nils-herrmann/clean_scirepeval huggingface dataset:
Back-projection algorithm based on elliptical polar coordinate for low frequency ultra wide band one-stationary bistatic SAR imaging

Low frequency ultra-wide band (UWB) one-stationary bistatic synthetic aperture radar (OS-BiSAR) has serious azimuth-variance and longer synthetic aperture, which greatly complicate the range-azimuth coupling and motion errors and limits the application of frequency domain algorithms. The back-projection (BP) algorithm has the well ability to handle the range-azimuth coupling and motion compensation, so it is a reliable method to process the low frequency UWB OS-BiSAR data. In this paper, a new derivation of BP algorithm based on elliptical polar coordinate for low frequency UWB OS-BiSAR is presented, which unites BP algorithm and fast BP algorithm in elliptical polar coordinate. Based on the deduction, the comparative analysis of the performances of the two algorithms is easily carried out, which will provide a sound basis for the following study of the fast BP algorithm for OS-BiSAR in theory. First of all, the imaging geometric model of the OS-BiSAR with motion errors is provided, and the characteristics of the echo signal are analyzed. Then, the elliptical polar coordinate system for OS-BiSAR is derived, and the analytic expression of BP algorithm in elliptical polar coordinate is deduced in detail. Finally, simulation experiment results are presented to prove the validity of the proposed algorithms.
A new heat flux coupled combustion model of sandwich propellant with complex gas reaction kinetics

This paper proposed a new heat flux coupled combustion model of sandwich propellant, a modified 37 species and 127 reactions kinetics mechanism was utilized to describe combustion flame in gas phase; a simple arrhenius pyrolyis law was used to calculate mass flux of oxidizer and binder constitute, initial species fraction released from burning surface was determined by experimental data, an “equivalent source term method” was applied to describe surface decomposition via pyrolysis relations; steady heat conduction equation was solved in inert solid phase. This model adopted full-coupled way of heat flux to determine temperature of burning surface and burning rate, then 2-D heat conduction behavior in solid phase was considered. Numerical studies have been conducted on 2-D AP(ammonium perchlorate)/HTPB(hydroxylterminated polybutadiene) sandwich models, and the flame structure, the temperature and temperature gradient along burning surface, and burning rate characteristics were discussed.
Recognizing partially occluded, expression variant faces from single training image per person with SOM and soft k-NN ensemble

Most classical template-based frontal face recognition techniques assume that multiple images per person are available for training, while in many real-world applications only one training image per person is available and the test images may be partially occluded or may vary in expressions. This paper addresses those problems by extending a previous local probabilistic approach presented by Martinez, using the self-organizing map (SOM) instead of a mixture of Gaussians to learn the subspace that represented each individual. Based on the localization of the training images, two strategies of learning the SOM topological space are proposed, namely to train a single SOM map for all the samples and to train a separate SOM map for each class, respectively. A soft k nearest neighbor (soft k-NN) ensemble method, which can effectively exploit the outputs of the SOM topological space, is also proposed to identify the unlabeled subjects. Experiments show that the proposed method exhibits high robust performance against the partial occlusions and variant expressions.
A zero velocity intervals detection algorithm based on sensor fusion for indoor pedestrian navigation

The Zero-velocity Update (ZUPT) algorithm is commonly used to suppress the error growth of foot-mounted indoor pedestrian navigation system. To use the ZUPT, it is necessary to detect zero velocity intervals reliably. The traditional zero velocity intervals detection algorithms cannot provides good performance at high gait speeds. In this paper, we propose a novel zero velocity intervals detection approach based on sensors fusion. In this method, the measurements of accelerometer, gyroscope and pressure sensor are combined together to detect the zero velocity intervals. Then the adaptive threshold is developed to improve the accuracy of detection under various motion modes. The performance of the proposed method is examined in real indoor scenarios. The results show that the proposed method can correctly detect the zero velocity intervals and improve the accuracy of positioning.
Interferogram Reconstruction of Cascaded Coaxial Cable Fabry-Perot Interferometers for Distributed Sensing Application

This paper describes a distributed sensing concept using coaxial cable-based Fabry-Perot interferometers (CCFPIs). Multiple reflectors are implemented along a coaxial cable, where every two consecutive reflectors can be considered as a low finesse CCFPI, which has a relatively weak reflection coefficient and insertion loss. The interferogram in a frequency domain of each individual CCFPI could be reconstructed through the proposed signal processing method, so that the phase detection could be applied to any CCFPIs on one cable to achieve high measurement accuracy. A large capacity sensor network with a relatively high measurement accuracy can be implemented simultaneously. The concept takes advantage of the time-domain multiplexing method and the pure frequency domain measurement, which is herein called a joint-time-frequency demodulation technique. Due to its effectiveness and robustness, the device is especially attractive for structural, downhole, or underwater applications.
An off-online fuzzy modelling method for fault prognosis with an application

