nils-herrmann/clean_scirepeval huggingface dataset:
A Novel Moving Target Imaging Algorithm for HRWS SAR Based on Local Maximum-Likelihood Minimum Entropy

For high-resolution wide-swath (HRWS) SAR based on multiple receive apertures in azimuth, this paper proposes a novel imaging approach for moving targets. This approach utilizes the wide bandwidth characteristics of the transmitted signal (multiple wavelengths) to estimate the moving target velocity. First, this paper explains that there is a phase mismatch (PM) between azimuth channels for the echo of a moving target, which depends on range frequency. In order to correct the PM, an algorithm based on local maximum-likelihood minimum entropy is proposed. The linear dependence of the PM on range frequency is employed to estimate the target velocity. Second, after the signal reconstruction in Doppler frequency and the compensation of the PM for a moving target, the estimated target velocity is utilized to implement the linear range cell migration correction and the Doppler centroid shifting. Then, the quadratic range cell migration is corrected by the keystone processing. After that, the focused moving target image can be obtained using the existing azimuth focusing approaches. Theoretical analysis shows that no interpolation is needed. The effectiveness of the imaging algorithm for moving targets is demonstrated via simulated and real measured ship HRWS ScanSAR data.
An Empirical Research on the Relevance of Fair Value

Research on the fair value is always the key problem and also the crucial problem of the world accounting research, especially in the financial crisis. This paper provides data support for the empirical research of the relevance of fair value in China. By analyzing the financial data of listed commercial banks in China in 2006 and 2007 through an empirical method, the changes of explanation of book value per share and earnings per share to share price before and after the application of fair value are compared. The results show that earnings per share under fair value measurement have incremental explanatory power to stock prices. That is, the economic effect of fair value is positive.
A fast algorithm for volume integral equation using interpolative decomposition and multilevel fast multipole algorithm

The fast algorithm which combines interpolative decomposition (ID) and the multilevel fast multipole algorithm (MLFMA) is extended to the method of moments (MoM) solution of volume integral equations (VIE) from that of surface integral equations (SIE). Boxes in the finest level in MLFMA are further divided and the rank-deficient submatrices of the near-field interactions are approximated by the data sparse representation through the ID. Numerical experiments are conducted to study the performance of the fast solver.
A Fast Search Algorithm of Anomaly in Data Streams Based on Shifted Wavelet Tree

Anomaly detection is to find aggregate which is different from the most aggregates. Aiming at the limitation of the anomaly detection algorithm of shifted wavelet tree (SWT) in data streams, we propose the improved algorithm which constructs the monotonic search space for binary search after removing the disturbance of bumps to increase the efficiency of detection, and uses the real-time incremental update algorithm for meeting the requirement of the online processing of the data streams. The simulation experiments using two data sets of the Gamma Ray and the Power Quality Disturbance (PQD) verify the high effectiveness and accuracy of our algorithm.
Adaptive Robust Finite-Time Trajectory Tracking Control of Fully Actuated Marine Surface Vehicles

In this brief, an adaptive robust finite-time tracking control (ARFTTC) scheme for trajectory tracking of a fully actuated marine surface vehicle with unknown disturbances is proposed. A new finite-time disturbance observer is incorporated into the proposed finite-time tracking control (FTTC) structure that facilitates faster convergence and better robustness to disturbances. Hence, in the presence of unknown disturbances, the ARFTTC can cause tracking error to converge to zero in a finite time. Simulation studies and comprehensive comparisons with conventional backstepping technique demonstrate remarkable performance and superiority of the ARFTTC in terms of both tracking accuracy and robustness.
Design of testing system for accelerometer based on GP-IB

Through analyzing the situation of accelerometer testing system, there are several bottleneck factors such as various testing procedure, complicated operation, and easy to cause mishandling which have restricted the accelerometer's automatic testing capability. To calibrate the performance parameters better, the testing system for accelerometer based on GPIB is designed, and the composition of the system is introduced. According to the characters of high accuracy and faint feedback signals of accelerometer, the digital multimeter technique is applied to realize the accurate test of the accelerometer's faint output signals through its favorable signal processing and noise limiting capacity. At the same time, for the small sample of test data, double modified interpolation is used to process the obtained data, enlarge the sample size and solve the problem that it's difficult to build times-series model with a small sample. Comparing with operational testing system, advanced technology, comprehensive function and powerful data processing & analyzing capacity are main advantages of this testing system. The application indicates that this precise and steady testing system can meet the requirement of accelerometer calibrating & testing completely.
An Improved Competitive and Cooperative Learning Approach for Data Clustering

