Hiromi Miyajima

Bagging and AdaBoost algorithms for vector quantization

In this paper, we propose VQ methods based on ensemble learning algorithms Bagging and AdaBoost. The proposed methods consist of more than one weak learner, which are trained in parallel or sequentially. In Bagging, the weak learners are trained in parallel by using randomly selected data from a given data set. The output for Bagging is given as the average among the weak learners. In AdaBoost, the weak learners are sequentially trained. The first weak learner is trained by using randomly selected data from a given data set. For the second and later weak learners, the probability distribution of learning data is modified so that each weak learner focuses on data involving higher error for the previous weak one. The output for AdaBoost is given as the weighted average among the weak learners. The presented simulation results show that the proposed methods can achieve a good performance in shorter learning times than conventional ones such as K-means and NG.

Optimal switch placement in distribution networks under different conditions using improved GA

This paper presents an optimization methodology for placement of switches in medium-voltage loop distribution networks. The primary objective is to minimize the total cost with consideration of achieving high distribution reliability levels. In this paper, four possible states are con-sidered and discussed, and the improved genetic algorithm is adopted to determine the optimal number and locations of sectionalizing switches and interconnection switches. The feasibility and superiority of the proposed algorithm is demonstrated by application to main feeder F1 and F2 among RBTS-BUS 5 system.

Some quantum search algorithms for arbitrary initial amplitude distribution

For searching any item in an unsorted database with N items, Grovers quantum searching algorithm takes only O(√N) steps, which is much faster than O(N) steps required for a classical computer. However, it is known that Grovers algorithm is effective only in the case where the initial amplitude distribution of dataset is uniform. On the other hand, Ventura has also proposed a quantum searching algorithm. The algorithm is effective only in the special case for the initial amplitude distribution. Therefore, we have proposed an effective quantum searching algorithm in another case in the previous paper. In this paper, we generalize the previous algorithm and propose several effective algorithms in several cases where initial amplitude distributions of dataset are non-uniform. Further, in order to show the effectiveness of the algorithms, we analyze their dynamics.

Effective initial route construction for mobile relay on wireless sensor network

In recent years, mobile relay has been studied in order to reduce the energy consumption in WSN El-Moukaddem et al. (Mobile Relay Configuration in Data-intensive Wireless Sensor Networks, 2009). The concept of mobile relay is that some movable nodes change their location so as to minimize the total energy consumed by both wireless transmission and locomotion. Mobile relay needs to determine an initial route that represents the sequence of relaying nodes. We have already proposed several initial route construction algorithms based on greedy approaches Shigei et al. (IAENG Int J Comput Sci 39: 321–328, 2012), Yudo et al. (Battery-Aware Initial Route Construction for Mobile Relay on Wireless Sensor Network, 2012). However, the conventional method cannot always provide optimal routes, because they do not examine all the possible routes. In this paper, we propose a battery-aware initial route construction method based on Dijkstra’s algorithm (BAIR-D), which utilizes the node information outside of the direct communication range. The algorithm has a capability to examine all the possible routes. We show the effectiveness by numerical simulation.

A Proposal of Fuzzy Inference Model Composed of Small-Number-of-Input Rule Modules

The automatic construction of fuzzy system with a large number of input variables involves many difficulties such as large time complexities and getting stuck in a shallow local minimum. In order to overcome them, an SIRMs (Single-Input Rule Modules) model has been proposed. However, such a simple model does not always achieve good performance in complex non-linear systems. This paper proposes a fuzzy reasoning model as a generalized SIRMs model, in which each module deals with a small number of input variables. The reasoning output of the model is determined as the weighted sum of all modules, where each weight is the importance degree of a module. Further, in order to construct a simpler model, we introduce a module deletion function according to the importance degree into the proposed system. With the deletion function, we propose a learning algorithm to construct a fuzzy reasoning system consisting of small-number-of-input rule modules (SNIRMs). The conducted numerical simulation shows that the proposed method is superior in terms of accuracy compared to the conventional SIRMs model.

