Controller design of desktop-size NC machine tool with multi-application function

In this paper, a desktop-size NC machine tool is first presented to have multi-application function, such as 3D-machining modes with and without handling a machining force, and profiling-control modes with and without using stick-slip motion. The NC machine tool consists of three single-axis devices with a high resolution of 1???m in position. A ball end mill called router bit or a thin wood-stick tool can be attached to the tip of the z-axis. The proposed machine tool realizes compliant motion required for the profiling control along a desired trajectory. The profiling-control mode can be applied to the lapping process of a metallic LED lens cavity. Next, a control strategy with fine stick-slip motion is further added to the profiling-control system to improve the lapping performance. The fine stick-slip motion is generated orthogonally to the direction of the tool movement. Generally, the stick-slip motion is an undesirable phenomenon to be eliminated in precision machineries. However, the proposed machine tool employs the fine stick-slip motion to partly regulate slight finishing energy. The effectiveness of the machine tool is demonstrated through an actual machining test of a wood material and a lapping test of a metallic LED lens cavity.     

Adaptive Learning with Large Variability of Teaching Signals for Neural Networks and Its Application to Motion Control of an Industrial Robot

Recently, various control methods represented by proportional-integral-derivative (PID) control are used for robotic control.To cope with the requirements for high response and precision, advanced feedforward controllers such as gravity compensator,Coriolis/centrifugal force compensator and friction compensators have been built in the controller.Generally, it causes heavy computational load when calculating the compensating value within a short sampling period.In this paper, integrated recurrent neural networks are applied as a feedforward controller for PUMA560 manipulator.The feedforward controller works instead of gravity and Coriolis/centrifugal force compcusators.In the learning process of the neural network by using back propagation algorithm, the learning coefficient and gain of sigmoid function are tuned intuitively and empirically according to teaching signals.The tuning is complicated because it is being conducted by trial and error.Especially, when the scale of teaching signal is large, the problem becomes crucial.To cope with the problcm which concerns the learning performance, a simple and adaptive learning technique for large scale teaching signals is proposed.The learning techniques and control effectiveness are evaluated through simulations using the dynamic model of PUMA560 manipulator.     

Application of magnetic pulse welding technique for flexible printed circuit boards (FPCB) lap joints

The magnetic pulse welding (MPW) is a high speed solid state welding process which has been used successfully to provide metallurgical and electrical bonds between flexible printed circuit board (FPCB) with using: (1) welding with aluminum driver sheet and (2) without driver sheet. The optimum bank energy for reliable bonding was about 1 kJ with 160–180 kA maximum current. The maximum tensile shearing for most welded samples was nearly same as tensile shearing strength of copper layer of FPCB sheet.     

Oral Squamous Cell Carcinoma Contributes to Differentiation of Monocyte-Derived Tumor-Associated Macrophages via PAI-1 and IL-8 Production

Tumor-associated macrophages (TAMs) promote cancer cell proliferation and metastasis, as well as anti-tumor immune suppression. Recent studies have shown that tumors enhance the recruitment and differentiation of TAMs, but the detailed mechanisms have not been clarified. We thus examined the influence of cancer cells on the differentiation of monocytes to TAM subsets, including CD163⁺, CD204⁺, and CD206⁺ cells, in oral squamous cell carcinoma (OSCC) using immunohistochemistry, flow cytometry, and a cytokine array. Furthermore, we investigated the effect of OSCC cells (HSC-2, SQUU-A, and SQUU-B cells) on the differentiation of purified CD14⁺ cells to TAM subsets. The localization patterns of CD163⁺, CD204⁺, and CD206⁺ in OSCC sections were quite different. The expression of CD206 on CD14⁺ cells was significantly increased after the co-culture with OSCC cell lines, while the expressions of CD163 and CD204 on CD14⁺ cells showed no change. High concentrations of plasminogen activator inhibitor-1 (PAI-1) and interleukin-8 (IL-8) were detected in the conditioned medium of OSCC cell lines. PAI-1 and IL-8 stimulated CD14⁺ cells to express CD206. Moreover, there were positive correlations among the numbers of CD206⁺, PAI-1⁺, and IL-8⁺ cells in OSCC sections. These results suggest that PAI-1 and IL-8 produced by OSCC contribute to the differentiation of monocytes to CD206⁺ TAMs.     

