nils-herrmann/clean_scirepeval huggingface dataset:
Research on the Evaluation Model of Urban Transportation Management Planning Project Based on Fuzzy Theory

It was an important to improve urban transportation management level by carrying out urban transportation management planning. According to the characteristics of urban transportation management planning, the evaluation model of urban transportation management planning project was studied based on fuzzy theory. First, the 14 evaluation index of urban transportation management planning project was constructed from three aspects, which are safety, smooth and environment. Second, the weights of evaluation indicators were determined by fuzzy AHP, and then a fuzzy comprehensive evaluation model for urban transportation management planning project was established. Finally, the example of Dongguan was presented to illustrate the model. The results show that the model is very effectual.
Guidance precision factors of terminal correction mortar projectile using pulse jets

Trajectory sag due to gravity and demodulation error caused by roll angle measurement error are the main factors that influence the guidance precision of Terminal Correction Mortar Projectile (following called TCMP for short) using pulse jets. TCMP dynamics model and guidance loop model are established, and then the impact of pulse jet impulse, pulse jet ignition waiting time and gravity on the guidance loop is obtained via analyzing phase trajectory of TCMP position deviation. According to the discrete control characteristics of impulse force, we propose bias threshold method for gravity compensation. The quantitative relationship between pulse jet firing waiting angle and roll angle measurement error is derived under gravity compensation. Moreover, roll coupling guidance loop simulation model of TCMP is established, and then the model is simulated under different waiting angle demodulation error. Simulation results show that TCMP has a certain tolerance to roll angle measurement error.
Switch-Linear Hybrid (SLH) Conversion Based on Source Follower

A family of switch-linear hybrid (SLH) converters is proposed, which integrates the advantages of linear amplifiers into switch-mode converters without sacrificing the energy efficiency of the system. Due to its high input resistance and low output resistance, a source (emitter) follower is introduced to be the core unit fed by a switching-filter supply, which constitutes the hybrid converter's topology. The proposed SLH converters feature excellent load robustness in addition to other advantages of pure linear systems, resulting in small volume and cheap cost. This paper describes the typical topologies and basic operating principle of the SLH converters. It is revealed that the special operation status that MOSFET devices possess uniquely has led to the derivation of a number of new converter topologies. Theoretical analysis and experimental results verify the excellent steady and dynamic performance of the SLH converters.
Modeling and Predicting Groundwater Pollution Caused by Oil Pipeline Leakage Using MODFLOW and MT3D

This article describes how to use a three-dimensional MODFLOW model, paired with MT3D, to predict groundwater pollution caused by oil pipeline leakage. Then using geological and hydraulic data, the potential area of oil pollutant transport and its concentration were simulated by MODFLOW and MT3D, making it possible to ultimately predict the scope and extent of groundwater pollution. The model is a valuable contribution to the ever-advancing technology of groundwater assessment.
An approach for fault diagnosis based on an improved k-nearest neighbor algorithm

The k-nearest neighbor (k-NN) algorithm has received many attentions for its applications in fault diagnosis. However, the conventional k-NN algorithm often provides a misleading diagnosis due to slight data variation near the threshold. This paper presents an improved k-NN algorithm based on D-S evidence theory. D-S evidence theory is adopted to identify and linearize the edge data. A set-valued decision information system is established based on fault diagnosis rules and mass functions. The k-NN algorithm is used to classify the features in the decision information system. The simulation experiments of oil-immersed power transformer are performed with the conventional algorithm and the improved algorithm. The simulation result shows that the accuracy rate of the proposed method is higher than conventional method.
A Fast and Robust Super Resolution Method for Intima Reconstruction in Medical Ultrasound

Accurate measurement of intima-media thickness (IMT) in ultrasound images is clinically meaningful but also difficult because of low resolution. This paper proposes an approach to reconstruct a resolution-enhanced intima from a sequence of acquired images using a maximum a-posteriori framework. Anisotropic diffusion is used to reduce speckle with edge enhancement during reconstruction. For real time application in an ultrasound system, a fast and robust approach is employed in the presence of outliers. Finally, an iterative process is used to achieve a single high-resolution image with a better defined intima boundary than the original images. Our approach may be a valuable preprocessing step to image segmentation and IMT measurement.
A method of quantitative analysis for dynamic fault tree

