nils-herrmann/clean_scirepeval huggingface dataset:
Axial Magnetic-Field Effects on an Argon Arc Between Pin and Plate Electrodes at Atmospheric Pressure

The heat transfer and plasma flow inside an argon arc exposed in an axial magnetic field (AMF) at atmospheric pressure are simulated with an axisymmetric-swirl model. The numerical results show that the AMF induces most of the plasma flowing by the arc mantel, and as a result, the AMF arc presents a hollow bell shape.
One-stationary bistatic UHF UWB SAR experiment and imaging

One-stationary bistatic ultrahigh frequency (UHF) ultrawideband (UWB) synthetic aperture radar (SAR) works in the UHF UWB signal with a large fractional bandwidth and has the wide antenna beamwidth with a large integration angle, thus it has the well ability of penetrating the foliage, high-resolution imaging and providing the increased information. Here, the onestationary bistatic UHF UWB SAR experiment is described and the imaging results are proposed. This bistatic SAR system was developed based on a UHF UWB SAR system as the vehicle-based transmitter and the other UHF UWB radar system as the ground-based receiver. In late 2015, the experiment was first conducted in China, and then the monostatic and bistatic scattered signal were simultaneity collected. The objective of this paper was to investigate the imaging performance of the onestationary bistatic UHF UWB SAR system.
Fuzzy Energy and Reserve Co-optimization With High Penetration of Renewable Energy

In this paper, we propose a fuzzy-based energy and reserve co-optimization model with consideration of high penetration of renewable energy. Under the assumption of a fixed uncertainty set of renewables, a two-stage robust model is proposed for clearing energy and reserves in the first stage and checking the feasibility and robustness of re-dispatches in the second stage. Fuzzy sets and their membership functions are introduced into the optimization model to represent the satisfaction degree of the variable uncertainty sets. The lower bound of the uncertainty set is expressed as fuzzy membership functions. The solutions are obtained by transforming the fuzzy mathematical programming formulation into traditional mixed integer linear programming problems.
Soft-biometric detection based on supervised learning

In the past 5 years, people re-identification has been a popular topic as an application using computer vision techniques. Among the models used for people re-identification, soft-biometric traits based models have great potential due to the semantic meaning and robust performance they have. In this paper, we will exploit the performance of supervised learning based method on the detection of three soft-biometric traits: Glasses, Cap and Clothes Pattern. Simple features like edge and frequency are extracted from sample images and used for learning. Two supervised learning methods - Support Vector Machine (SVM) and Extreme Learning Machine (ELM) are employed and compared. Different normalization methods are compared as well. Experiments are carried out on images from FERET dataset and images collected online, and discussion is provided.
Spatial resolution analysis of low frequency UWB one-stationary bistatic SAR

This study analyses the spatial resolution for the low frequency (LF) ultra-wideband (UWB) one-stationary bistatic synthetic aperture radar (OSBSAR) system. It considers the wavenumber coupling and radar squint angle to derive the finest possible spatial resolution. First, the spatial wavenumber of the LF UWB OSBSAR echo data of the target is analyzed. Then, its spatial resolution is derived based on the spatial wavenumber domain support. Simulation results are given to prove its validity.
Salient region detection based on local and global saliency

A new and effective salient region detection method based on local and global saliency information is proposed. To keep the completeness of salient regions, the input image is segmented into several regions firstly. Then for each region, local saliency and global saliency are generated respectively. The local saliency is computed by multi-scale neighborhood contrast, and the global saliency is measured according to global spatial distribution and inter-region isolation of features. Based on the local saliency and global saliency, the final saliency can be obtained by the weighted combination of them. The comparison experiment results demonstrate the effective performance of the proposed algorithm on salient region detection.
Individual Stable Space: An Approach to Face Recognition Under Uncontrolled Conditions

There usually exist many kinds of variations in face images taken under uncontrolled conditions, such as changes of pose, illumination, expression, etc. Most previous works on face recognition (FR) focus on particular variations and usually assume the absence of others. Instead of such a ldquodivide and conquerrdquo strategy, this paper attempts to directly address face recognition under uncontrolled conditions. The key is the individual stable space (ISS), which only expresses personal characteristics. A neural network named ISNN is proposed to map a raw face image into the ISS. After that, three ISS-based algorithms are designed for FR under uncontrolled conditions. There are no restrictions for the images fed into these algorithms. Moreover, unlike many other FR techniques, they do not require any extra training information, such as the view angle. These advantages make them practical to implement under uncontrolled conditions. The proposed algorithms are tested on three large face databases with vast variations and achieve superior performance compared with other 12 existing FR techniques.
Exploitation of label relationship in multi-label learning

