nils-herrmann/clean_scirepeval huggingface dataset:
Reverse Design and Finite Element Analysis of Tractor Panel Based on CATIA

According to the features of the tractor panel, the tractor panel is redesigned by the method of reverse design. The feature of CATIA, the flow of reverse design and the key technologies of date measurement, date processing and model reconstruction are introduced in this paper. Combing with engineering examples, the CAD modeling of tractor panel is reconstructed by CATIA, the specific reverse design flow of CATIA in the reverse design of tractor panel is described in this paper. The finite element analysis of the surface modeling is carried on, which provides a basis for the structure design of tractor panel.
Fast variable block-size motion estimation algorithm based on merge and slit procedures for H.264/MPEG-4 AVC

This paper proposes and investigates fast variable block-size motion estimation algorithms based on merge and split procedure for H.264/MPEG-4 AVC video encoding. The algorithms take advantage of the correlation of the motion vectors (MVs) of the different block-size modes, to achieve good computation reduction. Experimental results show that the number of search point can be reduced to about 4% of that using full-search motion estimation, with negligible quality degradation.
A new approach for landmine discrimination in SAR images

Landmine detection is an available application of Synthetic Aperture Radar (SAR). According to the double-hump structure of a landmine in SAR images, a new approach is proposed for landmine discrimination with sparse representation. First, the total variation (TV) algorithm is used to denoise. Second, a sub-dictionary is obtained within the narrow limits of the Gabor dictionary. Third, the sparse reconstruction mean square error (MSE) is calculated for classification. The experimental results indicate that the proposed approach is effective for landmine discrimination.
Imaging model of forward-looking ground penetrating radar with split-aperture transmitting configuration

In this paper, a novel antenna configuration for the forward-looking ground penetrating radar is studied. In the novel configuration, the two transmitting antennas are located at two ends of the receiving array, which is referred to as the Split-Aperture Transmitting (SAT) configuration. Firstly, a Modified Back-Projection (MBP) image formation is proposed according to the imaging geometry of the SAT configuration. And then the analytical azimuth resolution expression of the SAT configuration is derived. Based on the analytical form of the azimuth resolution, the relationship of target scattering information between received raw echo and focused imagery is obtained. The theoretic calculation and analysis in this paper are validated by the field data of the forward-looking ground penetrating radar with the SAT configuration developed by the National University of Defense Technology (NUDT).
Performance analysis for T-RN multistatic radar system

Multistatic radar system has a great potential for human detection and tracking for its fine localization precision, wide coverage and good observability. T-RN (one transmitting antenna and N receiving antennas) radar system is the most popular multistatic radar structure. In this paper, the localization performance of T-RN radar is evaluated using Cramér-Rao lower bound (CRLB). The localization precision of different radar topology types and different numbers of receiving antennas are compared. The localization performance is also evaluated by experimental data. The results reveal that more separated receiving antenna configurations and more numbers of receiving antennas have better localization precision.
Functional Module Design of Virtual Maintenance Training and Testing System for a Certain Type of Command and Control System

To enhance the training manintenance and training level of a certain type command and control system, the online virtual maintenance training system based on fault cases were developed. This system utilizes the modular structure. It is composed of the management modules, training modules, test modules and detection modules. By using the Access databases, the data access operation of users, administrators, papers and the operating procedures realize the data interaction with FlashActionScript.
Modeling by numerical reduction of modes for multivariable control of an optical-fiber draw process

Motivated by a need for a method to derive practical and physical-based dynamic models that capture the essential characteristics of an optical-fiber draw process for precision control of diameter uniformity, we extend the Karhunen-Loeve decomposition technique with a Galerkin procedure to derive a reduced-order model (ROM) for a multivariable distributed-parameter system. We validated the ROM derived from a high-fidelity physics-based model by simulating a modern optical-fiber draw process, the numerical solutions for which have been experimentally verified in our earlier studies. Perturbation studies demonstrated that the 24th-order ROM agrees remarkably well with the original nonlinear semi-two-dimensional and quasi-one-dimensional distributed models. We further examine the efficiency of the ROM in the context of a model-based H/sub /spl infin///LQG fiber drawing control system for the regulation of the fiber diameter and tension. The results show that variations in fiber diameter can be reduced significantly by appropriately distributing the number of retained eigenmodes among the physical state variables in the ROM. We also demonstrate that controlling the surrounding air temperature in addition to the draw speed is very effective in regulating both the fiber diameter and tension while simultaneously keeping the draw speed and temperature fluctuations to a minimum. Note to Practitioners-Because of the stringent production requirements (on draw speed, tension, and temperature), diameter uniformity is a challenging distributed control problem in modern fiber production where progressively larger diameter preforms are drawn at higher speeds. The reduced-order model offers an effective way to observe physical variables in a multivariable distributed-parameter system, which may not be physically measured. While developed in the context of fiber diameter control, the modeling techniques presented in this paper are applicable to other material processing systems, such as deposition thickness control in semiconductor wafer manufacturing.
Obstacle sensing using dechirp SAR

