nils-herrmann/clean_scirepeval huggingface dataset:
Hybrid simulation platform for VSC-HVDC-assisted large-scale system restoration studies

Several works have studied the process of system restoration using Voltage Source Converter-based High Voltage Direct Current (VSC-HVDC) links and shown its effectiveness through simulations performed using detailed switched models in software like EMTDC/PSCAD. Unfortunately, studying restoration of large power systems using such detailed models are computationally prohibitive. This paper proposes a hybrid simulation framework for power system restoration, which can represent a significant portion of the model in a phasor framework and co-simulate that with a detailed model of a smaller portion containing VSC-HVDC. To that end, a software called ETRANPLUS is used to interface the phasor model in PSSE software with the detailed model in EMTDC/PSCAD.
Wide-area phasor power oscillation damping controller: A new approach to handling time-varying signal latency

Using the present technology, latency associated with remote feedback signals can be determined from the time stamp information at both the phasor measurement unit (PMU) location and the control centre. This study illustrates how this latency could be accounted for in the implementation of a wide-area phasor power oscillation damping controller (phasor POD). The basic idea is to adjust the position of the rotating reference frame - used for phasor extraction - to account for the extra phase shift introduced because of latency. The oscillatory component of the original PMU measurement is retrieved out of the delayed signal received at the control centre. Thus, continuous compensation is achieved without requiring any Pade approximation and/or gain scheduling, unlike the techniques reported in the literature. With the proposed modification, a phasor POD is shown to continuously adapt to the actual latency and maintain the desired dynamic performance over a range of different operating conditions.
Synchrophasor-enabled power grid restoration with DFIG-based wind farms and VSC-HVDC transmission system

An innovative system restoration strategy using doubly-fed induction generator-based wind farms is proposed. The strategy involves retention of charge in the DC bus following a blackout and `Hot-Swapping' between direct flux control mode and conventional grid-connected mode, which does not require resetting of any controller dynamic states and avoids the need for energy storage. An autonomous synchronisation mechanism enabled by remote synchrophasors is also proposed. A blacked-out system, which includes a wind farm and a voltage source converter (VSC)-HVDC connected to a network unaffected by blackout, is used as the study system. Transmission line charging and load pickup is performed using the wind farm in flux control mode while the VSC-HVDC system conducts the same process for another portion of the system. The proposed `Hot-Swapping' and autonomous synchronisation approach is applied to connect the two parts of the grid and switch the wind farm to grid connected mode of operation. The results are demonstrated in a hybrid co-simulation platform where the aforementioned system is modelled in EMT-type software and the rest of the network is represented in a phasor framework.
Convolutional gated recurrent networks for video semantic segmentation in automated driving

Semantic segmentation is an important visual perception module of automated driving. Most of the progress has been focused on single-frame image segmentation with an optional temporal post-processing. In this paper, we propose a novel algorithm to utilize the temporal information in the semantic segmentation model using convolutional gated recurrent networks. The main motivation is to design a spatio-temporal network which can leverage motion cues for aiding segmentation and providing temporally consistent results. The proposed algorithm makes use of a fully convolutional network (FCN) that is embedded into a gated recurrent architecture. We use FCN because of its simplicity and ease of extension and the embedding can extend to other architectures. We also chose an FCN model with reasonable computational complexity suitable for real-time applications. Experimental results show consistent accuracy improvements over the baseline FCN in several datasets and it is also visually evident in our test videos shared on YouTube. The accuracy improvements for binary segmentation using F-measure were 5% and 3% in SegTrack2 and Davis respectively and the improvements for semantic segmentation in mean IoU were 5.7% and 1.7% in Synthia and Camvid respectively. To our knowledge, no prior work has been done for CNN based spatio-temporal video segmentation for automated driving and we hope that our results encourage further research in this area.
Median filter with absolute value norm spatial regularization

We provide a novel formulation for computing median filter with spatial regularization as minimizing a cost function composed of absolute value norms. We turn this cost minimization into an equivalent linear programming (LP) and solve its dual LP as a minimum cost flow (MCF) problem. The MCF is solved over a graph constructed for an input image, and the primal LP solution is retrieved as the filtered image. For solving the MCF, we utilize an efficient network simplex algorithm. Numerical results show that the proposed median filter with a spatial regularization term outperforms median filters and a decision theoretic filter for impulse noise removal.
Object Classification With Joint Projection and Low-Rank Dictionary Learning

