R line color的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到附近那裡買和營業時間的推薦產品

Work That Makes Sense: Operator-Led Visuality, Second Edition

為了解決R line color的問題，作者Galsworth, Gwendolyn D. 這樣論述：

Gwendolyn D. Galsworth, PhD, is president/founder of Visual Thinking Inc. and The Visual‐Lean Institute(R) (collectively ＂VTI＂). Dr. Galsworth formed her company in 1991 as a consulting, training, and research firm, specializing in the technologies of the visual workplace. Since then, she has focuse

d on codifying the field of visuality into a single coherent framework of thinking and application.In 2005, Dr. Galsworth established the Visual‐Lean(R) Institute where inhouse trainers and external consultants are trained and licensed in nine core visual workplace courses--including Visual Machine(

R), Visual‐Lean(R) Office, and Visual Leadership. In this way, they can train and implement these in their own companies (or with their clients) and achieve a fully‐functioning, self‐sufficient visual enterprise. VTI conducts seminars, workshops, and train‐the‐trainer events in the USA and around th

e world. A former Baldrige and Shingo Prize examiner (eight years), Dr. Galsworth helps companies all over the world to accelerate their rate of visual transformation, strengthen cultural alignment, and achieve long‐term, sustainable bottom‐line outcomes. Her clients include: Lockheed‐Martin, Hamilt

on Standard, Pratt & Whitney, Hitchcock Industries, Royal Nooteboom Trailers/Holland, Crown Equipment/Mexico, Trailmobile/Canada, Parker Denison, Crown/Mexico, Rolls‐Royce/UK, TVS Sundaram Clayton/India, Crompton Greaves/India, Sears Home Services, United Electric Controls, and Wilson Transformer /A

ustralia. She is a former Malcolm Baldrige and Shingo Prize Examiner.Dr. Galsworth and her team are continually transforming their research--new knowledge and knowhow-- into improved products to assist you on your journey to workplace visuality. We recently launched a new platform of fully‐narrated

online training systems on a range of topics, including Work That Makes Sense (English/Spanish); and, in collaboration with Dr. Martin Hinckley (author/Make No Mistake), Mistake‐Proofing for Perfect Quality.Recognizing the fit between The Shingo Prize and workplace visuality, Robert Miller, former e

xecutive director of The Prize, put it this way: ＂The visual workplace, as taught by Gwendolyn Galsworth, is in perfect harmony with the model of Operational Excellence represented by The Shingo Prize. The concepts, tools, and methods of visuality are essential as we pursue other guiding principles

of seeking perfection, quality at the source, and the continuous flow of value to customers.＂Dr. Galsworth is a Teaching Fellow on the faculty of the Shingo Institute and author of seven books on workplace visuality and strategic improvement. Her ground‐breaking book, Work That Makes Sense: Operator

‐Led Visuality, contains over 500 full‐color visual solutions and provides a step‐by-step process that operators follow to convert their work areas to visuality that is sustainable. Galsworth’s book, Visual Workplace‐Visual Thinking, provides a complete foundation in visual principles and practices

and her 10‐ Doorways model. Both books are winners of the prestigious Shingo Prize.Her book on the cost of complexity, Smart Simple Design/Reloaded, takes a hard look at the costs and challenges of product diversification. In it, she maps out the Variety Effectiveness Process (VEP), a proven method

for de‐complicating the organization by simplifying product architecture and dismantling layers of imbedded complication that choke a company’s infrastructure and product design process. Once VEP is effectively applied, the need for both visual and lean is significantly less urgent. Galsworth’s book

s are available globally from our website (visualworkplace.com) and on Amazon, in print and on Kindle.Dr. Galsworth’s career in operational excellence began as the head of training and development at Productivity Inc. in the early 1980s. There she worked closely with Dr. Ryuji Fukuda to adapt the CE

DAC(R) method for western audiences--and with Dr. Shigeo Shingo to develop, among many things, poka‐yoke as an implementation methodology for the West. While there, she was also principal developer and implementer of Visual Factory, TEIAN (operator‐led suggestion systems), and Hoshin Kanri/X‐Type Ma

trix Planning (policy deployment).Gwendolyn holds a Ph.D. from Indiana University, has led study missions to some of the world’s finest companies, including in Japan, and is a frequent keynote speaker. Podcasts from her weekly radio show, Visual Workplace Radio, and over 100 articles from The Visual

Thinker Newsletter can be found on her website, www.visualworkplace.com Dr. Galsworth now lives in New England where she happily works, hikes, and writes. She can be reached at 503‐233‐1784 or through visualworkplace.com.

