基層消防人員參與休閒活動、工作壓力與身心健康之研究-以台中市政府消防局外勤人員為例

[[abstract]]本研究探討臺中市外勤基層消防人員參與休閒活動、工作壓力及身心健康狀況之間的關係。瞭解到不同背景的外勤消防人員對於工作壓力及身心健康狀況有不同的影響及差異性，進一步的瞭解從參與休閒活動中如何調適工作壓力，進而對身心健康狀況有所幫助。 本研究以便利取樣方式進行問卷調查，整理後得到有效問卷共計210份。研究結果發現：1.不同年齡、教育程度、擔任職務、婚姻狀況、工作年資及個人每月平均所在參與休閒活動上顯著差異。2.不同年齡、教育程度及工作年資者其工作壓力有顯著差異。3.不同職稱、工作年資者於身心健康狀況上有顯著差異。4.研究發現消防人員參與休閒活動類型中以技藝與表演型活動與工作壓力呈現顯著負相關。5.從研究結果發現消防人員的工作壓力之程度及身心健康有顯著相關。 The main purpose of this study is to explore the relationship between the participation of outside firefighters in leisure activities, work stress and physical and mental health in Taichung City. It is learned that field firefighters with different backgrounds have different effects and differences on working pressure and physical and mental health, and further understand how to adjust work pressure from participation in leisure activities so as to be helpful for their physical and mental health. In this study, a questionnaire survey was conducted to facilitate sampling and a total of 210 valid questionnaires were collected. The findings are as follows: 1. Significant differences in participation in recreational activities among different age, educational level, job title, marital status, working age and average monthly attendance. 2. Different age, education level and working age have significant differences in working pressure. 3. Different titles, working years in physical and mental health were significantly different. 4. The study found that firefighters involved in leisure activities in the type of skills and performance type of work pressure was significantly negatively correlated.5.From the results of the study, we found that there is a significant correlation between the degree of stress of firefighters and the physical and mental health, and based on the results of the study, put forward the relevant suggestions to the fire units and follow-up researchers for reference

Study on Machine Learning for Recognition System of Breeder

本研究之目的為改善業者種雞選拔系統，建立一套透過影像資料進行種雞篩選的種雞選拔人工智慧(Artificial Intelligence, AI)系統，可以幫助有色雞業者發展有效且快速的種雞選拔系統，系統除選出生育率性能良好的種雞，並由即早淘汱不適任的種雞，而大幅降低種雞選拔成本，如飼料成本，同時減少作業人力的需求與時間的消耗，本系統利用Google團隊開發的Tensorflow與Keras學習框架進行卷積神經網路的辨識演算法，期望能找出種雞與非種雞之間的差異性，為取得足夠且具代表性的雞隻影像，本研究同時開發了利用誘食的方式所建立的種雞選拔校正試驗系統。在實驗中將種雞與非種雞的影像到卷積神經網路影像共600張，做出的結果辨識率最高達60.03 %。The purpose of this study was to establish a breeders selection system by adopting an artificial intelligence (AI) system by filtering breeders through machine vision. It can help the colored chicken industry to improve its productive efficiency by adopting this effective and rapid breeder selection system. In addition to selecting breeders based on the fertility performance via the system, and eliminating the breeders what were ineligibility as soon as possible will dramatically reduce the cost of breeder selection, such as feed costs, and also reduce the labor demand and time consumption. The system used the Tensorflow and Keras learning frameworks is developed by the Google team to proceed the identification algorithm of Convolutional Neural Network. The developed system is used to find the difference between breeders and non-breeders. In order to obtain sufficient representative images of chicken samples, this study developed a breeder calibration and test system, which by altering feed position periodically on both ends of the image taking tunnel in the calibrating system. In the experiment, 600 images of breeder and non-breeder were taken and sent to Convolutional Neural Network. So far, the recognition rate of the developed system was up to 60.03%.致謝 i 摘要 ii Abstract iii 目錄 iv 圖目錄 vii 表目錄 x 符號說明 xi 第一章 前言 1 1.1 研究背景 1 1.2 研究目的 2 第二章 文獻探討 3 2.1 台灣土雞 3 2.1.1 台灣土雞介紹 3 2.1.2 土雞(系)雜交 4 2.1.3 土雞的行為 5 2.2 機器學習 7 2.2.1 監督式學習 8 2.2.2 非監督式學習 8 2.2.3 增強式學習 10 2.3 深度學習 11 2.4 類神經網路 12 2.4.1 單層感知器 12 2.4.2 多層感知器 13 2.5 卷積神經網路 14 2.5.1 卷積層 21 2.5.2 激活函數 25 2.5.3 Dropout 30 2.5.4 池化層 31 2.5.5全連接層 32 2.5.6 Softmax 函數 32 2.5.7 損失函數 33 2.5.8 梯度下降 33 第三章 實驗設備與方法 34 3.1實驗設備 34 3.1.1 實驗場地 34 3.1.2 試驗雞隻 35 3.1.3 硬體設備 35 3.1.3.1 儲存影像電腦 36 3.1.3.2 攝像機 37 3.1.3.3 Gigabi 交換器 38 3.1.3.4 RFID Reader 39 3.1.3.5 腳環式電子標籤 40 3.1.4 軟體設備 41 3.2 實驗規劃 42 3.2.1 人工架設鐵網拍攝方式 43 3.2.2 誘食通道拍攝系統 45 3.2.3 資料預處理 49 3.2.3.1 影像擷取 49 3.2.3.2 資料擴增 49 3.2.3.3 資料正規化 51 3.2.4 建立CNN模型 51 第四章 結果與討論 52 4.1 人工架設鐵網拍攝方式結果 52 4.2 誘食通道拍攝系統結果 53 4.3 雞隻各別部位比較 55 4.3.1 雞冠數據集結果分析 55 4.3.2 腳脛數據集結果分析 57 4.3.3 身體數據集結果分析 58 第五章 結論與建議 60 5.1 結論 60 5.2 建議 61 參考文獻 6

