nils-herrmann/clean_scirepeval huggingface dataset:
Research on face recognition method based on deep learning in natural environment

In the present study, there are a number of recognition methods with high recognition accuracy, which are based on deep learning. However, these methods usually have a good effect in a restricted environment, but in the natural environment, the accuracy of face recognition has decreased significantly, especially in the case of occlusion, face recognition will appear inaccurate or unrecognized situation. Based on this, this paper presents a face recognition method based on the deep learning in the natural environment, hoping to achieve robust performance in the natural environment, especially in the case of occlusion. The main contribution of this paper is improving the method of multi-patches by using 4 areas' patches in the face. And in order to have a higher performance, we use a Joint Bayesian (JB) measure in face-verification. Finally, we trained the model by the set of CASIA-WebFace and test it in the Labeled Faces in the Wild (LFW).
A Preprocessing Method Based on the Wavelet Threshold De-Noising Technique for the Key Variables of Pulverized-Coal Concentration

The accurate measurement of the pulverized-coal concentration in the powder-transmission pipe of the coal pulverizing system of thermal power plant can effectively reflect the operation efficiency of the system, and the preprocessing of several key variables, that are adopted to represent the pulverized-coal concentration, is an important step. In this paper, we propose a preprocessing method based on the wavelet threshold de-nosing technique for the key variables of pulverized-coal concentration. The method uses wavelet transform on noises firstly. Then according to threshold functions and threshold, we do threshold processing on the wavelet coefficients to get the new wavelet coefficients. Finally we reconstruct signals by the new wavelet coefficients to get the de-noised signals. This method can eliminate the noise and remove the random error with maintaining the characteristic information of the original signals. The results of the simulation verify the effectiveness of the proposed method.
Application of Kalman filter in Three Beidou Geostationary Satellites passive dynamic positioning

Aimed at the movement model of Three Beidou Geostationary Satellites passive dynamic positioning receiver and the noise characteristics of the receiving signal, the turn model is applied to represent exactly the movement pattern of the receiver, and a Kalman filter algorithm based on turn model is presented in this paper. By filtering the Three Beidou Geostationary Satellites passive dynamic positioning results, the emulation experiment shows that the algorithm can eliminate most random errors in the dynamic positioning. Compared with the traditional Kalman filter algorithms, the algorithm presented in this paper is charactered with easy realization, well application and high positioning precision, and a good result is obtained in the practical application.
Assessing electrical impedance alterations in spinal muscular atrophy via the finite element method

Electrical impedance myography (EIM) is a surface-based, non-invasive technique of evaluation of muscle health, involving the application of high frequency, low-amplitude current to the skin over a muscle of interest. Results from a previous animal study suggest that the finite element method can relate disease-induced changes in electrical properties of the muscle to alterations in surface impedance measurements; however, whether such an approach will prove useful in human models is uncertain. Therefore, to further investigate this question, we have created a single finite element model of the human biceps muscle using data from one healthy subject and one with spinal muscular atrophy (SMA), each of whom had comparable age, limb girth, muscle size, and subcutaneous fat thickness. Since healthy human tissue was unavailable, permittivity and conductivity measurements were obtained from five healthy and five advanced amyotrophic lateral sclerosis rat gastrocnemius muscles immediately after sacrifice; their data were input into the human biceps model and the expected surface voltages calculated. We then compared the results of this model to the actual surface EIM data for both individuals. Although the actual resistance and reactance values varied and the peak values were displaced, the resulting maximum phase predicted by the model approximated that obtained with surface recordings. These results support that alterations in the primary characteristics of muscle impact the surface impedance measurements in meaningful and likely predictable ways.
A new phase image reconstruction method using Markov random fields

Phase images derived from imaging devices are usually wrapped into discontinuous images, so phase unwrapping is needed for phase image reconstruction. The wrap counts of every voxel are determined by the assumption that the true phase is spatially continuous. However, it is difficult to distinguish whether the phase jump is caused by phase wrap or noise. In this paper, a new 3D phase unwrapping method is proposed by using Markov Random Fields. Phase unwrapping is formulated as a discrete energy minimization problem defined on a 3D MRF. Experimental results show substantial improvements in phase unwrapping and quantitative susceptibility mapping reconstruction compared to a region growing method on Magnetic Resonance data with low SNR and rapid phase variation.
A Topos Induced by Qualitative Mapping

It is shown that the Reasoning Lattice-Monoid Category L-M(Au, Ù, →) of the attribute set Au of object u can be constructed by the conjunction Ù between two attributes, and a Qualitaive Mapping Topos TQM(Au, *, →) of the category L-M(Au, *, →) can be induced by a Qualitative Mapping t(x, [a, b]) with qualitative criterion transformation T.
Dynamic Soft-Frequency Reuse with Inter-Cell Coordination in OFDMA Networks

