دانلود و نمایش مقالات مرتبط با Dynamic Time Warping::صفحه 1
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نتیجه جستجو - Dynamic Time Warping

تعداد مقالات یافته شده: 7
ردیف عنوان نوع
1 Dynamic time warping for reducing the effect of force variation on myoelectric control of hand prostheses
تار شدن زمان پویا برای کاهش تأثیر تغییر نیرو در کنترل میو الکتریک پروتزهای دستی-2019
Research in pattern recognition (PR) for myoelectric control of the upper limb prostheses has been extensive. However, there has been limited attention to the factors that influence the clinical translation of this technology. A relevant factor of influence in clinical performance of EMG PR-based control of prostheses is the variation in muscle activation level, which modifies the EMG patterns even when the amputee attempts the same movement. To decrease the effect of muscle activation level variations on EMG PR, this work proposes to use dynamic time warping (DTW) and is validated on two databases. The first database, which has data from ten intact-limbed subjects, was used to test the baseline performance of DTW, resulting in an average classification accuracy of more than 90%. The second database comprised data from nine upper limb amputees recorded at three levels of force for six hand grips. The results showed that DTW trained at a single force level achieved an average classification accuracy of 60± 9%, 70± 8%, and 60± 7% at the low, medium and high force levels respectively across all amputee subjects. The proposed scheme with DTW achieved a significant 10% improvement in classification accuracy when trained at a low force level when compared to the traditional time-dependent power spectrum descriptors (TD-PSD) method.
Keywords: Electromyography (EMG) | Dynamic Time Warping (DTW) | Pattern Recognition (PR) | Force level variation | Classification
مقاله انگلیسی
2 FlatDTW – Dynamic Time Warping optimization for piecewise constant templates
FlatDTW - بهینه سازی چرخش زمان پویا برای الگوهای ثابت چند بعدی-2019
The aim of this work is to construct and analyze a method of optimization of the Dynamic Time Warping(DTW) algorithm – a well-known and popular pattern recognition tool. DTW is typically used to evaluate the degree of similarity between time series subjected to non-linear time warping, which is a common problem i.a. in speech recognition, music information retrieval, motion analysis and gesture recognition. Based on the dynamic programming principle, DTW is computed efficiently in O(N2)time, which contributes to its popularity and makes it applicable in many real-time scenarios. In this paper we propose amethod of DTW algorithm optimization in the case when one of the time series may be modeled as a piecewise constant sequence. Such sequences, composed of several “flat” sections, typically occur when real-world data are compared to some predefined templates, as in the task of matching a melody sung by a user to a MIDI-based template melody (a.k.a. Query-by-Singing/Humming, QbH). The modified DTW processes such templates in amore efficient way reducing the computational complexity. The obtained speed-up of the algorithm is not associated with any quality loss of the matching process results.
Keywords: Dynamic Time Warping | Time series matching | Pattern recognition | Query-by-Humming
مقاله انگلیسی
3 A trajectory clustering method based on Douglas-Peucker compression and density for marine traffic pattern recognition
روش خوشه بندی مسیر مبتنی بر فشرده سازی و تراکم داگلاس-پوکر برای تشخیص الگوی ترافیک دریایی-2019
Clustering analysis is applied extensively in pattern recognition. In marine traffic applications, the clustering results may exhibit a customary route and traffic volume distribution. In order to improve the clustering performance of ship trajectory data, which is characterized by a large data volume and distribution complexity, a method consisting of Douglas-Peucker (DP)-based compression and density-based clustering is proposed. In the first part of the proposed method, the appropriate parameters for the DP algorithm were determined according to the shape changes in the trajectories, which were used to compress the trajectories prior to calculating the dynamic time warping (DTW) distance matrix. In the second part, the density-based spatial clustering of applications with noise (DBSCAN) algorithm was improved in terms of determining the parameters. Based on the statistical characteristics of ship trajectory distribution, the appropriate DBSCAN parameters could be determined adaptively. Evaluation and comparison experiments were conducted based on massive real ship trajectories in the Chinese port of Beilun-Zhoushan. The results demonstrated that, compared to the traditional DTW distance, the proposed similarity measurement exhibits superior performance in terms of both time and quality. Furthermore, the results of the comparison experiment demonstrated that the improved DBSCAN outperforms two existing clustering methods in marine traffic c pattern recognition.
