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Author: 高昶易
Chang-Yi Kao
Thesis Title: 應用於人機介面的多階段學習框架
A Multi-Stage Learning Framework for Human Computer Interface Applications
Advisor: 范欽雄
Chin-Shyurng Fahn
Committee: 古鴻炎
Degree: 博士
Department: 電資學院 - 資訊工程系
Department of Computer Science and Information Engineering
Thesis Publication Year: 2013
Graduation Academic Year: 101
Language: 英文
Pages: 198
Keywords (in Chinese): 語音特徵值手勢辨識成本敏感人機介面安全庫存管理
Keywords (in other languages): and Vendor Managed Inventory, Native Voice Eigenvalue, Gesture Recognition, Cost-Sensitive, Human-Computer Interface
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隨著資訊發達以及介面易用性的觀念逐漸普及化,人與電腦、資訊裝置、新興消費性電子等的互動逐漸被重視。最被大家所熟悉的就是蘋果電腦(Apple)積極在人機介面的互動創新,因此在iPod、iPhon等產品上,均提出新穎性的互動作法,而SIRI行動助理就是一個典型應用機器學習的人機介面(HCI, Human-Computer Interface)的應用。學習的技術運用在許多的領域,如:人臉辨識、語者辨識、商品推薦系統等等,現有學習方式都是在大量資料中進行運算。本研究提出一個改良的學習排序演算法:除了多階段的作法,兼具(1).考量到成本敏感(Cost-Sensitive)的議題,先分類收斂到小額資料量、並以Boosting演算法可較快速到達排序功能。(2).參考方向一致性(Concordant/Discordant)作法,改善原本二元分類的模式,並計算出排名之間的距離,用以收斂出最適合之前幾筆少數資料量的排序清單。因此本研究研究所提出的方法改善了上述兩個重要地方。就本研究所提出的方法進行實驗,本研究方法在P@n、MAP以及NDCG三項指標皆略優於其它典型方法。本論文也依據所提出多階段適應學習架構,研究成果分別於三項不同領域進行實驗。
首先本研究應用多階段適應學習架構於手勢辨識。第二部份本研究將應用在語者辨識,以語音訊號轉換成語音特徵值後的原生資料進行比對辨識,而本研究中並非專注於數位訊號處理(DSP, Digital Signal Processing)技術的改善。最後,本研究以物聯網(IoT, Internet of Thing)架構的連結器業者為例,提出一個平衡生產線廠區生產力的架構雛型,並且以網頁作人機介面作為上下游業者拉式溝通的模式,並用本方法實驗以安全庫存為基準進行管理訂單派送。

As information technologies advance and user-friendly interfaces develop, the interaction between humans and computers, information devices, and new consumer electronics is increasingly gaining attention. One example that most people can relate to is Apple’s innovation in HCI (Human-Computer Interface) which has been used on many products such as iPad and iPhone. Siri, the intelligent personal assistant, is a typical application of machine-learning Human-Computer Interface.
Algorithms in machine learning have been employed in many disciplines, including gesture recognition, speaker recognition, and product recommendation systems. While the existing learning algorithms compute and learn from a large quantity of data. In this study, in addition to ranking data through multiple stages, algorithm significantly improves the existing algorithms in two ways. Firstly, it considers the cost-sensitive issue in the ranking algorithm. It classifies and filters data to small quantities and applies the Boosting algorithm to achieve faster ranking performance. Secondly, it enhances the original binary classification by using the concordant and discordant. Results from experiments demonstrate that our proposed algorithm outperforms the conventional methods in three evaluation measures: P@n, MAP, and NDCG. We have also proved in applications in three different areas.
The proposed method was applied to three areas. The experimental results of hand gesture recognition reveal that the efficiency of system execution turns out to be satisfying and the suggested method is desired for application in hand gesture recognition. As for the outcome of average accuracy rate of gesture recognition is more than 98%, a rate of satisfactory. We do not deal the technology improvement with the DSP (Digital Signal Processing). We only process the voice signal converted to the native voice eigenvalue which used to voice recognize. The experiments of the speech recognition show that the recognition optimization procedures established by this study are able to increase the recognition rate to over 96% in the personal computing device and industrial personal computer. It is expected that in the future this voice management system will accurately and effectively identify speakers answering the voice response questionnaire and will successfully carry out the functions in the choice of answers, paying the way for the formation of a virtual customer service person. Finally, we use the Web as the Human-Computer Interface to implement and manage the orders delivered by proposed method. The proposed method in the VMI (Vendor Managed Inventory) system framework achieves an order fulfillment rate 99%, up from the previous 94.75%, or an increase of 4.25% in our experiment result on connector industry. The system is also expected to improve the production efficiency and global competitiveness of the said connector maker.

