Back‐channel‐etched thin‐film transistor using c‐axis‐aligned crystal In–Ga–Zn oxide

Our crystalline In–Ga–Zn oxide (IGZO) thin film has a c‐axis‐aligned crystal (CAAC) structure and maintains crystallinity even on an amorphous base layer. Although the crystal has c‐axis alignment, its a‐axis and b‐axis have random arrangement; moreover, a clear grain boundary is not observed. We fabricated a back‐channel‐etched thin‐film transistor (TFT) using the CAAC‐IGZO film. Using the CAAC‐IGZO film, more stable TFT characteristics, even with a short channel length, can be obtained, and the instability of the back channel, which is one of the biggest problems of IGZO TFTs, is solved. As a result, we improved the process of manufacturing back‐channel‐etched TFTs.     

High-aspect-ratio through-hole array microfabricated in a PMMA plate for monodisperse emulsion production

Microchannel (MC) emulsification is a promising technique for producing monodisperse emulsions consisting of highly uniform droplets. The authors developed a high-aspect-ratio microstructure (HARMST) made of poly(methyl methacrylate) (PMMA) as a new MC emulsification device. A PMMA straight-through MC array plate consisting of 31,250 through-holes with a 7.3 × 22.9-μm oblong section and a 200-μm depth was fabricated by a process of synchrotron radiation (SR) lithography and etching. Oblong MCs fabricated in a PMMA straight-through MC array were highly uniform with a coefficient of variation of less than 2%. The fabricated PMMA straight-through MC array plate was used to produce water-in-oil (W/O) emulsions. Monodisperse W/O emulsions with average droplet diameters of approximately 25 μm and a minimum coefficient of variation of 3.2% were produced via a hydrophobic PMMA straight-through MC array. The PMMA straight-through MC array plate also produced monodisperse W/O emulsions at droplet production rates of up to 1.7 × 10⁴/s. The PMMA straight-through MC array plates developed in this work are expected to expand the application field of emulsification using straight-through MC array plates, which have previously been made of single-crystal silicon.     

Neural network technique for fuzzy multiobjective linear programming

Neural Network(NN) is well-known as one of powerful computing tools to solve optimization problems. Due to the massive computing unit-neurons and parallel mechanism of neural network approach we can solve the large-scale problem efficiently and optimal solution can be gotten. In this paper, we intoroduce improvement of the two-phase approach for solving fuzzy multiobjectve linear programming problem with both fuzzy objectives and constraints and we propose a new neural network technique for solving fuzzy multiobjective linear programming problems. The procedure and efficiency of this approach are shown with numerical simulations.     

Uniform carbon‐nanotube emitter for field‐emission displays

Abstract— The synthesis of carbon‐nanotube (CNT) field emitters for FEDs by thermal chemical vapor deposition (CVD) and their structural and emission characterization are described. Multi‐walled nanotubes (MWNTs) were grown on patterned metal‐base electrodes by thermal CVD, and the grown CNTs formed a network structured layer covering the surfaces of the metal electrode uniformly, which realized uniform distribution of electron emission. A technique for growing narrow MWNTs was also developed in order to reduce the driving voltage. The diameter of MWNT depends on the growth temperature, and it has changed from 40 nm at the low temperature (675°C) to 10–15 nm at the high temperature (900–1000°C). Moreover, narrower MWNTs were grown by using the metal‐base electrode covered with a thin alumina layer and a metal catalyst layer. Double‐walled nanotubes (DWNTs) were also observed among narrow MWNTs. The emission from the narrow CNTs showed a low turn‐on electric field of 1.5 V/μm at the as‐grown layer.     

Neural network approach for multicriteria solid transportation problem

In this paper, we propose neural network approach for multicriteria solid transportation problems(STP). First we suggest a neural network architecture to solve single-objective STP according to augmented Lagrange multiplier method. Due to the massive computing unit-neurons and parallel mechanism of neural network approach can solve the large scale problem efficiently and optimal solution can be got. Then we transform the original multicriteria problem into a single objective problem by global criteria method and adopt the neural network approach to solve it. By this way we can get the satisfactory solution of the original problem. The procedure and efficiency of this approach are shown with numerical simulations.     

FDCNet: filtering deep convolutional network for marine organism classification

Convolutional networks are currently the most popular computer vision methods for a wide variety of applications in multimedia research fields. Most recent methods have focused on solving problems with natural images and usually use a training database, such as Imagenet or Openimage, to detect the characteristics of the objects. However, in practical applications, training samples are difficult to acquire. In this study, we develop a powerful approach that can accurately learn marine organisms. The proposed filtering deep convolutional network (FDCNet) classifies deep-sea objects better than state-of-the-art classification methods, such as AlexNet, GoogLeNet, ResNet50, and ResNet101. The classification accuracy of the proposed FDCNet method is 1.8%, 2.9%, 2.0%, and 1.0% better than AlexNet, GooLeNet, ResNet50, and ResNet101, respectively. In addition, we have built the first marine organism database, Kyutech10K, with seven categories (i.e., shrimp, squid, crab, shark, sea urchin, manganese, and sand).     

Generation of uniform drops via through-hole arrays micromachined in stainless-steel plates

This study investigated the generation of oil drops using new symmetric and asymmetric through-hole-array devices made of stainless steel. The through-hole-array devices were built by piling up six stainless-steel plates, each having circular micro-holes with a diameter of 300 or 500 μm or micro-slots with a shorter line of 300 or 500 μm. Drops were generated by injecting a dispersed phase (refined soybean oil) via the through-hole array into a compartment filled with a continuous phase (Milli-Q water solution containing one of two emulsifiers). The drop detachment from symmetric and asymmetric through holes was observed in real time and analyzed. Uniform oil drops with average diameters of 1.0–4.1 mm and coefficients of variation of typically less than 6% were generated using symmetric and asymmetric through-hole-array devices. The resultant drop diameters for asymmetric through-hole arrays were significantly smaller than those for symmetric through-hole arrays. This paper also discusses experimental results regarding the effects of the microstructure, the dimensions of the through holes, and the type of emulsifier on drop generation and the resultant drop diameter.     

Effect of stress wave shape on the crack propagation velocity in cyclic delayed failure

Crack propagation velocity in delayed failure under superposed repeating load, ($\text{d}\text{a/}\text{d}\text{t)}{}_{\mathrm{R}}$, was compared with that under static load, ($\text{d}\text{a/}\text{d}\text{t)}{}_{\mathrm{S}}$Two peaks appear on the relation between decreasing rate of crack propagation velocity, $\text{1-β = 1 ? (}\text{d}\text{a/}\text{d}\text{t))}{}_{\mathrm{R}}\text{/(}\text{d}\text{a/}\text{d}\text{t)}{}_{\mathrm{S}}$ and frequency, ?, both under sinusoidal and square load. By changing the ratio of holding time at maximum stress intensity factor to that at minimum stress intensity factor in square load, it was deduced that the existence of two peaks on the 1 ? β vs $\text{f}$ curve was caused by an asymmetric interaction between hydrogen atoms and cyclic moving of the position with triaxial tensile stress at crack tip. Moreover, the relation between 1 ? β and $\text{f}$ under the positive or negative saw tooth load could be well explained by the interaction model.     

Solving fuzzy shortest path problems by neural networks

In this paper, we introduce the neural networks for solving fuzzy shortest path problems. The penalization of the neural networks is realized after transforming into crisp shortest path model. The procedure and efficiency of this approach are shown with numerical simulations.