Keywords: Ship trajectory clustering | Douglas–peucker algorithm | DBSCAN | DTW
مقاله انگلیسی
4 Automatic object detection using dynamic time warping on ground penetrating radar signals
ردیابی خودکار شی با استفاده از چرخش زمانی پویا در سیگنالهای رادار نافذ در زمین-2019
Ground Penetrating Radar (GPR) is a widely used non-destructive method in buried object detection. However, online, automatic, and accurate location and depth estimation methods using GPR are still un- der development. In this article, a cutting-edge expert system is proposed that compares signals from newly scanned locations to a target-free accumulated reference signal and computes a dissimilarity mea- sure using Dynamic Time Warping (DTW). By setting a threshold on DTW values and monitoring them online, a significant deviation of the DTW values from the reference signal is detected prior to reaching an object. A potential burial site is therefore automatically detected without having a complete GPR scan which is a huge advantage compared to existing methods. Following the scanning process and investi- gating the potential burial site, location and depth of multiple buried objects is estimated automatically and highly accurate. The fully-automated analytics eliminate the need of expert operators in estimating spatial burial locations and perform accurately even on noisy media. Statistical proofs are provided that support the validity of the developed expert system in theory. Moreover, the analytics run in real-time that is plausible for on-site applications
Keywords: Ground Penetrating Radar signals | Dynamic Time Warping | Sequential confidence intervals | Control process | Object detection
مقاله انگلیسی
5 Pattern graph tracking-based stock price prediction using big data
پیش بینی قیمت سهام مبتنی بر ردیابی الگو با استفاده از داده های بزرگ-2018
Stock price forecasting is the most difficult field owing to irregularities. However, because stock prices sometimes show similar patterns and are determined by a variety of factors, we propose determining similar patterns in historical stock data to achieve daily stock prices with high prediction accuracy and potential rules for selecting the main factors that significantly affect the price, while simultaneously considering all factors. This study is intended at suggesting a new complex methodology that finds the optimal historical dataset with similar patterns according to various algorithms for each stock item and provides a more accurate prediction of daily stock price. First, we use a Dynamic Time Warping algorithm to find patterns with the most similar situation adjacent to a current pattern. Second, we select the determinants most affected by the stock price using feature selection based on Stepwise Regression Analysis. Moreover, we generate an artificial neural network model with selected features as training data for predicting the best stock price. Finally, we use Jaro–Winkler distance with Symbolic Aggregate approXimation (SAX) as a prediction accuracy measure to verify the accuracy of our model.
Keywords: Stock price prediction ، Dynamic time warping، Feature selection ، Artificial neural network ، Jaro–Winkler distance ، Symbolic aggregate approXimation
مقاله انگلیسی
6 Pattern graph tracking-based stock price prediction using big data
پیش بینی قیمت سهام مبتنی بر ردیابی الگو با استفاده از داده های بزرگ-2017
Stock price forecasting is the most difficult field owing to irregularities. How ever, because stock prices sometimes show similar patterns and are determined by a variety of factors, we propose determining similar patterns in historical stock data to achieve daily stock prices with high prediction accuracy and potential rules for selecting the main factors that significantly affect the price, while simultane ously considering all factors. This study is intended at suggesting a new complex methodology that finds the optimal historical dataset with similar patterns accord ing to various algorithms for each stock item and provides a more accurate pre diction of daily stock price. First, we use a Dynamic Time Warping algorithm to find patterns with the most similar situation adjacent to a current pattern. Second, we select the determinants most affected by the stock price using feature selec tion based on Stepwise Regression Analysis. Moreover, we generate an artificial neural network model with selected features as training data for predicting the best stock price. Finally, we use Jaro-Winkler distance with Symbolic Aggregate approXimation (SAX) as a prediction accuracy measure to verify the accuracy of our model.
Keywords: Stock price prediction| Dynamic time warping| Feature selection|Artificial neural network| Jaro-Winkler distance| Symbolic Aggregate approXimation
مقاله انگلیسی
7 فناوری گرمایش ساختمان در خانه هوشمند با استفاده از ابزارهای مدیریتی سیستم PI
سال انتشار: 2016 - تعداد صفحات فایل pdf انگلیسی: 9 - تعداد صفحات فایل doc فارسی: 19
به منظور نظارت راحت بر برخی از عملکردهای فنی و عملیاتی در بخش داخلی ساختمان خانه هوشمند از دوردست، ممکن است از برنامه ها و ابزارهای سودمند زیادی استفاده نمود. اگرچه هر برنامه یا وسیله ای، متناسب با این هدف نیست، یا اینکه وظایف لازم را ارائه نمی دهد. هدف از این مقاله توصیف و بررسی استفاده از ابزار نرم افزاری مناسب سیستم PI برای نظارت آنی بر داده های بدست امده از بخش های اصلی فناوری مستقر در مرکز اموزش Moravian-Silesian Wood Cluster است. سپس، از سیستمی پیشرفته و عالی شامل برنامه های PI Coresight و PI ProcessBook برای تجزیه و تحلیل و پردازش این داده های اخذ شده (برای مثال با استفاده از انحراف یا تاب زمان دینامیک برای کمیت های خاص وابسته به فناوری) استفاده می شود. هر برنامه دارای مزایا و معایب مربوط به خود است که همراه با احتمال دستکاری داده ها براورد می شوند. در بخش آزمایشی، همچنین از استاندارد ارتباطات فنی BACnet برای کنترل گرمایش، خنک کننده و تهویه و از ابزار نرم افزاری ESIGO Insight برای نشان دادن داده ها به شکل جداول، نمودارهای چند لایه ای، و صفحه نمایش ها برای فناوری ویژه، استفاده می شود.
کلمات کلیدی: فناوری اتوماسیون | ساختمان | فناوری گرمایشی | ابزارهای مدیریتی | سیستم PI | خانه هوشمند
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