For an object classification system, the most critical obstacles toward real-world applications are often caused by large intra-class variability, arising from different lightings, occlusion, and corruption, in limited sample sets. Most methods in the literature would fail when the training samples are heavily occluded, corrupted or have significant illumination or viewpoint variations. Besides, most of the existing methods and especially deep learning-based methods, need large training sets to achieve a satisfactory recognition performance. Although using the pre-trained network on a generic large-scale data set and fine-tune it to the small-sized target data set is a widely used technique, this would not help when the content of base and target data sets are very different. To address these issues simultaneously, we propose a joint projection and low-rank dictionary learning method using dual graph constraints. Specifically, a structured class-specific dictionary is learned in the low-dimensional space, and the discrimination is further improved by imposing a graph constraint on the coding coefficients, that maximizes the intra-class compactness and inter-class separability. We enforce structural incoherence and low-rank constraints on sub-dictionaries to reduce the redundancy among them, and also make them robust to variations and outliers. To preserve the intrinsic structure of data, we introduce a supervised neighborhood graph into the framework to make the proposed method robust to small-sized and high-dimensional data sets. Experimental results on several benchmark data sets verify the superior performance of our method for object classification of small-sized data sets, which include a considerable amount of different kinds of variation, and may have high-dimensional feature vectors.
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.
Droop control of distributed electric springs for stabilizing future power grid

This paper describes the droop control method for parallel operation of distributed electric springs for stabilizing ac power grid. It provides a methodology that has the potential of allowing reactive power controllers to work in different locations of the distribution lines of an ac power supply and for these reactive power controllers to support and stabilize the ac mains voltage levels at their respective locations on the distribution lines. The control scheme allows these reactive power controllers to have automatically adjustable voltage references according to the mains voltage levels at the locations of the distribution network. The control method can be applied to reactive power controllers embedded in smart electric loads distributed across the power grid for stabilizing and supporting the ac power supply along the distribution network. The proposed distributed deployment of electric springs is envisaged to become an emerging technology potentially useful for stabilizing power grids with substantial penetration of distributed and intermittent renewable power sources or weakly regulated ac power grid.
Bad data detection in PMU measurements using principal component analysis

This paper presents an approach for bad data detection in PMU measurements during disturbances. Bad data in the form of outliers can have very similar appearance as that of system response following disturbances like faults. A principal component analysis (PCA) based approach is proposed to distinguish between the outlier caused by bad data from those caused by disturbances. The principal components (PCs) in the lower dimensional subspace capture the dynamical properties of the system and the PCs in the higher dimensional subspace represent noisy information. Using this property, it is hypothesized that outliers due to bad data will result in larger activity in the high dimensional subspace. Monte Carlo simulation results demonstrate the effectiveness of the proposed hypothesis in an example power system.
Damping and relative mode-shape estimation in near real-time through phasor approach

A technique for estimating damping and electromechanical mode-shape in near real-time as oscillations develop under transient condition is presented. At each sampling instant, measured signals are expressed as phasors using corrected values of modal frequencies. Damping is obtained from the exponential variation of estimated phasor magnitude using a moving window least squares (LS) algorithm. The relative mode-shape is computed directly from the magnitude and phase angle of the phasors. Random variations in loads are considered to examine possible impact on phasor estimation, especially the frequency correction loop. Accuracy and speed of convergence is validated by comparing the time variation of estimated dampings and relative mode-shapes against the actual values obtained from the linearized models under respective operating conditions. Besides the well-known four-machine, two-area test system, a 16-machine, five-area system is considered for illustration of the concept. Monte Carlo simulations are used to capture the statistical variability in estimation as a result of persistent disturbances (e.g., random fluctuations in loads) leading to different signal-to-noise ratios (SNRs). Results from a commercial real-time simulator illustrate the practical feasibility of the proposed approach.
Optimum Design of UPFC Controllers Using GEA: Decoupled Real & Reactive Power Flow and Damping Controllers

This paper presents a comprehensive approach for designing UPFC controllers (decoupled real & reactive power flow controllers, DC link and AC voltage regulators & UPFC based damping controller). For designing the controllers, the detailed model of the system with UPFC has been considered. Investigations clearly show that the real and reactive power flows on the line can be effectively regulated independently using decoupled controller. Studies also reveal that a significant improvement in damping of power system oscillations can be achieved with UPFC based damping controller.
Computing oil sand particle size distribution by snake-PCA algorithm

