nils-herrmann/clean_scirepeval huggingface dataset:
Ultra-wideband Synthetic Aperture Radar landmine detection based on landmine-enhanced imaging

Ultra-wideband Synthetic Aperture Radar (UWB SAR) has the characteristics of wide aspect angle and wide frequency band. UWB SAR data contains rich information about targets. However, the prior information of landmine is used until the discrimination in the conventional landmine detection procedure. Therefore, in order to ensure the probability of detection, there exist many false alarms in the prescreening results. That will increase the burden of the subsequent processing steps. By applying the prior knowledge of landmine to imaging, landmine-enhanced imaging can be realized and beneficial improvements in image quality and detection performance can be expected. In this paper, the unique feature of aspect invariance is used in landmine-enhanced imaging. Further, we apply the landmine-enhanced imaging to landmine detection. Thus, the detection probability of landmine increases under the same false alarms rate as the traditional landmine detection method. The experimental results demonstrate the effectiveness and practicability of the proposed algorithm.
Research on The underwater vehicle radiated noise model and the linear spectrum extraction method

Underwater vehicle radiated noise is the sound source of target detection and tracking of navy weapons. With the development of combat simulation and virtual training, the underwater vehicle radiated noise signal model was applied widely. Meanwhile, with the improvement of underwater vehicle radiated noise controlling technology, it becomes more and more difficult in extracting the low frequency linear spectrum noise from the other noise since the former is easily submerged by the latter. In the paper, the formation mechanism of underwater vehicle radiated noise is studied, and continuous spectrum component with underwater vehicle acoustic property is simulated by FIR filter based on LMS algorithm firstly, and then the radiated noise is created by combining continuous spectrum component with the line-spectrum component. To increase the signal to noise ratio of linear spectrum and suppress the continuous spectrum component, the adaptive linear spectrum extraction method is proposed based on EMD interval-threshold de-noising technology, which overcomes the disadvantages of traditional spectrum analysis and the wavelet analysis. Finally, the simulation model of underwater vehicle radiated noise and low frequency linear spectrum component extraction method are validated via simulation test.
Unsupervised metric fusion by cross diffusion

Metric learning is n fundamental problem in computer vision. Different features and algorithms may tackle a problem from different angles, and thus often provide complementary information. In this paper; we propose a fusion algorithm which outputs enhanced metrics by combining multiple given metrics (similarity measures). Unlike traditional co-training style algorithms where multi-view features or multiple data subsets are used for classification or regression, we focus on fusing multiple given metrics through diffusion process in an unsupervised way. Our algorithm has its particular advantage when the input similarity' matrices are the outputs from diverse algorithms. We provide both theoretical and empirical explanations to our method. Significant improvements over the state-of-the-art results have been observed on various benchmark datasets. For example, we have achieved 100% accuracy (no longer the bull's eye measure) on the MPEG-7 shape dataset. Our method has a wide range of applications in machine learning and computer vision.
A New Prediction Model Based on Belief Rule Base for System's Behavior Prediction

In engineering practice, a system's behavior constantly changes over time. To predict the behavior of a complex engineering system, a model can be built and trained using historical data. This paper addresses the forecasting problems with a belief rule base (BRB) to trace and predict system performance in a more interpretable and transparent way. More precisely, it extends the BRB method to handle a system's behavior prediction, and a new prediction model based on BRB is presented, which can model and analyze prediction problems using not only numerical data but human judgmental information as well. The proposed forecasting model includes some unknown parameters that can be manually tuned and trained. To build an effective BRB forecasting model, a multiple-objective optimization model is provided to locally train the BRB prediction model by minimizing the mean square error (MSE). Finally, a practical case study is provided to illustrate the detailed implementation procedures and examine the feasibility of the proposed approach in engineering application. Furthermore, the comparative studies with other state-of-the-art prediction methods are carried out. It is shown that the proposed model is effective and can generate better prediction in terms of accuracy, as well as comprehensibility.
Metamorphic testing of decision support systems: a case study

