74,453 research outputs found
An examination of the project method as an instrument of teaching religion
Full text linkThesis (Ph.D.)--Boston University
PLEASE NOTE: page iii appears to be missing from the thesis. Our determination is that this is the result of misnumbering by the author, and no substantive content is actually missing. If you are able to determine otherwise, please contact us
Optimization of operation rule curves and flushing schedule in a reservoir
No full textFlushing sediment through a reservoir has been practiced successfully and found to be inexpensive in many cases. However, the great amount of water consumed in the flushing operation might affect the water supply. To satisfy the water demand and water consumed in the flushing operation, two models combining the reservoir simulation model and the sediment flushing model are established. In the reservoir simulation model, the genetic algorithm (GA) is used to optimize and determine the flushing operation rule curves. The sediment-flushing model is developed to estimate the amount of the flushed sediment volume, and the simulated results update the elevation-storage curve, which can be taken into account in the reservoir simulation model. The models are successfully applied to the Tapu reservoir, which has faced serious sedimentation problems. Based on 36 years historical sequential data, the results show that (i) the simulated flushing operation rule curves model has superior performance, in terms of lower shortage index (SI) and higher flushing efficiency (FE), than that by the original reservoir operation; (ii) the rational and riskless flushing schedule for the Tapu reservoir is suggested to be set within an interval of every 2 or 4 years in the months of May or June
Wen xuan kao yi
No full textV.1-14. 文選 : 六十卷 -- v.15-16. 文選考異 : 十卷.梁昭明太子撰 ; 李善注. 文選考異 : 十卷 / 胡克家撰.綫裝, 2函.據鄱陽胡氏校刊宋淳熙本影印.《文選》內封背頁印有"宋淳熙本重雕, 鄱陽胡氏藏版" ; "上海會文堂新記書局精印". 《文選考異》內封背頁印有"鄱陽胡氏"卷一卷端題下載有"鄱陽胡氏果泉手校"印, "廣圻審定"印, 及"彭兆蓀讀"印.Liang Zhaoming tai zi zhuan ; Li Shan zhu. Wen xuan kao yi : shi juan / Hu Kejia zhuan.Xian zhuang, 2 han.Ju Poyang Hu shi jiao kan Song Chunxi ben ying yin."Wen xuan" nei feng bei ye yin you "Song Chunxi ben chong diao, Poyang Hu shi cang ban" ; "Shanghai Hui wen tang xin ji shu ju jing yin". "Wen xuan kao yi" nei feng bei ye yin you "Poyang Hu shi"Juan yi juan duan ti xia zai you "Poyang Hu shi guo quan shou jiao" yin, "guang qi shen ding"yin, ji"peng zhao sun du"yin.V.1-14. Wen xuan : liu shi juan -- v.15-16. Wen xuan kao yi : shi juan
A Study on Zhu He-Ling's "Explanatory Notes of the Poetry Collection of Li Yi-Shan"
No full textAmong those who interpreted the poetry of Li Shang-Yin in the early Qing Dynasty, Zhu He-Ling was the one that took the lead. He interpreted the poetry of Li Shang-Yin on Qian Qian-Yi\ue2s instructions. It was a time of dynasty changes and literary inquisition, similar to the late Tang Dynasty. The poetry of Li Shang-Yin, poet of the late Tang, was ambiguous and obscure, which provided an opportunity for Zhu who was in the same situation as Li-Shang-Yin to display his commentary ability. Zhu adopted the method of \ue2zhi ren lun shi\ue2 to interpret Li\ue2s poems, which is a method of understanding a person by researching the historical background. After \ue2Explanatory Notes of the Poetry Collection of Li Yi-Shan\ue2 was published, it evoked resonance among readers. Therefore, it is now an important book for studying Li\ue2s poems. If the process of how Zhu finished the book could be understood and the essence and features of the book could be outlined and summarized, it would be beneficial for researching the poet Li Shang-Yin and his poems.
