Enhanced path smoothing based on conjugate gradient descent for firefighting robots in petrochemical complexes*

The firefighting robot system (FFRS) comprises several autonomous robots that can be deployed to fire disasters in petrochemical complexes. For autonomous navigation, the path planner should consider the robot constraints and characteristics. Specifically, three requirements should be satisfied for a path to be suitable for the FFRS. First, the path must satisfy the maximum curvature constraint. Second, it must be smooth for robots to easily execute the trajectory. Third, it must allow reaching the target location in a specific heading. We propose a path planner that provides smooth paths, satisfy the maximum curvature constraint, and allows a suitable robot heading. The path smoother is based on the conjugate gradient descent, and three approaches are proposed for this path planner to meet all the FFRS requirements. The effectiveness of these approaches is qualitatively and quantitatively evaluated by examining the generated paths. Finally, the path planner is applied to an actual robot to verify the suitability of the generated paths for the FFRS, and planning is applied to another type of robot to demonstrate the wide applicability of the proposed planner.     

Efficient distribution-free population learning of simple concepts

We consider a variant of the ‘population learning model’ proposed by Kearns and Seung [8], in which the learner is required to be ‘distribution-free’ as well as computationally efficient. A population learner receives as input hypotheses from a large population of agents and produces as output its final hypothesis. Each agent is assumed to independently obtain labeled sample for the target concept and output a hypothesis. A polynomial time population learner is said to PAC-learn a concept class, if its hypothesis is probably approximately correct whenever the population size exceeds a certain bound which is polynomial, even if the sample size for each agent is fixed at some constant. We exhibit some general population learning strategies, and some simple concept classes that can be learned by them. These strategies include the ‘supremum hypothesis finder’, the ‘minimum superset finder’ (a special case of the ‘supremum hypothesis finder’), and various voting schemes. When coupled with appropriate agent algorithms, these strategies can learn a variety of simple concept classes, such as the ‘high–low game’, conjunctions, axis-parallel rectangles and others. We give upper bounds on the required population size for each of these cases, and show that these systems can be used to obtain a speed up from the ordinary PAC-learning model [11], with appropriate choices of sample and population sizes. With the population learner restricted to be a voting scheme, what we have is effectively a model of ‘population prediction’, in which the learner is to predict the value of the target concept at an arbitrarily drawn point, as a threshold function of the predictions made by its agents on the same point. We show that the population learning model is strictly more powerful than the population prediction model. Finally, we consider a variant of this model with classification noise, and exhibit a population learner for the class of conjunctions in this model. This revised version was published online in June 2006 with corrections to the Cover Date.     

Fabrication of X-rays mask with carbon membrane for diffraction gratings

We have produced diffraction gratings for obtaining high resolution X-ray phase imaging, such as X-ray Talbot interferometer. These diffraction gratings were required to have a fine, high accuracy, high aspect ratio structure. Therefore, we decided to use the X-rays lithography technique that used synchrotron radiation of the directivity for a manufacture process. The accuracy of the completed structure depends largely on the accuracy of the X-ray mask. In our group, a resin material is conventionally used for the membrane of large X-ray masks. However, X-ray masks comprising a resin membrane have the disadvantage that, after several cycles of X-ray exposure, they crease and sag due to X-ray-derived heat. As a substitute for the conventional resin membrane, we experimentally fabricated a new X-ray mask using a carbon wafer membrane. The newly fabricated X-ray mask was subjected to X-ray exposure experiment. We succeeded in making the structure body which was almost shape. And the experimental results verified that the new mask did not deteriorate even when used repeatedly, demonstrating that it was highly durable.     

An optimum design of robotic food handling by using Burger model

This paper discusses an optimum design approach on robotic food handling by considering the characteristics of viscoelasticity of object. We pick up a traditional Japanese food, “Norimaki” as a typical example with the viscoelastic characteristics. We first show that the dynamic characteristics of Norimaki can be expressed by utilizing the Burger model. After testing the parameter sensitivity, we show an example of the optimum design for determining the combination of the hand stiffness and the operating velocity. We further show that the resultant plastic deformation can be formulated with the exact solution.     

Growth monitoring and classification of rice fields using multitemporal RADARSAT-2 full-polarimetric data

Spaceborne synthetic aperture radar (SAR) can be used for agricultural monitoring. In this study, three single-polarimetric and four full-polarimetric observation data sets were analysed. A rice paddy field in northern Japan was used as the study site; the data for this site were obtained using RADARSAT-2, which carries a full-polarimetric C-band SAR. Soybean and grass fields were also present within the paddy fields. The temporal change in the backscattering coefficient of the rice paddy fields for the single-polarization data agreed with the temporal change obtained for a rice growth model based on radiative transfer theory. A three-component decomposition approach was applied to the full-polarimetric data. With each rice growth stage, the volume scattering component ratio increased, whereas the surface scattering component ratio generally decreased. The soybean and grass fields showed a smaller double-bounce scattering component than the rice fields for all the acquired data. The results of this study show that multitemporal observation by full-polarimetric SAR has great potential to be utilized for estimating rice-planted areas and monitoring rice growth.     

