Morphological control of laser-synthesized silicon nanoparticles

Size and crystallinity controlled silicon nanoparticles were prepared by a laser ablation, in situ annealing and mobility size-selection with a differential mobility analyzer (DMA). The shape and crystal structure of generated particles were observed by a transmission electron microscopy (TEM). Both densification of agglomerates and crystal growth of the particles were observed. The size of silicon primary particle was increased by the annealing, and the uniformity of the particle classified at 10 nm was improved as a result.     

Adaptability analysis of genetic network programming with reinforcement learning in dynamically changing environments

Genetic network programming (GNP) has been proposed as one of the evolutionary algorithms and extended with reinforcement learning (GNP-RL). The combination of evolution and learning can efficiently evolve programs and the fitness improvement has been confirmed in the simulations of tileworld problems, elevator group supervisory control systems, stock trading models and wall following behavior of Khepera robot. However, its adaptability in testing environments, where the situations dynamically change, has not been analyzed in detail yet. In this paper, the adaptation mechanism in the testing environment is introduced and it is confirmed that GNP-RL can adapt to the environmental changes using a robot simulator WEBOTS, especially when unexperienced sensor troubles suddenly occur. The simulation results show that GNP-RL works well in the testing even if wrong sensor information is given because GNP-RL has a function to automatically change programs using alternative actions. In addition, the analysis on the effects of the parameters of GNP-RL is carried out in both training and testing simulations.     

Genetic relation algorithm with guided mutation for the large-scale portfolio optimization

The survey of the relevant literatures shows that there have been many studies for portfolio optimization problems and that the number of studies which have investigated the optimum portfolio using evolutionary computation is quite large. But, almost none of these studies deals with genetic relation algorithm (GRA), where GRA is one of the evolutionary methods with graph structure. This study presents an approach to large-scale portfolio optimization problems using GRA with a new operator, called guided mutation. In order to pick up the most efficient portfolio, GRA considers the correlation coefficient between stock brands as strength, which indicates the relation between nodes in each individual of GRA. Guided mutation generates offspring according to the average value of correlation coefficients in each individual, which means to enhance the exploitation ability of evolution of GRA. A genetic relation algorithm with guided mutation (GRA/G) for the portfolio optimization is proposed in this paper. Genetic network programming (GNP), which was proposed in our previous research, is used to validate the performance of the portfolio generated with GRA/G. The results show that GRA/G approach is successful in portfolio optimization.     

Learning Petri network and its application to nonlinear systemcontrol

According to recent knowledge of brain science it is suggested that there exists functions distribution, which means that specific parts exist in the brain for realizing specific functions. This paper introduces a new brain-like model called Learning Petri Network (LPN) that has the capability of functions distribution and learning. The idea is to use Petri net to realize the functions distribution and to incorporate the learning and representing ability of neural network into the Petri net. The obtained LPN can be used in the same way as a neural network to model and control dynamic systems, while it is distinctive to a neural network in that it has the capability of functions distribution. An application of the LPN to nonlinear crane control systems is discussed. It is shown via numerical simulations that the proposed LPN controller has superior performance to the commonly-used neural network one.     

Temperature distribution in semiconductor wafers heated in ahot-wall-type rapid diffusion furnace

Transient temperature distribution was calculated for wafers heated in a new hot-wall-type rapid diffusion furnace. Two-dimensional radiative heat transfer was combined with unsteady conduction in wafers and the furnace. The furnace is composed of parallel plate heaters, and heats wafers to a temperature of about 1000°C. The heaters are divided into four zones and their heating powers are PID-controlled. Two wafers on a holder are inserted vertically from the bottom of the furnace, and heated for three minutes. The calculated results show the wafer temperature approached the desired heating temperature about one minute after insertion, agreeing with experimental results. The average temperature distribution in the wafers during heating is found to be within ±1°C at 1000°C, when the heating power (temperature) of the four zones is properly controlled. The effects of heater temperature, insertion speed, and holder thickness on the temperature distribution in wafers were calculated. The new hot-wall-type rapid diffusion furnace can be used to manufacture future VLSI     

Photoinduction of arginine decarboxylase activity in leaves of Pharbitis nil

The activity of arginine decarboxylase (ADC) in leaves of Pharbitis nil was induced by light. The ADC activity increased to a maximum 1 h after illumination, followed by a gradual decrease. This suggested light either induced synthesis of ADC protein de novo or was involved in its activation. Cycloheximide inhibited the photoinduction of ADC activity, and the half life of ADC in leaves was 30-40 min. The temperature and relative humidity in darkness before illumination had no effect on the photoinduction of ADC activity, contrary to the photoresponse of S-adenosylmethionine decarboxylase (SAMDC) activity where the conditions of darkness before lights-on have a marked effect. The light response of the polyamine (PA)-biosynthetic enzyme activity produced transient accumulation of PA. The level of spermidine increased in leaves in which activities of both SAMDC and ADC increased after illumination, while the level of putrescine increased in leaves in which the activity of ADC increased but that of SAMDC did not.     

Analysis of a cavity-backed annular slot antenna with one pointshorted

Analysis of a cavity-backed annular slot antenna with one point shorted is performed experimentally and theoretically. Resonance frequencies, bandwidths, and radiation patterns are studied with respect to slot width, cavity depth, and slot shorting position. In the theoretical analysis, the method of moments is applied to find the magnet current on the slot, and the Green's function for the field inside the cavity has been newly derived. By selecting a slot shorting position, circular polarization, and a bandwidth of more than 10%, the voltage standing-wave ratio (VSWR) ⩽2 for the input impedance is obtained     

MBFP generalized association rule mining and classification in traffic volume prediction

Genetic network programming (GNP)‐based time‐related association rules mining method provides a useful mean to investigate future traffic volume of road networks and hence helps us to develop traffic navigation system. Further improvements have been proposed in this paper about the time‐related association rule mining using generalized GNP with multibranches and full‐paths (MBFP) algorithm. For fully utilizing the potential ability of GNP structure, the mechanism of generalized GNP with MBFP is studied. The aim of this algorithm is to better handle association rule extraction from the databases with high efficiency in a variety of time‐related applications, especially in the traffic volume prediction problems. The generalized algorithm which can find the important time‐related association rules is described, and experimental results are presented considering a traffic prediction problem. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

A Double-Deck Elevator Group Supervisory Control System Using Genetic Network Programming

Elevator group supervisory control systems (EGSCSs) are designed so that the movement of several elevators in a building is controlled efficiently. The efficient control of EGSCSs using conventional control methods is very difficult due to its complexity, so it is becoming popular to introduce artificial intelligence (AI) technologies into EGSCSs in recent years. As a new approach, a graph-based evolutionary method named genetic network programming (GNP) has been applied to the EGSCSs, and its effectiveness is clarified. The GNP can introduce various a priori knowledge of the EGSCSs in its node functions easily, and can execute an efficient rule-based group supervisory control that is optimized in an evolutionary way. Meanwhile, double-deck elevator systems (DDESs) where two cages are connected in a shaft have been developed for the rising demand of more efficient transport of passengers in high-rise buildings. The DDESs have specific features due to the connection of cages and the need for comfortable riding; so its group supervisory control becomes more complex and requires more efficient group control systems than the conventional single-deck elevator systems (SDESs). In this paper, a new group supervisory control system for DDESs using GNP is proposed, and its optimization and performance evaluation are done through simulations. First, optimization of the GNP for DDSEs is executed. Second, the performance of the proposed method is evaluated by comparison with conventional methods, and the obtained control rules in GNP are studied. Finally, the reduction of space requirements compared with SDESs is confirmed.