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Blade of a Sword: Ernst J�nger and the 73rd Fusilier Regiment on the Western Front, 1914-18

為了解決1914的問題，作者Strohn, Matthias 這樣論述：

Dr Matthias Strohn was educated at the Universities of Münster (Germany) and Oxford. He has lectured at Oxford University and the Joint Services Command and Staff College at Shrivenham. Since 2006 he has been a lecturer in the Department of War Studies at the Royal Military Academy Sandhurst, and in

2011 he was also made a Senior Research Fellow at the University of Buckingham. He holds a commission in the German army and is currently a member of the military attaché reserve. He lives in Surrey, UK.Dr Strohn has published widely on 20th-century German and European military history and is an ex

pert on the German army in World War I and the inter-war period. He has advised British and German government bodies on the World War I centenary commemorations.

1914進入發燒排行的影片

基於積分同步學習之多軸無人機參數估測與控制

為了解決1914的問題，作者楊承澄 這樣論述：

在本篇論文中，基於積分同步學習 (ICL) 之多軸無人機參數估測與控制演算法被完整開發，此控制演算法將被用來同時控制無人機與估測未知的無人機質量與轉動慣量。據我們所知，這是首個將同步學習應用於估測無人機之質量與轉動慣量上的研究，此控制演算法搭配幾何追蹤控制提供無人機迴授控制。在此研究中，由於無人機的動態是在全域的空間上定義，因此開發出來的 ICL 控制器可以確保全域的狀態誤差收斂與參數估測誤差收斂。此外，此控制架構可以被應用在任何未知質量與轉動慣量的無人機上。經由穩定性證明，狀態誤差與參數估測誤差會以漸近的形式收斂。ICL 控制器的性能與效率將會經由模擬與實驗結果展現。

Both Sides of the Wire - Disaster at Dawn: Somme 1916: Preliminaries and First Moves

為了解決1914的問題，作者Cave, Nigel/ Sheldon, Jack 這樣論述：

Both Sides of the Wire is the first in a new series of battlefield guides that looks at the fighting on the Western Front, chiefly in the sectors in which the British Expeditionary Force was engaged. In these books, Nigel Cave and Jack Sheldon will look at engagements from both the allied and Ger

man perspective; at the end of each chapter there will be a tour section so that readers can place themselves in the best vantage points to follow the action that is described on the ground and which is directly related to the narrative account that will form the bulk of each chapter. This book deal

s with the Somme up to and including the first day or two of the infantry assault on 1 July. In addition to the familiar British sector, the authors intend to cover those parts of the Somme battlefield that were (largely) fought over by the French. Although there is much to see of the 'French' Secto

r, it is generally poorly served by published guides despite the fact that there are numerous signs of the Great War of the ground, many accessible to the public. With the concentration on 1st July and the subsequent months, there is relatively little about what happened on the Somme from the establ

ishment of the line there in October 1914 to the summer 1916 offensive. For example, this involved extensive mine warfare, of which very few traces remain, but for which there is excellent contemporary mapping and some useful accounts. Because of the considerable amount of literature already availab

le on the British sector of the Somme, the authors will be concentrating on particular aspects - areas selected include: Gommecourt (56th London Division); Serre (the French attacks of June 1915); the Heidenkopf; raids prior to I July around Beaumont Hamel; the Schwaben Redoubt, the German defence p

lan and what happened on the day; the Glory Hole and the fighting there from December 1914; the area around King George's Hill, near Fricourt; the attacks and counter attacks at Montauban on 1 - 3 July 1916; a very successful limited German assault at Feuilleres in January 1916; the line at Dompierr

e and Fey, in particular mine warfare (and also some coverage of the brief tenure of the line in this area by the British in the autumn of 1915). The book is aimed at anyone with an interest in the war, in the Somme in particular and, whilst acting as a guide, it will also be of value to those who c

annot get to the Somme themselves, with the authors aiming at a more balanced understanding of what happened and explaining the outcomes at the various locations.

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1914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。