nils-herrmann/clean_scirepeval huggingface dataset:
Landmine feature extraction in UWB SAR based on sparse representation

Ultrawide Band Synthetic Aperture Radar (UWB SAR) is an alternative to detect landmines. The echo of a landmine has “double-hump” signature, which corresponds to the returns of front and rear edges of the top of the landmine. However, their echo traces do not fit with the corresponding integral traces of the SAR imaging model, which can lead to defocusing in the SAR image. In this paper, we construct two dictionaries for the front peak and rear peak, respectively. Then we find the optimally sparse representation of each peak of the double-hump signature via basis pursuit algorithm. The results of the real data experiment show the validity of the method.
Ero-multiplication TX-filter structure for eband transmission system

In order to reduce the hardware complexity and unify the sampling rate of transmit filter (TX-filter) module, a novel lookup table (LUT) based zero-multiplication TX-filter structure is proposed for E-band microwave transmission system. Simulation results demonstrate that the proposed structure needs only 16.7% addition operation compared with conventional convolutional based solutions, no multiplexing operation included and an arbitrary sampling rate transform integrated simultaneously.
DC-Link Voltage Regulation of Bidirectional Quasi-Z-Source Inverter for Electric Vehicle Applications

In order to regulate the dc-link voltage and achieve the bidirectional power flow capability, a traction driving system with bidirectional quasi-Z-source inverter (BQ-ZSI) for electric vehicles (EV) is proposed in this paper. Based on analyzing the circuit structure and small signal model, oscillation of the dc-link voltage of BQ-ZSI could be caused when the disturbances on dc input voltage occur. By producing a dc-link voltage controller with a capacitor voltage feedback control in addition to dc input voltage and inductor current feed-forward compensation, the oscillation of the dc-link voltage and inductor current produces by the disturbance on dc input voltage can be suppressed. Simulation results show that EVs can be operated among startup, climbing, acceleration and regenerative braking modes with a stable dc-link voltage. The proposed control strategy can effectively stabilize the dc-link voltage of the BQ- ZSI traction driving system when the dc input voltage varies and the operation mode of EV changes.
Self-dual basis functions for solving surface Magnetic Field Integral Equation

Dual basis functions (DBFs) have been used widely in conjunction with primary basis functions (PBFs) in the Method of Moments (MoM) for solving electromagnetic integral equations. The DBFs are designed approximately orthogonal to the PBFs, and hence, the DBFs and PBFs reside in different functional spaces. This results in an asymmetry in the impedances of Magnetic Field Integral Equation (MFIE). A question naturally arises, i.e., whether there exist a set of basis functions (BFs) that are dual to themselves. We give an affirmative answer to this question by presenting a set of self-dual basis functions (SDBFs).
A study of preparation and optical properties of the white OLEDs stacked with YAG and Sr2Si5N8:Eu phosphors based color conversion layers

Hybrid organic-inorganic white light emitting devices were fabricated by the phosphor cerium-doped yttrium aluminum garnet (YAG) and nitridosilicate Sr2Si5N8: Eu (258) with blue OLEDs, which emit electroluminescence spectrum with a dominant peak at 470 nm. The phosphors with different concentration were doped become color conversion layers (CCL), which could absorb blue light effectively for red and yellow, and finally mixes the unabsorbed blue emission to produce white light emission. When the concentration of YAG and 258 phosphors were fixed at concentration of 10wt% and 6wt%, respectively, with 30μm thickness of CCL and current density of 10 mA/cm2, the CIE and the luminous efficiency were (0.28,0.43) and 13.1 cd / A, respectively. All the results indicated that the hybrid organic-inorganic is a feasible approach to obtain white light emitting devices.
A cross-layer adaptive resource allocation algorithm based on impartiality of wait time

A cross-layer adaptive resource allocation algorithm of multi-user MIMO OFDM systems is proposed and simulated. The aim is to maximize the transmit rate under the constraints of the fixed transmit power and QoS. The algorithm exploits the fair of wait time of each user to allocate subcarriers whose allocation standard is the spatial subchannel whose SNR is best, bits and power of subcarriers and spatial subchannels. Simulation results show that the algorithm can satisfy the fair of wait time and QoS of each user, and the total rate can be increased significantly.
Training Cost-Sensitive Neural Networks with Methods Addressing the Class Imbalance Problem

