nils-herrmann/clean_scirepeval huggingface dataset:
Chance-Constrained Goal-Programming based day ahead scheduling in wind power integrated system

To mitigate the impact of wind forecasting error uncertainty, a Chance-Constrained Goal Programming (CCGP) based day-ahead scheduling model is proposed in this paper. Compared with the traditional Chance Constrained Programming (CCP) method, the CCGP based model is more flexible, which allows higher violation probability than the predefined probability in necessary situations. In this way, the day-ahead scheduling and the uncertain range covered by reserves can be both optimized. Therefore, the reliability and economy of the system with wind power uncertainty can be considered in details with more flexibilities. In addition, because slack variables in CCGP model have corresponding physical meanings, they can provide more information to system operators. Furthermore, numerical tests are performed with the IEEE 118 bus system with wind power input. Results indicate that the proposed method achieves a good balance of cost and risk. And the total operation cost, especially the unit commitment cost, is reduced by the proposed method. Comparative evaluations of the proposed CCGP method and CCP method are presented in the paper.
Foliage-penetration human tracking by multistatic radar

Human target detection and tracking have great potential in military, safety, security and entertainment applications. In this paper, a complete processing procedure is proposed for human tracking in foliage-penetration environment by multistatic radar. It consists of five main steps, including clutter suppression, target detection, measurement estimation, target localization and target tracking. Exponential average background subtraction is applied for clutter suppression. Ordered statistics constant false alarm rate (OS-CFAR) detector is used to detect targets in the range profile. The range measurement estimation is realized by a window filter. The S-D assignment algorithm is introduced for multi-target localization. Target tracking is realized by a Kalman filter based multi-target tracking (MTT) system. The experimental results verify the effectiveness of the proposed human tracking procedure.
An iterative singular vector decomposition based micro-motion target indication in through-the-wall radar

Human target indication and detection are widely used in military and civil applications. For stationary human targets behind an obstacle, the micro-motion caused by vital sign results in a fairly low signal-to-clutter-and-noise (SCNR). This study presents an iterative singular vector decomposition (ISVD) based micro-motion indication method for detecting human targets with through-the-wall radar. First, singular vector decomposition (SVD) with stepped frequency signal is analyzed, and then iterative SVD is proposed to indication micro-motion human targets. Through this iterative process, the residual clutter and noise are effectively removed. Simulation and experimental results indicate that the ISVD based method is robust to noise and improve SCNR remarkably.
Improving Deployment Availability of Energy Storage With Data-Driven AGC Signal Models

Energy Storage (ES) provides great flexibility and large benefits to power system operations and control. When providing ancillary services (e.g., regulation, reserve, etc.), the real-time (RT) deployment of ES is uncertain, and it is important to manage state of charge accordingly. Aiming to improve the ES performance for providing energy and regulation service in the electricity market, we propose two data-driven Automatic Generation Control (AGC) signal models. The first one is a historical-data-driven AGC signal model, which is based on the analysis of the historical AGC signals, and is designed for ES participation in the day-ahead (DA) market. The second one is a prediction-data-driven AGC signal model, which is based on the prediction of the AGC signals, and is designed for ES participation in the RT market. We also develop a deployment availability check model and solution algorithm. The proposed framework is applied to an ES bidding problem in the DA and RT markets. The results indicate that deployment availability and operational performance of the ES are improved with the proposed data-driven AGC models compared to traditional benchmarks.
Motion information and coding mode reuse for MPEG-2 to H.264 transcoding

MPEG-2 to H.264/AVC transcoding is an interesting research topic and has wide applications. This paper proposes a motion vector and coding mode re-use process for MPEG-2 to H.264 main profile transcoding. Experimental results show that the proposed approach can significantly reduce the computational complexity of the transcoder without sacrificing the rate-distortion performance.
A Motion Compensation Technique Based on GPS and Refined MapDrift For Ultr-wide Band SAR

Traditional motion compensation for airborne synthetic aperture radar (SAR) based on hardware requires motion-measurement and navigation system with high-accuracy, which is expensive. Based on the character that attitude information of aircraft is unnecessary for ultra-wide band SAR (UWB-SAR), a new motion compensation method combining real-time global position system (GPS) and our refined MapDrift (RMD) is proposed in this paper. The GPS information is used to correct most errors of aircraft, that is to say, the GPS position data are used to correct range delay of SAR echoes, and the GPS velocity data are used to correct most of phase error. After coarse compensation according to GPS, the residual phase errors are corrected by our RMD. The new method is verified by real UWB-SAR data and is very attractive for UWB-SAR for its cheapness
Distributed lightning current monitoring and its application to lightning protection on transmission line