Decision support systems provide critical support to decision makers. These systems are increasingly complex and, as a result, they are very difficult to test because of the lack of an ideal test oracle. Lack of testing may result in poor software quality and wrong decisions being made and, hence, bring serious consequences. In the present study, the authors conduct a case study, where the authors apply a metamorphic testing method to alleviate the oracle problem in testing decision support systems. Experimental results show that metamorphic testing is effective in revealing faults in the absence of an ideal oracle.
A nystrom scheme based on cuboid elements for solving Volume Integral Equations

are indispensable for electromagnetic (EM) analysis by integral equation approach when inhomogeneous penetrable objects are involved. In the conventional method of moment (MoM) for solving the VIEs, unknown functions are expanded by a well-designed basis function like Schaubert-Wilton-Glisson (SWG) basis function, and a tedious testing procedure is required to transform the VIEs into matrix equations. Although the MoM is dominant, the Nyström method could be a good alternative because of its well-known merits. The objects are often meshed into tetrahedral elements for a good geometric approximation. Nevertheless, the objects take a cuboid shape in some applications like in packaging structures or in inverse scattering problems whose imaging domain is always a cubic box. Using cuboid elements to discretize the objects is a more favorable choice for convenience and efficiency in this situation. Also, it is easier to handle singular integrals over cuboid elements than tetrahedral elements because of their geometric characteristics. Albeit the Nyström method has been used for solving the VIEs with a tetrahedral discretization, it has not been employed based on the discretization with cuboid elements. We propose a Nyström scheme based on cuboid elements for solving the VIEs in this work. Unlike in the MoM, the matrix elements are not calculated by double numerical integrations involving scalar Green's function, but simply by sampling the dyad Green's function at observation points and source points when the distance between them is large. For near-interaction and self-interaction elements where an observation point is near or inside a source element, a polynomial, which is combined with the dyad Green's function, is used to interpolate unknown functions, leading to singular or badly-behaved integrals with different orders. The hypersingular integrals have been addressed in our previous work and weakly-singular integrals can be evaluated by Duffy's method. This work develops some analytical formulations for evaluating strongly-singular integrals based on a Cauchy-Principal-Value (CPV) sense. These formulations can be used to simplify the evaluation of matrix elements with strongly-singular integrands. Numerical examples are presented to demonstrate the scheme and good results have been obtained.
An Optimized Approach for Pansharpening Very High Resolution Multispectral Images

State-of-the-art pansharpening methods generally inject the spatial details extracted from the panchromatic (Pan) image into the multispectral (MS) images by considering different injection models. The fusion performances severely rely on the accuracy of the modeling and the estimation of model parameters. In this letter, we propose an optimized approach to avoid explicitly modeling the detail injection process. The solution employs the gradient field of the Pan image for spatial enhancement. The low-pass (LP) version of the fused bands are constrained to be the most similar to the original MS bands to preserve the spectral characteristics. We use the local correlation coefficients between the MS band and the LP version of the Pan image to adjust the two sources of information based on a simple observation, and it is further optimized by considering the overall quality index Q4. Experimental results demonstrate that the proposed method outperforms the state-of-the-art multiresolution analysis-based methods.
Three-component decomposition based on stokes vector for compact polarimetric SAR under π/4 mode

This paper formulates a Stokes vector (SV) based decomposition algorithm for compact polarimetric (CP) SAR under π/4 mode. The three-component scattering model is first established by using the relationship between covariance matrix and SV, and then the contribution of each component is estimated from algebraic calculation. To solve the underdetermined equations for π/4 decomposition, the contribution of volume scattering is taken as a free parameter, under the constraint that the weighting factor of each scattering component should be nonnegative. The feasibility of this algorithm is supported by two data sets from AIRSAR and RADARSAT-2 respectively.
An empirical study on “double dividend” in China's existing environmental taxation

Based on a literature review on the theory of “double dividend”, this paper is designed to carry out an empirical study on the issue whether there is a “double dividend” effect in China's existing environmental tax system. The empirical analysis shows that: (1) under the current scale of environmental tax revenues in China, there is no “double dividend” effect; (2) there is still a weak “green” effect on pollution reduction and this “green” effect may not come into effect instantly after the green tax reform is carried out and it needs more time; (3) taxes promote fixed asset investment of the society, and maintain it at a high level in the long term;(4) On the distribution effect and employment effect, environmental taxes do not play their corresponding role in China. As a result, there is no “double dividend” effect in China's existing environmental tax composition, but there is great potential for a “Green Tax” reform.
Weighted support vector machine based on association rules

This paper presents a weighted support vector machine (WSVM) based on association rules for two-class classification problems. The basic idea of the WSVM is to assign different weights to different data points to minimize impacts of outliers. In this paper, we apply association rules to generate weights to prevent bias to the majority class for imbalanced binary classification problems. Experimental results indicate that the proposed method yields a better generalization in comparison to the standard support vector machines.
Development of a new approach for formulating equations of motion of dynamic systems

