nils-herrmann/clean_scirepeval huggingface dataset:
Integration of Web2.0, Panorama Virtual Reality and Geological Information System

In filed geological observation, field outcrop reflects the lithology, the stratum attitude, the diastrophism and the mineral resource directly or indirectly. It has great significance on geological research. It is also the most important data in Geological Information System. However, there are some disadvantages in current system such as bad visibility and weak User Experience, etc. Because the field outcrop data is so complex and the information quantity is very large, the refreshing speed of webpage of the Geological Information System is always slow. Aiming at above problem, the author proposes an integration of Web2.0, panorama Virtual Reality and Geological Information System. As a demonstration, the author designs and develops a 360°panorama field outcrop Geological Information System for the field teaching base of Jilin University of XingCheng in Liaoning Province. The integration solution provides technology support for the application of the technology of Web2.0 and panorama Virtual Reality to the Geological Information System.
Distributional Features for Text Categorization

Text categorization is the task of assigning predefined categories to natural language text. With the widely used 'bag of words' representation, previous researches usually assign a word with values such that whether this word appears in the document concerned or how frequently this word appears. Although these values are useful for text categorization, they have not fully expressed the abundant information contained in the document. This paper explores the effect of other types of values, which express the distribution of a word in the document. These novel values assigned to a word are called distributional features, which include the compactness of the appearances of the word and the position of the first appearance of the word. The proposed distributional features are exploited by a tf idf style equation and different features are combined using ensemble learning techniques. Experiments show that the distributional features are useful for text categorization. In contrast to using the traditional term frequency values solely, including the distributional features requires only a little additional cost, while the categorization performance can be significantly improved. Further analysis shows that the distributional features are especially useful when documents are long and the writing style is casual.
An online reliability evaluation method based on evidential reasoning for space relay

Current reliability assessment approaches targeted at dynamic systems suffer from the challenge of uncertainties. This paper, we proposed a new online reliability evaluation method which based on evidential reasoning, which can integrate both historic and present status information to online assess the reliability of space relay under uncertainties. Firstly, the space relay is analyzed and the characteristic parameters which can reflect the health status are selected as reliability data. Secondly, the parameter data are collected and updated online weights using the variation-based weighting method which are further normalized. Thirdly, the appropriate forgetting factors are determined with the consideration of the influences on the assessment result from both historic and present data in the system operation process. Finally, a fusion scheme based evidential reasoning algorithm is applied to integrate the historic and present reliability data which are online monitored, and the system's present health status is then assessed. A case study for space relay reliability evaluation is examined to corroborate the effectiveness of the presented method. The result shows that the reliability of the space relay can be precisely evaluated and the presented method has also proved superior property in assessing the health state of dynamic systems.
Fault diagnosis based on nonlinear dynamic modeling in rolling element bearing systems

In order to diagnose the location and size of fault in rolling element bearing system accurately, two-DOF dynamic model and six-DOF dynamic model of fault bearings are built and compared with each other based on the generation mechanism of bearing fault. The models not only consider the influencing factors of variable stiffness caused by bearing clearance, but also introduce a unit resonator to simulate high frequency inherent vibration arising with the bearing fault in sixDOF dynamic model. Dynamic models of localised fault on outer race, inner race and rolling element of rolling element bearing are simulated and analyzed by using Runge-Kutta method. The simulating results are in accordance with experimental results, which proves the accuracy and practicality of the models used in engineering application.
Through-the-wall imaging and correction based on the estimation of wall parameters

Due to the refraction and multi-reflection of electromagnetic wave spread caused by the wall, the target behind the wall will become displaced and unfocused by using traditional imaging methods. In this paper, an approach of through-the-wall imaging and correction is adopted. To begin with, wall reflection is modeled and the estimation range of wall parameters is acquired. According to wall transmission model, compensation coefficients are then constructed with numerical values within the estimation range. Finally, real values of wall parameters are selected through the refocusing degree of compensated images. Simulation and experiment results demonstrate that, the proposed algorithm is able to realize precise target imaging and wall parameters estimation simultaneously when antenna array is 12.6m away from the wall.
Using an Ensemble Classifier on Learning Evaluation for E-learning System

Learning evaluation is an important part of cyber learning. Research studies aiming to increase the accuracy of performance evaluation in E-learning employ data mining technique. Here we describe a scheme for integrating classification algorithms that have been created by a machine learning method, trained on the real data set. The ensemble classifiers combine these classifiers, decision trees, neural networks, naive Bayesian into a single module and uses majority voting to fusion the output label. In this paper we discuss the theory behind the ensemble architecture, and present its implementation and a set of experiments using a variety of data sets. According to 10-fold cross validation bagging increases significantly the performance of every one classifier. Our work shows how the ensemble classifier performs remarkably for a specific data set on the application of E-learning evaluation system.
Energy Saving Potential of Public Building in Jiangbei District of Chongqing