Traditional supervised learning deals with problems where one instance is associated with a single class label, whereas in many real tasks, one instance may be associated with multiple class labels simultaneously. The paper introduces some of our recent findings in the exploitation of label relationship. First, it introduces an approach which is able to discover and exploit label relationship automatically. This approach does not require label relationship as input for the model construction process; instead, it is able to provide not only accurate predictions, but also reasonable estimate of label relationship. The label relationship discovered is usually asymmetric, which is quite different from symmetric label relationship assumed by previous studies, but is more consistent with realistic situations. Secondly, it advocates to exploit label correlations in the data locally, because in real tasks it is usually the case that the label correlation is shared by only a subset of instances rather than all instances.
Finite-Time Attitude Trajectory Tracking Control of Rigid Spacecraft

In this paper, we investigate the finite-time attitude tracking control problem for a rigid spacecraft on the special orthogonal group (<inline-formula><tex-math notation="LaTeX">$SO(3)$</tex-math></inline-formula>). The exponential coordinates, derived from the exponential and logarithmic maps of Lie group, are utilized to describe the attitude tracking error on <inline-formula><tex-math notation="LaTeX">$SO(3)$</tex-math></inline-formula> almost globally unique except for a zero measure set on which the logarithmic map is double valued. To avoid this issue, a novel nonsingular sliding surface is designed to guarantee that the attitude tracking error will never reach the zero measure set and provide fixed-time stability during the sliding phase. Then, a novel continuous second-order sliding mode control scheme is developed to force the system state to reach the desired sliding surface in finite time. The salient feature of the proposed control scheme is that it significantly suppresses the chattering phenomenon. In addition, to address the lack of angular velocity measurements, a finite-time observer is proposed to recover the unknown angular velocity information in finite time. Then, an observer-based finite-time output feedback control strategy is obtained. Finally, numerical simulations are presented to demonstrate the effectiveness of the proposed control scheme.
Processing bistatic forward-looking spotlight SAR data using fast backprojection algorithm

This study presents a fast backprojection algorithm (FBPA) for bistatic forward-looking spotlight synthetic aperture radar (BFSSAR) imaging. It represents the subimages on the polar grids to reduce the computational burden. First, the backprojection algorithm for the BFSSAR imaging is introduced. Then, FBPA for the BFSSAR imaging based on the subaperture processing is provided, and the sampling requirements for the polar grids of subimages are derived from the bandwidth angle. Simulation results are shown to demonstrate its validity.
Research on robot tactile display based on water jet technology

A new water jet tactile display technology is presented in this paper, which use water jet as the pressure stimuli for tactile telepresence. There is an open water jet nozzle connected to pressure water tank at each element of the tactile array, and there is also an electromagnetism valve between the nozzle and the water tank which is used to control the ON/OFF of the water current. When tactile telepresence needs to present information for the user, the corresponding nozzles standing for tactile information are connected to the pressure water tank, and the water jet on the surface of the user's finger will result in a ramp distribution of water pressure, and finally make the user obtain an artificial tactile sensation. As the water can take heat away from the skin of the finger, there should also be a ramp distribution of temperature which will cause a tactile sensation of another type. Pressure ramp and temperature ramp stimulate together and finally provide a pressure-temperature fusion tactile for the user
Clustering Analysis of Sports Performance Based on Ant Colony Algorithm

Cluster analysis is one of the modes of data mining, which classifies the sample data to different types according to similarity rules. It has also been used in education management field. This paper investigates the principle of k-means clustering algorithm. Because it is easy to converge into local minimum and is also sensitive to noise, isolated point data have a great impact on the average value, an improved clustering algorithm based on ant colony optimization is proposed. The improved algorithm is used in student sports performance management system. It can be concluded that clustering results obtained by the improved algorithm based on ant colony optimization is more scientific, fair and reasonable.
Characteristics of Microstrip Lines through a Metalized EBG Substrate

This paper presents the analysis of a microstrip line surrounded by metalized substrates. The metalized substrates are dielectric substrates with vias through and either with or without patch covers. The guided-wave characteristics are investigated by full-wave electromagnetic simulations with experimental verification. It is found that the metalized substrate would increase the phase constant (slow-wave factor) and decrease the characteristic impedance of a microstrip line. When a microstrip line is in contact with the surrounding vias, the structure is in analogy to a metallic waveguide with a cut-off frequency. The mode properties are also characterized
A D2D offloading approach to efficient mobile edge resource pooling

