the effectiveness of the proposed method comparing with other methods. Moreover, we confirm the effectiveness by using the implementation circuit of the three-phase inverter.
Nonlinear Theory and its Applications (NOLTA), IEICE, Apr 1, 2015
This paper studies the particle swarm optimization (PSO) with switched topology (SW-TOPO) and its... more This paper studies the particle swarm optimization (PSO) with switched topology (SW-TOPO) and its application to the multi-optima problems (M-OPT). Particles converge at multiple optima simultaneously, since SW-TOPO disconnect the transmission of information and separate the topology of the particles. We introduce the switching path length as a basic measure to evaluate the switched topology. Also, applying the proposed PSO to typical benchmark functions of the M-OPT, the algorithm efficiency is investigated.
Nonlinear Theory and its Applications (NOLTA), IEICE, Apr 1, 2015
Because of the huge growth in the number of Internet users, data packets flowing in communication... more Because of the huge growth in the number of Internet users, data packets flowing in communication networks have also growth, and as a result, some packets can become congested in communication networks. If packet congestion occurs in a communication network, the packets are trapped in congested nodes, and then the transmission of these packets to their destinations is delayed. Further, the packets could be removed from the communication network in the worst case. To overcome these undesirable problems, an efficient routing strategy based on mutually connected neural networks has been proposed. This neural-based routing strategy shows good performance for regular topological communication networks. However, the performance of the routing strategy declines in irregular topological communication networks. To improve its performance for irregular topological communication networks, we propose in this paper a new neural-based routing strategy with the transmission information. Numerical experiments show that the performance of the proposed strategy is enhanced by the newly added transmission information as compared to the conventional routing strategies. Further, the proposed routing strategy shows better performance for other topological complex communication networks.
Journal of Signal Processing, Nov 2004
The synthesis of analog electronic circuits involves the design of their input-output characteris... more The synthesis of analog electronic circuits involves the design of their input-output characteristic and the selection of suitable elements. The design of analog electronic circuits is difficult, and in general, there has been no general automated synthesis procedure. therefore, a number of automated synthesis procedure of analog circuits have been proposed. Koza et al. proposed an automated synthesis procedure using genetic programming. The automated synthesis procedure can automatically create parameterized topologies of the desired analog circuit. However, this synthesis procedure uses an infinite number of devices, and therefore the scale of the generated circuits is very large in many cases. In a real design process, only the finite number device can be used. Also, the generated circuits are only evaluated by a numerical simulation, namely, SPICE. The generated circuit is required to have stability and reproducibility. In this paper, we propose a novel automated synthesis procedure for a two-port circuit which has a simple nonlinear characteristic, by means of a genetic algorithm. The proposed method generates a suitable circuit which uses minimum resources.
Nonlinear Theory and its Applications (NOLTA), IEICE, Jul 20, 2012
In this study, we analyzed the kind of psychological effects that were caused by nonlinear, possi... more In this study, we analyzed the kind of psychological effects that were caused by nonlinear, possibly chaotic vibrations as compared to regular vibrations. For this analysis, we produced a chaotic low-frequency electrical therapy device to generate chaotic vibrations. Using the device, we analyzed the direct effects of chaotic vibrations on the human body. In the experiments, we generated fully chaotic vibrations, intermittent chaotic vibrations, and periodic vibrations. To evaluate the effects of the vibrations on the human body, we used one of the subjective methods; a paired comparison method. We identified a rank-order scale by comparing pairs of two vibrations. The results indicate that complicated vibrations are more effective than periodic vibrations.
Journal of Advanced Computational Intelligence and Intelligent Informatics, Nov 20, 2014
In this paper, the controlmethod based on recurrent neural networks is proposed for optimizing la... more In this paper, the controlmethod based on recurrent neural networks is proposed for optimizing large-scale wind and solar power generation systems. Recently, an optimal control method based on recurrent neural networks was proposed for wind and solar power generation systems. In this method, optimization problems are regarded as linear programming problems, which are solved by recurrent neural networks. Results suggest that this control method based on recurrent neural networks could be implemented in real world systems. However, only small power generation systems were used to evaluate this control method in previous studies. Then, the method for power generation systems is evaluated by more realistic conditions. The results of our numerical experiments show that this control method delivers high performance with large-scale power generation systems. Furthermore, if the power generation systems has specific topologies, almost 20% of the supplying capacity is improved.