Jiangbei belongs to Metropolitan Advanced Economic Sphere of Chongqing so its character of energy consumption can be very typical in Chongqing. A energy consumption investigation of 72 public buildings was organized by People's Local Congress in Jiangbei. Based on the investigation data and information coming from energy audit and site measurement for 14 public buildings, charcater of energy consumption for six diffrenet types of public building is summed up, and problems and its solutions of six energy consumption systems of public building are analysed also. It was pointed out that electricity is the main form of building energy and building electircity consumption was more than 50% of total electricity consumption in Jiangbei. Also, electricity consumption of public building, among which energy for heating, air conditioning and lighting can be half, is nearly 50% of building electricity consumption. At last, energy saving potential of public building in Jiangbei is evaluated. It is determined that if 289.3 million yuan is invested in energy saving retrofit project of public bulding in Jiangbei, it will create 9,640 work opportunities a year, drive 723.3 million yuan output of other industries and reduce 161,629 tons of CO2 emission. energy investigation; public building; energy saving potential;
A Novel Method of Landmines Detection Based on Improved SVM

To detect landmines in real SAR images, detection process based on PCA and SVM techniques is proposed in this paper. Firstly, a referential deflection-optimal linear-quadratic(DOLQ) detector is presented. Then, the proposed centering PCA+SVM method is developed and compared with the above benchmark. A morphological processing procedure is used to remove the isolated spurious pixels and to form landmine objects in the binary images. Finally, the experimental results show that the novel method can constantly outperform the DOLQ detector in terms of POD(probility of detection) and the SVM method in terms of training and classification times in landmine detection.
Exploration on the Cracking Resistance of Steel-Concrete Composite Beams with Perforated Stiffening Rib Plates

To prevent or limit flexural cracking in the negative moment region normally with a spiky rise and rapid reversal, a stiffened steel-concrete composite section (SCPR) was proposed The proposed section was composed of concrete and steel plate with perforated stiffening rib plate to reduce the dependency of shear transfer on interfacial bond. Eight scale specimens were tested for comparison. Six specimens were reinforced using steel plate with perforated stiffening rib plates, one specimen was reinforced using conventional reinforcing bars and one specimen was reinforced using epoxy-bonded steel plate for comparison. All beam specimens were tested to failure. The test results indicated the steel plate with rib stiffeners can offer a pronounced improvement to the cracking resistance of the concrete. The cracking load for the SCPR specimen with stiffening rib plate is three times higher than the specimen reinforced with conventional steel, and two and a half times higher than specimen reinforced with epoxy-bonded steel plate. The cracking mechanism of the SCPR beams was discussed in the paper.
A Double Layer BRB Model for Health Prognostics in Complex Electromechanical System

The health of a complex electromechanical system is dynamic and is accompanied by a full life cycle. Due to the complexity and coupling of complex electromechanical systems, the establishment of a dynamic and accurate model for the health state is difficult. A belief rule base (BRB) shows outstanding performance in modeling complex systems because it can combine both quantitative information and expert knowledge. In this paper, a double-layer BRB model is proposed to predict the health state of a complex electromechanical system. The two layers achieve different functions: BRB_layer1 is used to establish the dynamic change of the time series of features, BRB_layer2 is employed to combine the features for predicting the health state of the complex electromechanical system. During this process, the infinite irrelevance method is utilized for feature selection in reducing the scale of the BRB model. Considering the initial parameters are given by experts, which may have boundedness and may not be appropriate for engineering practice, the projection covariance matrix adaption evolution strategy (P-CMA-ES) is chosen as the optimization algorithm to train the initial parameters. To verify the rationality and effectiveness of the proposed model, the low-frequency vibration fault of a certain aero-engine is taken as an example. The results show that the proposed method can predict the health state of a complex electromechanical system precisely according to current and historical data.
Magnetic field aided heading estimation for indoor pedestrian positioning