To correctly record the process of lightning strike on power line and locate the fault site fast, serving to summarize their features and further take targeted protection measures against lightning strike, a distributed lightning current monitoring technology is proposed based on the simulation result. It's able to obtain necessary parameters of transient current, therefore make recognition of lightning fault and distinction of strike type, thus maximizing the effectiveness of protection measures. Preliminary trial operation suggests it works.
A Review on Multi-Label Learning Algorithms

Multi-label learning studies the problem where each example is represented by a single instance while associated with a set of labels simultaneously. During the past decade, significant amount of progresses have been made toward this emerging machine learning paradigm. This paper aims to provide a timely review on this area with emphasis on state-of-the-art multi-label learning algorithms. Firstly, fundamentals on multi-label learning including formal definition and evaluation metrics are given. Secondly and primarily, eight representative multi-label learning algorithms are scrutinized under common notations with relevant analyses and discussions. Thirdly, several related learning settings are briefly summarized. As a conclusion, online resources and open research problems on multi-label learning are outlined for reference purposes.
Parallel realization of moving target imaging in 2D MIMO Through-Wall Radar applications

Through-Wall Imaging Radar (TWIR) can reconstruct targets which are concealed behind the wall, so it is highly desired in antiterrorism, hostage situations and other fields. To detect, track and locate personnel targets behind the wall, the TWIR must have the ability of high data frame rate and fast imaging speed. In this paper, we analyze the imaging algorithm for two dimensional multiple input multiple output (MIMO) TWIR. To satisfy the real-time requirement of TWIR applications, we design a parallel realization method based on TMS320C6678. Experimental results show that this parallel method can adequately fulfill the real-time requirements.
Modeling and simulation study on radar Doppler signatures of pedestrian

This paper focuses on radar Doppler signatures of pedestrian. Firstly, an improved human walking model which describes a pedestrian as a linear-rigid target is introduced. Then radar echoes of the pedestrian under the condition of continuous wave (CW) radar signal are acquired, formulas of instantaneous Doppler are derived and movement signatures of the target are analyzed. Simulation experiments are carried out to validate the model and theoretic analysis.
Adaptive Random Testing for Image Comparison in Regression Web Testing

Web applications have become the most popular type of software in the past decade, attracting the attention of both the academia and the industry. In parallel with their popularity, the complexity of aesthetics and functionality of web applications have also increased significantly, creating a big challenge for maintenance and cross-browser compliance testing. Since such testing and verification activities require visual analysis, web application testing has not been sufficiently automated. In this paper, we propose a novel pairwise image comparison approach suitable for web application testing where the location of layout faults needs to be detected efficiently while insignificant variations being neglected. This technique is developed based on the characteristics of fault patterns of browser layouts. An empirical study conducted with the industry partner shows our approach is more effective and efficient than existing methods in this area.
Sequence-Based Prediction of microRNA-Binding Residues in Proteins Using Cost-Sensitive Laplacian Support Vector Machines

The recognition of microRNA (miRNA)-binding residues in proteins is helpful to understand how miRNAs silence their target genes. It is difficult to use existing computational method to predict miRNA-binding residues in proteins due to the lack of training examples. To address this issue, unlabeled data may be exploited to help construct a computational model. Semisupervised learning deals with methods for exploiting unlabeled data in addition to labeled data automatically to improve learning performance, where no human intervention is assumed. In addition, miRNA-binding proteins almost always contain a much smaller number of binding than nonbinding residues, and cost-sensitive learning has been deemed as a good solution to the class imbalance problem. In this work, a novel model is proposed for recognizing miRNA-binding residues in proteins from sequences using a cost-sensitive extension of Laplacian support vector machines (CS-LapSVM) with a hybrid feature. The hybrid feature consists of evolutionary information of the amino acid sequence (position-specific scoring matrices), the conservation information about three biochemical properties (HKM) and mutual interaction propensities in protein-miRNA complex structures. The CS-LapSVM receives good performance with an F1 score of 26.23 + 2.55% and an AUC value of 0.805 + 0.020 superior to existing approaches for the recognition of RNA-binding residues. A web server called SARS is built and freely available for academic usage.
The representation of moving targets in multi-channel UWB SAR images and it’s application in the generalization of image domain GMTI methods

To generalize the image domain GMTI methods for slow moving targets to be suitable for detection fast moving targets with multi-channel ultra-wide band (UWB) SAR system, the representation of moving targets in multi-channel UWB SAR images is studied in this paper. By analyzing the local back projection (LBP) SAR imaging process for the moving target, this paper gives the analytic expressions of the moving target in the multi-channel UWB SAR images. Following the results, the image domain GMTI methods can be generalized to be suitable for fast moving targets that defocus in multi-channel UWB SAR images. This generalization is demonstrated via the ATI method.
WiFi based trajectory alignment, calibration and crowdsourced site survey using smart phones and foot-mounted IMUs