Money, Output, and Reverse Causation

本文嘗試提出一個明確區分內、外在貨幣，且貨幣數量與貨幣政策均為內生的動態隨機全面均衡模型，期望能夠複製出實證上貨幣與產出的動態相關性與統計因果，並為逆向因果假說及支持該假說的實證研究提供具體且可檢驗的理論架構。結果發現，模型大致能複製出台灣實證上貨幣總計數M1與產出的動態相關性，但對於貨幣基數(MB)與產出的動態相關性，本文雖能以央行資訊落後的設定方式來改善模型的解釋能力，卻仍不足以完整詮釋實際資料的特徵。在統計因果方面，本文雖未能以模型中逆向因果傳導機制所產生的模擬值得到實證上“M1 單向Granger cause產出”的結論，但是Granger因果檢定誤判模型中MB與產出間真實的因果關係，的確削弱了傳統上以Granger統計因果來代表真實經濟因果的可信度。此外，藉由實證上貨幣法則轉變的案例來進行模擬比對，本文確認了模型的內生性貨幣政策具有近似中立性的特質。A dynamic general equilibrium model, in which money and monetary policy are both endogenous, is calibrated and simulated. It is then confronted with the dynamic cross-correlation and statistical causality of money and output in Taiwan time series data. The model economy performs well in matching the empirical dynamic cross-correlation of money aggregate M1 and output. However, it leaves few unexplainable causes of empirical dynamic cross-correlation of monetary base (MB) and output, even with a modification to reflect that the central bank cannot acknowledge the exogenous technology shock immediately. In addition, Granger causality test misjudged the economic causality of MB and output in the model. Therefore, the reliability of such statistical test should be weakened in the aspect of representing real economic causality. Furthermore, through the case in which monetary policy rule is different in two empirical stages, the model proves that endogenous monetary policy is nearly neutral.第一節 緒論……………………………………………………….………1.1貨幣變動引發產出變動 ─ 事實或是迷思?………….…………1.2研究目的與論文架構…………………………….……………….4二節 文獻回顧………………………………………….………………6.1貨幣與產出因果關係之實證研究…………………..……………6.2貨幣與產出逆向因果關係之理論模型………………..…………8三節 貨幣與貨幣政策均為內生的實質循環模型…………………11.1理論模型設定………………………………………………….…11.2模型求解…………………………………………………………20.3模型參數設定與模擬步驟………………………………………24四節 基準與延伸模型的模擬結果分析………………………….…28.1基準模型…………………………………………………………28.2延伸模型(I) ─ 央行資訊落後…………………………………32.3延伸模型(II) ─ 貨幣法則轉變…………………………...……36.4小結………………………………………………………………39五節 模型的動態調整過程……………………………………...……41.1技術衝擊下的動態調整過程……………………………………41.2貨幣衝擊下的動態調整過程……………………………………42六節 結論……………………………………………………………45考文獻…………………………………………………………………46錄一 貨幣基數變動法則之推導……………………………….……50錄二 恆定狀態值之求解……………………………………….……51錄三 決策函數之求解………………………………………….……55錄四 第二組參數的模擬結果…………………………………….…5

電力電驛故障檢測裝置

[[abstract]]本專題是由指導老師的指點而感興趣外，也是曾在指導老師的課程中學到工業配線的故障檢修課程，讓我們想要做故障檢測相關的專題，希望能用所學到的知識加以改善。曾經遇過電驛故障所導致動作錯誤並浪費許多時間在線路的查錯上面，往往在花費不必要的時間後才發現電驛的故障，而故障的電驛往往需要另外接上檢測用的電路才能確認故障的電驛，為了使其能更方便、便利的檢測電驛，本項專題透過程式控制Arduino板在控制電驛的腳位，讓我們能夠更快速找到故障電驛的故障接點，在檢查電驛時能更快速、便利的檢查電驛。我們這組希望這電驛故障檢測裝置可以運用在很多地方，例如在做控制盤接線與製造的相關產業，以及工業配線、室內配線相關的教育與培訓單位，皆能透過這項裝置來方便快速的檢測電驛。而本項裝置用法為將電驛插上去就能夠知道這顆電驛功能是否正常，也可以知道故障的電驛哪一個A(常開)、B(常閉)接點故障，讓那些配盤的接線人員能夠第一時間知道這個電驛的好壞，更好的做電驛的更換，提升接線人員配盤的速度並減少故障檢測的時間，此電驛故障檢測裝置也可利用在電驛設備的製造廠商，用以檢查電驛的成品並提升產品的合格率