路線牌
Multi tool use路線牌（Destination Sign）是一種通常被裝置在公車、路面電車或鐵路列車等大眾運輸交通工具上，作為指示路線編號與目的地（有時也會同時標示起點與中途停站）的顯示設備。由於經常被設計裝置於車輛前擋風玻璃上方醒目處，因此又常被稱為車頭牌（可對應英文中的Headsign之稱呼方式）。除此之外，車輛的尾部與側面有時也會設置有路線牌，以方便路邊或後方的其他用路人辨識。
配合時代的改變與技術的演進，路線牌的顯示方式也有不小的變化。在過去捲布式路線牌（Rollsign）是很常見的設計，除此之外預先將要顯示的資訊印刷在一片一片的壓克力板上、再根據需要以人工或機械方式切換的膠牌式路線牌也經常見到。今日的大眾運輸工具已陸續改用數位控制的電子式路線牌，並採用液晶顯示幕（LCD）或發光二極體顯示幕（LED）作為畫面顯示的方式。此類的新式路線牌能根據需求快速地編輯顯示畫面，除了方便調度之外，也能以較動態的方式（例如跑馬燈或循環切換畫面）顯示更豐富的內容資訊。[1]
另見
參考資料
^ Destination and route signs (guidelines for), section 39 within Part 38 (Accessibility Specifications for Transportation Vehicles) of the U.S. Americans with Disabilities Act of 1990.
外部連結
Rollsign Gallery, showing the history of public transit through their destination signs - USA, Canada, overseas: www.rollsigngallery.com
 |
维基共享资源中相关的多媒体资源：路線牌
|
显示器科技
|
|---|
| | 显示器 |
已停產 | |
|---|
| 当前 | - 電致發光顯示器 (ELD)
- 真空荧光显示器 (VFD)
- 发光二极管 (LED)
液晶显示器 (LCD)（TN液晶、STN液晶、TFT；Blue phase、IPS、VA；CCFL背光、LED背光）
- 數位光處理 (DLP)
- 液晶覆硅 (LCOS)
- 氧化銦鎵鋅(IGZO)
- 小尺寸主動矩陣有機發光二極體（AMOLED）
- 量子點顯示器 (QDLED)
- Eidophor
等離子顯示屏 (PDP)
电子纸
|
|---|
| 下一代 |
有机发光半导体 (OLED)/大尺寸AMOLED
- 有机发光晶体管 (OLET)
微發光二極體顯示器(Micro LED)/Mini LED
- 表面传导电子发射显示器 (SED)
- 场发射显示器 (FED)
- 有源驅動鐵電液晶顯示器 (FLCD)
激光电视
- 磁液显示器 (FLD)
- 干涉测量调节显示器 (IMOD)
- 厚膜電致發光 (TDEL)
- 时序复用光学快门 (TMOS)
- 伸縮像素顯示器 (TPD)
- 雷射熒光顯示器 (LPD)
|
|---|
|
|---|
| 非影片
| - 机械七段数字显示
- 机械翻页显示
- 点阵磁翻显示
- 可卷式显示器
- 點陣式顯示器
七段数码管
|
|---|
| | 三維顯示 | |
|---|
| | 靜態媒體 | |
|---|
| | 影像科技 | |
|---|
| 參見：显示技术的比较 ·  电脑基本部件 · 电脑显示标准 |
|
N,o5W8VD5kCtmnv,B8m Szrs4CF
Popular posts from this blog
Ramiro Burr's New Blog - to go back: www.ramiroburr.com From Latin rock to reggaeton, boleros to blues,Tex-Mex to Tejano, conjunto to corridos and beyond, Ramiro Burr has it covered. If you have a new CD release, a trivia question or are looking for tour info, post a message here or e-mail Ramiro directly at: musicreporter@gmail.com Top Tejano songwriter Luis Silva dead of heart attack at 64 By Ramiro Burr on October 23, 2008 8:40 AM | Permalink | Comments (12) | TrackBacks (0) UPDATE: Luis Silva Funeral Service details released Visitation 4-9 p.m. Saturday, Rosary service 6 p.m. Saturday at Porter Loring, 1101 McCullough Ave Funeral Service 10:30 a.m. Monday St. Anthony De Padua Catholic Church, Burial Service at Chapel Hills, 7735 Gibbs Sprawl Road. Porter Loring (210) 227-8221 Related New Flash: Irma Laura Lopez: long time record promoter killed in accident NewsFlash: 9:02 a.m. (New comments below) Luis Silva , one of the most well-known ...
1 I having trouble getting my ResourceDictionary.MergedDictionaries to load from app.xaml. My WPF app has a static class with a Main defined and startup object set to it. Within Main I created an instance of App and run it. The override OnStartup fires and the mainwindow.cs InitializeComponent gives the error "Message "Cannot find resource named 'MaterialDesignFloatingActionMiniAccentButton'. If I put the resources in the mainwindow.xaml everything is fine, but I wanted them to load at the app level so I they are not in each page. Any help appreciated. public partial class App protected override void OnStartup(StartupEventArgs e) base.OnStartup(e); var app = new MainWindow(); var context = new MainWindowViewModel(); app.DataContext = context; app.Show(); from the Main.. var app = new App(); app.Run(); app.xaml.. <Application x:Class="GS.Server.App" xmlns="http://schemas.microsoft.com/winfx/2006/xaml/presentation" xmlns:...
up vote 2 down vote favorite There is a clear pattern that show for two separate subsets (set of columns); If one value is missing in a column, values of other columns in the same subset are missing for any row. Here is a visualization of missing data My tries up until now, I used ycimpute library to learn from other values, and applied Iterforest. I noted, score of Logistic regression is so weak (0.6) and thought Iterforest might not learn enough or anyway, except from outer subset which might not be enough? for example the subset with 11 columns might learn from the other columns but not from within it's members, and the same goes for the subset with four columns. This bar plot show better quantity of missings So of course, dealing with missings is better than dropping rows because It would affect my prediction which does contain the same missings quantity relatively. Any better way to deal with these ? [EDIT] The nullity pattern is confirmed: machine-learning cor...
电脑基本部件 