Foot-mounted inertial positioning (FMIP) can face problems of inertial drifts and unknown initial states in real applications, which renders the estimated trajectories inaccurate and not obtained in a well defined coordinate system for matching trajectories of different users. In this paper, an approach adopting received signal strength (RSS) measurements for Wifi access points (APs) are proposed to align and calibrate the trajectories estimated from foot mounted inertial measurement units (IMUs). A crowdsourced radio map (RM) can be built subsequently and can be used for fingerprinting based Wifi indoor positioning (FWIP). The foundation of the proposed approach is graph-based simultaneously localization and mapping (SLAM). The nodes in the graph denote users' poses and the edges denote the pairwise constrains between the nodes. The constrains are derived from: (1) inertial estimated trajectories; (2) vicinity in the RSS space. With these constrains, an error functions is defined. By minimizing the error function, the graph is optimized and the aligned/calibrated trajectories along with the RM are acquired. The experimental results have corroborated the effectiveness of the approach for trajectory alignment, calibration as well as RM construction.
A geometric approach for modelling of unfixed-base manipulators

In this article, we establish a coordinate-free description of the kinematic and dynamic models of unfixed-base manipulator by using standard ideas from Lie group and Lie algebra. We use directly the exponential product formula to formulate the kinematic equation of an unfixed-base manipulator. In order to derive the dynamic equation of an unfixed-base manipulator, geometric variations, Riemannian metric, and Christoffel symbol are introduced. And, a concise, closed-form and coordinate-free dynamic equation is given according to the Hamilton's variational principle. This derived model has two characteristics. On one hand, the dynamic model of the fixed-base manipulator is obtained naturally from this dynamic model of the unfixed-base manipulator. On the other hand, it is a control-oriented model and has some important structural properties which may be used to construct the tracking control law.
Effect analysis after prohibition of pumping groundwater in Su-Xi-Chang area of China

Over-exploitation of groundwater has existed before 2000 in Su-Xi-Chang area. From the start of 2000, the program about prohibition of pumping groundwater was implemented stage by stage by Jiangsu provincial government, and the prohibition program was first conducted in over-exploitation area. By the end of 2005, an overall prohibition of pumping groundwater in Su-Xi-Chang was realized and the velocity of land subsidence was slowing down and groundwater table was recovered gradually. According to systematic subsidence monitoring data and observed groundwater table data in the study area in the recent years, the change laws of groundwater table and land subsidence are analyzed before and after prohibition of pumping groundwater, which show there are the tight relationship between recovery of groundwater table and the decrease of land subsidence. Simultaneously, the geological environmental effects were evaluated after prohibition of pumping groundwater. Some countermeasures and proposals are provided for geological environmental protection in the study area.
Optimized CQI Quantization Based PARC Scheme for Multi-Stream MIMO Systems

Per-antenna rate control (PARC) has been proved to be an efficient way to increase throughput of multi-stream MIMO system in previous studies and selected as a powerful candidate for Rel. 7 WCDMA and 3GPP LTE standardization. In this paper, we proposed an optimized modulation and coding set (MCS) selection scheme for PARC based multi-stream MIMO system considering the quantization effects of channel quality indicator (CQI) feedback, in which signal to interference and noise ratio (SINR) is used as CQI feedback. This scheme offers an easy way to set switching thresholds for rate control. Especially, the quantization effects of different bits to the rate control scheme of PARC are analyzed. Simulation results are presented to demonstrate the system performance of PARC considering quantization of feedback, and suggestions on the quantized bits are also obtained to achieve good performance.
A log-function power-constrained algorithm for OLED displays

Organic light-emitting diode(OLED) has emerged as the new generation technique to mobile displays. Although OLED plane has many advantages compared to LCD, it still accounts for a large amount of the power consumption in mobile devices. In this paper, we propose a log-function power constrained algorithm(LFPC) to save the power of OLED display. We choose superpixel as the basic unit and computer a saliency map of all superpixels. Next, we integrate the human visual characteristic into a log-function which is used to limit the pixel values thus achieving the power saving of OLED. Our experimental results show that LFPC algorithm can achieve 34.6% more OLED power saving for images with high luminance while retaining a high image visual experience. Also, our methodology performs better on power saving and image quality compared to PCCE.
Extraction of Landmine Features Using a Forward-Looking Ground-Penetrating Radar With MIMO Array

