Generalized measuring-worm algorithm: high-accuracy mapping and movement via cooperating swarm robots

Recently, many extensive studies have been conducted on robot control via self-positioning estimation techniques. In the simultaneous localization and mapping (SLAM) method, which is one approach to self-positioning estimation, robots generally use both autonomous position information from internal sensors and observed information on external landmarks. SLAM can yield higher accuracy positioning estimations depending on the number of landmarks; however, this technique involves a degree of uncertainty and has a high computational cost, because it utilizes image processing to detect and recognize landmarks. To overcome this problem, we propose a state-of-the-art method called a generalized measuring-worm (GMW) algorithm for map creation and position estimation, which uses multiple cooperating robots that serve as moving landmarks for each other. This approach allows problems of uncertainty and computational cost to be overcome, because a robot must find only a simple two-dimensional marker rather than feature-point landmarks. In the GMW method, the robots are given a two-dimensional marker of known shape and size and use a front-positioned camera to determine the marker distance and direction. The robots use this information to estimate each other’s positions and to calibrate their movement. To evaluate the proposed method experimentally, we fabricated two real robots and observed their behavior in an indoor environment. The experimental results revealed that the distance measurement and control error could be reduced to less than 3 %.     

Microscopic image restoration based on tensor factorization of rotated patches

In microscopic image processing for analyzing biological objects, structural characters of objects such as symmetry and orientation can be used as a prior knowledge to improve the results. In this study, we incorporated filamentous local structures of neurons into a statistical model of image patches and then devised an image processing method based on tensor factorization with image patch rotation. Tensor factorization enabled us to incorporate correlation structure between neighboring pixels, and patch rotation helped us obtain image bases that well reproduce filamentous structures of neurons. We applied the proposed model to a microscopic image and found significant improvement in image restoration performance over existing methods, even with smaller number of bases.     

Efficient Leave-<Emphasis Type="Italic">m</Emphasis>-out Cross-Validation of Support Vector Regression by Generalizing Decremental Algorithm

We propose a computationally efficient method for cross-validation of the Support Vector Regression (SVR) by generalizing the decremental algorithm of SVR. Incremental and decremental algorithm of Support Vector Machines (SVM) ^{2, 8, 9)} efficiently update the trained SVM model when a single data point is added to or removed from the training set. The computational cost of leave-one-out cross-validation can be reduced using the decremental algorithm. However, when we perform leave-m-out cross-validation (m > 1), we have to repeatedly apply the decremental algorithm for each data point. In this paper, we extend the decremental algorithm of SVR^{8, 9)} in such a way that several data points can be removed more efficiently. Experimental results indicate that the proposed approach can reduce the computational cost. In particular, we observed that the number of breakpoints, which is the main computational cost of the involved path-following, were reduced from \({\mathcal O}(m)\) to \({\mathcal O}(\sqrt{m})\).     

Accurate finite element method for atomic calculations based on density functional theory and Hartree–Fock method

An accurate finite element method is developed for atomic calculations based on density functional theory (DFT) within local density approximation (LDA) and Hartree–Fock (HF) method. The radial wave functions are expanded by cubic Hermite spline functions on a uniform mesh for , and all the associated integrals are analytically evaluated in conjunction with fitting procedures of the Hartree and the exchange–correlation potentials to the same cubic Hermite spline functions using a set of recurrence formulas. The total energy of atoms systematically converges from above, and the error algebraically decays as the mesh spacing decreases. When the mesh spacing d is taken to be , the total energy for an atom of atomic number Z can be calculated within error of ¹⁰−7 hartree for both the LDA and HF methods. The equal applicability of the method to DFT and the HF method with a similar degree of high accuracy enables the method to be a reliable platform for development of new functionals in DFT such as hybrid functionals.     

