Space Alignment Research Articles

Class conditional distribution alignment for domain adaptation

In this paper, we study the problem of domain adaptation, which is a crucial ingredient in transfer learning with two domains, that is, the source domain with labeled data and the target domain with none or few labels. Domain adaptation aims to extract knowledge from the source domain to improve the performance of the learning task in the target domain. A popular approach to handle this problem is via adversarial training, which is explained by the HΔH-distance theory. However, traditional adversarial network architectures just align the marginal feature distribution in the feature space. The alignment of class condition distribution is not guaranteed. Therefore, we proposed a novel method based on pseudo labels and the cluster assumption to avoid the incorrect class alignment in the feature space. The experiments demonstrate that our framework improves the accuracy on typical transfer learning tasks.

Optimizing Ontology Alignment in Vector Space

Ontology matching technique aims at determining the identical entities, which can effectively solve the ontology heterogeneity problem and implement the collaborations among ontology-based intelligent systems. Typically, an ontology consists of a set of concepts which are described by various properties, and they define a space such that each distinct concept and property represents one dimension in that space. Therefore, it is an effective way to model an ontology in a vector space, and use the vector space based similarity measure to calculate two entities’ similarity. In this work, the entities’ structure information is utilized to model an ontology in a vector space, and then, their linguistic information is used to reduce the number of dimensions, which can improve the efficiency of the similarity calculation and entity matching process. After that, a discrete optimization model is constructed for the ontology matching problem, and a compact Evolutionary Algorithm (cEA) based ontology matching technique is proposed to efficiently address it. The experiment uses the benchmark track provided by Ontology Alignment Evaluation Initiative (OAEI) to test our proposal’s performance, and the comparing results with state-of-the-art ontology matching systems show that our approach can efficiently determine high-quality ontology alignments.

Subspace Learning and Feature Selection via Orthogonal Mapping

A plethora of dimensionality reduction (DR) techniques, stemming from statistics, machine learning, or graph theory, has been developed. Their ultimate objective is to eliminate data redundancy without a significant information loss. A general formulation, known as graph embedding, offers a unified framework for describing several well-known DR algorithms. In this paper, the inclusion of several DR algorithms within this unified framework is demonstrated. The enforcement of orthogonality to the projection matrix within this framework is proven to be of vital importance, since Orthogonal Neighborhood Preserving Projections, Orthogonal Locality Preserving Projections, Orthogonal Isometric Projection, Orthogonal Linear Discriminant Analysis, and Orthogonal Local Tangent Space Alignment algorithms outperform their non-orthogonal counterparts, e.g., Neighborhood Preserving Embedding, Locality Preserving Projections, Isometric Mapping, Linear Discriminant Analysis, and Local Tangent Space Alignment. One may simultaneously also impose the $\ell _{2,1}$ norm regularization on the projection matrix, seeking for row-sparsity. This leads to the known Joint Feature Selection and Subspace Learning (JFSSL) framework. All DR algorithms are employed within JFSSL. It is proven that the use of orthogonal mapping algorithms within JFSSL against their non-orthogonal counterparts does not improve the recognition rate of NN classifier.

Fault diagnosis of high-speed train suspension systems using multiscale permutation entropy and linear local tangent space alignment

Online health monitoring of railway vehicle suspension systems is of critical importance to guarantee train running safety. The currently reported works on vehicle suspension health monitoring mainly adopt model-based approaches. However, detailed parameters of the vehicle suspension systems, usually, are complicated to be acquired. Moreover, an accurate model cannot be easily obtained due to the nonlinearities of vehicle components and the complexity of suspension systems. Considering the limitations of the model-based approaches, a data-driven method, which combines multiscale permutation entropy and linear local tangent space alignment (MPE-LLTSA), is proposed to diagnose the faults of vehicle suspension systems. To demonstrate the effectiveness and advantages of this method, an MBS model of the China CRH3 train is built to generate the bogie frame’s lateral acceleration containing various secondary suspension faults, and the simulated signals are then introduced to evaluate the MPE-LLTSA method. The evaluation results show that the proposed data-driven approach can accurately identify different types of suspension faults. Finally, the MPE-LLTSA method is further validated using the tracking data of the CRH3 train running on a high-speed railway line in China, and the test results show that the proposed method has the potential to be applied to the field of railway engineering.

