Academic literature on the topic 'XBART'
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Journal articles on the topic "XBART"
Sivilevičius, Henrikas. "CRITERIA AND METHODOLOGY OF COMPLEX EVALUATION OF BITUMINOUS CONCRETE MIXER QUALITY/ASFALTBETONIO MAIŠYTUVO KOKYBĖS KOMPLEKSINIO VERTINIMO KRITERIJAI IR METODIKA." JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 7, no. 3 (June 30, 2001): 213–23. http://dx.doi.org/10.3846/13921525.2001.10531727.
Full textUla, Taylan A. "CORRECTIONS FOR ESTIMATED XBAR CONTROL CHARTS." Advances and Applications in Statistics 47, no. 1 (December 3, 2015): 51–63. http://dx.doi.org/10.17654/adasoct2015_051_063.
Full textZhang, Ying, Philippe Castagliola, Zhang Wu, and Michael B. C. Khoo. "The synthetic [Xbar] chart with estimated parameters." IIE Transactions 43, no. 9 (September 2011): 676–87. http://dx.doi.org/10.1080/0740817x.2010.549547.
Full textElam, Matthew E., and Kenneth E. Case. "Two-Stage Short-Run ([Xbar],s) Control Charts." Quality Engineering 17, no. 1 (December 31, 2004): 95–107. http://dx.doi.org/10.1081/qen-200028714.
Full textDiniaty, Dewi. "Analisis Pengendalian Mutu (Quality Control) CPO (Crude Palm Oil) Pada PT. XYZ." Jurnal Teknik Industri: Jurnal Hasil Penelitian dan Karya Ilmiah dalam Bidang Teknik Industri 5, no. 2 (February 6, 2020): 92. http://dx.doi.org/10.24014/jti.v5i2.8316.
Full textBalázová, Zelmíra, Andrej Trebichalský, Zdenka Gálová, and Radomíra Hornyák-Gregáňová. "Application of wheat SSR markers for detection of genetic diversity in triticale (x Triticosecale witt. )." Genetika 47, no. 3 (2015): 983–92. http://dx.doi.org/10.2298/gensr1503983b.
Full textJacobo, Javier A., Masao Buentello, and Ramiro Del Valle. "C-methionine-PET-guided Gamma Knife radiosurgery boost as adjuvant treatment for newly diagnosed glioblastomas." Surgical Neurology International 12 (May 31, 2021): 247. http://dx.doi.org/10.25259/sni_706_2020.
Full textYakowec, Jing Jing Wang, Emily Regan, Andrew J. Wagner, and Christina Isabella Herold. "Improving efficiency of exam room use at a comprehensive cancer center." Journal of Clinical Oncology 36, no. 30_suppl (October 20, 2018): 307. http://dx.doi.org/10.1200/jco.2018.36.30_suppl.307.
Full textLee, Lei Yong, Michael Boon Chong Khoo, Sin Yin Teh, and Ming Ha Lee. "A Variable Sampling Interval Synthetic Xbar Chart for the Process Mean." PLOS ONE 10, no. 5 (May 7, 2015): e0126331. http://dx.doi.org/10.1371/journal.pone.0126331.
Full textELSAYED, E. A., and A. CHEN. "An economic design of [xbar] control chart using quadratic loss function." International Journal of Production Research 32, no. 4 (April 1994): 873–87. http://dx.doi.org/10.1080/00207549408956976.
Full textDissertations / Theses on the topic "XBART"
TEMIZ, OZLEM. "COMPARISION OF METHODS FOR DEVELOPING ESTIMATED PARAMETER Xbar CONTROL CHARTS PROPOSED BY NEDUMARAN & PIGNATIELLO, ALBERS & KALLENBERG and TSAI ET AL." Cleveland State University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=csu1209592013.
Full textJARDIM, FELIPE SCHOEMER. "XBAR CHART WITH ESTIMATED PARAMETERS: THE AVERAGE RUN LENGTH DISTRIBUTION AND CORRECTIONS TO THE CONTROL LIMITS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2018. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=34608@1.