This paper studies empirically the effect of sampling and threshold-moving in training cost-sensitive neural networks. Both oversampling and undersampling are considered. These techniques modify the distribution of the training data such that the costs of the examples are conveyed explicitly by the appearances of the examples. Threshold-moving tries to move the output threshold toward inexpensive classes such that examples with higher costs become harder to be misclassified. Moreover, hard-ensemble and soft-ensemble, i.e., the combination of above techniques via hard or soft voting schemes, are also tested. Twenty-one UCl data sets with three types of cost matrices and a real-world cost-sensitive data set are used in the empirical study. The results suggest that cost-sensitive learning with multiclass tasks is more difficult than with two-class tasks, and a higher degree of class imbalance may increase the difficulty. It also reveals that almost all the techniques are effective on two-class tasks, while most are ineffective and even may cause negative effect on multiclass tasks. Overall, threshold-moving and soft-ensemble are relatively good choices in training cost-sensitive neural networks. The empirical study also suggests that some methods that have been believed to be effective in addressing the class imbalance problem may, in fact, only be effective on learning with imbalanced two-class data sets.
A New Belief-Rule-Based Method for Fault Diagnosis of Wireless Sensor Network

In the technology of wireless sensor network (WSN), wireless sensor fault diagnosis based on fusion data analysis has attracted attention in the wireless sensor field. It can detect and correct the faults of sensor nodes in time to improve the accuracy of sensor data fusion. In this paper, the data characteristics of WSN are analyzed, and a method is proposed for fault diagnosis of WSN based on a belief rule base (BRB) model. First, the sensor fault diagnosis process is described based on the characteristics of a wireless sensor in WSN. Then, the characteristics of sensors are analyzed from the aspects of time, space, and attributes. Finally, a fault diagnosis model is proposed based on the hierarchical BRB model. To make the results more accurate, a covariance matrix adaptation evolution strategy algorithm is used to optimize the initial parameters of the proposed model. A case study using the Intel lab data set of sensors is designed to verify the effectiveness of the proposed model. The results show that the proposed method is effective in fault diagnosis of WSN.
A novel memetic algorithm with random multi-local-search: a case study of TSP

Memetic algorithms (MAs) have been shown to be very effective in finding near optimal solutions to hard combinatorial optimization problems. We propose a novel memetic algorithm (MsMA), in which a new local search scheme is introduced. We called this local search scheme as random multi-local-search (MLS). The MLS is composed of several local search schemes, each of which executes with a predefined probability to increase the diversity of the population. The combination of MsMA with the crossover operator edge assembly crossover (EAX) on the classic combinatorial optimization problem traveling salesman problem (TSP) is studied, and comparisons are also made with some best known MAs. We have found that it is significantly outperforming the known MAs on almost all of the selected instances. Furthermore, we have proposed a new crossover named M-EAX, which has more powerful local search ability than the EAX. The experimental results show that the MsMA with M-EAX has given a further improvement to the existing EAX.
A substrate integrated waveguide slot antenna for 79-GHz applications

A substrate integrated waveguide (SIW) slot antenna for 79GHz applications is proposed in this paper. In order to get the high gain and lower losses, the antenna is designed as a sub-array composed with eight slots, which fabricated in the wide side plane of the SIW, and placed along the central axis with the equal interval of half waveguide wavelength. The slots can radiate the electromagnetic energy through the field disturbance caused by the plated-through hole (PTH) positioned beside the slots. The antenna is fed by the 50 û coaxial-fed structure located from the bottom of SIW. This bottom-fed method is different from the traditional microstrip feeding method, it has more convenience to extend the 2-dimensional phased array through multiple independent subarrays in the 79-GHz applications. The simulated results of the subarray indicate that the frequency band with |S11|<−10dB is from 78GHz to 80GHz, and the cross-polarization is lower than −50dB. An array model with 3×5 subarrays is also fabricated and simulated, it is found that the array radiation efficiency can be evaluated larger than 80%, and the patterns are also extremely well qualified. The performances confirm that the proposed SIW slot antenna is suitable for the millimeter-wave electronic systems with the requirement of array antenna.
Image Segmentation Based on the Poincaré Map Method

Active contour models (ACMs) integrated with various kinds of external force fields to pull the contours to the exact boundaries have shown their powerful abilities in object segmentation. However, local minimum problems still exist within these models, particularly the vector field's “equilibrium issues.” Different from traditional ACMs, within this paper, the task of object segmentation is achieved in a novel manner by the Poincaré map method in a defined vector field in view of dynamical systems. An interpolated swirling and attracting flow (ISAF) vector field is first generated for the observed image. Then, the states on the limit cycles of the ISAF are located by the convergence of Newton-Raphson sequences on the given Poincaré sections. Meanwhile, the periods of limit cycles are determined. Consequently, the objects' boundaries are represented by integral equations with the corresponding converged states and periods. Experiments and comparisons with some traditional external force field methods are done to exhibit the superiority of the proposed method in cases of complex concave boundary segmentation, multiple-object segmentation, and initialization flexibility. In addition, it is more computationally efficient than traditional ACMs by solving the problem in some lower dimensional subspace without using level-set methods.
Reducing Electricity Price Forecasting Error Using Seasonality and Higher Order Crossing Information