Fault prognosis plays a key role in prognostics and health management (PHM). Currently, there are many methods to predict the occurrence of a fault, but the fuzzy model has become an effective alternative owing to its advantage of using not only quantitative data but also qualitative information with fuzzy uncertainty. It is particularly useful for a dynamic system with complexity, morbidity and nonlinearity. Compared with the prediction from an offline fuzzy model, an online prediction is more desired, since we can monitor the health condition of a system and achieve fault prognosis in real time. In this paper, we develop a prediction model by firstly establishing an initial fuzzy model with offline information, then, using the online information to adjust the initial offline model in real time. The offline fuzzy model is modelled through a relevance vector machine (RVM) method and the structure of the initial fuzzy model is adjusted based on the statistical utility of a fuzzy rule using online information. The model parameters are optimized by the gradient decent (GD) algorithm. To do so, a fault prognosis algorithm is proposed on the basis of our off-online fuzzy modelling method. Finally, the proposed fuzzy modelling method and its fault prognosis algorithm are applied to a practical example. The empirical results show that our developed method has a significant improvement over the existing fuzzy modelling methods in terms of accuracy and the corresponding fault prognosis algorithm is effective.
Pedestrian positioning using WiFi fingerprints and a foot-mounted inertial sensor

Foot-mounted inertial positioning (FMIP) and fingerprinting based WiFi indoor positioning (FWIP) are two promising solutions for indoor positioning. However, FMIP suffers from accumulative positioning errors in the long term while FWIP involves a very labor-intensive offline training phase. A new approach combining the two solutions is proposed in this paper, which can limit the error growth in FMIP and is free of any offline site survey phase. This approach is realized in the framework of a particle filter, where each particle denotes a potential trajectory of the user and is weighted according to its consistency in signal strength space. Compared with the traditional Gaussian process based approaches, the proposed one has less computational cost and is free from any prior information in the position domain, such as the positions of access points, received signal strengths at certain positions and so on. An experiment is carried out to demonstrate the performance of the proposed approach compared to the traditional Gaussian process based approach.
People apparel model for human appearance description

Many applications of computer vision, such as people identification and tracking in surveillance system, demand to describe the appearance of people. However, factors such as illumination, people pose and background may greatly affect the performance of the people appearance description. In this paper, a semantic model to describe people in surveillance cameras is introduced. Observing that in a short period of time, the clothes of people does not change, therefore, we construct a people apparel model based on their clothes colors, clothes pattern and dress style. These are all semantic traits which are invariant to illumination and people pose change, as well as various camera specifications. So they can provide robust description. Traits are extracted from two parts of the human body for more accurate description. Experiments are carried out on videos captured in public areas and the results show the good performance of this semantic people apparel model.
PCA and KPCA for Predicting Membrane Protein Types

Knowing type of an uncharacterized membrane protein often provides a useful clue in both basic research and drug discovery. With the explosion of protein sequences generated in the post genomic era, determination of membrane proteins types by experimental methods is expensive and time consuming. It therefore becomes important to develop an automated method to find the possible type of membrane protein. In view of this, the DC (Dipeptide Composition) is introduced to represent the protein sample. However, a high dimensional disaster may be caused by using this representation method. Thus, a linear dimensionality reduction algorithm PCA (Principle component analysis) and a nonlinear dimensionality reduction algorithm KPCA (Kernel Principle component analysis) are introduced to extract the indispensable features from the high-dimensional DC space,respectively. Based on the reduced low-dimensional features,K-NN (K-nearest neighbor) classifier is introduced to identify the types of membrane proteins. Finally, experiment results show that using the proposed method to cope with prediction of membrane proteins types are very effective.
A singular spectrum analysis based human life signal detection

Life signal detection of human beings behind an obstacle is a keen research topic in recent years. In this paper, a singular spectrum analysis (SSA) based human life signal detection approach is proposed. SSA is a nonparametric spectral estimation method, and is widely used in time series analysis. SSA can decompose a time series into a sum of components, such as trend, periodic components and noise, so it is more suitable to detect human life signal. Firstly, stationary human micro-motion model is established, and the received signal of continuous wave radar is analyzed. Then SSA is used to detect human life signal by four steps: embedding, singular value decomposition, grouping and reconstruction. Simulation and experimental results validate the correctness and effectiveness of SSA in human life signal detection.
Image-Domain Estimation of Wall Parameters for Autofocusing of Through-the-Wall SAR Imagery