Journal of Signal Processing, RISP, Jul 20, 2012
A particle swarm optimization (PSO) is one of the powerful systems for solving global optimizatio... more A particle swarm optimization (PSO) is one of the powerful systems for solving global optimization problems. The searching ability of such PSO depends on the inertia weight coefficient, and the acceleration coefficients. Since the acceleration coefficients are multiplied by a random vector, the system can be regarded as a stochastic system. In order to analyze the dynamics rigorously, we pay attention to deterministic PSO which does not contain any stochastic factors. On the other hand, the standard PSO may diverge depending on the random parameter. Due to this divergence property, the standard PSO has high performance compared to the deterministic PSO. Since the deterministic PSO does not have stochastic factors, the diversity of the particles of deterministic PSO is lost. Therefore its searching ability is worse. In order to give the diversity to the deterministic PSO, the golden angle is applied to the rotation angle parameter of the deterministic PSO. We confirm the performance of the searching ability of the proposed PSO.
Journal of Signal Processing, RISP, Nov 20, 2013
Blind source separation (BSS) is a technique for recovering original source signals from mixed si... more Blind source separation (BSS) is a technique for recovering original source signals from mixed signals without the aid on information of the source signals. The system restores the original source signals using the probability of the distribution of the original signal. In this paper, we consider the case where the original source signals are nonlinearly mixed. In general, the separation of the nonlinear mixed signals is difficult. In order to solve this problem, we apply a radial basis function (RBF) network with the nonlinear BSS system. The RBF network can approximate the nonlinear mapping. Therefore, the inverse mapping of the nonlinear mixture system is approximated by the RBF network. For the system to approximate the inverse mapping, it is necessary to adjust the parameters of the RBF network. We assume the original source signals to be independent of each other. In this case, if the mixed signals can be separated, the higher-order cross-moment of the output signals is decreased. In order to adjust the parameters of the RBF network, particle swarm optimization is used. We confirm the separation performance by numerical simulations. Simulation results indicate that the proposed approach has good performance.
Journal of Advanced Computational Intelligence and Intelligent Informatics, May 20, 2014
We consider the design procedure for a single-phase PWM DC-AC inverter using a particle swarm opt... more We consider the design procedure for a single-phase PWM DC-AC inverter using a particle swarm optimization algorithm. The switching operation is the most important component of the single-phase PWM DC-AC inverter. The PSO algorithm optimizes the switching angle effectively. The design procedure of the switching angle evaluates total harmonic distortion and the effective value of output. The proposed evaluation function restricts the scope to evaluating harmonic components. Based on numerical simulation results, we confirmed that the performance of the proposed design procedure was improved compared to the conventional sinusoidal PWM procedure. We develop an implementation circuit for our PWM DCAC inverter. By using the implemented circuit, we confirmed that results for implementation circuits are consistent with results for numerical simulations, indicating that the proposed algorithm exhibits better performance than the conventional sinusoidal PWM DC-AC inverter.
Nonlinear Theory and its Applications (NOLTA), IEICE, Oct 25, 2013
Particle Swarm Optimization (PSO) is a heuristic optimization value searching method. The method ... more Particle Swarm Optimization (PSO) is a heuristic optimization value searching method. The method can find the better solution quickly comparing with other heuristic algorithms. The searching ability of PSO is depended on the parameters. Since the parameters of PSO contain a stochastic factor, the rigorous theoretical analysis is not sufficient. In order to analyze the dynamics rigorously, a deterministic PSO has been proposed. This paper pays attention to such deterministic PSO. We derive a damping factor and a rotation angle of the trajectory from its eigenvalues. We discuss the relationship between the parameters and the searching ability. Based on the results of our numerical simulations, we clarify that the damping factor and the rotation angle influence the stability of the trajectory and the searching ability of the optimal value.
Conference Presentations by Kenya Jinno
—There are several types of switching pattern design of the matrix converter. In this paper, we d... more —There are several types of switching pattern design of the matrix converter. In this paper, we discuss the switching pattern design of the matrix converter. As a new switching pattern design method, the application of the PSO, which is one of the non-linear optimization method. Further, the switching pattern is confirmed generated from numerical experiments.
