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Our Cool Run to Sturgis 2017: Let it go with the flow ...

為了解決evo 10規格的問題，作者Schulz, Lothar R. 這樣論述：

The author, Lothar R. Schulz, was born in 1967 in a small town of Ettlingen near Karlsruhe, Germany, where he also grew up. After training as a mechanical engineer and subsequently continuing education as a quality manager and technical business economist, he has been working for over 10 years with

an international automotive supplier. As an American friend for the ＂cool way-of-life＂ and an avid big-twin fan, in 2017, he was drawn to the biggest bike event in the world -Sturgis, South Dakota ... The author himself rode a customized 1987 HD Evo and wants to present and an entertaining, informat

ive, as well as tingling travelogue with his ＂first work＂, in the language of unmistakable pictures, which should also be fun ...!

evo 10規格進入發燒排行的影片

觸控使用者介面改善設計研究

為了解決evo 10規格的問題，作者呂昭儒 這樣論述：

電子產品介面隨著數位產品的普及，數位平板電腦等相關產品興起，整體設計水準整體的提高，需要更加深入的使用者介面設計。觸控式智慧型手機與電腦的App需專注產品呈現，包括使用者與系統之間的溝通介面，產品本身的介面設計愈顯重要，不僅業者注重開發歷程，使用者對於產品品質與需求也隨之提高，觸控式介面設計的模式需考量使用者認知，以達成直覺式的介面操作特性，確立視覺體驗的統一。　　本研究運用圖像認知機制與人們行為心理，歸納Nilson與Susan Weinschenk所提出的易用性介面設計原則，參考心理學知識讓UI呈現更直覺及更貼近人性思維，考慮「產品用起來的感覺」應用於觸控式介面設計。透過設計原則進行AP

P Inventor案例之介面重新設計與POP原型操作測試，檢視原則運用之成效。企業案例藉由Qt designer實作雷射照射儀介面設計，使用高擬真度產品原型，並帶入使用者測試，觀察使用者的使用過程，依循線索修正現有問題，並在設計的前期階段，針對遺漏或誤解的使用者需求加以改進，將設計稿移交給工程師，記錄相關的溝通技巧，以使用者導向的角度讓UI呈現更貼近人。結合易用性原則與企業設計策略，規劃設計符合需求之觸控式介面設計，讓產品更符合與貼近實際使用的狀態，最後以介面的操作測試進行最終修正，達到預期且理想的效果，並歸納案例延伸之設計原則：「安全性為首要原則」重視操作流程的正確性，模糊的辨別方式將容易

導致錯誤的認知操作；「醫療類型產品的色彩應用原則」醫療類別的色彩應用與一般應用程式設計具有差異性，社會文化帶來的觀感與認知讓部分色彩具有特殊意義。

Domesticated: Evolution in a Man-Made World

為了解決evo 10規格的問題，作者Francis, Richard C. 這樣論述：

We would still be living at subsistence level as hunter-gatherers if not for domestication. It is no accident that the cradle of civilization--the Middle East--is where sheep, goats, pigs, cattle, and cats commenced their fatefully intimate association with humans.Before the agricultural revoluti

on, there were perhaps 10 million humans on earth. Now there are more than 7 billion of us. Our domesticated species have also thrived, in stark contrast to their wild ancestors. In a human-constructed environment--or man-made world--it pays to be domesticated.Domestication is an evolutionary proces

s first and foremost. What most distinguishes domesticated animals from their wild ancestors are genetic alterations resulting in tameness, the capacity to tolerate close human proximity. But selection for tameness often results in a host of seemingly unrelated by-products, including floppy ears, sk

eletal alterations, reduced aggression, increased sociality, and reduced brain size. It's a package deal known as the domestication syndrome.Elements of the domestication syndrome can be found in every domesticated species--not only cats, dogs, pigs, sheep, cattle, and horses but also more recent hu

man creations, such as domesticated camels, reindeer, and laboratory rats. That domestication results in this suite of changes in such a wide variety of mammals is a fascinating evolutionary story, one that sheds much light on the evolutionary process in general.We humans, too, show signs of the dom

estication syndrome, which some believe was key to our evolutionary success. By this view, human evolution parallels the evolution of dogs from wolves, in particular.A natural storyteller, Richard C. Francis weaves history, archaeology, and anthropology to create a fascinating narrative while seamle

ssly integrating the most cutting-edge ideas in twenty-first-century biology, from genomics to evo-devo.

以協同演化演算法求解單目標大規模全域最佳化問題

為了解決evo 10規格的問題，作者周彥辰 這樣論述：

隨著科學技術的進步及大數據的時代來臨，我們面臨的最佳化問題越來越龐大，變數也越來越多，甚至多達上千個；隨著最佳化問題的維度增加，大部分演化演算法的性能將因此迅速惡化而陷入高維度災難。因此，近年來有越來越多的演化計算領域學者投入大規模全域最佳化問題的研究並應用於求解生活中的實際問題。本研究結合目前兩大主流應用於大規模單目標實數最佳化的方法—協同演化框架 (CC) 和SHADE演算法，提出CBCCLS-mSHADE-RDG3 演算法。從大量的參數調整實驗到演算法行為設計與驗證，一步一步將CC與SHADE演算法結合。於CC框架下，我們採用RDG3 演算法做為問題分解策略；使用CBCC3 進行計算資

源的分配，給予對整體適應值貢獻度高的子族群更多必須的計算資源提升演算法效能；以改良版 mSHADE演化子族群。另外，我們提出一個新穎的設計，於 CBCC3 架構下，對適應值貢獻度高的子族群除了使用mSHADE演算法進行演化外，我們以額外區域搜尋演算法MTS-LS1 協助提升最佳解之品質。此獨特設計從實驗結果驗證得知，不僅可以穩定LSGO問題解的品質，更可以提升求解部分可疊加分解問題的效能，整體演算法表現與近兩年的LSGO比賽優勝演算法相比頗具競爭力。