nils-herrmann/clean_scirepeval huggingface dataset:
Performance evaluation for frequency estimation of transients using the ESPRIT: Measured noise versus white noise

A number of papers have proposed to use the ESPRIT method for estimating some power system transients. The mathematical model for using the ESPRIT requires that the additive noise be white. Fail to satisfy this could lead to degraded performance of frequency estimation. However, in practical situations the noise is often non-white. In this paper we study the performance of the ESPRIT frequency estimation method when the additive noise is measured from power systems. It is shown that tests using synthetic white noise are not sufficient for realistic power-system scenarios, as the noise is often non-white and not i.i.d. distributed. First, a set of measured voltage waveforms is superimposed to synthetic transients with known frequencies. This yields a set of semi-measured transients. The ESPRIT method is then applied to estimate the frequencies. The performance of the ESPRIT is then evaluated in term of the mean value and standard deviation. Our experiments show that, for a transient contains one sinusoid, the ESPRIT of sinusoidal model order K=2 results in an acceptable accuracy if the amplitude of the transient is well above the noise level and the transient frequency is not too close to the power-system frequency. This is sufficient for most practical applications. A better performance for higher noise levels is obtained by significantly increasing the sinusoidal model order in the ESPRIT.
The use of time-varying AR models for the characterization of voltage disturbances

A signal-processing tool, autoregressive (AR) modeling, has been introduced to analyze disturbance measurement data from events, like voltage dips, capacitor switching and transformer energization. We show that time-varying AR models may be utilized for detecting sudden transitions of the signal by examining the models' residuals. We also show a potential way of detecting and describing signal transients, using the time-evolved AR model spectra. To examine the disturbances over the entire spectral range, a multiresolution disturbance analysis scheme is presented.
Event-based transient categorization and analysis in electric power systems

Power system transients cause problems and disturbances that might be harmful to electronic equipment. However, very little has been done to systematically understand and analyze power system transients. There is also a lack of computational effective methods as transients are short and associated with a wide range of events and system conditions. This paper addresses the following issues: (1) to categorize the power system transients according to their underlying causes; (2) to propose a signal processing method, ESPRIT, for the analysis of a range of transients due to different causes. Experimental results are included to evaluate the usefulness of ESPRIT in power system transient analysis. Remaining open issues for research are also discussed.
Joint Feature Correspondences and Appearance Similarity for Robust Visual Object Tracking

A novel visual object tracking scheme is proposed by using joint point feature correspondences and object appearance similarity. For point feature-based tracking, we propose a candidate tracker that simultaneously exploits two separate sets of point feature correspondences in the foreground and in the surrounding background, where background features are exploited for the indication of occlusions. Feature points in these two sets are then dynamically maintained. For object appearance-based tracking, we propose a candidate tracker based on an enhanced anisotropic mean shift with a fully tunable (five degrees of freedom) bounding box that is partially guided by the above feature point tracker. Both candidate trackers contain a reinitialization process to reset the tracker in order to prevent accumulated tracking error propagation in frames. In addition, a novel online learning method is introduced to the enhanced mean shift-based candidate tracker. The reference object distribution is updated in each time interval if there is an indication of stable and reliable tracking without background interferences. By dynamically updating the reference object model, tracking is further improved by using a more accurate object appearance similarity measure. An optimal selection criterion is applied to the final tracker based on the results of these candidate trackers. Experiments have been conducted on several videos containing a range of complex scenarios. To evaluate the performance, the proposed scheme is further evaluated using three objective criteria, and compared with two existing trackers. All our results have shown that the proposed scheme is very robust and has yielded a marked improvement in terms of tracking drift, tightness, and accuracy of tracked bounding boxes, especially for complex video scenarios containing long-term partial occlusions or intersections, deformation, or background clutter with similar color distributions to the foreground object.
3D limb movement tracking and analysis for neurological dysfunctions of neonates using multi-camera videos.

