電腦科學與資訊工程

Wibrazz is a wearable communication tool that allows the teacher, the therapist, the parent to communicate information to the child remotely using the device. Haptic (vibrationbased) feedback is becoming increasingly important in everyday life. A vibrating device that transmits information through clothing can help people with disabilities who have no or limited sensory use to live an integrated life in society without barriers.

本研究提出了一種基於毫米波FMCW雷達的即時居家健康監測系統，與RGB攝影機感測器方案相比，該系統更具成本效益並保護用戶隱私。對於居家健康監測系統來說，除了走、站、坐、臥四種常見的身體動作外，及時發現緊急情況也至關重要。因此，我們支持跌倒偵測以及兩種手勢的識別，一種用於頭痛，另一種用於緊急事件。然而，由於雷達點雲的稀疏和不規律特性，我們提出一種新的身體動作波形表示方式和一套處理程序用來平滑動作波形，利用雷達點雲的三度空間座標和速度在時間序列上的變化波形來表示動作特徵並作為神經網路的輸入，再搭配一輕量級的1D-CNN+LSTM神經網路來實現即時動作辨識。根據實驗結果，此方法可以達到30FPS的輸出效率和94%的辨識準確率。

Medication errors in patients are a global problem. They can negatively affect patients and be costly for hospitals and medical clinics. In 2021, a 28-year-old man with heart problems was admitted to a hospital in Porto Alegre. Due to a pharmacy error and insufficient monitoring in the administration, he received a dose 10 times higher than prescribed. This caused serious and probably irreversible damage to the patient. Reading the news and following the case in the media has encouraged research in scientific databases, searching for information and data on medication errors, as well as emerging technologies to reduce the occurrence of adverse medication events. Based on the findings of an English study that proved that errors occur at the drug prescription stage, the first stage of this research focused on drug dosage errors. The aim of this study is to develop an application based on artificial intelligence that can recognise these errors and help prevent them. The application uses a neural network to analyse prescriptions and warn of possible cases of incorrect dosage. The computer program was developed using a neural network and the drug dosage error recognition system using Python and Keras. The system was trained with 10 drugs and correct and incorrect dosage cases. A graphical interface was created to input and display new case data. Neural networks with different configurations were tested to obtain high accuracy with the training and validation data. A confusion matrix was used to assess the accuracy of the network for cases not used for training. The accuracy was approximately 96%, but problems were found in certain intervals. The errors are due to the need for more training, higher processing capacity and a cloud server. The results of the first stage of the research indicate the feasibility of using a neural network to recognise medication dosage errors and thus preventing the associated risks. Such a method could prevent cases like the one in Porto Alegre. Future studies could incorporate more types of drugs, allergies, drug interactions, pre-existing illnesses and other relevant factors into the system.

本研究旨在探索大型語言模型如何應用於音樂生成故事。研究動機源自音樂作為文化中不可或缺的一部分，但若要以文字精準表達出音樂中的故事情緒尚屬困難，藉由本研究提出的方法可以使故事顧及到音樂的情緒起伏。隨著 AI 的發展，我 們開始看到它們在各領域的應用。這項研究的目的是製作出一個系統能以音樂作為輸入，輸出音樂內的故事，為達成目的，我們結合多個模型。研究使用 PyTorch等工具，並探討文句和音樂的共同表示方法，實現情感匹配。研究結果顯示，音樂和文句情感辨識模型表現不錯，也研發出一個完整的生成流程。目前已有直接生成音樂的模型，也有把音樂統整介紹的模型，卻沒有依據音樂中的情緒生成故事的模型。我們研究就是在解決這個問題，結合到 LLaMA2預訓練模型生成出具情緒浮動的故事，要注意的是 LLaMA2的輸出限制最多只能有 4096個token。我們將此產生過程稱為 MTSPL (Music To Story Procedure with LLaMA)。

Over 30 million souls live in a world of darkness, a number greater than the populations of both Norway and Sweden combined. Every individual deserves the chance to embark on a journey across our magnificent blue planet. Yet, regrettably, little has been done to assist them. With this project, we’re lighting the way for the blind to explore our beautiful world independently, breaking free from dependence and embracing boundless horizons. In order to put our theory of the project into practice & explore the use of artificial intelligence & computer science, we started by collecting the required materials for our project such as micro-controllers, sensors, a pair of glasses, a laptop, and a miniature camera. Then we moved onto creating the project itself in which the digital software programmed onto the hardware plays the key-role, as the sensors and the camera will record the details and information from the surroundings and send it to the laptop for further processing. The camera would be the backbone of our project, as it will stream real-time footage to the laptop which will be analyzed by an open-source object detection model ‘YOLOv8’ for identifying objects. After finishing the base model of our project, we tested it in-front of objects such as toy cars, bikes, people, etc, and the results of the object-detection would be shown on the laptop. To observe this data, we created a device which has different modules and integrations for different functions. For example, we will use our camera and then stream it onto a laptop so the reading and the data can be processed on the laptop by AI using YOLOv8. As mentioned in the start, many people do not possess the ability to see, to assist them we have thought of this device which uses all readings and its analytical skills to analyze data and help them navigate, travel or simply, live a better life.

