本研究旨在利用深度學習實作改變字體風格的模型，試圖讓生活中處處可見的文字變得活潑、生動，並省去許多設計字體所需的時間、精力。我們利用了風格矩陣、對抗式生成網絡(GAN)等概念設計了3種模型，並在實際運行後進行分析與比較。 VGG模型運用了卷積神經網絡的概念，擷取特徵以生成風格矩陣並將其進行字體風格轉換。Bicycle-GAN模型結合了Conditional GAN和VAE的優點，能夠生成多樣風格的字體。Zi2Zi模型則是在pix2pix的方法改進部分缺點，能讓生成的字體更逼真，且具有顯著的成效。 經過多次的嘗試和失敗經驗，我們發現VGG、Bicycle-GAN、Zi2Zi，3種模型在字體風格轉換上具有相當的潛力，在經過適當的後處理之後，能夠在耗費少量時間和樣本的情況下，生成實際套用在電腦中的個人化字體。

本研究以稠密式深度預測為基礎，進行環繞掃描３Ｄ還原建模。在這項研究中，我們擇手機作為唯一的外部數據提供設備。首先用手機同時錄影和記錄角度變化、位移數據後，我們同步各數據的時間點，並計算每一幀影像的位置，接著將拍攝的影像輸入深度預測模型轉換成深度圖並將其儲存為點雲，最後利用三維旋轉和平移矩陣將點雲轉回正確的位置以進行疊合。我們開發的３Ｄ還原建模系統能夠輸入影像、特定時間點的位置和角度變化來建立３Ｄ模型。此系統可以解決結構光掃描儀需要額外標記點的劣勢，且在機動性上較傳統的點對點雷射掃描儀更方便。我們希望可以在未來將這套系統集成為一個手機應用程式，方便使用者在手機上進行相關操作。

鐵路時刻表排點至今為止仍十分仰賴人工作業，且排班優劣對於乘客服務品質有顯著影響。本研究採用元啟發式最佳化演算法及模擬器進行旅客列車鐵路時刻表排點，旨在找出一份針對旅客需求、提升旅客運輸成功率且降低旅途時間的時刻表。我們提出一種班表編碼機制，可依此產生班表草稿。我們研發的模擬器可將班表草稿轉換為合法無衝突之班表。最後，透過登山演算法及基因演算法來搜尋班表草稿，並以模擬器評估優劣，我們實現了一個自動化排班系統。我們也以不同的乘客測資針對於不同演算法進行比較，並試圖找出表現最好的演算法。實驗結果指出我們的模擬器能有效率產生無衝突時刻表，且所提出之演算法操作相較現行班表可提升運輸成功率和降低旅途時間。

The number of natural disasters has risen significantly in recent years, and with climate change there is no end in sight. Consequently, the demands on rescue forces around the world are increasing. For this reason, I asked myself what I can do to improve the work of rescue teams. Advances in artificial intelligence and drone technology enable new possibilities for problem solving. Based on the technological advances mentioned above, an autonomous Search and Rescue drone was developed as part of this project. The system assists rescue workers in searching for survivors of natural disasters or missing people. This paper also suggests a method for prioritizing survivors based on their vitality. The system was implemented using a commercial Parrot ANAFI drone and Python. The software was tested on a simulated drone. To simplify the development, the whole system was divided into the following subsystems: Navigation System, Search System and Mission Abort System. These subsystems were tested independently. The testing of solutions and new concepts were performed using smaller test programs on the simulated drone and finally on the physical drone. The Search and Rescue system was successfully developed. The person detection system can detect humans and distinguish them from the environment. Furthermore, based on the movements of a person, the system can distinguish whether the person is a rescuer or a victim. In addition, an area to be flown over can be defined. If something goes wrong during the mission, the mission can be aborted by the Mission Abort System. In the simulation, the predefined area can successfully be flown over. Unfortunately, controlling the physical drone does not work. It stops in the air after takeoff due to the firmware of the drone. It does not change the flight state of the drone, which results in all subsequent commands from the system being ignored. This paper shows that artificial intelligence and drone technologies can be combined to deliver better rescue services. The same system can be applied to other applications.

