nils-herrmann/clean_scirepeval huggingface dataset:
Mechanical Analysis of High-Strength Bolts Based on Node Fatigue Test

Chongqing Chaotianmen Yangtze Bridge with the main span of 552m is the steel truss continuous tied arch bridge. Track cross beams of the lower layer and nodes of main truss are connected by high-strength bolts. To verify fatigue security performance of this node connection, large-scale model of 1/2 is applied in node fatigue test. In the 2 million fatigue tests during design life, the high-strength bolts at node connection don't loose. In the next 850 thousand failure tests, the high-strength bolts at node connection loose and fail. Based on the calculation of prying force of connective component, the prying force is 34%-38% of bolt tension. Then by the further calculation of internal force of the high-strength bolt's connection, the high-strength bolts fail when the external load are 2.5 times of design fatigue load amplitude, which coincides with experimental results. At last, summarize the failure reason and inductive failure law of high-strength bolts of this kind node connection, and provide some reference for the bridge design and bridge monitoring in the future.
RFID antenna size minimization using double-layer periodic structures

In this paper, we explored the possibility of using multilayer periodic structures to reduce the RFID antenna size while maintaining the required performance. Specific designs are laid out with two double-layer structures with L-C and L-L setups, respectively. As compared to a normal single-layer dipole, the new structures results in large size reductions while maintaining the self resonance and self tuned features. With the meander line shaping, the double- layer dipoles can be easily reduced by 50% without losing the performance requirements.
A method for extracting InSAR image features of negative and positive obstacles

Radar sensors have received more and more interest for unmanned ground vehicle to sense positive and negative obstacles in unstructured environments or out fields, especially on negative obstacle. In this paper, we present an approach for extracting the features of obstacles from radar images. Based on interferometric synthetic aperture radar (InSAR) images focused by the back-projection (BP) algorithm, range compensation, speckle filtering and threshold segmentation are performed. And morphological operations are used to perform some simple connectivity filtering to smooth the image and remove spurious pixels. Finally, feature fusion is applied to the amplitude and the correlation coefficient images. Both the theoretical analysis and the experimental results indicate that the proposed method is an efficient method.
Ensembling local learners ThroughMultimodal perturbation

Ensemble learning algorithms train multiple component learners and then combine their predictions. In order to generate a strong ensemble, the component learners should be with high accuracy as well as high diversity. A popularly used scheme in generating accurate but diverse component learners is to perturb the training data with resampling methods, such as the bootstrap sampling used in bagging. However, such a scheme is not very effective on local learners such as nearest-neighbor classifiers because a slight change in training data can hardly result in local learners with big differences. In this paper, a new ensemble algorithm named Filtered Attribute Subspace based Bagging with Injected Randomness (FASBIR) is proposed for building ensembles of local learners, which utilizes multimodal perturbation to help generate accurate but diverse component learners. In detail, FASBIR employs the perturbation on the training data with bootstrap sampling, the perturbation on the input attributes with attribute filtering and attribute subspace selection, and the perturbation on the learning parameters with randomly configured distance metrics. A large empirical study shows that FASBIR is effective in building ensembles of nearest-neighbor classifiers, whose performance is better than that of many other ensemble algorithms.
On the Effectiveness of Sampling for Evolutionary Optimization in Noisy Environments

In real-world optimization tasks, the objective (i.e., fitness) function evaluation is often disturbed by noise due to a wide range of uncertainties. Evolutionary algorithms are often employed in noisy optimization, where reducing the negative effect of noise is a crucial issue. Sampling is a popular strategy for dealing with noise: to estimate the fitness of a solution, it evaluates the fitness multiple () times independently and then uses the sample average to approximate the true fitness. Obviously, sampling can make the fitness estimation closer to the true value, but also increases the estimation cost. Previous studies mainly focused on empirical analysis and design of efficient sampling strategies, while the impact of sampling is unclear from a theoretical viewpoint. In this article, we show that sampling can speed up noisy evolutionary optimization exponentially via rigorous running time analysis. For the (11)-EA solving the OneMax and the LeadingOnes problems under prior (e.g., one-bit) or posterior (e.g., additive Gaussian) noise, we prove that, under a high noise level, the running time can be reduced from exponential to polynomial by sampling. The analysis also shows that a gap of one on the value of for sampling can lead to an exponential difference on the expected running time, cautioning for a careful selection of . We further prove by using two illustrative examples that sampling can be more effective for noise handling than parent populations and threshold selection, two strategies that have shown to be robust to noise. Finally, we also show that sampling can be ineffective when noise does not bring a negative impact.
Semi-supervised Learning by Disagreement

