A High-performance Classifier from K-dimensional Tree-based Dual-kNN

Swe Swe Aung,Itaru Nagayama,Shiro Tamaki 대한전자공학회 2018 IEIE Transactions on Smart Processing & Computing Vol.7 No.3

The k-nearest neighbors (kNN) method is highly effective in many application areas. Conceptually, its other good properties are simplicity and ease of understanding. However, according to measurements of the performance of algorithms based on three considerations (simplicity, processing time, and prediction power), the classic kNN algorithm lacks high-speed computation as well as maintenance of high accuracy for different values of k. The k-nearest neighbors algorithm is still influenced by varying k values and high variance in the training data set. Prediction accuracy diminishes when k approaches larger values. To overcome these issues, this paper introduces a k-dimensional (kd)-tree–based dual-kNN approach that concentrates on two properties to maintain classification accuracy at different k values and that also upgrades processing time performance. By conducting experiments on real data sets and comparing this algorithm with two other algorithms (dual-kNN and classic kNN), it was experimentally confirmed that the kdtree–based dual-kNN is a more effective and robust approach for classification than pure dual-kNN and classic kNN.

Two-stage feature selection for bearing fault diagnosis based on dual-tree complex wavelet transform and empirical mode decomposition

Van, Mien,Kang, Hee-Jun Professional Engineering Publishing Ltd 2016 Proceedings of the Institution of Mechanical Engin Vol. No.

<P>This paper presents an automatic fault diagnosis of different rolling element bearing faults using a dual-tree complex wavelet transform, empirical mode decomposition, and a novel two-stage feature selection technique. In this method, dual-tree complex wavelet transform and empirical mode decomposition were used to preprocess the original vibration signal to obtain more accurate fault characteristic information. Then, features in the time domain were extracted from each of the original signals, the coefficients of the dual-tree complex wavelet transform, and some useful intrinsic mode functions to generate a rich combined feature set. Next, a two-stage feature selection algorithm was proposed to generate the smallest set of features that leads to the superior classification accuracy. In the first stage of the two-stage feature selection, we found the candidate feature set using the distance evaluation technique and a k-nearest neighbor classifier. In the second stage, a genetic algorithm-based k-nearest neighbor classifier was designed to obtain the superior combination of features from the candidate feature set with respect to the classification accuracy and number of feature inputs. Finally, the selected features were used as the input to a k-nearest neighbor classifier to evaluate the system diagnosis performance. The experimental results obtained from real bearing vibration signals demonstrated that the method combining dual-tree complex wavelet transform, empirical mode decomposition, and the two-stage feature selection technique is effective in both feature extraction and feature selection, which also increase classification accuracy.</P>

Remote Sensing Image Fusion Based On IHS and Dual Tree Compactly Supported Shearlet Transform

Chang Duan,Qihong Huang,Xuegang Wang,Shuai Wang,Hong Wang 보안공학연구지원센터 2014 International Journal of Signal Processing, Image Vol.7 No.5

This paper presents a novel remote sensing image fusion algorithm, which implements the intensity-hue-saturation (IHS) transform on panchromatic sharpening of multispectral data and the dual-tree compactly supported shearlet transform (DT CSST) during fusion. Shearlet transforms can provide almost optimal representation of the anisotropic features of an image. The spatial domain discrete implementation, the compactly supported shearlet transform (CSST), which represents the directions by dilation operations, are selected in the proposed fusion method. Since most of the prominent features of images, such as edges and regions, have limited sizes in the spatial domain, CSST is very suitable for image fusion. However, the conventional CSST is shift-variant, which causes distortions in fused images. With the embedded dual-tree (DT) sturcture in the CSST, the shift-variant properties can be effectively reduced. Combining the IHS transform and the DT CSST, an effective panchromatic and multispectral image fusion method is proposed in this paper. The experiments’ results suggest that the proposed method extract more spatial information from panchromatic images with less lost in spectral consistency compared to other fusion methods which are based on discrete wavelet transform (DWT), à trous wavelet transform, à trous shearlet transform, the dual-tree complex wave transform ( DT CWT), or the Curvelet transform.

