nils-herrmann/clean_scirepeval huggingface dataset:
Edge Sensitive Variational Image Thresholding

In this paper we propose a locally adaptive image threshold technique via variational energy minimization. The novelty of the proposed method is that from an image it automatically computes the weights on the data fidelity and the regularization terms in the energy functional, unlike many other previously proposed variational formulations that require manual input of these weights by laborious trial and error. To achieve the automatic setting of the weighting parameters we propose a non-linear convex combination of the data fidelity and the regularization terms in the energy functional and seek the optimum threshold surface via minimax principle. Our choice of the novel energy functional allows fast computation of the unique minimax solution. As a specific segmentation application, the proposed technique shows promising results when applied to find lung boundary from MR imagery. Illustrative examples are also provided where the proposed method is observed to retain texture information better than other competing methods.
Solidity based local threshold for oil sand image segmentation

A novel local threshold algorithm for images with poor illumination and complex texture surface is presented in this paper. This algorithm improves segmentation quality by selecting local thresholds according to object level information incorporating prior knowledge, specifically the solidity features. Local thresholds are searched by maximizing the probability of solidity, and fragments with lower segmentation quality are filtered by the stability of solidity. Since thresholding results are produced with object level information, our algorithm is robust in dealing with images of poor quality. Experiments on oil sand images show the proposed algorithm has superior performance to existing local threshold approaches in terms of segmentation quality.
Impact of Inertia and Effective Short Circuit Ratio on Control of Frequency in Weak Grids Interfacing LCC-HVDC and DFIG-Based Wind Farms

Although virtually every overhead HVdc line is based on line commutated converter (LCC) technology, hardly any work has been reported on modeling and analysis of the impact of inertia and effective short circuit ratio on control of frequency in weak grids interfacing LCC-HVdc and onshore doubly fed induction generator (DFIG)-based wind farms. This paper develops a comprehensive modeling and stability analysis framework of a weak grid that interfaces an LCC-HVdc station and a DFIG-based wind farm. To that end, a nonlinear averaged phasor model of the system is derived, which is then benchmarked against a detailed switched model. The averaged model is linearized to design a frequency controller for the LCC-HVdc rectifier station. Participation factor and eigenvalue sensitivity measures indicate that the ac system electromechanical mode and the “HVdc PLL” mode are highly sensitive to changes in effective dc inertia constant and effective short circuit ratio, respectively. Root-locus analysis in a progressively weak grid validates these results. A systematic design process of the frequency controller parameters reveals a negative interaction between the “generator speed-HVdc PLL-frequency controller” mode and the “DFIG-GSC controller” mode.
People counting with image retrieval using compressed sensing

The estimation of the number of people present in an image has many applications such as intelligent transportation, urban planning and crowd surveillance. Rather than conventional counting by detection or regression/machine-learning methods, we propose an image retrieval approach, which uses an image descriptor to estimate the people count. We review the performance of several image descriptors. In addition, we propose a straightforward global image descriptor for image retrieval based on compressed sensing theory. Extensive evaluations on existing crowd analysis benchmark datasets demonstrate the effectiveness of our image retrieval-based approach compared to state-of-the-art regression-based people counting methods.
Synthetic Viewpoint Prediction

Determining the viewpoint (pose) of rigid objects in monocular 2D images is a classic vision problem with applications to robotic grasping, augmented reality, semantic SLAM, autonomous navigation and scene understanding in general. Using only 3D CAD models of an object class as input, we demonstrate the ability to accurately predict viewpoint in real-world images even in the presence of clutter and occlusion. We report results on eight datasets, one of which is new, in the hope of providing the community with new viewpoint prediction baselines. We show that deep representations (from convolutional networks) can bridge the large divide between purely synthetic training data and real-world test data to achieve near state-of-the-art results in viewpoint prediction but without the need for labeled, real-world training data. Our general approach to viewpoint prediction is applicable to any object class where 3D models are available.
Snake Validation: A PCA-Based Outlier Detection Method

We utilize outlier detection by principal component analysis (PCA) as an effective step to automate snakes/active contours for object detection. The principle of our approach is straightforward: we allow snakes to evolve on a given image and classify them into desired object and non-object classes. To perform the classification, an annular image band around a snake is formed. The annular band is considered as a pattern image for PCA. Extensive experiments have been carried out on oil-sand and leukocyte images and the performance of the proposed method has been compared with two other automatic initialization and two gradient-based outlier detection techniques. Results show that the proposed algorithm improves the performance of automatic initialization techniques and validates snakes more accurately than other outlier detection methods, even when considerable object localization error is present.
Tracking of multiple interacting objects using a novel prediction model