The Eminence of Co-Expressed Ties in Schizophrenia Network Communities

Exploring gene networks is crucial for identifying significant biological interactions occurring in a disease condition. These interactions can be acknowledged by modeling the tie structure of networks. Such tie orientations are often detected within embedded community structures. However, most of the prevailing community detection modules are intended to capture information from nodes and its attributes, usually ignoring the ties. In this study, a modularity maximization algorithm is proposed based on nonlinear representation of local tangent space alignment (LTSA). Initially, the tangent coordinates are computed locally to identify k-nearest neighbors across the genes. These local neighbors are further optimized by generating a nonlinear network embedding function for detecting gene communities based on eigenvector decomposition. Experimental results suggest that this algorithm detects gene modules with a better modularity index of 0.9256, compared to other traditional community detection algorithms. Furthermore, co-expressed genes across these communities are identified by discovering the characteristic tie structures. These detected ties are known to have substantial biological influence in the progression of schizophrenia, thereby signifying the influence of tie patterns in biological networks. This technique can be extended logically on other diseases networks for detecting substantial gene “hotspots”.

Supervised kernel discriminant local tangent space alignment for high-resolution range profile-based radar target recognition

We present a modified local tangent space alignment (LTSA) algorithm, called supervised kernel discriminant local tangent space alignment (SKDLTSA), for radar target recognition based on high-resolution range profile (HRRP). SKDLTSA aims to extract intraclass geometric structure embedded in local neighborhoods, as well as to maximize interclass separability characterized by overall distances among different classes. It is formulated with kernel technique to extract nonlinear features, which helps to obtain better performance than its linear counterparts. Extensive experiments on measured HRRP data from three flying airplanes demonstrate that the proposed method can significantly improve the recognition performance. Further results also indicate its robustness to target attitude variations and noise corruption.

Deep cycle autoencoder for unsupervised domain adaptation with generative adversarial networks

Deep learning is a powerful tool for domain adaptation by learning robust high‐level domain invariant representations. Recently, adversarial domain adaptation models are applied to learn representations with adversarial training manners in feature space. However, existing models often ignore the generation process for domain adaptation. To tackle this problem, deep cycle autoencoder (DCA) is proposed that integrates a generation procedure into the adversarial adaptation methods. The proposed DCA consists of four parts, a shared encoder, two separated decoders, a discriminator and a linear classifier. With the labelled source images and unlabelled target images as inputs, the encoder extracts high‐level representations for both source and target domains, and the two decoders reconstruct the inputs with the latent representations separately. The shared encoder is pitted against the discriminator; the encoder tries to confuse the discriminator while discriminator aims at distinguishing which domain the latent representations come from. DCA adopts both adversarial loss and maximum mean discrepancy loss in the latent space for distribution alignment. The classifier is trained with both the source original and reconstructed image representations. Extensive experimental results have demonstrated the effectiveness and the reliability of the proposed methods.

Rapid and Accurate Registration Method between Intraoperative 2D XA and Preoperative 3D CTA Images for Guidance of Percutaneous Coronary Intervention.

In this paper, we propose a rapid rigid registration method for the fusion visualization of intraoperative 2D X-ray angiogram (XA) and preoperative 3D computed tomography angiography (CTA) images. First, we perform the cardiac cycle alignment of a patient's 2D XA and 3D CTA images obtained from a different apparatus. Subsequently, we perform the initial registration through alignment of the registration space and optimal boundary box. Finally, the two images are registered where the distance between two vascular structures is minimized by using the local distance map, selective distance measure, and optimization of transformation function. To improve the accuracy and robustness of the registration process, the normalized importance value based on the anatomical information of the coronary arteries is utilized. The experimental results showed fast, robust, and accurate registration using 10 cases, each of the left coronary artery (LCA) and right coronary artery (RCA). Our method can be used as a computer-aided technology for percutaneous coronary intervention (PCI). Our method can be applied to the study of other types of vessels.

Optimal manifold neighborhood and kernel width for robust non-linear dimensionality reduction

The graph Laplacian approximation of the Laplace–Beltrami operator on the Riemann manifold requires estimation of the kernel width and the neighborhood which is usually obtained through cross-validation. In this work, we propose a method based on local tangent space alignment to determine the optimal neighborhood. The method works by selecting the most distant point with minimum acceptable misalignment in every local region to define the neighborhood size. The work also proposes to use local kernel width which is represented as a function of the number of data points in the optimal neighborhood and maximum neighborhood distance. Extensive experiments on synthetic and real-world data including brain computer interface readings confirm that the proposed method is able to extract the intrinsic representation and outperforms the related existing state-of-the-art methods in the area.

