nils-herrmann/clean_scirepeval huggingface dataset:
Chaotic Local Search Based Differential Evolution

To improve the performance of Differential Evolution (DE), a novel algorithm called Chaotic Differential Evolution (CDE) was proposed in this paper. Chaotic local search, which is characterized bits randomicity, is employed to overcome the weakness of the premature convergence of standard DE. Three multidimensional non-linear functions were used to verify the efficiency of CDE and the simulation results show that it is a promising method with the advantages of better search ability and robust.
Aggregating Multiple Ontology Similarity Based on IOWA Operator

Different ontology matching approaches which utilize diverse semantic information to bridge heterogenous ontologies perform different adaptability and use value on the same task. Usually, combination of a set of different matching approaches can achieve higher accuracy than single approach. Therefore, a method based on induced ordered weighted averaging operator (IOWA) is proposed in this paper to aggregate the similarities computed by multiple ontology matching methods. It first predicts the confidence of similarity, and then takes it as induce value to assign weight to similarity and sums up all the weighted similarities about a given element pair. Experiment shows the validity and practicability of this method.
Full-wave analysis of antenna-array mutual coupling using the FETI-DPEM algorithm

The FETI-DPEM method has been applied to the full-wave analysis of mutual coupling between antenna arrays. The full-wave simulation has been accelerated through parallel computation implemented on a distributed-memory system using MPI. Examples have been presented and the array mutual coupling at the presence of a platform has been characterized using the received power at one array due to the scanning operation of the other array.
A naiive bayes learning based website reconfiguration system

The continuous and sharp growth of web sites in terms of size and complexity has made improving the website organization to facilitate users' navigation something of an emergency. To address this problem, in this paper we propose a website reconfiguration system using the machine learning approach. First, a Naive Bayes Classifier is trained and then applied to identify each page in a web site as important or unimportant in terms of fulfilling visitors' information needs. For those important pages, we check the reasonableness of their locations, which is measured by the average number of hops needed to reach them during visitor sessions. Those important but difficult reach pages are considered for reconfiguration, which is done by either automatically moving them to some level closer to the visitors' starting point, making it easier for users to access them, or presenting webmasters with a list of suggestions. We also propose a formula to evaluate the "global structure" of a web site, and use it to examine the effect of our system on improving website design.
Motion control system simulation of cylindrical linear induction motor used in circuit breaker operating mechanism

According to the requirements of movement characteristics during the opening and closing of 40.5 kV vacuum circuit breaker on operating mechanism, this paper designed a new type of motor drive operating mechanism-cylinder linear induction motor (C-LIM) drive mechanism. Based on the characteristics of short trip, strong anti-force, high speed in mechanism, analysis the motor mechanism movement characteristics, calculated the dynamic characteristics of the contact under the optimized velocity curve and also plan the move curve of the contact in the paper. Pointing at proper border effect of C-LIM mathematical model is built of vector control system when consider dynamic vertical border effect, and simulated of the mechanism control system for driven by (C-LIM) in MATLAB environment. The simulation results show that it can get a good dynamic performance, small vibration and perfectly meet the requirements of mechanism movement characteristics during the opening and closing of circuit breaker when the linear motor direct drive circuit breaker actuator operating shaft, and still lay the foundations of circuit breaker intelligence control.
Dual-Mode Dual-Band Bandpass Filter Using Stacked-Loop Structure

In this letter, a dual-mode dual-band bandpass filter using stacked-loop structure is proposed, which allows two transmission paths to radio frequency (RF) signals. Each of them using dual-mode resonators results in respective passband. This can provide convenience to easily change one passband frequency, while another keeps almost the same. Several attenuation poles in the stopband are realized to improve the selectivity of the proposed bandpass filter. The theoretical and measured results are presented and show good agreement
Compact SIW microstrip magnetic Yagi antenna

A compact microstrip magnetic Yagi antenna in X band is designed using substrate integrated waveguide (SIW). The SIW works as driven element to generate vertically polarized wave and microstrip patches is connected to ground plane by plated through holes to work as magnetic dipole directors. A coupling patch is inserted between the driven element and the first director to improve impedance matching. With this arrangement, a magnetic Yagi antenna with vertical polarization is produced and the relative bandwidth reaches 16.84%. The peak gain is between 7.26dBi and 8.98dBi in the working band. Total area of the proposed antenna is 1.58λ1×0.95λ1. Numerical simulations are provided to demonstrate the radiation characteristics and experimental results show a good agreement with simulations.
Research on directed fault propagation graph model and optimization