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。首先在資料集方面，分為 PE資料集與 ELF資料集，均包含良性及惡意執行檔，且蒐集惡意程式時採用多種不同之惡意程式家族。接著對資料集進行反組譯及前處理。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、BERT 以及不同 n-gram對模型的影響。 研究結果顯示。詞袋模型以使用multi-hot編碼表現最佳，PE資料集之F1-score為96.87%；序列模型則是有位置編碼的 Transformerencoder 表現最優。在不同 n-gram 的比較，multi-hot詞袋模型與 TF-IDF 詞袋模型，分別在 2-gram 及 5-gram 有最高的 F1-score。

隨著氣候變遷對人類生活帶來越來越大的的影響，CO2 為氣候變遷的主要驅動因素之一，準確預測二氧化碳（CO2）排放量變得至關重要。 本研究深入探討了各種先進的單變量和多變量時間序列模型，並提出一種新穎的混合集成模型，旨在提升台灣CO2 排放的預測準確性。 我們採用了自1965 年至2022 年的年均數據集，涵蓋CO2 排放量以及天然氣、煤炭和石油的消耗數據，利用標準評估指標來評估模型表現。在多次實驗中，我們選定了三個表現最佳的模型，並通過疊加泛化技術將其預測結果整合至一個元模型。所提出的混合集成模型達到了1.398% 的MAPE 分數，顯示出相較於傳統模型更優越且穩定的性能。 經過全面優化後，本模型可為政策制定者和產業領袖在制定減少CO2排放的決策時提供了可靠的依據。

隨著氣候變遷對人類生活帶來越來越大的的影響，CO2 為氣候變遷的主要驅動因素之一，準確預測二氧化碳（CO2）排放量變得至關重要。 本研究深入探討了各種先進的單變量和多變量時間序列模型，並提出一種新穎的混合集成模型，旨在提升台灣CO2 排放的預測準確性。 我們採用了自1965 年至2022 年的年均數據集，涵蓋CO2 排放量以及天然氣、煤炭和石油的消耗數據，利用標準評估指標來評估模型表現。在多次實驗中，我們選定了三個表現最佳的模型，並通過疊加泛化技術將其預測結果整合至一個元模型。所提出的混合集成模型達到了1.398% 的MAPE 分數，顯示出相較於傳統模型更優越且穩定的性能。 經過全面優化後，本模型可為政策制定者和產業領袖在制定減少CO2排放的決策時提供了可靠的依據。

"Blindness keeps you from things, deafness keeps you from people" (Helen Keller) Wibrazz is a communication tool that can be placed inside sportswear. Two versions have been developed. The simpler one allows hearing-impaired footballers to compete in the league with other athletes. The referee is given an additional device to give a signal when he blows his whistle. The hearing-impaired footballer then senses the signal from the device he is wearing and knows that he must pay attention to the referee. The complex version speeds up communication between the coach and the players during training sessions. It allows the coach to send simple messages to his players using his smart device. The athlete senses the signal from the device and acts on what has been previously discussed (e.g. a long signal means, "Everyone come to me!") With over 70 million deaf people worldwide, and 2-4 out of 1000 people in the United States who are functionally deaf, this can affect an individual's mental and physical well-being, and it is therefore a pressing issue to provide these athletes with the means to develop their talents in a traditional team environment. In addition to the organisations within countries, the ICSD is present on the international stage. Their importance is demonstrated by the fact that the 2023 Deaf Football World Cup featured teams from countries such as the United States, Germany, England and Japan.

在高度數位化的社會，存在大量的數位影像資料，在不影響視覺品質之前提下如何標記持有者或資料來源是一重要課題，而不可視浮水印機制是可行的解決之道，本研究運用對抗性機器學習技術的概念及深度學習技術研發設計數位影像之浮水印機制，研製偵測器與註冊器。本研究設計研製之偵測器與註冊器可以處理任意大小的影像，經實驗分析具高度的保真性，並具可以承受 JPEG 中度品質(qlt=50)的失真壓縮攻擊，解壓縮還原之已嵌浮水印影像偵測器仍舊可以有效判定具浮水印。本機制可以結合網站、伺服器或影像設備為其提供的數位影像嵌入浮水印，在不影響視覺感官的前提下標記來源或持有者。