—Particle swarm optimization (PSO) is a stochastic population-based algorithm that is designed fo... more —Particle swarm optimization (PSO) is a stochastic population-based algorithm that is designed for real-parameter optimization problems. PSO is a simple and powerful algorithm. However, the performance of PSO is degraded in the case of non-separable and ill-conditioned problems. In this article, we discuss the relation between the Hessian matrix of a function and the covariance matrix of the search distribution. The covariance matrix adaptation mechanism is required to solve non-separable and ill-conditioned problems. Therefore, in order to solve such problems, we propose a simple covariance matrix adaptation mechanism that uses the difference vector of the personal best positons. In addition, we propose a selection rule to improve the local search ability. Finally, we clarify the effectiveness of the proposed method in solving non-separable and ill-conditioned problems by using test functions.
—Particle swarm optimization (PSO) is a stochastic population-based algorithm that is designed fo... more —Particle swarm optimization (PSO) is a stochastic population-based algorithm that is designed for real-parameter optimization problems. PSO is simple and powerful algorithm, and is applied to many real world problems. However, because the bias of the search area exists in the conventional PSO, the search performance is deteriorated in non-separable problems. In order to overcome this problem, standard particle swarm optimization 2011 (SPSO2011) was proposed. The performance of SPSO2011 is not affected by the dependencies among variables. In this article, we clarify that SPSO2011 performance is affected by the distribution of the center of the search range. Also, we clarify that the global search ability fades away by the update rule of the center. Therefore, we propose a novel update rule to improve the global search ability. We clarify the effectiveness of the proposed method by numerical experiments by using CEC2005 benchmark functions.
Given an undirected weighted graph G = (V, E, c) and a set T , where V is the set of nodes, E is ... more Given an undirected weighted graph G = (V, E, c) and a set T , where V is the set of nodes, E is the set of edges, c is a cost function, and T is a subset of nodes called terminals, the Steiner tree problem in graphs is that of finding the subgraph of the minimum weight that connects all of terminals. The Steiner tree problem is an example of an NP-complete combinatorial optimization problem [1]. Thus, approximate methods are usually employed for constructing the Steiner tree. In this study, the KMB algorithm [2], which is an efficient construction method for Steiner tree problems, is enhanced by considering edge betweenness [3]. The results of numerical simulations indicate that our improved KMB algorithm shows good performances for various types of benchmark Steiner tree problems.
SUMMARY In this study we investigate the synchronization of relaxation oscillators having individ... more SUMMARY In this study we investigate the synchronization of relaxation oscillators having individual differences by using non-periodic signal injection. When a common non-periodic signal is injected into the relaxation oscillators, the oscillators exhibit synchronization phenomena. Such synchronization phenomena can be classified as injection locking. We also consider the relation between the synchronization state and the individual difference. Further, we pay attention to the effect of the fluctuation range of the non-periodic injected signal. When the fluctuation range is wide, we confirm that the synchronization range increases if the individual difference is small. key words: injection locking, relaxation oscillator, non-periodic injected signal, piecewise linear system
The purpose of this paper is to improve the output quality of three-phase PWM DC-AC inverter. The... more The purpose of this paper is to improve the output quality of three-phase PWM DC-AC inverter. The improvement in output quality is required to reduce the harmonic components. The PWM control can reduce harmonic components by adjusting the width of each pulse. We design the switching phase to improve output quality, also propose an evaluation function for evaluating the frequency components. In order to optimize the switching phase, particle swarm optimization is applied.
We confirm the effectiveness of the proposed method comparing with other methods. Moreover, we confirm the effectiveness by using the implementation circuit of the three-phase inverter.
Nonlinear Theory and its Applications (NOLTA), IEICE, Apr 1, 2015
This paper studies the particle swarm optimization (PSO) with switched topology (SW-TOPO) and its... more This paper studies the particle swarm optimization (PSO) with switched topology (SW-TOPO) and its application to the multi-optima problems (M-OPT). Particles converge at multiple optima simultaneously, since SW-TOPO disconnect the transmission of information and separate the topology of the particles. We introduce the switching path length as a basic measure to evaluate the switched topology. Also, applying the proposed PSO to typical benchmark functions of the M-OPT, the algorithm efficiency is investigated.
Nonlinear Theory and its Applications (NOLTA), IEICE, Apr 1, 2015
Because of the huge growth in the number of Internet users, data packets flowing in communication... more Because of the huge growth in the number of Internet users, data packets flowing in communication networks have also growth, and as a result, some packets can become congested in communication networks. If packet congestion occurs in a communication network, the packets are trapped in congested nodes, and then the transmission of these packets to their destinations is delayed. Further, the packets could be removed from the communication network in the worst case. To overcome these undesirable problems, an efficient routing strategy based on mutually connected neural networks has been proposed. This neural-based routing strategy shows good performance for regular topological communication networks. However, the performance of the routing strategy declines in irregular topological communication networks. To improve its performance for irregular topological communication networks, we propose in this paper a new neural-based routing strategy with the transmission information. Numerical experiments show that the performance of the proposed strategy is enhanced by the newly added transmission information as compared to the conventional routing strategies. Further, the proposed routing strategy shows better performance for other topological complex communication networks.
