Synthesis of polyorganosiloxane retaining SiO4 framework from inosilicate mineral by trimethylsilylation

A polyorganosiloxane retaining a SiO₄ tetrahedral framework has been synthesized by the reaction of an inosilicate mineral (para-wollastonite) with a trimethylsilylating reagent. The glassy product dissolved in a wide range of organic solvents but was insoluble in water. The analyses of the product by means of infra-red spectroscopy, X-ray powder diffraction and elemental analysis indicated that trimethylsilyl side groups were linked to the SiO₄ framework. The thermal stability of the product was determined by thermal analysis.     

Copolymerization of unsaturated polyester with styrene in inverted emulsion

Copolymerization of unsaturated polyester with styrene in water-in-oil (W/O) type emulsion was carried out with the use of various basic compounds as emulsifiers. It was found that a stable, gellike W/O type emulsion of unsaturated polyester resin is formed only when pK_a's of the bases are above 6 and their concentrations are higher than some critical value. In these conditions, water can be dispersed in emulsion up to 900% to the resin. By polymerization, the stable W/O type emulsion is transformed to a white solid copolymer which is dry to the touch and which contains 90–95% of initially added water. It was confirmed that the basic compounds react with the carboxylic group of the polyester to form at the water-resin interface polyester salts, which act as true emulsifying agents. The stabilization mechanism of the emulsion at various concentrations of the polyester salt was investigated, mainly by microscopic observations, and an interpretation of the critical value of emulsifier concentration is proposed.     

Sequential Data Fusion via Vector Spaces: Fusion of Heterogeneous Data in the Complex Domain

A sequential data fusion approach via higher dimensional vector spaces is introduced. This is achieved by making use of the representation of directional signals within the field of complex numbers $C$ . The concept of data fusion is next introduced and the place of the proposed approach within that framework is identified. The benefits of such an approach are illustrated and a range of possible applications is shown. The concept introduced is supported by a real world case study which focuses on simultaneous forecasting of wind speed and direction. The architectures and learning algorithms which support this concept are introduced and their distributed sequential fusion nature is highlighted.     

Parity induced edge-current saturation and current distribution in zigzag-edged graphene nano-ribbon devices

We show computationally that the current-voltage characteristics of the zigzag-edged graphene nanoribbon (ZGNR) with the even width exhibit remarkable current saturation behavior in spite of the absence of the bandgap. Mechanism of such current-saturation behavior can be understood to be originated from the symmetries of the wavefunctions corresponding to the edge states in ZGNR. We further demonstrate that the current-voltage characteristics of ZGNR can be drastically changed even by the presence of a single lattice vacancy, with the strong dependence on the position of the vacancy. The origin of such properties is intuitively understood by analyzing the transmission probabilities through such systems.     

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.

Integrative Bayesian model on two opposite types of sensory adaptation

Adaptation is a fundamental property of human perception. Recently, it was found that there are two opposite types of adaptation to repetitive stimuli with a temporal difference. In this article, we construct an integrative model of adaptation. We model the perception as a Bayesian inference, and represent the two types of adaptation as changes in the likelihood function and the prior distribution in the Bayesian inference. We examine our model analytically and show how the types of adaptation depend on model parameters.     

Networked reinforcement learning

Recently, many models of reinforcement learning with hierarchical or modular structures have been proposed. They decompose a task into simpler subtasks and solve them by using multiple agents. However, these models impose certain restrictions on the topological relations of agents and so on. By relaxing these restrictions, we propose networked reinforcement learning, where each agent in a network acts autonomously by regarding the other agents as a part of its environment. Although convergence to an optimal policy is no longer assured, by means of numerical simulations, we show that our model functions appropriately, at least in certain simple situations. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

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.     

Region with velocity constraints: map information and its usage for safe motion planning of a mobile robot in a public environment

Recently, various autonomous mobile robots have been developed for practical use. To support the coexistence of robots and humans in real environments, we propose a concept named ‘Region with Velocity Constraints (RVC),’ which is set around hazardous areas. RVCs are regions where the velocities of the robot are constrained to predefined values. Inside the RVCs, the robot has to reduce its translational velocity to avoid predicted hazards such as collisions with obstacles, and to reduce its rotational velocity to prevent undesirable motions such as sharp turns. We also propose a motion planning method for navigating the mobile robot in an environment with RVCs based on the Navigation Function and Global Dynamic Window Approach. Our method generates a trajectory satisfying both translational and rotational velocity constraints to be compatible with the surroundings. Moreover, to demonstrate the validity of our method, we performed numerical simulations and experiments.     

A control law for vehicle merging inspired by dragonfly behavior

Autonomous control of vehicles has recently attracted considerable attention. In this sense, vehicle merging has become an important topic in this field of research. However, in conventional studies, the controlled vehicle must calculate the movement of other surrounding vehicles to complete the merge, requiring high computational costs. In this paper, we focus on dragonfly behavior to solve this issue. Indeed, insects can behave adaptively in the complex real world in spite of the limited size of their brains. They reduce the computational requirements of their brain by relying on different properties of their surroundings, basing their intelligent behaviors on simple strategies. The behavior of a dragonfly when chasing a prey is an example of these strategies. In this study, we address the vehicle merging maneuver by applying dragonfly’s strategies to control the movement of the merging vehicle. We propose a simple control method inspired by the aforementioned strategies and, finally, we present simulation results that were conducted to demonstrate the effectiveness of this method.