Tracking multiple interacting objects is an interesting and difficult task in computer vision. Two common problems in this field are a single object with multiple tracks and a single track with multiple objects. Most of the existing algorithms address the first problem but not the second one. In this paper, to solve the second problem we propose a new algorithm with a novel prediction model, which exploits the idea of penalizing outliers in statistics. The experiments show that our proposed algorithm is more robust than the existing algorithms in tackling both the aforementioned problems.
A two-stage minimum spanning tree (MST) based clustering algorithm for 2D deformable registration of time sequenced images

Significant cardiac and respiratory motion of the living subject, occasional spells of defocus, drifts in the field of view, and long image sequences make the registration of in-vivo microscopy image sequences used in atherosclerosis study an onerous task. In this study we developed and implemented a novel Minimum Spanning Tree (MST)-based clustering method for image sequence registration that first constructs a minimum spanning tree for the input image sequence. The spanning tree re-orders the images in such a way where poor quality images appear at the end of the sequence. Then the spanning tree is clustered into several groups based on the similarity of the images. Subsequently deformable registration is conducted locally within the group with respect to the local anchor image selected automatically from the images in the group. After that coarse registration is performed to find the global anchor and then a deformable registration is performed globally to incorporate larger drift and distortion. Two-stage deformable registration incrementally incorporates larger drifts and distortions present in the longer sequence. Our algorithm involves very few tuning parameters, the optimal value of these parameters can be easily learned from data. Our method outperforms other methods on microscopy image sequences of mouse arteries.
Impact of inertia and effective short circuit ratio on control of frequency in weak grids interfacing LCC-HVDC and DFIG-based wind farms

Although virtually every overhead HVDC line is based on LCC technology, hardly any work has been reported on modeling and analysis of the impact of inertia and effective short circuit ratio on control of frequency in weak grids interfacing LCC-HVDC and onshore DFIG-based wind farms. This paper develops a comprehensive modeling and stability analysis framework of a weak grid that interfaces an LCC-HVDC station and a DFIG-based wind farm. To that end, a nonlinear averaged phasor model of the system is derived, which is then benchmarked against a detailed switched model. The averaged model is linearized to design a frequency controller for the LCC-HVDC rectifier station. Participation factor and eigenvalue sensitivity measures indicate that the AC system electromechanical mode and the ‘HVDC PLL’ mode are highly sensitive to changes in Effective DC Inertia Constant and Effective Short Circuit Ratio, respectively. Root-locus analysis in a progressively weak grid validates these results. A systematic design process of the frequency controller parameters reveals a negative interaction between the ‘generator speed-HVDC PLL-frequency controller’ mode and the ‘DFIG-GSC controller’ mode.
Self-tuning feedback linearization controller for power oscillation damping

Power systems exhibit highly nonlinear behavior especially under large disturbances like faults, outages etc. necessitating application of nonlinear control techniques. Nonlinear estimation and control of power oscillations through FACTS devices is illustrated in this paper. A special form of nonlinear neural network compatible with the feedback linearization framework is used. Levenberg-Marquardt (LM) algorithm is adapted to work in sliding window batch mode for online estimation of system oscillatory behavior. At each sampling interval the estimated neural network parameters are used to derive appropriate control using the feedback linearization technique. Use of LM is shown to yield better closed-loop performance compared to conventional recursive least square (RLS) approach. A case study is presented to demonstrate the effectiveness of feedback linearization controller (FBLC), especially, under stressed operating conditions. Its performance is compared against pole-shifting controller (PSC) under different scenarios.
Recurrent Fully Convolutional Networks for Video Segmentation

Image segmentation is an important step in most visual tasks. While convolutional neural networks have shown to perform well on single image segmentation, to our knowledge, no study has been done on leveraging recurrent gated architectures for video segmentation. Accordingly, we propose and implement a novel method for online segmentation of video sequences that incorporates temporal data. The network is built from a fully convolutional network and a recurrent unit that works on a sliding window over the temporal data. We use convolutional gated recurrent unit that preserves the spatial information and reduces the parameters learned. Our method has the advantage that it can work in an online fashion instead of operating over the whole input batch of video frames. The network is tested on video segmentation benchmarks in Segtrack V2 and Davis. It proved to have 5% improvement in Segtrack and 3% improvement in Davis in F-measure over a plain fully convolutional network.
Stability analysis of VSC MTDC grids connected to multimachine ac systems

