Multivariate time series are found everywhere and they are important data in the field of data mining, but their high dimensionality often hinders the quality of techniques employed for classifying ...multivariate time series. In this study, we propose an accurate and efficient classification method based on common principal components analysis for multivariate time series. First, multivariate time series are divided into several clusters according to the number of class labels, and the high dimensionality of multivariate time series can then be reduced by common principal components analysis, which gives the reduced principal component series sufficiently high variance. Second, each cluster is used to construct the corresponding reduced coordinate space formed by the eigenvectors of the common covariance matrix. Third, any multivariate time series without a class label can be projected onto these coordinate spaces and its label can be predicted based on the minimal variance of the reduced principal components series according to the different projections. Our experimental results demonstrated that the proposed method for the classification of multivariate time series is more accurate and efficient than existing methods. It is also flexible for multivariate time series with different lengths.
•The CFD model with reduced PRF chemistry is able to simulate the combustion of methane in NG-diesel dual fuel engine.•The main combustion process consumes 43–53% of methane.•The post-combustion ...oxidation process consumes 17–29% methane.•The unburned methane observed at EVO was distributed mainly at the center of the combustion chamber.
Natural gas (NG)-diesel dual fuel engines have been criticized for their high emissions of unburned methane. The past research on methane emissions from dual fuel engines has focused on the measurement of methane concentration in exhaust gases. The development of approaches capable of minimizing methane emissions requests the detailed spatial distribution of methane in-cylinder during the combustion and post combustion processes. However, it is difficult to experimentally measure the spatial distribution of methane in-cylinder.
This research presents a numerical study on the combustion process of a NG-diesel dual fuel engine using the computational fluids dynamics (CFD) model CONVERGE coupled with a reduced primary reference fuel (PRF) mechanism. The model was validated against the heat release process and the emissions of nitrogen oxide, methane and carbon monoxide measured in a single cylinder dual fuel engine. The validated CFD model was applied to investigate the combustion of methane and n-heptane and the spatial distribution of methane in the dual fuel engine. This is most likely the first attempt to visualize the spatial distribution of methane in dual fuel engines using CFD. The objective of this study is to numerically simulate the methane combustion process, especially the methane present outside the pilot spray, quantify the methane combustion in each combustion stage, and visualize the spatial methane distribution in cylinder. The results showed that the momentum produced by the pilot fuel injection and combustion pushed the combustion products of pilot fuel and methane within the pilot spray plume toward the unburned methane-air mixture. Such a movement enhanced the mixing of the hot combustion products and the relatively cold unburned methane-air mixture during the main combustion process and dominated the combustion of methane presented outside the pilot fuel spray plume. Based on the simulation results at a low load condition (4.05bar), the main combustion process consumed 43–53% of the methane fumigated into the intake mixture. The post-combustion oxidation process consumed 17–29% of the intake methane, which was 36.2–51.8% methane that survived the main combustion process. In comparison, 27–35% methane emitted the engine without participating the combustion process. The unburned methane at exhaust valve opening was mainly observed at the center of the cylinder. In comparison, the contribution of the crevice and boundary layer around the cylinder liner to methane emissions was relatively small. The slip of methane through the dual fuel engines was due to the fact that the premixed mixture was too lean to support the propagation of the turbulent flame initiated by the pilot fuel and the lack of pilot fuel vapor reaching the center of the combustion chamber because of the geometric limitations of the fuel injection system and the reduced mass of pilot fuel injected into the cylinder. The approaches aiming to enhance the combustion of methane and minimize methane emissions from dual fuel engines should focus on those capable of increasing the volume of pilot fuel vapor formed after injected into the cylinder.
Abstract Smartphone-based biometric authentication has been widely used in various applications. Among several biometric characteristics, fingerphoto biometrics captured from smartphones are gaining ...popularity owing to their usability, scalability across different smartphones, and reliable verification. However, fingerphoto verification systems are vulnerable to both direct and indirect attacks. In this work, we propose a novel method to generate morphing attacks on fingerphoto biometrics captured using smartphones. We introduce three different image-level fingerphoto morphing attack generation algorithms that can generate high-quality fingerphoto morphing images with minimum distortions. Extensive experiments were conducted on two datasets captured using different smartphones under various environmental conditions. The results demonstrate that the proposed morphing algorithms are highly vulnerable to commercial off-the-shelf and block-directional fingerprint verification systems. To effectively detect morphing attacks on fingerphoto biometrics, we propose the use of fingerphoto morphing attack detection algorithms that utilize both handcrafted and deep features. However, our detection results showed a high error rate in accurately detecting these types of attacks.
•The underlying idea is: point p and point q should have similar neighbors, provided p and q are close to each other; given a certain eps, the closer they are, the more similar their neighbors ...are.•NQ-DBSCAN is an exact algorithm that may return the same result as DBSCAN if the parameters are same. While ρ-Approximate DBSCAN is an approximate algorithm.•The best complexity of NQ-DBSCAN can be O(n), and the average complexity of NQ-DBSCAN is proved to be O(n log(n)) provided the parameters are properly chosen. While ρ-Approximate DBSCAN runs only in O(n2) in high dimension.•NQ-DBSCAN is suitable for clustering data with a lot of noise.