R line color進入發燒排行的影片

調控高分子給體二維共軛側鏈與設計共軛中心核與pi-架橋小分子受體結構與性質之系統性研究

為了解決R line color的問題，作者陳重豪 這樣論述：

此研究中，我們通過引入具有（苯並二噻吩）-（噻吩）（噻吩）-四氫苯並惡二唑（BDTTBO）主鏈的新型供體-受體（D/A）共軛聚合物製備了用於有機光伏（OPV）的三元共混物。在BDTTBO單體中BDT供體單元上修飾不同的共軛側鏈聯噻吩 (BT)、苯並噻吩 (BzT) 和噻吩並噻吩 (TT)（記為 BDTTBO-BT、BDTTBO-BzT 和 BDTTBO-TT）。然後，我們將 BDTTBO-BT 或 BDTTBO-BzT 或 BDTTBO-TT 與聚（苯並二噻吩-氟噻吩並噻吩）（PTB7-TH）結合起來，以擴大太陽光譜的吸收並調整活性層中 PTB7-TH 和富勒烯的分子堆積，從而增加短路電流密

度。我們發現參入10%的BDTTBO-BT高分子以形成 PTB7-TH：BDTTBO-BT：PC71BM 形成三元共混物元件活性層可以將太陽能元件的功率轉換效率從 PTB7-TH 的二元共混物元件 9.0% 提高到 10.4%： PC71BM 轉換效率相對增長超過 15%。於第二部分，我們比較在BDTTBO單體中BDT供體單元上修飾硫原子或氯原子 取代和同時修飾硫原子和氯原子取代的側鏈聚合物供體與小分子受體光伏的功率轉換效率 (PCE) 的實驗結果與由監督產生的預測 PCE。使用隨機森林算法的機器學習 (ML) 模型。我們發現 ML 可以解釋原子變化的聚合物側鏈結構中的結構差異，因此對二元共混

系統中的 PCE 趨勢給出了合理的預測，提供了系統中的形態差異，例如分子堆積和取向被最小化。因此，活性層中分子取向和堆積導致的結構差異顯著影響 PCE 的預測值和實驗值之間的差異。我們通過改變其原始聚合物聚[苯並二噻吩-噻吩-苯並惡二唑] (PBDTTBO) 的側鏈結構合成了三種新的聚合物供體。同時修飾硫原子和氯原子取代的側鏈結構用於改變聚合物供體的相對取向和表面能，從而改變活性層的形態。 BDTSCl-TBO:IT-4F 器件的最高功率轉換效率 (PCE) 為 11.7%，與使用基於隨機森林算法的機器學習預測的 11.8% 的 PCE 一致。這項研究不僅提供了對新聚合物供體光伏性能的深入了解

，而且還提出了未明確納入機器學習算法的形態（堆積取向和表面能）的可能影響。於第三部分，為了理解下一代材料化學結構的設計規則提高有機光伏（OPV）性能。特別是在小分子受體的化學結構不僅決定了其互補光吸收的程度，還決定了與聚合物供體結合時本體異質結 (BHJ) 活性層的形態。通過正確選擇受體實現優化的OPV 元件性能。在本研究中，我們選擇了四種具有不同共軛核心的小分子受體——稠環核心茚二噻吩、二噻吩並茚並茚二噻吩（IDTT）、具有氧烷基-苯基取代的IDTT稠環核心、二噻吩並噻吩-吡咯並苯並噻二唑結構相同的端基，標記為 ID-4Cl、IT-4Cl、m-ITIC-OR-4Cl 和 Y7，與寬能帶高分子