Estimation of the basic parameters, wall thickness and dielectric constant, is important in through-the-wall radar imaging. Ambiguities in wall characteristics will degrade the image focusing quality of synthetic-aperture radar. In order to obtain a quick and precise estimation of wall parameters, an equivalent propagation model of an electromagnetic wave in the air-wall-air medium is first developed in this paper. According to the developed propagation model, two filter-based approaches, denoted respectively as the echo-domain-filter-based method and the image-domain-filter-based method, are proposed to estimate wall thickness and dielectric constant by adjusting the two corresponding parameters of the echo-domain filter or the image-domain filter to obtain the best focusing quality of behind-the-wall targets. The processing schemes of the two methods show that the image-domain-filter-based method is more efficient because it does not involve imaging processing in each adjustment. Moreover, the image-domain-filter-based method is accelerated by reducing the dimension of searching space to better the computational efficiency further. Simulation results show that the proposed accelerating image-domain-filter-based method can provide quick and precise estimation of wall parameters.
Joint tuning of physical carrier sensing, power and rate in high-density WLAN

Fast proliferation of IEEE 802.11 wireless devices has led to the emergence of High-Density WLAN, where a large number(10s-100s) of Access Points(AP) are deployed to service a larger number(100s-1000s) of users. In such network it is a challenge to improve the throughput because each device has to share channel with all the other devices within its carrier sensing range. So some traditional settings of 802.11 devices need to be self-adaptively tuned. In this paper, we investigate the joint tuning of physical carrier sensing, transmission power and data rate in High-Density WLAN. With introducing an analytical model, we propose two simple yet effective tuning rules to maximize spatial reuse and keep the fairness among users. We also develop a distributed joint tuning algorithm based on these rules. Extensive simulation results show that the proposed joint tuning algorithm achieves significant improvements in both aggregate network throughput and fairness.
Analysis on the implementation model of intelligent knowledge management based on data mining

The development and application of data mining technology puts forward new requirement to knowledge management in information era. Intelligent knowledge is the valuable knowledge that comes from data mining technology, and has the intelligent features such as memory, recognition, self-updating, disappearance etc after the transaction by man-machine interaction. On the basis of analyzing the goals and layers of intelligent knowledge management, this paper studies the implementation model of intelligent knowledge management from the aspects of its cultivation, expression and storage, audit and logic mechanism etc. It will afford new perspective for research on knowledge management.
A 3D circuit model for power distribution networks in complex package structures

We propose a three dimensional circuit model that is used for characterizing big power nets in complex flip chip packages. The model is based on block division of a multilayered package. Besides, the model breaks the package into three parts along the z-dimension according to layer stack ups. The model parameters can be evaluated individually and thus rapidly by using commercial software. The model is easy to maintain and update by reassigning power grid.
Simulation of features of electrons during a pulse period in rod-plate DC negative corona using a plasma chemical model

In order to discuss the features of electrons in negative corona, a 2D, multi-component, and plasma chemical improved model is detailedly presented for the simulation of rod-plate DC corona discharge in dry air (O2:N2=1:4). The model is based on plasma hydrodynamics and chemical models in which 28 reactions are considered. The simulation is established with a rod-plate electrode configuration with an inter-electrode gap of 3.3 mm, the voltage applied to the rod is DC −5.0 kV, the pressure in air discharge fixed at standard atmospheric pressure (1.0 atm), and the ambient temperature assumed to be a constant (300 K). Based on this improved model, electron mean energy distribution, electron density distribution, generation and dissipation rates of electron at 6 typical time points during a pulse period are analysed emphatically.
Interactive image segmentation with color and texture information by region merging

Interactive image segmentation is able to extract the user-specified foreground objects from the whole image, which remains to be a challenging problem in image processing and computer vision. The traditional pixel-based interactive segmentation is time-consuming and neglects the neighbor information, which is hard to achieve efficient and accurate results. To address this problem, a novel region-based approach is proposed for interactive image segmentation. The algorithm contains three stages: initial segmentation by Simple Linear Iterative Clustering (SLIC) superpixels, region representation combining color features and texture features, region merging based on the region similarity. A new region similarity metric based on the Normalized Cross correlation is raised to guide the region merging process with the aid of user markers. Moreover, since the region merging is a critical step in the entire process, a novel one-stage region merging strategy is exploited to improve the efficiency and robustness of the algorithm. Experiments on various images from the Berkeley dataset is conducted, and the results demonstrate the high speed and effectiveness of the proposed interactive image segmentation method.
Grating lobes suppression for ultra-wideband MIMO radar imaging

