Path-planning and navigation of a mobile robot as discrete optimization problems

There is huge diversity among navigation and path-planning problems in the real world because of the enormous number and great variety of assumptions about the environments, constraints, and tasks imposed on a robot. To deal with this diversity, we propose a new solution to the path-planning and navigation of a mobile robot. In our approach, we formulated the following two problems at each time-step as discrete optimization problems: (1) estimation of a robot's location, and (2) action decision. For the first problem, we minimize an objective function that includes a data term, a constraint term, and a prediction term. This approach is an approximation of Markov localization. For the second problem, we define and minimize another objective function that includes a goal term, a smoothness term, and a collision term. Simulation results show the effectiveness of our approach. This work was presented in part at the Fifth International Symposium on Artificial Life and Robotics, Oita, Japan, January 26–28, 2000     

Generation of templates for low-resolution text recognition using a hypothesis graph

We propose a recognition method of character-string images captured by portable digital cameras. A challenging task in character-string recognition is the segmentation of characters. In the proposed method, a hypothesis graph is used for recognition-based segmentation of the character-string images. The hypothesis graph is constructed by the subspace method, using eigenvectors as conditionally elastic templates. To obtain these templates, a generation-based approach is introduced in the training stage. Various templates are generated to cope with low-resolution. We have experimentally proved that the proposed scheme achieves high recognition performance even for low-resolution character-string images. The text was submitted by the authors in English. Hiroyuki Ishida. Received his B.S. and M.S. degrees from the Department of Information Engineering and from the Graduate School of Information Science, respectively, at Nagoya University. He is currently pursuing a Ph.D. in Information Science at Nagoya University. Ichiro Ide. Received his B.S. degree from the Department of Electronic Engineering, his M.S. degree from the Department of Information Engineering, and his Ph.D. from the Department of Electrical Engineering at the University of Tokyo. He is currently an Associate Professor in the Graduate School of Information Science at Nagoya University. Tomokazu Takahashi. Received his B.S. degree from the Department of Information Engineering at Ibaraki University, and his M.S. and Ph.D. from the Graduate School of Science and Engineering at Ibaraki University. His research interests include computer graphics and image recognition. Hiroshi Murase. Received his B.S., M.S., and Ph.D. degrees from the Graduate School of Electrical Engineering at Nagoya University. He is currently a Professor in the Graduate School of Information Science at Nagoya University. He received the Ministry Award from the Ministry of Education, Culture, Sports, Science and Technology in Japan in 2003. He is a Fellow of the IEEE.     

Minimizing False Positives of a Decision Tree Classifier for Intrusion Detection on the Internet

Machine learning or data mining technologies are often used in network intrusion detection systems. An intrusion detection system based on machine learning utilizes a classifier to infer the current state from the observed traffic attributes. The problem with learning-based intrusion detection is that it leads to false positives and so incurs unnecessary additional operation costs. This paper investigates a method to decrease the false positives generated by an intrusion detection system that employs a decision tree as its classifier. The paper first points out that the information-gain criterion used in previous studies to select the attributes in the tree-constructing algorithm is not effective in achieving low false positive rates. Instead of the information-gain criterion, this paper proposes a new function that evaluates the goodness of an attribute by considering the significance of error types. The proposed function can successfully choose an attribute that suppresses false positives from the given attribute set and the effectiveness of using it is confirmed experimentally. This paper also examines the more trivial leaf rewriting approach to benchmark the proposed method. The comparison shows that the proposed attribute evaluation function yields better solutions than the leaf rewriting approach.

Shear viscosity of the B phase of superfluid3He

The shear viscosity (T) in the Balian-Werthamer (BW) state of superfluid ³ He is calculated variationally throughout the region 0t 1(t=T/T _c) from the transport equation for Bogoliubov quasiparticles. Coherence factors are treated exactly in the calculation of the collision integral. The numerical result for =_s= _s(T)/_n(T_c) agree very well with experiment in the range 0.8t1.0. Analytic expressions = 0.577 (1–1.0008t) and =1–(23/64) [=(T)/k _B T] are obtained in the low-temperature region and in the vicinity ofT _c, respectively. From the numerical analysis it is shown that the latter equation is valid only in the temperature range 0.9997t1.0.Supported by the Research Institute for Fundamental Physics, Kyoto University.     

