Configure的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列必買單品、推薦清單和精選懶人包

ASP.NET Core Razor Pages in Action

為了解決Configure的問題，作者Brind, Mike 這樣論述：

Razor Pages lets you build, configure, and deploy amazing dynamic ASP.NET websites with ease. Razor Pages lets you build, configure, and deploy amazing dynamic ASP.NET websites with ease. In ASP.NET Core Razor Pages in Action, you’ll use Razor Pages to build a complete application, learning to itera

te amazing new features chapter-by-chapter. In ASP.NET Core Razor Pages in Action, you’ll go hands-on to build a complete vacation-booking application, incorporating new framework features and adding complexities as they’re introduced. You’ll see how Razor Pages simplifies all the essential tasks of

web development, how to enhance your web applications with the huge ecosystem of C# libraries, and how to locate the perfect tool for your task using centralized repositories. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. Mike Brind i

s an 11-time recipient of the Microsoft Most Valuable Professional (MVP) award for ASP.NET. He is a regular Razor Pages blogger and a moderator of the official ASP.NET forums.

Configure進入發燒排行的影片

以卷積神經網路優化5G時代下智慧家庭的服務流量分類

為了解決Configure的問題，作者蔡宗諺 這樣論述：

近年來，隨著物聯網及人工智慧技術的迅速發展與進步，愈來愈多業者將住宅結合新興科技打造智慧家庭，以提升住戶的生活品質。因此在未來的智慧家庭中，許多類似5G三大應用場景特性的服務將應用於不同種類的智慧裝置，智慧家庭的整體網路流量必然大量增加，使智慧家庭中的網路流量管理成為值得深入探討的議題。由於5G時代的網路流量大幅增加與網路加密技術的廣泛使用，無法輕易從大多數網路應用服務中解密流量取得資訊，更無法透過傳統的網路流量分類方法將各類服務流量進行分類，加以發送到對應的應用類別進行管理。為改善上述問題，本論文以網路服務商(ISP)管理數以萬計的物聯網智慧家庭為情境，針對智慧家庭中多樣化的智慧裝置，利用

可以解決複雜分類問題的深度學習技術，優化ISP業者對智慧家庭的網路封包分類的精準度。本論文藉由軟體定義網路技術模擬多租戶的智慧家庭環境，依據3GPP LTE QoS Class Identifier (QCI)表，篩選出適用於未來智慧家庭類別的服務，模擬不同類別的智慧家庭服務流量，並利用卷積神經網路對網路流量進行分類。透過本論文，ISP業者能依分類好的服務類別，設定頻寬比例並配置到對應的服務類別，達到有效提升QoS及使用者QoE的目的。實驗結果顯示，CNN模型對智慧家庭模擬流量的分類精準度，透過調整後的參數組合與設定大小為1500 bytes的Payload輸入，能有最佳的分類準確率86.5%

，相較一般神經網路模型準確率提升了6.5%。

Certmike Exam Essentials: Comptia A+ Core 1 Exam 220-1101

為了解決Configure的問題，作者Chapple, Mike,Soper, Mark 這樣論述：

Let CertMike help you prepare, practice, and pass the CompTIA A+ Core 1 exam! Sybex’s NEW CompTIA A+ CertMike: Prepare. Practice. Pass the Test! Get Certified! Core 1 Exam 220-1101 lets you skip the fluff and focus only on the material you need to understand for passing CompTIA A+ Core 1 Exam 220-11

01--and working towards earning your certification! Ace the test using the proven CertMike approach: Prepare - As your personal study coach, CertMike guides you through all the exam objectives, helping you gain an understanding of important topics and how they apply to on-the-job scenarios!Practice

- Each chapter includes two multiple choice practice questions. Work through the detailed explanations to evaluate each answer option and understand the reason for the best answer.Pass - On exam day, take the critical knowledge you’ve learned into the test. You’ll feel ready and confident to pass th

e exam and earn your certification! The CompTIA A+ certification validates you have the knowledge and skills required to install, configure, and maintain computer equipment, mobile devices, and software for end users; service components based on customer requirements; understand networking basics an

d apply basic cybersecurity methods to mitigate threats; properly and safely diagnose, resolve, and document common hardware and software issues; apply troubleshooting skills and provide customer support using appropriate communication skills; and understand the basics of scripting, cloud technologi

es, virtualization, and multi-OS deployments in corporate environments.Readers also have access to additional online study tools, including a bonus practice exam and audio recordings of the book’s Exam Essentials to help you review for the actual exam. Prepare smarter with Sybex’s NEW CertMike Get C

ertified! for achieving the CompTIA A+ certification!

應用機器學習於永磁同步馬達轉子溫度預測之研究

為了解決Configure的問題，作者儲玉瑄 這樣論述：

　　工業4.0自動化產業興盛，電動車產業為現代主要趨勢，則多數廠商配置永磁同步馬達（PMSM）作為汽車的核心驅動系統，當驅動馬達時會因轉子溫度變化而影響系統效能，如何有效控制溫度變化，實現馬達高效率控制策略，確保PMSM於安全運作與最大使用率的狀態，可降低內部零組件的壽命耗損和提升整體運轉效率。　　本研究使用Kaggle提供的PMSM溫度資料集的轉子溫度作為主要探討，因此欲透過傳統迴歸分析與機器學習方法之模型對轉子溫度進行預測，分別使用貝氏嶺迴歸、隨機森林、XGBoost及LightGBM模型，並將上述各預測方法比較之各模型績效。經由各預測方法比較之各模型績效後，得知最佳預測模型為XGBoo

st模型，以利未來將本研究提供於電動車產業配置PMSM的研發與技術，能施以預測性維護馬達溫度狀態，進而防止關鍵性設備故障與停機。