本研究旨在研究一款自創棋類遊戲「梅花棋」，找出效率最佳的演算法及分析AI的適用性。遊戲規則如下：雙方玩家輪流在19階的棋盤上下棋，先手執黑子，後手執白子，任一方形成梅花即獲勝。隨著棋子的增加，肉眼判斷勝負愈發困難，因此希望借助電腦的力量完成它。我們透過C++編寫程式，持續改良優化演算法，提升電腦的精確度與流暢度。過程中依序提出了平均演算法、畢氏定理演算法、向量演算法、以及網狀編碼演算法。目前最新版本中，我們使用含有螺旋編碼表的網狀編碼演算法，這可使電腦快速正確地判斷勝負。得到最佳的演算法後，我們嘗試運用撰寫Minimax演算法編寫AI，並且不斷增加演算法的深度，從而提升電腦的實力。透過Victory notion的概念分析兩者間的相似度，判斷其對於梅花棋的適用性。透過不斷與Minimax演算法測試遊戲，將梅花棋規則中，先後手的優勢差距逐漸縮小。目前本研究已可順利進行單純的雙人對戰與複雜的人機對戰模式。

本次科展主題為探究屏東與臺灣其他地區海灘樣本的差異。我們選擇了具代表性的屏東地區海灘作為主要採樣點，利用數位顯微鏡比對外觀特徵，電腦影像軟體分析色調，透過篩網分析海灘樣本顆粒大小比例，並檢測其酸鹼性、硬度和KH等性質。 結果顯示，屏東和小琉球海灘主要由珊瑚、貝殼和少量有孔蟲殼體等生物碎屑組成，呈白黃色調；而臺灣其他地區的海灘沙粒主要由岩石碎屑組成，呈灰黑色。單位體積海灘樣本的重量測量結果並無明顯差異，水溶液的酸鹼值大多呈弱鹼性，TDS數值顯示不同區域的海水組成和溶解物有所差異，但屬於珊瑚砂海灘樣本的KH值平均超過10。這些差異是臺灣地理位置、河流侵蝕搬運堆積和沿海海流等因素共同作用的結果。

Triage is the process by which nurses manage hospital emergency departments by assigning patients varying degrees of urgency. While triage algorithms such as the Emergency Severity Index (ESI) have been standardized worldwide, many of them are highly inconsistent, which could endanger the lives of thousands of patients. One way to improve on nurses’ accuracy is to use machine learning models (ML), which can learn from past data to make predictions. We tested six ML models: random forest, XGBoost, logistic regression, support vector machines, k-nearest neighbors, and multilayer perceptron. These models were tasked with predicting whether a patient would be admitted to the intensive care unit (ICU), another unit in the hospital, or be discharged. After training on data from more than 30,000 patients and testing using 10-fold cross-validation, we found that all six models outperformed ESI. Of the six, the random forest model achieved the highest average accuracy in predicting both ICU admission (81% vs. 69% using ESI; p<0.001) and hospitalization (75% vs. 57%; p<0.001). These models were then added to an Android application, which would accept patient data, predict their triage, and then add them to a priority-ordered waiting list. This approach may offer significant advantages over conventional triage: mainly, it has a higher accuracy than nurses and returns predictions instantaneously. It could also stand-in for triage nurses entirely in disasters, where medical personnel must deal with a large influx of patients in a short amount of time.

