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.     

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.     

A dynamic default revision mechanism for speculative computation

In this work a default revision mechanism is introduced into speculative computation to manage incomplete information. The default revision is supported by a method for the generation of default constraints based on Bayesian networks. The method enables the generation of an initial set of defaults which is used to produce the most likely scenarios during the computation, represented by active processes. As facts arrive, the Bayesian network is used to derive new defaults. The objective with such a new dynamic mechanism is to keep the active processes coherent with arrived facts. This is achieved by changing the initial set of default constraints during the reasoning process in speculative computation. A practical example in clinical decision support is described.     

Gender differences in hand stability of normal young people assessed at low force levels

To examine gender differences in hand stability, finger position and force holding tasks at low force levels were conducted with 30 male and 30 female young adults. Total fluctuation was defined as the standard deviation of measured data and fluctuation and the 10-Hz component of the physiological tremor were compared between maleand female subjects. In all tasks, the total fluctuation and the 10-Hz tremor were significantly larger in male subjects than females. On average, the fluctuation was 1.3 times larger and the 10-Hz tremor was 1.6 times larger. The results of this study suggest that women have superior hand stability compared with men at low force levels. Finger length, maximal voluntary contraction and surface electromyography were also measured and factors related to gender differences in hand stability are discussed. STATEMENT OF RELEVANCE: Hand stability is crucial for precise manual operations. This study demonstrated gender differences in hand steadiness at low force levels. Though hand dexterity cannot be explained only by hand stability, the results of this study are useful not only for occupational aptitude tests but also for neuropsychological tests.     

Vibration control of load for rotary crane system using neural network with GA-based training

A neuro-controller for vibration control of load in a rotary crane system is proposed involving the rotation about the vertical axis only. As in a nonholonomic system, the vibration control method using a static continuous state feedback cannot stabilize the load swing. It is necessary to design a time-varying feedback controller or a discontinuous feedback controller. We propose a simple three-layered neural network as a controller (NC) with genetic algorithm-based (GA-based) training in order to control load swing suppression for the rotary crane system. The NC is trained by a real-coded GA, which substantially simplifies the design of the controller. It appeared that a control scheme with performance comparable to conventional methods can be obtained by a relatively simple approach. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008