In this paper, we develop a downlink dynamic soft frequency reuse (SFR) scheme with inter-cell coordination in orthogonal frequency division multiple access (OFDMA) cellular networks. A clustered base station (BS) coordination strategy is employed to facilitate frequency resource allocation among coordinated sectors. Compared to the traditional static SFR scheme, the proposed scheme can exploit frequency selectivity of wireless channels and multi-user diversity through inter-cell coordination and achieve better performance. Since the proposed approach performs resource allocation in a distributed way, only limited information needs to be shared or exchanged even within a cluster. As shown by computer simulation, compared with the traditional scheme, the proposed one can improve the throughput for those users at the edge of a sector by 41.9% while maintaining the same throughput for those users at the center of a sector.
On-line monitoring and evaluation of mechanical characteristic condition of switchgear

In this paper, through the monitoring case on the mechanical characteristics of the circuit breaker in service operation, analyzed the problems such as when the circuit breaker is in the running process whether is abnormal, potential faults or defect types, the potential fault parts, etc, and had a maintenance to the products with potential failures that may endanger the operation, and verified the potential fault diagnosis by the actual products. This paper uses the advanced on-line monitoring system of high voltage circuit breaker for monitoring circuit breaker closing and opening motions and current waveform of closing and opening coil, to obtain spring running state and monitoring the spring fatigue performance at any time. It solves the problem that the spring which is most important component of operating mechanism is difficult to monitoring in the running status. Meanwhile, we can determine common fault from the current waveform of closing/opening coil, such as whether the coil is binding or auxiliary switch is failures and so on. It provides effective guarantee of reliable safety runs for circuit breakers.
Processes of Raising Voltage and Reducing Voltage in Needle-Plate Dielectric Barrier Discharge

In this paper, the raising voltage process is defined as stepping up the applied voltage, whereas the reducing voltage process means turning down the applied voltage, which follow on the termination of the raising voltage process. Discharge images, the waveforms of applied voltage and discharge current, Lissajous figures, and the optical emission spectra of the two different processes were acquired in needle-plate alternating current dielectric barrier discharge in humid nitrogen at atmospheric pressure. Conclusively, the characteristics of the discharges with the same applied voltage in the raising and reducing voltage processes are pretty different. The discharge in the reducing voltage process is more intense and the power consumption is about three times than that in the raising voltage process at the same applied voltage. Analysis shows that the phenomena would be interpreted by the effect of the space charges in the discharge gap, and worth taking more efforts in the practical applications.
A Unified Framework for Bias Compensation Based Methods in Correlated Noise Case

This technical note presents a unified framework for bias compensation principle (BCP)-based methods applied for identification of linear systems subject to correlated noise. By introducing a non-singular matrix and an auxiliary vector uncorrelated with the noise, the unified framework is established. Since there are rich possibilities of the choices of the introduced matrix and vector, the proposed unified framework is very flexible. It can be verified that the existing BCP-based methods are special cases of the achieved result. It also shows that the unified framework can be used for deriving new or simplified versions of the BCP type methods.
Robust Coordinated Transmission and Generation Expansion Planning Considering Ramping Requirements and Construction Periods

Two critical issues have arisen in transmission expansion planning with the rapid growth of wind power generation. First, severe power ramping events in daily operation due to the high variability of wind power generation pose great challenges to multi-year planning decision making. Second, the long construction periods of transmission lines may not be able to keep pace with the fast growing uncertainty due to the increasing integration of wind power generation. To address such issues, we propose a comprehensive robust planning model considering different resources, namely, transmission lines, generators, and FACTS devices. Various factors are taken into account, including flexibility requirements, construction period, and cost. We construct the hourly net load ramping uncertainty (HLRU) set to characterize the variation of hourly net load including wind power generation, and the annual net load duration curve uncertainty (LDCU) set for the uncertainty of normal annual net load duration curve. This results in a two-stage robust optimization model with two different types of uncertainty sets, which are decoupled into two different sets of subproblems to make the entire solution process tractable. Numerical simulations with real-world data show that the proposed model and solution method are effective in coordinating different flexible resources and rendering robust expansion planning strategies.
A spectral approach to statistical polar shape modeling

Accounting for uncertainty in three-dimensional (3D) shapes is important in a large number of scientific and engineering areas including: biometrics, biomedical imaging, and multimodality image registration. It is well known that 3D star-shaped objects can be represented by Fourier descriptors such as spherical harmonics and double Fourier series. However, the statistics of these spectral shape models have not been widely explored. This article presents a spectral theory and its applications in 3D shape modeling. Spherical harmonic (SH) expansions over the unit sphere not only provide a low dimensional polarimetric parameterization of stochastic shape, but also correspond to the Karhunen-Loeve (K-L) expansion of any isotropic random field on the unit sphere. Spherical harmonic expansions permit estimation and detection tasks, such as optimal shape filtering, object registration, and shape classification, which can be performed directly in the spectral domain with low computational complexity.
An Improved Multi-objective Genetic Algorithm Based on Agent