In real-world applications, assigning labels to examples usually requires human effort and therefore, labeled training examples are expensive; unlabeled training examples, however, are cheap and abundant. As a consequence, semi-supervised learning which attempts to exploit unlabeled data to help improve learning performance has become a very hot topic in machine learning and data mining. In this talk, I will introduce some of our research advances in disagreement-based semi-supervised learning, a paradigm covers a broad range of algorithms and has been successfully applied to many real tasks such as statistical parsing, noun phrase identification, image retrieval, etc.
Facial Age Estimation by Learning from Label Distributions

One of the main difficulties in facial age estimation is that the learning algorithms cannot expect sufficient and complete training data. Fortunately, the faces at close ages look quite similar since aging is a slow and smooth process. Inspired by this observation, instead of considering each face image as an instance with one label (age), this paper regards each face image as an instance associated with a label distribution. The label distribution covers a certain number of class labels, representing the degree that each label describes the instance. Through this way, one face image can contribute to not only the learning of its chronological age, but also the learning of its adjacent ages. Two algorithms, named IIS-LLD and CPNN, are proposed to learn from such label distributions. Experimental results on two aging face databases show remarkable advantages of the proposed label distribution learning algorithms over the compared single-label learning algorithms, either specially designed for age estimation or for general purpose.
On arbitrating the power-performance tradeoff in SaaS clouds

In this paper, we present an analytical framework for characterizing and optimizing the power-performance tradeoff in Software-as-a-Service (SaaS) cloud platforms. Our objectives are two-fold: (1) We maximize the operating profit when serving heterogeneous SaaS applications with unpredictable user requests, and (2) we minimize the power consumption when processing user requests. To achieve these objectives, we take advantage of Lyapunov Optimization techniques to design and analyze an optimal control framework to make online decisions on request admission control, routing, and virtual machine (VMs) scheduling. In particular, our control framework can be flexibly extended to incorporate various design choices and practical requirements of a data-center in the cloud, such as enforcing a certain power budget for improving the performance (dollar) per watt. Our mathematical analyses and simulations have demonstrated both the optimality (in terms of a cost-effective power-performance tradeoff) and system stability (in terms of robustness and adaptivity to time-varying and bursty user requests) achieved by our proposed control framework.
Real time fault monitoring and diagnosis method for power grid monitoring and its application

In Energy Internet mode, a large number of alarm information is generated when equipment exception and multiple faults in large power grid, which seriously affects the information collection, fault analysis and delays the accident treatment for the monitors. To this point, this paper proposed a method for power grid monitoring to monitor and diagnose fault in real time, constructed the equipment fault logical model based on five section alarm information, built the standard fault information set, realized fault information optimization, fault equipment location, fault type diagnosis, false-report message and missing-report message analysis using matching algorithm. The validity and practicality of the proposed method by an actual case was verified, which can shorten the time of obtaining and analyzing fault information, accelerate the progress of accident treatment, ensure the safe and stable operation of power grid.
Polarimetric characteristics analysis of interior structures of a building in through-the-wall radar imaging

Motivated by the desire to obtain the interior structures and layouts of buildings, we have conducted several preliminary through-the-wall imaging experiments and collected real building penetrating data on a simple room. The aperture data measurements are taken using a network analyzer. Two horn antennas are mounted on a 3D scanner that moves the antenna to obtain a synthetic aperture. This paper provides polarimetric characteristics analysis and two kinds of polarization imaging results (VV and HV). The phenomenological results obtained in this paper can be used as reference in order to optimize the performance of through-the-wall synthetic aperture radar imaging systems and identify building interior structure. (5 pages)
Study on risk early warning system of asphalt concrete pavement design

There are a lot of uncertainty risk factors in the design of highway asphalt concrete pavement. The design is affected not only by the various uncertain factors of the design phase, such as reliability, and pavement construction, but also of using stage, such as vehicle overloading etc. Being traditional pavement design theory not availably considering the uncertain risk factors, so it is resulted the quality risk of asphalt concrete pavement design. A large number of cases of early damage of asphalt pavement, by the uncertainty risk factors, is the exemplification. On the basis of the risk characteristics of the asphalt concrete pavement design and the risk management theory, the paper has put forward the early risk warning system in the design of highway asphalt concrete pavement, and has structured the early warning index system. And then, studied and proposed the risk warning threshold and the warning limit of the asphalt concrete pavement design. Based on extension theory, established the early risk warning model of the asphalt concrete pavement design, and illustrated the specific application of the model. Finally, briefly introduce the early risk warning computer system. With the early risk warning system, the risk warning state of particular design can be evaluated overall and systematically. So, according to the warning state of design quality risk, it is able to guide the designers to modify or redesign, as well as optimizate the original design results. By this procedure, it is realized to improve the existing design theory and reduce the quality risk of asphalt concrete pavement design.
Dynamic distribution network reconfiguration considering travel behaviors and battery degradation of electric vehicles