Obstacle sensing is one of the most difficult problems in perception for unmanned ground vehicle (UGV) in outdoor environments. The regular obstacle sensors, such as stereo vision, infrared detectors and ladar, have their limited performance in unconstructed environments. In this paper, we used an ultra wideband synthetic aperture radar (SAR) to sense obstacle for UGV. The dechirp method was applied to SAR imaging to reduce the high sample rate and the system storage capacity. The ultra-wideband SAR echo model was built, and the dechirp process was reviewed. Back projection (BP) was adopted to form the two-dimensional complex image. Finally, we have conducted a field experiment on a ground covered by sparse grasses to test the obstacle sensing performance of our ultra wideband SAR, and obstacles have shown special scattering properties in scattering image.
Fault-based testing in the absence of an oracle

Although testing is the most popular method for assuring software quality, there are two recognized limitations, known as the reliable test set problem and the oracle problem. Fault-based testing is an attempt by Morell to alleviate the reliable test set problem. In this paper, we propose to enhance fault-based testing to address the oracle problem as well. We present an integrated method that combines metamorphic testing with fault-based testing using real and symbolic inputs.
Research and practice on disaster preventive scheme management

This paper presents a disaster preventive scheme, which is based on the specific circumstances of Tonglu county. The scheme includes three-tier plans among county, township and villages. The system architecture uses the model of Browser/Server, which only need be deployed on server and the client could easily perform its plans, updating and maintenance without installation of any other software. Hibrante in the framework and MySQL in database, this architecture is safe, reliable and convenient. The authorized officers of FCDRH could operate the system on computer at any place where the Internet is available to be connected.
Efficient Optimization of Performance Measures by Classifier Adaptation

In practical applications, machine learning algorithms are often needed to learn classifiers that optimize domain specific performance measures. Previously, the research has focused on learning the needed classifier in isolation, yet learning nonlinear classifier for nonlinear and nonsmooth performance measures is still hard. In this paper, rather than learning the needed classifier by optimizing specific performance measure directly, we circumvent this problem by proposing a novel two-step approach called CAPO, namely, to first train nonlinear auxiliary classifiers with existing learning methods and then to adapt auxiliary classifiers for specific performance measures. In the first step, auxiliary classifiers can be obtained efficiently by taking off-the-shelf learning algorithms. For the second step, we show that the classifier adaptation problem can be reduced to a quadratic program problem, which is similar to linear $({\rm SVM}^{\rm perf})$ and can be efficiently solved. By exploiting nonlinear auxiliary classifiers, CAPO can generate nonlinear classifier which optimizes a large variety of performance measures, including all the performance measures based on the contingency table and AUC, while keeping high computational efficiency. Empirical studies show that CAPO is effective and of high computational efficiency, and it is even more efficient than linear $({\rm SVM}^{\rm perf})$.
Experimental study on the behavior of steel-concrete composite arch ribs to combined bending and compression

In order to achieve accelerated and less expensive construction for arch bridges, optimized steel composite sections with variations of material and dimensions were proposed for the arch ribs. The proposed arch rib is made of fabricated steel tubular section with concrete fill or reinforced concrete topping based on stress distribution. An important advantage of this composite section is that a base arch frame of steel tubular section can be erected vertically and rotated to closure in limited workspace and within a short period. A particular composite with hollow steel box and reinforced concrete topping is used for combined compression and positive bending in the mid-span region, which is quarter-spans from the crown, to reduce the self-weight and cost. An in-depth understanding of the load carrying characteristics theoretically and experimentally can be very crucial for the design optimization. The objective of this research is to study experimentally the behavior, failure mechanism and performance of this composite section. The test includes the simulation of pure bending with lateral load, and pure compression with central axial loading as well as combined compression and bending with eccentric axial loading. This paper presents the experimental results of a series of test specimen tested under different load conditions. The results demonstrated that the proposed composite section can meet both strength needs and performance requirements. The composite section achieved its ultimate design strength with fully developed concrete compressive strength and steel tensile strength, without premature failures such as crushing or cracking of concrete or buckling of steel.
Evaluation and simulation of detection effectiveness of airborne early warning radar