An improved multi-objective Genetic Algorithm based on agent is offered. In the improved algorithm, agents were co-evolution with different control parameters to increase the diversity of candidate solutions. Two kinds of crossover strategies of Arithmetic and Simulated binary (SBX) were introduced in order to complete the competition behavior of the agent, these strategies increased the choice range of the agent, and improved the search performance. To construct non-dominated set, Arena Principle (AP) was used in the process of self-learning behavior, and the clustering method was used to narrow the non-dominated set, so as to obtain the set of Pareto optimal front. The idea of the elitism retaining was used to quicken the convergence rate, and then formed the elitism of individuals, this performance was similarly to the local climbing for self-learning operation. Finally, we saved these individuals to the elitism population, several standard test functions are used to verify this improved algorithm. The results indicated that the improved Genetic Algorithm (GA) obtained good performance.
Research on the passenger comfort evaluation system of urban rail transit

The development of economy brings changes to people's lifestyle. The growing requirements for service quality puts forward new challenges to the operation and development of urban rail transit. Therefore, the research of train comfort degree has positive significance. The paper analyzes the main influencing factors of train comfort degree based on scientific methods of system evaluation, and a suitable evaluation method for each evaluation index of the system is determined. Then we use subjective survey, analytic hierarchy process (AHP) and correlation matrix evaluation method to determine the weight coefficient of each evaluation index. Finally, we present the mathematical model of the whole evaluation system using the centesimal system, realizing the overall evaluation of the train comfort degree.
Scattering Properties of Near-Field Evanescent Wave From Medium With Ellipsoidal and Cylindrical Distributions

The evolution properties of the near-field evanescent wave scattered from two particles with the ellipsoidal and cylindrical scattering potentials are studied, respectively. Within the accuracy of the first-order Born approximation, the integrations are derived for the scattered fields from these two kinds of particles. It is found that the resultant scattered intensities are closely related to the scattering potential types. Compared with the ellipsoidal particle, the cylindrical one gives rise to less fluctuations of the scattered intensities. It also is shown that the scattered intensity strongly depends on the z-component of the effective radii and summation indices of both particles. Results indicate that the scattered intensity distribution can be tuned by controlling the Effective Radius of Scattering potentials (ERSPs) and summation indices of the medium. The obtained results are useful for determining the structural parameters of tiny particles and molecules by converting the scattering formulas.
Mapping of Interception Loss of Vegetation in the Heihe River Basin of China Using Remote Sensing Observations

Interception loss is an important component of the regional water balance for the Heihe River Basin which is an inland basin with limited precipitation. We used a modified Gash analytical model by combining remote sensing observations to estimate the interception loss of several vegetation types, e.g., grass, crop, forest and shrub for the years 2003-2012 in the Heihe River Basin. The estimated monthly interception ratio (in percent) was compared with field measurements made in Dayekou and Pailugou forest hydrology experimental sites and the results showed reasonable accuracy with RMSE of 5.0% and 4.3% at the two sites, respectively. The regional distribution of the interception loss showed strong spatial and temporal variability at monthly scale. At annual scale, the interception ratio could be treated as a stable indicator for long-term water balance research. The annual average interception loss is about 7.2% of gross rainfall for the vegetation covered area in the Heihe River Basin.
Notice of Retraction A Method for Obtaining Regions of Interest with Adaptive Gaussian Mixture Model

In this paper an adaptive Gaussian mixture model is introduced firstly to remove the shadow of regions of interest in the detection of moving human body from current video sequences. Then use a proposed method of obtaining ROI. From the view of the tracking effect, it can be concluded that this method of removal shadow of regions of interest can improve the precision rate of segment of moving people and is good for subsequent tracking. The experimental result showed the ROI produced by this method and is robust, and is suitable for real- time tracking.
AppBot: A Novel P2P Botnet Architecture Resistant to Graph-Based Tracking

