nils-herrmann/clean_scirepeval huggingface dataset:
Back-propagation network and its configuration for blood vessel detection in angiograms

A neural-network classifier for detecting vascular structures in angiograms was developed. The classifier consisted of a multilayer feedforward network window in which the center pixel was classified using gray-scale information within the window. The network was trained by using the backpropagation algorithm with the momentum term. Based on this image segmentation problem, the effect of changing network configuration on the classification performance was also characterized. Factors including topology, rate parameters, training sample set, and initial weights were systematically analyzed. The training set consisted of 75 selected points from a 256x256 digitized cineangiogram. While different network topologies showed no significant effect on performance, both the learning process and the classification performance were sensitive to the rate parameters. In a comparative study, the network demonstrated its superiority in classification performance. It was also shown that the trained neural-network classifier was equivalent to a generalized matched filter with a nonlinear decision tree.
Research on VRP of Optimizing Based on Fuzzy C-Means Clustering and Iga Under Electronic Commerce

The logistic distribution under electronic commerce has the characteristic of dispersive customer positions, large order forms, little batches and many repeated routes. The traditional optimizing vehicle routing problems meet with diversified problems in different extents and are difficult to play their roles. Therefore, according to the particularity of logistic distribution under electronic commerce, the improved two-phase algorithm needs to be adopted to get solutions. Namely, the customer group can be divided into several regions using fuzzy c-means clustering algorithm in first phase. And it is decomposed into some small-scale subsets according with restraint conditions with scan algorithm in each region. In second phase, get the solutions of the customer point in every group using the improved genetic algorithm. In fact it is route optimization problems of several single TSP model. In the end, the test proves the validity of this improved algorithm combining with examples
Career-oriented information service talents cultivation mode

With the developing of modern service industry, an emerging information services, characterized by intensive intelligence, have the new requirements, such as the ability of knowledge of information technology, knowledge of foreign languages, and international business professional. Career-oriented information service talents cultivation has become the hot topic of many studies educators. Analyses the Status of talents cultivation nowadays, aimed at information services career, the cultivation mode, professional orientation, basic form of school-enterprise cooperation are presented in this paper. It will provide an education mode to comply the conversion from the “subjects-logical” to “Employment — Employability” Training Mode disciplines, which can be a model for the information service engineering construction.
Interacting multiple model filter based moving object tracking with mobile robot in unknown environments

This paper presents a simultaneous localization,mapping (SLAM) and object tracking(OT) method based on interacting multiple model (IMM) filter to achieve simultaneous estimation of robot and object's trajectories in unknown environments. The proposed approach integrates IMM based object tracking and EKF-based SLAM. Since the implementation of IMM, the method can help the robot to track object and estimate its motion model, which improves the accuracy of object localization. Moreover, this approach can help robot to track the object that have more ability to escape ,and improve it practicality .Simulation and real robot experiment results validate the effectiveness of the proposed method in robot and target localization and mapping. The results also show the accuracy of target motion model estimation and the system ability to track maneuvering target.
Gradient Threshold Based Image Transition Region Extraction and Segmentation

Traditional transition region extraction methods depend much on the clip limits low L and high L . In which methods low L and high L can not often be obtained correctly from real images, which will result in incorrect extraction of transition region and finally bad quality of segmentation. A novel gradient threshold-based transition region extraction method (GT-TREM) is presented. Transition regions can steadily be extracted by GT-TREM. Experimental results demonstrated the robustness and effectiveness of the algorithm. Keywords-transition region; image segmentation; gradient; effective average gradient; threshold
A frequency diversity scheme for OFDM systems based on space-time block coding

In this paper, a frequency diversity scheme for space-time block coded orthogonal frequency division multiplexing (STBC-OFDM) systems is investigated. The scheme can simultaneously obtain frequency diversity and space diversity gain. Extensive simulations show that the proposed scheme with two transmit antennas and a single receive antenna almost has the same performance as STBC-OFDM systems with four transmit antennas and a single receive antenna. Under the same symbol error rate (SER) performance, the number of transmit antenna is not required to be increased because of using frequency diversity.
Development of Magnetostriction Sensor for on-line Liquid Level and Density Measurement

A new magnetostriction sensor and an on-line liquid level and density measuring equipment were developed. The operating principle of the measuring equipment with magnetostrictive sensor was described and a multi-floater relative displacement algorithm based on Wiedemann and Viuary effect was brought forward. The effect of density change on liquid level was investigated by considering of five liquids which density varies from 0.69 to 0.89 g/cm3 . Density correction algorithm was given to increase measuring precision of liquid density. With these correction algorithms, the measuring resolution of the magnetostrictive liquidometer may reach 0.1mm and the precision of magnetostrictive densimeter reaches 0.0001g/cm3. Good agreement has been obtained between the experiments and predictions for liquid level and density measurement. The equipment was installed on four tanks of a gas station in Jiangsu Province, China; one year site service showed that the equipment works very well
Human recognition for following robots with a Kinect sensor