Polarization-insensitive silicon nitride arrayed waveguide grating.

Next-generation passive optical networks require integrated, polarization-insensitive wavelength-division multiplexing solutions, for which the recently emerging low-loss silicon nitride nanophotonic platforms hold great potential. A novel polarization-insensitive arrayed waveguide grating (AWG) built with silicon nitride waveguides is presented in this Letter. Polarization insensitivity is obtained when both the channel spacing and the center wavelength of the two orthogonal polarization states (i.e., the TE and TM waveguide modes) are simultaneously aligned. In our design, the channel spacing alignment between the polarization states is obtained by optimizing the geometry of the arrayed waveguides, whereas the central wavelength polarization insensitivity is obtained by splitting the two polarization states and adjusting their angle of incidence at the input star coupler to compensate for the polarization mode dispersion of the AWG. A 100GHz 1×8 wavelength-division multiplexer with crosstalk levels below -16 dB is demonstrated experimentally.

Rolling bearing fault diagnosis based on adaptive smooth ITD and MF-DFA method

To effectively utilize a feature set to further improve fault diagnosis of a rolling bearing vibration signal, a method based on multi-fractal detrended fluctuation analysis (MF-DFA) and smooth intrinsic time-scale decomposition (SITD) was proposed. The vibration signal was decomposed into several proper rotation components by applying this new SITD method to overcome noise effects, preserve the effective signal, and improve the signal-to-noise ratio. Wavelet analysis was embedded in iteration procedures of intrinsic time-scale decomposition (ITD). For better results, an adaptive threshold function was used for signal recovery from noisy proper rotation components in the wavelet domain. Additionally, MF-DFA was used to reveal the multi-fractality present in the instantaneous amplitude of the proper rotation components. Finally, linear local tangent space alignment was applied for feature dimension reduction and to obtain fault characteristics of different types, further improving identification accuracy. The performance of the proposed method is determined to be superior to that of the ITD-MF-DFA method.

Critical Time, Space, and Decision-Making Agent Considerations in Human-Centered Interdisciplinary Hurricane-Related Research.

In hazard and disaster contexts, human-centered approaches are promising for interdisciplinary research since humans and communities feature prominently in many definitions of disaster and the built environment is designed and constructed by humans to serve their needs. With a human-centered approach, the decision-making agent becomes a critical consideration. This article discusses and illustrates the need for alignment of decision-making agents, time, and space for interdisciplinary research on hurricanes, particularly evacuation and the immediate aftermath. We specifically consider the fields of sociobehavioral science, transportation engineering, power systems engineering, and decision support systems in this context. These disciplines have historically adopted different decision-making agents, ranging from individuals to households to utilities and government agencies. The fields largely converged to the local level for studies' spatial scales, with some extensions based on the physical construction and operation of some systems. Greater discrepancy across the fields is found in the frequency of data collection, which ranges from one time (e.g., surveys) to continuous monitoring systems (e.g., sensors). Resolving these differences is important for the success of interdisciplinary teams in protective-action-related disaster research.

리드용 와이어의 평탄도 측정 방법에 따른 비교 분석

Recently, the demand of wire quality improvement has been increasing due to the development of the textile market, and the demand for round type flat wire having superior performance compared to the existing reed wire is also increasing greatly. The round wire requires equal spacing and precise alignment of the wire at reed knitting. So, there is a need for a method for measuring the flatness of Reed in real time. In this paper, it is shown how the contact and non - contact methods for the flatness of Reed used in a power loom are easy to measure in real - time process. The contact-type method provides excellent measurement accuracy and precision because it directly touches the object. The non-contact type does not touch the object, so it does not scratch the surface, and the result can be obtained faster than the contact type. Contact type measuring device was used as contact type and laser displacement sensor was used as non - contact type. It was confirmed that the measurement method using the laser displacement sensor (non-contact type) is suitable for the real time process.