The number of natural disasters has risen significantly in recent years, and with climate change there is no end in sight. Consequently, the demands on rescue forces around the world are increasing. For this reason, I asked myself what I can do to improve the work of rescue teams. Advances in artificial intelligence and drone technology enable new possibilities for problem solving. Based on the technological advances mentioned above, an autonomous Search and Rescue drone was developed as part of this project. The system assists rescue workers in searching for survivors of natural disasters or missing people. This paper also suggests a method for prioritizing survivors based on their vitality. The system was implemented using a commercial Parrot ANAFI drone and Python. The software was tested on a simulated drone. To simplify the development, the whole system was divided into the following subsystems: Navigation System, Search System and Mission Abort System. These subsystems were tested independently. The testing of solutions and new concepts were performed using smaller test programs on the simulated drone and finally on the physical drone. The Search and Rescue system was successfully developed. The person detection system can detect humans and distinguish them from the environment. Furthermore, based on the movements of a person, the system can distinguish whether the person is a rescuer or a victim. In addition, an area to be flown over can be defined. If something goes wrong during the mission, the mission can be aborted by the Mission Abort System. In the simulation, the predefined area can successfully be flown over. Unfortunately, controlling the physical drone does not work. It stops in the air after takeoff due to the firmware of the drone. It does not change the flight state of the drone, which results in all subsequent commands from the system being ignored. This paper shows that artificial intelligence and drone technologies can be combined to deliver better rescue services. The same system can be applied to other applications.

本研究係運用多項式擬合技術，表示出股價的年週期循環特性及趨勢，製作出可顯示個股股價年週期循環趨勢的應用程式。 先觀察市面上常見的股票技術分析，接著用Octave進行研究，最後運用Python製作出包含使用者介面的應用程式，並將分析結果量化及輸出，本研究將此應用程式稱為「股價年週期循環技術分析應用程式」。

「溫室效應」會造成地球暖化，使地球環境惡化。但「溫室效應」並非顯而易見，因此不易理解，現在結合自然科學與科技課程，於微型玻璃屋中，探討二氧化碳對溫室效應的影響。 首先在玻璃屋外進行「空白試驗」，再於玻璃屋內進行溫室實驗。打氣幫浦輸出的氣體經由三通管控制「繞流」(Bypass)，通過氯化鈣去除水氣，約略可視為「零空氣」(Zero Air)，以人工光源照射充滿「零氣體」的玻璃屋作為「對照組」，再比較充滿不同濃度的二氧化碳氣體作為「實驗組」，應用Arduino開發板、感測器、液晶螢幕與記憶卡，能即時顯示數據、記錄資料，再以Excel繪製圖表，也可連接電腦進行現場環境監測。 從數據分析，「溫室效應」確實發生，所以應該儘量減少二氧化碳排放至大氣中。

本研究旨在改善聽障人士無法完整接收影音類型資訊的狀況，探討各種影片處理技術，尋找、嘗試並比較各種方法，整合出最適合的系統自動替影片嵌入情境化字幕——用視覺的方式呈現影片聽覺訊息，讓聽障人士便於理解各種類型的影片內容與資訊。 為此，我們呈現的情境化字幕有主要幾個特點： 1、將聲音對話轉為字幕標記在說話者旁，透過畫面中語句位置就可以了解跟語者的對應關係。 2、畫面中字幕會以漸漸上飄消失的泡泡字幕來呈現，使觀影者有充足時間閱讀字幕理解內容。 3、將環境音效如電話聲、雷聲與貓叫聲等各種能傳達資訊的聽覺訊息標示在畫面中。 藉由這些處理使畫面呈現更豐富的影片資訊，最終達到改善聽障人士資訊接收權益不平等的目標。

本研究探討平面中三圓的關係，其中圓O固定不動，圓O1以逆時針方向滾動且繞行圓O；而圓O2同時也以逆時針方向滾動且繞行動圓圓O1。其中圓O1與圓O2上各有一動點P、Q。三圓一開始為圓O2分別與圓O和圓O1外切，且O 、Q、P三點成一直線，Q點介於O 、P兩點之間。當 ̅OO1與x軸夾角為θ時，先以繪圖軟體了解動點的軌跡；其次以三角比的概念求得P、Q兩點的坐標；最後再以電腦繪圖軟體，製作當三點共線時之θ值，並藉由函數圖形了解三點之中何者介於其他兩點之間。本研究由原本的三圓外切的情形，邁向討論三圓外離的情況，猶如恆星、行星、衛星三者的運行，進而以各圓半徑、兩圓連心距、繞行角度為變數，歸納合理的數學式，以利日後進行更廣泛的研究。