Free vector的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列懶人包和總整理

Humans and Animals: The Things We Have in Common

為了解決Free vector的問題，作者 這樣論述：

Pavla Hanackova (1990-2021), born in the Czech Republic. She came from a picturesque Moravian village surrounded by vineyards. Her greatest passions were travelling, food, and everything associated with it. Dasha Lebesheva is an illustrator from Moscow, Russia. After she graduated from the British H

igher School of Art and Design Dasha discovered vector illustration and developed her own style. It is based on smooth lines, angular silhouettes and precise geometry. Dasha likes working with shapes and colors, simplifying, seeking and finding the best solution - minimalistic but expressive. Dasha

loves dogs, gorillas, funny socks and pumpkin soup. She usually spends her free time playing video games, reading comics and beautiful children’s books, rewatching Disney animated films and eating chocolate cereal (preferably at night).

Free vector進入發燒排行的影片

深度學習應用於結構耐震性能評估 – 以後拉式預力預鑄混凝土節塊橋樑為例

為了解決Free vector的問題，作者曾揚 這樣論述：

非線性增量動力分析（Incremental Dynamic Analysis）是一個用以評估結構耐震性能的方法，該方法透過將地動記錄的強度縮放到不同尺度對結構進行模擬實驗，以獲得強度與結構行為的關係作為評估結構耐震性能的參考。然而，由於非線性增量動力分析耗費的分析成本極高，故通常用於分析的地動記錄數量不多，也因此使得繪製易損性函數（Fragility Function）時可能存在著取樣誤差。且當結構性能產生變化，如預應力損失等，會影響結構行為，需要花費高成本來重新進行分析。故本研究以後拉式預應力預鑄混凝土節塊橋梁為例，嘗試使用深度學習模型來預測結構物的耐震行為，並使用此結果繪製易損性函數來評

估結構的耐震性能，與非線性增量動力分析繪製的結果做比較，評估此方法的可行性。本研究以監督式學習的方式訓練模型，為了獲得監督式學習的訓練數據，以建模軟體(ABAQUS)建構離散有限元素模型，使其承載震波並進行非線性增量動力分析。藉由非線性增量動力分析的位移量製作標籤，並提取地動記錄的特徵及預拉應力的改變倍率作為深度學習模型的輸入資料，進行模型訓練與優化，期望使模型可以延伸使用數值分析的結果來增加耐震性能的評估準確性及降低成本。

Sustaining Empire: Venezuela’’s Trade with the United States During the Age of Revolutions, 1797-1828

為了解決Free vector的問題，作者Pompeian, Edward P. 這樣論述：

Why did trade with the United States prolong Spanish colonial rule during the Venezuelan independence struggles?From 1790 to 1815, much of the Atlantic World was roiled by European imperial wars. While the citizens of the United States profited from the waste of blood and treasure, Spanish Americ

an colonists struggled to preserve their prosperity on an imperial periphery. Along the Caribbean coast of South America, colonial elites and officials fought to secure Venezuela from threats of foreign invasion, slave rebellion, and revolution. For these elites, trading with the United States and o

ther neutral nations was not a way to subvert colonial rule but to safeguard the prosperity and happiness of loyal subjects of the Spanish Crown. Food insecurity, deprivation, and political uncertainty left Venezuela vulnerable to revolution, however.In Sustaining Empire, Edward P. Pompeian lets rea

ders see liberal free trade just as colonial Venezuelans did. From the vantage point of the slave-holding elite to which revolutionary figures like Simón Bolívar belonged, neutral commerce was a valuable and effectual way to conserve the colonial status quo. But after Spain’s crisis of sovereignty i

n 1808, it proved an impediment to Venezuelan independence. Analyzing the diplomatic and economic linkages between the new US republic and revolutionary Latin American governments, Pompeian reminds us that the United States did not, and does not, exist in a vacuum, and that the historic relationship

s between nations mattered then and matters now.Examining an overlooked region, Pompeian offers a novel interpretation of early United States relations with Latin America, showing how US merchants executed government contracts and established flour, tobacco, and slave trading monopolies that facilit

ated the maintenance of colonial rule and the Spanish Empire. Trading with the United States, Pompeian argues, kept both colony and empire under a tenuous hold despite revolutionary circumstances. A fascinating revisionist history, Sustaining Empire challenges long-standing assertions that this comm

erce served primarily as a vector for the one-way transmission of revolutionary, liberal ideas from the North to South Atlantic.

以機器學習輔助之進化演算法 實現考量參數變異的快速類比電路尺寸調整方法

為了解決Free vector的問題，作者郭東杰 這樣論述：

進化演算法被廣泛應用於各種優化問題，因其高準確度和對不同電路的強適應性，相當適合被應用在類比電路尺寸設計上。然而，若在電路尺寸設計中考慮製程變異的影響，將會大量增加電路模擬次數，使其無法被應用於大規模電路上。儘管最近的一些相關研究採用了機器學習技術來加速優化過程，但很少有人在他們的方法中考慮製程變異的影響。在本篇論文中，我們提出了一種應用於類比電路尺寸設計的進化演算法，可以快速地考慮製程變異對良率影響。透過機器學習模型，我們能夠在進行模擬前初步預測新電路樣本的效能好壞，並過濾掉表現可能較差的新電路樣本，節省許多不必要的模擬時間，加快收斂的速度。此外，我們也提出了一種新的類力學模型來引導演算法

優化良率。基於先前過程中的電路樣本，所提出的類力學模型可以預測設計是否具有更好的良率，而無需執行耗時的蒙特卡羅分析。與先前的研究相比，我們所提出的方法顯著減少了進化演算法過程的模擬次數，有助於產生具有高可靠性和低成本的實用設計。相同的概念也可以用在類比電路遷移，大幅縮短改變製程時的尺寸再優化時間。從幾個類比電路的實驗來看，我們的方法確實非常有效率。