An important measure in various stages of oil sand mining is particle size distribution (PSD) of oil sand particles. Currently PSD is found by time consuming manual inspection. An effective automation of PSD computation can play a significant role in improving the mining process. Toward this goal we propose an algorithm (snake-PCA) to detect oil sands from conveyor belt images, which pose considerable challenges to automated analysis. The novelty in snake-PCA is as follows. First, snake-PCA evolves a number of snakes based on a novel variation of gradient vector flow requiring only a point as initialization. Oil sand is then detected by applying a threshold on PCA reconstruction error of a novel pattern image formed on each evolved snake. We show the discriminative property of the proposed pattern image here. Also, our detection experiments with snake-PCA produce a PSD matching well with a manually found PSD.
Fast Large-Scale Spectral Clustering via Explicit Feature Mapping

We propose an efficient spectral clustering method for large-scale data. The main idea in our method consists of employing random Fourier features to explicitly represent data in kernel space. The complexity of spectral clustering thus is shown lower than existing Nyström approximations on large-scale data. With <inline-formula> <tex-math notation="LaTeX">${m}$ </tex-math></inline-formula> training points from a total of <inline-formula> <tex-math notation="LaTeX">${n}$ </tex-math></inline-formula> data points, Nyström method requires <inline-formula> <tex-math notation="LaTeX">${O(nmd+m^{3}+nm^{2})}$ </tex-math></inline-formula> operations, where <inline-formula> <tex-math notation="LaTeX">${d}$ </tex-math></inline-formula> is the input dimension. In contrast, our proposed method requires <inline-formula> <tex-math notation="LaTeX">${O(nDd+D^{3}+n'D^{2})}$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">${n}'$ </tex-math></inline-formula> is the number of data points needed until convergence and <inline-formula> <tex-math notation="LaTeX">${D}$ </tex-math></inline-formula> is the kernel mapped dimension. In large-scale datasets where <inline-formula> <tex-math notation="LaTeX">${n' \ll n}$ </tex-math></inline-formula> hold true, our explicitly mapping method can significantly speed up eigenvector approximation and benefit prediction speed in spectral clustering. For instance, on MNIST (60 000 data points), the proposed method is similar in clustering accuracy to Nyström methods while its speed is twice as fast as Nyström.
Continuous blood pressure prediction from pulse transit time using ECG and PPG signals

High blood pressure (BP) is the most common cause of death and disability in the world, and is the largest contributor to heart and kidney disease. Current methods of measuring and monitoring blood pressure require either invasive procedures or intermittent inflation of a cuff to restrict blood flow. Thus a non-invasive method for continuous blood pressure monitoring is needed. Pulse transit time (PTT), has been reported to be highly correlated with blood pressure but data examining the effect of posture and activity on PTT based BP estimation are very limited. In this paper, PTT was computed using the windowed correlation between ECG and PPG signals. Continuous blood pressure was estimated using a previously published linear regression model. In fourteen healthy subjects, BP was estimated using PTT in 5 different positions (recumbent, seated, standing, walking, cycling) for each subject according to a preset protocol. Accuracy was increased when sparsified, preprocessed PPG signals were used. Furthermore, the observed errors of PTT measurement were within 1% of manual PTT measurement. The Root-Mean-Squared Errors (RMSE) in systolic and diastolic blood pressure between the reference standard oscillometric cuff-based device and the estimated BP from PTT were lowest when seated or standing and highest when walking or cycling. The mean difference ±standard deviation (SD) of the difference between the PTT-based estimated systolic BP and the reference standard was 0.07±5.8 mmHg in the seated position; however, this increased to 4.4±20.9 and 10.2±16.0 when walking and cycling respectively. Therefore, PTT-based BP estimation was reasonably accurate while stationary but not during motion and further improvements in estimation are required before its use for the estimation of ambulatory BP.
Seacast: A protocol for peer-to-peer video streaming supporting multiple description coding

