nils-herrmann/clean_scirepeval huggingface dataset:
Risk Adjustable Day-Ahead Unit Commitment With Wind Power Based on Chance Constrained Goal Programming

The impact of wind power forecast uncertainty has been amplified by the deepening wind power penetration. To guarantee system security and reliability, sufficient dispatchable generation and transmission capacities have to be reserved. Currently, research has been carried out to improve system operational performance by optimizing schedules considering uncertainty. However, most methods are designed to cover a given risk level of uncertainty, which is determined ex ante. With the increase in wind power capacity, defining the risk level a priori without considering the unit commitment (UC) may limit the scheduling efficiency. Essentially, there is no absolute standard for acceptable risk exposure. Instead, there is a tradeoff between the risk and the cost of reserve capacity. Therefore, it is necessary to develop a tool to enable a flexible and comprehensive consideration of the risk level. In this paper, by combining chance constrained programming and goal programming, a novel model based on chance constrained goal programming is proposed to optimize the risk adjustable UC problem. To facilitate an efficient solution, the proposed model is transferred into a tractable mixed integer linear programming problem by a deterministic equivalent and piecewise linearization. Case studies are performed on an IEEE 118-bus system to illustrate the effectiveness and efficiency of the model.
A new algorithm in network performance forecast based on FAC and episode rule

The performance forecast is a main aspect in network performance management. Aiming at multiple elements performance forecast, this paper proposes an algorithm based on fuzzy associative classifier (FAC) and enhanced frequent episode rule. Firstly, we present the framework of forecast system. Then introduce a new algorithm based on FAC and frequent episode rule. At last, combined with an instance, we recount the reasoning process in reasoning machine.
Segmented Reconstruction for Compressed Sensing SAR Imaging

The compressed sensing (CS) synthetic aperture radar (SAR) imaging scheme can use random undersampled data to reconstruct images of sparse or compressible targets. However, compared to Nyquist sampling, the cost of the CS imaging scheme is the long reconstruction time, particularly for the conventional reconstruction strategy, which reconstructs the whole scene in one process. It also needs a large memory to access the sensing matrix used for reconstruction. In this paper, a segmented reconstruction strategy for the CS SAR imaging scheme is proposed. The whole scene is split into a set of small subscenes, so that the reconstruction time can be reduced significantly. The proposed method also needs much less memory for computation than the conventional method. In this proposed method, the range profiles are reconstructed first, and then, the range profiles can be split into subpatches. Subscenes can be reconstructed by using the subpatch data, and the whole scene can be obtained by combining the reconstructed subscenes. Simulation and experimental results are shown to demonstrate the validity of the proposed method.
Performance of a chaotic binary sequence based multiple-access DCSK communication system

A new kind of multiple-access differential chaos shift keying (MA-DCSK) is presented. It is based on chaotic Binary sequence and Walsh codes, for convenience, we call it BW-MA-DCSK. In the proposed BWMA-DCSK system, the signals that come from the transmitter and the corresponding receiver are multiplied with the same Walsh codes. Due to the Walsh codes orthogonality and the constant instantaneous power of the chaotic binary signal, the interference between users will be greatly reduced. So, the BWMA-DCSK is capable of multiple access ability. The systempsilas bit error rate (BER) performance is analyzed and compared with that of other kind of MA-DCSK. The BER formula is evaluated and the simulation results match the numerical ones well which support the theoretical analysis.
Exploratory Under-Sampling for Class-Imbalance Learning

Under-sampling is a class-imbalance learning method which uses only a subset of major class examples and thus is very efficient. The main deficiency is that many major class examples are ignored. We propose two algorithms to overcome the deficiency. EasyEnsemble samples several subsets from the major class, trains a learner using each of them, and combines the outputs of those learners. BalanceCascade is similar to EasyEnsemble except that it removes correctly classified major class examples of trained learners from further consideration. Experiments show that both of the proposed algorithms have better AUC scores than many existing class-imbalance learning methods. Moreover, they have approximately the same training time as that of under-sampling, which trains significantly faster than other methods.
Switch linear hybrid power converter and its applications

A novel power conversion mode is structured by hybridizing switch power conversion mode with linear amplification mode, so as to acquire their synthetic optimal performances. The theoretical analyses and experimental results are presented in this paper. It is proved that the novel converters can match multi-kind of loads (including nonlinear loads) with strong robustness, good waveforms and high efficiency. Its application prospect can be expected, particularly for the conditions where serious static and dynamic performances are required. Simulation and experimental result show the truth of Switch Linear Hybrid power converter.
Image Retargeting Using Mesh Parametrization