Modern equipment is large structure and complex function with redundant components within the system. As a result, the complexity is increased when fault diagnosis and analysis model is set up. In order to reduce diagnosis model complexity, a method of directed fault propagation model and optimization is proposed. Fault relationship among the components is analyzed based on the probability and influence of fault propagation, the multiple fault modes and fault propagation relationship is also studied. The directed fault propagation graph model is optimized according to the fault influence factor on the basis of quantitative assessment, and redundant component within fault diagnosis model is cut, so the model is simplified, the computational process is reduced and the efficiency for fault diagnosis and analysis is improved. A practical model shows the validation of the model and optimization method.
Adaptive Feedback-Feedforward Control for a Class of Nonlinear Chemical Processes

To circumvent the drawbacks in nonlinear controller designing of chemical processes, an adaptive feedback-feedforward control scheme is proposed in this paper. A class of nonlinear processes with modest nonlinearities is approximated by a composite model consisting a linear ARX model and a fuzzy neural network-based linearization error model. In addition, the stable analysis is also discussed. Simulation results show that the feedforward control plays a major role in improving the control performance, and the proposed feedback-feedforward control possesses better performance
Differential detection acquisition for time-hopping sequence in ultra-wideband radio

In this paper, a novel acquisition approach termed differential detection acquisition (DDA) is proposed for time-hopping ultra-wideband (TH-UWB) signals. The approach is different from traditional acquisition approaches for it succeeds in transferring acquisition of whole TH sequence to detection of arriving times of several pulses. The mean acquisition time for this approach is not limited by the length of PN sequence and it can obtain the same acquisition probability performance as sliding correlation acquisition approach. Computer simulation results are presented to confirm the theoretical analysis.
Effect of including detector response in SPECT quantification of focal I-131 therapy

With a regularized strip-integral (1D) SAGE reconstruction, circular-orbit SPECT estimates of phantom focal 131-I activity vary with changes in the level of uniform background. They also vary with changes in image resolution due to different settings of the radius of rotation. To solve these problems, we investigated the effect of employing two different depth-dependent detector-response models. A regularized plane-by-plane (2D) SAGE algorithm reduced dependence of the counts-to-activity conversion factor on relative background concentration by 37% compared to the 1D SAGE. With unregularized multi-plane (3D) OSEM reconstruction, initial results showed: 1) a conversion factor that was independent of relative background concentration, and 2) a recovery coefficient that was approximately 1 for any sphere volume down to 20cc. We conclude that using a 3D detector-response model has the potential to eliminate bias problems. For a patient, the preliminary activity-estimate changes using 3D OSEM compared to 1D SAGE were: 1) +16% for a large tumor, and 2) -35% for a small tumor for which recovery-coefficient-based-correction-factor errors can be large.
Compact microstrip lowpass filter with sharp roll-off and wide stop-band

A microstrip lowpass filter with enhanced performance is developed. The studied filter is based on a novel patch resonator that features strong slow-wave effects, and can also implement either an improved roll-off or an enhanced stop-band. A demonstrator filter with 3 dB cutoff frequency fc =2.4=GHz is optimally designed, fabricated and measured. Results indicate that a roll-off of 92.5=dB/GHz together with a relative stop-band bandwidth of 135.5= (referred to a 30=dB suppression) is obtained while achieving a highest figure-of-merit of 10106. Both simulations and measurements are presented and compared.
Rudder/Flap Joint Intelligent Control for Ship Course System

The mathematic model of ship course rudder/flap joint control system is founded. The thought that rudder and flap are considered as two independent planes to design control rules is put forward for the first time. The calculation models of hydrodynamic coefficients of rudder/flap are acquired by means of the least square algorithm. To make full use of rudder/flap, the index function is given from the angle of system control performance and driving energy. The decision-making rules of rudder/flap angles are researched. And the genetic algorithm is adopted to optimize the rudder/flap angles. The method of infeasibility degree is applied to deal with the constrained condition of the main/flap angle. And some measures are used to improve the searching efficiency of the genetic algorithm. The system simulation is given, and the simulation result shows that, compared with the common rudder control system, this system has better control performance, stronger sea-situation-adapting ability, and much less energy consumption, etc. So this research has theory research meaning and engineering application value.
A computationally efficient approach to the estimation of two- and three-dimensional hidden Markov models

Statistical modeling methods are becoming indispensable in today's large-scale image analysis. In this paper, we explore a computationally efficient parameter estimation algorithm for two-dimensional (2-D) and three-dimensional (3-D) hidden Markov models (HMMs) and show applications to satellite image segmentation. The proposed parameter estimation algorithm is compared with the first proposed algorithm for 2-D HMMs based on variable state Viterbi. We also propose a 3-D HMM for volume image modeling and apply it to volume image segmentation using a large number of synthetic images with ground truth. Experiments have demonstrated the computational efficiency of the proposed parameter estimation technique for 2-D HMMs and a potential of 3-D HMM as a stochastic modeling tool for volume images.
Detecting Masked Faces in the Wild with LLE-CNNs

