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Color Mixing Recipes for Portraits: More Than 500 Color Cominations for Skin, Eyes, Lips, and Hair : Featuring Oil and Acrylic -

為了解決Mockup free的問題，作者Powell, William F. 這樣論述：

Color Mixing Recipes for Portraits features master mixes for an array of skin colors in oil, acrylic, and watercolor--plus recipes for hair, eye, and lip colors. The concealed wire-o bound book also includes a plastic color-mixing grid for measuring out paints, as well as a handy conversion chart fo

r finding acrylic equivalents of oil paints and vice versa. With recipes for more than 500 color combinations, this comprehensive guide is your ultimate source for painting realistic skin, mouths, ears, noses, eyes, and hair. This convenient guide details three simple steps to mix virtually any skin

toneMatch a palette of skin tones from this book to your portrait subject.Create the Master Skin Tone Recipe for this palette using the Color Mixing Grid to measure each paint color.Follow the recipes to create all the skin tones you'll need to complete your portrait--from light values to shadows.A

lso featured: Color theoryTinting, toning, and shadingGraying skin tones naturallyIdentifying facial planesMouth, nose, and ear color tonesEye and hair color tonesWatercolor skin tone recipesWith recipes for more than 500 color combinations, Color Mixing Recipes for Portraits is your ultimate source

for painting realistic skin, mouths, ears, noses, eyes, and hair. William F. Powel is an internationally recognized artist and one of America’s foremost colorists. He is the author of many art instructions books, including The Art of Watercolor, Color Mixing Recipes for Watercolor, Color Mixing R

ecipes for Portraits, and Drawing Made Easy: Dynamic Composition. A native of Huntington, West Virginia, Bill studied at the Art Student’s Career School in New York; Harrow Technical College in Harrow, England; and the Louvre Free School of Art in Paris, France. He has been professionally involved i

n fine art, commercial art, and technical illustrations for more than 45 years. His experience as an art instructor includes oil, watercolor, acrylic, colored pencil, and pastel--with subjects ranging from landscapes to portraits and wildlife. As a renowned master of color, Bill has conducted numero

us ＂Color Mixing and Theory＂ workshops in various cities throughout the US. Additionally, he has performed as an art consultant for national space programs and for several artist’s paint manufacturers. Bill’s work has also included the creation of background sets for films, model making, animated ca

rtoons, and animated films for computer mockup programs. He also produces instructional painting, color mixing, and drawing art videos.

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邁向高時空分辨率環境影響監測

為了解決Mockup free的問題，作者伊瑟曼邁克 這樣論述：

市政當局及其公共事業在全球面臨越來越大的壓力,需要製定可持續的資源戰略並實現碳中和,只能依靠數據驅動的規劃方法,來解決耦合基礎結構系統和創建循環供應路徑的複雜性。但是,由於所需的能力和成本過高,很少有市政當局進行全面的數據收集。因此,本研究試圖透過以高時空分辨率,進行對城市環境影響的模型建造和模擬來減輕負擔。能源需求和城市徑流的影響是起點,住宅能源需求估算由城市建築能源模型(UBEM)執行,該模型通過 Functional Mock-up Interface(FMI)實施。以 FMI 為基礎並與 UBEM 的交互作用,無需重新初始化即可模擬各種環境條件。它能讓建築能源模擬 EnergyPlu

s 與外部模型耦合, 根據每棟建築物的鄰近環境條件,估算具有高時間分辨率的能源需求。為改善適用性,以 FMI 為基礎的 UBEM 通過合併自動程序,通過篩選的 Poisson 曲面重建算法,從城市範圍的點雲派生 3D 建築模型,顯示高幾何保真度,進而豐富該模型。建築和建築系統是根據歐盟 TABULA 項目編制的住宅建築類型定義的。為了演示擬議的 UBEM 的功能,使用城市小氣候數據和熱泵代替其他供熱和熱水系統構建了一個方案。在變化的佔用率和天氣條件下,對能源需求的時間密集估計,可以預測對下降的峰值負荷做出需求響應的需求。並且將模擬年度能源使用強度,與參考研究的結果進行比較,得出了令人滿意的結果

,因此,該建模概念被認為是適當的。關於城市徑流,簡化的數學模型“元胞自動機”與“運動成本”字段相結合,將穿越區域的難度記入城市洪水模型 CAMC。它通過簡單的矩陣和邏輯運算,達到高計算效率。該開發,集中應用在具有眾多建築物的密集城市建成區中。CAMC 足夠高效和靈活,可用於具有當前天氣狀況的“實況”城市洪水預警系統。將 CAMC 與基於淺水方程的 ANUGA 模型進行了比較。在高空間分辨率下,CAMC 大約比 ANUGA 快 5 倍,並且能夠保持數值穩定性。案例研究在德國伍珀塔爾市的市區中進行,該市區有大約 12,000 座建築物,據估能源需求約為5736 座。根據翻新及供暖系統的狀態,歸納出

41 種類型和 8 種可能配置的多種建築材料。通過網絡界面可視化使用“實況”天氣的 3D 建築模型和模擬結果,該界面通過地理空間 3D 框架 NASA WorldWind 呈現。“實況”模擬模式可以估算出住宅能源和其碳濃度、住宅需水量以及降雨事件下的徑流深度。為了評估時空模型中的不確定性,基於點估計方法(PEM)開發了不確定性量化方法。相較對通用但計算要求很高的 Monte Carlo(MC)模擬,PEM 提供了的替代方法。PEM 使用不確定變量的概率分佈之代表值,重新運轉模型。結果可以估算輸出分佈的統計動差。Hong 的方法是針對案例研究實施的特定 PEM。該案例研究使用 ANUGA模型對

英國格拉斯哥地區的水流進行了模擬,海拔高度為不確定性的來源。逐步高斯模擬法會產生隨機誤差範圍,可用在輸入任何空間模型,MC 模擬的輸出用於驗證。使用 Grams-Charlier 擴展來擬合分佈並生成概率洪水氾濫圖。PEM 不需要至少進行 500 次 MC 模擬,而只需進行 3 次模擬。因此,Hong 的方法對於近似時空模型的不確定性似乎很有吸引力。