Encrypted network behaviors identification based on dynamic time warping and k-nearest neighbor

Large scale optical character recognition (OCR) refers to or means the computerization of large amounts of documents such as news papers. Despite the diversity of commercial OCR products, this task still remains too far from the mature especially if the input documents are insufficient quality or cursive writing such as the Arabic documents (Vinciarelli, 2002). Indeed, in their project (Holley, 2009), the national library of Australia reports that existing OCR systems are, commonly, weak. Moreover, their conducted experiments on historical newspapers show that the corresponding accuracy raw varied from 71% to 98.02%. This is surely due to the weakness of the approaches and techniques used in these systems. Printed cursive written documents such as the Arabic one presents, in addition, other difficulties which are behind the weaknesses of the existing commercialized systems especially when the quality of the input binary image of the document is not good enough. The first difficulty encountered for such writing is the segmentation of any given input word or sub-word into isolated characters given that the size of each ofwhich is variable. In practice, if the segmentation process is conducted successfully, then it eases the recognition step to a large extent. That is why Latin printedOCR systems are, commonly, more powerful compared to those devoted to the cursive writing documents. Dynamic Time Warp (DTW) algorithm is a well known procedure especially in pattern recognition (Alves et al., 2002; Khemakhem et al., 2005; Philip, 1992; Vuori et al., 2001), (Khemakhem et al., 2009; Kumar et al., 2006; Tapia et al., 2007). The DTW algorithm is the result of the adaptation of dynamic programming to the field of pattern recognition. Printed cursive writing OCR by the DTW algorithm provides very interesting recognition rates without prior character segmentation (such as: the Arabic, Persian, Urdu, latin connected characters,...), (Khemakhem et al., 2005). The purpose of the DTW algorithm is to perform optimal time alignment between a reference pattern and an unknown pattern and evaluate their difference. Intensive experiments show that the recognition rate of the DTW algorithm remains acceptable compared to the existing commercialized systems even when the quality of the input documents is not good enough. Intensive tests on more than 100.000 connected characters (most of them are Arabic cursive and including some important noise) show that the segmentation average rate is greater than 98% and the recognition average rate is 5

A time series analysis method for spacecraft telemetry data is presented in this paper. For spacecraft testing and on-orbit flight, this method can monitor the changes of telemetry data automatically and identify the failure modes of spacecraft. Using dynamic time warping (DTW) algorithm, combining historical data samples as well as fault cases with this method analyzes the similarity of telemetry data transformed into time series. By comparing the results of analysis with the results of DTW distance calculation, the relative deviation of data is measured and the abnormal data in fault mode is identified. The results show that the telemetry data analysis method based on DTW algorithm can effectively detect data anomalies and realize fault identification, which has a certain application prospect.

Abstrak Kondisi permukaan jalan merupakan faktor yang memengaruhi kenyamanan dan keselamatan pengendara. Sehingga perlu dilakukan pemeriksaan kondisi jalan secara berkala. Pemantauan kondisi permukaan jalan di Indonesia pada umumnya masih masih dilakukan secara konvensional. Oleh karena itu, penelitian ini bertujuan untuk menerapkan pendekatan yang lebih efisien dalam mengukur kondisi permukaan jalan dengan menggunakan sensor accelerometer dan global positioning sistem (GPS) yang terinstall pada smartphone. Penelitian ini menggunakan metode Dynamic Time Warping (DTW) untuk mengevaluasi kondisi permukaan jalan secara umum dan metode K-means clustering untuk mengidentifikasi lokasi anomali yang terdapat pada permukaan jalan. Data hasil perekaman kondisi permukaan jalan USAID dari SMA Negeri 1 Lhoknga sampai Pantai Lhokseudu di Provinsi Aceh (lebih kurang 16 kilometer) dianalisis pada penelitian ini. Hasil analisis menunjukkan bahwa bentuk topografi jalan menurun dan menanjak cenderung memiliki kondisi yang lebih buruk dibandingkan segmen jalan yang mendatar. Selain itu, hasil penelitian juga menunjukkan bahwa kawasan dengan kepadatan penduduk yang lebih rendah memiliki kondisi jalan yang kurang baik dibandingkan kawasan dengan kepadatan penduduk yang lebih tinggi. Metode DTW dan K-means clustering berpotensi untuk digunakan sebagai metode alternatif dalam mengukur kondisi permukaan jalan yang lebih hemat tenaga, waktu dan biaya jika dibandingkan dengan pemeriksaan secara konvensional. Kata-kata Kunci: DTW, k-means clustering, permukaan jalan, smartphone Abstract The condition of the road surface is a factor that affects the comfort and safety of the driver. It is necessary to examine the condition of the road on a regular basis. Monitoring of road surface conditions in Indonesia is generally still done conventionally. Therefore, this study aims to apply a more efficient approach in measuring road surface conditions using accelerometer sensors and a global positioning system (GPS) installed on a smartphone. This study employs the Dynamic Time Warping (DTW) method to evaluate general road surface conditions and the K-means clustering method to identify the location of anomalies on the road surface. Recording data of USAID road surface condition from SMA Negeri 1 Lhoknga to Lhok Seudu Beach in Aceh Province (approximately 16 kilometers) were analyzed in this study. The results of the analysis show that the topography of downhill and uphill roads tends to possess worse conditions than the horizontal road segments. In addition, the results also show that areas with lower population densities have adverse road conditions than areas with higher population densities. The DTW method and K-means clustering demonstrate the potential to be used as an alternative method in measuring road surface conditions which is more efficient in energy, time and cost when compared to conventional inspection. Keywords: DTW, k-means clustering, road surface, smartphone

