Simultaneous design of the sensory morphology and controller of mobile robots

This paper proposes a method for automatic design of the sensory morphology of a mobile robot. The proposed method employs two types of adaptations, ontogenetic and phylogenetic, to optimize the sensory morphology of the robot. In ontogenetic adaptation, reinforcement learning searches for the optimal policy, which is highly dependent on the sensory morphology. In phylogenetic adaptation, a genetic algorithm is used to select morphologies with which the robot can learn tasks faster. Our proposed method was applied to the design of the sensory morphology of a line‐following robot. We performed simulation experiments to compare the design solution with a hand‐coded robot. The results of the experiments revealed that our robot outperformed the hand‐coded robot in terms of the following accuracy and learning speed, although our robot had fewer sensors than the hand‐coded one. We also built a physical robot using the design solution. The experimental results revealed that this physical robot used its morphology effectively and outperformed the hand‐coded robot. © 2010 Wiley Periodicals, Inc. Electr Eng Jpn, 172(1): 48–57, 2010; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20965     

Mixing speed-controlled gold nanoparticle synthesis with pulsed mixing microfluidic system

Gold nanoparticles with diameters of a few tens of nanometer and a narrow size distribution were synthesized using a pulsed mixing method with a microfluidic system which consists of a Y-shaped mixing microchannel and two piezoelectric valveless micropumps. This mixing method enables control of the mixing speed of gold salts and reducing agent by changing the switching frequency of the micropumps, which was our focus to improve the particle size distribution, which is an essential parameter in gold nanoparticle synthesis. In the proposed method, the mixing time was inversely proportional to the switching frequency and the minimum mixing time was 95 ms at a switching frequency of 200 Hz. During synthesis experiments, the mean diameter of the synthesized gold nanoparticles was found to increase, and the coefficient of variation of particle size was found to decrease with decreasing mixing time. We successfully improved the coefficient of variation to less than 10% for a mean diameter of around 40 nm.     

Foundations

Foundation engineering emerged as a discipline of modern engineering science in 1940s. Design and analysis as well as practice of foundation engineering have made a significant progress up to the present time. This paper gives an overview of the development of foundation engineering, mainly referring to papers published in Soils and Foundations. The paper describes the theoretical development of foundation analysis and a recent increasing trend of foundation practice of adopting various hybrid foundations. The paper points out the importance of environmental considerations in foundation design and practice, including low noise and vibration reduction, reuse of existing foundations and use of natural energy through foundation elements. The paper then provides authors’ views of future directions in foundation studies and practice.     

OPTIMIZATION OF MATERIAL COMPOSITION OF FUNCTIONALLY GRADED PLATE FOR THERMAL STRESS RELAXATION USING A GENETIC ALGORITHM

In this article, a genetic algorithm is applied to an optimization problem of material composition for a plate of step-formed functionally graded materials. The step-formed functionally graded plate is analyzed as a laminated composite plate made of numerous layers with homogeneous and different isotropic material properties. First, the onedimensional transient temperature distribution for a laminated plate is analyzed theoret ically. In addition, the thermal stress components for such an infinitely long plate are formulated under the mechanical condition of being traction-free. As a numerical example, a plate composed of zirconium oxide and titanium alloy is considered. In addition, for the optimization problem of minimizing the thermal stress distribution, the numerical calculations are made using a genetic algorithm without supposing a distribution function of material composition and the optimal material composition of each layer is determined taking into account the effect of the temperature dependency of material properties. Furthermore, the results obtained when a distribution function isn't specified and the results found when a distribution function is specified are compared.     

Viscoelastic behavior of poly(butyl methacrylate) densely grafted on silica nanoparticles

A series of poly(butyl methacrylate)s (PBMAs) with various molar masses (33 000–270 000 g mol^?1), which were densely grafted on fumed silica nanoparticles (PBMA–SiO₂), were synthesized by surface‐initiated atom transfer radical polymerization. The dynamic viscoelastic behavior of PBMA–SiO₂ was systematically investigated in the solid and molten states with oscillatory strains, and compared to that of a conventional nanocomposite (PBMA/SiO₂). The storage moduli of PBMA–SiO₂ and PBMA/SiO₂ are equivalent in the solid state, whereas the storage modulus of PBMA–SiO₂ is lower than that of PBMA/SiO₂ in the molten state, especially at high silica loading. This is because the formation of a network structure composed of the silica nanoparticles in PBMA–SiO₂ is strongly suppressed by the polymer brushes on the particles. In contrast, even at low silica loading, the PBMA–SiO₂ system exhibits a gel‐like behavior resulting from a steric repulsion between the composite particles, because all of the tethered polymers behave as bound polymers. Copyright © 2011 Society of Chemical Industry     

Application of nonlinear fitting and selection of the most fitted equation by AIC in stability test of pharmaceutical ingredients

We proposed a method for objectively selecting the most fitted rate equation among candidate rate equations based on chemical kinetics by both nonlinear regression analysis and Akaike's information criterion (AIC), to express the decrease of pharmaceutical ingredient as an appropriate equation. Pseudo-zero, first, and second-order rate equations were prepared as candidates beforehand, and AIC was introduced for selecting the most fitted rate equation among the candidates. We compared the proposed method to the Weibull method that expressed any decrease patterns as a single equation.

