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Porsche - Silver Steeds: Porsche Racing: A Dedication 1948 to 1965

為了解決Benz UK的問題，作者Smith, Roy 這樣論述：

Roy Smith is a retired company director who has a lifelong passion for motorsport, and, since 1968, Alpine Renault in particular. He began writing in the 1960s for club magazines, mainly interviews with the notables of the time. A 40-year business career in sales and marketing developed an attention

for detail that was required for the preparation of his published works Alpine & Renault - the sports prototypes (two volumes), Alpine & Renault- the development of the first Turbo Formula 1 cars and AMEDEE GORDINI - a true racing legend. As a journalist he has written many historic motorsport and

Alpine Renault-related articles for various magazines in Europe and USA. He is the UK historic correspondent for the French magazine Retro Passion and the Alpine Renault magazine, Mille Miles. He also contributes to several websites. His aim is to provide the newcomer and enthusiasts alike with a ＂g

ood read,＂ as well as being technically informative. Widely acclaimed in reviews, he was presented with the Guild of Motoring Writers Mercedez Benz award and the Montagu of Beaulieu Trophy in 2010.

Benz UK進入發燒排行的影片

基於目的地導向之道路潛在危險社交行為預測

為了解決Benz UK的問題，作者林明志 這樣論述：

本論文主要開發一套基於目的地導向之道路潛在危險社交行為預測，如行人或車輛無預期性的突然闖入車道、行人不遵守道路規則橫跨馬路等道路危險情境，藉由所發展的深度學習演算策略預測動態物件的短期軌跡，以進一步達到駕駛安全預警輔助系統之功效。首先，為了提取道路環境中動態物件一小段連續時間的辨識結果，故本論文主要是採用深度學習模型進行物件辨識，並於辨識後使用件追蹤演算法，以確保獲得的邊界框為同一行人、四輪車輛或者兩輪車輛。接著我們發展一套基於目的地導向之社交行為預測模型，並搭配自我迴歸訓練策略，以實現物件彼此之間的社交軌跡預測，其中該網路模型主要分成五大部分 (1)特徵提取器；(2)編碼器；(2)目的地導

向預測器；(3)條件變分自動編碼器；(4)解碼器。首先，透過特徵提取器由輸入資訊中提取動態物件與自車彼此間的距離、動態物件速度、動態物件軌跡以及自車的狀態等時序特徵。接著，輸入至編碼器中進行編碼，此編碼器主要由長短期記憶與多頭自注意力機制組成，分別針對目標物件的時序特徵以及社交關係進行編碼。接著，目的地導向預測器則是透過長短期記憶與多頭自注意力機制先行預測未來軌跡，並分別向前回饋給編碼器以輔助特徵編碼生成；同時向後輸出至後續的條件變分自動編碼器，以用來輔助最終的軌跡預測結果。第三部分為條件變分自動編碼器將未來軌跡做為條件，生成符合條件的未來軌跡多模態(multimodal)分佈。最終透過基於多

頭自注意力機制的解碼器，有效預測出更準確的軌跡路徑。最後本文主要是採用TITAN公開資料庫，以進行本文所發展的演算模型驗證與量化分析。經實驗結果發現，本文所提方法其預測軌跡的平均位移誤差(ADE)能有效改善5%、最終位移誤差(FDE)更能有效改善21%，同時最終交並比(FIOU)也提升9%。

Ecological, Societal, and Technological Risks and the Financial Sector

為了解決Benz UK的問題，作者 這樣論述：

Thomas Walker is Professor of Finance at Concordia University, Canada, and previously studied at Washington State University, USA.Prior to academia, he worked for several years in the German consulting and industrial sector at Mercedes Benz, Utility Consultants International, Lahmeyer International,

Telenet, and KPMG Peat Marwick.Dieter Gramlich is Professor of Banking & Finance at DHBW - Baden-Wuerttemberg Cooperative State University in Heidenheim, Germany, where he serves as Head of the Banking Department. He previously studied at the University of Mannheim and was an interim professor and

Chair of Banking and Finance at the University of Halle in Germany.Mohammad Bitar is Assistant Professor of Finance at Nottingham University Business School, UK, where he teaches banking and FinTech courses. Previously, Mohammad taught at the John Molson School of Business at Concordia University, C

anada, and studied Finance at Grenoble Alps University, France.Pedram Fardnia is a PhD candidate and staff researcher at the John Molson School of Business at Concordia University, Canada. Among others things, his research focuses on corporate governance issues within the finance sector, with a spec

ific focus on the aviation industry.

一套運用在全球汽車產業的永續製造績效評估之多準則決策方法

為了解決Benz UK的問題，作者阮氏庄 這樣論述：

由於受 COVID-19 疫情的影響，世界經濟受到了嚴重影響。生產“新常態”的生產策略對於平衡社會、經濟和環境至關重要。特別是可持續生產問題已受到全球許多製造業的關注，特別是汽車行業。本研究提出了一套結合 Malmquist 生產力指數 (MPI) 和序數優先方法 (OPA) 的混合模型，用於分析20 家製造商的表現，並選出可永續生產的製造商。以2016-2020年期間世界領先的汽車生產商為例。用MPI評估技術效率、技術進步和整體生產力因素。並根據 MPI 的結果，應用 OPA 方法在選定的公司中選擇最可永續的生產企業。研究結果顯示，豐田、特斯拉和上汽是可永續生產前三名的製造商。儘管本文只研

究了汽車行業，但提出的方法和結果可以為其他行業提供很好的參考和應用。