Detecting faces with occlusions is a challenging task due to two main reasons: 1) the absence of large datasets of masked faces, and 2) the absence of facial cues from the masked regions. To address these two issues, this paper first introduces a dataset, denoted as MAFA, with 30, 811 Internet images and 35, 806 masked faces. Faces in the dataset have various orientations and occlusion degrees, while at least one part of each face is occluded by mask. Based on this dataset, we further propose LLE-CNNs for masked face detection, which consist of three major modules. The Proposal module first combines two pre-trained CNNs to extract candidate facial regions from the input image and represent them with high dimensional descriptors. After that, the Embedding module is incorporated to turn such descriptors into a similarity-based descriptor by using locally linear embedding (LLE) algorithm and the dictionaries trained on a large pool of synthesized normal faces, masked faces and non-faces. In this manner, many missing facial cues can be largely recovered and the influences of noisy cues introduced by diversified masks can be greatly alleviated. Finally, the Verification module is incorporated to identify candidate facial regions and refine their positions by jointly performing the classification and regression tasks within a unified CNN. Experimental results on the MAFA dataset show that the proposed approach remarkably outperforms 6 state-of-the-arts by at least 15.6%.
Nonlinear hybrid adaptive inverse control using neural fuzzy system and its application to CSTR systems

A hybrid adaptive inverse control based on a neural fuzzy system is presented in this paper. It consists of two control loops, inverse control and PID control. PID control is a complement for inverse control and is mainly used to eliminate static error existing in direct inverse control when the inverse model is uncertain. For inverse control, the neural fuzzy system is utilized to construct the inverse controller (inverse model), which consists of two learning phases, improved nearest-neighborhood clustering algorithm and gradient descent algorithm. Finally, in order to test the effect of the proposed algorithm, it is applied to an exothermic CSTR. Simulation comparison with directed inverse control and PID control are also performed. The results show that the presented hybrid adaptive inverse control possesses better performance both in speediness and robustness and can be applied to a nonlinear system.
Linearization of phase modulation link using stimulated brillouin scattering

In this paper, we propose and experimentally demonstrate a linearization technique to modify the nonlinear transfer function of a phase modulation link. The phase modulation signal is converted into intensity modulation signal, and the phase of the optical carrier band is shifted. Eventually the third-order intermodulation distortion (IMD3) components produced by different optical spectrum components have opposite phase, with destructive combination in electronic domain, the IMD3 will be eliminated. Theoretical analysis is given and an experiment is conducted, results show that the spurious dynamic range is 106.7 dBHz2/3 for the linearized link, as compared with the link without linearization, an enhancement of 10.4 dB is achieved.
A printed trapezoid dipole broadband antenna

The feed part which uses gradually changing structure usually has wider impedance bandwidth. A kind of trapezoid dipole antenna that is similar to the parallel dual-feed is analyzed in the essay. Adopting the software of CST Microwave to simulate how the structure parameter of antenna influences impedance character, a type of dipole antenna working within 1.85∼3.75GHz is designed. The simulation results and experiments indicate that the antenna possesses wide impedance bandwidth. Meanwhile, the pattern is approximately omni-directional within the frequency band. So the antenna can be used in many fields, such as PCS, WLAN, WiMAX, and so on.
Chinese Prosodic Word Prediction Using the Conditional Random Fields

As the basic prosodic unit, the prosodic word play an important role for the naturalness and the intelligibility for the Chinese TTS system. Although many research work have been on this research direction, the precision of the prosodic word prediction is still not satisfying. In this paper, Conditional Random Fields is introduced to model the prosodic predicting process. In this model, more efficient features can be fused together. Compared with the ME model, the CRF model can describe the interacting relations between the neighboring prosodic words. The experiment results show that this Conditional Random Fields Model is competent for the prosodic word prediction task. The f-score of the prosodic word boundary prediction reaches 96.81%.
Correlation Function Algorithm of Time-Hopping Sequences for TH-UWB

Time-hopping (TH) sequences play an important role in time-hopping ultra wideband (TH-UWB) communication systems. The proper measure for the performance of TH sequences is time-hopping correlation function (TH-CF). For TH sequences with a long period, the total number of computations to implement directly TH-CF will be huge, and it will increase very rapidly as the period increases. Hence, it is of practical interest to develop more efficient algorithm for computing TH-CF. In this paper, by transforming the decimal TH-CF into a binary one, a fast TH-CF algorithm is proposed on the basis of the well-known fast Fourier transform (FFT). The presented algorithm can obviously decrease the number of computations of TH-CF, which will result in significant improvement in the computer-based evaluation of TH correlation properties
Layout design of multi-finger power SiGe HBTs for thermal stability improvement