Partial matching and search space reduction for QbE-STD

Sleep plays a significant role in human’ smental and physical health. Recently, the associations between lack of sleep and weight gain, development of cancer and many other health problems have been recognized. Then monitoring the sleep and wake state all night is becoming a hotspot issue. Traditionally it classified by a PSG recording which is very costly and uncomfortable. Nowadays, with the advance of internet of things, many convenient wearable devices are being used for medical use, like measuring the heart rate (HR), blood pressure and other signals. With the sleep quality monitor problem, the key question is how to discriminate the sleep and weak stage from these signals. This paper proposed a Bayesian approach based on dynamic time warping (DTW) method for sleep and wake classification. It used HR and surplus pulse O2 (SPO2) signals to analyze the sleep states and the occurrence of some sleep-related problems. DTW is an algorithm that searches an optimal alignment between time series with scaling and shifting and Bayesian methods have been successfully used for object classification in many study. In this paper, a three-step process is used for sleep and wake classification. In the first step, the DTW is used to extract features of the original HR and SPO2 signals. Then a probabilistic model is introduced for using the Bayesian classification for uncertain data. And in the classification step, the DTW features are used as the training dataset in the Bayesian approach for sleep and wake classification. Finally, a case study form a real-word applications, collected from the website of the Sleep Heart Health Study, is presented to shown the feasibility and advantages of the DTW-based Bayesian approach.

Insects play significant role in the human life. And insects pollinate major food crops consumed in the world. Insect pests consume and destroy major crops in the world. Hence to have control over the disease and pests, researches are going on in the area of entomology using chemical, biological and mechanical approaches. The data relevant to the flying insects often changes over time, and classification of such data is a central issue. And such time series mining tasks along with classification is critical nowadays. Most time series data mining algorithms use similarity search and hence time taken for similarity search is the bottleneck and it does not produce accurate results and also produces very poor performance. In this paper, a novel classification method that is based on the dynamic time warping (DTW) algorithm is proposed. The dynamic time warping algorithm is deterministic and lacks in modeling stochastic signals. The dynamic time warping (DTW) algorithm is improved by implementing a nonlinear median filtering (NMF). Recognition accuracy of conventional DTW algorithms is less than that of the hidden Markov model (HMM) by same voice activity detection (VAD) and noise-reduction. With running spectrum filtering (RSF) and dynamic range adjustment (DRA). NMF seek the median distance of every reference of time series data and the recognition accuracy is much improved. In this research work, optical sensors are used to record the sound of insect flight, with invariance to interference from ambient sounds. The implementation of our tool includes two parts, an optical sensor to record the "sound" of insect flight, and a software that leverages on the sensor information, to automatically detect and identify flying insects.

