nils-herrmann/clean_scirepeval huggingface dataset:
Finding representative wind power scenarios and their probabilities for stochastic models

This paper analyzes the application of clustering techniques for wind power scenario reduction. The results have shown the unimodal structure of the scenario generated under a Monte Carlo process. The unimodal structure has been confirmed by the modes found by the information theoretic learning mean shift algorithm. The paper also presents a new technique able to represent the wind power forecasting uncertainty by a set of representative scenarios capable of characterizing the probability density function of the wind power forecast. From an initial large set of sampled scenarios, a reduced discrete set of representative scenarios associated with a probability of occurrence can be created finding the areas of high probability density. This will allow the reduction of the computational burden in stochastic models that require scenario representation.
Method for detecting ground moving target with range migration

For airborne radar detecting ground moving target, the range migration of the moving target was often ignored. However, for the cases of high relative speed between the radar and the target, the range migration should be considered, especially for high range resolution (HRR) airborne radar. Since the range migration can dramatically reduce the performance of detecting the moving target for airborne radar, the effect of range migration must be removed. In this paper, the data model of moving target with range migration is discussed, and a STAP method is proposed for detecting the moving target with range migration. Simulation results show that the proposed method can remove the range migration of all moving targets simultaneously, and improve the detection capability for the ground moving target with range migration.
Squared distance matrix completion through Nystrom approximation

In this paper, the completion of missing measurements in a squared distances matrix through Nystrom completion algorithm has been investigated. This missing occurred due to limitation of power when the sensors are deployed in a large area. The Nystrom algorithm has overcome the classical multidimensional scaling in a low and moderate signal to noise ratio, in addition it performs well as the number of missing entries increase. The plotted figures show admissible consequences for the proposed algorithm.
Analyzing way for elastic body in MBS based on superelement method

By the use of theories of Finite Element Method(FEM) and multibody system(MBS), the kinematics expression of elastic body in multibody system, its elastic deformation described with Rayleigh-Ritz method and its equation of motion based on d'Alembert law and Jourdain's virtual power principle are studied in detail. And the processing method to express the characteristic information of elastic body with FE means and to apply the information in vehicle system are also studied and analyzed. Based on above theories, the complete FE model of a frame in vehicle system is condensed by the superelement method, which greatly reduces the DOF of frame expressed in multibody system. And the precision of results is guaranteed simultaneously. Solutions of the frame show that the method presented here can solve elastic body in vehicle system exactly and fast, and has high precision, stability and reliability in concerned frequency range to meet the need of scientific researches and the practical analysis of structural vibration.
Active contour energy used in object recognition method

Object recognition finds wide application and is a challenge topic in computer vision and pattern recognition. In this paper, a new object detection method is proposed, which depends on active contour models (ACMs) energy function with pattern matching method. Firstly, object detection with shape prior based on energy function is presented to extract candidate targets. A novel geometric feature based on energy function of prior shape is put forward. The object detection method is invariant to scaling of shape and is also suitable for prior shapes set for general cases. Several candidate targets may be extracted when the object detection method is applied in object detection because most objects have similar shape. Then Speed up Robust Features (SURF) algorithm is employed to recognize the desired target from candidate targets. Experimental results show that proposed method is effective and efficient.
Human tracking using range and velocity measurements by multistatic radar

Multistatic radar system has a great potential for human detection and tracking for its fine localization precision, wide coverage and good observability. Traditional human detection and tracking are performed on the one dimensional (1-D) range profile. If targets are close or overlapped in range, it is difficult to distinguish these targets and obtain the measurements for target tracking. It can lead to the discontinuities of the human trajectories. As velocity provides another dimension to distinguish targets, in this paper, we propose to detect the targets on the range-Doppler (RD) image. Target localization and tracking is based on both range and velocity measurements. The performance of the human tracking method is verified by the experimental data in the foliage-penetration environment.
A segmentation-based CFAR algorithm for subsurface targets detection in FLGPSAR

Forward-Looking Ground Penetrating Synthetic Aperture Radar (FLGPSAR) has the capability of forming two-dimensional high-resolution images of subsurface objects from a standoff distance. This paper addresses the detection of subsurface targets, i.e. landmines, in FLGPSAR images. The conventional Constant False-Alarm Rate (CFAR) algorithm has been widely used in SAR image target detection, but its performance will degrade in subsurface targets detection because of the presence of interfering targets and clutter power transition. In this paper, a segmentation-based CFAR (S-CFAR) algorithm is proposed. The S-CFAR algorithm can remove the interfering targets or clutter power transition before estimating the parameters of the clutter background to achieve a better performance than the conventional CFAR algorithm. The real data processing results are given to validate the efficiency of the proposed method.
Preliminary results of ultra-wideband through-the-wall life-detecting radar

