A Linear-Time Algorithm for Symmetric Convex Drawings of Internally Triconnected Plane Graphs

We study a crossing minimization problem of drawing a bipartite graph with a radial drawing of two orbits. Radial drawings are one of well-known drawing conventions in social network analysis and visualization, in particular, displaying centrality indices of actors (Wasserman and Faust, Social Network Analysis: Methods and Applications. Cambridge University Press, Cambridge, 1994). The main problem in this paper is called the one-sided radial crossing minimization, if the positions of vertices in the outer orbit are fixed. The problem is known to be NP-hard (Bachmaier, IEEE Trans. Vis. Comput. Graph. 13, 583–594, 2007), and a number of heuristics are available (Bachmaier, IEEE Trans. Vis. Comput. Graph. 13, 583–594, 2007). However, there is no approximation algorithm for the crossing minimization problem in radial drawings. We present the first polynomial time constant-factor approximation algorithm for the one-sided radial crossing minimization problem.     

Brain-like Computing Based on Distributed Representations and Neurodynamics

A key to overcoming the limitations of classical artificial intelligence and to deal well with enormous amounts of information might be brain-like computing in which distributed representations of information are processed by dynamical systems without using symbols. We present a method for such computing. We constructed an inference system using a nonmonotone neural network, which is a kind of recurrent neural network with continuous-time dynamics. This system deduces a conclusion according to state transitions of the network in which knowledge is embedded as trajectory attractors. It has the powerful ability of analogical reasoning without special treatment for exceptional knowledge. We also propose a method of linking different neurodynamical systems and show that two mutually interacting systems can process complex spatiotemporal patterns.     

Universality Issues in Reversible Computing Systems and Cellular Automata (Extended Abstract)

In this survey, we deal with the problem how a universal computer can be constructed in a reversible environment. We discuss this problem based on the frameworks of reversible Turing machines, reversible logic circuits, and reversible cellular automata. We can see that in spite of the constraint of reversibility, there are several very simple reversible systems that have universal computing ability.     

Prediction and analysis of flow behavior of a polymer melt through nanochannels using artificial neural network and statistical methods

A new methodology, namely, artificial neural network (ANN) approach was proposed for modeling and predicting flow behavior of the polyethylene melt through nanochannels of nanoporous alumina templates. Wetting length of the nanochannels was determined to be a function of time, temperature, diameter of nanochannels, and surface properties of the inner wall of the nanochannels. An ANN was designed to forecast the relationship between the length of wetting as output parameter and other aforementioned parameters as input variables. It was demonstrated that the ANN method is capable of modeling this phenomenon with high accuracy. The designed ANN was then employed to obtain the wetting length of the nanochannels for those cases, which were not reported by the wetting experiments. The results were then analyzed statistically to identify the effect of each independent variable, namely, time, temperature, diameter of nanochannels, and surface properties of the inner wall of nanochannels as well as their combinations on the wetting length of the nanochannels. Interesting results were attained and discussed.     

Optimal configuration for the self-identification of a two-dimensional variable geometry truss

This study addresses the optimal changes in geometry of a two-dimensional variable geometry truss (VGT) to identify the stiffness matrix of the truss using the concept of self-identification. The optimization of the geometry changing of the VGT is a problem of selecting the optimal combination of multiple design variables from a large number of candidate sets. This study proposes a simple optimization method for determining a set of optimal geometric parameters; in this method, the approximated mode shape matrix obtained using spline interpolation techniques is used to calculate the objective function for self-identification. The objective function used in this paper is a function of the condition number of the coefficient matrix of a linear matrix equation and a criterion for self-identification. The proposed algorithm can be used to reduce the number of actual vibration tests required for measuring the mode shapes and modal frequency while it maximizes the objective function. Numerical experiments are conducted to investigate the relationship between the convergence characteristics of the optimization and the target vibration modes. The effectiveness of the optimized geometry changing is verified by comparing the identification error for the uniform geometry changing, the optimized one for the three lower modes of the VGT, and the one found by a classical QR decomposition. Furthermore, the numerical results show that the identification sensitivity with respect to noisy data is reduced by the optimization.     

Shape Reconstruction and Camera Self-Calibration Using Cast Shadows and Scene Geometries

Recently, various techniques of shape reconstruction using cast shadows have been proposed. These techniques have the advantage that they can be applied to various scenes, including outdoor scenes, without using special devices. Previously proposed techniques usually require calibration of camera parameters and light source positions, and such calibration processes limit the range of application of these techniques. In this paper, we propose a method to reconstruct 3D scenes even when the camera parameters or light source positions are unknown. The technique first recovers the shape with 4-DOF indeterminacy using coplanarities obtained by cast shadows of straight edges or visible planes in a scene, and then upgrades the shape using metric constraints obtained from the geometrical constraints in the scene. In order to circumvent the need for calibrations and special devices, we propose both linear and nonlinear methods in this paper. Experiments using simulated and real images verified the effectiveness of this technique.     

Ergonomic design and evaluation of the handle for an endoscopic dissector

The purpose of this study was to design an endoscopic dissector handle and objectively assess its usability. The handles were designed with increased contact area between the fingers and thumb and the eye rings, and the eye rings were modified to have a more perpendicular insertion angle to the finger midline. Four different handle models were compared, including a conventional product. Subjects performed dissection, exclusion, grasping, precision manipulation and precision handling tasks. Electromyography and subjective evaluations were measured. Compared to conventional handles, the designated handle reduced the muscle load in the extensor and flexor muscles of the forearm and increased subjective stability. The activity of the first dorsal interosseous muscle was sometimes influenced by the shape of the other parts. The ergonomically designed endoscopic dissector handle used in this study achieved high usability. Medical instrument designs based on ergonomic concepts should be assessed with objective indices.

Practitioner Summary: The endoscopic dissector handles were designed with increased contact area and more suitable insertion angle between the fingers and thumb and the eye rings. Compared to conventional handles, the designated handle reduced the muscle load in the extensor and flexor muscles of the forearm and increased subjective stability.     

Descent three-term conjugate gradient methods based on secant conditions for unconstrained optimization

The conjugate gradient method is an effective method for large-scale unconstrained optimization problems. Recent research has proposed conjugate gradient methods based on secant conditions to establish fast convergence of the methods. However, these methods do not always generate a descent search direction. In contrast, Y. Narushima, H. Yabe, and J.A. Ford [A three-term conjugate gradient method with sufficient descent property for unconstrained optimization, SIAM J. Optim. 21 (2011), pp. 212–230] proposed a three-term conjugate gradient method which always satisfies the sufficient descent condition. This paper makes use of both ideas to propose descent three-term conjugate gradient methods based on particular secant conditions, and then shows their global convergence properties. Finally, numerical results are given.