Croplands are commonly mapped using time series of remotely sensed images. The dynamic time warping (DTW) algorithm is an effective method for realizing this. However, DTW algorithm faces the challenge of capturing complete and accurate representative cropland time series on a national scale, especially in Asian countries where climatic and topographic conditions, cropland types, and crop growth patterns vary significantly. This study proposes an automatic cropland extraction method based on the DTW algorithm and density-based spatial clustering of applications with noise (DBSCAN), hereinafter referred to as ACE-DTW, to map croplands in Pakistan in 2015. First, 422 frames of multispectral Landsat-8 satellite images were selected from the Google Earth Engine to construct monthly normalized difference vegetation index (NDVI) time series. Next, a total of 2409 training samples of six land cover types were generated randomly and explained visually using high-resolution remotely sensed images. Then, a multi-layer DBSCAN was used to classify NDVI time series of training samples into different categories automatically based on their pairwise DTW distances, and the mean NDVI time series of each category was used as the standard time series to represent the characteristics of that category. These standard time series attempted to represent cropland information and maximally distinguished croplands from other possible interference land cover types. Finally, image pixels were classified as cropland or non-cropland based on their DTW distances to the standard time series of the six land cover types. The overall cropland extraction accuracy of ACE-DTW was 89.7%, which exceeded those of other supervised classifiers (classification and regression trees: 78.2%; support vector machines: 78.8%) and existing global cropland datasets (Finer Resolution Observation and Monitoring of Global Land Cover: 87.1%; Global Food Security Support Analysis Data: 83.1%). Further, ACE-DTW could produce relatively complete time series of variable cropland types, and thereby provide a significant advantage in mountain regions with small, fragmented croplands and plain regions with large, high-density patches of croplands.

Ecological patterns are often fundamentally chronological. However, generalization of data is necessarily accompanied by a loss of detail or resolution. Temporal data in particular contain information not only in data values but in the temporal structure, which is lost when these values are aggregated to provide point estimates. Dynamic time warping (DTW) is a time series comparison method that is capable of efficiently comparing series despite temporal offsets that confound other methods. The DTW method is both efficient and remarkably flexible, capable of efficiently matching not only time series but also any sequentially structured data set, which has made it a popular technique in machine learning, artificial intelligence, and big data analytical tasks. DTW is rarely used in ecology despite the ubiquity of temporally structured data. As technological advances have increased the richness of small‐scale ecological data, DTW may be an attractive analysis technique because it is able to utilize the additional information contained in the temporal structure of many ecological data sets. In this study, we use an example data set of high‐resolution fish movement records obtained from otolith microchemistry to compare traditional analysis techniques with DTW clustering. Our results suggest that DTW is capable of detecting subtle behavioral patterns within otolith data sets which traditional data aggregation techniques cannot. These results provide evidence that the DTW method may be useful across many of the temporal data types commonly collected in ecology, as well other sequentially ordered “pseudo time series” data such as classification of species by shape.

Audio forensics is the application of science and scientific methods in handling digital evidence in the form of audio. In this regard, the audio supports the disclosure of various criminal cases and reveals the necessary information needed in the trial process. So far, research related to audio forensics is more on human voices that are recorded directly, either by using a voice recorder or voice recordings on smartphones, which are available on Google Play services or iOS Store. This study compares the analysis of live voices (human voices) with artificial voices on Google Voice and other artificial voices. This study implements the audio forensic analysis, which involves pitch, formant, and spectrogram as the parameters. Besides, it also analyses the data by using feature extraction using the Mel Frequency Cepstral Coefficient (MFCC) method, the Dynamic Time Warping (DTW) method, and applying the K-Nearest Neighbor (KNN) algorithm. The previously made live voice recording and artificial voice are then cut into words. Then, it tests the chunk from the voice recording. The testing of audio forensic techniques with the Praat application obtained similar words between live and artificial voices and provided 40,74% accuracy of information. While the testing by using the MFCC, DTW, KNN methods with the built systems by using Matlab, obtained similar word information between live voice and artificial voice with an accuracy of 33.33%.

Dynamic Time Warping(DTW) algorithm has strong resistance to synchronous error. Aiming at the similarity and asynchrony of meteorological factor, a method of applying DTW to photovoltaic power prediction is proposed. The meteorological factors which have great influence on photovoltaic power generation are selected by Pearson correlation coefficient method. Based on the DTW algorithm, the similarity of meteorological factors between the predicting days and the historical days can be measured. Through weighted calculation of similarity, the similar days are used to obtain training data of Partial Least Squares(PLS) prediction model. The simulation study on a photovoltaic power station verifies the correctness and effective of the proposed prediction method. The results show that the prediction model has great forecasting accuracy and certain feasibility and practicability.