Journal of Signal Processing, Nov 2004
The synthesis of analog electronic circuits involves the design of their input-output characteris... more The synthesis of analog electronic circuits involves the design of their input-output characteristic and the selection of suitable elements. The design of analog electronic circuits is difficult, and in general, there has been no general automated synthesis procedure. therefore, a number of automated synthesis procedure of analog circuits have been proposed. Koza et al. proposed an automated synthesis procedure using genetic programming. The automated synthesis procedure can automatically create parameterized topologies of the desired analog circuit. However, this synthesis procedure uses an infinite number of devices, and therefore the scale of the generated circuits is very large in many cases. In a real design process, only the finite number device can be used. Also, the generated circuits are only evaluated by a numerical simulation, namely, SPICE. The generated circuit is required to have stability and reproducibility. In this paper, we propose a novel automated synthesis procedure for a two-port circuit which has a simple nonlinear characteristic, by means of a genetic algorithm. The proposed method generates a suitable circuit which uses minimum resources.
Nonlinear Theory and its Applications (NOLTA), IEICE, Jul 20, 2012
In this study, we analyzed the kind of psychological effects that were caused by nonlinear, possi... more In this study, we analyzed the kind of psychological effects that were caused by nonlinear, possibly chaotic vibrations as compared to regular vibrations. For this analysis, we produced a chaotic low-frequency electrical therapy device to generate chaotic vibrations. Using the device, we analyzed the direct effects of chaotic vibrations on the human body. In the experiments, we generated fully chaotic vibrations, intermittent chaotic vibrations, and periodic vibrations. To evaluate the effects of the vibrations on the human body, we used one of the subjective methods; a paired comparison method. We identified a rank-order scale by comparing pairs of two vibrations. The results indicate that complicated vibrations are more effective than periodic vibrations.
Journal of Advanced Computational Intelligence and Intelligent Informatics, Nov 20, 2014
In this paper, the controlmethod based on recurrent neural networks is proposed for optimizing la... more In this paper, the controlmethod based on recurrent neural networks is proposed for optimizing large-scale wind and solar power generation systems. Recently, an optimal control method based on recurrent neural networks was proposed for wind and solar power generation systems. In this method, optimization problems are regarded as linear programming problems, which are solved by recurrent neural networks. Results suggest that this control method based on recurrent neural networks could be implemented in real world systems. However, only small power generation systems were used to evaluate this control method in previous studies. Then, the method for power generation systems is evaluated by more realistic conditions. The results of our numerical experiments show that this control method delivers high performance with large-scale power generation systems. Furthermore, if the power generation systems has specific topologies, almost 20% of the supplying capacity is improved.
Journal of Signal Processing, RISP, Jul 20, 2012
A particle swarm optimization (PSO) is one of the powerful systems for solving global optimizatio... more A particle swarm optimization (PSO) is one of the powerful systems for solving global optimization problems. The searching ability of such PSO depends on the inertia weight coefficient, and the acceleration coefficients. Since the acceleration coefficients are multiplied by a random vector, the system can be regarded as a stochastic system. In order to analyze the dynamics rigorously, we pay attention to deterministic PSO which does not contain any stochastic factors. On the other hand, the standard PSO may diverge depending on the random parameter. Due to this divergence property, the standard PSO has high performance compared to the deterministic PSO. Since the deterministic PSO does not have stochastic factors, the diversity of the particles of deterministic PSO is lost. Therefore its searching ability is worse. In order to give the diversity to the deterministic PSO, the golden angle is applied to the rotation angle parameter of the deterministic PSO. We confirm the performance of the searching ability of the proposed PSO.