Central nervous system dysfunction in infants may be manifested through inconsistent, rigid and abnormal limb movements. Detection of limb movement anomalies associated with such neurological dysfunctions in infants is the first step towards early treatment for improving infant development. This paper addresses the issue of detecting and quantifying limb movement anomalies in infants through non-invasive 3D image analysis methods using videos from multiple camera views. We propose a novel scheme for tracking 3D time trajectories of markers on infant's limbs by video analysis techniques. The proposed scheme employ videos captured from three camera views. This enables us to detect a set of enhanced 3D markers through cross-view matching and to effectively handle marker self-occlusions by other body parts. We track a set of 3D trajectories of limb movements by a set of particle filters in parallel, enabling more robust 3D tracking of markers, and use the 3D model errors for quantifying abrupt limb movements. The proposed work makes a significant advancement to the previous work in  through employing tracking in 3D space, and hence overcome several main barriers that hinder real applications by using single camera-based techniques. To the best of our knowledge, applying such a multi-view video analysis approach for assessing neurological dysfunctions of infants through 3D time trajectories of markers on limbs is novel, and could lead to computer-aided tools for diagnosis of dysfunctions where early treatment may improve infant development. Experiments were conducted on multi-view neonate videos recorded in a clinical setting and results have provided further support to the proposed method.
Adaptive particle filters for visual object tracking using joint PCA appearance model and consensus point correspondences

This paper addresses issues on moving object tracking from videos. We propose a novel tracking scheme that jointly exploits local object features using consensus point correspondences, and global object appearance and shape models using adaptive particle filter-based eigen-tracking. The paper include the following main novelties: (a) employ consensus feature point correspondences to estimate the motion vector of shape model; (b) employ adaptive particle filters and motioncorrected state vector for joint appearance- and shape-based eigen-tracking. An adaptive number of particles is chosen automatically based on an updated estimation of covariancematrix. Further, online learning is made adaptive to avoid learning using partially-occluded objects. The proposed scheme is realized by integrating SURF and RANSAC [8, 9] for estimating consensus point correspondences, and modify an existing particle filter-based eigen-tracking . Experiment results on tracking moving objects in videos have shown that the proposed scheme provides more accurate tracking, especially for objects with fast motion or long-term partial occlusions. The average number of particles is significantly reduced. Comparisons have been made with an existing method, results have shown that the proposed scheme has provided an improved tracking accuracy at the cost of more computations.
Robust visual tracking via inverse nonnegative matrix factorization

The establishment of robust target appearance model over time is an overriding concern in visual tracking. In this paper, we propose an inverse nonnegative matrix factorization (NMF) method for robust appearance modeling. Rather than using a linear combination of nonnegative basis vectors for each target image patch in conventional NMF, the proposed method is a reverse thought to conventional NMF tracker. It utilizes both the foreground and background information, and imposes a local coordinate constraint, where the basis matrix is sparse matrix from the linear combination of candidates with corresponding nonnegative coefficient vectors. Inverse NMF is used as a feature encoder, where the resulting coefficient vectors are fed into a SVM classifier for separating the target from the background. The proposed method is tested on several videos and compared with seven state-of-the-art methods. Our results have provided further support to the effectiveness and robustness of the proposed method.
Robust Visual Object Tracking Using Multi-Mode Anisotropic Mean Shift and Particle Filters

This paper addresses issues in object tracking where videos contain complex scenarios. We propose a novel tracking scheme that jointly employs particle filters and multi-mode anisotropic mean shift. The tracker estimates the dynamic shape and appearance of objects, and also performs online learning of reference object. Several partition prototypes and fully tunable parameters are applied to the rectangular object bounding box for improving the estimates of shape and multiple appearance modes in the object. The main contributions of the proposed scheme include: 1) use a novel approach for online learning of reference object distributions; 2) use a five parameter set (2-D central location, width, height, and orientation) of rectangular bounding box as tunable variables in the joint tracking scheme; 3) derive the multi-mode anisotropic mean shift related to a partitioned rectangular bounding box and several partition prototypes; and 4) relate the bounding box parameter computation with the multi-mode mean shift estimates by combining eigen decomposition, geometry of subareas, and weighted average. This has led to more accurate and efficient tracking where only small number of particles (<;20) is required. Experiments have been conducted for a range of videos captured by a dynamic or stationary camera, where the target object may experience long-term partial occlusions, intersections with other objects with similar color distributions, deformable object accompanied with shape, pose or abrupt motion speed changes, and cluttered background. Comparisons with existing methods and performance evaluations are also performed. Test results have shown marked improvement of the proposed method in terms of robustness to occlusions, tracking drifts and tightness and accuracy of tracked bounding box. Limitations of the method are also mentioned.
Support Vector Machine for Classification of Voltage Disturbances

The support vector machine (SVM) is a powerful method for statistical classification of data used in a number of different applications. However, the usefulness of the method in a commercial available system is very much dependent on whether the SVM classifier can be pretrained from a factory since it is not realistic that the SVM classifier must be trained by the customers themselves before it can be used. This paper proposes a novel SVM classification system for voltage disturbances. The performance of the proposed SVM classifier is investigated when the voltage disturbance data used for training and testing originated from different sources. The data used in the experiments were obtained from both real disturbances recorded in two different power networks and from synthetic data. The experimental results shown high accuracy in classification with training data from one power network and unseen testing data from another. High accuracy was also achieved when the SVM classifier was trained on data from a real power network and test data originated from synthetic data. A lower accuracy resulted when the SVM classifier was trained on synthetic data and test data originated from the power network.
Nonlinear smoothing filter using adaptive radial clustering

