Make consistent的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列問答集和整理懶人包

Classical and Quantum Cosmology: The Einstenian Picture of the Universe

為了解決Make consistent的問題，作者Montani, Giovanni/ Cianfrani, Francesco/ Lattanzi, Massimiano 這樣論述：

This volumes gives a complete description of the Universe's birth and evolution, based on the implementation of General Relativity and Quantum Field Theory. The presentation has a pedagogical profile and aims to make accessible, at a Master's Degree level, a wide number of subtle topics concernin

g the thermal history of the Universe. This also includes the discussion of themes still open to the scientific debate, such as the quantum nature of the cosmological singularity and the nature of the dark components of the Universe. To this aim, this volume is divided into three parts, the first tw

o being dedicated to introducing the paradigms of Einsteinian gravitation and quantum physics. These are necessary for a self-consistent presentation of the third part which fully develops the analysis of the cosmological framework. The first two parts have their own independent value as compact, pe

dagogical introductions to the corresponding areas, and are completed by relevant monographic appendices. In this respect, the picture outlined in the two chapters devoted to quantum physics of gravitation is distinguished by the clear and complete synthesis it offers to the reader.The main targets

of this volume are Master's Degree students; however, because of the advanced character of some selected topics, it can also be fruitfully adopted by PhD students and even researchers wishing to obtain an up-to-date knowledge of some of the topics addressed in the book.

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生成對抗網路應用於AOI樣本數擴增

為了解決Make consistent的問題，作者蔡棠介 這樣論述：

AOI(Automated Optical Inspection)自動光學檢測於台灣製造業中，為應用廣泛之一，因社會勞動力老年化及人口的趨減，加上人會因為疲勞而降低專注力，故製造業逐漸導入AOI光學檢測設備來取代傳統目檢人力，在應用於工廠內之產品瑕疵檢測時，常發生正確率不高／漏檢之問題，主要原因是以訓練樣品數不足為主，因瑕疵品在產品生產初期所發生之數量及類別不多，若出現不同的瑕疵內容，機器未先學習過，就會造成AOI漏檢。因此本研究主要利用GAN(Generative Adversarial Nets)中文譯為生成對抗網路來生成樣本，來彌補AOI開發初期樣本數不足的問題， 利用兩種生成對抗網路

模型Cycle GAN與Bicycle GAN在兩種不同情境的情況下，生成樣本來擴增AOI樣本資料庫，研究的架構中應用YoloV4(You Only Look Once V4)來當替代AOI系統，在資料集分配上，模擬剛開發初期只有少量的樣本時需讓AOI有基本的檢測能力，故只抽取少量的訓練資料來生成，其餘的當作測試集來驗證生成的樣本是否有效。有別於其他研究應用，本文利用VAE(Variational autoencoders)及GAN結合的生成對抗網路，控制特徵潛在空間向量來生成多樣性的AOI樣本，實驗結果說明利用生成對抗網路生成瑕疵樣本，相較於擴增前兩者準確率差異準確率可達12%，在實驗過程中

生成出多樣性的AOI樣本已與原先輸入的圖像截然不同，故也可應用於生成不同的瑕疵樣本來測試AOI系統的檢驗可靠度。

Machine Learning for Engineers

為了解決Make consistent的問題，作者 這樣論述：

This self-contained introduction to machine learning, designed from the start with engineers in mind, will equip students with everything they need to start applying machine learning principles and algorithms to real-world engineering problems. With a consistent emphasis on the connections betwee

n estimation, detection, information theory, and optimization, it includes: an accessible overview of the relationships between machine learning and signal processing, providing a solid foundation for further study; clear explanations of the differences between state-of-the-art techniques and more c

lassical methods, equipping students with all the understanding they need to make informed technique choices; demonstration of the links between information-theoretical concepts and their practical engineering relevance; reproducible examples using Matlab, enabling hands-on student experimentation.

Assuming only a basic understanding of probability and linear algebra, and accompanied by lecture slides and solutions for instructors, this is the ideal introduction to machine learning for engineering students of all disciplines.

探討熔膠黏彈性對射出成型充填階段影響與並應用於射出重量校正方法研究

為了解決Make consistent的問題，作者黃珮絜 這樣論述：

現今射出產業中從過去以來皆透過具備相關經驗的師傅為為主，近年來電腦輔助工程分析技術出現，可以事先進行模擬使現場實際的材料損失減少因而降低成本等，但模擬分析與實際射出仍存有差異，一般利用模擬分析進行機台校正流程並探索機台校正的影響，發現模擬結果與實機生產的結果完全一致是非常具有挑戰性，目前主要面臨的是因為模擬分析無法考量許多的實際狀況，像是慣性效應、噴泉效應、壓縮效應… 等。 在CAE模擬分析過去已有許多理論方法用以修正熔膠黏彈性效應行為且大多數黏彈性效應模型會需要進階材料相關參數，但仍有存有奇異點、不準確等問題出現，使分析與實際存有差異。因此須依靠統計模型校正彌補CAE不足之處，才可以

使模擬分析更為趨近於實際實驗。 本論文研究目的是為了提高黏彈性效應在分析之中準確度並靈活的預測重量且成本較低，結合數值方法使分析與實際實驗一致，然後預測不同模具在充填階段之重量，並以融熔塑膠為黏彈體去了解對重量影響，為此利用充填階段常使用因子製作不同的預測模型，因子分別為射出速度、澆口厚度、模具溫度、材料溫度以及緩衝量，先製作預測機台實際射出速度的預測模型，比較校正後機台實際射出速度之分析、未校正射出速度之分析與實際射出產品重量進行，再製作預測產品重量來了解各因子間與重量之影響，利用五因子實驗製作預測產品重量、校正後射速之分析產品重量與實際射出產品重量進行比較，最後利用驗證模具進行驗證。

研究結果顯示，將分析之中考慮黏彈性能減少分析與實際的誤差，利用校正分析準確度從97.25%提升至98.65%。預測產品重量方面，使用驗證模具時準確度在99.73%，能準確預測不同模具時之產品重量。