In order to efficiently perform reliability quantitative analysis for dynamic system, whose behaviors containing temporal dependency make the analysis very complicated, a quantitative analysis method applied to dynamic fault tree is proposed. Above all, the conception of extended cut sequence is brought forward. Extended cut sequence includes temporal logic and has more expressive capability than previous similar conceptions. Secondly, generates the set of minimal extended cut sequence. Thirdly, the conflict detection, the reduction of tem poral constraints and the topological sort of basic events are carried out to quantitatively analyze each cut item. Finally, the system reliability parameters are acquired through the combination. One example is particularly described to evaluate this method which is compared to another method. The result shows that this method has concise expressive form of cut sequence, efficient process, precise data and low time cost. This method also provides a novel foundation for further research on reliability quantitative analysis.
An integrated model of visual attention for homecare robot with self-awareness

This paper proposes an integrated model of top-down and bottom-up visual attention with self-awareness for a homecare robot. For mimicking the human attention processes, a robot self-awareness model with fuzzy decision making system is developed and utilized, which is an important improvement on the existing robot attention models. Besides the task-driven object-based biasing, a robot self-awareness model can generate other parts of top-down biases in a robot visual attention model. Some results from the self-awareness model are obtained here. In order to update the weights in robot memory, the learning process is carried out through the Bayes' rule. Three images are tested to evaluate the developed methods. Four types of saliency maps are compared to see how self-awareness can affect robot attention selection.
Improving triplet-wise training of convolutional neural network for vehicle re-identification

Vehicle re-identification (re-id) plays an important role in the automatic analysis of the drastically increasing urban surveillance videos. Similar to the other image retrieval problems, vehicle re-id suffers from the difficulties caused by various poses of vehicles, diversified illuminations, and complicated environments. Triplet-wise training of convolutional neural network (CNN) has been studied to address these challenges, where the CNN is adopted to automate the feature extraction from images, and the training adopts triplets of (query, positive example, negative example) to capture the relative similarity between them to learn representative features. The traditional triplet-wise training is weakly constrained and thus fails to achieve satisfactory results. We propose to improve the triplet-wise training at two aspects: first, a stronger constraint namely classification-oriented loss is augmented with the original triplet loss; second, a new triplet sampling method based on pairwise images is designed. Our experimental results demonstrate the effectiveness of the proposed methods that achieve superior performance than the state-of-the-arts on two vehicle re-id datasets, which are derived from real-world urban surveillance videos.
Robust Information Fusion Estimator for Multiple Delay-Tolerant Sensors With Different Failure Rates

In this paper, the robust information fusion Kalman filtering problem is considered for multi-sensor systems with parameter uncertainties, randomly delayed measurements and sensor failures. The stochastic parameter perturbations are included in the state space models such that the proposed fusion estimator has robustness for the varying system parameters. For each observation subsystem, multiple binary random variables with known probabilities are introduced to model sensor failures and random delays in the measurements. Without resorting to the augmentation of system states and measurements, a robust optimal recursive filter for each subsystem is derived in the linear minimum variance sense by using the innovation analysis method, and the estimation error cross-covariance matrix between any two subsystems is given recursively. Based on the optimal fusion algorithm weighted by matrices, a robust distributed state fusion Kalman filter is derived for the considered system, and the dimension of the designed filter is the same as the original system, which can reduce computation costs as compared with the augmentation method. Moreover, the performance of the designed fusion filter is dependent on the sensor failure rates. Finally, two illustrative examples are given to show the effectiveness of the proposed method.
A Cloud Oriented Account Service Mechanism for SME SaaS Ecosystem

Account services are playing important roles in building IT ecosystems, such as Google Accounts, Apple ID and Microsoft Your Account for the forthcoming windows 8. In this paper, we propose a novel mechanism to build a cloud oriented account service for SME SaaS Ecosystem (SSE), a public cloud ecosystem among small and medium sized enterprises of China. Different from the account services listed above, our account service has been established not only for individuals, but also for enterprises. Characteristics of the account service are as follows: 1) multi-tenant oriented implementation through virtual directory Information Tree (DIT) views based on a hybrid data structure; 2) partition based distributed architecture enables the service sufficiently calable and available; and 3) GUI based interfaces for configuration management, RESTful and SOAP based interfaces for the third party services invoking.
Local histogram specification using learned histograms for face recognition

In the field of face recognition, most existing preprocessing methods only try to filter the low frequency part of the spectrum of face images to eliminate illumination variations. In this paper, we introduce the Local Histogram Specification (LHS) to preprocess face images using learned histograms. Each local histogram to be specified is learned by estimating the distribution of gray values in the corresponding local region of all normal lighting images in the training set. The proposed method is able to alleviate both the low and high frequency parts of illumination on face images as well as enhance face features lying in the low frequency part. Reasonable window size is also empirically studied. Experimental results on two standard illumination variation datasets demonstrate the effectiveness and stability of our proposed method.
New Improvement Approaches to H&#8734;Controller Designs Based on Fuzzy Observers for T-S Fuzzy Systems with Time-varying Delay