Commodity prices are volatile and their volatility changes over time. Electricity prices are even more volatile in general because customers cannot easily switch to other energy source when the electricity prices fluctuate. Electricity prices also have strong seasonal behavior because the demand of electricity depends on the season. To address these characteristics, a time series forecasting model with seasonality and higher order crossing information for electricity prices is proposed. The model measures the similarity between two time series by using their higher order crossing information. The model can predict not only day-ahead prices (short term forecasting), but also a series of prices for one week ahead or even longer simultaneously (medium term price-curve forecasting). The model is tested on both high and low volatile data sets to evaluate its robustness and generality. It is also shown that the model can produce more accurate prediction than some well-known models.
New design of a three-terminal memristor emulator

An emulator circuit is newly designed in this paper by making use of common off-the-shelf active devices for mimicking the dynamic behaviors of the three-terminal memristors (3T-MRs). The theoretical calculation shows that the equivalent memductance of the 3T-MRs emulator can be flexibly controlled by imposing excitation voltage with different amplitude, frequency and duty cycle on the third terminal. For the purpose of testing the controllability and emulation performance of the proposed emulator, simulation investigation is carried out based on PSpice software. The simulation results show good agreement with theoretical analysis, which confirms that the memductance of this 3T-MRs emulator can be conveniently controlled and can be utilized for further investigating the dynamic behaviors of 3T-MRs.
Image formation techniques for Vehicle-Mounted Forward-Looking Ground Penetrating SAR

A vehicle-mounted forward-looking ground penetrate synthetic aperture radar (VMFL-GPSAR) is newly developed in China for real-time mine detection. The VMFL-GPSAR is an impulse radar system. With a 6 meter long antenna array, the radar can output 0.15 m by 0.15 m real-time images at a velocity of 15 Km/h. This paper presents some key techniques in developing the system, including antenna dispersion compensation, channel equalization, RFI suppression, fast backprojection image formation algorithm and along range multi-look image enhance algorithm. Tests conducted in Changsha last year indicate that the system can detect both metallic and plastic landmines.
Energy Storage Arbitrage Under Day-Ahead and Real-Time Price Uncertainty

Electricity markets must match real-time supply and demand of electricity. With increasing penetration of renewable resources, it is important that this balancing is done effectively, considering the high uncertainty of wind and solar energy. Storing electrical energy can make the grid more reliable and efficient and energy storage is proposed as a complement to highly variable renewable energy sources. However, for investments in energy storage to increase, participating in the market must become economically viable for owners. This paper proposes a stochastic formulation of a storage owner's arbitrage profit maximization problem under uncertainty in day-ahead and real-time market prices. The proposed model helps storage owners in market bidding and operational decisions and in estimation of the economic viability of energy storage. Case study results on realistic market price data show that the novel stochastic bidding approach does significantly better than the deterministic benchmark.
An Efficient Parallel Approach for Sclera Vein Recognition

Sclera vein recognition is shown to be a promising method for human identification. However, its matching speed is slow, which could impact its application for real-time applications. To improve the matching efficiency, we proposed a new parallel sclera vein recognition method using a two-stage parallel approach for registration and matching. First, we designed a rotation- and scale-invariant Y shape descriptor based feature extraction method to efficiently eliminate most unlikely matches. Second, we developed a weighted polar line sclera descriptor structure to incorporate mask information to reduce GPU memory cost. Third, we designed a coarse-to-fine two-stage matching method. Finally, we developed a mapping scheme to map the subtasks to GPU processing units. The experimental results show that our proposed method can achieve dramatic processing speed improvement without compromising the recognition accuracy.
A stereo vision system for traffic accident investigation

In this paper, an application of stereo vision on traffic accident investigation is presented. Self-calibration of the stereo rig is utilized to get affine reconstruction. The external parameters between the two cameras are pre-calibrated which help us upgrade the affine structure to Euclidean. So the system not only can measure the real distance in Euclidean space, but also can be a varying focus system. This means the system is more flexible than most measurement systems. Experiment results validate the performance of the system
A novel genetic algorithm based on multi-species

This paper presents a novel genetic algorithms based on multi-species with a new mutation operator. The method is designed for optimizing complex multimodal functions in which the standard genetic algorithms (SGA) always gets struck into a local optimum. The paper introduces a new structure based on main species and additional species to avoid the premature convergence of SGA. In this structure, the main species use a new mutation operator to keep the population diversity in the entire search space and acquire the fast increasing of better models, and the additional species are designed to get local optima in the specified regions. The storing of the research history and the communication between the main and additional species help to decrease the research space for acquiring a global optimum and several local optima. The experiments performed to optimize several complex multimodal functions have acquired good results.
Application of Multi-Agent for Collaborative Product Design Engineering