The explosion of resource-hungry mobile applications has posed great challenges on the underlying mobile devices which typically have limited computation resource. In response, device-to-device (D2D) computation offloading is envisioned as a promising approach to the problem by gearing resource-rich devices and resource-poor devices. Towards real-time and efficient computation offloading, in this paper, we proposed a novel edge resource pooling framework called ERP, in which a massive crowd of devices at the network edge exploit D2D collaboration for pooling and sharing computation resource with each other. Specifically, we first formulate the utility maximization problem under both computation and communication constraints as a mixed-integer linear programming (MILP), which is further proven to be NP-hard. To address this challenge, we propose a centralized greedy heuristic based on the classical maximum network flow problem, which schedules the task offloading in a cost-efficient manner. Rigorous theoretical analysis and extensive evaluations demonstrate the effectiveness of the heuristic to some extent.
Big Data Opportunities and Challenges: Discussions from Data Analytics Perspectives [Discussion Forum]

"Big Data" as a term has been among the biggest trends of the last three years, leading to an upsurge of research, as well as industry and government applications. Data is deemed a powerful raw material that can impact multidisciplinary research endeavors as well as government and business performance. The goal of this discussion paper is to share the data analytics opinions and perspectives of the authors relating to the new opportunities and challenges brought forth by the big data movement. The authors bring together diverse perspectives, coming from different geographical locations with different core research expertise and different affiliations and work experiences. The aim of this paper is to evoke discussion rather than to provide a comprehensive survey of big data research.
The improvement and application of acoustic emission inspection algorithm for metal vessel

A method of acoustic emission defect inspection based on wavelet packet analysis and BPNN (BP neural network)is introduced. The method of wavelet packet based on sections and energy-moment feature is used to replace the traditional “wavelet packet-energy” to pick-up characteristics of AE signals. The efficiency of this method is validated by experiment of metal vessel defect diagnosis. The result shows that compared with ordinary way, the method of feature extraction based on wavelet packet of sections and energy-moment feature, can make better use of the major band of defect signals and the wavelet's time-frequency information, and reduce the complexity of system and increase the identification rate.
A Theme-Based Blended E-learning System

This paper discusses a new e-learning method which use thematic learning to make knowledge integrated into a whole and use blended learning mode to improve the feasibility of thematic learning and make e-learning surrounded with a meaningful environment. At first, the definition and the model of theme-based blended e-learning are proposed. The model shows how the two learning mode are linked in the activities of the e-learning processes. In the theme-based blended learning, teachers design the themes and control the learning process which make the process of students’ self-exploring an under-control effective learning. An elaborate definition of process control is proposed as well. Finally a theme-based blended e-learning system is developed and a case is studied to show the efficient of the model.
Teaching automated test case generation

Software testing is a major approach to software quality assurance, but it is relatively neglected in universities' computing curricula. For students majoring in computer science or software engineering, several basic testing methods need to be taught. These testing methods generate test cases based on either specifications or program code. When introducing the testing methods based on program code, it is not easy to let the students experience automated test case generation due to the lack of supporting tools and limited teaching hours. In this paper we report our experience in teaching this topic with limited resources. The evaluation result indicates that our teaching method is effective and can also be adopted in other computer science/software engineering subjects where similar constraints exist.
Study on Fault Diagnosis Expert System for Power Supply Circuit Board on Vxi Bus

A high-tech information electronic equipment of some given type is designed in order to proceed automatically fault detection and improve the efficiency and accuracy of diagnosis. This thesis which is a part of the program introduces the research of algorithm of fault diagnose expert system of a power supply circuit board of an electronic device and algorithm realization and example proving on the hardware platform. It's quicker and more convenient to locate fault on the circuit boards with this equipment. It's proved that this expert system can solve the problems of high cost and long intervals of maintenance and keep the equipment in a stable status
Fast Factorized Backprojection Algorithm for One-Stationary Bistatic Spotlight Circular SAR Image Formation

In this paper, a fast factorized backprojection (FFBP) algorithm is proposed for the one-stationary bistatic spotlight circular synthetic aperture radar (OS-BSCSAR) data processing. This method represents the subimages on polar grids in the slant-range plane instead of the ground plane, which can be accurately referenced to the tracks of both transmitter and receiver. It can not only accurately accommodate the complicated circular track including the motion error, scene topographic information, large spatial variances and significant range-azimuth coupling of the echo data, but also improve the imaging efficiency compared with the backprojection (BP) algorithm. First, OS-BSCSAR imaging geometry is provided, and then the bistatic BP algorithm for the OS-BSCSAR imaging is derived to provide a basis for the proposed FFBP algorithm. Second, based on the subaperture imaging model, the polar grids for calculating the subimages are defined, and the sampling requirements for the polar grids are derived from the viewpoint of the bandwidth, which can offer the near-optimum tradeoff between the imaging quality and the imaging efficiency. Finally, implementation and computational burden of the proposed FFBP algorithm is discussed, and then the speed-up factor of the proposed FFBP algorithm with respect to the bistatic BP algorithm is derived. Experiment results are given to prove the correctness of the theory analysis and validity of the proposed FFBP algorithm.
New Semi-Supervised Classification Method Based on Modified Cluster Assumption