PTQ10 形成二共混物元件。我們發現基於 Y7 受體的器件在所有二元混合物器件中表現出最好的光伏性能，功率轉換效率 (PCE) 達到 14.5%，與具有 10.0% 的 PCE 的 ID-4Cl 受體相比，可以提高 45%主要歸因於短路電流密度 (JSC) 和填充因子 (FF) 的增強，這是由於熔環核心區域中共軛和對稱梯型的增加，提供了更廣泛的光吸收，誘導面朝向並減小域尺寸。該研究揭示了核心結構單元在影響有源層形態和器件性能方面的重要性，並為設計新材料和優化器件提供了指導，這將有助於有機光伏技術的發展。最後，我們比較了具有 AD-A´-DA 結構的合成小分子受體——其中 A、A´ 和 D 分

別代表端基、核心和 π 價橋單元—它們與有機光伏聚合物 PM6 形成二共混物元件。 增加核苝四羧酸二亞胺 (PDI) 單元的數量並將它們與噻吩並噻吩 (TT) 或二噻吩吡咯 (DTP) π 橋單元共軛增強了分子內電荷轉移 (ICT) 並增加了有效共軛，從而改善了光吸收和分子包裝。 hPDI-DTP-IC2F的吸收係數具有最高值（8 X 104 cm-1），因為它具有最大程度的 ICT，遠大於 PDI-TT-IC2F、hPDI-TT-IC2F和 PDI-DTP-IC2F。 PM6:hPDI-DTP-IC2F 器件提供了 11.6% 的最高功率轉換效率 (PCE)；該值是 PM6:PDI-DTP-

IC2F (4.8%) 設備的兩倍多。從一個 PDI 核心到兩個 PDI 核心案例的器件 PCE 的大幅增加可歸因於兩個 PDI 核心案例具有 (i) 更強的 ICT，(ii) 正面分子堆積，提供更高的和更平衡的載波遷移率和 (iii) 比單 PDI 情況下的能量損失更小。因此，越來越多的 PDI 單元與適當的髮色團共軛以增強小分子受體中的 ICT 可以成為提高有機光伏效率的有效方法

Work That Makes Sense: Operator-Led Visuality, Second Edition

為了解決R line color的問題，作者Galsworth, Gwendolyn D. 這樣論述：

Gwendolyn D. Galsworth, PhD, is president/founder of Visual Thinking Inc. and The Visual‐Lean Institute(R) (collectively ＂VTI＂). Dr. Galsworth formed her company in 1991 as a consulting, training, and research firm, specializing in the technologies of the visual workplace. Since then, she has focuse

d on codifying the field of visuality into a single coherent framework of thinking and application.In 2005, Dr. Galsworth established the Visual‐Lean(R) Institute where inhouse trainers and external consultants are trained and licensed in nine core visual workplace courses--including Visual Machine(

R), Visual‐Lean(R) Office, and Visual Leadership. In this way, they can train and implement these in their own companies (or with their clients) and achieve a fully‐functioning, self‐sufficient visual enterprise. VTI conducts seminars, workshops, and train‐the‐trainer events in the USA and around th

e world. A former Baldrige and Shingo Prize examiner (eight years), Dr. Galsworth helps companies all over the world to accelerate their rate of visual transformation, strengthen cultural alignment, and achieve long‐term, sustainable bottom‐line outcomes. Her clients include: Lockheed‐Martin, Hamilt

on Standard, Pratt & Whitney, Hitchcock Industries, Royal Nooteboom Trailers/Holland, Crown Equipment/Mexico, Trailmobile/Canada, Parker Denison, Crown/Mexico, Rolls‐Royce/UK, TVS Sundaram Clayton/India, Crompton Greaves/India, Sears Home Services, United Electric Controls, and Wilson Transformer /A

ustralia. She is a former Malcolm Baldrige and Shingo Prize Examiner.Dr. Galsworth and her team are continually transforming their research--new knowledge and knowhow-- into improved products to assist you on your journey to workplace visuality. We recently launched a new platform of fully‐narrated

online training systems on a range of topics, including Work That Makes Sense (English/Spanish); and, in collaboration with Dr. Martin Hinckley (author/Make No Mistake), Mistake‐Proofing for Perfect Quality.Recognizing the fit between The Shingo Prize and workplace visuality, Robert Miller, former e

xecutive director of The Prize, put it this way: ＂The visual workplace, as taught by Gwendolyn Galsworth, is in perfect harmony with the model of Operational Excellence represented by The Shingo Prize. The concepts, tools, and methods of visuality are essential as we pursue other guiding principles

of seeking perfection, quality at the source, and the continuous flow of value to customers.＂Dr. Galsworth is a Teaching Fellow on the faculty of the Shingo Institute and author of seven books on workplace visuality and strategic improvement. Her ground‐breaking book, Work That Makes Sense: Operator