Using selective precipitant for uranyl ions — Fundamental studies for evaluating the precipitant performance

We have developed a simple reprocessing process for spent FBR fuels using N-cyclohexyl-2-pyrrolidone (NCP) which has selective precipitation ability for UO₂²⁺ ions. It was confirmed that NCP has sufficient precipitation ability for UO₂²⁺ ions, decontamination capability (separation of UO₂²⁺ from simulated fission products), and resistance to γ-ray radiation in nitric acid solutions. These findings indicate that NCP is applicable to our reprocessing process. We have also evaluated performances of other precipitants such as N-n-propyl-2-pyrrolidone (NProP), N-n-butyl-2-pyrrolidone (NBP), and N-n-butyl-2-pyridone (NBPyr). It was found that higher decontamination factors (DFs) are obtained by using NProP and NBP. This can be interpreted that the hydrophobicity of NProP and NBP is lower than that of NCP. Furthermore, we have obtained an experimental result that the resistance of NBPyr to γ-ray radiation is superior to that of NCP.     

A Knowledge Discovery System with Support for Model Selection and Visualization

The process of knowledge discovery in databases consists of several steps that are iterative and interactive. In each application, to go through this process the user has to exploit different algorithms and their settings that usually yield multiple models. Model selection, that is, the selection of appropriate models or algorithms to achieve such models, requires meta-knowledge of algorithm/model and model performance metrics. Therefore, model selection is usually a difficult task for the user. We believe that simplifying the process of model selection for the user is crucial to the success of real-life knowledge discovery activities. As opposed to most related work that aims to automate model selection, in our view model selection is a semiautomatic process, requiring an effective collaboration between the user and the discovery system. For such a collaboration, our solution is to give the user the ability to try various alternatives and to compare competing models quantitatively by performance metrics, and qualitatively by effective visualization. This paper presents our research on model selection and visualization in the development of a knowledge discovery system called D2MS. The paper addresses the motivation of model selection in knowledge discovery and related work, gives an overview of D2MS, and describes its solution to model selection and visualization. It then presents the usefulness of D2MS model selection in two case studies of discovering medical knowledge in hospital data—on meningitis and stomach cancer—using three data mining methods of decision trees, conceptual clustering, and rule induction.     

Stress distribution of coated film with a range of coated film thickness and elastic properties under a single EHL operating condition

In recent years, the techniques improving sliding performances have progressed by using coated films possessing superior tribological properties, to reduce the failures of the mechanical elements. Those techniques are often used under severe conditions such as elastohydrodynamic lubrication (EHL). In this paper, numerical three-dimensional analysis of the maximum shear stress applied into the coated film and substrate under a single EHL operating condition was performed with a range of coated film thickness and elastic properties. The strength of coated film as one of those techniques was evaluated numerically, resulting in an optimum design of coated film. As a result, coated films with a larger value of thickness and a smaller modulus of elasticity than that of substrate are preferable.     

Polymer coating method developed for carbon stripper foils

The problem of handling the fragile carbon foils (mounting on the frame, placing in the stripper changer) that easily break when self-supporting has been solved by coating carbon foils with poly-monochloro-para-xylylene. It was found that the polymer-coating method could also be used to produce carbon foils thicker than 100 μg/cm² by alternated deposition of carbon and poly-monochloro-para-xylylene layers. Carbon foil of 500 μg/cm² thick and 10 cm in diameter was produced by this method and mounted to a foil holder. Results of lifetime measurement for singly coated foils are also presented.     

黄土包气带土壤水特征曲线研究

本文主要介绍在中国辐射防护研究院野我试验场从地表到潜水ｍ深范围内４６个土芯样的水特征曲线的测量方法及测量结果。     

Experimental examination of the characteristics of bright-field scanning confocal electron microscopy images

We experimentally examined the characteristics of bright-field (BF) scanning confocal electron microscopy (SCEM) images by changing the observation conditions and comparing the images with those obtained by BF transmission electron microscopy (TEM) and BF scanning TEM (STEM) modes. The observation of 5-nm-diameter Au nanoparticles demonstrated that BF-SCEM produces object elongation of more than 2000?nm along the optical axis, as do BF-TEM and BF-STEM. We demonstrated the relationship between elongation length and geometric effects such as convergence and collection angles of a probe and the lateral size of an object; the relationship is consistent with previous theoretical prediction. Further, we observed interesting features that are seen only in the BF-SCEM images; the film contrast was strongly enhanced, compared with that of BF-STEM. In addition, a bright contrast appeared around the object position in the elongated images. Using this characteristic, we could determine the object position and structure.