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Discrimination of Out-of-Control Condition Using AIC in (x, s) Control Chart
Takemoto, Yasuhiko,Arizono, Ikuo,Satoh, Takanori Korean Institute of Industrial Engineers 2013 Industrial Engineeering & Management Systems Vol.12 No.2
The $\overline{x}$ control chart for the process mean and either the R or s control chart for the process dispersion have been used together to monitor the manufacturing processes. However, it has been pointed out that this procedure is flawed by a fault that makes it difficult to capture the behavior of process condition visually by considering the relationship between the shift in the process mean and the change in the process dispersion because the respective characteristics are monitored by an individual control chart in parallel. Then, the ($\overline{x}$, s) control chart has been proposed to enable the process managers to monitor the changes in the process mean, process dispersion, or both. On the one hand, identifying which process parameters are responsible for out-of-control condition of process is one of the important issues in the process management. It is especially important in the ($\overline{x}$, s) control chart where some parameters are monitored at a single plane. The previous literature has proposed the multiple decision method based on the statistical hypothesis tests to identify the parameters responsible for out-of-control condition. In this paper, we propose how to identify parameters responsible for out-of-control condition using the information criterion. Then, the effectiveness of proposed method is shown through some numerical experiments.
Effects of Crystal Grain Size and Particle Size on Core Loss For Fe-Si Compressed Cores
Takemoto Satoshi,Saito Takanobu 한국분말야금학회 2006 한국분말야금학회 학술대회논문집 Vol.2006 No.1
Core loss of soft magnetic powder cores have been focused on to achieve high efficiency of power supplies. In this study the effects of crystal grain size on core loss were investigated by changing heat treatment conditions. It was found that core loss is influenced by crystal grain size because eddy current loss decreased and hysteresis loss increased by making crystal grain size smaller, and it is also influenced by particle size.
Estimation of Change Point in Process State on CUSUM ($\bar{x}$, s) Control Chart
Takemoto, Yasuhiko,Arizono, Ikuo Korean Institute of Industrial Engineers 2009 Industrial Engineeering & Management Systems Vol.8 No.3
Control charts are used to distinguish between chance and assignable causes in the variability of quality characteristics. When a control chart signals that an assignable cause is present, process engineers must initiate a search for the assignable cause of the process disturbance. Identifying the time of a process change could lead to simplifying the search for the assignable cause and less process down time, as well as help to reduce the probability of incorrectly identifying the assignable cause. The change point estimation by likelihood theory and the built-in change point estimation in a control chart have been discussed until now. In this article, we discuss two kinds of process change point estimation when the CUSUM ($\bar{x}$, s) control chart for monitoring process mean and variance simultaneously is operated. Throughout some numerical experiments about the performance of the change point estimation, the change point estimation techniques in the CUSUM ($\bar{x}$, s) control chart are considered.
Takemoto, Yasuhiko,Arizono, Ikuo Korean Institute of Industrial Engineers 2009 Industrial Engineeering & Management Systems Vol.8 No.2
The ultimate goal of inventory management is to decide the timing and the quantity of ordering in response to uncertain demands. Recently, some researchers have focused upon an impact of distortions in the information, e.g., customer order cancellation, on an economical inventory policy. The customer order cancellation is considered a kind of distortions in demands, because a demand that is eventually cancelled is equivalent to a phony demand. Also, there are some additional distortions in the inventory information. For instance, the procurement of suppliers may include some nonconforming items as a result of imperfect production and inspection by the suppliers, and/or damage in transit. The nonconforming item should be considered a kind of distortions in the inventory information, because the nonconforming item is equivalent to a phony stock. In this article, we consider an inventory model under the situation that customers can cancel their orders and the procurement of suppliers may include some nonconforming items. Then, we introduce the customer order reservation into the inventory model for the purpose of avoiding the costly backlogs, because the customer order reservation gives retailers a period to fulfill customer's requests. We formulate a periodic review (s, S) inventory model and investigate the economical operation under the situation mentioned above. Further, through the sensitivity analysis, we show the impact of these distortions and the effect of the customer order reservation on the inventory policy.
Takemoto, Shigeki,Iwanaga, Masako,Sagara, Yasuko,Watanabe, Toshiki Asian Pacific Journal of Cancer Prevention 2015 Asian Pacific journal of cancer prevention Vol.16 No.18
Elevated levels of soluble CD30 (sCD30) are linked with various T-cell neoplasms. However, the relationship between sCD30 levels and the development of adult T-cell leukemia (ATL) in human T-cell leukemia virus type 1 (HTLV-1) carriers remains to be clarified. We here investigated whether plasma sCD30 is associated with risk of ATL in a nested case-control study within a cohort of HTLV-1 carriers. We compared sCD30 levels between 11 cases (i.e., HTLV-1 carriers who later progressed to ATL) and 22 age-, sex- and institution-matched control HTLV-1 carriers (i.e., those with no progression). The sCD30 concentration at baseline was significantly higher in cases than in controls (median 65.8, range 27.2-134.5 U/mL vs. median 22.2, range 8.4-63.1 U/mL, P=0.001). In the univariate logistic regression analysis, a higher sCD30 (${\geq}30.2U/mL$) was significantly associated with ATL development (odds ratio 7.88 and the 95% confidence intervals 1.35-45.8, P = 0.02). Among cases, sCD30 concentration tended to increase at the time of diagnosis of aggressive-type ATL, but the concentration was stable in those developing the smoldering-type. This suggests that sCD30 may serve as a predictive marker for the onset of aggressive-type ATL in HTLV-1 carriers.
A New Strategy of Space Vector Control in a 3-Phase PWM Inverter
Nobuyuki Takemoto,Syoichi Inoue 전력전자학회 1992 ICPE(ISPE)논문집 Vol.1992 No.4
The tolerance-band (hysteresis) control method is widely used in three-phase PWM inverters. This paper presents a new Space Vector Control System of a three-phase instantaneous follow up control(IFUC) PWM inverter that uses hysteresis comparators with variable threshold voltage. The IFUC-PWM three-phase inverter is operated automatically by the chosen voltage vector. In this paper we show clearly this operation of the IFUC-PWM system with the space vector approach and then discuss the undesirable extraordinary oscillation phenomenon of this system. In this system, the integrated complex plane of error-voltage between the reference voltage and the selected output voltage vector has the defined domain of a trapezoid about each output vector. A new output voltage vector is chosen by moving the integrated voltage in the domain of the current trapezoid. We also discuss the extraordinary oscillation in the same domain, andpropose a method for its prohibition. Finally, the effect of this prohibition method is shown by experimental results.
Estimation of Change Point in Process State on CUSUM (x, s) Control Chart
Yasuhiko Takemoto,Ikuo Arizono 대한산업공학회 2009 Industrial Engineeering & Management Systems Vol.8 No.3
Control charts are used to distinguish between chance and assignable causes in the variability of quality characteristics. When a control chart signals that an assignable cause is present, process engineers must initiate a search for the assignable cause of the process disturbance. Identifying the time of a process change could lead to simplifying the search for the assignable cause and less process down time, as well as help to reduce the probability of incorrectly identifying the assignable cause. The change point estimation by likelihood theory and the built-in change point estimation in a control chart have been discussed until now. In this article, we discuss two kinds of process change point estimation when the CUSUM (x, s) control chart for monitoring process mean and variance simultaneously is operated. Throughout some numerical experiments about the performance of the change point estimation, the change point estimation techniques in the CUSUM (x, s) control chart are considered.