ENGLISH

26卷/1期

26卷/1期

華藝線上圖書館

Pages:

1-12

論文名稱

結合土地利用迴歸與極限梯度提升演算法發展高雄都會區二氧化氮之推估模型

Title

Development of an Integrated Model for NO2 Variation Prediction in Kaohsiung Metropolis Using Land-Use Regression and XGBoost

作者

翁佩詒、吳治達、蘇慧貞

Author

Pei-Yi Wong, Chih-Da Wu, Huey-Jen Su

中文摘要

暴露二氧化氮 (NO2) 會對人體造成不良健康效應,然而過去空氣污染暴露評估模式仍有高估或低估的問題,因此使用高時空解析度之預測模型探討大範圍暴露濃度有其必要性。本研究以高雄都會區為研究區,使用土地利用迴歸模型、並結合極限梯度提升 (Extreme Gradient Boosting, XGBoost) 演算法,發展高時空解析度之NO2濃度推估模型。結果顯示,土地利用迴歸結合XGBoost模型R2為0.82,均方根誤差為4.53 ppb,具有高度預測與解釋力,十折交叉驗證R2為0.82,顯示模型沒有過度擬合的問題,最後利用此模型推估高雄都會區NO2之時空變異情形,結果發現高值熱點出現在南高雄之工商業發達以及人口密集處。

Abstract

Exposing to Nitrogen Dioxide (NO2) may cause adverse health effects. Previous air pollution estimating models still face overfitting or underfitting problems. Thus, using estimation model with high spatial and temporal resolution to assess NO2 exposure is important. This study utilized Land-Use Regression (LUR) coupled with Extreme Gradient Boosting (XGBoost) algorithm to feature NO2 concentration distribution in Kaohsiung metropolis. The results showed that R2 value for LUR integrated XGBoost model was 0.82, RMSE was 4.53 ppb, which had highly explanatory ability. Besides, 10-fold cross validation R2 for the proposed model was 0.82, which showed that the model did not encounter overfitting issue. Finally, this study used the model to depict estimation maps for NO2 concentration variation in Kaohsiung. The results showed that higher polluted regions were clustered in south Kaohsiung where industries were well developed and population was densely distributed.

關鍵字

空氣污染、二氧化氮、土地利用迴歸、極限梯度提升演算法

Keywords

Air Pollution, Nitrogen Dioxide (NO2), Land-Use Regression (LUR), Extreme Gradient Boosting (XGBoost)

附件檔名

華芸線上圖書館

https://www.airitilibrary.com/Publication/alDetailedMesh?DocID=10218661-202103-202104010009-202104010009-1-12

備註說明

N / A

Pages:

13-29

論文名稱

以虛擬原鄉輔助傳統生態知識教學之研究

Title

Virtual Indigenous Tribe for Teaching Traditional Ecological Knowledge

作者

周孜恆、王聖鐸

Author

Tzu-Heng Chou, Sendo Wang

中文摘要

本研究提出虛擬原鄉 (Virtual Indigenous Tribe),透過沉浸式虛擬實境技術,重現原鄉環境,並融入傳統生態知識,讓都市原住民族學生以更具臨場感的方式認識部落的環境與知識。本研究以臺東成功重安部落作為虛擬原鄉建置對象,將傳統生態知識融入利用無人飛行載具 (Unmanned Aerial Vehicle, UAV)所拍攝之像片和360°全景相機所拍攝的全景像片和全景影片,以Unity軟體開發沉浸式虛擬原鄉APP。本研究與樹林高中原住民族專班合作進行虛擬原鄉教學實測,以認知試題和情意量表前後測問卷作為評量工具。研究結果顯示,虛擬原鄉教學相較於一般教學更能明顯提升學生對於傳統生態知識的認知程度。

Abstract

The indigenous peoples have been living in Taiwan for thousands of years. Comparing to the western-style science education, they have completely different pedagogy to establish and to pass their own environmental knowledge, living styles, and cultures. However, the young generation of the indigenous people is losing their traditional point of view to their environment under current western-style science education system. Especially for the indigenous peoples who moved to cities for living, the opportunities for their young generation to learn how to interact with natural environmental resources are significantly decreased. Based on the above, we proposed a concept called “Virtual Indigenous Tribe” in this study. We integrate traditional ecological knowledge into Immersive Virtual Reality to let urban indigenous youths learn the knowledge in a more intuitive way. We collected traditional ecological knowledge through the interviews with elders, integrating it into panoramic videos and images taken by 360° panoramic cameras and the Unmanned Aerial Vehicle (UAV) to build Virtual Indigenous Tribe. Finally, we use Unity to develop immersive virtual tribe APP. We cooperate with urban indigenous class of Shulin Senior High School to evaluate the effectiveness of Virtual Indigenous Tribe teaching. The results show that Virtual Indigenous Tribe teaching can significantly improve more students' cognition of traditional ecological knowledge than general teaching. In this study, we hope that “Virtual Indigenous Tribe” can be used as an auxiliary teaching tool to enable urban indigenous students to break through the limitations of time and space to learn traditional ecological knowledge in a more authentic way, and increase students’ curiosity about their own tribal history, culture and knowledge.