The cluster assumption, which assumes that “similar instances should share the same label,” is a basic assumption in semi-supervised classification learning, and has been found very useful in many successful semi-supervised classification methods. It is rarely noticed that when the cluster assumption is adopted, there is an implicit assumption that every instance should have a crisp class label assignment. In real applications, however, there are cases where it is difficult to tell that an instance definitely belongs to one class and does not belong to other neighboring classes. In such cases, it is more adequate to assume that “similar instances should share similar label memberships” rather than sharing a crisp label assignment. Here “label memberships” can be represented as a vector, where each element corresponds to a class, and the value at the element expresses the likelihood of the concerned instance belonging to the class. By adopting this modified cluster assumption, in this paper we propose a new semi-supervised classification method, that is, semi-supervised classification based on class membership (SSCCM). Specifically, we try to solve the decision function and adequate label memberships for instances simultaneously, and constrain that an instance and its “local weighted mean” (LWM) share the same label membership vector, where the LWM is a robust image of the instance, constructed by calculating the weighted mean of its neighboring instances. We formulate the problem in a unified objective function for the labeled, unlabeled data and their LWMs based on the square loss function, and take an alternating iterative strategy to solve it, in which each step generates a closed-form solution, and the convergence is guaranteed. The solution will provide both the decision function and the label membership function for classification, their classification results can verify each other, and the reliability of semi-supervised classification learning might be enhanced by checking the consistency between those two predictions. Experiments show that SSCCM obtains encouraging results compared to state-of-the-art semi-supervised classification methods.
Design of joint structure for upper limb exoskeleton robot system

Stroke endangers the health of the elder, sequelae of which bring great harms to patient and rehabilitation training can rebuild injured parts' motor function of brains. These can use multi-functional upper limb rehabilitation robot at a lower cost but in high efficiency. However, today's rehabilitation robots can only do simple move, whose design using a series structure, movement space is not ideal with small stiffness and poor wearability. The paper's purpose is to analyze the design of the articular structure of the exoskeleton robot. First, a summary of the study of upper limb coordination movement in stroke patients were given. Secondly, we discuss the mechanical design of motion-assisted prototypes, exoskeleton and robot movement space based on the human anatomy. Then, a virtual model of the external skeleton robot rehabilitation system is simulated to enhance the effect and interest of the exercise. And the trying to use the EMG signal to control the robot is tested.
VAT competition among China's provincial governments and the regulation suggestions

Since the tax distribution reform implemented in 1994, China has begun general VAT system, and has determined the above as the tax categories shared by both the central government and the local governments. Whereas, “Base Number Technique” sharing is implemented for the VAT revenue between the central and local governments; that is to say, the VAT revenues turned over by the provincial governments are shared by the central and local governments as per the proportion of 75%–25%. The local governments have independent economic benefit, and have strong motivation to perform VAT system, and thereby the VAT revenue increases greatly; however, a new tax competition of VAT is put on the stage. The paper, in the first place, reviews the relevant theories of tax competition, then lists the cross-provincial VAT competition forms in China and analyzes the economic effects caused by VAT competition, and finally, as per the latest theories of the international tax revenue circle, raises some regulation suggestions on the VAT competition behaviors between the provincial governments.
Forward-looking InSAR image pixel-level fusing for unmanned ground vehicle unconstructed field perception

We have present a forward-looking InSAR sensor for Unmanned Ground Vehicle (UGV) to sense the complex unconstructed roads which are challenge environments face by UGVs. The forward-looking InSAR can provide a scattering image, a coherence image and a Digital Terrain Model (DTM) of the same scene ahead the radar during each scan. Each type of image can highlight some unique features of an obstacle and the features are complementary. In the scattering image, a positive obstacle features a strong leading edge and a weak shadow; while a negative obstacle features a weak body and a strong trailing edge. In the coherence image, the weak areas are emphasized and are easy to be segmented. In the DTM, the height profiles of the visible parts of the positive and negative obstacle can be extracted for target discrimination. Pixel-level image fusing results have demonstrated the potential to boost topography mapping fidelity and to improve discrimination performance between positive and negative obstacles.