Adaptive interpolation scheme for NURBS curves with the integration of machining dynamics

This paper develops a comprehensive interpolation scheme for non-uniform rational B-spline (NURBS) curves, which does not only simultaneously meet the requirements of both constant feedrate and chord accuracy, but also real-time integrates machining dynamics in the interpolation stage. Although the existing work in this regard has realized the importance to simultaneously consider chord error and machining dynamics, none has really incorporated these in one complete interpolation scheme. In this paper, machining dynamics is considered for three aspects: sharp corners or feedrate sensitive corners on the curves, components with high frequencies or frequencies matching machine natural ones and high jerks. A look-ahead module was developed for detecting and adaptively adjusting the feedrate at the sharp corners. By Fast Fourier Transform (FFT) analysis with a moving window in the interpolation stage identified were some special frequency components such as those containing high frequencies or with frequencies matching machine natural ones. Then, the notch filtering or time spacing method was used to eliminate these components. To more completely reduce feedrate and acceleration fluctuations, the jerk-limited algorithm was also developed. Finally, the interpolated feedrate was further smoothened with B-spline fitting method and the NURBS curves were re-interpolated with the smoothened feedrate. During the interpolation, the chord error was repeatedly checked and confined in the prescribed tolerance. Two NURBS curves were used as examples to test the feasibility of the developed interpolation scheme.     

Simulation of ultrasonic-vibration drawing using the finite element method (FEM)

In ultrasonic-vibration drawing, wires are drawn while ultrasonic vibration is applied to a drawing die. Prior studies provide experimental proof that ultrasonic-vibration drawing reduces drawing resistance, improves lubrication and prevents wire breakage. In the future, ultrasonic-vibration drawing is expected to contribute to the drawing of difficult-to-draw materials and operations, such as shaped wires, ultrafine wires, and the wire drawing operation in semidry or dry condition. However, a detailed analysis and understanding of the mechanism of improvement is not possible on the basis of conventional experimental observations because the ultrasonic-vibration processing phenomenon occurs at high speed. Therefore, we attempted to understand the processing mechanism of ultrasonic-vibration drawing using the finite element method (FEM). ABAQUS was used for the FEM. Drawing force and stress–strain distributions in drawn wires were analyzed. From these studies, we quantitatively clarified the mechanism of improved drawing characteristics, such as decreased drawing force.     

Hand shape classification in various pronation angles using a wearable wrist contour sensor

Hand gestures are potentially useful for communications between humans and between a human and a machine. However, existing methods entail several problems for practical use. We have proposed an approach to hand shape recognition based on wrist contour measurement. Especially in this paper, two assignments are addressed. First is the development of a new sensing device in which all elements are installed in a wrist-watch-type device. Second is the development of a new hand shape classifier that can accommodate pronation angle changes. The developed sensing device enables wrist contour data collection under conditions in which the pronation angle varies. The classifier recognizes the hand shape based on statistics produced through data forming and statistics conversion processes. The most important result is that no large difference exists between classification rates that include or those that exclude the independent (preliminary) pronation estimation process using inertia measurement units. This result shows two possible insights: (1) the wrist contour has some features that depend on the hand shape but which do not depend on the pronation angle, or (2) the wrist contour potentially includes pronation angle variation information. These insights indicate the possibility that hand shape can be recognized solely from the wrist contour, even while changing the pronation angle.     

TMN implementation strategy based on OSI management standards

In order to facilitate the implementations ofTMN interface protocols/services studied inITU-T, it is very important to define profiles for supportingTMN management service. This paper proposes a concrete method for achieving this based on osi management standards as a promisingTMN implementation method. It proposes an idea of structuring theTMN ISP’S based on the structure of the osi managementISP’S. The paper discusses aTMN based on the osi managementISP’S. Finally the implementation as software is discussed and a software architecture for efficient application development is proposed.     

Multiphase composites of tetragonal zirconia agglomerate dispersed into alumina and alumina-zirconia matrices

Multiphase composites of yttria- and ceria-doped tetragonal zirconia agglomerates (10–50 m) dispersed into an alumina or alumina-zirconia matrix were sintered at 1500–1600 °C in air, followed by post-Hot Isostatic Pressing (HIP) at 1450°C and 150 MPa in an Ar gas atmosphere. The relative density of the recovered composites was above 98% of the theoretical density. By chemically etching on the surface of zirconia agglomerates, the sinterability of composites was apparently improved; and no microcracks nor pores were observed at the interface of agglomerate and matrix. According to scanning electron microscopy (SEM) observation, tetragonal and tetragonal-monoclinic zirconia agglomerates were highly dispersed into the alumina or alumina-zirconia matrix. The multiphase composites containing 10 vol% spherical agglomerates demonstrate the relatively low value of bending strength, < 400 MPa, and a high value of fracture toughness, > 11 MPa m^1/2. The crack propagation introduced by Vickers indentation was efficiently suppressed and deflected by the agglomerates.     