‐Led Visuality, contains over 500 full‐color visual solutions and provides a step‐by-step process that operators follow to convert their work areas to visuality that is sustainable. Galsworth’s book, Visual Workplace‐Visual Thinking, provides a complete foundation in visual principles and practices

and her 10‐ Doorways model. Both books are winners of the prestigious Shingo Prize.Her book on the cost of complexity, Smart Simple Design/Reloaded, takes a hard look at the costs and challenges of product diversification. In it, she maps out the Variety Effectiveness Process (VEP), a proven method

for de‐complicating the organization by simplifying product architecture and dismantling layers of imbedded complication that choke a company’s infrastructure and product design process. Once VEP is effectively applied, the need for both visual and lean is significantly less urgent. Galsworth’s book

s are available globally from our website (visualworkplace.com) and on Amazon, in print and on Kindle.Dr. Galsworth’s career in operational excellence began as the head of training and development at Productivity Inc. in the early 1980s. There she worked closely with Dr. Ryuji Fukuda to adapt the CE

DAC(R) method for western audiences--and with Dr. Shigeo Shingo to develop, among many things, poka‐yoke as an implementation methodology for the West. While there, she was also principal developer and implementer of Visual Factory, TEIAN (operator‐led suggestion systems), and Hoshin Kanri/X‐Type Ma

trix Planning (policy deployment).Gwendolyn holds a Ph.D. from Indiana University, has led study missions to some of the world’s finest companies, including in Japan, and is a frequent keynote speaker. Podcasts from her weekly radio show, Visual Workplace Radio, and over 100 articles from The Visual

Thinker Newsletter can be found on her website, www.visualworkplace.com Dr. Galsworth now lives in New England where she happily works, hikes, and writes. She can be reached at 503‐233‐1784 or through visualworkplace.com.

基於FPGA單晶片及像素趨勢車道線檢測法實現車道線感測系統之研究

為了解決R line color的問題，作者廖國欽 這樣論述：

車輛自動駕駛系統目前主要是由自動跟車 (Adaptive Cruise Control, ACC) 以及車道偏離警示 (Lane Departure Warning System, LDWS) 兩大系統所組成。然而，自動跟車系統在實現過程中，由於必須藉由前方車輛實現車輛跟隨功能，因此若無前方車輛時則無法實現此功能。反觀車道偏離警示系統是依據車道線軌跡來幫助車輛保持於車道內，因此具備較高實用性。在此，本研究特別針對車道感測進行研究。由於傳統的車道線感測必須仰賴高效率的電腦才能有效地完成運算，為了克服傳統車道線辨識的缺點，本研究專注於如何將車道線辨識演算法簡化，並實現在單晶片上，達到低功耗之目的

。本研究以單一數位相機及單一現場可程式邏輯閘陣列 (Field Programmable Gate Array, FPGA) 實線以精簡之硬體電路達到即時於白天及黃昏情況下進行車道線辨識。透過像素趨勢車道檢測法 (Pixel Trend Lane Detection, PTLD) 擷取特徵，並將所得之車道位置利用線性回歸 (Linear Regression, LR) 決定車道線的軌跡，再透過左右車道回歸線取得車道的中心線，藉此引導車輛穩定行駛於車道中。另外，本研究還搭配語音辨識擴充模組 (DFR0177 Voice Recognition) 來辨識由Google Map路線規劃所傳出的語音指

令。根據辨識的結果，輸出行車指令給FPGA，以此決定車輛轉彎或直線行車路線模式。根據本研究之實驗結果，在使用每秒90張畫面播放速度以及640×480影像解析度情況下，只需11 ms即可擷取車道線特徵。而由左右車道線線性回歸決定出的中心線與實際影像中的中心線，誤差僅在5個像素以內。故本研究不管在運算速度以及準確度上均符合實際運用需求，未來可以有效幫助車輛穩定行駛於車道，達成自動駕駛之目的。