This dissertation is composed of five chapters. The first chapter- introduction- contains the purpose and method of the study. The life and characteristics of Zhu and the gist of \ue2Explanatory Notes of the Poetry Collection of Li Yi-Shan\ue2 were also introduced. The second chapter-the historical background of Zhu He-ling\ue2s interpretation of Li Yi-Shan\ue2s poems-indicates that the author wrote and developed the concept of shishi (\ue8\ua9\ua9\ue5\ub2) and bixin (\ue6\uaf\ue8) under the influence of the political and social environment and the academic atmosphere at that time. This chapter also discusses the process of how Zhu finished the book and compares the differences in different versions in order to highlight the outcomes of Zhu\ue2s studies. In chapter three-the style and structure of \ue2Explanatory notes of the poetry collection of Li-Yi-Shan\ue2- the style and structure of the explanation, interpretation and quotation in the book were analyzed. By doing this, Zhu\ue2s devotion to interpreting Li\ue2s poems and to preserving predecessors\ue2 works could be easily seen. Chapter four-the contribution and defect of \ue2Explanatory notes of the poetry collection of Li-Yi-Shan\ue2-depicts the contribution and defect of the book for those who study Li Shang-Yin\ue2s poems. Chapter five-conclusion- summarizes the main ideas from chapter two to four in the hope that the whole picture of \ue2Explanatory notes of the poetry collection of Li-Yi-Shan\ue2 could be understood
A Study of Artificial Intelligence Techniques for the Estimation of the Arsenic Variation in the Regional Groundwater System
No full text人工智慧廣泛應用於水文系統中，提升水文量預測及推估準確性，但是鮮少案例應用於地下水水質推估，而地下水水質具有污染不易察覺、變異性大、影響因子不確定、易受到周邊水域環境影響及資料取得不易等特性，一般傳統模式難以推估，另一方面，砷物質存在於地層中，已被證實是造成烏腳病主要原因，對人體健康危害相當嚴重，實有必要建立可靠地下水中砷濃度推估模式，掌握地下水中砷污染情形，因此，本研究主要目的為應用類神經網路模式推估地下水中砷濃度變化。
本研究以地下水受嚴重砷污染之台灣西部雲林縣沿海地區為研究區域，並採用水利署1992年至2005年設置於本區28座監測井之水質資料為分析對象，地下水水質採樣期間，因經費或人為因素，部份監測井停止採樣或資料缺漏，影響後續對地下水中砷污染擴散機制之瞭解，為補遺本區監測井之砷濃度資料，本研究第一部分採用倒傳遞類神經網路，建立空間模式補遺地下水中砷濃度資料，在建立模式過程中遭遇到資料過少，模式過度訓練問題，應用主成份分析、交叉驗證法及修正型目標函數加以改善，有效提升模式推估精確度；另考量在尋求最佳推估模式架構及參數過程中，由於模式架構不確定與參數過多需耗用大量優選時間，且無法獲得可最佳模式，故本研究應用遺傳演算法之強大搜尋能力優選最佳模式之架構，其中優選項目包括輸入層因子、隱藏層之神經元個數及修正型目標函數之係數，成功解決模式之架構與參數不確定問題。
本研究之第二部份，主要考量砷在地下水環境中受到多種水質因子影響，故深入探討砷與其他地下水水質間之相關性，從而利用水質因子建立地下水中砷濃度之推估模式，模式分為單一水井模式及區域模式，整體而言，以單一水井模式推估結果較佳，但是，區域模式應用範圍較廣，可展現區域地下水中濃度特性，最後，本研究將觀測與本模式推估之地下水中砷變化結果繪出地下水中砷污染潛勢圖，展現本研究區域內1992至2005年砷濃度在時間與空間變化，提供政府單位與相關研究者了解地下水中砷變化情形及傳輸機制，有效減少居民誤飲用高砷地下水之風險，達到有效管理及利用地下水之目的Artificial intelligence is extensively applied to hydrological systems and is successfully implemented in the quantitative estimation of water quality. However, artificial intelligence techniques are seldom employed in the prediction of groundwater quality. The features of the groundwater pollution include imperceptibility, complex affective factors and limited data. It is not easy to employee traditional models for estimating the water quality in groundwater systems. Arsenic (As) proves to be a main factor of black-foot disease and threatens the health of residents. Constructing a reliable model for estimating arsenic concentration in groundwater is essential. Therefore, the aim of this study is to construct an artificial neural network (ANN) model for estimating arsenic concentration in groundwater systems.
From 1992 to 2005, the government takes into account the serious arsenic pollution that occurred in the coastal area of the Yun-Lin County in Taiwan and set up 28 monitoring wells for investigating the pollution in groundwater. The collected water quality data were used when constructing models in this study. However, due to limited budget and/or human factors, some arsenic concentration data from these wells were missing, which affects the realization of the pollution in groundwater. The first subject of this study is to construct a spatial model for estimating missing data by applying ANN. During the process of model construction, inaccuracy and over-fitting commonly occur in sparse data. To overcome these problems, the principal component analysis, the cross-validation and the modified performance function are employed when constructing the model. These methods have the ability to effectively alleviate the over-fitting problem and improve model accuracy. On the other hand, searching and identifying the optimal ANN structure is quite time and labor consuming. Genetic algorithm is used to identify the effective input factors and the suitable number of neurons in hidden layer.