A novel adaptive nonlinear filter is proposed aimed at smoothing homogenous regions while maintaining image structures. The filter can be utilized as a pre-processing tool in image segmentation and edge estimation for improving the results. Several special features are introduced to the filter, including using local adaptive radial clustering and pixel filtering to exclude the influence of outliers and to maintain image structures; using the steepest-ascent method to iteratively update pixels to the nearest clusters obtained by mean-shift; and introducing highly parallel processing by using random seed samples and their associated data blocks which enables fast processing and the global optimum solution of the nonlinear filter. Experiments were done on images of various complexities, and good results were obtained. Evaluations of the filter were also done in terms of edge preservation and image segmentation.
Signal processing and classification tools for intelligent distributed monitoring and analysis of the smart grid

This paper proposes a novel framework for an intelligent monitoring system that supervises the performance of the future power system. The increased complexity of the power system could endanger the reliability, voltage quality, operational security or resilience of the power system. A distributed structure for such a monitoring system is described and some of the advanced signal processing techniques or tools that could be used in such a monitoring system are given. Several examples for seeking the spatial locations and finding the underlying causes of disturbances are included.
A fault location technique using high frequency fault clearing transients

This letter suggests that a voltage recorder, placed next to a circuit breaker not as usual on the side of the substation, but on the side of the transmission line, may reveal the location of permanent faults. Two different approaches to estimating fault location are presented here: spectrum estimation and wavelet analysis. The methods are tested and compared with simulations of typical transmission systems using the Electromagnetic Transients Program (EMTP).
Human fall detection via shape analysis on Riemannian manifolds with applications to elderly care

This paper addresses issues in fall detection from videos. The focus is on the analysis of human shapes which deform drastically in camera views while a person falls onto the ground. A novel approach is proposed that performs fall detection from an arbitrary view angle, via shape analysis on a unified Riemannian manifold for different camera views. The main novelties of this paper include: (a) representing dynamic shapes as points moving on a unit n-sphere, one of the simplest Riemannian manifolds; (b) characterizing the deformation of shapes by computing velocity statistics of their corresponding manifold points, based on geodesic distances on the manifold. Experiments have been conducted on two publicly available video datasets for fall detection. Test, evaluations and comparisons with 6 existing methods show the effectiveness of our proposed method.
Traffic sign recognition using salient region features: A novel learning-based coarse-to-fine scheme

Traffic sign recognition, including sign detection and classification, is essential for advanced driver assistance systems and autonomous vehicles. This paper introduces a novel machine learning-based sign recognition scheme. In the proposed scheme, detection and classification are realized through learning in a coarse-to-fine manner. Based on the observation that signs in the same category share some common attributes in appearance, the proposed scheme first distinguishes each individual sign category from the background in the coarse learning stage (i.e. sign detection) followed by distinguishing different sign classes within each category in the fine learning stage (i.e. sign classification). Both stages are realized through machine learning techniques. A complete recognition scheme is developed that is effective for simultaneously recognizing multiple categories of traffic signs. In addition, a novel saliency-based feature extraction method is proposed for sign classification. The method segments salient sign regions by leveraging the geodesic energy propagation. Compared with the conventional feature extraction, our method provides more reliable feature extraction from salient sign regions. The proposed scheme is tested and validated on two categories of Chinese traffic signs from Tencent street view. Evaluations on the test dataset show reasonably good performance, with an average of 97.5% true positive and 0.3% false positive on two categories of traffic signs.
Music Signal Synthesis using Sinusoid Models and Sliding-Window Esprit

This paper proposes a music signal synthesis scheme that is based on sinusoid modeling and sliding-window ESPRIT. Despite widely used audio coding standards, effectively synthesizing music using sinusoid models, more suitable for harmonic rich music signals, remains an open issue. In the proposed scheme, music signals are modeled by a sum of damped sinusoids in noise. A sliding window ESPRIT algorithm is applied. A continuity constraint is then imposed for tracking the time trajectories of sinusoids in music and for removing spurious spectral peaks in order to adapt to the changing number of sinusoid contents in dynamic music. Simulations have been performed to several music signals with a range of complexities, including music recorded from banjo, flute and music with mixed instruments. The results from listening and spectrograms have strongly indicated that the proposed method is very robust for music synthesis with good quality
Parameter estimation of multidimensional NMR signals based on high-resolution subband analysis of 2D NMR projections

