Effect of Elastic Energy due to Atomic Size Factor on Ordering and Decomposition Behaviour of Binary Solid Solutions

A theory recently developed by the present authors is applied to the study of the effect of elastic energy due to atomic size factor on the transformation behaviour of binary solid solutions. lt is found that elastic interaction energy (EIE), which is a part of the total elastic energy plays a key role in both ordering elastic interaction ordering (EIO) and spinodal decomposition. The present study gives a reasonable explanation to the historical dilemmas, "elastic energy paradox" and "atomic size factor paradox . By solving these confusing problems, the coexistence of ordering (EIO) and decomposition, which has been regarded as impossible by conventional theories. can be well understood. The mechanism is as follows: lowering of elastic energy demands EIO, and such an ordering provides a driving force for spinodal decomposition. Therefore, in alloys with large atomic size factor, spinodal decomposition is preceded and induced by ordering. Ordering and spinodal decomposition are thus closely related processes to each other     

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.     

Dielectric Dispersion of Polycrystalline Aluminum Nitride at Microwave Frequencies

Complex permittivity of polycrystalline AlN was measured over the frequency range 100 MHz to 13 GHz using reflection coefficient and cavity resonator methods. Dielectric relaxation peaks of loss tangent were observed in the frequency range 200 MHz to 3 GHz. The maximum value of loss tangent was ∼200 × 10⁻⁴. Relative permittivities decreased by ∼4% with increased frequency at the region of relaxation peaks. It was shown that these dispersion frequencies were inversely proportional to the grain sizes of AlN and independent of purities and processes. Model calculations for elastic vibration of a single-crystal grain of AlN were performed to investigate the origin of the dielectric dispersion due to piezoelectric effect. The calculated results of elastic sphere and 14-faced-polyhedron models showed a good quantitative agreement with the observed dispersion frequencies, which suggested that the dispersion was due to a piezoelectric vibration of a single grain of AlN ceramics at microwave frequencies.     

MOSAIC for multiple-reward environments

Reinforcement learning (RL) can provide a basic framework for autonomous robots to learn to control and maximize future cumulative rewards in complex environments. To achieve high performance, RL controllers must consider the complex external dynamics for movements and task (reward function) and optimize control commands. For example, a robot playing tennis and squash needs to cope with the different dynamics of a tennis or squash racket and such dynamic environmental factors as the wind. In addition, this robot has to tailor its tactics simultaneously under the rules of either game. This double complexity of the external dynamics and reward function sometimes becomes more complex when both the multiple dynamics and multiple reward functions switch implicitly, as in the situation of a real (multi-agent) game of tennis where one player cannot observe the intention of her opponents or her partner. The robot must consider its opponent's and its partner's unobservable behavioral goals (reward function). In this article, we address how an RL agent should be designed to handle such double complexity of dynamics and reward. We have previously proposed modular selection and identification for control (MOSAIC) to cope with nonstationary dynamics where appropriate controllers are selected and learned among many candidates based on the error of its paired dynamics predictor: the forward model. Here we extend this framework for RL and propose MOSAIC-MR architecture. It resembles MOSAIC in spirit and selects and learns an appropriate RL controller based on the RL controller's TD error using the errors of the dynamics (the forward model) and the reward predictors. Furthermore, unlike other MOSAIC variants for RL, RL controllers are not a priori paired with the fixed predictors of dynamics and rewards. The simulation results demonstrate that MOSAIC-MR outperforms other counterparts because of this flexible association ability among RL controllers, forward models, and reward predictors.     

Electrodeposition of mercury from the HgNO3-CH3OH-H2O system at low temperature

Peculiar dendritic deposits of mercury were obtained galvanostatically from the mercurous nitratemethanol-water system at –50° C. These deposits could not be maintained at a constant shape because, when the current was interrupted, they transformed and shrank immediately to a spherical shape with hydrogen evolution. It seems that when the electrical potential was removed, the mercury deposits liquefied and the hydrogen stored in them was released during the period of shrinking.