P2P architectures have been widely adopted by botnets for their high robustness. As the complex Command and Control (C&C) mechanism inevitably leads to too many interactions among bots, the P2P topology characteristics of traditional P2P botnet architectures is likely to be exposed to defenders, which makes them vulnerable to graph-based tracking. Inspired by the infeasibility of monitoring the global Internet and the difficulty of IP traceback problem, we propose a novel P2P botnet architecture, called AppBot, which is resistant to graph-based tracking. The key idea behind our proposal is two-folds: i) interactions are strictly restricted across different domains, and ii) IP spoofing is used to hide the origins of bots' interactions. Based on the real botnet distribution and background traffic of mainland China, we provide a realistic scenario for our experiments. In order to systematically evaluate AppBot, we compare it with two other typical P2P botnet architectures (HppBot and KppBot) in terms of the performance against one of the best graph-based tracking methods. We repeat our experiments by varying the botnet distribution (Gafgyt distribution and IMDDOS distribution). Each experiment is conducted on the mixture of synthetic botnet traffic and real background traffic of Xinjiang domain and Hainan domain, respectively. Experimental results show that AppBot shows a significantly high anti-tracking performance over all experimental settings. The average antitracking performances of AppBot, HppBot and KppBot are 46.88%, 20.45% and 24.28%, respectively.
Minimum Spanning Tree Fusing Uniform Sub-Sampling Points and High-Dimensional Features for Medical Image Registration

In this paper, we propose a novel registration algorithm based on minimal spanning tree. First, we extracted uniform sub-sampling points from image. Second, based on the feature points, in addition to using pixel intensity, we also added region based feature to include more spatial information. The proposed method is evaluated by performing registration experiments on BrainWeb database. The experimental results show that the proposed method achieves better robustness while maintaining good registration accuracy, compared to the conventional normalized mutual information (NMI) based registration method.
Error analysis of the position and velocity of arc-welding robot based on flexible mechanics and ADAMS

With the development of aerospace manufacturing automation, the demand for high-precision welding robot is increasing. The analysis method of robot pose error is the key technology to improve the accuracy. This paper presents a method of analyzing the robot posture error by using flexible mechanics theory combined with ADAMS virtual prototype technology, which the elastic deformation of the link arm during working is considered, and the effect of deformation on the position and velocity error of robot can be analyzed. The analysis result of engineering case shows that the maximum position error caused by elastic deformation accounts is up to 25% of static error, the velocity fluctuation of mechanical arm is obvious which affects the welding quality. Therefore the error analysis of robot position and velocity should be combined with flexible mechanics. Method proposed in this paper is used to analyze the position and velocity errors in the design and manufacture of welding robot, which has important significance to improve the welding quality.
Eco-hydrological study on restoration threshold of the Lhalu wetland

As the largest wetland with the highest elevation located in urban region, the Lhasa Lhalu wetland is chosen as a typical area in this research. The wetland is regarded as “The Lung of Lhasa City” due to its outstanding ecological benefits. It will become more efficient and reasonable for the protection of wetland eco-environment by applying the principle and method of ecohydrology to set a series of ecological restoration thresholds of the Lhalu wetland. After analysis on ecohydrological changing process and characteristics of the Lhalu wetland in recent twenty years, a new coupled model is developed on the basis of groundwater flow model, soil water movement model, water sucking model of plant roots and plant evapotranspiration model. This coupled model can be applied to describe the eco-hydrological process of the Lhalu wetland and focuses on how plant biomasses react to various ground water levels and annual precipitation levels, based on which, we bring forward the restoration threshold value which provides scientific strategic base for ecological construction and management of the Lhalu wetland.
Building and using a semantivisual image hierarchy

A semantically meaningful image hierarchy can ease the human effort in organizing thousands and millions of pictures (e.g., personal albums), and help to improve performance of end tasks such as image annotation and classification. Previous work has focused on using either low-level image features or textual tags to build image hierarchies, resulting in limited success in their general usage. In this paper, we propose a method to automatically discover the “semantivisual” image hierarchy by incorporating both image and tag information. This hierarchy encodes a general-to-specific image relationship. We pay particular attention to quantifying the effectiveness of the learned hierarchy, as well as comparing our method with others in the end-task applications. Our experiments show that humans find our semantivisual image hierarchy more effective than those solely based on texts or low-level visual features. And using the constructed image hierarchy as a knowledge ontology, our algorithm can perform challenging image classification and annotation tasks more accurately.
Packet Detection for Onboard Switching Broadband IP Satellite Networks

The signal detection performance is studied for an onboard switching broadband IP satellite network. The probability of false alarm, miss and early detection is analyzed and simulated using either CFAR or the classic method. The unique word length is 108 symbols and the SNR is Eb/N0=1.8 dB. It is shown that the overall probability of packet loss can be less than 10-9
Blind equalization Using ν- support vector regressor for constant modulus signals

The support vector machine has been recently developed for blind equalization of constant modulus signals. In this paper we propose to use a v-support vector regressor (nu-SVR) for blindly equalizing multipath channels because of the high generalization ability of the SVR for short burst sequences. A weighted least square procedure is presented for solving the blind nu-SVR equalizer. The performance of the proposed algorithm is analyzed by means of computer simulations.