Self-reflective segmentation of human bodily motions using recurrent neural networks

For realizing a naturalistic collaboration between the human and the robot, we have to establish the intention sharing from the series of motion data that are observed and exchanged between the human and the machine. In a word, this is a problem to detect "meanings" out of the digitized data stream. In this paper, we propose a novel approach based on semiosis, and present a method of interpreting bodily motions using recurrent neural networks called Elman networks. We made some experiments using the raw data acquired while a human performs a simple task of fetching objects by stretching and folding his/her arm, and demonstrate that the network can learn invariant features of the generalized motion concepts, classify the motion by referring to self-organized memory structure, and understand a task structure of the observed human bodily motion. These capabilities are essential for machine intelligence to establishing the human-robot shared autonomy, a new style of human-machine collaboration proposed in the area of robotics.     

Evaluation of the Properties of Si3N4/Si3N4 Joint Brazed Using a Filler Alloy Containing Pd

Si3N4 ceramic was jointed to itself using a filler alloy of Cu76.5Pd8.5Ti15, and the mechanical properties of the jointwere measured and analyzed. By using a filler alloy of Cu76.5Pd8.5Ti15, the SisN4/SisN4 joints were obtained bybrazing at 1373～1473 K f     

Minimax estimation of deterministic parameters in linear models with a random model matrix

We consider the problem of estimating an unknown deterministic parameter vector in a linear model with a random model matrix, with known second-order statistics. We first seek the linear estimator that minimizes the worst-case mean-squared error (MSE) across all parameter vectors whose (possibly weighted) norm is bounded above. We show that the minimax MSE estimator can be found by solving a semidefinite programming problem and develop necessary and sufficient optimality conditions on the minimax MSE estimator. Using these conditions, we derive closed-form expressions for the minimax MSE estimator in some special cases. We then demonstrate, through examples, that the minimax MSE estimator can improve the performance over both a Baysian approach and a least-squares method. We then consider the case in which the norm of the parameter vector is also bounded below. Since the minimax MSE approach cannot account for a nonzero lower bound, we consider, in this case, a minimax regret method in which we seek the estimator that minimizes the worst-case difference between the MSE attainable using a linear estimator that does not know the parameter vector, and the optimal MSE attained using a linear estimator that knows the parameter vector. For analytical tractability, we restrict our attention to the scalar case and develop a closed-form expression for the minimax regret estimator.     

X-ray or UV adjustment of MOS threshold voltage: Analytical and numerical modeling

A model is formulated that describes how radiation-induced charge accumulates in the gate oxide of a MOS structure and how it decays through tunneling and thermal emission. The model is used in a numerical analysis of the x-ray or UV adjustment of threshold voltage in MOS-circuit manufacture. The limits of this process technique are evaluated.     

Using the magneto-optical Kerr effect to study magnetization processes in two-dimensional magnonic crystals based on YIG thin films

The magneto-optical Kerr effect for red (628 nm) and green (532 nm) light is used to study magnetization processes in 2D magnonic crystals obtained by etching pits with the diameter D ≈ 32 μm to a depth of t ≤ 2 μm in a 16.1-μm-thick film of yttrium iron garnet (YIG). Hysteresis loops obtained in the case of the inplane crystal magnetization at 628 nm are characterized by lower saturation fields H _s and higher remanent magnetizations than those obtained at 532 nm, a result that is attributed to different absorption coefficients of the YIG film at these wavelengths. This difference between the magnetization curves reflects the fact that the magnonic-crystal surface probed with the green light makes a greater contribution to the magneto-optical Kerr effect. Therefore, the green light is more sensitive to the demagnetizing fields, which govern magnetization processes in the magnonic crystals.     

Nondegenerate Phase-Locking of a Semiconductor Laser by Sideband Injection

A semiconductor laser rate equation theory is presented that describes sideband injection locking under both weak optical injection and current modulation. By simultaneous optical injection and current modulation, control of both the phase and the frequency of a semiconductor laser is demonstrated. The phase-locked semiconductor laser operates at a different frequency to the optical injection source, with a frequency-difference given by the current modulation frequency. This method can be used to produce broadband sources, such as those producing ultrashort pulses and those required for coherent control, or to create high-frequency electronic oscillator sources with phase control by interference beating     

Second harmonic generation from a ferroelectric film

We derive an expression for transmittivity (T_SHG) of second harmonic generation (SHG) signals from a ferroelectric (FE) film. Intensities of up and down fields in the medium are investigated in relation to T_SHG. The derivations are made based on undepletion of input fields and nonlinear wave equation derived from the Maxwell equations. We present two cases: film without mirrors and with partial mirrors. Expressions for the newly derived nonlinear susceptibility coefficients of SHG for real crystal symmetry [J. Opt. Soc. Am. B 19 (2002) 2007] are used to get more realistic results. Variations in T_SHG with respect to film thickness are illustrated.     

Adaptive agents and multiagent systems

New information systems and recent applications (grid computing, Web Services, and so on) are often distributed, large-scale, open, heterogeneous, and characterized by a dynamic environment. To model these complex systems, researchers have spent much effort during the last few years on multiagent systems. The aim is to model complex distributed systems as a set of (possibly organized) software agents that interact in a common environment. The decomposition of a system into a number of agents lets the system react and adapt better in a changing environment. Moreover, organized structures ("social" structures) can emerge from interactions between agents, which in turn constrain and coordinate the agents' behavior. A multiagent system takes its metaphors of interaction from social systems rather than using the metaphor of the isolated thinker that early artificial intelligence researchers preferred. An important issue when dealing with this increasing complexity is to build adaptive agents and multiagent systems. Agents and multiagent systems must be aware of their own capabilities and of changes to other agents and their environment. To remain effective, agents must be able to adapt their structures and knowledge while they execute.