SEACAST is a peer-to-peer live streaming protocol developed at Politecnico di Torino, which aims at improving current systems in two key areas. The first is the use of fullfledged flow control using RTP/UDP and session signaling. The second is the use of multiple description coding to handle error resilience and user heterogeneity. In this paper we overview SEACAST, highlighting its main innovations, and providing a short summary of performance evaluation over a local testbed at Politecnico di Torino. The results show a definite performance improvement with respect to existing systems, and point out the usefulness of multiple description coding in the peer-to-peer context.
Automatic license plate recognition with convolutional neural networks trained on synthetic data

We present an Automatic License Plate Recognition system designed around Convolutional Neural Networks (CNNs) and trained over synthetic plate images. We first design CNNs suitable for plate and character detection, sharing a common architecture and training procedure. Then, we generate synthetic images that account for the varying illumination and pose conditions encountered with real plate images and we use exclusively such synthetic images to train our CNNs. Experiments with real vehicle images captured in natural light with commodity imaging systems show precision and recall in excess of 93% despite our networks are trained exclusively on synthetic images.
Distributed joint source-channel arithmetic coding

We address distributed source coding with decoder side information, when the decoder observes the source through a noisy channel. Existing approaches employ syndromeor parity-based channel codes. We propose a new approach based on distributed arithmetic coding (DAC).We introduce a DAC with forbidden symbol, which allows to tune the redundancy according to the amount of channel noise. We propose a novel sequential decoder that employs the known side information to decode the corrupted codeword. Experimental results show that the proposed scheme is better than parity-based turbo codes at relatively short block lengths.
A hardware-friendly architecture for onboard rate-controlled predictive coding of hyperspectral and multispectral images

In this paper we propose an efficient architecture for onboard implementation of rate-controlled predictive lossy compression of hyperspectral and multispectral images. In particular, we consider the recent state-of-the-art rate control algorithm for onboard predictive compression , and propose an architecture addressing two fundamental aspects of its hardware implementation. Specifically, this architecture overcomes the serial nature of the algorithm, as well as the large memory requirements of the entropy coding stage, achieving a pipelined implementation suitable for high-throughput onboard implementation, at a negligible cost in terms of coding efficiency.
Error sensitivity data structures and retransmission strategies for robust JPEG 2000 wireless imaging

In this paper we address the problem of JPEG 2000 imaging in a wireless environment. We first define a flexible and efficient data structure for the description of the error sensitivity of different parts of a JPEG 2000 codestream or file format; the data structure is designed in such a way that it can seamlessly integrated as payload of a JPEG 2000 marker segment or file format box. Moreover, we investigate ARQ policies for robust packet-based JPEG 2000 image transmission over 3G mobile communication systems, and highlight how the proposed data structure can be exploited to improve the end-to-end performance.
Parallel rate-distortion optimised fast motion estimation algorithm for H.264/AVC using GPU

Recently, the parallel processing capability of the Graphics Processing Unit (GPU) has been employed for accelerating motion estimation in H.264/AVC encoder implementations. However, while implementing parallel motion estimation on GPU, the bit rate cost of the motion vector is generally ignored. This is due to the unavailability of the spatially predicted motion vectors, which leads to rate-distortion performance degradation. This paper presents a fast parallel motion estimation algorithm implemented on GPU using OpenCL to tackle this problem. The predicted motion vectors are estimated from temporally predicted motion vectors and used for evaluating the bit rate cost of the motion vectors simultaneously. The experimental results show that the proposed scheme achieves significant speedup and has comparable rate-distortion performance with respect to sequential fast motion estimation algorithm.
Embedded lossy to lossless compression of hyperspectral images using JPEG 2000

Hyperspectral image compression has recently attracted a remarkable interest for remote sensing applications. In this paper we propose a unified embedded lossy-to-lossless compression framework based on the JPEG 2000 standard. In particular, we exploit the multicomponent transformation feature of Part 2 of JPEG 2000 to devise a compression framework based on a spectral decorrelating transform followed by JPEG 2000 compression of the transformed coefficients. We evaluate several possible choices for the spectral transform, including a floating-point DCT, an integer DCT, and a wavelet transform. The final version of the proposed algorithm has been compared to 3D-SPIHT in the lossy case, and to several state-of-the-art compression algorithms including JPEG-LS and 3D-CALIC in the lossless case. Experimental results on AVIRIS data show that the proposed technique exhibits very competitive performance for both reversible and irreversible compression, with significantly lower complexity than DPCM-based methods, and memory requirements compatible with typical onboard processing subsystems of remote sensing platforms.