中文摘要iv Abstractv Abstract Remarksvii 致謝x Contentsxi List of Figuresxvi List of Tablesxx Chapter 1 Introduction1 1.1 Overview1 1.2 Background and motivation1 1.2.1 HCI (Human-Computer Interface)1 1.2.2 Hand gesture recognition5 1.2.3 Speech recognition7 1.2.4 VMI application system8 1.3 Motivation12 1.4 Ph.D. dissertation organization13 Chapter 2 Related Works15 2.1 Gesture recognition17 2.1.1 Face detection and tracking17 2.1.2 Hand detection and tracking19 2.1.3 Hand gesture recognition20 2.2 Speaker recognition22 2.3 The Vendor Managed Inventory application in business26 2.4 The learning method29 2.4.1 Point-wise34 2.4.2 Pair-wise35 2.4.3 List-wise37 2.4 Section summary39 Chapter 3 Proposed Method41 3.1 System algorithm structure41 3.2 Ranking models45 3.2.1 Binary classifier45 Support vector machine model47 Linear support vector machines48 Non-linear support vector machines53 The SVM-based multi-classifier56 3.2.2 Boosting introduction58 Boosting and machine learning59 The Boosting algorithm60 3.3 Boosting schema61 3.3.1 AdaBoost61 3.3.2 The weak classifier68 3.3.3 The AdaBoost-base multi-classifier70 3.4 MultiStageBoost72 3.6 Section summary80 Chapter 4 Proposed Method Experimental Results82 4.1 Performance evaluation82 4.1.1 Precision at position n (P@n)83 4.1.2 Mean Average Precision83 4.1.3 Normalized Discount Cumulative Gain83 4.2 Experimental source data84 4.3 NP200485 4.4 HP200488 Chapter 5 Case Study92 5.1 Exploiting MultiStageBoost model and trajectory of hand motion for hand gesture recognition92 5.1.1 Introduction92 5.1.2 Hand detection and tracking93 Skin detection95 Face detection96 Hand detection97 Hand tracking98 5.1.3 Hand gesture recognition100 Feature extraction101 Gesture definition101 5.1.4 Exploiting proposed method procedure103 5.1.5 Experimental results106 5.1.6 Section summary109 5.2 Application for interactive voice system111 5.2.1 Introduction111 5.2.2 Recognition concept112 5.2.3 Identification model113 5.2.4 A concept of the analysis model120 5.2.5 Experimental results125 5.2.6 Section summary128 5.3 Exploit sensing data to analysis to achieve order arrangement, management and tracking process system130 5.3.1 Introduction130 5.3.2 Operation strategy and goal of connector industry132 Short-term goal132 Medium-term goal133 Long-term goal134 5.3.3 Optimization of value chain134 5.3.4 A case: PHILIPS’ major supplier of connectors136 5.3.5 A design of manufacturing order arrangement and management expert system138 5.3.6 Framework of manufacturing order arrangement order management expert system140 Subsystem 1 - M2MICT system141 Subsystem 2 - pull model based inventory demand system144 Subsystem 3 - manufacturing order arrangement and management service and production transparency systems146 5.3.7 Self- learning model149 5.3.7 Introduction of system and analysis of benefits156 Operation process before introduction of system (As-Is)156 Operation process after introduction of system (To-Be)157 5.3.8 Section summary159 Chapter 6 Conclusions and Future Works162 6.1 Conclusion162 6.2 Future works164 References165 Appendix176 作者簡介177

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