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Formula 1 2020/2021: Technical Insights (Preview 2022)

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為了解決F1 2021的問題，作者Filisetti, Paolo 這樣論述：

The 2020 and 2021 Formula 1 seasons are tightly intertwined. The second, in fact, saw a form of technological status quo caused by the pandemic which ensured the technical revolution focussing on ground effects was postponed by a year and will underpin the 2022 cars. Within a context of absolute reg

ulatory stability, the 2020 championship, which was completed over the course of just a little over five months, with races repeated on the same circuits within a week of one another, nonetheless saw developments on various cars. However, it was 2021 that proved to be one of the most competitive of

seasons on a technical level, with the introduction of a number of features by all the teams. Once again Paolo Filisetti will be analysing the technical evolution of the Formula 1 cars during the course of these two seasons through hundreds of drawings accompanied by painstakingly accurate technica

l/sporting texts. Born in Milan, Paolo Filisetti is a journalist and an authoritative technical commentator. He has cultivated a passion for motorsport since childhood. His first complete season following the F1 circus was in 1997, the year he began working for RAI, the principal Italian TV broadc

aster, as a technical analyst reporting from the various circuits. There followed years in which he contributed to Italian daily newspapers and Italian and international periodicals. He currently writes for some of the leading specialist motorsport titles.

F1 2021進入發燒排行的影片

使用 Azure 實現預知保養系統架構-以 TFT-LCD 廠為 例

為了解決F1 2021的問題，作者張翔淨 這樣論述：

以往設備維護的方式是設備壞了才修，以此降低維護成本，又或是計畫性維修，維修人員依照過往經驗，到了機器運行的一定次數或是時間來定期更換，但這樣的方式無法考量到環境及不同元件造成的差異，仍會造成設備損壞，而非預期的停機，讓不管是產能還是維修等費用都大大的損失。工業 4.0 的興起帶起全球邁向智慧製造，製造業結合物聯網、大數據及 AI 等技術，讓現在設備維護的工作可以透過收集機器的電流、溫度及其他機台參數資訊，進一步進行數據分析來做到機台的預知保養，提早進行機台保養、維修，避免非預期的停機，影響產線運行。本論文將以 TFT-LCD 面板零組件製造業作為實驗場域，實作透過 Azure 雲端服務平台來

建置 TFT-LCD 機台預知保養系統，透過皮爾森相關性等分析，找到適合本實驗場域使用的參數，利用 PySpark 提高資料處理的速度，並利用分區方式優化資料表，Operator Cost、I/O Cost 和 CPU Cost 分別提升了 98.77%、98.78% 和 98.74%，且在面對不同機台數據會有差異的情況下，每一個機台建置一個隨機森林模型來進行數據的分析，模型準確率為 0.99，且將模型部屬至 Azure Kubernetes 來進行即時的評分，最後也將數據以及模型分析結果視覺化，讓工廠的維修人員能夠透過數據以及分析結果來調整製程參數、提早了解機台健康狀況，達到預知保養的工作。

Autocourse 2021-2022: The World’’s Leading Grand Prix Annual

為了解決F1 2021的問題，作者 這樣論述：

71st YEAR - First published in 1951 - The longest running motor racing yearbook. Independent and authoritative editorial combined with the sport’s finest photography Despite the pandemic, AUTOCOURSE celebrated its 70th year of publication in 2020, a year turned on its head by Covid-19. Thanks to

the FIA and racing’s management, F1 was rescued, with a compelling 17-race championship spanning Europe and the Middle East. For 2021 a record 22-race schedule was planned, and despite changes to venues, the F1 championship was very much up and running by March 2021. Mercedes and Lewis Hamilton -

now the winningest driver in history - broke more records. The Briton smashed through the 100 pole-position barrier, and moved inexorably toward a century of Grand Prix wins in the face of a real championship challenge from Max Verstappen. Red Bull Racing and Honda seemed to have all but matched M

ercedes to provide the fans with a thrilling wheel-to-wheel battle, which would end up with some controversial collisions as the season reached boiling point. Authors Tony Dodgins and Maurice Hamilton, combining almost 80 years of F1 expertise, examine each round in depth. Full race reports are bac

ked by detailed results, including lap charts and tyre strategies. The nuances of F1’s designs and development are analysed team by team by the much-respected Mark Hughes, enhanced by Adrian Dean’s handsome F1 car illustrations. Motor racing’s other major categories are also fully covered: Toyota’

s WEC and Le Mans sports car successes; the closely-fought F2 and Formula 3 championships, featuring emerging young talent from around all continents the world; and Nick de Vries emerging victorious in the tight fought Formula E series for electric powered single seaters. AUTOCOURSE includes all th

e hectic action from the top Touring Car series - the World Touring Car Cup and the British Touring Car Championships as well as the reconfigured DTM Series now running GT cars from Audi, Mercedes, BMW and Ferrari. From America, Gordon Kirby recounts a thrilling Indycar series, featuring a mix of y

outhful talent such as Alex Palou and Pato O’Ward, both of who were vying to overturn the established veterans who have dominated proceedings over the past decade. In an emotional return, Helio Castroneves took record equalling fourth Indy 500 victory, whilst an appreciation is made to paid to the l

egendary three-time Indy 500 winner the late Bobby Unser. The ever-popular NASCAR stock car series ran from February to November with barely a weekend’s break, to feature more than forty races before the final championship play-off round at Phoenix, Arizona. In a single essential volume, AUTOCOURSE

provides the most comprehensive record of world motor sport, complete with full results not found anywhere in a single volume. It is required reading for all motor sport fans worldwide

植基於支援向量機模型之良品預測—以石英振盪器銲線製程為例

為了解決F1 2021的問題，作者黃俊傑 這樣論述：

銲線製程於石英振盪器的封裝流程屬於前段製程，銲線品質的優劣會直接對電子產品電子訊號的傳輸、阻抗干擾等造成影響，且銲線製程所造成的報廢無法再次重工，故該製程對石英振盪器十分重要。而在銲線製程中的金球球厚、金球球徑、金線弧高則是銲線品質判定的其中幾個重要關鍵因子，目前業界普遍使用放大倍數較高的電子顯微鏡由品管人員人工量測再進行判斷，但因人員量測手法有些微差異或是測量過多而使人員產生視覺疲勞、或注意力分散等因素而產生誤判。本研究使用支援向量機(Support Vector Machine, SVM)進行品管的分類預測，分類模型的應變數為品管分類(即良品與不良品)，自變數為金球球厚、金球球徑與金線弧

高。本研究實驗的結果顯示，以支援向量機模型為基礎的石英振盪器品管分類模型，透過 70/30訓練資料與測試資料進行模型的訓練與測試後，其Recall、F1-Score、Precision評估本研究所提出之分類模型之精準度，可提供準確的石英振盪器品管分類預測。