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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 彭雲明(Yun-Ming Pong) | |
dc.contributor.author | Shu-Chun Chen | en |
dc.contributor.author | 陳淑君 | zh_TW |
dc.date.accessioned | 2021-06-13T00:04:36Z | - |
dc.date.available | 2007-07-31 | |
dc.date.copyright | 2007-07-31 | |
dc.date.issued | 2007 | |
dc.date.submitted | 2007-07-30 | |
dc.identifier.citation | chapter 1
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Belknap Press of Harvard University, Cambridge, Massachusetts, London, England). | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/28298 | - |
dc.description.abstract | 在這個研究中,分成兩大部分。第一部份主要是提出一套新的分析方法,稱
為層級切割法(Hierarchical Segmentation) ,尤其在處理非穩定性的時間序列 資料。層級切割法主要的想法,是利用編碼的方式,對資料進行分類 (clustering)。經過三層的編碼後,我們很輕易的將連續的資料,依據其本身所 表現的特性,切割成數段。在最上層的編碼裡,代碼相同的區段,表示他們具有 相似的特性。這樣的分析方法,尤其在連續資料中,對於模式(pattern)或是某一 特別區段的辨識(identifying)上,是一個非常有用的工具。對於分析方法的理論 發展上,也不需要像傳統時間序列對於資料需要有穩定性的假設,是一個很值得 期待的方法。我們也將這個方法實際應用在不同領域的分析上。在此論文中,我 們探討了在生物方面,有關生物時鐘、豆象產卵行走的軌跡的問題;在心理學方 面,探討夫妻之間情緒的變化和之間的相互影響。 在生物時鐘方面,每一個時間點,對於生物而言都是一個相位(phase) 。找 出穩定且可靠的相位(phase) 來定義每天活動的起始或是結束是一個研究的關 鍵。利用層級切割法,我們很成功的找出每天起始的相位,並進一步探討週期的 變化,活動的波形(waveform) ,和因著外來刺激而產生的相位移動(phase shift) ,並發展出一套架構相位反應曲線(phase response curve, PRC) 的方法。 我們也透過模擬的方式來比較HS 和Onset 這兩個方法的優劣。結果發現,HS 相對 於Onset 具又較佳的穩定性,亦即從活動的波形的角度而言,HS 其所得到的相位 標誌能標示出固定的相位。 在豆象產卵行為方面,主要是利用層級切割法,找出豆象行走軌跡的模式, 並探討在三種不同的資源環境下,豆象是否改變其行為反應。發現豆象在豐富的 資源環境行走軌跡呈現較簡單的模式且偏好近距離的搜尋。隨著資源使用的耗 竭,其行走軌跡出現愈多的長距離的搜尋。 在夫妻情緒資料的分析上,透過層級切割,將情緒變動較大的期間區分出來, 經由chi-square 檢定,發現不論在正、負面情緒上,夫妻之間具有一致性 (coherent)的情緒反應。經由HS 的方式,我們將資料切割成三段。並且利用模擬 的方式,證實這樣的分斷,更能幫助我們解釋情緒變動的相互影響。最後運用了 stochastic small-world network (SSWN),動態影片,以其更能捕捉夫妻之間情 緒的互動。 第二部分是對於灰色蟑螂其階級的不穩定性的探討。我們發展了兩個指標, 分別用來量化在一個族群中階層制度的不穩定性和其競爭的程度。我們發現不穩 定性和族群內的個體數目具有高度的相關,而競爭的程度與階層的不穩定具有高度的正相關。亦即,在一族群中,若其階層制度越不穩定,表示族群內競爭程度 就越激烈。 | zh_TW |
dc.description.abstract | There are two parts in this dissertation. First is that we propose a new method,
Hierarchical Segmentation (HS), to analyze non-stationary time series data. The main idea of HS is to divide a series of data into several segments according to the characteristics of data through three level coding. At the upper level, there resemble each other if they have the same codewords. So, it is useful in identifying patterns or special segments with appropriate coding. Here, we apply HS in three different fields: circadian rhythm, moving trajectory and the emotional interaction of couples. In circadian rhythm study, a phase represents each time point for the experiment duration. It is crucial to decide robust and reliable markers as the onset or offset points for the daily activity. We define the onset phase markers via HS; furthermore, we discuss the variation of periods, waveform, and the phase shift inducing by the outside stimulation and develop a new approach in constructing phase response curve (PRC). We compare HS and Onset methods via simulation. It reveals that HS is more robust than Onset; in other words, the phase markers inducing by HS can mark the benchmark in waveform. In the study of oviposition behavior, we explore the moving trajectory of bean weevil and try to find out the moving patterns via HS. We are interested in exploring that if subject changes her moving pattern under three different resource environments. It reveals that the moving patterns is simpler and local search preferred in rich patch than in half and poor. With resource consumed, the moving patterns are increased into lots of long walk. In the couple data analysis, we divide the segments of highly emotional response. After examining with chi-square test, we find that there is coherence situation in both positive and negative emotion. We separate the series data into three segments, and we prove that the approach is helpful in catching the interaction patterns. Finally, we use stochastic small-world network (SSWN) and dynamic movie to represent the dyadic dynamic interactions. The other part is to explore the unstable hierarchy formation in cockroach, Nauphoeta cinerea. We develop two indices to quantify the degree of unstable and the competitive pressure in a group. It reveals that the degree of unstableness is highly related with group size, and there is highly positive relation between unstableness and competitive pressure. In other words, within a group, the more unstable a hierarchy formation is, the stronger the competition appears. | en |
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dc.description.tableofcontents | Table of Contents