Though high range and azimuth resolutions can be achieved through back projection imaging for ultra-wideband multi-input multi-output (MIMO) radar, significant grating lobes are also observed that deteriorate the imaging performance. Since the existing coherence factor approaches may also reduce the magnitude of extended targets when suppressing the grating lobes, a novel correction, named as power spectrum coherence factors (PSCF) approach is proposed in this work. The real data experiment of a MIMO through-the-wall imaging radar shows that PSCF correction can preserve higher magnitude for extended targets than traditional approaches while suppressing grating lobes effectively.
Online Updating With a Probability-Based Prediction Model Using Expectation Maximization Algorithm for Reliability Forecasting

Recently, a novel prediction model based on the evidential reasoning (ER) approach is developed to forecast reliability in engineering systems. In order to determine the parameters of the ER-based prediction model, some optimization models have been proposed to train the ER-based prediction model. However, these models are implemented in an offline fashion and thus it is very expensive to train and retrain them when new information is available. This correspondence paper is concerned with developing the recursive algorithms for updating the ER-based prediction model from the probability-based point of view. Using the recursive expectation maximization algorithm, two recursive algorithms are proposed for updating the parameters of the ER-based prediction model under judgmental and numerical outputs, respectively. As such, the proposed algorithms can be used to fine tune the ER-based prediction model online once new information becomes available. We verify the proposed method via a realistic example with missile reliability data.
Differential evolution algorithm with variable neighborhood search for hybrid flow shop scheduling problem

Hybrid flow shop scheduling problem (HFSP) is a complex combinatorial optimization problem in real world applications. Due to the complexity and importance, the HFSP has been studied intensively. This paper presents differential evolution algorithm with variable neighborhood (DEVNS) search to solve HFSP. Experiments have been conducted and comparison has been made with the state-of-the-art algorithms. Experimental results show that the DEVNS algorithm is highly competitive in solving the HFSP.
Fast ECG anomaly detection on Android platform

This paper is to present a fast and real-time algorithm for detecting abnormal ECG signals by using Welch Method to preprocess the data of normal sinus rhythm database of MIT-BIH, and then analyze with K-means algorithm to get the normal form of ECG categories and store them in Android mobile terminal. In real-time detection, extract the ECG waveform and match with the normal ECG categories to get whether it is abnormal. The time of measuring 10s electrocardiogram was 3.03s, the precision rate was 94.05%, and the recall rate was 93.12%. This method can measure the ECG abnormity rapidly in real time, and the accuracy is similar to the traditional method.
Study on Hydro-Mechanical Differential Dynamic Turning Process of Tracked Vehicle

The dynamic turning process of tracked vehicle is the transition turning process. The hydro-mechanical differential turning system is the new turning system of tracked vehicle using hydro-mechanical stepless transmission theory and being made up of hydraulic transmission and gear transmission. The hydro-mechanical differential turning theory is analyzed. Basing on turning dynamics and kinematics of tracked vehicle, the simulated model of hydro-mechanical differential dynamic turning process of tracked vehicle is established. The variety trend of turning angular velocity and turning radius with displacement ratio of hydraulic turning close loop system and transmission ratio of transmission case is obtained in this paper. The simulated results can be used to design the steering system of tracked vehicle.
Landmine detection using boosting classifiers with adaptive feature selection

In order to solve the problem of landmine detection in Forward-Looking Ground Penetrating Virtual Aperture Radar (FLGPVAR), the AdaBoost classification with adaptive feature selection (AFS-AdaBoost) is proposed. The feature selection is added into the traditional AdaBoost, which can reduce the training error of weak classifiers and improve the generalization capability of a strong classifier. The feature selection is based on a wrapper model, whose cost function is the performance of the classifier. Considering landmine detection one-class classification problem, the false alarm rate with constant probability of detection is chosen to be the cost function, which ensures the detection performance of strong a classifier. Processing of a real dataset show that AFS-AdaBoost is applicable to the landmine detection in FLGPVAR. Compared with traditional AdaBoost, the detection performance and generalization capability of AFS-AdaBoost are significantly improved.
Estimation and Mitigation of Time-Variant RFI in Low-Frequency Ultra-Wideband Radar

The work presented in this letter focuses on the time-variant radio frequency interference (RFI) issue in the low-frequency ultra-wideband (UWB) radar. Different from many previous studies, we first analyze the characteristics of RFIs and scattered echoes in the slow-time dimension and take advantage of overlapped short-time Fourier transform to adapt to the time-variant RFIs and update the frequency Doppler spectrum. Then, in the frequency Doppler spectrum, we adopt the minimum statistic combined with 1-D cell-averaging constant false alarm rate to estimate and separate the RFI power spectrum from the scattered echoes based on their differences. Finally, to mitigate the estimated RFIs, a suboptimal filter controlled by the defined entropy of range profiles after math filtering is designed. Employing a UWB radar, different experiments were conducted, and results verify the proposed method.