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Printable Mesoscopic Perovskite Solar Cells

為了解決China energy的問題，作者 這樣論述：

Hongwei Han, professor and doctoral supervisor of Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology. He has published more than 150 papers in journals such as Science, Adv Mater, Nature Comms, JACS, etc. One of his papers has been cited more than 2,200 time

s. Hosted many international and domestic academic conferences. He presided over the National Natural Science Foundation of China integrated projects, key projects, 863 projects and other projects. In 2016, he was awarded the Changjiang Scholar Distinguished Professor, and in 2017, he was awarded th

e leading talent in science and technology innovation of the National Ten Thousand Talents Program. Michael Grätzel, professor of Ecole Polytechnique Federale de Lausanne, created the field of molecular photovoltaics, being the first to conceive and realize mesoscopic photo-systems based on molecul

ar light harvesters that by now can rival and even exceed the performance of conventional solar cells. Furthermore, he played a pivotal role in the recent development of perovskite solar cells (PSCs) that directly emerged from the DSC. Their meteoric rise to reach a solar to electric power conversio

n efficiency of over 25 % in 2019 has attracted wide research interest with over 10,000 papers being published on the subject over the last 7 years. Graetzel is also a leader in the field of fuel generation by sunlight, which is a key technology to provide future renewable energy sources that can be

stored. His group uses tandems of two photosystems to split water into hydrogen and oxygen and reduce carbon dioxide by visible light. His 1645 publications have received some 284,000 citations and his h-index is 243. Anyi Mei got his PHD in Huazhong University of Science and Technology in 2018. A

fter that, he continues his research in the university and becomes an associate professor in Wuhan National Laboratory for Optoelectronics in Huazhong University of Science and Technology. His research focuses on printed mesoscopic solar cell materials and devices. He has published more than 60 peer

-reviewed articles with H-index 27. Dr. Yue Hu is currently associate professor of Huazhong University of Science and Technology. Dr. Hu has carried out a series of pioneering work in the fields of dye-sensitized solar cells and perovskite solar cells. From the perspectives of molecular design, mat

erial synthesis, interface modification, device optimization and mechanism analysis, Dr. Hu Yue has developed a series of new low-cost light absorbing materials focusing on the photoelectric conversion efficiency, stability and cost of mesoscopic solar cells. She has participated in many scientific

research projects of the Royal Society of chemistry and the National Natural Science Foundation of China. She has published more than 80 research articles in international journals including Science, Advanced Materials, and Advanced Energy Materials etc. with H-index 25.

China energy進入發燒排行的影片

銀行、證券及保險動態併行效率評估

為了解決China energy的問題，作者陳軒頎 這樣論述：

本研究探討的是在台灣金控體系裡同時擁有銀行、證券及保險的金控公司，子公司的效率是如何影響金控本身，多元化的大型金控要如何去針對各子公司的弱項去強化，讓資源的運用更有效率。在過去的文獻中，發現擁有高效率的銀行比同行要更能抵擋金融危機，以及大型銀行較可能從規模經濟中獲益，多元經營的金控子公司可以從多元化的結構中獲得更多的收益，並且金控的子公司可以通過分擔風險以減少不良產出，這些文獻已經證實了大型且多元經營的銀行擁有比一般單純銀行更高的績效。在此前的研究缺少了保險部門的績效比較，對於以保險業為主的金控公司就顯得吃虧，本文要在這一基礎上更進一步，加入保險部門，並配合視窗分析法解決決策單元數量減少的問

題。透過同時分析金控公司底下的銀行、證券及保險子公司三個部門的績效，能夠解析金控內部這三個部門各自營運情形，結果更能貼近金融業真實情況，有利於決策管理人瞭解參考與決策。從結果中發現保險部門是這10家大型金控中較需改進的部門，但因保險業加入臺灣金融體系的時間還不夠長，盼將來的資料增加後能進行更完整更詳細的分析。

木工技能速成一本通

為了解決China energy的問題，作者 這樣論述：

木工是傳統工種，在建築、裝飾裝修、傢俱等領域發揮著不可替代的作用。本書面向一線工人，介紹了木工材料、木工識圖、常用木工工具等基礎內容，著重介紹了木工接合、塗裝、裝飾裝修木工操作以及傢俱生產等內容。同時，根據技術工人的實際需求，結合常用木工工具及設備，提供了大量實際操作的教學視頻，讀者可以直接學習的仿製。   《木工技能速成一本通》注重基礎，深入淺出，使讀者可以快速入門，熟練掌握相關操作技巧，適宜廣大木工朋友參考學習。

一個使用智慧型手機實現深度學習心電圖分類的心臟疾病辨識系統

為了解決China energy的問題，作者何亞恩 這樣論述：

目錄誌謝 i摘要 iiAbstract iii目錄 v圖目錄 viii表目錄 xi第一章 緒論 11.1研究動機 11.2研究目的 21.3研究架構 2第二章 研究背景 32.1心電圖與疾病介紹 32.1.1心臟導程 32.1.2心臟疾病介紹 52.2Android系統 102.2.1 Android的基礎 102.2.2 Android系統框架 102.3相關文獻探討 11第三章 研究方法 173.1資料庫介紹 173.2訊號前處理 193.2.1小波濾波 193.2.2訊號正規化 213.3一維訊號轉二維影像 213.3.1手機螢幕上

繪製圖形 213.3.2影像儲存於智慧型手機 233.3.3資料擴增Data Augmentation 243.4深度學習架構 253.4.1多卷積核架構 253.4.2注意力模型 283.4.2.1通道注意力模組Channel attention 293.4.2.2空間注意力模組Spatial attention 303.4.2.3激活函數Activation function 303.5損失函數Loss function 313.6交叉驗證Cross validation 323.7優化訓練模型 333.8移動端應用 343.9硬體設備、軟體環境與開發環境 36

3.9.1硬體設備 363.9.2軟體環境與開發環境 37第四章 研究結果與討論 3834.1評估指標 384.2訓練參數設定 404.3實驗結果 414.3.1深度學習模型之辨識結果 414.3.1.1比較資料擴增前後之分類結果 414.3.1.2不同模型架構之分類結果 424.3.2智慧型手機應用結果 464.4相關文獻比較 48第五章 結論與未來展望 525.1結論 525.2未來展望 53參考文獻 54