Clustering is an important technique to deal with large scale data which are explosively created in internet. Most data are high-dimensional with a lot of noise, which brings great challenges to retrieval, classification and understanding. No current existing approach is “optimal” for large scale data. For example, DBSCAN requires O(n2) time, Fast-DBSCAN only works well in 2 dimensions, and ρ-Approximate DBSCAN runs in O(n) expected time which needs dimension D to be a relative small constant for the linear running time to hold. However, we prove theoretically and experimentally that ρ-Approximate DBSCAN degenerates to an O(n2) algorithm in very high dimension such that 2D > > n. In this paper, we propose a novel local neighborhood searching technique, and apply it to improve DBSCAN, named as NQ-DBSCAN, such that a large number of unnecessary distance computations can be effectively reduced. Theoretical analysis and experimental results show that NQ-DBSCAN averagely runs in O(n*log(n)) with the help of indexing technique, and the best case is O(n) if proper parameters are used, which makes it suitable for many realtime data.
Dynamic time warping is one of the most important similarity measurement methods for time series data mining. Owing to the different influence of various time points, an extension of dynamic time ...warping based on time weight analysis is proposed, where the weights of pairs of time points from two series can be automatically calculated through measuring how far the history time points are from the latest ones. The time weights of the matching pairs in the warping path obtained by dynamic time warping represent the importance of the corresponding time points and will make different contributions to the accumulated cost matrix. The hierarchical clustering results in various types of time series data, including UCI data and financial stock exchange data, demonstrate that time works wonders, and different history time points have different influence on the contribution of the minimal distance between two time series. Compared to state-of-the-art methods, the proposed technique takes the time factor into consideration and can be advantageously used for similarity measurement in time series data mining.
Time series clustering is often applied to pattern recognition and also as the basis of the tasks in the field of time series data mining including dimensionality reduction, feature extraction, ...classification and visualization. Due to the high dimensionality of multivariate time series and most of the previous work concentrating on univariate time series clustering, a novel method which is based on common principal component analysis, is proposed to achieve multivariate time series clustering more fast and accurately. It is inspired by the traditional clustering method K-Means and can construct a common projection axes as prototype of each cluster. Moreover, the reconstruction error of each multivariate time series projected on the corresponding common projection axes are used to reassign the member of the cluster. The detailed algorithm of the proposed method Mc2PCA is given and the time complexity is analyzed, which shows that the proposed method is very fast and its time complexity is linear to the number of multivariate time series objects. Unlike the traditional methods, the proposed method considers the relationship among variables and the distribution of the original data values of multivariate time series. The experimental results in the various datasets demonstrate that Mc2PCA is superior to the traditional methods for multivariate time series clustering.
An appropriate control strategy can play an important role in further improving the fuel economy performance of hybrid electric vehicle (HEV). This research developed a novel adaptive control ...strategy to achieve optimal power distribution for a series-parallel hybrid electric bus (SPHEB) to adapt driving pattern instantaneously. First, a methodology of extracting mode transition control and power distribution strategy from dynamic programming (DP) solution is proposed for the development of the hierarchical energy management strategy. A SPHEB energy management problem under the Chinese typical bus driving schedule at urban district (CTBDS_UD) is investigated as a case study. Second, an approach of driving pattern recognition (DPR) module is developed. For adaptive learning, four typical driving patterns are selected as the database of driving condition and using the extraction method described above to acquire optimal control strategies for four driving patterns. Third, a framework of adaptive control strategy has been proposed based on the extracted hierarchical energy management strategy from DP and combined with DPR. Finally, the simulation results demonstrate the proposed adaptive strategy can make power distribution proper adjustments in real time and be capable of improving significantly the fuel efficiency of the SPHEB.
•The proposed method APCA can process the time series of different length.•A synchronous or an asynchronous correlation can be reflected by APCA.•APCA retains much more information than PCA in the ...reduced dimension.
Principal component analysis (PCA) is often applied to dimensionality reduction for time series data mining. However, the principle of PCA is based on the synchronous covariance, which is not very effective in some cases. In this paper, an asynchronism-based principal component analysis (APCA) is proposed to reduce the dimensionality of univariate time series. In the process of APCA, an asynchronous method based on dynamic time warping (DTW) is developed to obtain the interpolated time series which derive from the original ones. The correlation coefficient or covariance between the interpolated time series represents the correlation between the original ones. In this way, a novel and valid principal component analysis based on the asynchronous covariance is achieved to reduce the dimensionality. The results of several experiments demonstrate that the proposed approach APCA outperforms PCA for dimensionality reduction in the field of time series data mining.
Target azimuth information can help further improve the accuracy of magnetic orientation, but the current periodic magnetic field generated by the magnetic beacon is multivalued, so it is not ...suitable for azimuth measurement. According to the distribution of a rotating magnetic field and the phase angle measuring principle, we put forward a new magnetic source structure design of a multiple rotating permanent magnet array by adjusting the spacing d, the rotating speed ω and the initial rotation angle φ, and then verified the mathematical model using COMSOL simulation software. A triple structure was obtained by comparison (d3=3d1=3d2=43 m, d3=3d1=3d2=43 m, φ1=0, φ2=4π5 rad. φ3=π rad), which can produce a strong characteristic magnetic signal similar to a heart-shaped field pattern. Finally, a signal transceiver system was set up for the experiment. The experimental result shows that the waveform of the magnetic signal generated by the real beacon meets the requirement of having a unique maximum value and good directivity within a period, which proves the practical application effect of the structure.
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