Primal Tree의 공간 분할 샘플링 분석 및 구현

박태정(Taejung Park) 한국디지털콘텐츠학회 2014 한국디지털콘텐츠학회논문지 Vol.15 No.3

The general octree structure is common for various applications including computer graphics, geometry information analysis and query. Unfortunately, the general octree approach causes duplicated sample data and discrepancy between sampling and representation positions when applied to sample continuous spatial information, for example, signed distance fields. To address these issues, some researchers introduced the dual octree. In this paper, the weakness of the dual octree approach will be illustrated by focusing on the fact that the dual octree cannot access some specific continuous zones asymptotically. This paper shows that the primal tree presented by Lefebvre and Hoppe can solve all the problems above. Also, this paper presents a three-dimensional primal tree traversal algorithm based the Morton codes which will help to parallelize the primal tree method.

디지털 영상 처리를 위한 Quincunx 표본화가 사용된 이중 트리 이산 웨이브렛 변환

신종홍 (사)디지털산업정보학회 2011 디지털산업정보학회논문지 Vol.7 No.4

In this paper, we explore the application of 2-D dual-tree discrete wavelet transform (DDWT), which is a directional and redundant transform, for image coding. DDWT main property is a more computationally efficient approach to shift invariance. Also, the DDWT gives much better directional selectivity when filtering multidimensional signals. The dual-tree DWT of a signal is implemented using two critically-sampled DWTs in parallel on the same data. The transform is 2-times expansive because for an N-point signal it gives 2N DWT coefficients. If the filters are designed is a specific way, then the sub-band signals of the upper DWT can be interpreted as the real part of a complex wavelet transform, and sub-band signals of the lower DWT can be interpreted as the imaginary part. The quincunx lattice is a sampling method in image processing. It treats the different directions more homogeneously than the separable two dimensional schemes. Quincunx lattice yields a non separable 2D-wavelet transform, which is also symmetric in both horizontal and vertical direction. And non-separable wavelet transformation can generate sub-images of multiple degrees rotated versions. Therefore, non-separable image processing using DDWT services good performance.

Remaining useful life prediction of circuit breaker operating mechanisms based on wavelet‑enhanced dual‑tree residual networks

Tailong Wu,Yuan Yao,Zhihao Li,Binqiang Chen,Yue Wu,Weifang Sun 전력전자학회 2024 JOURNAL OF POWER ELECTRONICS Vol.24 No.1

The remaining useful life prediction of circuit breaker operating mechanisms is crucial for the condition-based maintenance of national power grids. To realize accurate remaining useful life prediction, a novel wavelet-enhanced dual-tree residual network is proposed in this paper. Through this wavelet transform, the time series is decomposed into two components (high frequency and low frequency). Then the two decomposed components are fed into two lightweight residual neural network structures. By concatenating the dual-tree features, the remaining useful life of a circuit breaker operating mechanism can be predicted. The proposed network is validated using a full-life cycle experiment of the circuit breaker operating mechanism. Results show that the proposed method has good capability when it comes to predicting the remaining useful life of the circuit breaker operating mechanism. Along with application in the construction of smart grids and green energy, it is expected that the proposed method has potential in running state prognostics of circuit breakers.

State detection of explosive welding structure by dual-tree complex wavelet transform based permutation entropy

Yue Si,ZhouSuo Zhang,Wei Cheng,FeiChen Yuan 국제구조공학회 2015 Steel and Composite Structures, An International J Vol.19 No.3

Recent years, explosive welding structures have been widely used in many engineering fields. The bonding state detection of explosive welding structures is significant to prevent unscheduled failures and even catastrophic accidents. However, this task still faces challenges due to the complexity of the bonding interface. In this paper, a new method called dual-tree complex wavelet transform based permutation entropy (DTCWT-PE) is proposed to detect bonding state of such structures. Benefiting from the complex analytical wavelet function, the dual-tree complex wavelet transform (DTCWT) has better shift invariance and reduced spectral aliasing compared with the traditional wavelet transform. All those characters are good for characterizing the vibration response signals. Furthermore, as a statistical measure, permutation entropy (PE) quantifies the complexity of non-stationary signals through phase space reconstruction, and thus it can be used as a viable tool to detect the change of bonding state. In order to more accurate identification and detection of bonding state, PE values derived from DTCWT coefficients are proposed to extract the state information from the vibration response signal of explosive welding structure, and then the extracted PE values serve as input vectors of support vector machine (SVM) to identify the bonding state of the structure. The experiments on bonding state detection of explosive welding pipes are presented to illustrate the feasibility and effectiveness of the proposed method.