Numerical evaluation of clustering methods with kernel PCA

Kernel methods are ones that, by replacing the inner product with positive definite function, implicitly perform a nonlinear mapping of the input data into a high-dimensional feature space. The clustering methods using kernel function (kernel clustering methods) are superior in accuracy to the conventional ones such as K-Means (KM) and Neural-Gas (NG). But, it seems that kernel clustering methods do not always show sufficient ability of clustering. One method to improve them is to find expression of approximation for data in the feature space. In this paper, we introduce the kernel PCA and apply it to clustering methods as KM and NG. Further, we apply it to derived kernel method, which means twice application of kernel functions. The simulation results show that the proposed clustering methods are superior in terms of accuracy to the conventional methods.

A Learning Algorithm with Boosting for Fuzzy Reasoning Model

There have been proposed many learning algorithms for fuzzy reasoning models based on the steepest descend method. However, any learning algorithm known as a superior one does not always work well. This paper proposes a new learning algorithm with boosting. Boosting is a general method which attempts to boost the accuracy of any given learning algorithm. The proposed method consists of three sub-learners. The first sub-learner is constructed by performing the conventional learning algorithm with randomly selected data from given data space. The second sub-learner is constructed by performing the conventional learning algorithm with the data selected with equal probability from correctly and incorrectly learned data in the first learning. The third sub-learner is constructed with the data for which either the first or the second sub-learner is incorrectly learned. The output for any input data is given as decision by majority among the outputs of three sub-learners. That is, the method attempts to boost correctly learned data by learning incorrectly learned data repeatedly. In order to show the effectiveness of the proposed algorithm, numerical simulations are performed.

Hybrid of ideal movement method and virtual rail method for network construction in mobile sensor network

Mobile sensor network (MSN) has attracted attention because it can perform sensing at the place where is wide range or difficult to place directly sensor nodes. In MSN, nodes need to connect with the network for sending the sensing data to the base station. So far, an ideal movement method and a virtual rail method have been proposed as network construction methods. In this paper, we propose a hybrid method of the ideal movement method and the virtual rail method, and demonstrate the effectiveness of the proposed method by simulation.

Design of hardware circuit based on a neural network model for rapid detection of center of gravity position

This paper proposes a rapid detection method for the center of gravity based on a neural network model. It is suitable for the rapid response requirement such as attitude control of a gait robot or real time torque control of a running car. The proposed method detects the center of gravity position on a straight line by using only the hardware circuit composing of common electronic devices instead of software, microprocessor and AD converter. The circuit employs some neural based comparators without the learning function to simplify the circuit structure. The detection circuit using some parallel processing neural comparators rapidly detects the center of gravity position on a straight line. In this paper, the circuit is designed and fabricated with electronic devices, and the circuit experiment shows the performance of the position detection.

A∗ algorithm-based initial route construction for mobile relay on WSN

Energy consumption is the most crucial issue in Wireless Sensor Network (WSN). Mobile relay is one of the techniques for saving the energy in WSN. In mobile relay, initial route construction determines the sequence of relaying nodes, which is provided to mobile relay algorithms. Several methods have been proposed for determining the initial route for mobile relay in WSN. We have proposed Battery-Aware Initial Route construction by Dijkstras Algorithm (BAIR-D). BAIR-D employs Dijkstras algorithm to find the initial route. It takes into account nodes battery level into the cost function, such that this algorithm avoids using node with low battery levels. We have implemented this method in a centralized fashion. However, from a scalability point of view, the initial route construction should be performed distributedly In this paper, we present a new initial route construction based on A-star algorithm, which is referred as BAIR-A*. Unlike BAIR-D, BAIR-A* is suitable for distributed implementation, because the method can significantly reduce the communication needed for initial route construction. We also present the distributed algorithm of BAIR-A*. We demonstrate the effectiveness of BAIR-A by using numerical simulations.