Micro-Electro-Mechanical Systems (MEMS) are miniature systems composed
ofintegratedelectricalandmechanicalpartstosenseand/orcontrolthingsonaμmscale.
The concept of MEMS is attributed to Richard Feynman’s famous talk on December
29th, 1959 [2,3]. Dr. Feynman foresaw many aspects of future MEMS development
with his insight in Microphysics. In particular, material properties in the μm scale are
differentfrombulkpropertiesandthescalingdownofintegratedcircuits(IC)fabrication
technology has been a major driving force of MEMS development.
Recent advancements in nanotechnology (NT) materials and growth of Micro/
nanotechnology have opened the door for potential applications of Microelectro-
mechanical systems (MEMS)- and NT-based sensors and devices. Such sensors and
devices are best suited for communications, medical diagnosis, commercial, military,
aerospace, and satellite applications. This book comes at a time when the future and
well-being of Western industrial nations in the twenty-first century’s global eco-
nomy increasingly depend on the quality and depth of the technological innovations
they can commercialize at a rapid pace.
The solid high-polymer-film-type fuel cell (PEM-FC) system is used as the power
supply equipment for transportation and replaces an internal combustion engine. A
reduction of the environmental load is expected through the cogeneration system’s
(CGS) use of the PEM-FC system as a distributed power supply to individual
houses, apartments, and so forth [1–3]. The growing use of distributed power
systems, such as fuel cells, the reduction of power-transmission losses, and an
increase of waste heat recovery are expected. Therefore, the reduction of carbon-
dioxide emission is also expected as compared to conventional energy supply
methods using commercial electric power.
這是我在做大學教授期間推薦給我學生的一本書，非常好，適合入門學習。《python深度學習》由Keras之父、現任Google人工智能研究員的弗朗索瓦?肖萊（Franc?ois Chollet）執筆，詳盡介紹了用Python和Keras進行深度學習的探索實踐，包括計算機視覺、自然語言處理、產生式模型等應用。書中包含30多個代碼示例，步驟講解詳細透徹。作者在github公布了代碼，代碼幾乎囊括了本書所有知識點。在學習完本書后，讀者將具備搭建自己的深度學習環境、建立圖像識別模型、生成圖像和文字等能力。但是有一個小小的遺憾：代碼的解釋和注釋是全英文的，即使英文水平較好的朋友看起來也很吃力。本人認為，這本書和代碼是初學者入門深度學習及Keras最好的工具。作者在github公布了代碼，本人參照書本，對全部代碼做了中文解釋和注釋，并下載了代碼所需要的一些數據集（尤其是“貓狗大戰”數據集），并對其中一些圖像進行了本地化，代碼全部測試通過。（請按照文件順序運行，代碼前后有部分關聯）。以下代碼包含了全書約80%左右的知識點，代碼目錄：2.1: A first look at a neural network（ 初識神經網絡）3.5: Classifying movie reviews（電影評論分類：二分類問題）3.6: Classifying newswires（新聞分類：多分類問題 ）3.7: Predicting house prices（預測房價：回歸問題）4.4: Underfitting and overfitting（ 過擬合與欠擬合）5.1: Introduction to convnets（卷積神經網絡簡介）5.2: Using convnets with small datasets（在小型數據集上從頭開始訓練一個卷積網絡）5.3: Using a pre-trained convnet（使用預訓練的卷積神經網絡）5.4: Visualizing what convnets learn（卷積神經網絡的可視化）