關鍵字

虛擬實境、傳統生態知識、都市原住民、數位學習、全景像片

Keywords

Virtual Reality, Traditional Ecological Knowledge, Urban Indigenous People, Digital Learning, Panoramic Image

附件檔名

華芸線上圖書館

https://www.airitilibrary.com/Publication/alDetailedMesh?DocID=10218661-202103-202104010009-202104010009-13-29

備註說明

N / A

Pages:

31-43

論文名稱

點雲自動建構向量模型之策略

Title

Automatic Point Cloud Structuralization for Vector Models

作者

宋政哲、莊子毅

Author

Cheng-Che Sung, Tzu-Yi Chuang

中文摘要

本研究基於深度學習技術提出物件角點萃取模型及角點向量化模型,並針對建物點雲中的幾何結構,如板、梁、柱、牆作為向量化之標的物,建立點雲轉換向量模型的自動化機制。經實際測試,本研究在物件分類品質的平均精確度可達到50%以上,物件角點位置之誤差最高不超過25 cm,其中梁柱類別的角點誤差更小於10 cm,而在向量模型的角點連結正確率可達70%以上。顯示本研究方法能有效地將構件點雲自動地轉換為向量模型,同時賦予物件之語義屬性,模型成果可視為後續加值應用或成果精煉的基礎模型,達到提升自動化作業之效益。

Abstract

This study proposes a novel learning-based point cloud structurization method especially for building components such as plates, beams, columns, and walls. The proposed model net learns from the point clouds generated by existing building information modeling (BIM) components to predict the geometric model of a newly given point cloud consequently. It is worthy to note that the BIM-to-Point cloud approach overcomes the difficulty of 3D training data acquisition that is usually confronted in most deep learning applications. The average precision (AP) of each category achieved a precision of above 50% in the classification task, in which the wall category yielded a precision of 77%. On the other hand, the predicted object corner positions reported an accuracy better than 25cm, in which the beam category revealed an accuracy of up to 10cm. Moreover, the quality of the geometric model in vector-linking prediction reached a precision of 70% suggesting that the proposed learning-based method can indeed reconstruct the geometric model of a given point cloud automatically.

關鍵字

點雲結構化、三維深度學習、建築資訊模型、角點偵測、角點向量化

Keywords

Point Cloud Structuring, 3D Deep Learning, Building Information Modeling, Corner Detection, Corner Vectorization

附件檔名

華芸線上圖書館

https://www.airitilibrary.com/Publication/alDetailedMesh?DocID=10218661-202103-202104010009-202104010009-31-43

備註說明

N / A

Pages:

45-55

論文名稱

水清石見-台灣北部水庫水質監測

Title

“Seeing the Pebbles on the Riverbed Only through the Clear Water”: Monitor Water Quality of the Reservoirs in Northern Taiwan

作者

劉芊妤、徐安驊、廖苡均、陳正儒、張維凱、王奕甯

Author

Qian-Yu Liu, An-Hua Hsu, Yi-Jun Liau, Cheng-Ru Chen, Wei-Kai Zhang, Yi-Ning Wang

中文摘要

水質監測是當代重要的環境課題,透過遙測進行水庫水質推估較傳統的人工測點採樣省時省力,且有迅速獲知大範圍水質資訊的優點。本研究使用福衛五號、陸域衛星八號影像與地理資訊系統,選定北台灣石門與寶山水庫,以遙測光譜和庫區水質現地資料建立迴歸模型進行水質監測。數據分析成果顯示兩個庫區水質的迴歸模型在葉綠素a、總磷、化學需氧量的水質推估判定係數皆在0.61~0.75之間。利用迴歸模型能呈現庫區水體整體水質分布,估測水質與岸區土地利用有很高的關聯性。利用遙測以迴歸模型進行水庫水質之空間推估,未來能應用於台灣其他水庫。

Abstract

Water quality monitoring is nowadays an essential environment issue. Compared to the traditional survey, using remote sensing data to estimate the water quality in the reservoir is more time-saving, efficient in a large area. This study utilized the satellite images of FormoSat-5 (FS5), Landsat 8 (L8), and geographic information system (GIS) to monitor the water quality of Shimen and the Baoshan Reservoir. The predicted regression model was built based on the spectral data of the collected satellite images and ground-referenced data of the water quality of the Shimen Reservoir and Baoshan Reservoir. The R2 of chlorophyll-a (ChIa), secchi disk depth (SDD), chemical oxygen demand (COD), and total phosphorus (TP) in Shimen and Baoshan Reservoir are between 0.61 and 0.75. The mapping results can not only present the spatial distribution of water quality in the reservoir area, but also indicate that water quality has high correlation with the land use in the reservoir coastal area. In this study, the procedure of combining remote sensing data and the regression model to estimate the spatial distribution of water quality of the reservoir can be promoted to monitor the water quality of other reservoirs in Taiwan in the future.

關鍵字

水質監測、遙測、福衛五號、陸域衛星八號

Keywords

Water Quality Monitoring, Remote Sensing, FormoSat-5, Landsat 8

附件檔名

華芸線上圖書館

https://www.airitilibrary.com/Publication/alDetailedMesh?DocID=10218661-202103-202104010009-202104010009-45-55

備註說明

N / A

更多活動學刊