Full textCOORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
PROGRAMA DE SUPORTE À PÓS-GRADUAÇÃO DE INSTS. DE ENSINO
Os gráficos de controle estão entre as ferramentas indispensáveis para monitorar o desempenho de um processo em várias indústrias. Quando estimativas de parâmetros são necessárias para projetar esses gráficos, seu desempenho é afetado devido aos erros de estimação. Para resolver esse problema, no passado, pesquisadores avaliavam o desempenho desses métodos em termos do valor esperado do número médio de amostras até um alarme falso condicionado às estimativas dos parâmetros (denotado por 0). No entanto, esta solução não considera a grande variabilidade do 0 entre usuários. Então, recentemente, surgiu a ideia de medir o desempenho dos gráficos de controle usando a probabilidade de o 0 ser maior do que um valor especificado – que deve estar próximo do desejado nominal. Isso é chamado de Exceedance Probability Criterion (EPC). Para aplicar o EPC, a função de distribuição acumulada (c.d.f.) do 0 é necessária. No entanto, para um dos gráficos de controle mais utilizados, o gráfico Xbarra, também conhecido como gráfico x (sob a suposição de distribuição normal), a expressão matemática da c.d.f. não está disponível na literatura. Como contribuição nesse sentido, o presente trabalho apresenta a derivação exata da expressão matemática da c.d.f. do 0 para três possíveis casos de estimação de parâmetros: (1) quando a média e o desvio-padrão são desconhecidos, (2) quando apenas a média é desconhecida e (3) quando apenas o desvio-padrão é desconhecido. Assim, foi possível calcular o número mínimo de amostras iniciais, m, que garantem um desempenho desejada do gráfico em termos de EPC. Esses resultados mostram que m pode assumir valores consideravelmente grandes (como, por exemplo, 3.000 amostras). Como solução, duas novas equações são derivadas aqui para ajustar os limites de controle garantindo assim um desempenho desejado para qualquer valor de m. A vantagem dessas equações é que uma delas fornece resultados exatos enquanto a outra dispensa avançados softwares de computador para os cálculos. Um estudo adicional sobre o impacto desses ajustes no desempenho fora de controle (OOC) fornece tabelas que ajudam na decisão do melhor tradeoff entre quantidade adequada de dados e desempenhos IC e OOC preferenciais do gráfico. Recomendações práticas para uso desses resultados são aqui também fornecidas.
Control charts are among the indispensable tools for monitoring process performance in various industries. When parameter estimation is needed to design these charts, their performance is affected due to parameter estimation errors. To overcome this problem, in the past, researchers have evaluated the performance of control charts and designed them in terms of the expectation of the realized in-control (IC) average run length (0). But, as pointed recently, this solution does not account for what is known as the practitioner-to-practitioner variability (i.e., the variability of 0). So, a recent idea emerged where control chart performance is measured by the probability of the 0 being greater than a specified value - which must be close to the nominal desired one. This is called the Exceedance Probability Criterion (EPC). To apply the EPC, the cumulative distribution function (c.d.f.) of the 0 is required. However, for the most well-known control chart, named the two-sided Shewhart Xbar (or simply X) Chart (under normality assumption), the mathematical c.d.f. expression of the 0 is not available in the literature. As a contribution in this respect, the present work presents the derivation of the exact c.d.f. expression of the 0 for three cases of parameters estimation: (1) when both the process mean and standard deviation are unknown, (2) when only the mean is unknown and (3) when only the standard deviation is unknown. Using these key results, it was possible to calculate the exact minimum number of initial (Phase I) samples (m) that guarantees a desired in-control performance in terms of the EPC. These results show that m can be prohibitively large (such as 3.000 samples). As a solution to this problem, two new equations are derived here to adjust the control limits to guarantee a desired in-control performance in terms of the EPC for any given value of m. The advantage of these equations (compared to the existing adjustments methods) is that one provides exact results and the other one does not require too many computational resources to perform the calculations. A further study about the impact of these adjustments on the out-of-control (OOC) performance provides useful tables to decide the appropriate amount of data and the adjustments that corresponds to a user preferred tradeoff between the IC and OOC performances of the chart. Practical recommendations for using these findings are also provided in this research work.
Nodzon, Lisa A. "A member of the Arabidopsis thaliana XBAT family of ubiquitin protein ligases, XBAT32, is a positive regulator of lateral root development." [Gainesville, Fla.] : University of Florida, 2005. http://purl.fcla.edu/fcla/etd/UFE0013082.
Full text"A Study of Accelerated Bayesian Additive Regression Trees." Master's thesis, 2019. http://hdl.handle.net/2286/R.I.53698.
Full textDissertation/Thesis
Masters Thesis Statistics 2019
Tang, Tsuei-Ling, and 湯翠玲. "A Study on Economic .Xbar. - Control Charts." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/08894428277726811513.
Full text輔仁大學
數學系
83
Economic models for the design of control charts based on Duncan's approach have been studied in the recent past.However, the economic design of control charts has not been developed in a systematic manner so far.Consequently, various assumptions and approaches have been made. The most researchers consider the independence of occurrence for the multiple assignable cau- ses,but to confine in a short time interval the probablity is zero for the common occurrence of the assignable causes. The structure of this study is : (1) To flex the confinement of the independence assumption, we allow the occurence for the multip- le assignable causes in the short time interval. (2) To apply the $\bar{X}$ - control charts to the generalized process model . (3) To obtain the derived cost function. It is believed that the expected cycle time and expected cycle cost are more easily obtained by the proposed Markov chain method than by extending the Duncan's approach and others approaches. The design method can be applied to mul - tiple process variables and to a variety of control charts.