Detection effectiveness of the airborne early warning radar is affected by its detection range as well as its detection probability. This paper presents a method for simulating and evaluating the detection effectiveness of the airborne early warning radar. Firstly, the detection model of the airborne early warning radar is designed. Secondly, based on the radar system parameter as well as the motion parameters of the airplane and target, the detection effectiveness of the early warning radar is simulated and displayed dynamically. Finally, the detection effectiveness of the early warning radar is validated based on the simulated data. The simulation results show that the proposed method can effectively simulate the detection effectiveness of the airborne early warning radar.
Aspect-invariant feature extraction and associated landmine detector in UWB SAR

Vehicle- or air-borne ultra-wide band synthetic aperture radar (UWB SAR) can perform landmine detection over large areas, where too many false alarms is the major problem for UWB SAR application in practice. In this paper, landmine aspect-invariant feature extraction using the space-wavenumber distribution (SWD) and its associated support vector machine (SVM) with a hypersphere classification boundary are proposed in landmine detection procedure. The proposed methods have been tested by the real data collected with the rail-GPSAR system
Evolution of Cathodic Arc Roots in a Large-Scale Magnetically Rotating Arc Plasma

The behavior of the cathode arc root is one of the most fundamental phenomena associated with arc discharges. The formation of isolated multiple cathodic roots and the transition from discrete spots to a diffuse cathodic attachment in an argon arc plasma at 1 atm have been observed with short exposure time ICCD imaging in our attempt to generate a uniform large-area plasma, using magnetically rotating arc plasma.
Carbon-Aware Online Control of Geo-Distributed Cloud Services

Recently, datacenter carbon emission has become an emerging concern for the cloud service providers. Previous works are limited on cutting down the power consumption of datacenters to defuse such a concern. In this paper, we show how the spatial and temporal variabilities of the electricity carbon footprint can be fully exploited to further green the cloud running on top of geographically distributed datacenters. Specifically, we first verify that electricity cost minimization conflicts with carbon emission minimization, based on an empirical study of several representative geo-distributed cloud services. We then jointly consider the electricity cost, service level agreement (SLA) requirement, and emission reduction budget. To navigate such a three-way tradeoff, we take advantage of Lyapunov optimization techniques to design and analyze a carbon-aware control framework, which makes online decisions on geographical load balancing, capacity right-sizing, and server speed scaling. Results from rigorous mathematical analysis and real-world trace-driven evaluation demonstrate the effectiveness of our framework in reducing both electricity cost and carbon emission.
The transient stability analysis based on WAMS and online admittance parameter identification

With the scale of interconnected power grid expanding, the topology of fault network and admittance parameters become unavailable, which makes it difficult for line personnel in power system to do transient stability analyses. Aiming to solve the problem, this paper proposed the multivariate linear regression algorithm (multiple linear regression, the MLR) to identify the node admittance parameters of power system based on the data of the WAMS (wide area measurement system, the WAMS). Firstly, the MLR algorithm was used to identify the pre-fault and after-fault admittance parameters; after that, the transient energy function (transient energy function, TEF) was applied to solve the energy function of the system, CCT (critical clearing time, CCT) could be obtained ultimately. Simulation results show that the presented algorithm can simultaneously consider specific failure type of the power system and uncertain network topology structure; Furthermore, the calculation precision could get relatively higher as the admittance parameters' calculating procedures get simplified when the algorithm is applied into TEF method to analyze the transient stability. When the algorithm applied in TEF method to analyze the transient stability, the calculation precision gets relatively higher and the admittance parameter calculating procedure gets simplified.
Adaptive Blocking Artifacts Reduction Algorithm Based on DCT-Domain

Block-based DCT compression methods usually result in discontinuities called blocking artifacts at the boundaries of blocks due to the coarse quantization of the coefficients. In order to save more computations, an algorithm directly based on DCT-domain is proposed. It reduces the blocking artifacts as much as possible and preserve edge adequately as well. The algorithm presents a specific and convenient edge detection criterion. For the non-edge blocks, one-dimension DCT is used on each row of two adjacent blocks and the shifted block, and then the transform coefficients of the shifted block are replaced by adaptively weighted average of three blocks coefficients. For the edge blocks, Sigma filter is used to smooth the block boundaries. Simulation results show that the proposed reducing blocking artifacts algorithm outperforms the existing algorithms
Improving Software Testing Cost-Effectiveness through Dynamic Partitioning

We present a dynamic partitioning strategy that selects test cases using online feedback information. The presented strategy differs from conventional approaches. Firstly, the partitioning is carried out online rather than off-line. Secondly, the partitioning is not based on program code or specifications; instead, it is simply based on the fail or pass information of previously executed test cases and, hence, can be implemented in the absence of the source code or specification of the program under test. The cost-effectiveness of the proposed strategy has been empirically investigated with three programs, namely SPACE, SED, and GREP. The results show that the proposed strategy achieves a significant saving in terms of total number of test cases executed to detect all faults.
Condition monitoring of power transformers with neural networks

