General structure and characteristics of quick response production system

The diversification and shorter production cycles urge manufacturers to shift their production systems from “make to stock” to “make to order” or an intermediate production system between them. The present paper deals with a production system developed by some manufacturers to meet requirements in the last decade. The system, which we call “Quick response to orders production system” or in brief “quick response production system (QRPS)”, is characterized by two key factors, i.e., “acceptable response time” and “semifinished product.” An acceptable response time is decided through the explicit or implicit approval of customers beforehand and manufacturers guarantee to ship products within an acceptable response time in a stochastic sense by processing the semifinished product according to customer order. As a result, manufacturers are able to reduce the inventory of finished products, while customers are able to enjoy the diversification of products and a stable supply from manufacturers. The general structure and characteristics of QRPS are discussed primarily from a quantitative point of view and a numerical example is shown to assist in understanding the design and operation of QRPS.     

Improving the Performance of Feedforward Neural Networks by Noise Injection into Hidden Neurons

The generalization ability of feedforward neural networks (NNs) depends on the size of training set and the feature of the training patterns. Theoretically the best classification property is obtained if all possible patterns are used to train the network, which is practically impossible. In this paper a new noise injection technique is proposed, that is noise injection into the hidden neurons at the summation level. Assuming that the test patterns are drawn from the same population used to generate the training set, we show that noise injection into hidden neurons is equivalent to training with noisy input patterns (i.e., larger training set). The simulation results indicate that the networks trained with the proposed technique and the networks trained with noisy input patterns have almost the same generalization and fault tolerance abilities. The learning time required by the proposed method is considerably less than that required by the training with noisy input patterns, and it is almost the same as that required by the standard backpropagation using normal input patterns.     

Legacy system program transformation by Lyee methodology

Based on the comparative review of several approaches to legacy system conversion and revitalization, the Lyee methodology application for the issue is presented to clarify its idea, the associated procedure, and the implemented tools. It could be said that with the tools and manual developed by ICBSM&T, the mechanical transformation of the conventional program to a Lyee-structured one becomes possible as long as the programs are made in a procedure-oriented language. In addition to the program structure conversion, the Lyee methodology permits people to choose any application language in the transformed program. At the same time, quite a new approach related to the system conversion is introduced, in which the chunk of data extracted from an old program is edited to make a new conventional structure program that has a logical sequence instead of a Lyee type of declarative program. These features can be realized through the concept of LyeeBELT, which is a set of word-information about the attributes, formulae, and conditions for an independent data item.

The overall workflow of the legacy program transformation is shown in the following.

A critical part in its implementation is the feasibility study (pre-analysis) stage where necessary information is supposed to be secured, and an appropriate plan and policy about the system to be revitalized in the new system environment should be clarified so as to customize the tools accordingly. If the initial process is completed, the mechanical legacy system conversion will be realized by registering the parameters in the tool, and the reestablishment of business knowledge in the LyeeBELT will be enabled. With the regulated business logic on the LyeeBELT, the program maintenance afterwards becomes drastically simplified and stable without the ‘spaghetti’ problem, so that software evolution can be possible.     

Semantic Web: A road to the knowledge infrastructure on the internet

In this article, I describe the basic technologies for Semantic Web and relationship between Semantic Web and Knowledge Representation in Artificial Intelligence. Semantic Web is planned as an extension of the current web in order to help cooperation between computers and humans, i.e., computers and humans are expected to understand each other in the knowledge level. I first describe the vision of the Semantic Web, then introduce the current Semantic Web technologies, i.e., RDF, RDFS, and OWL. I describe relationship between the trend of Semantic Web and Knowledge Representation, and clarify challenges and difficulties of Semantic Web from the point of view of Knowledge Representation. Hideaki Takeda: He is a professor at National Institute of Informatics (NII) and a professor in Department of Informatics at the Graduate University of Advanced Studies (Sokendai). He received his Ph.D. from the University of Tokyo in 1991. His research interest in computer science includes ontology engineering, community informatics and knowledge sharing systems.     