Acknowledgments ii Abstract iv List of Tables xi List of Figures xiii General Introduction 1 1 Hierarchical Segmentation 3 1.1 The original of Hierarchical segmentation . . . . . . . . . . . . . . . . 3 1.2 The algorithm of Hierarchical segmentation . . . . . . . . . . . . . . . 4 1.3 An example in illustration . . . . . . . . . . . . . . . . . . . . . . . . 5 1.3.1 Generating data . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.3.2 Data analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2 Hierarchical Segmentation in Circadian Rhythm 12 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.2 Empirical data and Analysis Methods . . . . . . . . . . . . . . . . . . 14 2.2.1 Biological background and experiment design . . . . . . . . . 14 2.2.2 Hierarchical Segmentation and optimizing parameters . . . . . 15 2.2.3 Two adaptive alignment algorithm (AAA) . . . . . . . . . . . 18 2.3 Phase Shift and Phase Response Curve . . . . . . . . . . . . . . . . . 21 2.3.1 Measurement of phase-shift . . . . . . . . . . . . . . . . . . . 22 2.3.2 Nonparametric curve estimetion for PRC . . . . . . . . . . . . 23 2.3.3 Real data analysis . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3 The Simulating Experiment in Circadian Rhythm 35 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.2 The role of waveform in pattern recognition . . . . . . . . . . . . . . 37 3.3 The experiment setting and analyzing method . . . . . . . . . . . . . 39 3.3.1 Waveform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 3.3.2 Integrate-and-Fire dynamics . . . . . . . . . . . . . . . . . . . 40 3.3.3 Initial phase and Simulation plan . . . . . . . . . . . . . . . . 41 3.3.4 Two rhythmic pattern recognition regimes . . . . . . . . . . . 41 3.4 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 3.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 4 Hierarchical Segmentation in analyzing the trajectory movement of bean weevil 52 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 4.2 Biological background and experimental setting . . . . . . . . . . . . 53 4.3 Coding regime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 4.4 Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 4.5 Hierarchical segmentation . . . . . . . . . . . . . . . . . . . . . . . . 58 4.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 5 Dyadic dynamics analysis via Hierarchical Segmentation 68 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 5.2 Mapping non-stationary dynamics of multi-faced dyadic interactions. 71 5.2.1 Background of data . . . . . . . . . . . . . . . . . . . . . . . . 71 5.2.2 Analysis Method . . . . . . . . . . . . . . . . . . . . . . . . . 71 5.2.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 5.2.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 5.3 Computations of algorithmic complexity of dyadic dynamics and its synthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 5.3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 5.3.2 Analysis methods . . . . . . . . . . . . . . . . . . . . . . . . . 81 5.3.3 Computation on first order histogram . . . . . . . . . . . . . . 83 5.3.4 Computation on second order histogram . . . . . . . . . . . . 85 5.3.5 Comparison of results between 1st- and 2nd-order histograms . 86 5.3.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 5.4 Computational composition of dyadic dynamics of affective processing 89 5.4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 5.4.2 Hierarchical segmentation approaches in recognizing dynamic patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 5.4.3 Neural firing patterns of individual dynamics of affective process 91 5.4.4 Dyadic dynamics with neural firing dynamic pattern . . . . . . 92 5.4.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 5.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 6 Measuring the degree of unstable dominance hierarchy in cockroach, Nauphoeta cinerea 124 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 6.2 Experiment design and results . . . . . . . . . . . . . . . . . . . . . . 125 6.3 Two measuring indices . . . . . . . . . . . . . . . . . . . . . . . . . . 127 6.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 Conclusion 136 | |
dc.language.iso | en | |
dc.title | 非平穩資料的時間序列分析:層級切割法及其運用 | zh_TW |
dc.title | A non-stationary analysis method in time series: hierarchical segmentation and its applications | en |
dc.type | Thesis | |
dc.date.schoolyear | 95-2 | |
dc.description.degree | 博士 | |
dc.contributor.coadvisor | 謝復興(Fushing Hsieh),寇融(Rong Kou) | |
dc.contributor.oralexamcommittee | 蔡紋琦,李後晶 | |
dc.subject.keyword | 層級切,割法,生物時鐘,行走軌跡,夫妻情緒相互影響,階層制度, | zh_TW |
dc.subject.keyword | hierarchical segmentation,circadian rhythm,moving trajectory,small-world dyadic dynamic interactions,hierarchy fornation, | en |
dc.relation.page | 138 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2007-07-30 | |
dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
dc.contributor.author-dept | 農藝學研究所 | zh_TW |
顯示於系所單位: | 農藝學系 |
文件中的檔案:
檔案 | 大小 | 格式 | |
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ntu-96-1.pdf 目前未授權公開取用 | 1.29 MB | Adobe PDF |
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