Greedy versus social: resource-competing oscillator network as a model of amoeba-based neurocomputer

A single-celled amoeboid organism, the true slime mold Physarum polycephalum, exhibits rich spatiotemporal oscillatory behavior and sophisticated computational capabilities. The authors previously created a biocomputer that incorporates the organism as a computing substrate to search for solutions to combinatorial optimization problems. With the assistance of optical feedback to implement a recurrent neural network model, the organism changes its shape by alternately growing and withdrawing its photosensitive branches so that its body area can be maximized and the risk of being illuminated can be minimized. In this way, the organism succeeded in finding the optimal solution to the four-city traveling salesman problem with a high probability. However, it remains unclear how the organism collects, stores, and compares information on light stimuli using the oscillatory dynamics. To study these points, we formulate an ordinary differential equation model of the amoeba-based neurocomputer, considering the organism as a network of oscillators that compete for a fixed amount of intracellular resource. The model, called the “Resource-Competing Oscillator Network (RCON) model,” reproduces well the organism’s experimentally observed behavior, as it generates a number of spatiotemporal oscillation modes by keeping the total sum of the resource constant. Designing the feedback rule properly, the RCON model comes to face a problem of optimizing the allocation of the resource to its nodes. In the problem-solving process, “greedy” nodes having the highest competitiveness are supposed to take more resource out of other nodes. However, the resource allocation pattern attained by the greedy nodes cannot always achieve a “socially optimal” state in terms of the public cost. We prepare four test problems including a tricky one in which the greedy pattern becomes “socially unfavorable” and investigate how the RCON model copes with these problems. Comparing problem-solving performances of the oscillation modes, we show that there exist some modes often attain socially favorable patterns without being trapped in the greedy one.     

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.     

Amoeba-based Chaotic Neurocomputing: Combinatorial Optimization by Coupled Biological Oscillators

We demonstrate a neurocomputing system incorporating an amoeboid unicellular organism, the true slime mold Physarum, known to exhibit rich spatiotemporal oscillatory behavior and sophisticated computational capabilities. Introducing optical feedback applied according to a recurrent neural network model, we induce that the amoeba’s photosensitive branches grow or degenerate in a network-patterned chamber in search of an optimal solution to the traveling salesman problem (TSP), where the solution corresponds to the amoeba’s stably relaxed configuration (shape), in which its body area is maximized while the risk of being illuminated is minimized.Our system is capable of reaching the optimal solution of the four-city TSP with a high probability. Moreover, our system can find more than one solution, because the amoeba can coordinate its branches’ oscillatory movements to perform transitional behavior among multiple stable configurations by spontaneously switching between the stabilizing and destabilizing modes. We show that the optimization capability is attributable to the amoeba’s fluctuating oscillatory movements. Applying several surrogate data analyses, we present results suggesting that the amoeba can be characterized as a set of coupled chaotic oscillators.

Buoyancy engine developed for underwater gliders

A buoyancy engine with a swashplate-type axial piston pump was developed. Its oil extrusion and drawing properties under high hydraulic pressure were evaluated. This buoyancy engine is now installed in an underwater glider that will achieve long-term monitoring of ocean environments up to 2100 m depth in a designated area with lower operational costs. This bidirectionally functioning pump can control the amount of oil in extrusion and draw operations. When drawing oil under high pressure, the hydraulic pump and the electric motor, respectively, act as a hydraulic motor and an electric generator. The generated electric power is absorbed by a damping resistor. The oil-drawing and extrusion properties were measured using a large hyperbaric chamber that is able to produce an almost identical environment to that of actual operations. Results confirmed stable oil extrusion operations up to 21 MPa. Regarding oil-drawing properties, although it was measured only up to 10 MPa in the hyperbaric chamber, it can be inferred that the system can draw the oil and can control the buoyancy precisely up to 21 MPa by replacing the two-way ball valve with an electromagnetic latching solenoid valve.     

Simulation of postoperative 3D facial morphology using a physics-based head model

We propose a prototype of a facial surgery simulation system for surgical planning and the prediction of facial deformation. We use a physics-based human head model. Our head model has a 3D hierarchical structure that consists of soft tissue and the skull, constructed from the exact 3D CT patient data. Anatomic points measured on X-ray images from both frontal and side views are used to fire the model to the patient's head. The purposes of this research is to analyze the relationship between changes of mandibular position and facial morphology after orthognathic surgery, and to simulate the exact postoperative 3D facial shape. In the experiment, we used our model to predict the facial shape after surgery for patients with mandibular prognathism. Comparing the simulation results and the actual facial images after the surgery shows that the proposed method is practical.