Ku, Chih-Chiang, and 辜誌強. "A Statistical Design of Xbar-R Control Chart." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/17728293248195170986.
Full text逢甲大學
工業工程與系統管理學研究所
96
Numerous methods, including Cumulative Sum(CuSum) , Exponentially Weighted Moving Average(EWMA), variable sample size, Variable Sampling Interval(VSI) , variable Control limit coefficient, have been proposed to improve the capability of monitoring process in Shewhart control chart. In practice, these methods aren’t simple, resulting in mistakes and loading. The capability of the control chart monitoring process is represented in two ARL (average run length). Essentially, the ARL is the average number of points that must be plotted before a point indicated an out-of control condition. When the process is in-control, the ARL of the control chart is the larger the better. Adversely, the process is out-of-control, the ARL of the control chart is the smaller the better. The ARL is affected by control chart parameters: sample size, sampling interval, control limit coefficient, out-of-control probability, the process shift amount and the rules of judgment. In this study, the central composite design (CCD) was used to allocate factor-level and data was obtained by computer simulation for the aim that the optimal parameters of Shewhart Xbar-R control chart were obtained.
Mao, Wei Shieng, and 毛威翔. ".Xbar control chart expert system using in poly etching process." Thesis, 1994. http://ndltd.ncl.edu.tw/handle/40803906786657016193.
Full text國立中山大學
企業管理研究所
82
.Xbar 管制圖利用基本的統計理念,以簡單的圖表及相關的判斷準則來顯 示品質狀況,是製程管制的主要工具之一。以往皆以人工繪製及判讀 .Xbar管制圖,耗時且易誤判,例如;因為趨勢與連串異常模式不易被發現。 將專家系統融入.Xbar管制圖,解決上述問題的可行方案。本研究以半導體 複晶蝕刻製程為對像,和廠商製程管制工程師、製程工程師及現場操作人 員共同合作,藉由瞭解製造程序、廠商使用的管制圖異常模式判讀與製程 異常分析步驟,及藉SmartQ--通用型管制圖專家系統建構工具(Shell),分 析三個月的製程歷史資料,歸納出五個常見異常模式。然後與合作廠商利 用魚骨圖、柏拉圖等品質改善之技巧,找出五個異常模式的相對應成因與 改善方法。再以此五個異常規則修改SmartQ之智庫,建構了第一個複晶蝕 刻.Xbar管制圖專家系統(First-Q)。最後藉由軟體測試方法--隨機測試, 瞭解First-Q專家系統的能力。發現它的可靠度達99%並且在降低異常製程 的發生上具有相當的效用。 .Xbar control chart uses fundamental statistics concepts, simple charting and correlative judgement rules to express states of product and service quality. Traditionally,.Xbar control chart are graphed and interpreted by human experts. It is time consuming and prone to be erroneous. Abnormal process models,e.g.,trends and runs,can not be eaily detected. Expert systems technique is one of the most effective methods to solve these problems. The underlying study is poly etching process in semiconductor industry. While knowledge for control chart interpretation was extracted from statistical process control textbook and related literatures,diagnostic rules were obtained by working with process control engineers,process engineers and shop floor operators. First,three-month historical process data are analyzed and five out-of-control patterns of .Xbar control chart are repetitive occurred. Then,cause and effect diagram, Pareto diagram are used to look for preventive method and the causes of abnormal process models. Finally,First-Q the first control chart expert system in poly etching process,are constructed by implementing five more rules for abnormal process diagnosis into Smart-Q,which is a general purpose quality control expert system shell. By utilizing a software reliability technique,random method and new data from processes, the reliability of First-Q expert system is 99%. So,First-Q is a powerful in correcting abnormal process and can be used to assist shop floor operators.
Wang, Shin-bau, and 王信堡. "Skewness and Kurtosis Correction for Xbar and R Control Charts." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/6vkb5s.
Full text國立高雄大學
統計學研究所
97
This thesis proposes a skewness and kurtosis correction (SKC) method to set up Xbar and R control charts for process monitoring. The developed SKC control limits take account of the skewness and kurtosis of process distribution, and are simply adjustments of the conventional Shewhart control charts. Type I risks of the proposed control charts are compared with those of some existing control charts when the underlying distribution is Weibull, lognormal, and Burr. Simulation results show that if the underlying distribution is asymmetric and leptokuric, then our SKC method offers considerable improvement over the existing control charts when it is desirable for Type I risks to close to 0.27% .
