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Estimation Of Various Walking Intensities And Plantar Tissue Stiffness Based On Plantar Pressure Data By Using Artificial Intelligence Technology

為了解決Python list(range)的問題，作者SUNARDI 這樣論述：

Walking has been shown to benefit individuals include Diabetes Mellitus (DM) patients and peripheral artery disease. However, brisk walking and continuous walking could produce repetitive loads and stresses on the plantar foot resulting in increased plantar tissue stiffness and peak plantar pressur

e (PPP), leading to a high risk of foot ulcer formation and tissue injury. Therefore, quantifying the walking intensity is essential for rehabilitation interventions to indicate suitable walking exercise.This study is divided into three objectives. First, this study aims to identify differences in w

alking speeds to the plantar pressure response using deep learning methods, including Resnet50, InceptionV3, and MobileNets. Second, this study proposed a machine learning model to classify the walking speed and duration using plantar region pressure images. Third, prediction of plantar tissue stiff

ness based on plantar stress pattern using vision transformer. The F-scan system (Tekscan, South Boston, MA, USA) was used to measure plantar pressures during walking. An elastographic ultrasound (Aloka Pro Sound Alpha 7, Hitachi Healthcare Americas, Twinsburg, OH, USA) with a linear array transduce

r (UST-5412, 5–13 MHz, Hitachi Healthcare Americas) was used to measure plantar tissue mechanical property. In the first study, the deep learning models were used to classify the plantar pressure images of healthy people walking on a treadmill. The design consisted of three walking speeds (0.8 m/s,

1.6 m/s, and 2.4 m/s). The second study, an Artificial Neural Network (ANN), was adopted to develop a model for walking intensity classification using different plantar region pressure images, including the first toe (T1), the first metatarsal head (M1), the second metatarsal head (M2), and the heel

(HL). The classification consisted of three walking speeds (i.e., slow at 0.8 m/s, moderate at 1.6 m/s, and fast at 2.4 m/s) and two walking durations (i.e., 10 min and 20 min). The third study used vision transformers to predict the relationship between plantar tissue stiffness with a plantar pres

sure pattern image.The experimental results show that artificial intelligence technology could predict walking intensity and analyze the relationship between plantar tissue stiffness and plantar pressure pattern image. The first study indicated that Resnet50 had the highest accuracy compared to Ince

ptioanV3 and MobileNets on analyzing plantar pressure distribution images. Furthermore, the experimental results of estimation of walking speed and duration based on four regions of plantar pressure (i.e., T1, M1, M2, and HL) with an ANN showed that the T1 region was more easily recognized by the AN

N model, as evidenced by the highest F1-score value than other regions. Meanwhile, detection of the relationship between plantar tissue stiffness with plantar pressure pattern images showed that vision transformers could map the relationship between plantar tissue stiffness and plantar stress patter

n images.

自然語言處理技術應用於科技華語詞彙分析

為了解決Python list(range)的問題，作者廖邵瑋 這樣論述：

本研究透過自然語言處理技術進行科技華語真實語料分析，以《泛科學》11067篇文本作為訓練資料，分別訓練LDA主題模型以及Word2Vec詞向量模型，欲藉此輔助科技華語詞彙教學。在國際化與科技發展的交互作用之下，來華就讀自然科學相關科系的外籍學習者日益增加。為滿足其修習專業課程以及與同儕進行學術交流之學習需求，科技華語課程需銜接通用華語與科技學術華語之間的落差。然對此專業領域的語言相關研究尚有不足，使得科技華語課程與教材存在兩大問題，一是無法針對學習者不同科系的專業選出合適的詞彙進行教學，二是缺乏科技文本語境中詞彙的使用方式分析。為此本研究將聚焦於以下研究目的：第一，篩選不同學科領域主題之科技

華語選詞範圍，並提出參考詞表；第二分析科技華語詞彙於通用華語及科技華語語境中的共現詞差異；第三，比較科技華語近義詞之使用情境與共現詞。首先，本研究根據LDA主題模型的建模結果發現，科普文本中存在「食品科學、營養學」、「生物學、生命科學」、「醫學、藥學、公共衛生」、「學術生活」、「資訊通訊科技、電機電子工程」、「地球科學、環境科學」、「天文學、航太工程」、「物理學、化學、材料科學」與「神經心理學、統計學」九個潛在的科技主題。接著，將各主題的關聯詞彙以國家教育研究院的詞語分級標準檢索系統進行詞彙難易度分級，建置科技華語各領域主題推薦詞表。其後，以上述詞表中的科技詞彙作為示例，應用Word2Vec模

型計算詞彙之間的語義相似度，比較科技詞彙於通用華語和科技華語語境中的使用差異，並進行科技華語近義詞分析，以期作為科技華語詞彙教學之參考。