In this paper the principle of stationary total potential energy in structural dynamics first presented by the author is thoroughly examined. It is clearly shown that when being used in conjunction with a new matrix assembly technique called the “set-in-right-position” rule also proposed by us, which is inherently different from the most widely used direct stiffness method, the proposed principle is suitable for formulating the finite element equations of motion of the vibrating system. The main advantage of present method lies in its simplicity and efficiency in modeling the dynamic behaviors of more complicated systems that are usually characterized by the presence of very complex layouts as well as the applying of various kinds of external forces. Then, in order to demonstrate the applicability and capability of present method, two illustrated examples have been given. By doing so, one is able to acquire an in-depth understanding on the influence of individual parts of the whole system on its overall dynamic behavior.
Double layer salient parts based multi-people tracking

Multi-people tracking has been studied for decades. It is already applied in many computer vision tasks. In this paper, the proposed framework performs frame to frame tracking and follows tracking-by-detection approach. Saliency detection is introduced to enhance multi-people tracking. It is performed on two layers in this method. Salient parts inside the human patch denote representative regions of the target, while parts around the target capture context information. Short term tracking of salient parts is applied along with data association. When data association with detections fails, the supporting models on-line learned are used to indicate the locations of targets based on the tracking results of salient parts. A Bayesian based method is used for mutual occlusion reasoning. Experiments are carried out on several public datasets to evaluate the proposed method. The experimental results show the promising performance of the proposed method compared with state-of-the-art works.
DFIG-based wind farm equivalent model for power system short circuit current calculation

This paper presents a simple DFIG-based wind farm equivalent model which can be used to quantify a wind farm's short circuit current contributions to the grid, e.g. for the purpose of protective relaying setting. The model has taken the equivalent impedances Xss and the equivalent potential E'q as the characteristic parameters. By analyzing the short circuit behavior of a doubly fed induction generator (DFIG) with crowbar protection, the amplitude range of short circuit current supplied by the wind farm is determined. Case study has compared the results of calculation and simulation, and the conclusion shows that the proposed simple model may be utilized in engineering calculation.
Single Droplet Evaporation Model in Laser Treatment of PWS in Conjunction with Cryogen Spray Cooling

Cryogen spray is an effective cooling technique used for laser treatment of port wine stain. The cooling process involves complex droplet evaporation and strong convective heat and mass transfer, therefore a deep understanding of spray characteristics is essential in order to optimize the nozzle design and improve the cooling efficiency of the spray. This paper presents a theoretical model to describe the equilibrium evaporation process of a single cryogen droplet in cryogen spray. The model considers mass transfer through mass transfer number method, the momentum transfer by selecting the suitable empirical correlation of drag coefficient, and heat transfer by taking into consideration of droplet evaporation and convective effect with ambient air. The influence of variable physical properties of R134a is also considered. Based on the validation by simulating the cooling stage of a hanging water droplet, this model is used to predict the temperature variation of cryogen droplets in cryogen spray, which is in reasonable agreement with the experimental measurements. A parametric study of the influences of initial diameter and velocity on the droplet evaporation is then carried out. The results can be used to guide the cryogen spray cooling of laser therapy.
A heading error estimation approach based on improved Quasi-static magnetic Field detection

The Zero-velocity Update (ZUPT)-aided Extended Kalman Filter(EKF) algorithm is commonly used to suppress the error growth of the inertial based pedestrian navigation systems, but it still suffers from long-term heading drift. The magnetic field was suggested to mitigate the heading errors of for positioning and navigation, but it undergoes severe perturbation in indoor scenarios. The Quasi-Static magnetic Field (QSF) method was developed to estimate heading errors using magnetic field in perturbed environments. However, this method may bring extra errors to system because of the high false alarm probability of detecting the quasi-static field. In this paper, we propose a heading error estimation approach based on the improved QSF approach for foot-mounted Inertial Navigation Systems (INS). For the approach, a magnetometer calibration method is developed to eliminate the deviation caused by the positioning system platform and shoes firstly. Then an improved QSF detection approach is proposed and then used for generating the desired magnetic measurements which are fed into the EKF to estimate the heading errors. The experiments indicate that the proposed method is effective in suppressing the heading errors.
Fast macroblock inter mode decision and motion estimation for H.264/MPEG-4 AVC