The manufacturing industries are now experiencing fierce pressure of competition. In addition, the advancement in computer networks and information technologies has been gradually reshaping the manufacturing companies by shifting from the industrial age to the information era. A number of new manufacturing and management strategies have emerged for the innovation of manufacturing enterprises. This paper proposes a multi-agent collaborative design system to support the enterprise functions/resources integration and collaboration over the networked environment. The realizing method of the design project of the multi-agent collaborative design system is discussed. An Application example is also presented.
A New Evidential Reasoning-Based Method for Online Safety Assessment of Complex Systems

It is vital to online assess the safety of a complex dynamic system by taking into account the current state, degradation trend, and historical records together. This paper proposes a new safety assessment model with an online algorithm based on the evidential reasoning (ER) approach. It does not only take into account the relative importance of each safety indicator, but also consider the reliability of each indicator. To obtain the integrated safety level, multiple safety indicators are fused at first and the “history,” “current,” and “future” safety states are then integrated. First, a forecasting model based on the third-order Volterra filter is proposed to predict the safety indicators’ information online. Second, an adaptive weighting model is developed to automatically adjust to various conditions and track the characteristics of the dynamic system, and the reliability of each indicator is considered to diminish the influence of inherent disturbance and/or noise. Finally, a safety assessment aggregation scheme based on the ER approach is presented to fuse the history, current, and future safety indicators to obtain the corresponding safety state, and the safety states are then fused synthetically to obtain a comprehensive safety assessment result of the complex dynamic system. A numerical study is examined to demonstrate the implementation and effectiveness. Moreover, a practical example of the inertial navigation system is studied to show the potential applications of the proposed ER-based safety assessment method.
A calibration method based on fusing multiple calibrators for UWB-VAR system using stepped frequency

The system calibration is an important factor for the detecting effect of UWB-VAR (Ultra Wide Band Virtual Aperture Radar) which can penetrate the earth to explore the weak scattering target flush buried in the shallow seam. The regular system calibration method developed for high frequency narrow band radar is based on one single calibrator, which is not applicable to the UWB-VAR system owning to its ultra bandwidth and inconsistency of multi-channel. After the research on the system errors and the electromagnetism of both calibrators and landmines, a new method based on the fusion of multi-calibrators and multi-band is introduced in this paper. This new method would not only calibrate the system errors efficiently, but enhance the performance of imaging. Finally the real data has proved that such a method can be effective.
Perceptual-Based Fusion of Infrared and Visual Images in Nonsubsampled Contourlet Transform

In this paper, a perceptual-based fusion method for infrared and visual images is proposed. Many methods based on gradient and energy have been used in the literature, but these methods are not considered the human visual system (HVS) and their experiment results are less than ideal. So a method depending on contrast sensitivity function (CSF) based on the respond of HVS as a criteria to determine the salient features of infrared and visual images to decide how to fusion is proposed. In this method Fourier transform is taken, which is sensitive to contrast change and is as an important criteria to form a salient metric. Comparing with other fusion methods, experiment results also prove that the proposed method is more outstanding.
A statistics based approach for extracting priority rules from trained neural networks

In this paper, a statistics based approach named STARE (statistics-based rule extraction) that is designed to extract symbolic rules from trained neural networks is proposed. STARE deals with continuous attributes in a unique way so that not only different attributes could be discretized to different number of clusters but also unnecessary discretization could be avoided. STARE introduces statistics to the generation and evaluation of priority rules that have concise appearance. Since it is independent of the network architectures and training algorithms, STARE could be applied to diversified neural classifiers. Experimental results show that rules extracted via STARE are comprehensible, compact and accurate.
Improve Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples

In computer-aided diagnosis (CAD), machine learning techniques have been widely applied to learn a hypothesis from diagnosed samples to assist the medical experts in making a diagnosis. To learn a well-performed hypothesis, a large amount of diagnosed samples are required. Although the samples can be easily collected from routine medical examinations, it is usually impossible for medical experts to make a diagnosis for each of the collected samples. If a hypothesis could be learned in the presence of a large amount of undiagnosed samples, the heavy burden on the medical experts could be released. In this paper, a new semisupervised learning algorithm named Co-Forest is proposed. It extends the co-training paradigm by using a well-known ensemble method named Random Forest, which enables Co-Forest to estimate the labeling confidence of undiagnosed samples and easily produce the final hypothesis. Experiments on benchmark data sets verify the effectiveness of the proposed algorithm. Case studies on three medical data sets and a successful application to microcalcification detection for breast cancer diagnosis show that undiagnosed samples are helpful in building CAD systems, and Co-Forest is able to enhance the performance of the hypothesis that is learned on only a small amount of diagnosed samples by utilizing the available undiagnosed samples.