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相對 R 平方方法理論性質的探討
(2012)
王秀瑛
;
WANG HSIUYING
;
國立交通大學統計學研究所
相對R-squared 方法(RRSM) 是近幾年來被提出來尋找miRNA 的標靶基因或致病基因的有效方法。傳統上，生物學家在分析microarray data來尋找miRNA標靶基因時大多應用相關性分析，但在實際的例子發現相關性分析無法得到與生物實驗相同的結果。而其它被提出的統計方法也無法在一些實驗數據上得到好的結果。所以，Wang and Li (2009) 利用迴歸模式去建立一個相對R-squared 方法，而此方法在利用microarray data 來尋找miRNA 的標靶基因的分析上比相關分析和其它被提出的方法有更好的表現。雖然相對R-squared 方法在實際應用已有好的結果，但它的理論性質還沒有被仔細的探討。在此研究計劃中，我們著力於相對R-squared 方法的理論性質的探討並利用統計推論方法來推導它的統計性質如區間估計等。
相對 R 平方方法理論性質的探討
(2013)
王秀瑛
;
WANG HSIUYING
;
國立交通大學統計學研究所
相對R-squared 方法(RRSM) 是近幾年來被提出來尋找miRNA 的標靶基因或致病基因的有效方法。傳統上，生物學家在分析microarray data來尋找miRNA標靶基因時大多應用相關性分析，但在實際的例子發現相關性分析無法得到與生物實驗相同的結果。而其它被提出的統計方法也無法在一些實驗數據上得到好的結果。所以，Wang and Li (2009) 利用迴歸模式去建立一個相對R-squared 方法，而此方法在利用microarray data 來尋找miRNA 的標靶基因的分析上比相關分析和其它被提出的方法有更好的表現。雖然相對R-squared 方法在實際應用已有好的結果，但它的理論性質還沒有被仔細的探討。在此研究計劃中，我們著力於相對R-squared 方法的理論性質的探討並利用統計推論方法來推導它的統計性質如區間估計等。
Estimating driving performance based on EEG spectrum analysis
(2005)
Lin, CT
;
Wu, RC
;
Jung, TP
;
Liang, SF
;
Huang, TY
The growing number of traffic accidents in recent years has become a serious concern to society. Accidents caused by driver's drowsiness behind the steering wheel have a high fatality rate because of the marked decline in the driver's abilities of perception, recognition, and vehicle control abilities while sleepy. Preventing such accidents caused by drowsiness is highly desirable but requires techniques for continuously detecting, estimating, and predicting the level of alertness of drivers and delivering effective feedbacks to maintain their maximum performance. This paper proposes an EEG-based drowsiness estimation system that combines electroencephalogram (EEG) log subband power spectrum, correlation analysis, principal component analysis, and linear regression models to indirectly estimate driver's drowsiness level in a virtual-reality-based driving simulator. Our results demonstrated that it is feasible to accurately estimate quantitatively driving performance, expressed as deviation between the center of the vehicle and the center of the cruising lane, in a realistic driving simulator.
Adaptive feature extractions in an EEG-based alertness estimation system
(2005)
Lin, CT
;
Chao, WH
;
Chen, YC
;
Liang, SF
;
腦科學研究中心
During the past years, the public security has become an important issue, especially, the safe manipulation and control of various vehicles. Maintaining high cognition is particularly important for the drivers behind the steering wheel. It requires an optimal estimation system to online continuously detect drivers' cognitive state related to abilities in perception, recognition, and vehicle control. In this paper, we proposed an EEG-based alertness estimation system with automatic feature selection mechanism. The independent component analysis (ICA) is used first to decompose the measured electroencephalogram (EEG). Then, a time-frequency analysis is performed to evaluate the time-frequency characteristic of each ICA component. We also proposed a new adaptive feature extracting mechanism for selections of frequency bands and ICA components. Different ranges of the alpha rhythm of subjects can be evaluated by the adaptive feature extracting mechanism according to the correlation coefficient between the ICA time-frequency response and the driving performance. The extracted features are then trained both by linear regression model and Self-cOnstructing Neuro-Fuzzy Inference Network (SONFIN) for the estimation of driving performance. The training and testing results of SONFIN are 96% and 91%, while the results of linear regression model are 90% and 85%, respectively. It demonstrates that the proposed adaptive feature extracting mechanism can achieve a great performance in alertness estimation with frequency band and component selection.
Toward incorporating a task-stage identification technique into the long-term document support process
(2008-09-01)
Wu, I-Chin
;
Liu, Duen-Ren
;
Chang, Pei-Cheng
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Effective knowledge management in a knowledge-intensive environment can place heavy demands on the information filtering (IF) strategies used to model workers' long-term task-needs. Because of the growing complexity of knowledge-intensive work tasks, a profiling technique is needed to deliver task-relevant documents to workers. In this study, we propose an IF technique with task-stage identification that provides effective codification-based support throughout the execution of a task. Task-needs pattern similarity analysis based on a correlation value is used to identify a worker's task-stage (the pre-focus, focus formulation, or post-focus task-stage). The identified task-stage is then incorporated into a profile adaptation process to generate the worker's current task profile. The results of a pilot study conducted in a research institute confirm that there is a low or negative correlation between search sessions and transactions in the pre-focus task-stage, whereas there is at least a moderate correlation between search sessions/transactions in the post-focus stage. Compared with the traditional IF technique, the proposed IF technique with task-stage identification achieves, on average, a 19.49%, improvement in task-relevant document support. The results confirm the effectiveness of the proposed method for knowledge-intensive work tasks. (c) 2007 Elsevier Ltd. All rights reserved.

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