The recently proposed Competitive and Cooperative Learning algorithm(CCL) (Cheung 2004) has provided a promising way to perform the data clustering without know- ing the number of clusters. Nevertheless, its performance is somewhat sensitive to the initialization of seed points. Also, its cooperative mechanism is applicable to the homogenous clusters only. In this paper, we will therefore suggest using the FSCL algorithm to initialize the seed points such that each cluster of data will at least have a seed point. Fur- thermore, we update the cooperation radius of seed points in CCL, whereby the improved CCL (ICCL for short) can be applicable to the heterogeneous clusters as well. Exper- iments show the efficacy of the proposed algorithm.
A new morphological algorithm for contour extraction on ultrasound image

This paper describes a new algorithm for contour extraction employing fuzzy sets theory together with morphological image processing techniques. The contour extraction is accomplished in two stages. The first stage employs fuzzy sets theory to enhance image. The quality of ultrasound image is improved in this stage. The second stage extracts the image contour effectively employing morphological theory. The validity of the approach is demonstrated using several ultrasound images.
A New High Efficiency Current Source Driver With Bipolar Gate Voltage

A novel bipolar current source driver (CSD) for power MOSFETs is proposed in this paper. The proposed bipolar CSD alleviates the gate current diversion problem of the existing CSDs by clamping the gate voltage to a flexible negative value (such as -3.5 V) during turn-off transition. Therefore, the proposed driver is able to turn off the MOSFET much faster with a higher effective gate current. The idea presented in this paper can also be extended to other CSDs to further improve the efficiency with high output currents. The experimental results verify the benefits of the proposed CSD. For buck converters with 12 V input at 1 MHz switching frequency, the proposed driver improves the efficiency from 80.5% using the existing CSD to 82.5% (an improvement of 2%) at 1.2 V/30 A, and at 1.3 V/30 A output, from 82.5% using the existing CSD to 83.9% (an improvement of 1.4%).
A method to locate neighborhood of global optimum

Locating the neighborhood of global optimum (NGO) is critical for global optimization. For this purpose, the relationship between the barycenter and the global maximum is investigated in this paper. And a space transform technique is proposed such that the barycenter of the transformed space is more close to NGO. Consequently, NGO can be readily located by means of estimating the barycenter of search space. The validity of the proposed method is demonstrated by multimodal function optimization.
A Digital Adaptive Discontinuous Current Source Driver for High-Frequency Interleaved Boost PFC Converters

A digital adaptive current source driver (CSD) is proposed for the interleaved Boost PFC converter. The adaptive drive current is achieved for the CSDs to optimize the switching loss and gate drive loss according to different turn-on and turn-off drain currents in a wide load range. Compared to the adaptive CSDs with the linear regulator, the digital adaptive CSD is able to eliminate the additional loss and cost introduced by the linear regulator. The most important benefit of the digital CSD is that the turn-on and turn-off drive current can be adjusted independently according to the different turn-on and turn-off drain currents. At the same time, the proposed digital CSD is compatible with other advanced digital control technique. The proposed digital CSD is applied to the interleaved Boost PFC converter under critical conduction mode (CRM) to reduce the high turn-off loss and gate drive loss. A 220-V AC input, 380-V/400-W output, two-phase interleaved Boost PFC converter with the switching frequency of 90-500 kHz under CRM was built to verify the functionality and advantages of the digital CSD.
A high accuracy and robust machine learning network for pattern recognition based on binary RRAM devices

Utilizing the binary RRAM devices, a hardware implemented network based on the modified k-nearest neighbor (KNN) algorithm is proposed for pattern recognition. Regarding to the recognition of the handwritten digits, the hardware network shows brilliant performance with simple training scheme, high tolerance of input noise (up to 40%) and variation (up to 60%), and high recognition accuracy rate (more than 90%). The simulations show that the stable binary rather than multilevel resistance characteristics of RRAM enable the better coupling of device and algorithm for the proposed learning network.
Domain regularized transfer component analysis

Anti-drift is an emergent and challenging issue in data-related subjects. In this paper, we propose to address the time-varying drift (e.g. electronic nose drift). By viewing drift to be with different probability distribution from the regular data, a robust subspace projection approach with PCA synthesis is proposed for anti-drift. The main idea behind is that given two clusters of data points with different probability distribution caused by drift, we tend to find a latent projection P (i.e. a group of basis), such that the newly projected subspace of the two clusters is with similar distribution (i.e. anti-drift). The merits of the proposed domain regularized component analysis (DRCA) method are threefold: 1) the proposed subspace projection mechanism is unsupervised, without using any label information of data in anti-drift; 2) a simple but effective concept of domain distance is proposed to represent the mean distribution discrepancy metric; 3) the proposed anti-drift method can be easily solved by Eigen decomposition, and anti-drift is manifested with a well solved projection matrix in real time application. Experiment on a benchmark drift dataset demonstrates the effectiveness of the proposed DRCA method.
A Statistical Method for Middleware System Architecture Evaluation