Interaction between multimachine ac systems and a multiterminal dc (MTDC) grid and the impact on the overall stability of the combined ac-MTDC system is studied in this paper. A generic modeling framework for voltage-source converter (VSC) - based MTDC grids, which is compatible with standard multimachine ac system models, is developed to carry out modal analysis and transient simulation. A general asymmetric bipole converter configuration comprising positive and negative pole converters and dc cable network with a positive, negative, and metallic return circuit is considered to enable different types of faults and dc-side unbalance studies. Detailed dynamic representation of the dc cables with distributed pi-section models is used along with the averaged model and decoupled control for the converter stations. An averaged model in Matlab/SIMULINK is validated against the detailed switched model in EMTDC/PSCAD by comparing the responses following small and large disturbances (e.g., faults on the dc side). Modal analysis is performed to identify the nature and root cause of the dynamic responses. Interaction between a multimachine ac system and an MTDC grid is examined following faults on the ac and dc sides and outage of converters. It is shown that the cause of instability in certain cases could only be attributed to the dc-side state variables. An averaged model of the converter along with the dc cable network is shown to be essential to analyze the stability and dynamics of combined ac-MTDC grids.
Malicious Corruption-Resilient Wide-Area Oscillation Monitoring Using Principal Component Pursuit

A principal component pursuit (PCP)-based interface is proposed between raw synchrophasor data and the algorithms used for wide-area monitoring application to provide resilience against malicious data corruption. The PCP method-based preprocessor recovers a low rank matrix from the data matrix despite gross sparse errors originating from cyber-attacks by solving a convex program. The low-rank matrix consists of the basis vectors obtained from the system response and the sparse matrix represents corruption in each position of the data matrix. An augmented Lagrangian multiplier-based algorithm is applied to solve the PCP problem. The low rank matrix obtained after solving PCP represents the reconstructed data and can be used for estimation of poorly damped modes. A recursive oscillation monitoring algorithm is tested to validate the effectiveness of the proposed approach under both ambient and transient conditions.
Online Analytical Characterization of Outliers in Synchrophasor Measurements: A Singular Value Perturbation Viewpoint

This paper presents a principal component (PC) analysis based method for online characterization of outliers in synchrophasor measurements. To that end, a linearized framework is established to analyze dynamical response from a system under nominal and off-nominal (e.g., faulted) conditions, which are contained in the same window of synchrophasor data. Inspired by the singular value perturbation theory, a bound on the change in the norm of the PC scores as a function of system state matrices is presented. It is shown that in the presence of bad data outliers these bounds for higher dimensional PC scores will be significantly larger compared to lower dimensions. The effect of the number of samples in the data window on the results of the analysis is established. Case studies on a simulated test system and on field data collected from a US utility are presented to support the analytical results. Finally, an online classifier for the characterization of outliers is developed to illustrate the usefulness of the proposed framework for machine learning based methods.
A novel framework to integrate convolutional neural network with compressed sensing for cell detection

The ability to detect certain types of cells in a microscopy image is important for a wide range of clinical applications. Cells often present huge variations in density and appearance, and often occupy only a small portion of an image. Consequently, general object detection methods in computer vision do not meet accuracy requirements: false / missed detections prevail. In this paper, we apply convolutional neural network (CNN) to regress a fixed length vector from a microscopy image. Then, L1 minimization / compressed sensing (CS) recovers a variable number of cell locations from this fixed-length predicted vector. Our contribution in this work is combining CS with CNN to solve cell detection problem in a regression framework, which needs to handle a variable number of cells. Our method relies on the observation that the number of pixels indicating cell centroid locations is a tiny fraction of the total image size. Thus, we utilize this sparsity property by CS. The proposed method is evaluated with several state-of-the-art approaches on public cell datasets and it obtains superior or comparable performances.
Wide-Area Damping Control Using Multiple DFIG-Based Wind Farms Under Stochastic Data Packet Dropouts

Data dropouts in communication network can have a significant impact on wide-area oscillation damping control of a smart power grid with large-scale deployment of distributed and networked phasor measurement units and wind energy resources. Remote feedback signals sent through communication channels encounter data dropout, which is represented by the Gilbert-Elliott model. An observer-driven reduced copy (ORC) approach is presented, which uses the knowledge of the nominal system dynamics during data dropouts to improve the damping performance where conventional feedback would suffer. An expression for the expectation of the bound on the error norm between the actual and the estimated states relating uncertainties in the cyber system due to data dropout and physical system due to change in operating conditions is also derived. The key contribution comes from the analytical derivation of the impact of coupling between the cyber and the physical layer on ORC performance. Monte Carlo simulation is performed to calculate the dispersion of the error bound. Nonlinear time-domain simulations demonstrate that the ORC produces significantly better performance compared to conventional feedback under higher data drop situations.
VFCCV snake: A novel active contour model combining edge and regional information