This paper provides an improved result over that in (X. Liu and Q. Zhang,  2003) and (Y. Yuan. and W. Che, Control and Decision, 2005) for the problem of observer-based Hinfin control for nonlinear systems with time-varying delay in Takagi-Sugeno (T-S) fuzzy model. It contributes in three aspects: (i) The present result is less conservative than that in (Y. Yuan. and W. Che, Control and Decision, 2005); (ii) The present strict LMI method is a single step approach which overcomes the drawback of a two-step approach in (Y. Yuan. and W. Che, Control and Decision, 2005); (iii) The matrix dimensions are largely reduced compared with the corresponding ones in (Y. Yuan. and W. Che, Control and Decision, 2005)
A New Method for Fuzzy Clustering Analysis Based on AFS Fuzzy Logic

In this paper, first, the AFS fuzzy logic clustering algorithm has been studied further. Then, based on the fuzzy implicator, an algorithm of selecting optimal subsets of relevant features for fuzzy clustering is proposed. Thus a new AFS fuzzy logic clustering algorithm is achieved. Finally, the proposed clustering algorithm is applied to the well known real-world wine data set. Experimental results demonstrate that a high clustering accuracy can be obtained by the proposed clustering algorithm only according to the order relations of the attributes, in stead of the numerical representations of the attributes. The proposed clustering algorithm can be applied to the data sets with various data types such as real numbers, Boolean values, partial orders, even human intuition descriptions.
Research on optimal stationing strategy of distribution automation terminals based on power supply reliability analysis

High power supply reliability is the target of modern distribution network development and distribution automation system is an important method to improve the power supply reliability of distribution network. There exist big differences between the distribution network structure and power supply reliability of different distribution region, thus we should use different stationing modes of distribution terminals based on actual conditions to reach the harmony and consistency of economy and practicability. This paper analyzes the fault processing mode of distribution automation system for Shanghai A+ region. It also builds a simplified calculation model of power supply reliability considering the distribution automation system, and then uses typical 10kV grids to analyze. It calculates the power supply reliability in different terminal stationing modes and considers other related factors comprehensively to propose the optimal stationing strategy of distribution automation terminals. Related research achievements have been contained in the “Technical Guide for Distribution Automation System Planning and Designing for State Grid Shanghai Municipal Electric Power Company” and will provide good guidance for planning and constructing distribution automation systems in Shanghai and other areas.
A parallel fuzzy rule discovery algorithm and future goods automated trading system

Nowadays, with the rapid development of information technology, many business fields gradually usher in the era of big data. Extracting valuable information from the large data is very urgent. However, because of some limitations such as memory restrictions, data complexity or time complexity, the majority of traditional data mining methods are time consuming and low efficiency working on big data. In contrast, parallel computing is a common and reliable choice to solve that problem. In this paper, we propose a fuzzy rule discovery algorithm — fuzzy rule based classification system in the framework of MapReduce (FRBCS-MR). The proposed FRBCS-MR algorithm applies parallel computing technology to extract fuzzy rules and build fuzzy rule-base, which combines the advantage of dealing with uncertainty of fuzzy system and the ability of MapReduce in parallel computing. Future goods trading has been gradually transformed from the original manual trading to stylized trading and the stylized trading is considered to be the forefront and the most scientific mode of investment. In the experimental studies, the rules extracted by FRBCS-MR algorithm using future goods data are turned into trading strategies which are to be used in the real automated trading platform TradeBlazer (TB) to compute the profit and loss situation. The result shows that the FRBCS-MR algorithm has good profit ability and possesses a certain guiding significance for investors.
An 8×8 CPW MKIDs developed for terahertz superconducting imaging array

China is planning to construct a 5-meter THz telescope named DATE5 at Dome A,  This paper reports on the development of an 8×8 CPW microwave kinetic detectors (MKIDs) for the terahertz superconducting imaging array (TeSIA), which is an instrument proposed for the DATE5. The 8×8 CPW MKIDs, operating at 0.35THz, make use of TiN superconducting film of Tc approximately equal to 4.3K. The resonance frequencies of the MKIDs span the 4-5.575GHz band with an interval of 25MHz. The design, fabrication and characterization of the 8×8 CPW MKIDs are presented.
Evaluation of the regional entrepreneurship based on PCA — A demonstration study of 31 regions in China

