民眾在生活採買與準備食材中，經常面臨一些問題，例如：忘記冰箱食品而導致重複購買與浪費、食品放置過期…等。因此我們提出一個可以解決上述問題的系統，藉由影像辨識來判斷食品品項與移動軌跡，藉此建立冰箱內部的食品清單，並且可透過冰箱觸控螢幕與手機APP，查看與設定清單內容。針對無法辨識的食品，系統可以學習訓練建立影像辨識模型，並針對現有的食品類別進行增量訓練，提高辨識食品的準確率。藉由本研究所提出的系統，可以學習與辨識各項冰箱食品、設定過期提醒通知，與冰箱異常偵測，協助使用者有效且便利地管理冰箱雜物、掌握冰箱的狀態。

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.

目前水冷散熱系統普遍應用在電腦CPU降溫中，雖然水的「比熱容」比空氣及大部分介質都高，但因水冷散熱系統內冷卻液的熱量，最終仍靠風扇送到機殼外，故CPU之最低溫度仍存在一個臨界值。本研究旨在對於「一體式」與「分離式」電腦水冷散熱系統及「熱電致（製）冷晶片」（Thermoelectric Cooling Module）結合進行模組開發設計，將此兩類相關元件搭配結合，以突破傳統水冷式散熱所無法降達的溫度。本研究將「致（製）冷晶片」之致冷端及水冷系統作結合，利用致冷端作為吸收CPU主要熱量，結果發現：與單純只利用風扇將熱量帶走的方式相比，本研究所開發之『第一代』一體式散熱模組與『第二代』分離式散熱模組皆成功地將頂級CPU之工作溫度再壓低，使電腦工作效率維持在最佳範圍。

本研究以廢棄的蚵殼和回收紙作為研究材料，先將鍛燒後的蚵粉溶於水後，噴灑於手機螢幕與電腦鍵盤，並利用ATP生物冷光儀檢測微生物的殘存量，研究發現10ppm與100ppm濃度的自製蚵粉水在手機與鍵盤皆可達98.14%與96.08%以上的殺菌效果。之後再將自製蚵粉水與市售蚵粉水、自製文蛤粉水、水，在門把上做殺菌效果的比較，結果顯示殺菌效果最佳的是100ppm的自製蚵粉水，可達93.23%以上的殺菌效果。接著利用回收紙製作環保鞋墊，並加入自製蚵粉，用以探討加入自製蚵粉後的鞋墊中是否具有抑菌的效果，結果顯示加入5公克自製蚵粉微生物最佳可達89.5%的殺菌效果。

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.

為了解決自動光學檢測的非穿透性檢測物體方式，使用自動X光檢測能解決此問題，因此，本研究嘗試開發自動X光檢測技術，並藉由常見的印刷電路板作為應用。作為結果，本研究能進行X光模擬理想化印刷電路板，搭配實體X光取像，藉由平移堆疊法重建出2.5D印刷電路板影像，並藉由霍夫法圓形辨識圈選錫球，輸入卷積神經網路，辨識錫球焊點之優劣。

本研究使用電腦模擬藥物的對接技術，快速有效的預測新冠肺炎病毒潛力防護精油。第ㄧ階段研究依據化學分子分類蒐集82種精油成份，建立分子結構資料集與新冠肺炎病毒蛋白和人類受體蛋白ACE2共11種蛋白質結構資料集，使用AutoDock Vina 分子模擬對接技術，計算對接能量數值及溫度圖，預測潛力防護精油成分，並探討預測成效。根據對接結果數值，並與目前已發表研究成果交叉比對，預測效果達80%。因此第二階段以30種台灣常見精油之主要成分擴充精油分子資料集，進行分子模擬對接計算，預測具新冠病毒防護力的潛力常見精油。本次科展研究結果所得的潛力防護精油，不僅具深入研究價值，也為後疫情時代提供具高度參考性的生活防疫方式，用以保護我們身邊所愛的家人。

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.

本研究主要整合實驗測量、田口實驗與人工智慧機器學習等方法，發展優化泵浦旋葉技術。首先以3D列印開發多種相異外型族群與不同葉片數目共計82種設計，以實驗探討旋葉構造形狀與泵浦之流量、揚程及效率，進而找出效率較佳的旋葉並作為基底，過程中應用電腦輔助分析軟體進行旋葉內部流場與應力場分析驗證，搭配透明運轉泵浦觀察不同轉速下旋葉內部流體流動狀態，田口法研究結果發現由信躁比與均值分析結果顯示入口斜率為最重要的影響參數、其次分別為旋葉數與出口斜率，影響最小則是上蓋厚度，且優化設計旋葉T3C-10-2-4-4最佳。機器學習方面，經由多元線性回歸訓練模型預測出未知的旋葉效率(Y值)，訓練完成後得到平均絕對誤差Mean Absolute Error (MAE)皆小於1.5。

本研究旨在探討層雲結構產生上的一些物理機制，一般情況為地表附近空氣經由日照加熱而對流上升，而後與冷空氣混和凝結，我們藉由建立一些相關的模型來探討此一現象，利用水中紊流模擬大氣，設計密度界面模擬大氣中密度層變界面，透過染劑以及雷射誘導螢光等技術來觀察，而後探討紊流穿透界面或與界面上溶液混和時的相關現象，並利用一些計算，例如達西-魏斯巴赫方程或渦量方程式等，去解釋這樣的模型，配合電腦程式輔助分析紊流的速度及形狀等特性的關係,，再用不確定度去評估實驗精確性，連結這個紊流模式與大氣流體中的現象，並可推廣至諸如海底火山噴泉或是工廠汙染物排放。

本研究旨在運用自然語言處理技術，建立辨識惡意程式的模型。首先搜集良性及惡意執行檔，進行反組譯及前處理以建立資料集。使用反組譯後的組合語言檔作為文本，訓練模型以區分良性和惡意程式。研究比較詞袋模型、序列模型、fastText以及不同n-gram對模型的影響，並將結果與其他相似研究比較。 研究結果顯示。詞袋模型以使用multi-hot編碼表現最佳，序列模型有位置編碼的Transformer encoder表現最優。在不同n-gram的比較，2-gram詞袋模型識別惡意程式達到99.6%的F1-score，且本研究的識別準確率優於其他相似研究。

自2020年受到新冠肺炎疫情的影響，許多原有的生活、工作與學習型態都受到了影響。為了控制疫情，減少面對面接觸是其中一種方法，學習模式亦從實體轉變成線上。因學習都是面對鏡頭進行，老師很難掌握學生實際的學習狀況，也不易確認學習的品質。 沒有專注就沒有辨識、學習記憶。鄭朝明（2006）提到專注力與學習有密切關係，線上學習容易受到許多外在環境的誘惑導致專注力下降。本作品提出利用人工智慧中的深度學習，透過學生學習時的鏡頭畫面進行臉部特徵擷取，作為深度學習之分類器的輸入進行辨識，並將辨識出的狀態分析後得到結果。教師可利用分析出的結果進行教學模式的調整，以提升學生學習的狀態與品質。