Image retargeting aims to adapt images to displays of small sizes and different aspect ratios. Effective retargeting requires emphasizing the important content while retaining surrounding context with minimal visual distortion. In this paper, we present such an effective image retargeting method using saliency-based mesh parametrization. Our method first constructs a mesh image representation that is consistent with the underlying image structures. Such a mesh representation enables easy preservation of image structures during retargeting since it captures underlying image structures. Based on this mesh representation, we formulate the problem of retargeting an image to a desired size as a constrained image mesh parametrization problem that aims at finding a homomorphous target mesh with desired size. Specifically, to emphasize salient objects and minimize visual distortion, we associate image saliency into the image mesh and regard image structure as constraints for mesh parametrization. Through a stretch-based mesh parametrization process we obtain the homomorphous target mesh, which is then used to render the target image by texture mapping. The effectiveness of our algorithm is demonstrated by experiments.
Combination application of outpatient informationization and the SAFE-CARE system improve the level of outpatient medical service

This article is to investigate the role of outpatient informationization and the SAFE-CARE system in raising the level of outpatient high-quality medical service. It summarizes combination application experiences of outpatient informationization and the SAFE-CARE system to improve the level of outpatient medical service during the past year. Outpatient informationization has provided an efficient operational tool to improve the treatment efficiency; the SAFE-CARE system has guaranteed medical safety and quality, and enhanced the level of hospital management. Therefore, the combination application of outpatient informationization and the SAFE-CARE system is one of important measures to alleviate the difficulties of the masses medical treatment.
GPU-based FFBP algorithm for high-resolution spotlight SAR imaging

This paper presents the fast factorized back projection (FFBP) algorithm for high-resolution spotlight synthetic aperture radar (SAR) data processing. To improve the imaging efficiency by using FFBP algorithm, the FFBP imaging in graphics processing unit (GPU) is presented, and the processing efficiency under the different platforms (CPU/GPU) for the different scene sizes is analyzed, which lay the foundation for developing the quasi real-time/real-time spotlight SAR imaging strategy with the FFBP algorithm. Both simulated and real-measured SAR data experiments are studied to verify the feasibility and validity of the method.
One-Pass Learning with Incremental and Decremental Features

In many real tasks the features are evolving, with some features vanished and some other features being augmented. For example, in environment monitoring some sensors expired whereas some new ones were deployed; in mobile game recommendation some games dropped whereas some new ones were added. Learning with such incremental and decremental features is crucial but rarely studied, particularly when the data comes like a stream and thus it is infeasible to keep the whole data for optimization. In this paper, we study this challenging problem and present the OPID approach. Our approach attempts to compress important information of vanished features into functions of survived features, and then expand to include the augmented features. It is an one-pass learning approach, which only needs to scan each instance once and does not need to store the whole data, and thus satisfies the evolving streaming data nature. After tackling this problem in one-shot scenario, we then extend it to multi-shot case. Empirical study on a broad range of data sets shows that our approach can address this problem effectively.
Enhanced imaging for through-the-wall radar with noise power spectrum estimation

Due to the attenuation effects of walls, the signal to noise ratio (SNR) for targets indoor is unavoidably decreased, which seriously influences the quality of through-the-wall imaging (TWI) results. To suppress the deterioration of noise and clutters yield by walls, an adaptive noise power spectrum estimation method is put forward in this paper to enhance the performance of TWI. Through a combination of a pre-treatment on the received signal and a replacement of the short-time power spectrum with the Frequency-Doppler spectrum, the minimum statistic based noise estimation method can be effectively performed in TWI. A threshold based on the method is chosen then to suppress the interference of noise. Experiment on practical TWI radar echo data validates the proposed method.
Automatic Detection of Lumina in Mouse Liver Immunohistochemical Color Image Using Support Vector Machine and Cellular Neural Network

A novel method for automatic lumina detection is proposed according to the characteristics of immunohise chemical color images for mouse liver. Firstly, the classification model of support vector machine was generated by the trained sample pixels based on the texture features: entropy, standard deviation and range in G component of the original color image. Secondly, the trained model is used to classify all pixels in image. Thirdly, Extracting Closed Domain Cellular Neural Network and median filtering were applied in turn to eliminate the misclassified pixels in last step. The expert's evaluation of our method showed that our method can get 76% classification accuracy, which are comparable to an pathologist's judgement.
Agent-based electricity market simulation with plug-in hybrid electric vehicle penetration