In this paper, a neural network technique is applied to a system named NEUCOMS that is designed for the condition monitoring of power transformers. Through employing paired neural networks, NEUCOMS has the ability of analyzing data of electrical inspections as well as data of the chromatogram of oil-dissolved gases. It utilizes redundant input attributes to speed the training and reduce the size of the neural networks. Moreover, it exploits fuzzy techniques to preprocess the input data so that features with small values will not be blocked off by features with big values. Experiments show that NEUCOMS works well in real-world situations.
Design Considerations of Miniaturized Least Dispersive Periodic Slow-Wave Structures

Slow-wave structures using distributed periodic inductive and capacitive loadings have found many microwave circuit applications as left-handed (bandpass) or right-handed (low-pass) transmission lines. A large slow-wave factor (SWF) could result in a much smaller passive component, but also a much lower bandgap (cutoff) frequency and a larger dispersion. This paper addresses the issues and the design tradeoff between the SWF, group delay (dispersion), and the cutoff frequency of a right-handed (low-pass) quasi-lumped transmission line. A new two-layer transmission line structure using 3-D substrate metallization with an SWF of 5.8 is designed. A prototype of a 3-GHz branch-line coupler with a 70% size reduction using such a transmission line structure is fabricated and tested.
Moving target focusing and parameter estimation based on ROI of multi-channel UWB SAR images

Image domain GMTI methods are very suitable for the multi-channel UWB SAR system. However, present image domain GMTI methods are mainly on the moving target detection and 1D range velocity estimation, but seldom refer to moving target focusing and 2D velocity/position parameter estimation. This paper proposes a new method for moving target focusing and parameter estimation based on the ROI of multi-channel UWB SAR images. As the consequent step after GMTD of image domain, the proposed method firstly focuses the moving target and estimates its relative velocity based on its ROI. Secondly, the method estimates the range velocity of the moving target based on its focused multi-channel images. Finally, the method estimates other parameters, including azimuth velocity, azimuth position and range position. The method has been proved to be valid based on multi-channel UWB SAR data.
The CRLB analysis for target localization behind wall and in free space

In through-the-wall target localization area, wall effect including absorption and refraction will arise, wall effect needs to be compensated. If wall parameters including thickness and dielectric constant are exactly known, the target can be compensated to its correct position. In this paper, based on the transmitting model of through-the-wall radar, we deduced the Cramer-Rao lower bound (CRLB) of target localization behind wall. Due to the wall effect, the target localization CRLB behind wall is better than that in free space. We also validated our conclusions using wavenumber domain analysis. Simulation results evaluated the effect of wall thickness and dielectric constant on CRLB qualitatively.
Lightning risk assessment method of transmission line using distributed traveling wave monitoring

Lightning current parameters, type and location of lightning fault are key factors for making a lightning risk assessment of transmission line scientifically. An assessment method is proposed on the basis of differentiated lightning protection ideology using distributed traveling wave monitoring, which treats lightning risk with more details and is employed in an actual 500kV transmission line. It is suggested that back striking risk is much higher than shield failure. Besides, the assessment result also provides guidance for lightning protection alteration in adopting measures.
Corrections to "Transforming Traditional Iris Recognition Systems to Work in Nonideal Situations" [Aug 09 3203-3213]

The authors of the above titled paper (ibid., vol 56, no. 8, pp. 3203-3213, Aug. 09) point out that the credits for Figs. 14 and 17 were missing in the final print version. These two figures were edited from the figures in another paper ) and used to introduce the background of the Multimodal Biometrics Grand Challenge organized by the National Institute of Standards and Technology.
Flaws identification in ultrasonic testing based on evidential reasoning rule

In order to solve the problem that the accuracy of flaws identification in ultrasonic testing is not high enough due to the error in sensor information acquisition and the noise interference in the detection environment, a method of flaws identification in ultrasonic testing based on evidential reasoning rule (ER rule) is studied. Firstly, ER rule is proposed to consider the reliability and weights factors of evidence. Secondly, the method of flaws identification in ultrasonic testing is proposed, which mainly includes ultrasonic flaws feature extraction, reliability factors acquisition, identification features fusion and evidence weights optimization. Finally, the flaws data of a composite material are obtained by means of ultrasonic testing, and the proposed method is verified. The results show that this method can overcome the influence of evidence reliability on flaws identification, and improve the accuracy rate compared with the other methods.