It is often a difficult task of knowing the location or movement of people behind barriers. To address this problem, we develop a through-the-wall life-detecting radar (TWLDR) prototype. It is a kind of ultra-wideband (UWB) radar using impulse waveform, which can penetrate non-metallic walls to detect static and moving targets behind walls. We first introduce configurations of the radar system, then describe signal processing techniques, and present experimental results in different situations at last. Results indicate that the UWB TWLDR can penetrate non-metallic walls to detect people with different locomotion states such as standing, walking along cross-range direction, walking along range direction, and the signal processing algorithm could be applied to other complex environments.
Multi-Label Learning with Global and Local Label Correlation

It is well-known that exploiting label correlations is important to multi-label learning. Existing approaches either assume that the label correlations are global and shared by all instances; or that the label correlations are local and shared only by a data subset. In fact, in the real-world applications, both cases may occur that some label correlations are globally applicable and some are shared only in a local group of instances. Moreover, it is also a usual case that only partial labels are observed, which makes the exploitation of the label correlations much more difficult. That is, it is hard to estimate the label correlations when many labels are absent. In this paper, we propose a new multi-label approach GLOCAL dealing with both the full-label and the missing-label cases, exploiting global and local label correlations simultaneously, through learning a latent label representation and optimizing label manifolds. The extensive experimental studies validate the effectiveness of our approach on both full-label and missing-label data.
A novel control strategy based on modified gating scheme for a dual-active-bridge converter

High frequency (HF) isolated dual active bridge (DAB) converters are featured with high power density, high efficiency for bidirectional applications. For the conventional single-phase-shift (SPS) control, all the switching devices of the DAB converter can work at zero-voltage switching (ZVS) for the entire power range, only when the voltage conversion ratio is equal to one. Various pulse width modulation (PWM) techniques for bidirectional converters exist to extend the zero-voltage-switching (ZVS) operation range when the voltage conversion ratio is not equal to one. In this paper, a novel control strategy based on a modified gating scheme is adopted for the DAB converter to realize ZVS operation for the whole load range. Verification of the analysis are performed through simulation and experimental results.
The Development of Brand Relationships Index Model

There is some problem about indicator system in most current researches of brand relationships evaluation. It is necessary to create a new method. Four brand relationship partners and three brand relationship natures are put forward to make up of a hypothesis of twelve-dimension brand relationships. Through a series of empirical studies, a five-dimension indicator system is obtained. Indicator weights are calculated by the path coefficients. Based on indicators and their weights, brand relationship index model is advanced
Price responsive demand for operating reserves and energy in electricity markets with wind power

Similar to demand response for energy, operating reserve can be elastic to prices given that minimal reliability criteria are satisfied. We present a probabilistic-based framework to estimate a demand curve for operating reserves. The operating reserve is estimated accounting for uncertainties from load, thermal unit contingencies, and wind power. The resulting demand for reserves is dynamically scheduled and co-optimized with energy in the market clearing. In a case study, the framework is applied to a two-settlement electricity market with centralized unit commitment, also considering demand response for energy. We find that the proposed demand curve for operating reserves contributes to improved handling of system uncertainties and more efficient pricing of operating reserves. Price elastic demand for energy has a similar effect on dispatch flexibility and cost, but has a less significant impact on the prices for spinning reserves.
Experimental Evaluation of Ad Hoc Testbed in Indoor Scenarios

To date, indoor applications are becoming more and more important for ad hoc networks. However, indoor scenarios pose new challenges for ad hoc networks. In this paper, we verify the problem of best offered load in chain topology and experimentally evaluate the local repair function. Our observation indicates that unidirectional links and unstable links are two cutthroats of ad hoc networks in indoor scenarios. This paper aims to aid researchers in refining and developing on-demand ad hoc routing protocols to cope with challenges from indoor scenarios
Optimal heat transfer criterion and inclination angle effects on non-boiling regime spray cooling

Based on the maximum CHF (critical heat flux) criterion, an optimal heat transfer criterion, which is called H criterion, was proposed. Experimental apparatuses were conducted. Distilled water was used as the working fluid. Three different DANFOSS nozzles with cone angles being 54°, 50° and 54° respectively were used to cool a 30×30mm2 square copper surface. Experimental results indicated that the volumetric fluxes were proportioned to P0.5, where P is the pressure drop across the nozzles. The optimal distance between the nozzles and the heated surface were derived. The results indicated that the optimal heat transfer appeared while the outside of the impellent thin spray film inscribed in the square heated surface. Based on the H criterion aforementioned, two DANFOSS nozzles of the three were used to study the temperature distribution of the heated surface experimentally while there were spray inclination angles. Distilled water was also used impacting on the 30×30mm2 square copper surface aforementioned and a circular heated copper surface with diameters being 30mm respectively. The heat flux of the surface was kept in constant (about 26−35W/cm2). The inclination angles were 0°, 10°, 20°, 30°, 40° and 50° respectively. Experimental results indicated that the grads of the temperature of the surface increases first and then decreases with the increase of the inclination angle.
Ground penetrating SAR buried landmine focusing in ground-plane