The architecture of complex software systems is a collection of decisions that are very expensive to change. This makes effective software architecture evaluation methods essential in today’s system development for mission critical systems. We have previously developed MEMS for evaluating middleware architectures, which provides an effective assessment of important quality attributes and their characterizations. To provide additional quantitative assessments on the overall system performance using actual runtime data, we employed a set of statistical procedures in this work. Our proposed assessment procedures comprises a standard sensitivity analysis procedure that utilizes leverage statistics to identify and remove influential data points, and an estimator for evaluating system stability and a metric for evaluating system load capacity. Experiments were conducted using real runtime datasets. Results show that our procedures effectively identified and isolated abnormal data points, and provided valuable statistics to show system stability. Our approach thus provides a sound statistical basis to support software architecture evaluation.
Adaptive fuzzy control for a class of chaotic discrete-time system

The direct heuristic dynamic programming (HDP) is used to handle the optimal tracking control for the Henon Mapping chaotic system. The fuzzy logic system (FLS) is applied to measure the long-term utility function. Lyapunov analysis approach is utilized to assure the stability of the chaotic system. Results have proven that the tracking error, the adaptation law and the control input of the system retain uniform ultimate boundedness.
A new predictive control strategy for power factor correction

A new predictive control strategy for PFC is presented. Its basic idea is that all of the duty cycles required to achieve unity power factor in a half line period are generated in advance by using a predictive algorithm. Based on the average output voltage in the previous half line period, the duty cycles in the current half line period can be calculated by the predictive algorithm, which is derived from the differential equations of boost topology. An optimization process is incorporated with the predictive algorithm to fine tune the parameter of model for the purpose of further reducing the harmonic current. Benefited from the proposed digital control strategy, the switching frequency of the PFC does not directly depend on the processing speed of the DSP. Simulation results show that the proposed strategy works well and unity power factor can be achieved with wide input voltage and load current variation range.
A research on developing tools based on formal OMT

OMT (Object Modeling Technique) is an informal software development tool. The main feature of the informal method is that it describes a system adopting natural language and intuition graphics. In order to increase the tightness and decrease the ambiguity, the paper presents a formalization of OMT. Moreover, the paper discusses an automatic translation development tool from a formal OMT to a C++ program. It may he used to shorten the translation period and avoid programming mistakes in manual implementation.
A Pedestrian Re-Identification Method Based on Multi-Feature Fusion

Single feature of pedestrian is difficult to accurately describe the target using traditional algorithms. A new reidentification algorithm combing global features and local features with different distance metric function is introduced. First, weighted color histogram feature for whole pedestrian is extracted and combined with Bhattacharyya distance to roughly recognize targets. Then pedestrians’ maximum stable color area (MSCR) of torso and legs and histograms of oriented gradients (HOG) are combined with weights for fusion feature. Finally, all obtained features are combined and compared with pedestrian original characteristics for final recognition. Pearson correlation coefficient is used as similarity measurement to finely recognize pedestrian. Experimental results show that proposed method can achieve high identification accuracy.
A Novel Approach for Service Capabilities Representation Based on Statistical Study on WSDL

Web services (WS) are becoming more and more popular nowadays and service-oriented architecture (SOA) have been widely used in the construction of information systems. But most of the SOA applications are not brand new and usually evolved from legacy systems. Our research group is building a service identification framework used for the SOA reengineering of existing large-scale information applications. Service capabilities have become an important issue we care about which are the actions performed or the information delivered by a service. But the semantic description for service capabilities has not been fully addressed in the current approach. In this paper, we analyze the contributions and limitations of current approach. In order to find the practical features of existing web services, we conducted a statistical study on more than four hundred WSDL documents collected from XMethods.net, Amazon and Google. Then we propose a new approach for Semantic Web Services description. Service capabilities are represented by informative entities and standard actions are specified for each informative entity.
Dual-band conformal antenna with omnidirectional radiation pattern on cylindrical surface