Entrepreneurship is the intangible resource of economic growth, and increasingly becomes the important microcosmic impetus of the technology innovation and economic growth. According to the connotation of entrepreneurship and the theory of regional economic development, this paper established an evaluation index system of entrepreneurship and the method of principal components analysis (PCA) was adopted to estimate the distribution of regional pioneering spirit of the entrepreneurs in China and analyzed the developing tendency of it. Comparison and cluster analysis were used to the results of Chinese 31 regions in 2005–2009. The result indicates that the enthusiasm of entrepreneurship in eastern China is more stronger than the less developed central and western China, and the difference between central and western areas is slight.
Model of projection pursuit drought evaluation based on improved artificial fish swarm algorithm of Sanjiang Plain

The model of projection pursuit drought evaluation based on artificial fish swarm algorithm was proposed to optimize the function and seek the optimum projection for solving the problems such as discrete evaluation grade and low resolved results obtained by using other methods. In order to avoid local optimization and improve global search ability and convergence speed of the algorithm, the adaptive artificial fish step and crowded degree factor were applied to optimize artificial fish swarm algorithm. The departure ratio of rain-fall, Z index and homogenization of rain-temperature were selected to establish evaluation model of Sanjiang Plain based on above methods. The results showed that model effectively avoided the incompatibility of single index. It was feasible to evaluate actual drought situation, and provide a new way for drought evaluation.
Development of a 0.85 THz Nb-AlN-NbN superconductor-insulator-superconductor mixer

China is planning to build a 5-m THz telescope named DATE5 at Dome A in Antarctica. The telescope will operate in 0.85 THz and 1.5 THz frequency bands. A waveguide superconductor-insulator-superconductor (SIS) mixer is developed for the 0.85 THz frequency band by utilizing parallel-connected tunnel junctions (PCTJ) with Nb/AlN/NbN trilayer and NbTiN/SiO2/Al microstrip tuning circuit on quartz substrate. Its frequency response is measured by a Fourier-transform spectrometer (FTS), showing an FWHM frequency coverage of 762–925 GHz. The measured uncorrected double sideband (DSB) receiver noise temperature is approximately equal to 450 K at 840 GHz. With the vacuum window and beamsplitter losses corrected, the receiver noise temperature is less than five times the quantum-limited noise. Detailed mixer design and measurement results will be presented.
Distributed Video Coding based on advanced side information generation framework

Distributed Video Coding (DVC) is a new compression paradigm in video coding, which is receiving lots of interests nowadays. Side information creation is a key function in the DVC decoder, and plays a key-role in determining the performance of the codec. This paper proposes a novel framework to create side information at the block level with two modes: Wyner-Ziv mode and Intra/WZ mode, corresponding to Motion Compensated Frame Interpolation and motion compensated quality enhancement technique, respectively. While in the second mode, a low quality Intra block sent by the encoder is used to generate side information by doing motion estimation with the help of the reference frames. Experimental results demonstrate that the proposed scheme can achieve up to 2.1dB improvement in rate-distortion performance when compared to state-of-the-art DVC codec, especially for high motion video sequences and long group of pictures sizes.
Adaptive mesh optimization and nonrigid motion recovery based image registration for wide-field-of-view ultrasound imaging

Wide field of view (WFOV) imaging mode obtains an ultrasound image over an area much larger than the real time window normally available. As the probe is moved over the region of interest, new image frames are combined with prior frames to form a panorama image. Image registration techniques are used to recover the probe motion, eliminating the need for a position sensor. Speckle patterns, which are inherent in ultrasound imaging, change, or become decorrelated, as the scan plane moves, so we pre-smooth the image to reduce the effects of speckle in registration, as well as reducing effects from thermal noise. Because we wish to track the movement of features such as structural boundaries, we use an adaptive mesh over the entire smoothed image to home in on areas with feature. Motion estimation using blocks centered at the individual mesh nodes generates a field of motion vectors. After angular correction of motion vectors, we model the overall movement between frames as a nonrigid deformation. The polygon filling algorithm for precise, persistence-based spatial compounding constructs the final speckle reduced WFOV image.
Low speed servo control for single Gimbal control moment gyros using back-stepping time-varying sliding model control

Single Gimbal Control moment gyroscope(SGCMG) is a typical servo system application and has a broad prospect in spacecraft attitude adjustment. In order to optimize the performance of SGCMG outer frame servo system on smooth speed, position tracking and robustness, a nonlinear servo control algorithm for the permanent-magnet synchronous motor(PMSM) system using back-stepping adaptive switch gain time-varying sliding mode control(SMC) is designed in this paper. First, a time-varying SMC approach based on back-stepping and adaptive switch gain is presented. The time-varying sliding mode controller refresh the slide surface, and the switch gain of controller is adaptively changed. Then, a SMC disturbance observer is proposed to reduce the impact of load torque and coefficient disturbance on the control system. Simulation results show the validity of the proposed control method.