High temperature superconductor micro-superconducting-quantum-interference-device magnetometer for magnetization measurement of a microscale magnet

We have developed a high temperature superconductor (HTS) micrometer-sized dc superconducting quantum interference device (SQUID) magnetometer for high field and high temperature operation. It was fabricated from YBa2Cu3O7-delta of 92 nm in thickness with photolithography techniques to have a hole of 4x9 microm2 and 2 microm wide grain boundary Josephson junctions. Combined with a three dimensional magnetic field coil system, the modulation patterns of critical current Ic were observed for three different field directions. They were successfully used to measure the magnetic properties of a molecular ferrimagnetic microcrystal (23x17x13 microm3), [Mn2(H2O)2(CH3COO)][W(CN)8]2H2O. The magnetization curve was obtained in magnetic field up to 0.12 T between 30 and 70 K. This is the first to measure the anisotropy of hysteresis curve in the field above 0.1 T with an accuracy of 10(-12) J T(-1) (10(-9) emu) with a HTS micro-SQUID magnetometer.     

Comparison of isolation methods using commercially available kits for obtaining extracellular vesicles from cow milk

Extracellular vesicles (EV) are important for delivering biologically active substances to facilitate cell-to-cell communication. Milk-derived EV are widely known because of their potential for immune enhancement. However, procedures for isolating milk-derived EV have not been fully established. To obtain pure milk-derived EV and accurately reveal their function, such procedures must be established. The aim of the present study was to compare methods using commercially available kits for isolating milk-derived EV. Initially, we investigated procedures to remove casein, which is the major obstacle in determining milk-derived EV purity. We separated whey using centrifugation only, acetic acid precipitation, and EDTA precipitation. Then, we isolated milk-derived EV by ultracentrifugation, membrane affinity column, size exclusion chromatography (SEC), polymer-based isolation, or phosphatidylserine-affinity isolation. Using EV count per milligram of protein, which is a good indicator of purity, we determined that acetic acid precipitation was the best method for removing casein. Using nanoparticle tracking analysis, protein quantity analysis, and RNA quantity analysis, we comprehensively compared each isolation method for its purity and yield. We found that SEC-based qEV column (Izon Science) could collect purer milk-derived EV at higher quantities. Thus, a combination of acetic acid precipitation and qEV can effectively isolate high amounts of pure extracellular vesicles from bovine milk.     

An evaluation of an olfactory sensor and recognition system using metal-oxide semiconductor gas sensor with variable heater controller

An artificial olfactory, which is called an electronic nose system (e-nose), is studied for realizing new human–machine interface. The system consists of sensor unit and a signal processing unit. There are some types of sensors for the sensor unit, metal oxide semiconductor gas sensors (MOGS) and quartz crystal microbalance gas sensors are useful in our study. Our system in this paper has MOGS. Many of MOGS utilize an effect of an oxidation–reduction reaction on the surface of the sensors. One of the features of the sensor, the character of the sensor is changed by temperature of the sensors. In this paper, we build a extend output sensor unit using this feature, and show experimental result of classification applying multilayer perceptron. In the experiment, we choose soy sauce as classification targets because we are considering applying the system for management of cooked foods.     

Measurements of gamma-ray emission probabilities of 241, 243Am and 239Np

Gamma-ray emission probabilities of ^{241, 243}Am and ²³⁹Np have been precisely measured with gamma- and alpha-ray spectroscopic methods. The activities of the samples were determined by measuring alpha particles using a Si semiconductor detector. Gamma rays emitted from the samples were measured with a planar type High-Purity Germanium (HPGe) detector. An efficiency curve of the HPGe detector was derived with uncertainties from 0.7% to 2.5% by combining measured efficiencies and Monte Carlo simulation. The gamma-ray emission probabilities for the major gamma rays of these nuclides were determined with uncertainties less than 1.2%.     

Voltage Control of Static Var Compensator for a Remote System Interconnected by Long‐Distance AC Cables

The voltage variation in a remote system is large when the system is connected by long‐distance AC cables due to the cable capacitance. In Japan, the longest 54‐km 66‐kV AC submarine cable interconnection between the Kyushu mainland and Goto Islands was commissioned in 2005. It was requested to mitigate the voltage variation caused by switching off and on one circuit of the two circuits in the AC cables when a fault occurs. Since the conventional voltage control methods such as transformer tap changer or shunt capacitor and reactor banks are not sufficient because of their slow response time, a static var compensator (SVC) was installed on the Goto Islands. In such an application, an SVC control method should be developed so as not to override the existing voltage control systems. This paper describes the SVC control method developed for the Goto Islands AC interconnection project, which can be applied to similar situations. The effectiveness of the control method was verified by the results of effective value simulation and of field testing, which was implemented before the SVC was commissioned in 2007. © 2013 Wiley Periodicals, Inc. Electr Eng Jpn, 186(3): 19–30, 2014; Published online in Wiley Online Library ( wileyonlinelibrary.com ). DOI 10.1002/eej.22337