Huan-Tang, Lin. "The Statistical Design of the Xbar Control Chart for ARTA processes." 2006. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0017-1901200710300638.
Full textYu-Chin, Chu. "Variance Reduction Techniques for Estimating the Xbar Chart Average Run Length." 2006. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0017-1901200710305055.
Full textBook chapters on the topic "XBART"
Jardim, Felipe S., Subhabrata Chakraborti, and Eugenio Kahn Epprecht. "Effects on the Power of the Xbar Chart After Adjustments to Guarantee an In-Control Performance." In Operations Management for Social Good, 743–51. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23816-2_73.
Full textConference papers on the topic "XBART"
Hofmann, Matthew, Zhiyao Tang, Jonathan Orgill, Jonathan Nelson, David Glanzman, Brent Nelson, and Andre DeHon. "XBERT: Xilinx Logical-Level Bitstream Embedded RAM Transfusion." In 2021 IEEE 29th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM). IEEE, 2021. http://dx.doi.org/10.1109/fccm51124.2021.00009.
Full textCheng, Longsheng, and Zhifang Guo. "The Economically Designed SVSSI Xbar Control Chart." In 2011 International Conference on Computer and Management (CAMAN). IEEE, 2011. http://dx.doi.org/10.1109/caman.2011.5778893.
Full textYandrapalli, Soumya, Victor Plessky, Julius Koskela, Ventislav Yantchev, Patrick Turner, and Luis Guillermo Villanueva. "Analysis of XBAR resonance and higher order spurious modes." In 2019 IEEE International Ultrasonics Symposium (IUS). IEEE, 2019. http://dx.doi.org/10.1109/ultsym.2019.8925993.
Full textYang, Mei. "The optimal sample sizes of the Xbar and CUSUM charts." In 2009 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, 2009. http://dx.doi.org/10.1109/ieem.2009.5373024.
Full textChakraborty, Indranil, Mustafa Fayez Ali, Dong Eun Kim, Aayush Ankit, and Kaushik Roy. "GENIEx: A Generalized Approach to Emulating Non-Ideality in Memristive Xbars using Neural Networks." In 2020 57th ACM/IEEE Design Automation Conference (DAC). IEEE, 2020. http://dx.doi.org/10.1109/dac18072.2020.9218688.
Full textGu, Xiyu, Jieyu Liu, Yao Cai, Yan Liu, Chao Gao, Zhiwei Wen, Shishang Guo, and Chengliang Sun. "Laterally-excited bulk-wave resonators (XBARs) with embedded electrodes in 149.5° Z-cut LiNbO3." In 2021 IEEE 16th International Conference on Nano/Micro Engineered and Molecular Systems (NEMS). IEEE, 2021. http://dx.doi.org/10.1109/nems51815.2021.9451522.
Full textKoskela, J., V. P. Plessky, B. A. Willemsen, P. J. Turner, B. Garcia, R. B. Hammond, and N. O. Fenzi. "Fast GPU-Assisted FEM Simulations of 3D Periodic TCSAW, IHP, and XBAR Devices." In 2019 IEEE International Ultrasonics Symposium (IUS). IEEE, 2019. http://dx.doi.org/10.1109/ultsym.2019.8926183.
Full textBalta, Berna, Fazıl O¨nder So¨nmez, and Abdu¨lkadir Cengiz. "Gage Repeatability and Reproducibility Investigations of a Test Rig Using ANOVA/Xbar-R Method." In ASME 2011 International Mechanical Engineering Congress and Exposition. ASMEDC, 2011. http://dx.doi.org/10.1115/imece2011-62130.
Full textFeng, Xiaolong, Rajendra Patel, Roger Pons, Ramon Casanelles, Daniel Wappling, Jakob Westrom, and Hans Andersson. "Optimization-based development of ultra high performance Twin Robot Xbar press tending robot system." In 2013 44th International Symposium on Robotics (ISR). IEEE, 2013. http://dx.doi.org/10.1109/isr.2013.6695702.
Full textPlessky, Victor, Soumya Yandrapalli, Patrick J. Turner, Luis G. Villanueva, Julius Koskela, Muhammad Faizan, Annalisa De Pastina, Bryant Garcia, Jim Costa, and Robert B. Hammond. "Laterally excited bulk wave resonators (XBARs) based on thin Lithium Niobate platelet for 5GHz and 13 GHz filters." In 2019 IEEE/MTT-S International Microwave Symposium - IMS 2019. IEEE, 2019. http://dx.doi.org/10.1109/mwsym.2019.8700876.
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