In this paper, a human recognition method based on soft biometrics is proposed for the human following robot. Two soft biometric traits (clothes color and body size) are calculated as features of the human. First, the human region detected by the Kinect is segmented to obtain the torso and leg parts of the body. Then the weighted HSV histograms of the body parts are calculated to describe the clothes color information. Body height, arm length and shoulder width values are measured to represent the body size information. Finally, human recognition is implemented by evaluating the similarity among the objects. The effectiveness and robustness of the proposed method is verified by the experiments. And the robot system with the recognition method can recognize and follow a human target reliably.
Study on excitation frequency of longitudinal and torsional guided waves

Guided ultrasonic wave is a new non-destructive pipe inspection method for rapid, long distance inspection. It can be used to inspect pipes, especially those buried underground or difficult to access using other conventional NDT methods. In this paper, the excitation frequency of two most commonly used ultrasonic guided waves, longitudinal wave and torsional wave were researched, and the lower and upper frequency limit were discussed in detail. Furthermore, factor that may affect the selection of experimental frequency was studied and optimum frequency of guided wave for a carbon steel experimental pipe was selected.
Design By Expectation: a framework for engineering design optimization

An engineering design often requires sophisticated understanding of the domain knowledge and the designer's creativity. To optimize the engineering design, one needs to explore a combinatorial search space. We propose a framework, called Design By Expectation (DBE), which provides a collaborative scheme for genetic algorithms (GA's) and domain-specific knowledge to carry out the engineering design optimization. DBE exploits the adaptation of GA's in approaching better solutions, which compensates for the weak creativity of computers. We make the DBE framework more flexible by developing a set of expectation interpreters that release the strict requirements on the formalities of objectives and constraints required in traditional optimization techniques. Finally, we use two practical examples: power system stabilizer and streetlight placement, to demonstrate the applicability of the DBE framework.<<ETX>>
A universal instrument for biological measurements, stimulation, clamping, and processing

In this paper, the design of an integrated instrument for biological experiments is presented, namely Universal Clamp. It combines the functions usually performed by separate electronic biological instruments to provide a universal solution. After simple cabling to pipette electrodes, it enables users to do membrane potential measurement, current stimulation, varieties of clamping, data acquisition, processing, and storing through a single PC-based graphic user interface.
CPICH power settings in irregular WCDMA macro cellular networks

In a WCDMA system, mobiles select cells based on received common pilot channel (CPICH) signal quality from the different base stations. The cell size therefore depends on radio propagation properties and transmitted CPICH power. For efficient resource utilization, it is important that the traffic is shared fairly evenly over the base stations. This is specifically challenging in irregular networks with inhomogeneous site locations and traffic distribution. This paper discusses methods for automated CPICH power planning, based on differently detailed traffic density information and predicted radio propagation. Simulations using data from a realistically planned network indicate that the tuning results in a more evenly utilized base station power. This in turn improves the overall capacity. It is furthermore fairly robust with respect to traffic density deviations from the true traffic density.
Regularized robust estimation of mean and covariance matrix under heavy tails and outliers

In this paper we consider the regularized mean and covariance estimation problem for samples drawn from elliptical family of distributions. The proposed estimator yields robust estimates when the underlying distribution is heavy-tailed or when there are outliers in the data samples. In the scenario that the number of samples is small, it shrinks the estimator of the mean and covariance towards arbitrary given prior targets. Numerical algorithms are designed for the estimator based on the majorization-minimization framework and the simulation shows that the proposed estimator achieves considerably better performance.
Numerical Simulation of Colloidal Drop Deposition Dynamics on Patterned Substrates for Printable Electronics Fabrication

One of the major challenges in printable electronics fabrication is the print resolution and accuracy for high precision applications such as printable displays. In this paper, the lattice Boltzmann method (LBM) is used for the direct numerical simulation of an inkjet-printed colloidal drop wetting on a patterned substrate for confined deposition. The two-dimensional multiphase particle suspension LBM model previously developed by the authors is extended to three-dimensional with the addition of the particle rotational dynamics and an improved treatment of particle-particle forces. The model is used to study the contact angle hysteresis and the stick-slip behavior of the contact line motion as a liquid drop wetting and evaporating on a patterned substrate. Finally, the dynamics of a colloidal drop containing many suspended particles are examined with and without evaporation. Results show that colloidal jamming occurs at the liquid-vapor interface as the drop preferentially wets and evaporates on a patterned substrate. This model development is an important first step towards understanding the complex transport phenomena present in an inkjet-printed evaporating drop for printable electronics fabrication.
Clustering methods based on rough estimate of cluster core