Increasing remote-controlled switches (RCSs) in the distribution network enhances not only the restoration capability, but also the flexibility to cope with the uncertainties of renewable generation and variable loads through dynamic reconfiguration. However, the switching frequency remains a significant concern for system stability. In this paper, we investigate the potential benefits of reducing switching frequency by coordinating electric vehicles (EVs) with distribution network operation, considering EVs' daily travel patterns and battery degradation cost. The problem is formulated as mixed-integer quadratic programming (MIQP). Testing our model on the IEEE 33-node test feeder, we find that EVs can potentially reduce the frequency of switching while maintaining desirable system performance. In addition, battery degradation cost, incurred by vehicle-to-grid (V2G) services, is only a small portion of the total system operational cost.
Extraction of micro-Doppler signal based on the combination of CLEAN and L-statistics method

A micro-Doppler signal extraction method base on the combination of the CLEAN algorithm and the L-statistics algorithm is proposed. L-statistics algorithm is adapted to estimate the parameter of the local polynomial Fourier transform (LPFT) process. The LPFT process is used to compensate the bulk motion of the target. And then a CLEAN method is used to remove the fuselage echo. Comparing to the L-statistic algorithm, the proposed method can get rid of the fuselage interference without influence on the micro-Doppler part. A simulated data containing fuselage echo, micro-Doppler echo and noise signal is given to verify the validity of the proposed method.
Query-Sensitive Similarity Measure for Content-Based Image Retrieval

Similarity measure is one of the keys of a high- performance content-based image retrieval (CBIR) system. Given a pair of images, existing similarity measures usually produce a static and constant similarity score. However, an image can usually be perceived with different meanings and therefore, the similarity between the same pair of images may change when the concept being queried changes. This paper proposes a query-sensitive similarity measure, Qsim, which takes the concept being queried into account in measuring image similarities, by exploiting the query image as well as the images labeled by user in the relevance feedback process. Experimental comparisons to state-of-the-art techniques show that Qsim has superior performance.
Adaptive Differential Evolution With Sorting Crossover Rate for Continuous Optimization Problems

Differential evolution (DE) is one of the best evolutionary algorithms (EAs). The effort of improving its performance has received great research attentions, such as adaptive DE (JADE). Based on the analysis on the aspects that may improve the performance of JADE, we introduce a modified JADE version with sorting crossover rate (CR). In JADE, CR values are generated based on mean value and Gaussian distribution. In the proposed algorithm, a smaller CR value is assigned to individual with better fitness value. Therefore, the components of the individuals, which have better fitness values, can appear in the offspring with higher possibility. In addition, the better offspring generated from last iteration are supposed to have better schemes, hence these schemes are preserved in next offspring generation procedure.  The experiments results with several excellent algorithms show the effectiveness of JADE_sort.
FMCW radar near field three-dimensional imaging

A system with 3-D imaging capability can be implemented by using a frequency modulated continuous wave (FMCW) radar which synthesizes a two-dimensional (2-D) planar aperture. A millimeter-wave FMCW three-dimensional (3-D) imaging system can be used for the detection of concealed weapons and contrabands at airports or other security checkpoints, since millimeter-wave can readily penetrate common clothing material. A 3-D image can be formed by coherently integrating the backscatter data over the measured frequency bandwidth and the two spatial coordinates of the 2-D synthetic aperture. This paper presents a 3-D imaging algorithm for near field FMCW radar. This algorithm is an extension of the 2-D range migration algorithm (RMA). We derive the formulation in detail by using the principle of stationary phase (POSP). A 3-D version Stolt interpolation is used in this algorithm. Accurate image reconstruction and high computational efficiency of this algorithm are demonstrated through simulation results.
A comprehensive sciera image quality measure

Sciera recognition can be used for human identification. However, if the sciera images cannot be properly segmented by the system or the images of sciera patterns are defocused and/or saturated, it can significantly affect the accuracy of sciera recognition. In this paper, we propose a comprehensive sciera image quality measure which can quickly detect if the image has a valid eye1, assess the image quality, evaluate the segmentation accuracy, and measure if the image has sufficient feature information for recognition. In addition, it used Dempster Shafer Theory to fuse the quality score, segmentation score, and feature score together to generate the overall combination score. It is empirically verified using the UBIRIS database that the proposed quality measure is highly correlated with the performance of sciera recognition.
Transforming Traditional Iris Recognition Systems to Work in Nonideal Situations