NMR spectroscopy is a powerful technique used in protein research for comprehensive functional characterizations, e.g. structure determination at atomic resolution. Due to the molecular size (typically≫1000 atoms), protein NMR spectra contain a large number of signal frequencies. Resolving these requires high-dimensional spectroscopy. However, when the number of frequency exceeds three, conventional approaches start to demand unrealistic long experiment time, and the data analysis becomes challenging. In this paper we explore a combination of novel methods: Data from 5D NMR experiments are recorded as a series of 2D projections, which are then subjected to 2D subband filters and 2D LS-ESPRIT for estimation of signal parameters. Based on the relations established between 5D NMR signals and their 2D counterparts, projection spectroscopy allows to extract highly similar information as what would be found in conventional 5D NMR experiments; however, while the latter would require months of experiment time, the recording of all necessary projections can be accomplished within 1–2 days. Preliminary results show the efficiency of the method with respect to accuracy and resolution of the parameter estimates as compared with conventional methods.
Icosahedral gradient encoding scheme for an arbitrary number of measurements

The icosahedral gradient encoding scheme (GES) is widely used in diffusion MRI community due to its uniformly distributed orientations and rotationally invariant condition number. The major drawback with this scheme is that it is not available for arbitrary number of measurements. In this paper (i) we propose an algorithm to find the icosahedral scheme for any number of measurements. Performance of the obtained GES is evaluated and compared with that of Jones and traditional icosahedral schemes in terms of condition number, standard deviation of the estimated fractional anisotropy and distribution of diffusion sensitizing directions; and (ii) we introduce minimum eigenvalue of the information matrix as a new optimality metric to replace condition number. Unlike condition number, it is proportional to the number of measurements and thus in agreement with the intuition that more measurements leads to more robust tensor estimation. Furthermore, it may independently be maximized to design GESs for different diffusion imaging techniques.
Visual trackingand dynamic learning on the Grassmann manifold with inference from a Bayesian framework and state space models

We propose a novel visual tracking scheme that exploits both the geometrical structure of Grassmann manifold and piece-wise geodesics under a Bayesian framework. Two particle filters are alternatingly employed on the manifold. One is used for online updating the appearance subspace on the manifold using sliding-window observations, and the other is for tracking moving objects on the manifold based on the dynamic shape and appearance models. Main contributions of the paper include: (a) proposing an online manifold learning strategy by a particle filter, where a mixture of dynamic models is used for both the changes of manifold bases in the tangent plane and the piecewise geodesics on the manifold, (b) proposing a manifold object tracker by incorporating object shape in the tangent plane and the manifold prediction error of object appearance jointly in a particle filter framework. Experiments performed on videos containing significant object pose changes show very robust tracking results. The proposed scheme also shows better performance as comparing with three existing trackers in terms of tracking drift and the tightness and accuracy of tracked boxes.
A LSTM-based deep learning method with application to voltage dip classification

In this paper, a deep learning (DL)-based method for automatic feature extraction and classification of voltage dips is proposed. The method consists of a dedicated architecture of Long Short-Term Memory (LSTM), which is a special type of Recurrent Neural Networks (RNNs). A total of 5982 three-phase one-cycle voltage dip RMS sequences, measured from several countries, has been used in our experiments. Our results have shown that the proposed method is able to classify the voltage dips from learned features in LSTM, with 93.40% classification accuracy on the test data set. The developed architecture is shown to be novel for feature learning and classification of voltage dips. Different from the conventional machine learning methods, the proposed method is able to learn dip features without requiring transition-event segmentation, selecting thresholds, and using expert rules or human expert knowledge, when a large amount of measurement data is available. This opens a new possibility of exploiting deep learning technology for power quality data analytics and classification.
Feature characterization of power quality events according to their underlying causes

This paper addresses the problem of extracting effective features for the analysis of underlying causes of power quality (PQ) disturbances. For each underlying cause, we define and extract a set of features based on analysis of voltage/current waveforms or the combination of them. The proposed feature sets are then used for building a rule-based classification framework for automatic identification of the underlying causes stored in PQ databases. These rules are based on the extracted features. Using the proposed features and rules, the proposed classifier has yielded a correct classification rate of 95.8% for a total of 96 disturbance sequences, demonstrating a high accuracy distinguishing between the different underlying causes in PQ events.