A novel speed model for safety evaluation of freeway alignment in Euclidean 3D space

Objective: Operating speed is a critical indicator to evaluate consistency of road alignment and safety. Although extensive studies have been conducted on developing operating speed models, few researchers have considered the interactive influence of horizontal and vertical alignment in 3D space. The purpose of this study is to develop a speed model based on 3D alignment in Euclidean space rather than traditional horizontal and vertical alignment.Methods: According to the curve theory of differential geometry, a novel method to estimate operating speed is proposed in our study using 3D space curvature instead of traditional horizontal or vertical parameters to describe the spatial geometric properties for a freeway alignment. Speeds of 54 different alignment segments are observed to develop the speed model. Several observing sites of each segment are selected beforehand, and the speeds of more than 300 vehicles in each site are observed. Space curvature is used as an important index to estimate operating speed.Results: The findings of this study indicated that both horizontal alignment and vertical alignment contribute to space curvature. Space curvature mainly affects direction control operating performance. However, vehicles overcome the effects of gravity along the vertical alignment in the z direction. Results indicate that operating speed exponentially declines with space curvature and that quadratic parabola decline with vertical grade.Conclusions: It can be concluded that there is a clear correlation between velocity and spatial curvature, which is proved by variance analysis. The estimation results of the speed models are reliable as tested using a real engineering example. The study would provide a scientific basis for safety evaluation of freeway alignment.

Local tangent space alignment and relevance vector machine as nonlinear methods for estimating sensory quality of tea using NIR spectroscopy

The quality of green tea is conventionally tested by human sensory panel, who assign quality scores to different teas. In order to overcome the deficiency of sensory evaluation methods, near infrared (NIR) reflectance spectroscopy combined with nonlinear methods was used to evaluate the sensory quality of green tea in this study. Nonlinear dimension reduction methods such as Kernel Principal Component Analysis (KPCA), Sparse Principal Component Analysis (SPCA), and Local Tangent Space Alignment (LTSA) were used to reduce the dimension of the spectral data, and compared with linear dimension reduction methods such as Principal Component Analysis (PCA), Multidimensional Scaling (MDS). Relevance vector machine (RVM) as a nonlinear quantitative analysis method was performed comparatively to develop calibration models between the sensory scores of the sensory panels and objective determination (NIR spectra measurements), and compared with back-propagation artificial network and linear PLS model. Experiment results showed that the non-linear reduction algorithm LTSA could obtain better effect on spatial reduction dimensional for NIR spectral data of tea. And when the number K of the neighborhoods in LTSA was 15, the RMSECV of RVM model was the minimum. The performance of LTSA-RVM model was superior to the others when 8 intrinsic variables were used. In the prediction set, Rp, RMSEP and RPD were 0.963, 1.461 and 3.788, respectively. The absolute value of prediction error of all prediction samples in LTSA-RVM are below 3.0, which indicated that the sensory scores predicted by NIR were consistent with the scores by tea assessors. The results of recovery study were 98.42%–102.81% at 5 real tea samples and the relative error lower than 3%. The research demonstrates that NIR technology combined with nonlinear analysis methods can effectively estimate sensory quality of tea and also present a potential way for fast and effective determination of the sensory quality of green tea.

Local tangent space alignment based on Hilbert–Schmidt independence criterion regularization

Local tangent space alignment (LTSA) is a famous manifold learning algorithm, and many other manifold learning algorithms are developed based on LTSA. However, from the viewpoint of dimensionality reduction, LTSA is only a local feature preserving algorithm. What the community of dimensionality reduction is now pursuing are those algorithms capable of preserving both local and global features at the same time. In this paper, a new algorithm for dimensionality reduction, called HSIC-regularized LTSA (HSIC–LTSA), is proposed, in which a HSIC regularization term is added to the objective function of LTSA. HSIC is an acronym for Hilbert–Schmidt independence criterion and has been used in many applications of machine learning. However, HSIC has not been directly applied to dimensionality reduction so far, neither used as a regularization term to combine with other machine learning algorithms. Therefore, the proposed HSIC–LTSA is a new try for both HSIC and LTSA. In HSIC–LTSA, HSIC makes the high- and low-dimensional data statistically correlative as much as possible, while LTSA reduces the data dimension under the local homeomorphism-preserving criterion. The experimental results presented in this paper show that, on several commonly used datasets, HSIC–LTSA performs better than LTSA as well as some state-of-the-art local and global preserving algorithms.