Evaluation of energy saving and CO2 emission reduction technologies in energy supply and end‐use sectors using a global energy model

Assessments of global warming mitigation technologies are important for achieving the Kyoto target and planning post‐Kyoto regimes. Regional differences in energy resources, growth in energy consumption, current technology diffusions, etc., should be considered in the assessments. A global energy systems model, DNE21+, with high regional resolution had treated the energy supply sectors in a bottom‐up fashion and the end‐use sectors in a top‐down fashion, which was expressed by using long‐term price elasticity. However, the assessments of technological options in the end‐use sectors are currently more important, particularly for the near and middle terms. In order to evaluate the technological options not only in the energy supply sectors but also in the end‐use sectors for energy savings and CO₂ emission reductions, DNE21+ has been modified for treating two energy‐intensive end‐use sectors, i.e. steel and cement sectors, in the bottom‐up fashion. The results reveal that the cost‐effective global CO₂ emission reductions in 2030 for stabilizing the atmospheric CO₂ concentration at 550 ppmv in comparison with that in the reference case would be approximately 68 MtC/yr and almost zero in the steel and cement sectors, respectively. The cost‐effective options include next‐generation coke ovens and coke dry quenching (CDQ) in the steel sector. Copyright © 2007 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Introduction of subsidisation in nascent climate-friendly learning technologies and evaluation of its effectiveness

Given its importance as a practical phenomenon underlying the progress of learning technologies, attention should be paid to the role of subsidisation in learning theory, particularly in the case of nascent climate-related sociable learning technologies, in order to examine its benefits. Thus, this study focuses on subsidy procurement of energy technologies in several economies in the context of the component learning track in endogenous global clusters in order to suggest improvements to the adoption mechanism and examine the climate stabilization constraint. At the same time, the study attempts to determine the global progress ratio of the lithium-ion battery in order to analyse various endogenous learning scenarios for hybrid technologies. An integrated energy system model with highly disaggregated global regions (DNE21+) is used to execute this research in a medium time frame. Subsidisation of the learning track of battery technology encourages greater development of plug-in hybrid vehicles, promotes early diffusion of hybrid technologies, and relieves heavy dependency on crude oil and biofuels. The subsidies in the common learning domains in few economies benefit the nearby economies because of the technology spillover that occurs through numerous cross-feedback learning mechanisms. Endogenous learning with subsidies augments diffusion potentials, abates emissions, and shifts sectoral emissions.     

A rechargeable lithium metal battery operating at intermediate temperatures using molten alkali bis(trifluoromethylsulfonyl)amide mixture as an electrolyte

The physicochemical properties of molten alkali bis(trifluoromethylsulfonyl)amide, MTFSI (M = Li, K, Cs), mixture (x_LiTFSI = 0.20, x_KTFSI = 0.10, x_CsTFSI = 0.70) were studied to develop a new rechargeable lithium battery operating at intermediate temperature (100–180 °C). The viscosity and ionic conductivity of this melt at 150 °C are 87.2 cP and 14.2 mS cm⁻¹, respectively. The cyclic voltammetry revealed that the electrochemical window at 150 °C is as wide as 5.0 V, and that the electrochemical deposition/dissolution of lithium metal occurs at the cathode limit. A Li/MTFSI (M = Li, K, Cs)/LiFePO₄ cell showed an excellent cycle performance at a constant current rate of C/10 at 150 °C; 95% of the initial discharge capacity was maintained after 50 cycles. Except for the initial few cycles, the coulombic efficiencies were approximately 100% for all the cycles, indicating the stabilities of the molten MTFSI mixture and all the electrode materials.     

A solution to SLAM problems by simultaneous estimation of kinematic parameters including sensor mounting offset with an augmented UKF

Accurate localization is required for autonomous robots to navigate in cluttered environments safely. Therefore, Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF), which incorporate probabilistic concepts as localization methods, have been researched up to now. It should be noted, however, that the errors of kinematic parameters such as wheel diameter, tread, and mounting sensor offset are not enough considered in conventional works. We propose an Augmented UKF-SLAM (AUKF-SLAM), which is an extension of the UKF-SLAM and can estimate the kinematic parameters including a sensor mounting offset together with the localization and mapping. The UKF-SLAM and the AUKF-SLAM are compared through some simulations to show that the proposed AUKF-SLAM is more accurate than the UKF-SLAM. Furthermore, localization experiments with only odometry are conducted using a real robot. The experimental results show to demonstrate that the localization using kinematic parameters estimated by the AUKF-SLAM is more accurate than that using values measured by hand in advance. Through some experimental verifications in an elevator hall, cluttered rooms, and a long distance corridor, it is confirmed that the proposed AUKF-SLAM which simultaneously estimates the effective kinematic parameters largely contributes to the total accuracy improvement of SLAM.