This paper proposes a dual-band conformal antenna which resonates at lower WLAN (2.4 GHz) and high WLAN (5.8 GHz) frequency bands. And for both the bands, the conformal antenna has perfect omnidirectional radiation pattern. The proposed antenna is conformed onto a cylindrical structure of radius 200 mm. And the electromagnetic model and parametric analysis of the proposed antenna is developed in HFSS for optimization and analysis. The performance simulation results of the antenna are presented.
A new resonant gate drive circuit utilizing leakage inductance of transformer

This paper presents a new resonant gate drive circuit that utilizes transformer leakage inductance as resonant component. The new gate driver circuit can drive a pair of MOSFETs, which can be used in both with same reference and different reference circuits such as MOSFETs in buck, bridge converters. Pulse controlled resonance is used to reduce the conduction loss of resonant circuit. Also it provides fast transition, thus the limitation on the duty cycle is minimized. In the paper, the theoretical analysis and simulation waveforms are provided. A prototype board is built and experimental results are also given
A novel self-selection bipolar RRAM cell with ultra-low operation currents for cross-point application

A self-selection and self-compliance bipolar RRAM cell based on TiN/HfOx/n+-Si structure is proposed for the CMOS compatible and ultra low power cross-point array application. Electrical forming scheme is important to achieved desired resistive switching performance due to the existing of interface sub-oxide layer. For RRAM cell, ultra-low RESET current <; 0.5 μA and good reliability (retention @ 150 °C > 104 seconds and endurance > 104 cycles) are demonstrated after optimization by material-oriented methodology. The write and read operation could be performed correctly without additional selector.
An improved nonlinear control strategy for induction motor drive

In this paper, a new configuration called the auto disturbance rejection controller (ADRC) is used in induction motor speed control. The key part of the ADRC is the extended state observer (ESO). By using ESO, the ADRC can estimate accurately the derivative signals and accurate decoupling of the induction motor is also achieved. In addition, the external disturbances and parameter variations could also be estimated and compensated by ADRC, so that the closed loop motor drive system under ADRC control does not depend on the accurate mathematical model of induction motors. Therefore, the robustness and adaptability of the control system is improved too. The simulation and experiment results show that the controller operates quite robustly under modeling uncertainty and external disturbance, and it can provide good dynamic performance such as small overshoot and fast transient time in the speed control.
A remote sensing-based net primary productivity model for semi-arid grasslands: model description and validation

This paper presents a production efficiency model (PEM) of regional net primary productivity parameterized with remote sensing information, and tests it for inner Mongolia semi-arid grassland. The model uses the concept of light use efficiency (LUE) that solar radiation is absorbed by plants to provide energy for photosynthesis, while change of air temperature, plant-water deficits and respiration reduce the efficiency of light usage, and the model is modified to better represent regional environmental characteristics of the semi-arid grasslands on a month time step. In this study, high quality weather and field data were introduced to calibrate model parameters, and the ratio of aboveground biomass and underground biomass was regarded as a constant. Accuracy of model products, such as leaf area index (LAI), aboveground biomass and aboveground NPP, was assessed by comparing them with field measurement. Aboveground NPP simulated has good linear relation with the measured (R/sup 2/=0.9698), and the absolute error of estimates is mostly less than 11% for no grazing and moderate grazing areas. Moreover, the model was used to evaluate aboveground NPP in response to water, temperature and grazing pressure. Results show that the response is consistent with the actual environmental and climatic condition of semi-arid grassland, the water-stress is the main reason of the decrease in light use efficiency, and grazing pressure can not be neglected especially for overgrazing areas.
A Cost-effective Approach to Water Utilization Property

Our country faces serious water resource issues, water excess exploitation, water waste, water pollution, water shortage issue becomes one of the key factors for the development of all over the world in the future. This paper defines water property, and Cost-effective analyses about water utilization property trade, by using sustainable development strategy, figures our water utilization property trade not only can improve service efficiency, speed up build water conservation society and carry out sustainable development about water resource but also can be a contribution to providing a certain theory and practice.
Association analysis based on mobile traffic flow for correlation mining of mobile apps

As the smartphone market increasingly gains its prosperity nowadays, Internet traffic generated by mobile terminals is also booming. The detailed traffic flow data is helpful to discover association relationship of applications and user behavior patterns. In this paper, we study the correlation of mobile apps based on a mobile network traffic dataset from a data mining perspective. This dataset is acquired by a traffic monitor which is deployed in the backbone network of a mobile network operator in a province of China. We analyze both the mobile applications and the websites which the mobile user has accessed. Then an association analysis method is conducted to extract rules which can reflect characteristics and preference of users when accessing the Internet by smartphones. By employing the Apriori algorithm on our mobile traffic flow data, we discover some association rules among several mobile apps and websites which can be helpful for understanding the mobile network user behavior.