国产bbaaaaa片,成年美女黄网站色视频免费,成年黄大片,а天堂中文最新一区二区三区,成人精品视频一区二区三区尤物

首頁(yè)> 外文期刊>The International Journal of Advanced Manufacturing Technology >Modeling of breakout prediction approach integrating feature dimension reduction with K-means clustering for slab continuous casting
【24h】

Modeling of breakout prediction approach integrating feature dimension reduction with K-means clustering for slab continuous casting

機(jī)譯：分組預(yù)測(cè)方法的建模與K-MERIAL Clasting用于平板連續(xù)鑄造的特征尺寸減小

獲取原文
獲取原文并翻譯 | 示例
  • 摘要
  • 著錄項(xiàng)
  • 相似文獻(xiàn)
  • 相關(guān)主題

摘要

Breakout is a catastrophic accident in continuous casting. The existing breakout prediction methods based on logical judgment and neural networks need to constantly adjust the prediction parameters or prepare high-quality samples as the input, resulting in poor robustness and unstable precision stability. Therefore, it is particularly important to develop a breakout prediction method that not only can predict breakout accurately but also avoid human intervention significantly. This work proposes a novel approach for breakout prediction combining K-means clustering and feature dimension reduction. The method uses feature dimension reduction to obtain the typical feature vector (TFV) that can characterize the original temperature change trend, and then a K-means clustering model is established to realize online detection of breakout prediction. The results show that the model has a 100% alarm rate for the true breakout, and meanwhile, reduces the number of false alarms from 555 to 217 compared with the on-line breakout prediction system (BPS). The proposed method does not need to adjust the prediction parameters frequently or prepare the input samples carefully, which not only avoids the human intervention but also meets the requirements of online monitoring for the practicality and applicability of the breakout prediction method.
機(jī)譯：突圍是連鑄災(zāi)難性事故。基于邏輯判斷和神經(jīng)網(wǎng)絡(luò)的現(xiàn)有分支預(yù)測(cè)方法需要不斷地調(diào)整預(yù)測(cè)參數(shù)或作為輸入制備高品質(zhì)的樣品，從而導(dǎo)致魯棒性差且不穩(wěn)定精度穩(wěn)定。因此，開發(fā)一種不僅能準(zhǔn)確地預(yù)測(cè)分支又避免人為干預(yù)顯著突破預(yù)測(cè)方法就顯得尤為重要。這項(xiàng)工作提出了漏鋼預(yù)報(bào)組合K-均值聚類和特征降維的新方法。該方法使用特征尺寸減少以獲得能夠表征原始溫度變化趨勢(shì)的典型特征向量（TFV），然后一個(gè)K-均值聚類模型被建立為實(shí)現(xiàn)突破預(yù)測(cè)在線檢測(cè)。結(jié)果表明，該模型具有用于真正突破100％的警率，同時(shí)，減少了假警報(bào)從555到217與上線漏鋼預(yù)報(bào)系統(tǒng)（BPS）相比的數(shù)量。該方法并不需要經(jīng)常調(diào)整預(yù)測(cè)參數(shù)或認(rèn)真準(zhǔn)備輸入樣本，這不僅避免了人工干預(yù)，也滿足了在線監(jiān)測(cè)的漏鋼預(yù)報(bào)方法的實(shí)用性和適用性的要求。

著錄項(xiàng)

相似文獻(xiàn)

  • 外文文獻(xiàn)
  • 中文文獻(xiàn)
  • 專利
獲取原文

客服郵箱：kefu@zhangqiaokeyan.com

京公網(wǎng)安備：11010802029741號(hào) ICP備案號(hào)：京ICP備15016152號(hào)-6 六維聯(lián)合信息科技 (北京) 有限公司?版權(quán)所有

  • 客服微信

  • 服務(wù)號(hào)