Under a nonideal situation, the image quality may vary. As a result, the traditional iris recognition systems would not work well. However, these kinds of iris recognition systems have been widely deployed in law enforcement and homeland security. It will be desirable to transform the traditional systems to perform in nonideal situations without a costly update. In this paper, we propose a method that upgrades the traditional iris recognition system to work on nonideal situations. The proposed method takes into consideration not only the effect of image quality but also the segmentation accuracy. It employs video-based image-processing techniques to quickly identify and eliminate the bad quality images from iris videos for further processing. The proposed method is tested on public databases using in-house recognition algorithms and also evaluated using a commercialized system. The research results show that the proposed methods can be used to improve the performance of iris recognition systems in a nonideal situation.
Design of Dynamic Measurement System of Torque Based on MSP430

Based on rotation angle measurement function by photo-electricity multiple code-tracks technology, proposed a improved method of torque measurement by photoelectric technology. With MSP430 process the signal of the encoder. The encoder output E signals with a fixed difference phase, So each count cycle were subdivided into E by the rest of the E-1 signals, have Estates. Using processing unit I/O ports read the state of these signals when interrupt of Timer, Calculate the state and the torque, which sub-division of the relative rotation angle, improved torque measurement accuracy. And the torque measurement of real-time signal was transmissed to the host computer for processing and displaying, analysis the torque signal of the vibration.
A 3D Reconstruction Strategy of Vehicle Outline Based on Single-Pass Single-Polarization CSAR Data.

In the last few years, interest in circular synthetic aperture radar (CSAR) acquisitions has arisen as a consequence of the potential achievement of 3D reconstructions over 360° azimuth angle variation. In real-world scenarios, full 3D reconstructions of arbitrary targets need multi-pass data, which makes the processing complex, money-consuming, and time expending. In this paper, we propose a processing strategy for the 3D reconstruction of vehicle, which can avoid using multi-pass data by introducing a priori information of vehicle's shape. Besides, the proposed strategy just needs the single-pass single-polarization CSAR data to perform vehicle's 3D reconstruction, which makes the processing much more economic and efficient. First, an analysis of the distribution of attributed scattering centers from vehicle facet model is presented. And the analysis results show that a smooth and continuous basic outline of vehicle could be extracted from the peak curve of a noncoherent processing image. Second, the 3D location of vehicle roofline is inferred from layover with empirical insets of the basic outline. At last, the basic line and roofline of the vehicle are used to estimate the vehicle's 3D information and constitute the vehicle's 3D outline. The simulated and measured data processing results prove the correctness and effectiveness of our proposed strategy.
Gabor Descriptor based cancelable iris recognition method

For positive human identification, the iris has been shown to be a stable and reliable biometric. However, it is permanently bounded with an individual and its pattern is very hard to reissue when “stolen”. Thus, cancelable biometrics is a desirable solution to ensure the revocability and alternative of an iris template when compromised. The traditional cancelable biometrics approaches often compromise the recognition accuracy. In this paper, we propose a new cancelable iris recognition method using Gabor Descriptor, which can not only achieve “cancelability”, but also increase the recognition accuracy.
Topologies of Switch-Linear Hybrid Power Conversion & Special Operation States

Typical topologies of switch-linear hybrid (SLH) power converter are summed up by former research practices. The main frame, hybrid criteria, category classification and extension regulation for the development of SLH-topology are presented by practical examples. Different operation states by different devices are also discussed for applying the topologies reasonably. All the above will set up the foundation of the SLH to develop into a complete self-family in power conversion branches and obtain wide applications
Traffic Signal Optimization with Vehicles Queue and the Number of Pedestrians Non-Complying at Single Intersection

In urban traffic signal optimization, the length of vehicle queue, waiting for green signal, is regarded as a performance index (PI). However, pedestrians are reluctant to waiting for green signal without person specially assigned to manage pedestrians in developing city, such as Xi'an. To the signal optimization problem of domestic urban traffic with pedestrians' noncompliance, a hybrid optimization algorithm is developed. The PI includes the length of vehicles queue and the number of pedestrians breaching signal. The number of pedestrians breach is estimated by a Monte Carlo method. The simulations are conducted in the condition of heavy traffic demands. The validation results indicate that the proposed method is able to improve the traffic condition for weak colony compared with the accustomed control method.