Airborne or vehicle-borne Ground Penetrating Synthetic Aperture Radar (GPSAR) can image buried landmines from a safe standoff distance. Traditional image formation, for example, Back-Projection (BP), focuses the image in the slant-plane without considering the refraction of air-soil interface and soil dispersion effects on subsurface landmines. Furthermore, geometric correction has to be performed to map the image from the slant-plane onto the ground-plane to get the ground truth. In this paper, a modified BP, denoted as Ground-Plane BP (GPBP), and its associated post-filter are proposed to form the image directly in the ground-plane with refraction and dispersion effects compensation. The proposed GPBP and its post-filter compensation scheme are validated using real data collected by the Rail-GPSAR system in terms of azimuth resolution and locating accuracy.
Multibaseline phase unwrapping through robust Chinese remainder theorem

Multibaseline phase unwrapping (PU) is a critical processing procedure of multibaseline synthetic aperture radar interferometry (InSAR). The presence of phase noise makes Chinese remainder theorem (CRT) method not robust enough and inapplicable in practical cases. In this letter, a method based on a robust Chinese remainder theorem (RCRT) is presented to improve the noise robustness of the multibaseline PU. Experimental results prove the effectiveness of RCRT method and demonstrate that the RCRT method is more robust to noise than the CRT method.
Efficient Raw Signal Generation Based on Equivalent Scatterer and Subaperture Processing for One-Stationary Bistatic SAR Including Motion Errors

This paper presents an efficient raw signal generation method based on equivalent scatterer and subaperture processing for the one-stationary bistatic synthetic aperture radar (SAR). It considers the moving platform's motion error to generate the precise raw signal for real SAR systems. First, the imaging geometry in elliptical polar coordinates is built, and then, the scene is divided into several equidistant elliptical rings. Based on the equivalent scatterer model, the approximate SAR system transfer function is derived; thus, each pulse's raw signal is calculated by the convolution of the transmitted signal and transfer function, via the fast Fourier transform (FFT). To further improve the simulation speed, the subaperture and subscene processing is used. The transfer function for the same subaperture's pluses is calculated simultaneously by the weighted sum of all subscenes' equivalent scattering coefficient in the same equidistant elliptical ring, which is performed by the nonuniform FFT (NUFFT). This method only involves the FFT, NUFFT, and complex multiplication, which means the easier operation and higher efficiency. The implementation, limitation, and evaluation of the proposed method are discussed. Simulation results are given to prove the correctness and validity of the proposed method.
Isolation Forest

Most existing model-based approaches to anomaly detection construct a profile of normal instances, then identify instances that do not conform to the normal profile as anomalies. This paper proposes a fundamentally different model-based method that explicitly isolates anomalies instead of profiles normal points. To our best knowledge, the concept of isolation has not been explored in current literature. The use of isolation enables the proposed method, iForest, to exploit sub-sampling to an extent that is not feasible in existing methods, creating an algorithm which has a linear time complexity with a low constant and a low memory requirement. Our empirical evaluation shows that iForest performs favourably to ORCA, a near-linear time complexity distance-based method, LOF and random forests in terms of AUC and processing time, and especially in large data sets. iForest also works well in high dimensional problems which have a large number of irrelevant attributes, and in situations where training set does not contain any anomalies.
Extended Nonlinear Chirp Scaling Algorithm for High-Resolution Highly Squint SAR Data Focusing

In this paper, an extended nonlinear chirp scaling (ENLCS) algorithm for focusing synthetic aperture radar data acquired at high resolution and highly squint angle is proposed. The whole processing of the ENLCS consists of the following three steps. First, a linear range walk correction is used to remove the linear component of target range cell migration (RCM) and to mitigate the range-azimuth coupling of the 2-D spectrum. Second, a bulk second range compression (SRC) is performed in the 2-D frequency domain for compensating the residual RCM, SRC term, and higher order range-azimuth coupling terms. Third, a modified azimuth NLCS (ANLCS) operation is applied to equalize the azimuth frequency modulation rate for azimuth compression. By adopting higher order approximation processing and by properly selecting the scaling coefficients, the proposed modified ANLCS operation has better accuracy and little image misregistration. The overall focusing procedure of the ENLCS algorithm only involves fast Fourier transform and complex multiplication, which means easier implementation and higher efficiency. The experimental results with simulated data prove the effectiveness of the proposed algorithm.
A Quantized Analyzing Method for Finding a User's Interested Web Pages Based on the User's Browsing Actions and Query Words

