情緒辨識是增進人際溝通的重要能力。如生命線、電話客服等應用情境缺乏表情、肢體語言等輔助時，單以語音進行情緒辨識有極高的實用價值。 本研究探討比較支持向量機（SVM）及卷積神經網路（CNN）兩種機器學習方法於訓練「AI語音情緒辨識」分類器模型的表現。我們採用SAVEE和RAVDESS兩個英文語音資料庫，並自行製作與標註「逼逼中文情緒語料庫」。研究結果顯示SVM對SAVEE資料庫單一情緒的辨識正確率達84~94%，個別錄音員正確率達75%，超越官網紀錄的73.7%。同時，實驗顯示深度學習的模型在訓練資料不足的狀況下，反而相對遜色。

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.

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.

本研究使用機器學習方法，改善年長者使用手機時觸控系統對於點按位置判斷之能力。首先設計實驗比較年長者使用手機時，點按位置及手勢判斷的準確率，接著收集年長使用者的觸控軌跡及裝置相關資料，並訓練模型以減少系統判斷的錯誤率和誤差幅度。再比較及分析不同機器學習模型對於本研究之資料的適用程度及經校準後點按位置準確率的提升，進而挑選出一個能夠最有效提升點按位置準確率的模型進行點按位置的預測。實驗過後選擇最有效提升準確率的Random Forest Regressor進行其他的校正實驗及分析。使用者點按位置的預測準確率能被有效提升，準確率能提高32.3%。而最終，將訓練後的模型套回實驗用的手機程式，系統判斷受測者的點按位置能從原本的63.7%提升至97.5%。

平常聽音樂時經常有種似曾相識的感覺。為了描述這種感覺，我們展開了複音音樂片段相似性度量的研究。因為曾經使用過最長公共子序列實作卻效果不如預期，我們將音樂片段正規化後，視為座標平面上的時間、音高點對的集合，使用點對應與二分圖匹配的方法，定義兩個複音音樂片段的相似度為最大權重匹配的平均邊權。我們計算了資料集(JKUPDD)中相同、相異的音樂片段的相似性，調整算法中的參數，找出最適合的參數組合，並且透過音符之間的權重，畫出自相似度矩陣，發現樂曲中的重複片段。

2019年新型冠狀病毒的大流行，佩戴口罩已成為全球防止飛沫傳播病毒成本最低且有效的方法，目前雖已有團隊針對口罩有無正確配戴提出解決方案，但根據收集的資料，目前針對口罩有無正確配戴解決方案通常是使用類神經網路YOLO進行實作，YOLO使用於口罩辨識雖可達到有一定的效果，但對口鼻密合度不佳的細微狀態常有一些誤判的現象，就算民眾有配戴口罩，但若未與臉部、口鼻密合，仍有50％的空氣洩漏機會，無法有效阻隔飛沫傳染，形成防疫破口。 而本研究在這樣的基礎架構下再整合目前最強大的姿勢辨識之一的OpenPose，針對口罩與口鼻密合度不佳的細微狀態進行更深一步地探討，以期達到更好的偵測判斷效果。本研究針對的改善的方向為當神經網路YOLO判定為有配戴正確的資料時，再利用OpenPose以及本研究開發出的鼻心物件演算法，就鼻部密合度做細部偵測，進行誤判修正，最後證實出本算法能篩出56.25%被神經網路YOLO誤判為有戴好口罩的資料，可顯著提升口罩配戴辨識精準度，減少形成防疫破口的機會。

利用深度學習解決醫學問題一直是受矚目的研究主題。鑒於近期新冠肺炎疫情上升，有關新冠肺炎檢測的研究便成了熱門研究主題。目前，最有效的檢測方法是聚合酶連鎖反應 (PCR)，然而，PCR耗時甚久且有人為誤差。因此，以X光影像圖透過深度學習來診斷並分級是一個有效率且安全的做法。在研究中，我們利用深度學習進行疾病診斷，在五元分類上有相當高的準確率(84.91%)、在COVID-19單獨辨識時得到了極高的準確率(99.35%)、產生出疾病熱區及設計了新的分級系統( X-ray Severity Grading System , XSGS)，並將其用於嚴重程度分類，在不同分級下具有可辨別的差異。

According to CNN Indonesia 2020, the demand for e-Commerce in Indonesia has nearly doubled during this pandemic. This surge in demand calls for a time-efficient method for warehouse order-picking. One approach to achieve that goal is by incorporating automation in their warehouse systems. Globally, the market of warehouse robotics is expected to reach 12.6 billion USD by 2027 (Data Bridge Market Research, 2020). In this research, the warehouse system studied would utilize AMR (Autonomous Mobile Robots) to lift and deliver movable shelf units to the packing station where workers are at. This research designed a heuristic algorithm called A.N.T.s (Algorithm for Navigating Traffic System) to conduct task assigning and pathfinding for AMR in the automated warehouse. The warehouse layout was drawn as a two-dimensional map in grids. When an order is placed, A.N.T.s would assign the task to a robot that would require the least amount of time to reach the target shelf. A.N.T.s then conducted pathfinding heuristically using Manhattan Distance. A.N.T.s would help the robot to navigate its way to the target shelf unit, lift the shelf and bring it to the designated packing station. A.N.T.s algorithm was tested in various warehouse layouts and with a varying number of AMRs. Comparison against the commonly used Djikstra’s algorithm was also conducted (Shaikh and Dhale, 2013). Results show that the proposed A.N.T.s algorithm could execute 100 orders in a 27x23 layout with five robots 9.96 times faster than Dijkstra with no collisions. The algorithm is also shown to be able to help assign tasks to robots and help them find short paths to navigate their ways to the shelf units and packing stations. A.N.T.s could navigate traffic to avoid deadlocks and collisions in the warehouse with the aid of lanes and directions.

以將古文翻譯成白話文為初衷，以爬蟲擷取古文解譯網站「讀古詩詞網」中的大量古文及其白話翻譯作為訓練用的資料，並按照不同文體分開訓練。我們先嘗試用Bert模型做選擇題：給一句古文讓機器從四個選項中選出其翻譯。一開始隨機挑選其餘三個選項，正確率高達96%。因此我們挑戰更困難的設置，撰寫搜尋關鍵字的程式，將有與題目古文相同字的白話文放入選項。雖然準確率有些許降低，但仍高於只選重複字最多選項的結果，代表模型有發展出獨立的判定標準。選擇題成功後，我們用MT5 模型嘗試更困難的翻譯，並在訓練集中新增提供不同前後文的注釋資料幫助訓練。雖然還無法翻得非常準確，但仍在某些句子有不錯的表現。我們也發現了模型對某些特定類型字詞的翻譯有待加強，未來希望透過加強代名詞判斷訓練及持續新增注釋來增加整體翻譯能力。

現今的大型活動，如：校慶活動、新北耶誕城等，缺乏互動性與參與感，其中原因大多是觀眾時常埋沒於手機中的社交軟體當中所導致。而我們的研究將利用此特性，探討大眾對於活動的觀點，搭配網路爬蟲抓取使用者的貼文，觀眾只需在Instagram、Twitter等社交軟體中發布文章，系統就會即時推播至活動中的大螢幕上，並且結合圖像辨識快速審核貼文，設計出一套能改善互動性低落的解決方案。研究中我們探討不同的網路爬蟲演算法、圖像辨識技術，及問卷調查等來使作品更加精進，且搭配Line Bot、後臺管理，及常駐貼文等功能來為各類大型活動量身打造，也能夠運用於政令宣導或文宣廣告等用途上，大幅提升活動的互動性與精采程度。