We present a Condensation Nucleus Clustering (CNC) method based on our study of SVC algorithm. In CNC, data points are mapped by means of a Gaussian kernel to a high dimensional feature space, where we search for the minimal enclosing sphere. We consider the inner data images as the rough estimate of each cluster's core, and they can be easily clustered. We name the groups of cluster result as Condensation Nucleus. Then assign the remaining data points into each cluster by linear discriminant analysis. Furthermore, we improve the CNC method to GCNC (Gradational Condensation Nucleus Clustering). In GCNC, the remaining data are assigned to each cluster gradationally. With the Condensation Nucleus bigger and the remaining data less, the Condensation Nucleus grow up to the final cluster results. We compare our methods with other similar clustering algorithm to demonstrate the performance of the proposed method on several datasets.
Identity Based Multi-proxy Multi-signcryption Scheme for Electronic Commerce

A new concept called multi-proxy multi-signcryption is introduced in this paper. In a multi-proxy multi-signcryption scheme, a group of original signcrypters can authorize a group of proxy signcrypters under the agreement of all signcrypters in the original group. Then only the cooperation of all signcrypters in the proxy group could generate a multi-proxy multi-signcryption. We propose a multi-proxy multi-signcryption scheme based on bilinear pairings. The proposed scheme has the following characteristics: (i) the size of multi-proxy multi-signcryption is independent of the number of original and proxy signcrypters. (ii) it provides the fair protection for the original signcrypter group and the proxy group.(iii) the key management problem is simplified because of using ID-based cryptography. Finally, we give an application of the proposed scheme in electronic commerce.
Dynamic analysis on the impact of monetary policy tools on real estate price

By adapting deposit reserve ratio, interest rates and money supply to the SVAR model, using the data from the first quarter of 2000 to the fourth quarter of 2013, this paper analyzes the strength and time of the impacts of the monetary policy tools to the real estate price. The results show that: Deposit reserve ratio has a negative impact on real estate price in medium term, and a positive one in short term long term. Interest rates have a negative impact on real estate price in short and medium term, but the regulation effect is more significant in medium term. The money supply has a positive impact on real estate price in short and medium term, and a negative one in long term. The impacts of the deposit reserve ratio and the money supply are more significant than the one of interest rates. As a result, the central bank could use the deposit reserve ratio as an important means of regulation, promote interest rates marketization, reasonably control the money supply, and improve the flexibility of monetary policy decisions.
Floorplanning based on particle swarm optimization

This paper presents a floorplanning method based on particle swarm optimization (PSO). We adopted the B*-tree floorplan structure to generate an initial stage with overlap free for placement and utilized PSO to find out the potential optimal placement solution. Unlike other related research, our method can avoid the solution from falling into the local minimal and has ability of more efficiency and robustness for explored solution space. Experiments employing MCNC and GSRC benchmarks show that the performance of our method for placement by the ability of exploring better solutions. The proposed approach exhibited rapidly convergence and led to more optimal solutions than other related approach.
Computer model study of regulatory mechanisms during airway obstruction

The baroreflex regulation counteracts with the systemic autoregulation in controlling the arterial resistance: baroreflex attempts to maintain a certain arterial pressure and systemic autoregulation attempts to maintain a certain cardiac output. In addition, baroreflex also controls heart rate and myocardial contractility based on arterial pressure changes. A computer model based on an electrical circuit has been developed and helped studying various cardiopulmonary mechanisms in health and disease. The model was capable of correctly predicting the hemodynamic consequences by incorporating simple baroreflex and autoregulatory gains and further suggested that baroreflex regulation slightly dominates autoregulation. With a postulation that baroreflex of heart rate is the major determinant, this study examined how the model fitted hemodynamic data obtained from humans with or without these regulation mechanisms. Twenty two patient data sets obtained retrospectively were used and classified into young and older group. Due to severe airway obstruction, inspiratory fall of arterial pressure was magnified as 31plusmn10 mmHg for the young and 29plusmn12 mmHg for the old. The performance of the model was accessed by comparing simulated arterial pressure and clinical data. With a simple linear model of baroreflex function on heart rate, the performance was noticeably increased: correlation coefficients (t) were 0.87plusmn;0.06 (meanplusmnSD). Baroreflex effects on total peripheral resistance and ventricular contractility were also evaluated, but much less significant than those from the heart rate regulation
Nonlinear fuzzy model for operating condition evaluation of power cables in tunnels

