本計畫係為期三年（2018年8月至2021年7月）的基礎研究計畫，研究主題為「複雜系統實驗資料的適應性數據分析方法」，主要目的係以適應性數據分析方法研究複雜系統的實驗資料，從中理出系統的本徵特性，進而建構對應的物理模型。本計畫擬探討的具體問題包括：1. 基於量測量之最大資訊量的適應性數據分析架構2. 蛋白質的力致活化動力學3. 實驗生醫信號分析4. 對應於金融系統數據特性的理論力學及臨界性質本計畫可望達成的目標有：1. 發展出適於複雜系統研究之基於最大資訊量之量測量的適應性數據分析方法2. 建立以力致活化的奈米力學識別蛋白質功能局域的一般性理論，並應用於分子對接問題3. 將所建構之適應性數據分析方法應用於實驗生醫信號分析4. 了解金融數據的性質、改良現有以經驗法則為基礎的模式，及建立對應的力學理論本計畫的研究成果對相關領域的基礎研究具有應用與參考價值。本計畫將有兩位博士生參與上述具體問題；計畫執行期間可望每年在SCI期刊發表數篇研究論文。 ;This is a three-year-term (2018.8~2021.7) research project entitled “adaptive data analysis approaches for empirical data from complex systems”. The aim of the project is to study empirical data from complex systems using adaptive data analysis approaches, and from which to extract intrinsic properties of the systems, thereby to construct corresponding physics models. Specifically, this project plans to deal with the following problems: 1. Adaptive data analysis frameworks based on the maximum information content of measures2. Dynamics of protein mechanoactivation3. Empirical biomedical signal analysis4. Theoretical mechanics corresponding to the properties of financial data and its critical propertiesThis project will reach the following achievements:1. Adaptive data analysis approaches based on the maximum information content of measures for complex systems2. A general theory for identifying protein functional domains from nanomechanics of mechanoactivation, and its applications to molecular docking problems3. Applications of the developed adaptive data analysis approaches on empirical biomedical signals4. Better understanding of the properties of financial data, improving the experience-based models, and developing its mechanical correspondenceOur research results will provide useful references for fundamental researches of related fields. Two PhD students will involve in this project and join the study of the above-mentioned research problems. A number of research papers can be published in SCI Journals every year, based on the results of this project.