Copolymerization of Vinyl Chloride and Ethylene with Cp<Superscript>*</Superscript>Ti( OCH<Subscript>3</Subscript>)<Subscript>3</Subscript>/MAO Catalyst

Summary Copolymerization of vinyl chloride (VC) and ethylene with Cp^*Ti( OCH₃)₃/MAO catalyst was investigated. The Cp^*Ti( OCH₃)₃/MAO catalyst initiated the copolymerization of VC with ethylene, although the copolymer yields were low. In the ¹³C NMR spectra of the copolymers, the peaks based on junction part between VC and ethylene was observed, but the signals were small. From DSC measurement of the copolymers, only one glass transition temperature was observed. Thus, it is clear that the copolymerization with Cp^*Ti( OCH₃)₃/MAO catalysts gave the copolymer, and the copolymer consisting of block sequence rather than random copolymer. Received: 13 November 2002/Revised: 6 January 2003/Accepted: 10 January 2003 Correspondence to Kiyoshi Endo     

Grain refinement and superplasticity in a magnesium alloy processed by equal-channel angular pressing

The extrusion/equal channel angular pressing (EX-ECAP) processing procedure, in which magnesium-based alloys are subjected to extrusion followed by ECAP, was applied to a Mg-7.5 pct Al-0.2 pct Zr alloy prepared by casting. Microstructural inspection showed the EX-ECAP process was effective in reducing the grain size from ∼21 μm after extrusion to an as-pressed grain size of ∼0.8 μm. It is shown through static annealing that these ultrafine grains are reasonably stable up to 473 K, but grain growth occurs at higher temperatures. Tensile specimens were cut from the billets prepared by EX-ECAP and testing showed these specimens exhibited superplasticity at relatively low temperatures with maximum elongations up to >700 pct. By processing through EX-ECAP to a higher imposed strain and thereby increasing the area fraction of high-angle boundaries, it is demonstrated that there is a potential for achieving high-strain-rate superplasticity. This article is based on a presentation made at the Symposium entitled “Phase Transformations and Deformation in Magnesium Alloys,” which occurred during the Spring TMS meeting, March 14–18, 2004, in Charlotte, NC, under the auspices of the ASM-MSCTS Phase Transformations Committee.     

Modeling of debris cooling with annular gap in the lower RPV and verification based on ALPHA experiments

For severe accident assessment in a light water reactor, heat transfer models in a narrow annular gap between the overheated core debris and the reactor pressure vessel (RPV) are important for evaluating RPV integrity and emergency procedures. Using existing data, the authors developed heat transfer models on the average critical heat flux (CHF) restricted by countercurrent flow limitation (CCFL) and local boiling heat fluxes, and showed that the average CHF depended on the steam–water flow pattern in the narrow gap and that the local heat fluxes were similar to the pool boiling curve. We evaluated the validity of heat transfer models by simple calculations for ALPHA experiments performed at Japan Atomic Energy Research Institute. Calculated results showed that heat fluxes on the crust surface were restricted mainly by thermal resistance of the crust after the crust formation, and emissivity on the crust surface did not have much effect on the heat fluxes. The calculated vessel temperature during the heat-up process and peak vessel temperature agreed well with the measurements, which confirmed the validity of the average CHF correlation. However, the vessel cooling rate was underestimated mainly due to underestimation of the gap size.     

Parallel processing circuit of line image using neuristor

A simultaneous detecting circuit system of centers and lengths of some lines in one dimension is presented applying an electronic circuit model of the nervous axon with respect to signal transmission. This model consists of series of one basic section, and has functions of Neuristor. The circuit system has a paralell processing function. It is constructed by three layers. In the first layer each visual signal of lines is changed to electric pulse signal. In the second layer edges of each line are detected, and in the third layer these detected signals are converted into voltages which correspond to lengths of the lines. These functions were confirmed experimentally using its hardware.     

Parametric Feature Detection

Most visual features are parametric in nature, including, edges, lines, corners, and junctions. We propose an algorithm to automatically construct detectors for arbitrary parametric features. To maximize robustness we use realistic multi-parameter feature models and incorporate optical and sensing effects. Each feature is represented as a densely sampled parametric manifold in a low dimensional subspace of a Hilbert space. During detection, the vector of intensity values in a window about each pixel in the image is projected into the subspace. If the projection lies sufficiently close to the feature manifold, the feature is detected and the location of the closest manifold point yields the feature parameters. The concepts of parameter reduction by normalization, dimension reduction, pattern rejection, and heuristic search are all employed to achieve the required efficiency. Detectors have been constructed for five features, namely, step edge (five parameters), roof edge (five parameters), line (six parameters), corner (five parameters), and circular disc (six parameters). The results of detailed experiments are presented which demonstrate the robustness of feature detection and the accuracy of parameter estimation.     

Image retrieval using efficient local-area matching

We present an efficient and accurate method for retrieving images based on color similarity with a given query image or histogram. The method matches the query against parts of the image using histogram intersection. Efficient searching for the best matching subimage is done by pruning the set of subimages using upper bound estimates. The method is fast, has high precision and recall and also allows queries based on the positions of one or more objects in the database image. Experimental results showing the efficiency of the proposed search method, and high precision and recall of retrieval are presented. Received: 20 January 1997 / Accepted: 5 January 1998