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跨世紀的產業推手：20個與台灣共同成長的故事

為了解決義隆電子104的問題，作者李俊明,李翠卿,謝其濬,傅瑋瓊,沈勤譽,王胤筠 這樣論述：

機遇無法複製，態度可以學習 　　★成就第一的關鍵態度 　　★20個成功者的答案 　　沒有莫衷一是的說法 　　只有追求成功者最佳的解答 　　無論時代如何演進，改變的力量，一直都在你身上，你用什麼樣的眼光看待世界、你用什麼樣的態度面對未來，才是影響人生精采或黯淡的關鍵。不要讓環境決定你想做的事，每個人都是獨一無二的作家，負責寫下自己的生命故事。 　　面對大環境不景氣，許多年輕人，為了追求改變與夢想，選擇自行創業，希望藉此改變人生與未來，卻也同樣有一群人輕人，對成功沒有自信，對未來沒有信心。 　　為了幫助有志創新創業年輕人的年輕人能順利實現理想，《跨世紀的產業推手──20個與台灣共同成

長的故事》一書，收藏二十位成功人士的生命故事，舉凡立志創業、擔任公職、投入研究、獻身公益……，無論你在哪個領域，都能從中得到啟發，幫助絕大多數人找到成功的方法。 　　這二十位傑出人士，包括： 　　長春石化總裁林書鴻 　　光寶科技董事長宋恭源 　　SIGMU集團總裁林孝信 　　新普科技董事長宋福祥 　　陽光電子董事長林宏裕 　　億光電子董事長葉寅夫 　　力成科技董事長蔡篤恭 　　鐿鈦科技總裁林寶彰 　　研揚科技董事長莊永順 　　群光集團副董事長兼群光電能董事長林茂桂 　　義隆電子創辦人暨董事長葉儀晧 　　美超微電腦董事長梁見後 　　經濟部常務次長沈榮津 　　友達光電董事長暨執行長彭双浪 　　日

本東北大學多元物質科學研究所教授蔡安邦 　　和碩聯合科技董事長童子賢 　　璞園團隊璞永建設董事長楊岳虎 　　華碩執行長沈振來 　　喬訊電子董事長張水美 　　臺北科技大學校長姚立德 　　透過本書，他們分享自己全方位的實戰經驗。他們的成功，或許無法複製，但做事的態度與方法，卻值得世代相傳。 強力推薦 　　每個時代都有自己的社會現象與責任，新一代年輕人必須在新世界裡找尋機會。 蕭萬長・前副總統 　　讀這本書，可以學習到二十位產業巨擘、學術賢達的思考方向。 鄭崇華・台達集團創辦人 　　當年宏碁集團的人才雖有許多來自國立大學，但台北工專的畢業生也是我們最喜歡晉用的人才。 施振榮・宏碁集團創辦人

／智榮基金會董事長 　　台灣要轉型，需要有扎實的技術力做為基礎，長期重視實作精神的臺北科大，將會是未來轉型再成長最實在的力量。 翁淑貞・臺北科技大學校友會全國總會總會長

義隆電子104進入發燒排行的影片

緊急鳴笛車輛之自動偵測方法研究

為了解決義隆電子104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.