In this paper, we simulate an electricity market with PHEV penetration by using agent-based modeling and simulation (ABMS). We model the PHEV users as agents which have unique decision making processes to plan their daily trips and determine their charging schedules. Moreover, we study the impact of different PHEV penetration levels on the power system. The electricity demand from PHEVs is supplied by the electricity market. Based on the simulation model, we observe the PHEV owners' payment and investigate the relation to different market structures.
A closed-form robust Chinese remainder theorem Based multibaseline phase unwrapping

Multibaseline phase unwrapping (PU) is a critical processing procedure of multibaseline synthetic aperture radar interferometry (InSAR). The presence of phase noise makes Chinese remainder theorem (CRT) method not robust enough and inapplicable in practical cases. In this letter, a method based on a closed-form robust Chinese remainder theorem (CFRCRT) is presented to improve the noise robustness of the multibaseline PU. Experimental results prove the effectiveness of CFRCRT method and demonstrate that the CFRCRT method is more robust to noise than the CRT method.
Research on Real-Time Flight Path Planning of UAV Based on Grey Prediction

Unmanned aerial vehicle (UAV) path planning under complex environment, especially under the threat of moving targets is a research difficulty in the field in those years. Aiming at above problem, a method based on MGM (1, n) model for predicting threat of moving targets three-dimensional coordinate of real-time path planning is putted forward. The method is to build three-dimensional search space base on UAV mobility firstly, and organize optimization function by considering the fuel consumption, threat of moving targets location and other variable multi-objective, then get the best track points by the improved chaos fish swarm algorithm to avoid the hit of dynamic targets. The experimental result shows the path planning method we studied can avoid the randomly moving target effectively on flying space, verified the feasibility and effectiveness of the proposed method.
Stationary human micro-motion trajectory extraction based on edge detection in through-the-wall radar

Stationary human micro-motion trajectory in slow time range domain provides more original motion information, including periodical respiration and heart beating, as well as random body movement and sudden body shaking, whose range often outstrips the periodical movement. Phase contour, amplitude contour and peak locus are all the represent of stationary human micro-motion trajectory. Due to the phase sudden change from π to -π, the phase contour is more robust and stable than the other two loci. In this paper, a stationary human micro-motion trajectory extraction approach based on edge detection is presented. Firstly, the stationary human micro-motion model is established and the effect brought by the wall is analyzed. Then the phase contour in slow-time range domain is introduced as a representation of micro-motion trajectory. Finally, the phase contour extraction based on edge detection is presented. Experimental results demonstrate that the proposed approach can extract stationary human trajectory correctly and accurately, after band pass filtering, the vital sign of human breathing can be extracted effectively.
The reduction of electromagnetic interference in RF integrated circuits through the use of metalized substrates

This paper discussed the use of metalized substrates for coupling (EMI) reduction of integrated circuit components. Coupled microstrip lines with arrays of shorting vias or mushrooms were used for demonstration. It was found that the metalized substrate could reduce the circuit coupling at lower frequencies due to the shielding effect and enhance the coupling at much higher frequencies due to the scattering and radiation of the metalized substrate. It was also found that the use of metalized EBG elements over the entire substrate, although eliminating the surface wave, would enhance the circuit coupling by introducing additional capacitance between strips at higher frequencies. At lower frequencies, the inductive vias are effectively shorted to the ground and provide shielding to reduce circuit coupling
The Finite Element Analysis of the Large Span Steel Truss Arch Bridge's Node Fatigue Test

Chongqing Chaotianmen Yangtze River Bridge with the main span of 552m is an urban bridge of double deck bridges for highway and rail, which need to verify fatigue security performance of the node on bottom chord . Through the calculation of the full bridge's space finite element, the node for study is determined as the M29 and calculate and calculate the internal force influence line of M29. Load according to standard fatigue vehicle of AASHTO, BS5400 and metro standard and obtain the internal force history of M29. Based on rain-flow counting method and Miner's law of fatigue damage, identify the load of 2 million cycles of fatigue test in this model of joint test. According to the result of finite element calculation,complete the conversion from the nodes of actual bridge to the large-scale model of 1/2 and the node's fatigue test. Through the comparison of the experimental results and theoretical calculation, the authenticity and reliability of test data and the safety of the joint connection during the 2 million's fatigue life period are verified. Finally, the calculation results of the node model' fatigue life shows that the lower part of the connection between the transverse beam's web and angle steel, which is verified by fatigue tests. The research results provide reference for the fatigue test and structure design of this kind of bridge in the future.
Constrained Monotone  $k$ -Submodular Function Maximization Using Multiobjective Evolutionary Algorithms With Theoretical Guarantee