Active contour models have been widely used for image segmentation. Among leading models of active contour is vector-field convolution (VFC), a parametric active contour that improves the popular gradient vector flow (GVF) model. However VFC is still sensitive to noise and can be easily trapped in cluttered regions of an image because it only considers edge information. Based on the geometric active contour model proposed by Chan and Vese, this paper introduces a novel active contour model that incorporates region information in VFC in order to take advantage of edge and regional information. This new model, which we refer to as VFCCV snake, is implemented in the parametric active contour framework, and has control on topology especially in noisy images and images with boundary gaps. Experimental results on both synthetic and real images show superior performance of our VFCCV snake to state-of-the-art leading active contour methods.
Change Detection and Object Segmentation: A Histogram of Features-Based Energy Minimization Approach

We consider here a change detection problem: to find regions of change on a test image with respect to a reference image. Unlike the state-of-the-art change detection and background subtraction algorithms that compute only local (pixel location-based) changes, we propose to minimize a novel region-based energy functional based on Bhattacharya coefficient involving histograms of image features. The optimization of the proposed energy functional simply consists of two very efficient searches if a crude segmentation such as a bounding box around the region of change is sufficient. Also, it allows variational optimization via level set-based curve evolution for supervised binary image labeling. The framework is demonstrated to cope well with considerable camera motion and shifts of objects between the test and the reference images. We illustrate encouraging results on finding bounding box around abnormality from brain MRI, object detection for maritime surveillance, and segmenting oil-sand particles from conveyor belt images.
Stability Analysis of Vector-Controlled Modular Multilevel Converters in Linear Time-Periodic Framework

Stability analysis of average value models (AVMs) of vector-controlled modular multilevel converters (MMCs) is the subject matter of this paper. Stability analysis of fundamental frequency phasor-based AVMs of MMCs can be conducted in a traditional linear time-invariant framework through eigenvalue computation. This class of models does not consider circulating current control loop and hence fails to capture system instability that occurs in a certain range of gains of the circulating current controller. We propose stability analysis in a linear time-periodic (LTP) framework to solve this issue. To that end, a nonlinear AVM is presented that considers the submodule capacitor insertion dynamics and takes into account the output and the circulating current control schemes in the vector control approach. Upon linearization, an LTP model is derived from this averaged model. It is shown that the Poincare multipliers are indicative of system instability corresponding to a certain range of gains of the circulating current controller.
An architecture for FACTS controllers to deal with bandwidth-constrained communication

Bandwidth constraints could have an adverse impact on the flexible AC transmission system (FACTS) controllers relying on signals communicated from distant locations. An observer-driven system copy (OSC) architecture is adopted here to deal with low data rates caused by the limited bandwidth availability. The basic idea is to use the knowledge of the nominal system to approximate its actual behavior during the intervals when data from the remote phasor measurement units (PMUs) are not available. This is corrected whenever the most recent states are obtained from the reduced-order observer at the PMU location. The closed-loop behavior deteriorates as the operating condition drifts away from the nominal. Nonetheless, significantly better response is achieved under limited bandwidth availability compared to the conventional approach of communicating the measured outputs. The deterioration is quantified in terms of the difference between the nominal and the offnominal condition.
Challenges of model reduction in modern power grid with wind generation

This paper presents the challenges of model order reduction of modern power grids with inverter-interfaced wind generation. To that end, a widely used model reduction technique called Balanced Truncation (BT) is compared with a relatively new moment matching approach known as the Iterative Rational Krylov Algorithm (IRKA). It is demonstrated that both BT and IRKA can produce an acceptable accuracy during model reduction for power grids with synchronous generators whereas IRKA produces better accuracy when Doubly Fed Induction Generator (DFIG)-based wind generation is considered.
Online bad data outlier detection in PMU measurements using PCA feature-driven ANN classifier

This paper presents a method for online bad data outlier detection in PMU measurements. High resemblance of bad data in the form of outliers with that of system disturbances can cause control centers to take false decisions. The proposed method utilizes artificial neural network (ANN) in association with principal component analysis (PCA)-based feature extraction technique in order to classify bad data outliers and outliers caused by disturbances like faults. The extracted features using PCA from the reduced dimensional representation of the data is given as input to ANN. Bayesian regularization back-propagation-based learning algorithm is used for training ANN. The test results demonstrate online classification/detection of bad data of different amplitudes injected in measurements, both before and after disturbance, in two example power systems.