Iterative Local Tangent Space Alignment with Adaptive Neighbourhood for Social Network Mining

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

Early fault detection of bearings based on adaptive variational mode decomposition and local tangent space alignment

PurposeEarly fault detection of bearing plays an increasingly important role in the operation of rotating machinery. Based on the properties of early fault signal of bearing, this paper aims to describe a novel hybrid early fault detection method of bearings.Design/methodology/approachIn adaptive variational mode decomposition (AVMD), an adaptive strategy is proposed to select the optimal decomposition level K of variational mode decomposition. Then, a criterion based on envelope entropy is applied to select the optimal intrinsic mode functions (OIMF), which contains most useful fault information. Afterwards, local tangent space alignment (LTSA) is used to denoising of OIMF. The envelope spectrum of the OIMF is used to analyze the fault frequency, thereby detecting the fault. Experiments are conducted in a simulated signal and two experimental vibration signals of bearings to verify the effect of the new method.FindingsThe results show that the proposed method yields a good capability of detecting bearing fault at an early stage. The new method can extract more useful information and can reduce noise, which can provide better detection accuracy compared with the other two methods.Originality/valueAn adaptive strategy based on center frequency is proposed to select the optimal decomposition level of variational mode decomposition. Envelope entropy is used to fault feature selection. Combining the advantage of the AVMD-envelope entropy and LTSA, which suits the nature of the early fault signal. So, the proposed method has better detection accuracy, which provides a good alternative for early fault detection of bearings.

Abstract No. 474 A Pilot study of the feasibility of ultrasound-PET/CT fusion–guided supraclavicular brown adipose tissue biopsy

To determine the feasibility of ultrasound-PET/CT fusion imaging guidance for supra-clavicular adipose tissue biopsies This IRB-approved prospective study included seven healthy volunteers (5F/2M) who had a prior 18F-FDG PET/CT showing metabolically active BAT. Manual registration of the ipsilateral sterno-clavicular joint was performed using an electromagnetically tracked 15-7io probe. A 2D ultrasound sweep of the ipsilateral clavicle was then obtained after manual alignment of the electromagnetic tracking space to the patient. A 3D ultrasound volume was reconstructed and then registered to the CT portion of the PET/CT image. Manual correction of image fusion was carried out on an as-needed basis. Ultrasound/CT/PET fusion was displayed in real-time for biopsy guidance. Biopsies were performed with 14-16G Temno needles under direct ultrasound visualization in the fused image. BAT was confirmed via 1) the presence of mRNA for uncoupling protein 1 (UCP1), the thermogenic protein specific to brown adipocytes, and 2) comparison of levels obtained from adjacent subcutaneous white adipose tissue (WAT) obtained from each subject. From the seven subjects enrolled, deep supraclavicular brown fat was acquired from 6/7 and superficial white fat from 7/7. The deep biopsies had 40X higher levels of UCP1 (P<0.001), consistent with BAT, validating the accuracy of the fused imaging guidance. Complications included self-limited site erythema (1/7), pressure (1/7), and paresthesias (1/7). Ultrasound-PET/CT fusion is a feasible approach for site-specific sampling human supraclavicular BAT and WAT. This novel method should enable a new, wide range of studies of human BAT physiology.

Bispectrum Texture Feature Manifold for Feature Extraction in Rolling Bear Fault Diagnosis

Effectively classify the fault types and the degradation degree of a rolling bearing is an important basis for accurate malfunction detection. A novel feature extract method - bispectrum image texture features manifold (BTM) of the rolling bearing vibration signal is proposed in this paper. The BTM method is realized by three main steps: bispectrum image analysis, texture feature construction and manifold feature dimensionality reduction. In this method, bispectrum analysis is employed to convert the mass vibration signals into bispectrum contour map, the typical texture features were extracted from the contour map by gray level co-occurrence matrix (GLCM), then the manifold dimensionality reduction method liner local tangent space alignment (LLTSA) is used to remove redundant information and reduce the dimension from the extracted texture features and obtain more meaningful low-dimensional information. Furthermore, the low-dimensional texture features were identified by support vector machine (SVM) which was optimized by genetic optimization algorithm (GA). The validity of BTM is confirmed by rolling bear experiments, the result show that the proposed feature extraction method can accurately distinguish different fault types and have a good performance to classify the degradation degree of inner race fault, outer race fault and rolling ball fault.