How to capture a user’s interested Web pages (IWPs) is very important in a Web personalized service. In this paper, a method to capture a user’s IWPs by combining a quantized analysis of the user’s browsing actions and query words has been presented. To distinguish the differences among users and at different time of a user, a set of parameters have been introduced when calculating the contribution of browsing actions to the interest value on a Web page for a user. The parameters are adjusted periodically according to the current Web pages browsed by the user. The interest value is used to adjust if a Web page is interested by the user. Then, the method has been improved by introducing the analysis of the user’s query words in order to determine more precisely if a border interested page is really an interested one. The method has been implemented using the technologies of Hook and BHO. Experiments have been given to show its feasibility and validity.
Scalable Algorithms for Multi-Instance Learning

Multi-instance learning (MIL) has been widely applied to diverse applications involving complicated data objects, such as images and genes. However, most existing MIL algorithms can only handle small- or moderate-sized data. In order to deal with large-scale MIL problems, we propose MIL based on the vector of locally aggregated descriptors representation (miVLAD) and MIL based on the Fisher vector representation (miFV), two efficient and scalable MIL algorithms. They map the original MIL bags into new vector representations using their corresponding mapping functions. The new feature representations keep essential bag-level information, and at the same time lead to excellent MIL performances even when linear classifiers are used. Thanks to the low computational cost in the mapping step and the scalability of linear classifiers, miVLAD and miFV can handle large-scale MIL data efficiently and effectively. Experiments show that miVLAD and miFV not only achieve comparable accuracy rates with the state-of-the-art MIL algorithms, but also have hundreds of times faster speed. Moreover, we can regard the new miVLAD and miFV representations as multiview data, which improves the accuracy rates in most cases. In addition, our algorithms perform well even when they are used without parameter tuning (i.e., adopting the default parameters), which is convenient for practical MIL applications.
An Adaptive Channel Tracking Algorithm Combined LMS and KALMAN Filter

Adaptive channel tracking algorithms are studied based on LMS and KALMAN in MIMO-OFDM system. A novel adaptive channel tracking algorithm, which combines the LMS and KALMAN filter to make use of the stabilization of LMS and the accuracy of KALMAN filter, is proposed. On the one side, the algorithm can track and estimate the channel with the optimal MSE, and on the other hand, the algorithm conquers the problem that the KALMAN filter need the transforming between learning pattern and tracking pattern frequently. For the number of training sequence is depressed, so the performance of the communication system is improved.
Feature Extraction and Discriminator Design for Landmine Detection on Double-Hump Signature in Ultrawideband SAR

An air- or vehicleborne ultrawideband synthetic aperture radar (UWB SAR) has ground penetrating capability, which provides a sufficient approach to detect landmines over wide areas from a safe standoff distance. In this paper, a support vector machine (SVM) with hypersphere classification boundary, which is referred to as HyperSphere-SVM (HS-SVM), using a hidden Markov model (HMM) kernel on the feature vector extracted by a postfilter-based method is proposed for landmine detection. The postfilter-based method can extract the feature containing not only the amplitude but also the amplitude varying information of the double-hump signature of metallic and plastic landmines. Compared with simple kernels, e.g., the Gaussian kernel, the HMM kernel employs the state-transition information in the extracted feature into the discrimination procedure and, thus, can improve detection performance. The proposed postfilter-based feature extraction method and the HMM kernel HS-SVM are verified on the field data collected by a UWB SAR system in different scenarios.
Ultra-wide band SAR subsurface metallic landmine images: simulation and measurement

Air- or vehicle-borne ultra-wide band synthetic aperture radar (UWB SAR) has the capability to detect landmines over large area from a standoff distance quickly, which is the trend of landmine detection. The study of electromagnetic characteristic of landmine is eagerly required to improve the landmine detection performance. In this paper, simulation and measurement of UWB SAR subsurface metallic landmine images are compared. Simulation and measurement results both show that metallic landmines have double-peak characteristic, which is valuable for landmine detection
A new genetic algorithm for Web-based negotiation support system

Web-based negotiation support system not only should provide the convenience for communication, but also give efficient solution in reasonable time to eliminate conflict and improve the success probability of the negotiation. Therefore, we construct a multi object combination optimization model that can be handled easily by genetic algorithm for the negotiation problem first, and then, considering the global convergence, concurrency features, we use a new genetic algorithm that hybridize local meta heuristic mechanism to speed the search process, improve the efficiency and overcome shortcomings such as the premature and lacking of diversity etc. Finally, we give an example to demonstrate that the proposed method is feasible and efficient.