In H.264/MPEG-4 AVC, MacroBlock (MB) mode decision and motion estimation (ME) is one of the most computationally expensive processes. This paper proposes a fast intermode decision and ME algorithm. Experimental results show that the proposed algorithm could achieve similar Rate-Distortion (R-D) performance with about 50% computation saving, compared to the JM low-complexity mode with a Fast Full-Search (FFS) motion estimation algorithm.
Interference cancellation based receive scheme to combat ICI for OFDM systems

In his paper, we analyses and proposes a new receiver structure to mitigate the effect of interchannel interference (ICI) on the reception of OFDM signals. The system operates in a frequency selective Rayleigh fading channel. Interchannel interference is caused by the time variations in channel gain within OFDM symbol and leads to an error floor, which is a function of Doppler frequency. In this study, it was found that by performing parallel interference cancellation at the receiver, we were able to significantly reduce the error floor.
A Model for Hidden Behavior Prediction of Complex Systems Based on Belief Rule Base and Power Set

It is important to predict the hidden behavior of a complex system. In the existing models for predicting the hidden behavior, the hidden belief rule base (HBRB) is an effective model which can use qualitative knowledge and quantitative data. However, the frame of discernment (FoD) of HBRB which is composed of some states or propositions and the universal set including all states or propositions is not complete. The global ignorance and local ignorance cannot be considered at the same time, which may lead to the inaccurate forecasting results. To solve the problems, a new HBRB model named as PHBRB in which the hidden behavior is described on the FoD of the power set is proposed in this correspondence paper. Furthermore, by using the evidential reasoning rule as the inference tool of PHBRB, a new projection covariance matrix adaption evolution strategy is developed to optimize the parameters of PHBRB so that more accurate prediction results can be obtained. A case study of network security situation prediction is conducted to demonstrate the effectiveness of the newly proposed method.
Research on driving permit of monocular people

Advocations of monocular people for driving rights have been more and more prominent in recent years. This paper aimed on research findings of monocular vision such as definition, criterion, compensatory mechanism and effect on driving behavior, and compared domestic vision requirements and test procedures of drivers with the counterparts of other countries. Based on analysis of advanced methods including conditional vehicle licensing system which has been applied for years in the U.S. and Australia, some feasibility plans were suggested to support the driving rights of monocular people, such as adjustment of requirement of visual acuity, introduction of requirement of vision field, stimulation and real-vehicle test of driving behavior of monocular drivers, application of conditional vehicle license, etc.
A Panoramic Geology Field Trip System Using Image-Based Rendering

Geology field trip is an important teaching step for the majors of geology, natural resources prospecting engineering and geographical sciences to learn geology knowledge and grasp field working skills. There are some problems in the traditional geology field trip teaching. Inspired by the Google Maps Street View, the author proposes applying the Image-Based Rendering (IBR), a branch of Virtual Reality (VR), to the field of geology field trip teaching. In this paper, the author designs and implements a panoramic geology field trip system for College of Earth Sciences of Jilin University, in which students can preview the geology field trip location by roaming in the three-dimension (3D) virtual environment. The practice shows that not only can the system help teach 3D thinking, but also reduce the teaching cost, improve students' learning efficiency and enhance learning interest.
The steady-state operation map of a semi-dual-active-bridge converter

In this work, the steady-state operation map of of a semi-dual-active-bridge converter is fully explored. Different operation modes are identified and the boundary condition between different modes are found. Related soft-switching behavior in each mode are discussed in detail. Experimental results are presented to validate the existence of the the operation modes. It is shown this converter is suitable for high voltage unidirectional application. Comparison with previous similar topology is also included to highlight the converter's advantageous feature.
Study on Analyzing Method for Vehicle System Based on Rigid-Flexible Coupling

The development requirements of the high-speed and heavy-loaded trend put forward new research subjects and challenges for vehicle systems and their dynamics, especially the dynamics questions caused by elastic body and rigid-flexible coupling. By combining theories of multibody system and FE, the kinematics expression of elastic body in heavy haul system, its elastic deformation and its equation of motion based on d’Alembert law and Jourdain’s virtual power principle are studied in detail. The processing method to express the characteristic information of elastic body with FE means and to apply the information in heavy haul system are also studied and analyzed. Based on above presented theories, the complete FE model of a key component in heavy haul vehicle system is condensed by the super element method, Calculated results of the component show that the method presented in the paper can solve elastic bodies in heavy haul system exactly and fast to provide reliable and correct data for further research. And practice computation and application also show that this method has high precision, stability and reliability to meet scientific research and the practical analysis of structural vibration.