The application of dynamic time warping in intelligent wheelchair 基于虛擬樣機(jī)技術(shù)的輪椅耐久性仿真分析
One-sided autocorrelation sequence; linear predictive coding; cepstrum; dynamic time warping 單邊自相關(guān)序列線性預(yù)測編碼倒譜動(dòng)態(tài)時(shí)間規(guī)正
Linear predictive coding; lpc prediction error; cepstrum; dynamic time warping 線性預(yù)測編碼lpclpcpe線性預(yù)測誤差倒譜動(dòng)態(tài)時(shí)軸彎曲或動(dòng)態(tài)時(shí)間規(guī)正dtw
The experiments show that the proposed method can work and the gray relation grade measure is better suited for the problem than the dynamic time warping measure 通過實(shí)驗(yàn)研究,發(fā)現(xiàn)基于灰色關(guān)聯(lián)度的層次化聚類方法能較好地實(shí)現(xiàn)交通流時(shí)間序列的進(jìn)一步有效分離。
The matching of the templates designs two kinds of solutions to solve it . dynamic time warping technique and vector quantization techniques are used in detecting real-time data 本文為檢出處理采用了兩種方案,便于實(shí)時(shí)處理的動(dòng)態(tài)時(shí)間規(guī)整和聚類的矢量量化技術(shù)。
The research includes following parts : 1 . online signature verification based on improved dynamic time warping and the elastic matching of 1d curve segments 本文對聯(lián)機(jī)手寫簽名鑒別技術(shù)進(jìn)行研究,主要研究內(nèi)容包括:1.基于改進(jìn)動(dòng)態(tài)時(shí)間規(guī)整和一維曲線段彈性匹配的聯(lián)機(jī)手寫簽名鑒別。
To solve this problem, fluctuation similarity measure, such as dynamic time warping and gray relation grade, and the hierarchical clustering algorithm were used to further separate the traffic flow time series 針對此問題,提出了將動(dòng)態(tài)時(shí)間彎曲及灰色關(guān)聯(lián)度引入交通流時(shí)間序列相似性度量,且結(jié)合層次化聚類方法對交通流時(shí)間序列進(jìn)一步分離的方法。
Following is the main work of this thesis : 1 . after researching on the basic theory of speech recognition, this thesis realizes talker-independent recognizer and speaker identification system with dtw ( dynamic time warping ) and vq ( vector quantization ) algorithm respectively 本文的主要研究工作如下:1.在研究語音識別的一些基本理論基礎(chǔ)上,分別采用dtw和vq兩種識別算法實(shí)現(xiàn)了非特定人孤立詞識別系統(tǒng)和說話人辨認(rèn)系統(tǒng)。
The method first segments time series based on a series of perceptually important points, use segment dynamic time warping distance as measurement, and then time series are converted into meaningful symbol sequences in terms of the segment's features and math categorization . after that, use above index model-irst, to achieve fast similarity retrieval in multiple time series 該方法提出通過基于重要點(diǎn)分段技術(shù)的分段動(dòng)態(tài)挖掘距離作為相似性度量,既保證了度量的魯棒性,又減少計(jì)算復(fù)雜度;利用各個(gè)分段的抽取六個(gè)主要特征,將時(shí)間序列轉(zhuǎn)化成一種特定的符號序列,在此基礎(chǔ)上利用海量全文索引結(jié)構(gòu)實(shí)現(xiàn)了相似性的索引查找。
What ’ s more, if the left-right structure character being written radicals apart, the segmentation algorithm will mistakenly segment one character to two . to resolve this problem, the paper proposes an approach to recognize continuous handwriting chinese characters using level building and dynamic time warping algorithm 針對現(xiàn)有識別系統(tǒng)的這些弱點(diǎn),本文提出將分層構(gòu)筑(levelbuilding,lb)和動(dòng)態(tài)時(shí)間歸正(dynamictimewarping,dtw)算法相結(jié)合(lbdtw)的聯(lián)機(jī)手寫漢字串識別方法。