Journal of Signal Processing, RISP, Nov 20, 2013
Blind source separation (BSS) is a technique for recovering original source signals from mixed si... more Blind source separation (BSS) is a technique for recovering original source signals from mixed signals without the aid on information of the source signals. The system restores the original source signals using the probability of the distribution of the original signal. In this paper, we consider the case where the original source signals are nonlinearly mixed. In general, the separation of the nonlinear mixed signals is difficult. In order to solve this problem, we apply a radial basis function (RBF) network with the nonlinear BSS system. The RBF network can approximate the nonlinear mapping. Therefore, the inverse mapping of the nonlinear mixture system is approximated by the RBF network. For the system to approximate the inverse mapping, it is necessary to adjust the parameters of the RBF network. We assume the original source signals to be independent of each other. In this case, if the mixed signals can be separated, the higher-order cross-moment of the output signals is decreased. In order to adjust the parameters of the RBF network, particle swarm optimization is used. We confirm the separation performance by numerical simulations. Simulation results indicate that the proposed approach has good performance.
Journal of Advanced Computational Intelligence and Intelligent Informatics, May 20, 2014
We consider the design procedure for a single-phase PWM DC-AC inverter using a particle swarm opt... more We consider the design procedure for a single-phase PWM DC-AC inverter using a particle swarm optimization algorithm. The switching operation is the most important component of the single-phase PWM DC-AC inverter. The PSO algorithm optimizes the switching angle effectively. The design procedure of the switching angle evaluates total harmonic distortion and the effective value of output. The proposed evaluation function restricts the scope to evaluating harmonic components. Based on numerical simulation results, we confirmed that the performance of the proposed design procedure was improved compared to the conventional sinusoidal PWM procedure. We develop an implementation circuit for our PWM DCAC inverter. By using the implemented circuit, we confirmed that results for implementation circuits are consistent with results for numerical simulations, indicating that the proposed algorithm exhibits better performance than the conventional sinusoidal PWM DC-AC inverter.
Nonlinear Theory and its Applications (NOLTA), IEICE, Oct 25, 2013
Particle Swarm Optimization (PSO) is a heuristic optimization value searching method. The method ... more Particle Swarm Optimization (PSO) is a heuristic optimization value searching method. The method can find the better solution quickly comparing with other heuristic algorithms. The searching ability of PSO is depended on the parameters. Since the parameters of PSO contain a stochastic factor, the rigorous theoretical analysis is not sufficient. In order to analyze the dynamics rigorously, a deterministic PSO has been proposed. This paper pays attention to such deterministic PSO. We derive a damping factor and a rotation angle of the trajectory from its eigenvalues. We discuss the relationship between the parameters and the searching ability. Based on the results of our numerical simulations, we clarify that the damping factor and the rotation angle influence the stability of the trajectory and the searching ability of the optimal value.
—There are several types of switching pattern design of the matrix converter. In this paper, we d... more —There are several types of switching pattern design of the matrix converter. In this paper, we discuss the switching pattern design of the matrix converter. As a new switching pattern design method, the application of the PSO, which is one of the non-linear optimization method. Further, the switching pattern is confirmed generated from numerical experiments.
—Particle swarm optimization (PSO) is a stochastic population-based algorithm that is designed fo... more —Particle swarm optimization (PSO) is a stochastic population-based algorithm that is designed for real-parameter optimization problems. PSO is a simple and powerful algorithm. However, the performance of PSO is degraded in the case of non-separable and ill-conditioned problems. In this article, we discuss the relation between the Hessian matrix of a function and the covariance matrix of the search distribution. The covariance matrix adaptation mechanism is required to solve non-separable and ill-conditioned problems. Therefore, in order to solve such problems, we propose a simple covariance matrix adaptation mechanism that uses the difference vector of the personal best positons. In addition, we propose a selection rule to improve the local search ability. Finally, we clarify the effectiveness of the proposed method in solving non-separable and ill-conditioned problems by using test functions.
—Particle swarm optimization (PSO) is a stochastic population-based algorithm that is designed fo... more —Particle swarm optimization (PSO) is a stochastic population-based algorithm that is designed for real-parameter optimization problems. PSO is simple and powerful algorithm, and is applied to many real world problems. However, because the bias of the search area exists in the conventional PSO, the search performance is deteriorated in non-separable problems. In order to overcome this problem, standard particle swarm optimization 2011 (SPSO2011) was proposed. The performance of SPSO2011 is not affected by the dependencies among variables. In this article, we clarify that SPSO2011 performance is affected by the distribution of the center of the search range. Also, we clarify that the global search ability fades away by the update rule of the center. Therefore, we propose a novel update rule to improve the global search ability. We clarify the effectiveness of the proposed method by numerical experiments by using CEC2005 benchmark functions.