YouTube channel Anal的問題,透過圖書和論文來找解法和答案更準確安心。 我們找到下列問答集和資訊懶人包

國立雲林科技大學 企業管理系 劉彥青所指導 Vina Aristantia的 擴增實境對客戶滿意度與重購意願影響之研究 (2021),提出YouTube channel Anal關鍵因素是什麼,來自於擴增實境、行動商務、滿意度、回購意向、S-O-R模型、科技整合接受模型。

而第二篇論文國立臺北科技大學 電子工程系 蔡偉和所指導 TRAN VAN THUAN的 緊急鳴笛車輛之自動偵測方法研究 (2021),提出因為有 Emergency vehicle detection、convolutional neural networks、object detection、traffic safety、siren sounds、warning signals、audio recognition、autonomous driving的重點而找出了 YouTube channel Anal的解答。

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YouTube channel Anal進入發燒排行的影片

趙氏曾任教於美國Bryant University 、北京理工大學珠海學院、深圳大學、香港樹仁大學、臺灣中央研究院近史所等多間學府,歷任助理教授、副教授、研究員、客座教授、高級訪問學者;

2018年獲中國經濟思想史優秀(一等)著作獎,研究題目包括經濟思想、經濟史、政治經濟學,出版著作二十一部,論文三十餘篇。英國權威出版社 Routledge給予 "中國和西方頂尖學者(leading Chinese and Western scholar)" 的評價。

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擴增實境對客戶滿意度與重購意願影響之研究

為了解決YouTube channel Anal的問題,作者Vina Aristantia 這樣論述:

本研究利用「刺激-機制-反應」模型 (S-O-R, Stimulus-Organism-Response model)探討行動商務擴增實境的生動特性和使用者互動,並以整合科技接受模型 (UTAUT) 進行使用者接受分析。這項研究採樣 431 名受訪者,其中包括 254 名擴增實境新手消費者,發現作為刺激的生動性和交互性顯著且積極地影響使用者的績效預期、努力預期、便利條件、社會影響和享樂動機。然而,便利條件並沒有受到生動性的顯著影響。本研究發現行動商務滿意度影響行動商務的回購意願。本研究通過使用 UTAUT 模型和 SOR 框架探討擴增實境對行動商務滿意度和回購意願的影響,除對學術貢獻外,建議

相關業者需要增強其生動性和互動性,以吸引消費者的回購意向關鍵詞 : 擴增實境, 行動商務, 滿意度, 回購意向, S-O-R模型, 科技整合接受模型

緊急鳴笛車輛之自動偵測方法研究

為了解決YouTube channel Anal的問題,作者TRAN VAN THUAN 這樣論述:

Emergency vehicles (EV), such as fire trucks, police cars, and ambulances, are the crucial components of the emergency service system (EMS), which provides quick responses and professional aids in urgent situations. For example, in case of reported serious illness or injury, the focus of EMS, inclu

ding ambulance, is providing rapid transportation and emergency medical care of the patients. Due to the need of driving at high speed to reach the destination, the EV’s drivers may put themselves at risk. Furthermore, in certain driving scenarios, car drivers may sometimes be unaware of the approac

hing EV, for instance, when an EV’s siren is unclear due to the use of the in-vehicle audio system, or when an EV is out of the car driver’s vision, so non-emergency vehicles may block or even collide with the EV. This work studies automatic methods for emergency vehicle detection (EVD) to warn car

drivers of the nearby priority vehicle(s) and paying attention.This dissertation investigates audio-based and vision-based approaches to build deep learning-based EVD systems that can accurately detect the EV using their siren sounds and/or their visual presence. Firstly, we build different convolut

ional neural networks (CNNs) for two kinds of EVD systems, namely A-EVD and V-EVD, which are based on siren sound detection and object detection approaches, respectively. Then, we integrate models from A-EVD and V-EVD to develop a prototype of the audio-vision EVD system (AV-EVD). To our knowledge,

there is no prior work examining such an AV-EVD system. In A-EVD, besides investigating the combined use of acoustic handcrafted features, including MFCCs and log-mel spectrogram, to train the 2D-CNN model (MLNet), we propose to train the end-to-end network (WaveNet) directly on audio raw waveforms.

Our experiments on a custom dataset of three audio classes (i.e. siren sound, vehicle horn, and traffic noise) show the efficiency of the proposed handcrafted feature aggregation as well as the raw feature extraction methods. Also, we propose two-stream models, namely PreCom-SirenNet, PostCom-Siren

Net, and DF-SirenNet, which are trained on both handcrafted features and raw wave features to further boost the classification accuracy. Our proposed A-EVD models can work well with various input lengths between 0.25 seconds and 1.5 seconds to obtain accuracies ranging from 92.2% to 98.51%. In V-EVD

, we propose to apply and modify the YOLOv4 object detection algorithm to build a single-stage V-EVD system, namely YOLO-EVD, which achieves 95.5% mean average precision on our custom dataset. The AV-EVD system comprised of YOLO-EVD and the WaveResNet, an improved version of the WaveNet, also yields

promising results, showing the potential of fusing information from acoustic signals and visual information to enhance the reliability of the system’s predictions. The application of A-EVD, V-EVD, and AV-EVD from this work will not only be able to help drivers avoid car accidents, but also provide

a necessary safety function for other smart vehicles and traffic infrastructures, such as self-driving cars and intelligent traffic light control systems.