Change in Oxygen Content of Y2O3-Nd2O3-Doped Silicon Nitride During Firing

The oxygen content of silicon nitride with 1 mol% Y₂O₃—Nd₂O₃ additive was measured after firing to determine the compositional change during gas-pressure sintering. Oxygen content decreases from 2.5 to 0.94 wt% during firing for 4 h at 1900°C and 10-MPa pressure in N₂. This decrease in oxygen results from the release of SiO gas generated by a thermaldecomposition reaction between Si₃N₄ and SiO₂. The resultant sintered silicon nitride material contains less than 1 wt% oxygen.     

Increasing Efficiency of Force-Controlled Robotic Assembly: -Design of Damping Control Parameters Considering Cycle Time-

Parameter tuning of force control to achieve operations efficiently in robotic assembly is essential but time-consuming. In this paper, an optimal set of parameters for damping control is computed, which can reduce the cycle time of an assembly operation. The proposed method is formulated as a nonlinear optimization using a dynamic simulator based on 3D geometric model of assembled parts. The method is applied to clutch assembly for practical use. The results verify how the operations can be sped up using the obtained parameters. The proposed method enables users to introduce force control agilely.     

Analytical study on cantilever resonance type magnet-integrated sensor device for micro-magnetic field detection

A beam-shaped cantilever resonance type magnetic sensor device has been proposed with a micro magnet. Two structural designs, named as design 1 and design 2, have been comparatively analyzed using ANSYS in order to obtain larger frequency shifts (higher magnetism sensitivity) due to the applied exterior magnetic field. The analytical results show that, in the range of 0–10 mT, the frequency shifts are small, while under 100 mT, a relatively larger frequency shift of about 30 Hz can be theoretically obtained. The power consumption of the proposed devices has been further theoretically studied for preliminary understanding. Using the well-known displacement equations, the estimated power consumption is around 0.21 μW, which is very lower than that of the reported magnetic field sensors. This implies that it is possible to fabricate higher sensitive magnetic field sensor with lower power consumption.     

Set-based particle swarm optimization with status memory for knapsack problem

Set-based particle swarm optimization (S-PSO) operates on discrete space. S-PSO can solve combinatorial optimization problem with high quality and is successful to apply to the large-scale problem. In S-PSO, a velocity is a set with possibility and a position is a candidate solution. In this paper, we present a novel algorithm of set-based particle swarm optimization with status memory (S-PSOSM) to decide the position based on the previous position for solving knapsack problem. Some operators are redefined for S-PSOSM. S-PSOSM is a simple algorithm because the state of probability reduces. In addition, the weight of S-PSOSM is discussed. S-PSOSM shows high qualities in experimental results.     

Repeatedly foldable AMOLED display

In this study, a 5.9‐inch foldable active‐matrix organic light emitting diode (AMOLED) display was developed. A folding test was performed repeatedly. The display survived the folding test (100,000 folds) with a curvature radius of 2 mm. To protect an organic light emitting diode (OLED) against moisture, inorganic passivation layers are provided on the upper and lower sides of the flexible display. Using our transfer technology, high density passivation layers can be obtained. The measured water vapor transmission rate of the layer is 7 × 10^?6 g/m²?day or less, which improves OLED reliability. With these techniques, we have developed a book‐type display, which is repeatedly foldable like a book, and a tri‐fold display including a display area, which is foldable in three.     

Development of a high pressure automated lag time apparatus for experimental studies and statistical analyses of nucleation and growth of gas hydrates

Nucleation in a supercooled or a supersaturated medium is a stochastic event, and hence statistical analyses are required for the understanding and prediction of such events. The development of reliable statistical methods for quantifying nucleation probability is highly desirable for applications where control of nucleation is required. The nucleation of gas hydrates in supercooled conditions is one such application. We describe the design and development of a high pressure automated lag time apparatus (HP-ALTA) for the statistical study of gas hydrate nucleation and growth at elevated gas pressures. The apparatus allows a small volume (≈150 μl) of water to be cooled at a controlled rate in a pressurized gas atmosphere, and the temperature of gas hydrate nucleation, T(f), to be detected. The instrument then raises the sample temperature under controlled conditions to facilitate dissociation of the gas hydrate before repeating the cooling-nucleation cycle again. This process of forming and dissociating gas hydrates can be automatically repeated for a statistically significant (>100) number of nucleation events. The HP-ALTA can be operated in two modes, one for the detection of hydrate in the bulk of the sample, under a stirring action, and the other for the detection of the formation of hydrate films across the water-gas interface of a quiescent sample. The technique can be applied to the study of several parameters, such as gas pressure, cooling rate and gas composition, on the gas hydrate nucleation probability distribution for supercooled water samples.     

Shadow Doppler technique for sizing particles of arbitrary shape

The output from a linear diode array is used in a modified laser Doppler velocimeter to measure the size and shape of irregular particles. The sizing accuracy for transparent and opaque particles between 30 and 140 μm is better than 10%. The inaccuracy caused by trajectories that lay at angles of less than 24° to the axis of the array was less than +5%, and a further inaccuracy of +5% was caused by defocusing of the particle from the center of the velocimeter measuring volume by up to ±500 μm. The advantages of the shadow Doppler technique over other techniques for sizing irregular particles, such as amplitude systems with pointer volumes, are that the shadow Doppler technique records shape, the optical arrangement is more robust, less precise alignment is required, and the equipment can be constructed at low cost.     

Optical signal inverter of erbium-doped yttrium aluminum garnet with red shift of laser diodes

An optical signal inverter was demonstrated in a simple structure that combined a laser diode with Er-doped YAG crystal. The optical signal inversion occurred at a response time of 7 ns and was caused by the decrease of transmission of Er:YAG against the red shift of the wavelength of the laser diode.     

Evaluation of vector magnetic property in a three-phase induction motor model core with visualization of streamline

In this paper, a streamline display method for magnetic flux density and magnetic field strength vectors in a three-phase induction motor model core is presented, and the relationship between streamlines and magnetic properties is investigated. It is possible to visualize the vector magnetic properties distribution by representing the magnetic flux density and the magnetic field strength vector with the streamlines. In addition, the time variation of the streamlines of the magnetic flux density and magnetic field strength vectors, and magnetic energy was compared. It was clear that the magnetic energy did not necessarily increase in the areas where the magnetic flux density and magnetic field strength increased because these vectors have the spatial phase difference.     

Development of an epileptic seizure prediction algorithm using R–R intervals with self-attentive autoencoder

Epilepsy is a neurological disorder that may affect the autonomic nervous system (ANS) from 15 to 20 min before seizure onset, and disturbances of ANS affect R–R intervals (RRI) on an electrocardiogram (ECG). This study aims to develop a machine learning algorithm for predicting focal epileptic seizures by monitoring R–R interval (RRI) data in real time. The developed algorithm adopts a self-attentive autoencoder (SA-AE), which is a neural network for time-series data. The results of applying the developed seizure prediction algorithm to clinical data demonstrated that it functioned well in most patients; however, false positives (FPs) occurred in specific participants. In a future work, we will investigate the causes of FPs and optimize the developing seizure prediction algorithm to further improve performance using newly added clinical data.