To assure the construction and operation security of the smart grid, it is necessary to assess the real-time operation reliability of the power cables which work in power tunnels by using on-line monitoring data. An online assessment system, which presents the important operating characteristics of the power cables, has been built up based on the following data: some preventive experiments results data, working environment, operation records, maintenance records, etc. With analytic hierarchy process method, an improved fuzzy integrated evaluation assessment model for cable assessment is proposed, the relative interiority indicator is adopted, and different methods are used to determine the membership functions for the qualitative indices and the quantitative indices. Through assessing the layer and sub-layer factors, the comprehensive operating conditions of the cables can be estimated. Based on the actual monitor data for a 220kV cable in power tunnel, the analysis result shows the proposed assessment model is correct and effective.
Nonlinear partial least squares with Hellinger distance for nonlinear process monitoring

This paper proposes an efficient data-based anomaly detection method that can be used for monitoring nonlinear processes. The proposed method merges advantages of nonlinear projection to latent structures (NLPLS) modeling and those of Hellinger distance (HD) metric to identify abnormal changes in highly correlated multivariate data. Specifically, the HD is used to quantify the dissimilarity between current NLPLS-based residual and reference probability distributions. The performances of the developed anomaly detection using NLPLS-based HD technique is illustrated using simulated plug flow reactor data.
A new scheme for constructing training resources of design graphics

A new scheme for training answers preparing and its teaching on design graphics is presented. In this method the answers are prepared firstly from 3D design, then these 3D models will be converted to 2D engineering graphics. 2D graphics will be further modified by 2D graphing software and the final answer will then be obtained. In which method the 2D answers and their 3D models could be obtained at the same time and will both be shown for help trainees understanding. The teaching efficiency is improved by proposed scheme.
Simulation of the Incentive Mechanism for Knowledge Sharing between Employees Based on the Organization Design

There exists "the prisoner's difficulty" between employees in the process of their knowledge sharing. Only by the way of being shared can knowledge develop and increase. Meanwhile, the effectiveness of knowledge sharing depends highly on the design of the organization structure. The author of this thesis which is based on the Game theory, by introducing the concept of "knowledge potential", analyzes the law of their knowledge sharing between different roles and gets the key source of knowledge sharing---introducing the strategy of stock ownership incentive to the staff with high-potential knowledge. The author makes a simulation experiment of an enterprise with nearly one-hundred employees on the Netlogo platform in this thesis, and sets different parameters of the knowledge sharing level to see how incentive mechanism can bring in the maximum income to the enterprise.
Notice of RetractionHuman Error Rate Assessment for Power Company Based on Management by Objective

The normal operation of power companies is quite important for the development of economy and human error is the key reason of accidents in power companies. In order to promote the branches to bring down the human error rate with effectual measures, encouragement and punishing impartially are needed for power companies. Aiming at the problem of how to impartially evaluate the human error rate of power companies and the branches, the concept of contribution rate is given in this paper in order to embody the effect of branches under the particular condition including the surrounding and their task according to the theory of management by objective. The mathematical programming model is founded to get the due human error rate of power companies and to evaluate the branches' contribution to the company. At last the algorithm of the model is given based on the bisection method.
A measurement-based fault detection approach applied to monitor robots swarm

Swarm robotics requires continuous monitoring to detect abnormal events and to sustain normal operations. Indeed, swarm robotics with one or more faulty robots leads to degradation of performances complying with the target requirements. This paper present an innovative data-driven fault detection method for monitoring robots swarm. The method combines the flexibility of principal component analysis (PCA) models and the greater sensitivity of the exponentially-weighted moving average control chart to incipient changes. We illustrate through simulated data collected from the ARGoS simulator that a significant improvement in fault detection can be obtained by using the proposed methods as compared to the use of the conventional PCA-based methods.
Model-based fault detection algorithm for photovoltaic system monitoring

Reliable detection of faults in PV systems plays an important role in improving their reliability, productivity, and safety. This paper addresses the detection of faults in the direct current (DC) side of photovoltaic (PV) systems using a statistical approach. Specifically, a simulation model that mimics the theoretical performances of the inspected PV system is designed. Residuals, which are the difference between the measured and estimated output data, are used as a fault indicator. Indeed, residuals are used as the input for the Multivariate CUmulative SUM (MCUSUM) algorithm to detect potential faults. We evaluated the proposed method by using data from an actual 20 MWp grid-connected PV system located in the province of Adrar, Algeria.