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Influence of Cobalt Content on the Fatigue Strength of WC-Co Hardmetals
Nakajima Takeshi,Hosokawa Hiroyuki,Shimojima Koji 한국분말야금학회 2006 한국분말야금학회 학술대회논문집 Vol.2006 No.1
The behavior of hardmetals under cyclic loads is investigated. Unnotched specimens were employed to obtain practical information regarding fatigue in hardmetals. All the tested hardmetals exhibit an increase in the number of cycles until failure with a decrease in the maximum stress, i.e., the hardmetals exhibit a high fatigue sensitivity. The fatigue strength increases with the cobalt content. Although distinct fatigue limits, as observed in metals, cannot be observed, the calculated fatigue limit stress at cycles is found to be approximately 70% of the flexural strength, and the stress value exhibits a linear relationship with the flexural stress.
박철영,中島康治 대구대학교 과학기술연구소 1998 科學技術硏究 Vol.5 No.2
We discuss the performance of the neural networks with quantized interconnections of +1, -1 and 0(Quantized Connection Neural Networks: QCNN, and how to choose the connection weights for the networks from the training set of examples. The basic characteristics of the networks and algorithm to decide the connection weights are presented. The layered QCNN to solve the parity problem with arbitrary number N of inputs is obtained by using the algorithm. The layered QCNN has a single hidden layer and no bias input when N is odd. When N is even, the network requires only one additional input as bias. The networks which perform any logic functions can be designed on the basis of the algorithm, which is slightly different from the way for solving N-parity problems. The network may be expected to have the same ability of generalization as the network trained with learning rules, because it is possible to decide the connection weights even if the given training set is small. It takes rather long time for the learning of the connection weights, however, one can decide them without learning in our case. Hence, we may expect some applications of QCNN for real-time processings.
박철영,中島康治 대구대학교 과학기술연구소 1998 科學技術硏究 Vol.5 No.1
To aim at improving performance of a neural network as an associative memory or as an optimization problem solver, we propose two models using a nonmonotone analog neuron model which differs from traditional ones. Using the proposed model, we construct the energy function which can have two minimums. It is shown that our model can recall embedded patterns successfully. We also discuss the simulation method and the performance of the model by numerical simulations. The memory capacity strongly depends on the shape of input-output function as well as the sharpness. This model should be useful to devise a class of models for associative memory of temporal patterns.
Yasuyuki Yamada,Kensuke Nakajima,Koji Nakajima 한국물리학회 2006 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.48 No.5I
We investigate experimentally the dynamics of Josephson vortices interacting with electromagnetic waves in Bi2Sr2CaCu2O8+y intrinsic Josephson junction stacks by means of millimeter wave irradiation and numerical simulations based on coupled sine-Gordon equations. We have found that an introduction of pancake vortices by tilting the external magnetic field from CuO2 planes reveals Shapiro-step-like features on the flux-flow characteristics. Those steps are assumed to be a manifestation of an in-phase coherent motion of Josephson vortices caused by a periodic potential developed by pancake vortices. The results of the numerical simulation, taking into account the periodic potential, supports the validity of such an assumption.?
A Hardware Neural Network for External Inspection
Seungwoo CHUN,Yoshihiro HAYAKAWA,Koji NAKAJIMA 대한전자공학회 2007 ITC-CSCC :International Technical Conference on Ci Vol.2007 No.7
At factories, the external inspection of final products relies on visual inspection, which is based on experience and instinct. With such method it is difficult to reduce time and production cost, and hence an automatic inspection system is desired. We used a hardware backpropagation(BP) system enabling high-speed judgment and learning. In order to increase the operation speed, we have implemented parallel and pipeline processing in the hardware. Our hardware BP system could complete the process within 1.37㎲. However, there was a case where learning could not be completed by such method. Therefore, we suggest an improved method to solve the problem in this paper.
Masanori Shimodaira,Tomohiro Niwa,Koji Nakajima,Mutsuhiro Kobayashi,Norinao Hanyu,Tomohiro Nakayama 대한당뇨병학회 2014 Diabetes and Metabolism Journal Vol.38 No.4
Background: Increased triglycerides (TGs) and decreased high density lipoprotein cholesterol (HDL-C) levels are established as diabetic risks for nondiabetic subjects. The aim of this study was to investigate the relationship among TG, HDL-C, TG/HDL-C ratio, and early-phase insulin secretion in normoglycemic and prediabetic subjects. Methods: We evaluated 663 Japanese subjects who underwent the 75-g oral glucose tolerance test. On the basis of these results, the subjects were divided into four groups: those with normal glucose tolerance (NGT; n=341), isolated impaired fasting glucose (i-IFG; n=211), isolated impaired glucose tolerance (i-IGT; n=71), and combined IFG and IGT (IFG+IGT; n=40). Insulin secretion was estimated by the insulinogenic index (IGI) (Δinsulin/Δglucose [30 to 0 minutes]) and disposition index (DI) (IGI/homeostasis model assessment of insulin resistance). Results: In prediabetic subjects (i-IFG, i-IGT, and IFG+IGT), linear regression analyses revealed that IGI and DI were positively correlated with HDL-C levels. Moreover, in subjects with i-IGT and (IFG+IGT), but not with i-IFG, the indices of insulin secretion were negatively correlated with the log-transformed TG and TG/HDL-C ratio. In both the subjects with i-IGT, multivariate linear regression analyses revealed that DI was positively correlated with HDL-C and negatively with log-transformed TG and TG/HDL-C ratio. On the other hand, in subjects with NGT, there was no association between insulin secretion and lipid profiles. Conclusion: These results revealed that serum TG and HDL-C levels have different impacts on early-phase insulin secretion on the basis of their glucose tolerance status.