Molar-Volume Dependence of the Pressure in hcp Solid 3He in Magnetic Fields

Pressure measurements have been made in a hcp solid ³ He at molar volumes from 18.90 to 19.45 cm ³ /mol, at temperatures down to 1 mK and in magnetic fields up to 0.16 Tesla. The results are analyzed by a high-temperature series expansion with multiple-exchange processes. The molar volume dependence of the multiple-exchange interactions has been determined by pressure measurements in magnetic fields. The results indicate that the exchange frequencies determined by the pressure measurements in a hcp solid ³ He are consistent with those obtained by magnetization measurements.     

Return Link Systems for Wireless Mobile Camera Using 42 GHz-Band with Frequency and Space Diversity Techniques

In this paper, return link systems for wireless mobile camera using 42 GHz-band in multipath fading environments are proposed. The return link systems for wireless mobile camera require wireless transmission with high reliability as same as the conventional wired camera systems. The proposed return link systems achieve transmission with high reliability by taking into consideration frequency and space diversity techniques. The proposed systems can be classified as 3 three types of return link systems according to diversity techniques： FD （frequency diversity） systems, SD （space diversity） systems and FD＋SD system （frequency and space diversity）. Computer simulations are carried out in order to confirm the effectiveness of the proposed return link systems. It is confirmed that the proposed three types of return link systems have an advantage about BER performances and effectively exploit frequency and space resources.     

Simultaneous rate-synchrony codes in populations of spiking neurons

Firing rates and synchronous firing are often simultaneously relevant signals, and they independently or cooperatively represent external sensory inputs, cognitive events, and environmental situations such as body position. However, how rates and synchrony comodulate and which aspects of inputs are effectively encoded, particularly in the presence of dynamical inputs, are unanswered questions. We examine theoretically how mixed information in dynamic mean input and noise input is represented by dynamic population firing rates and synchrony. In a subthreshold regime, amplitudes of spatially uncorrelated noise are encoded up to a fairly high input frequency, but this requires both rate and synchrony output channels. In a suprathreshold regime, means and common noise amplitudes can be simultaneously and separately encoded by rates and synchrony, respectively, but the input frequency for which this is possible has a lower limit.     

Status and research programs of the multinuclide accelerator mass spectrometry system at the University of Tsukuba

We present the current status and research programs of a multinuclide accelerator mass spectrometry (AMS) system on the 12UD Pelletron tandem accelerator at the University of Tsukuba (Tsukuba AMS system), Japan. A maximum terminal voltage of 12 MV is available for the AMS system. The Tsukuba AMS system can measure environmental levels of long-lived radioisotopes of ¹⁴C, ²⁶Al, ³⁶Cl and ¹²⁹I by employing a molecular pilot beam. Recently, enhancements in AMS techniques and equipment, including sample preparation, the ion source and the data acquisition system, have improved the performance of ³⁶Cl-AMS. The standard deviation of fluctuations is typically ±2%, and the machine background level for the ³⁶Cl/Cl ratio is lower than 1 × 10^?15 with a halite sample. We have measured over 500 samples in 1 year, including samples for earth and environmental sciences and nuclear safety research.     

Learning commutative deterministic finite state automata in polynomial time

We consider the problem of learning the commutative subclass of regular languages in the on-line model of predicting {0,1∼-valued functions from examples and reinforcements due to Littlestone [7,4]. We show that the entire class of commutative deterministic finite state automata (CDFAs) of an arbitrary alphabet sizek is predictable inO(s ^k) time with the worst case number of mistakes bounded above byO(s ^kk logs), wheres is the number of states in the target DFA. As a corollary, this result implies that the class of CDFAs is also PAC-learnable from random labeled examples in timeO(s ^k) with sample complexity, using a different class of representations. The mistake bound of our algorithm is within a polynomial, for a fixed alphabet size, of the lower boundO(s+k) we obtain by calculating the VC-dimension of the class. Our result also implies the predictability of the class of finite sets of commutative DFAs representing the finite unions of the languages accepted by the respective DFAs. Part of this work was supported by the Office of Naval Research under contract number N00014-87-K-0401 while the author was at the Department of Computer and Information Science, University of Pennsylvania, and N0014-86-K-0454 while at the Department of Computer and Information Sciences, U.C. Santa Cruz. The author’s email address is abe@IBL.CL.nec.co.jp