In the field of indoor pedestrian positioning, heading estimation plays an important role. Although gyroscopes are considered to be the primary sensors for attitude estimation, the errors associated with gyroscopes require periodic updates from other measurements. These measurements are magnetometers for the heading and accelerometers for roll and pitch estimates. The Quasi-Static magnetic Field (QSF) method has been proposed to estimate angular rate error in magnetically perturbed environments. However, this method may induce extra errors to the system due to the high false alarm probability in detecting quasi-static magnetic field. To address this issue, we propose a novel quasi-static magnetic detection approach for heading error estimation. The proposed quasi-static field detector detects the quasi-static field periods depending on the change rate of inclination angle, headings and the magnitude of the total magnetic field. In this manner, the detection accuracy and robustness are improved. Then the detected quasi-static magnetic field is utilized as the measurement fed into the ZUPT-aided EKF during the stance phase to estimate the heading errors. The experiment results indicate that the proposed method can provide superior performance in suppressing the heading errors with the comparison to original QSF method.
A Refined Cluster-Analysis-Based Multibaseline Phase-Unwrapping Algorithm

As is well known, multibaseline phase unwrapping (PU) is put forward to overcome single-baseline PU in discontinuous-terrain-height estimation. This letter presents a refined algorithm based on the cluster analysis (CA)-based noise-robust efficient multibaseline PU algorithm proposed by H. Yu. The basic idea is to combine multiple interferometric synthetic aperture radar interferograms with different baseline lengths by a linear combination. The new interferograms after the linear combination increase the ambiguity heights. The number of resulting groups on the envelope of the intercept histogram is decreased and the distance between different intercept groups is widened. Compared with the conventional CA method, the significant advantage of the refined CA (RCA) algorithm is that it improves noise robustness when the intercept groups are densely distributed. The proposed RCA algorithm is validated using the simulated interferometric data. The results demonstrate that the noise robustness performance is better than that of the CA method.
Different Space-Time Modulation with Adaptive Loaded Eigen-Beamforming for Correlated Fading Channels

A differential space-time modulation transmission scheme with adaptive transmit eigen-beamforming and power loading is developed exploiting the correlation of the channels at the transmitter. Based on the analysis of the error probability of spatially correlated Rayleigh fading channels, adaptive transmit eigen-beamforming and power loading are carried out according to the change of channels and the total transmit power. The analytical and simulation results show that the proposed scheme can achieve considerable performance gain than traditional DSTM in correlated fading channels.
Three-component decomposition based on stokes vector for compact polarimetric SAR under the CTLR mode

In this paper, a three-component decomposition algorithm is proposed for processing compact polarimetric SAR images under the CTLR mode. By using the correspondence between the covariance matrix and the Stokes vector, a three-component scattering model for CTLR mode is established. The explicit expression of decomposition results is then derived by setting the contribution of volume scattering as a free parameter, within the constraint that the weighting factor of each scattering component should be nonnegative. The feasibility of this algorithm is validated by the AIRSAR data over San Francisco.
A Multi-AP Architecture for High-Density WLANs: Protocol Design and Experimental Evaluation

Fast proliferation of IEEE 802.11 wireless devices has led to the emergence of High-Density (HD) Wireless Local Area Networks (WLANs), where it is challenging to improve the throughput because each device has to share channel with all the other devices within its carrier sensing range. Although the existing adaptive Physical Carrier Sensing (PCS) techniques can improve the throughput, they result in high frame loss rate. In this paper, we investigate a Multi-AP (MAP) architecture, in which each user can associate with multiple APs according to the network topology and traffic distribution, for adaptive PCS based HD-WLANs. One of important features of the MAP architecture is that it can obtain Multi-AP diversity in both uplink and downlink. In uplink (from users to APs) the frame loss rate can be decreased by combining the reception of all associated APs, and in downlink the throughput can be improved significantly by dynamically selecting one of associated APs for transmissions according to the channel fading and traffic distribution. We first study the uplink and downlink performance of the MAP theoretically, and then propose an AP association algorithm for deciding which APs to associate with, an AP selection algorithm for dynamically selecting an AP for downlink transmissions, and an ACK management solution for avoiding ACK collisions. We build a testbed based on Intel StarEast platform to make real experiments for performance evaluation. In a typical experiment scenario, compared to the scheme with the adaptive PCS only, up to 30% throughput gain can be observed in uplink, and nearly 100% throughput gain can be found in downlink.
NeC4.5: Neural Ensemble Based C4.5

Decision tree is with good comprehensibility while neural network ensemble is with strong generalization ability. These merits are integrated into a novel decision tree algorithm NeC4.5. This algorithm trains a neural network ensemble at first. Then, the trained ensemble is employed to generate a new training set through replacing the desired class labels of the original training examples with those output from the trained ensemble. Some extra training examples are also generated from the trained ensemble and added to the new training set. Finally, a C4.5 decision tree is grown from the new training set. Since its learning results are decision trees, the comprehensibility of NeC4.5 is better than that of neural network ensemble. Moreover, experiments show that the generalization ability of NeC4.5 decision trees can be better than that of C4.5 decision trees.
The Influence of Class Imbalance on Cost-Sensitive Learning: An Empirical Study