A vehicle-mounted forward-looking ground-penetrating radar (GPR) with multiple-input and multiple-output (MIMO) array can obtain the high-resolution image of its front area to perform the standoff detection of landmines. The major challenge for the GPR landmine detection over wide areas is the very high false alarm rate when maintaining a high detection probability. In this paper, a novel feature extraction method is proposed to obtain the bistatic scattering information from the MIMO array image to discriminate landmines from clutter. To realize the goal, an imaging model of the MIMO array is firstly developed. Based on the imaging model, the bistatic scattering function of a suspected object is estimated from its MIMO array image using the space-wavenumber processing. Images of different incident angles and bistatic angles at some resonance frequencies are selected from the estimated bistatic scattering function to represent the scattering characteristics. In order to obtain the scale, rotation, and translation invariant feature vector, Hu moment invariants of the selected images are calculated to form the low-dimensional feature vector. The experimental results show that the proposed method can offer an efficient feature vector for the landmine discriminator to improve the detection performance.
Learning a distance metric from multi-instance multi-label data

Multi-instance multi-label learning (MIML) refers to the learning problems where each example is represented by a bag/collection of instances and is labeled by multiple labels. An example application of MIML is visual object recognition in which each image is represented by multiple key points (i.e., instances) and is assigned to multiple object categories. In this paper, we study the problem of learning a distance metric from multi-instance multi-label data. It is significantly more challenging than the conventional setup of distance metric learning because it is difficult to associate instances in a bag with its assigned class labels. We propose an iterative algorithm for MIML distance metric learning: it first estimates the association between instances in a bag and its assigned class labels, and learns a distance metric from the estimated association by a discriminative analysis; the learned metric will be used to update the association between instances and class labels, which is further used to improve the learning of distance metric. We evaluate the proposed algorithm by the task of automated image annotation, a well known MIML problem. Our empirical study shows an encouraging result when combining the proposed algorithm with citation-kNN, a state-of-the-art algorithm for multi-instance learning.
A Novel Inshore Ship Detection via Ship Head Classification and Body Boundary Determination

In this letter, we propose a novel method for inshore ship detection via ship head classification and body boundary determination. Compared with some traditional ship head detection methods depending on accurate ship head segmentation, we generate novel ship head features in the transformed domain of polar coordinate, where the ship heads have an approximate trapezoid shape and can be more easily detected. Then, these features are used in the classification based on support vector machine to detect the ship head candidates, and give the important information of initial ship head direction. Next, the surrounding consistent line segments are utilized to refine the ship direction, and the ship boundary is determined based on the saliency of directional gradient information symmetrical about the ship body. Finally, the context information of sea areas is introduced to remove false alarms. Experimental results show that the proposed method can accurately and robustly detect the inshore ships in high-resolution optical remote sensing images.
Secure Design of RFID Tags in the New Type License Plates Automatic Identification System

Vehicle License Plate of GA36-2007 has overcome most defects of GA36-1992, but there are still some disadvantages on visibility, uniqueness and intelligence. At present the automatic vehicle identification system is limited on image identification technology, which still has many shortages. In this paper, we propose a new type design of vehicle license plate and develop a matched secure automatic identification system. In the system, new accumulating energy self-luminous materials are adopted and RFID tags are embed with the supporting of RFID technology. To ensure data security, secrete starting address and random number initialization are implemented, as greatly improve the security, visibility, uniqueness and intelligence of license plate. Through repetitious experiments and long-running stable status of system, we have proved this identification system to be feasible and secure.
Computer Base Research on Human Living Organ Transparent Visualization Method

Objective through interesting organ segmentation, realize organ and tumor 3D transparent visualization and give clinical support.  Use personal computer to conduct morphology image process, technical analyses, process threshold segmented tissue and set grade by using grade trans-function to give organ multilayer transparent visualization.  Experimental results demonstrated that using this method on personal computer can conveniently and fast to construct 3D image and retain relating environmental information and at same time can also observe internal organ construction qualitatively and quantitatively.
Subjective Fabric Evaluation

The number of various existing fabric materials for different usages is unlimited. Therefore it is important to judge each textile material regarding quality and suitability before any manufacturing process. Related fabric characteristics can be subjectively assessed or objectively measured. A new field of research tries to imitate the subjective fabric evaluation method by virtually simulating the touch of fabrics with new haptic and tactile technologies. However, the today existing technology does not allow the rendering of complex interactions between hand and fabric, as it occurs during the real assessment method. Thus, the subjective fabric evaluation needs to be simplified to allow a direct comparison of the real and the virtual process. The main difference of the traditional subjective assessment, where the fabric is touched with both hands, to the simplified one lays basically in the fixation of the fabrics, so that the specimen can be judged with two fingers. The main mechanical properties such tensile, shear, bending, compression, friction, surface and weight have been assessed with the new test arrangement. The results of the assessment have been reported for all tested properties.