We quantified the contents of thiamine nitrate (VB1) and taurine after storage for one, three, and six months under 40°C-75% relative humidity by high-pressure liquid chromatography. Decrease patterns of each sample were most fitted to one of the candidate rate equations, that is, pseudo-zero, first, and second-order rate equations, respectively, and the degree of fit in the most fitted equation was superior to that of the Weibull method, except for the pseudo-first-order rate equation.

Moreover, we confirmed that the proposed method was considerably precise for estimating the stability of pharmaceuticals by comparing estimated residual rates and confidence intervals to experimental data.     

Effect of mixing ratio on bactericidal action of MgO–CaO powders

MgO–CaO powders were prepared with different molar ratios (MgO/CaO) at 1400 °C for 2 h in air. By using the powder samples obtained, the change in bactericidal effect as a function of MgO–CaO composition was studied by colony count method. From the XRD measurements, it was found that CaO solid solution was formed by the replacement of Mg²⁺ ion with larger Ca²⁺ ion. However, no formation of MgO solid solution was observed. The average particle size and the specific surface area of the samples used in this study were about 0.2 m and 10.5m²g^–1, respectively. The pH values of physiological saline containing powder samples increased with the increase of CaO content, and the value reached 12.1 in sample with the molar ratio (MgO/CaO) of 0.25. From the results of bactericidal tests for Staphylococcus aureus, it was found that the bactericidal effect increased with the increase of CaO content in the samples.     

Beating in Membrane Potential Mediated by Cortical γ-Oscillations for Reading Out Synchronous Neuronal Activities Over Distant Cortical Areas

I investigate whether cortical -oscillations are relevant to reading out synchronous neuronal firings that are distributed over the brain. A cortical neural network model is proposed and simulated. The model consists of two sensory networks (SI and SII) and one coincidence-sensitive network (CS). -oscillatory inputs and external stimuli are applied to interneurons and projection neurons of the sensory networks, respectively. The SI and SII networks project to the CS network in a divergent/convergent manner. Here I show that beating in membrane potential of CS neurons mediates the detection of synchronous neuronal firings between the SI and SII networks. A slight increase (a few Hz) in difference between frequencies of the -oscillations in the SI and SII networks generates the beating in the CS network. The synchronous neuronal firings are detected at each peak of the beating, regardless of difference in signal transmission time between SI-to-CS and SII-to-CS pathways.     

Surface modification of an organosilane self-assembled monolayer on silicon substrates using atomic force microscopy: scanning probe electrochemistry toward nanolithography

Organosilane self-assembled monolayers (SAMs) have been applied to resist materials for nanolithography based on scanning probe microscopy. An organosilane SAM was prepared on Si substrates from a precursor, that is octadecyltrimethoxysilane. Using an atomic force microscope with a conductive probe, current was injected from the probe into the SAM-covered Si substrate so that the SAM was locally degraded at the probe-contacting point. Nanoscale patterns drawn on the SAM was clearly imaged by lateral force microscopy. The patterning could be conducted in air while, in vacuum at the order of 10(-6) Torr, no detectable patterns were fabricated. The presence of adsorbed water at the probe/sample junction was confirmed to be crucial for the patterning of the SAM/Si. Its mechanism was, thus, ascribed to electrochemical reactions of both the SAM and Si with adsorbed water.     

Adaptive Sampling Methods for Scaling Up Knowledge Discovery Algorithms

Scalability is a key requirement for any KDD and data mining algorithm, and one of the biggest research challenges is to develop methods that allow to use large amounts of data. One possible approach for dealing with huge amounts of data is to take a random sample and do data mining on it, since for many data mining applications approximate answers are acceptable. However, as argued by several researchers, random sampling is difficult to use due to the difficulty of determining an appropriate sample size. In this paper, we take a sequential sampling approach for solving this difficulty, and propose an adaptive sampling method that solves a general problem covering many actual problems arising in applications of discovery science. An algorithm following this method obtains examples sequentially in an on-line fashion, and it determines from the obtained examples whether it has already seen a large enough number of examples. Thus, sample size is not fixed a priori; instead, it adaptively depends on the situation. Due to this adaptiveness, if we are not in a worst case situation as fortunately happens in many practical applications, then we can solve the problem with a number of examples much smaller than required in the worst case. We prove the correctness of our method and estimates its efficiency theoretically. For illustrating its usefulness, we consider one concrete task requiring sampling, provide an algorithm based on our method, and show its efficiency experimentally.