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Arnold Schoenberg의 three piano pieces, Op.11 분석·연구 : 제1, 3곡을 중심으로
In the early 20th century, most European countries underwent sudden changes in the field of politics, economy and art during World War I(1914-1918). Especially, in the field of art, the new trend "Expressionism", which started in Germany, first came out in paintings. And then, this trend spreaded into the fields of literature, drama, movie, and music. The themes of Expressionism are conflict tension, anxiety, and fear which are inherent in human subconsciousness, and neglected humans who are against convention. To express these things, composers had to try new methods against typical ways, and one of new ones was Atonal. One of major Expressionist is Arnold Schoenberg(1874-1951), and he is called the second Vienna school with Anton Webern(1883-1945), and Alban Berg(1885-1935). Among his piano music, Three Piano Pieces Op. 11 was his first Expressionistic work written in Atonal. In this essay, the first piece was analyzed with emphasis on interval structure, and the third piece on rhythm. As a result, concerning interval structure, it was unnecessary dividing consonant interval and dissonant interval by every single note within Octave having different functions. And a range was expanded by melody's frequent leaps and sudden changes between extremely upper and lower ranges. Concerning rhythm, meter became free by using syncopation and accent. Also, through Dynamic's rapid changes and contrasts and using Harmonics, Schoenberg created new Music usage.
DAMAGE DETECTION FOR BRIDGES USING DEEP EMBEDDED CLUSTERING
ARNOLD JAN BITANGJOL 인하대학교 대학원 2022 국내석사
Bridge damage detection has been employed in structural health monitoring (SHM) system techniques. Techniques such as supervised and unsupervised deep learning can be efficiently utilized in bridge damage detection. However, because unsupervised deep learning does not require labelled data, it is considered a more ideal damage detection technique compared supervised deep learning. Convolutional autoencoder (CAE), an unsupervised deep learning model uses damage-sensitive features (DSF) to detect damage. CAE-based damage detection is done by training the undamaged state DSF from the sensors installed on the bridge and detect outliers in the test data that deviates a threshold. However, identifying the types of damage present on the bridge is a difficult challenge for the CAE-based damage detection. Therefore, this study aims to address the limitation of the CAE-based damage detection by categorizing the types of damage present on the bridge by using deep autoencoder clustering. Specifically, deep embedded clustering (DEC) model, which is based on a long short-term memory (LSTM) network, will be utilized to perform damage detection and clustering. DEC model is composed of two phases: (1) parameter initialization using deep autoencoder and (2) a K-means algorithm to initialize cluster centers and minimize Kullback-Leibler (KL) divergence loss to optimize parameters. Additionally, the data used is based on the study conducted by Lee (2021) on the Sanseong-woocheon-gyo bridge in South Korea.