The Dynamic Time Warping (DTW) algorithm is widely used in finding the global alignment of time series. Many time series data mining and analytical problems can be solved by the DTW algorithm. However, using the DTW algorithm to find similar subsequences is computationally expensive or unable to perform accurate analysis. Hence, in the literature, the parallelisation technique is used to speed up the DTW algorithm. However, due to the nature of DTW algorithm, parallelizing this algorithm remains an open challenge. In this paper, we first propose a novel method that finds the similar local subsequence. Our algorithm first searches for the possible start positions of subsequence, and then finds the best-matching alignment from these positions. Moreover, we parallelize the proposed algorithm on GPUs using CUDA and further propose an optimization technique to improve the performance of our parallelization implementation on GPU. We conducted the extensive experiments to evaluate the proposed method. Experimental results demonstrate that the proposed algorithm is able to discover time series subsequences efficiently and that the proposed GPU-based parallelization technique can further speedup the processing.

The technique of dynamic time warping (DTW) is relied on heavily in isolated word recognition systems. The advantage of using DTW is that reliable time alignment between reference and test patterns is obtained. The disadvantage of using DTW is the heavy computational burden required to find the optimal time alignment path. Several alternative procedures have been proposed for reducing the computation of DTW algorithms. However these alternative methods generally suffer from a loss of optimality or precision in defining points along the alignment path. In this paper we propose another alternative procedure for implementing a DTW algorithm. The procedure is based on the well known class of techniques for a directed search through a grid to find the shortest' path. An adaptive version of a directed search procedure is defined and shown to be capable of obtaining the exact DTW solution with reduced computation of distances but with increased overhead. It is shown that for machines where the time for distance computation is significantly larger than the time for combinatorics and overhead, a potential gain in speed of up to 3 to 1 can be realized with the directed search algorithm. Formal comparison of the directed search algorithm with a standard DTW method, in an isolated word recognition test, showed essentially no loss in recognition accuracy when the parameters of the directed search were selected to realize the 3 to 1 reduction in distance computation.

The gesture sequence recognition method based on the traditional Dynamic Time Warping(DTW) algorithm will lead to too long calculation time when the sample data sequence length is too large, and the accuracy of the recognition result will be obviously low due to the influence of noise. The FastDTW algorithm is mainly used for DTW acceleration, but it cannot suppress noise, and its time complexity is O(N). Edit Distance on Real sequence(EDR) algorithm can only suppress noise, but its efficiency is not improved, and its time complexity is O(N2). Based on the above problems, FastEDR algorithm is proposed for the matching verification of gesture sequences. The results show that the computational efficiency of FastEDR algorithm is significantly improved compared with DTW algorithm, the error acceptance rate FAR is reduced by 2.94%, and the error rejection rate FRR is reduced by 4.5%. The gesture sequence recognition based on FastEDR algorithm can achieve a better recognition state. If it is applied to unlock mobile phones, it is expected to become a new generation of unlocking method.

This paper proposes an improved dynamic time warping (DTW) algorithm with a nonlinear median filtering (NMF). Recognition accuracy of conventional DTW algorithms are less than the hidden Markov model (HMM) by same voice activity detection (VAD) and noise-reduction with running spectrum filtering (RSF) and dynamic range adjustment (DRA). For analyzing some incorrect results, unlike in conventional DTW, we do not use the minimum distance to recognize. we employ NMF and seek the median distance of the every reference word with the unknown speech waveform. All recognition accuracy of conventional DTW algorithms are improved much more by NMF. The recognition accuracy of Itakura's DTW algorithm is the best. Its recognition accuracy similar to that of the HMM approach in 10 dB and 20 dB white noise.

The pant-leg design is typical for higher capacity circulating fluidized bed (CFB) boilers because it allows for better secondary air penetration, maintaining good air-coal mixing and efficient combustion. However, the special risk, nominated as bed inventory overturn, remains a big challenge and it hinders the application of pant-leg CFB boilers. For a time series risk, it is critical to do the bed inventory overturn prevention to leave enough time for the adjustment. This paper proposed a new framework combing long short-term memory (LSTM) and dynamic time warping (DTW) methods to do the risk prediction. Pattern matching of data difference discrimination is employed for DTW algorithm, instead of the traditional Euclidean metric. The pattern matching has the merits in reduction of calculation and improvement of the adaptability to variables with different dimensions. After variable processing of the time series data by the variant DTW algorithm, the bed pressure drop prediction model is established based on the LSTM structure in this framework. Compared with some traditional prediction method, the framework in this paper has achieved superior results in the application of bed inventory overturn prevention.