In real-world applications the number of examples in one class may overwhelm the other class, but the primary interest is usually on the minor class. Cost-sensitive learning has been deeded as a good solution to these class-imbalanced tasks, yet it is not clear how does the class-imbalance affect cost-sensitive classifiers. This paper presents an empirical study using 38 data sets, which discloses that class-imbalance often affects the performance of cost-sensitive classifiers: When the misclassification costs are not seriously unequal, cost-sensitive classifiers generally favor natural class distribution although it might be imbalanced; while when misclassification costs are seriously unequal, a balanced class distribution is more favorable.
Detection of Human Beings in Motion Behind the Wall Using SAR Interferogram

This letter presents a novel change detection technique based on stepped-frequency continuous-wave synthetic aperture radar interferometry to detect human beings in motion inside a building. The proposed approach to moving-target indication consists of radar image formation, noncoherent energy change detection, and interferometric phase detection. Experimental results demonstrated that the proposed approach can dramatically reduce false alarms compared with a conventional noncoherent change detection approach.
A k-nearest neighbor based algorithm for multi-label classification

In multi-label learning, each instance in the training set is associated with a set of labels, and the task is to output a label set whose size is unknown a priori for each unseen instance. In this paper, a multi-label lazy learning approach named ML-kNN is presented, which is derived from the traditional k-nearest neighbor (kNN) algorithm. In detail, for each new instance, its k-nearest neighbors are firstly identified. After that, according to the label sets of these neighboring instances, maximum a posteriori (MAP) principle is utilized to determine the label set for the new instance. Experiments on a real-world multi-label bioinformatic data show that ML-kNN is highly comparable to existing multi-label learning algorithms.
Predictive Resilience Analysis of Complex Systems Using Dynamic Bayesian Networks

Uncertain and potentially harsh operating environments are often known to alter the operational performance of a system. In order to maintain system performance while coping with varying operating environments and potential disruptions, the resilience of engineered systems is desirable. Engineering systems are often interconnected in a dimensional way inherently from basic components to subsystems to the system of systems, which poses a grand challenge for system designers to analyze the resilience of such a complex system. Moreover, further complications in the assessment of resilience in the engineering domain are attributed to time-varying system performances, random perturbation occurrences, and probable failures caused by adverse events. This paper presents a dynamic Bayesian network (DBN) approach for the modeling and predictive resilience analysis for dynamic engineered systems. With the inter-time-slice links and the conditional probability tables in a DBN, the system performance could be molded as changing in a discrete time slice, while capturing the temporal probabilistic dependencies between the variables. An industrial-based case study of an electricity distribution system is further studied to demonstrate the effectiveness of the DBN approach for resilience analysis. The approach presented in this paper hopes to aid in realizing resiliency in system designs and to pave the way toward enhancements in developing resilient engineered systems.
ICCD Imaging of Coexisting Arc Roots and Arc Column in a Large-Area Dispersed Arc-Plasma Source

Arc attachments to cathode, as well as to anode, had been investigated with the bulk arc plasma between the coaxial electrodes with short-exposure-time ICCD imaging. The images captured suggests that multiple cathodic spots and diffuse attachment to the anode and cathode might contribute to the stability of rotating annular plasma dispersed by strong external coaxial magnetic field.
A New BRB-ER-Based Model for Assessing the Lives of Products Using Both Failure Data and Expert Knowledge

It is vital to assess the lives of newly developed products by using failure data from various testing environments. In the current methods, two steps are generally included. The first step is transforming the failure data under one testing environment into the actual working environment, and the second step is integrating all failure data under the actual working environment into a unified result. However, most available methods cannot use information that includes part failure data and part expert knowledge simultaneously. To resolve the above issue, based on the belief rule base (BRB) and the evidential reasoning (ER) approach, a new BRB-ER-based model is proposed, where the BRB is used to transform the failure data from one testing environment into the actual working environment. The ER approach, which is adopted to aggregate the failure data from different testing environments, is used to assess the life of a product. To conclude, the BRB-ER-based model is applied to represent and integrate asynchronous multisource information. In the proposed model, the initial BRB system is constructed based on experts' knowledge, which results in uncertainty because of the ambiguous nature of human judgment and calls for training the parameters in the BRB-ER-based model. Therefore, an optimal algorithm that employs the differential evolutionary algorithm is proposed. The proposed model and the optimal algorithm operate in an integrated manner to improve the assessment precision by using both failure data and expert knowledge effectively. A case study in three scenarios and use of the conventional approach is examined to demonstrate the capability and potential applications of the new BRB-ER-based model.