The problem of maximizing monotone  ${k}$ -submodular functions under a size constraint arises in many applications, and it is NP-hard. In this paper, we propose a new approach which employs a multiobjective evolutionary algorithm to maximize the given objective and minimize the size simultaneously. For general cases, we prove that the proposed method can obtain the asymptotically tight approximation guarantee, which was also achieved by the greedy algorithm. Moreover, we further give instances where the proposed approach performs better than the greedy algorithm on applications of influence maximization, information coverage maximization, and sensor placement. Experimental results on real-world data sets exhibit the superior performance of the proposed approach.
Fast Time-Domain Imaging in Elliptical Polar Coordinate for General Bistatic VHF/UHF Ultra-Wideband SAR With Arbitrary Motion

In this paper, a fast time-domain imaging algorithm called bistatic fast factorized back projection algorithm (BFFBPA) is proposed for the general bistatic VHF/UHF ultra-wideband synthetic aperture radar. It cannot only accurately dispose the large spatial variant range cell migrations, serious range-azimuth coupling and complicated motion error, but also achieve the imaging efficiency similar to frequency-domain algorithms. It represents subimages in elliptical polar coordinate to reduce the computational load. The imaging geometry with arbitrary motion in this coordinate system is provided, and the bistatic back projection algorithm (BPA) is derived to provide a basis for the proposed BFFBPA. Considering motion errors, the more accurate sampling requirements for elliptical subimages is deduced to offer the near-optimum tradeoff between the imaging quality and efficiency, and the constraint of motion errors for acceptable sampling requirements is discussed. Based on this sampling requirement, the advantage of using elliptical subimage grids for this BFFBPA is analyzed. A phase error correction is performed to reduce the impact of phase errors caused by interpolations in the BFFBPA. The speed-up factor of this BFFBPA with respect to the bistatic BPA is derived. Simulation results and evaluations are given to prove the correctness of the theory analysis and validity of the proposed method.
A trajectory prediction method based on aircraft motion model and grey theory

Aiming at the problem of trajectory prediction in air traffic control automation system, a new method of trajectory prediction based on aircraft motion model and grey theory is proposed. This method combines the aircraft motion model and the prediction method of grey theory, which realizes the real-time online trajectory prediction and improves the prediction accuracy. This prediction method has strong applicability and is more simple and easy compared with the traditional method, and it is more accurate than the traditional data mining method. Finally, the effectiveness of the method proposed in this paper is validated through the case of civil aviation.
A Study on the Velocity Ratio Control of the Tractor Continuously Variable Transmission

The speed ratio regulation law is one of the main bases in the matter of the continuously variable transmission control; it is also the foundation for bringing into play the superior transmission function of the continuously variable transmission. Based on the study of the working procedure of the engine, this paper elaborates the optimal fuel economy and the optimal power performance work curve of the engine. In accordance with the working features of agricultural tractors, a speed ratio regulation law on the optimal fuel economy and the optimal power performance is formulated concerning the continuously variable transmission of agricultural tractors; meanwhile the controller of velocity ratio is also engineered and has undergone experimental verification by way of simulation mode.
Error concealment based on robust optical flow

During transmission of encoded digital video sequences over noisy communication channels, the information of the images is altered or lost. A new error concealment method based on robust optical flow is proposed, as a post-processing tool at the decoder side to solve this problem. The conventional error concealment methods often result in blocking artifacts because of inaccurate motion vector estimation. We use the attractive property of optical flow technique on motion estimation to recover the lost block in a pixel-wise manner. In order to increase the robustness, the Lorentzian function is used to construct the energy function. Simulation results show that the proposed method can recover the higher quality image on both subjective visual evaluation and objective numerical metrics, compared to the existing error concealment methods.
Random-Frequency SAR Imaging Based on Compressed Sensing

Stepped-frequency waveforms can achieve an ultrawide bandwidth by using a sequence of single-frequency pulses. The advantages of stepped-frequency waveforms are low hardware requirements and high resolution. However, the stepped-frequency waveform requires a long time period to transmit the signals, which limits its application in synthetic aperture radar (SAR). The available imaging range width is usually very narrow, unless the range and azimuth resolutions are both decreased. In this paper, a random-frequency SAR imaging scheme based on compressed sensing is proposed. If the targets are sparse or compressible, it is sufficient to transmit only a small number of random frequencies to reconstruct the image of the targets. This means that the limitations of the stepped-frequency technique for SAR can be overcome. The available imaging range width can be enlarged significantly, while the range and azimuth resolutions are both maintained. Random undersampling is very easy to implement for both range and azimuth dimensions, and no new hardware components are needed. Simulation and experimental results are presented to demonstrate the validity of the proposed method.