Improved DTCWT-LMS and FastICA Based sEMG Signals Filtering

Li Lin,Wang Jianhui,Fang Xiaoke,Gu Shusheng 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.5

A novel design of dual-tree complex wavelet transform (DTCWT) and fastICA was proposed, aiming at the noise interference and aliasing between multi-channels sEMG signals. Firstly, DTCWT was utilized to decompose signals to different frequency band. Secondly, an improved LMS adaptive filter was designed for filtering sub band noise layer by layer. Finally, fastICA algorithm was introduced to separate crosstalk between channels. Some experiments were carried out to compare the proposed method with other algorithms, and the results showed that the algorithm proposed could filter noise effectively, keep better convergence especially in low signal-to-noise ratio and eliminate crosstalk more thoroughly by fastICA.

ICS 사이버 공격 탐지를 위한 딥러닝 전처리 방법 연구

박성환(Seonghwan Park),김민석(Minseok Kim),백은서(Eunseo Baek),박정훈(Junghoon Park) 한국스마트미디어학회 2023 스마트미디어저널 Vol.12 No.11

주요 산업현장에서 설비를 제어하는 산업제어시스템(ICS, Industrial Control System)이 네트워크로 다른 시스템과 연결되는 사례가 증가하고 있다. 또한, 이러한 통합과 함께 한 번의 외부 침입이 전체 시스템 마비로 이루어질 수 있는 지능화된 공격의 발달로, 산업제어시스템에 대한 보안에 대한 위험성과 파급력이 증가하고 있어, 사이버 공격에 대한 보호 및 탐지 방안의 연구가 활발하게 진행되고 있으며, 비지도학습 형태의 딥러닝 모델이 많은 성과를 보여 딥러닝을 기반으로 한 이상(Anomaly) 탐지 기술이 많이 도입되고 있다. 어어, 본 연구에서는 딥러닝 모델에 전처리 방법론을 적용하여 시계열 데이터의 이상 탐지성능을 향상시키는 것에 중점을 두어, 그 결과 웨이블릿 변환(WT, Wavelet Transform) 기반 노이즈 제거 방법론이 딥러닝 기반 이상 탐지의 전처리 방법론으로 효과적임을 알 수 있었으며, 특히 센서에 대한 군집화(Clustering)를 통해 센서의 특성을 반영하여 Dual-Tree Complex 웨이블릿 변환을 차등적으로 적용하였을 때 사이버 공격의 탐지성능을 높이는 것에 가장 효과적임을 확인하였다. Industrial Control System(ICS), which controls facilities at major industrial sites, is increasingly connected to other systems through networks. With this integration and the development of intelligent attacks that can lead to a single external intrusion as a whole system paralysis, the risk and impact of security on industrial control systems are increasing. As a result, research on how to protect and detect cyber attacks is actively underway, and deep learning models in the form of unsupervised learning have achieved a lot, and many abnormal detection technologies based on deep learning are being introduced. In this study, we emphasize the application of preprocessing methodologies to enhance the anomaly detection performance of deep learning models on time series data. The results demonstrate the effectiveness of a Wavelet Transform (WT)-based noise reduction methodology as a preprocessing technique for deep learning-based anomaly detection. Particularly, by incorporating sensor characteristics through clustering, the differential application of the Dual-Tree Complex Wavelet Transform proves to be the most effective approach in improving the detection performance of cyber attacks.

Interactive Semantic Image Retrieval

( Pushpa B. Patil ),( Manesh B. Kokare ) 한국정보처리학회 2013 Journal of information processing systems Vol.9 No.3

The big challenge in current content-based image retrieval systems is to reduce the semantic gap between the low level-features and high-level concepts. In this paper, we have proposed a novel framework for efficient image retrieval to improve the retrieval results significantly as a means to addressing this problem. In our proposed method, we first extracted a strong set of image features by using the dual-tree rotated complex wavelet filters (DT-RCWF) and dual tree-complex wavelet transform (DT-CWT) jointly, which obtains features in 12 different directions. Second, we presented a relevance feedback (RF) framework for efficient image retrieval by employing a support vector machine (SVM), which learns the semantic relationship among images using the knowledge, based on the user interaction. Extensive experiments show that there is a significant improvement in retrieval performance with the proposed method using SVMRF compared with the retrieval performance without RF. The proposed method improves retrieval performance from 78.5% to 92.29% on the texture database in terms of retrieval accuracy and from 57.20% to 94.2% on the Corel image database, in terms of precision in a much lower number of iterations.