This paper proposes the layout design of multi-finger power SiGe HBTs with non-uniform finger spacing to improve the thermal stability. Two types of 20-finger SiGe HBTs with uniform and non-uniform finger spacing are fabricated. Experimental results shown that, for the HBT with non-uniform finger spacing, the power level for thermal regression is 22.8% higher than that of the uniform one, which contributes to the improvement of the maximum temperature and the non-uniformity of the temperature profile in the device. Therefore, the temperature profiles of HBTs are simulated to directly show thermal stability and verified by the experiment result. Basing on the verified simulation, a further optimum layout design of HBT with non-uniform spacing is proposed. It is shown that the maximum temperature difference is improved by 79.28% for the further optimum HBT, when compared with that of HBT with uniform finger spacing. These results indicate that optimizing the layout of HBTs with non-uniform finger spacing is very useful for improving the thermal properties of power HBTs.
Fast Discrete Distribution Clustering Using Wasserstein Barycenter With Sparse Support

In a variety of research areas, the weighted bag of vectors and the histogram are widely used descriptors for complex objects. Both can be expressed as discrete distributions. D2-clustering pursues the minimum total within-cluster variation for a set of discrete distributions subject to the Kantorovich–Wasserstein metric. D2-clustering has a severe scalability issue, the bottleneck being the computation of a centroid distribution, called Wasserstein barycenter, that minimizes its sum of squared distances to the cluster members. In this paper, we develop a modified Bregman ADMM approach for computing the approximate discrete Wasserstein barycenter of large clusters. In the case when the support points of the barycenters are unknown and have low cardinality, our method achieves high accuracy empirically at a much reduced computational cost. The strengths and weaknesses of our method and its alternatives are examined through experiments, and we recommend scenarios for their respective usage. Moreover, we develop both serial and parallelized versions of the algorithm. By experimenting with large-scale data, we demonstrate the computational efficiency of the new methods and investigate their convergence properties and numerical stability. The clustering results obtained on several datasets in different domains are highly competitive in comparison with some widely used methods in the corresponding areas.
Experimental Studies of Cavity-Backed Bowtie Antenna with Pattern Improvement

In this paper, a cavity-backed antenna is proposed, and a bowtie exciter with four round corners is employed for wideband operation. The stable radiation patterns of the proposed antenna are achieved by a ring placed between the bowtie exciter and the ground plane. It is fed by a conventional balun (or a transition from microstrip line to a parallel stripline) and shows an impedance bandwidth of 78% for SWR les 2, together with stable and unidirectional radiation patterns along the whole operating band.
An improved method of using icesat altimetry data to extract Tibetan Plateau glacier thickness change rate

Glacier thickness change is a sensitive factor in response to global climate change, its quantitative assessment is critical to evaluate the variation of glacier mass balance. This paper analyzed and improved the method of planes fitted to repeat-tracks which uses Geoscience Laser Altimeter System (GLAS) data to extract glacier thickness change and applied it in the Tibetan Plateau area. The original plane fitting method was revised by curve fitting. Two extra parameters were added in the improved method so that the influence of complex terrain and of uncertainty due to sparse GLAS data on the estimate of glacier thickness change can be depressed. The improved method was applied to Naimona'Nyi glacier and Karakoram glacier in Tibetan Plateau area and results show that the glacier thinning rates reached -0.68±0.02 m/yr and -0.014±0.34m/yr respectively.
Multiresolution image classification by hierarchical modeling with two-dimensional hidden Markov models

This paper treats a multiresolution hidden Markov model for classifying images. Each image is represented by feature vectors at several resolutions, which are statistically dependent as modeled by the underlying state process, a multiscale Markov mesh. Unknowns in the model are estimated by maximum likelihood, in particular by employing the expectation-maximization algorithm. An image is classified by finding the optimal set of states with maximum a posteriori probability. States are then mapped into classes. The multiresolution model enables multiscale information about context to be incorporated into classification. Suboptimal algorithms based on the model provide progressive classification that is much faster than the algorithm based on single-resolution hidden Markov models.
Application of Artificial Neural Network in Urban Landscape Water Quality Evaluation

Water quality model is a useful tool to evaluate the future state of river water through evaluation of actual pollution loading or different management options. Based on the human brain physiology research, artificial neural network(ANN), which simulates the structure and mechanism of the human brain, is a kind of dynamic information processing system that eventually achieves certain functions of the human brain. At present, there are hundreds of neural network approaches&#65292;and also BP neural network is one of the most frequently used techniques in recent years&#65292;whose application in environmental quality assessment of environmental science field was presented in this paper. In this paper, BP neural network was used to establish the landscape water quality evaluation system. This paper also gave an optimization method of BP artificial neural network, which mainly used MATLAB programming whose toolbox provided a function of BP network to avoid complicated mathematical calculations and cumbersome code editor. On the basis of sampling and monitoring of water quality continuously, artificial neural network was used here to establish landscape water evaluation model in order to evaluate the landscape water quality objectively and quickly.