Is shift safe的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列懶人包和總整理

Future Sea: How to Rescue and Protect the World’s Oceans

為了解決Is shift safe的問題，作者 這樣論述：

A counterintuitive and compelling argument that existing laws already protect the entirety of our oceans--and a call to understand and enforce those protections. The world’s oceans face multiple threats: the effects of climate change, pollution, overfishing, plastic waste, and more. Confronted wi

th the immensity of these challenges and of the oceans themselves, we might wonder what more can be done to stop their decline and better protect the sea and marine life. Such widespread environmental threats call for a simple but significant shift in reasoning to bring about long-overdue, elemental

change in the way we use ocean resources. In Future Sea, ocean advocate and marine-policy researcher Deborah Rowan Wright provides the tools for that shift. Questioning the underlying philosophy of established ocean conservation approaches, Rowan Wright lays out a radical alternative: a bold and fa

r-reaching strategy of 100 percent ocean protection that would put an end to destructive industrial activities, better safeguard marine biodiversity, and enable ocean wildlife to return and thrive along coasts and in seas around the globe. Future Sea is essentially concerned with the solutions and n

ot the problems. Rowan Wright shines a light on existing international laws intended to keep marine environments safe that could underpin this new strategy. She gathers inspiring stories of communities and countries using ocean resources wisely, as well as of successful conservation projects, to bui

ld up a cautiously optimistic picture of the future for our oceans--counteracting all-too-prevalent reports of doom and gloom. A passionate, sweeping, and personal account, Future Sea not only argues for systemic change in how we manage what we do in the sea but also describes steps that anyone, fro

m children to political leaders (or indeed, any reader of the book), can take toward safeguarding the oceans and their extraordinary wildlife.

Is shift safe進入發燒排行的影片

基於語意之輪廓表示法及全連結捲積類神經網路之單晶片多車輛辨識系統

為了解決Is shift safe的問題，作者徐雋航 這樣論述：

鑒於現今智慧車輛發展迅速，前方車輛辨識及車距檢測為先進駕駛輔助系統 (Advanced Driver Assistance Systems, ADAS) 設計中相當重要的一環，此項技術通常藉由攝影鏡頭擷取前方影像，並透過影像辨識技術來判斷前方是否存在車輛、障礙物等等，進而控制車輛減速以保持安全距離。而這些複雜的圖形辨識技術往往需要透過高功耗之大型運算系統來實現，並且，若將傳統電腦安裝於車內常需要克服體積過大、耐震性不佳等缺點。因此，本研究專注於如何將車輛辨識及車距檢測演算法實現於單晶片，以達到高性能、低功耗，以及體積小之目的。為實現前方車輛辨識及車距檢測，本研究透過單一彩色相機模組收集前方影

像資訊，並於單一現場可程式邏輯閘陣列 (Field Programmable Gate Array, FPGA) 晶片中以最精簡之硬體電路實現白平衡 (White Balance)、影像對比度強化技術 (Image Contrast Technique)、物體邊緣檢測、利用基於模糊語意影像描述 (Semantics-based Vague Image Representation, SVIR) 改良之基於語義之輪廓表示法 (Semantic-based Contour Representation, SCR) 特徵表達物體、再透過不同的卷積核 (Convolution Kernel) 重釋SC

R特徵並交由全連接類神經網路(Fully Connected Neural Network, FCN) 進行車輛辨識。最後，以多個邊界框 (Bounding Box) 同時檢測前方多台車輛，達到單頁多目標辨識 (Single Shot MultiBox Detector，SSD) 之功能，而邊界框之座標可以透視法 (Perspective View) 計算前車相對距離。根據本研究之實驗結果，在相機以每秒90張影像攝影速度以及影像解析度在640×480像素的條件下，本研究僅須3.61us即可完成單台車輛辨識，車輛辨識率可達到94%，且車輛與非車輛至少保持38%以上之分離度，有效減少感測錯誤的情況

發生。因此，實現一真正高性能、低功耗以及體積小之前方車輛辨識晶片。

Skate for Your Life

為了解決Is shift safe的問題，作者Baker, Leo 這樣論述：

Leo Baker is one of the most iconic skateboarders in the world. A staple at the X Games, Leo took their abilities to Street League for back-to-back victories in 2016 and 2017. Their work has been featured on outlets like Thrasher, ESPN, Vice, Nike’s 30th Anniversary Just Do It Campaign, New York Tim

es Magazine, Rolling Stone, Vogue, and more. In 2019, they founded the NYC Skate Project, which is an effort to shift the dynamics in skateboarding and create a safe space for non-male skaters to build community with one another. Their undying commitment to themselves and their scene has earned them

global adoration from fans and friends both near and far. Ashley Lukashevsky is an illustrator and visual artist born and raised in Honolulu, Hawaii, currently based in Los Angeles. Ashley uses illustration and art as tools to strengthen social movements against systemic racism, sexism, and anti-im

migrant policy. She aims to tear down these systems of oppression through first envisioning and drawing a world without them. Her clients include Refinery29, Broadly, The Washington Post, Planned Parenthood, Girls Who Code, GOOD Magazine, Brooklyn Magazine, ACLU, Red Bull, Snapchat, Air Jordan, and

Logo TV.

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Is shift safe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。