Another subject of this study is to build a water quality assessment model for arsenic concentration by analyzing the relationship between arsenic concentration and other water quality factors in groundwater. This subject has two scenarios: one is for the single well model; and the other is for the regional model. Results indicate that the affective factors of arsenic concentration significantly vary from the north to the south in the coastal area of the Yun-Lin County. Overall, the single well model performs better than the regional model, despite that the regional model can be extensively applied over the study area. Finally, the results of the spatial and water quality models are applied to displaying the distribution map of arsenic pollution so that groundwater managers can easily realize the temporal and spatial variation in arsenic concentration during 1992 and 2005. The information of arsenic variation can reduce the risk of drinking contaminated groundwater for local residents and effectively enhance the control and management of arsenic pollution in groundwater
Feature Analysis for Emotion Recognition from Mandarin Speech Considering the Special Characteristics of Chinese Language
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Artificial neural networks for estimating regional arsenic concentrations in a blackfoot disease area in Taiwan
No full textHigh arsenic concentrations in groundwater have been detected in the south-western coastal area of Taiwan. In this study, artificial neural networks (ANNs) were investigated for their applicability to recovering the missing arsenic data and constructing the spatial distribution of arsenic concentration based on the arsenic concentration data of 28 groundwater observation wells. Due to a limited number of data sets, several strategies were proposed to construct the backpropagation neural networks (BPNs). The leave-one-out (LOO) cross-validation was adopted to diminish the bias in choosing validation data, and the modified performance function (MPF) was applied to reducing an over-fitting situation. Principal component analysis (PCA) was employed to transform the arsenic concentration of the regional wells into a limited number of main factors that were used as the input variables for the ANNs. Results showed that the LOO cross-validation was an effective tool for model selection, and the parameter, γ, of MPF played an important role for reducing errors in the model training and validation processes and alleviating the problem of over-fitting. Although sparse data sets have been used to construct ANNs, the models still achieved acceptable performance. The predicted spatial distribution of the arsenic concentration can provide useful information to local residents when groundwater achieves high levels of arsenic concentrations in non-functioning groundwater monitoring wells
Applying ANNs for Estimating the Regional Arsenic Pollution in Groundwater
No full text本研究中應用類神經網路建立地下水砷濃度推估模式，以解決高度非線性砷汙染傳輸問題，及提高砷濃度推估之準確性。本研究以雲林縣沿海地區為研究區域，採用類神經網路建構地下水中砷推估模式，模式分為單一水井水質及區域水井水質之類神經網路模式，單一水井水質類神經網路模式主要針對單一監測井藉由砷與其他水質因子相關性，建立類神經網路推估模式，區域水井水質類神經網路模式則是應用全區資料，建立適用研究區域範圍之砷濃度推估模式，研究中除探討輸入因子及網路架構對模式誤差之影響外，並針對地下水水質模式較易遭遇到資料過少問題，提出交叉驗證法及修正型目標函數加以改善模式推估誤差，其中單一水井水質類神經網路模式之平均誤差 (rmse) 為 65.7 ug/l，區域水井水質類神經網路模式之平均誤差 (rmse) 為 112 ug/l，大部分監測井推估結果屬可接受範圍，僅#7監測井誤差較大，但對於本區砷變動較大且複雜地區，而監測資料有限下，藉由類神經網路達到可接受誤差，本模式成功解決過去傳統模式不易推估區域地下水中砷污染問題。最後，本研究將模式推估結果結合地理資訊系統 (GIS) 展示雲林縣沿海地區地下水中砷污染分布情形，可作為日後政府管理地下水之參考依據。
The groundwater extracted by some regional farmers leads to a lower level of the groundwater and a release of the poisonous substance in the groundwater. That affects the health of local residents, even the inhabitants who do not ingest the local agriculture and aquaculture products. The aim of the study is to build the arsenic water quality model by adopting the artificial neural networks (ANNs). Taking the YUN-LIN County as an example, the ANNs were constructed and assessed. The models are divided into two parts: (1) single well models and (2) regional well models. In the process of constructing the models, the optimal input factors and structures of the ANN models were discussed in this study. At the same time, we applied the cross validation method and the modified objective function to solving the data scarce problems of the monitoring wells. The results produced by the single well models and the regional well models were compared and demonstrated their applicability. Finally, the results obtained by the ANN models were integrated with GIS to display the distribution of the arsenic concentration at the coastal area in the YUN-LIN County. The results can offer a good reference to government decision-makers for the management of the groundwater and installation of monitoring wells
Ch-u-tz'u Found in the Annotations of Li-Shan What(e)n-hsuan (III)
No full text本稿は、第2報に引続き『文選』李善注に引用されている『楚辞』について考察したものである。In the previous study,the differences between Ch'u-tz'u quoted in Li-Shan Chu and the present Ch'u-tz'u were investigated from the viewpoint of the revision of the present Ch'u-tz'u.In the present study,the author has investigated the following problems
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