nils-herrmann/clean_scirepeval huggingface dataset:
An improved strategy based on center of solutions for differential evolution algorithm

This paper proposed center of solutions to improve the solutions and convergence speed of Differential Evolution (DE) algorithm. Center of mass has been used in many studies and it is assumed that each particle has the same mass to be regarded as mean vector of target vector. In our method, we regard the solutions as mass of each particle and calculates the center of solutions. We handle the center of solutions as one element in mutation strategy to lead the direction of evolution. The performance of this strategy is reported and proves it can improve performance of conventional DE.
Modeling and Co-simulation of Hydraulic Power Steering System

Hydraulic power steering systems, which can provide large steering assist force, are widely used for heavy-duty commercial vehicles to enhance the driving comfort. Such systems have great influences on vehicle controllability, steering stability and safety. However, interpolated table from experiment data was used as the assist force to study the vehicle dynamics in many previous works, so the transient behavior of the power steering systems due to time delays, non-linear dynamic characteristic, external disturbance and parameter uncertainty of the hydraulic components was neglected. This paper presents a detailed hydraulic power steering model consisting of a steering cylinder, a rotary valve, a pump, a pressure relief valve, and hydraulic pipes, etc., and the model is established by AMESim software. The other mechanical parts of the vehicle such as the steering system, the suspension and the power train, etc., are built by ADAMS software. A co-simulation is carried out to study the steering stability of the vehicle affected by the hydraulic power steering system. Comparisons between measured data and simulation results validate the correctness of the model, and demonstrate the effectiveness of the co-simulation method. Then some important parameters were altered to show the influences on the vehicle on-center handling performance.
Regularized Tyler's Scatter Estimator: Existence, Uniqueness, and Algorithms

This paper considers the regularized Tyler's scatter estimator for elliptical distributions, which has received considerable attention recently. Various types of shrinkage Tyler's estimators have been proposed in the literature and proved work effectively in the “large p small n” scenario. Nevertheless, the existence and uniqueness properties of the estimators are not thoroughly studied, and in certain cases the algorithms may fail to converge. In this work, we provide a general result that analyzes the sufficient condition for the existence of a family of shrinkage Tyler's estimators, which quantitatively shows that regularization indeed reduces the number of required samples for estimation and the convergence of the algorithms for the estimators. For two specific shrinkage Tyler's estimators, we also proved that the condition is necessary and the estimator is unique. Finally, we show that the two estimators are actually equivalent. Numerical algorithms are also derived based on the majorization-minimization framework, under which the convergence is analyzed systematically.
Convergence of ant colony optimization on first-order deceptive systems

Deceptive problems have been considered difficult for ant colony optimization (ACO) and it was believed that ACO will fail to converge to global optima of deceptive problems. This paper presents a convergence analysis of ACO on deceptive systems. This paper proves, for the first time, that ACO can achieve reachability convergence but not asymptotic convergence for a class of first order deceptive systems (FODS) without assuming a minimum pheromone at each iteration. Experimental results confirm the analysis.
D2L: Decentralized dictionary learning over dynamic networks

The paper studies a general class of distributed dictionary learning (DL) problems where the learning task is distributed over a multi-agent network with (possibly) time-varying (non-symmetric) connectivity. This setting is relevant, for instance, in scenarios where massive amounts of data are not collocated but collected/stored in different spatial locations. We develop a unified distributed algorithmic framework for this class of non-convex problems and establish its asymptotic convergence. The new method hinges on Successive Convex Approximation (SCA) techniques while leveraging a novel broadcast protocol to disseminate information and distribute the computation over the network, which neither requires the double-stochasticity of the consensus matrices nor the knowledge of the graph sequence to implement. To the best of our knowledge, this is the first distributed scheme with provable convergence for DL (and more generally bi-convex) problems, over (time-varying) digraphs.
Automated identification of vessel contours in coronary arteriograms by an adaptive tracking algorithm.

A tracking algorithm for identification of vessel contours in digital coronary arteriograms was developed and validated. Given an initial start-of-search point, the tracking process was fully automated by utilizing the spatial continuity of the vessel's centerline, orientation, diameter, and density. The incremental sections along a major vessel were sequentially identified, based on the assumptions of geometric similarity and continuation between adjacent incremental sections. The algorithm consisted of an extrapolation-update process which was guided by a matched filter. The filter parameters were adapted to the measured lumen width. The tracking process was robust and extremely efficient as indicated by test results on synthetic images, digital subtraction angiograms, and cineangiograms. The algorithm provided accurate measurement of lumen width and percent stenosis that was relatively invariant to the vessel's orientation, dynamic range, background variation, and degree of blurring.
Active image: A shape and topology preserving segmentation method using B-spline free form deformations

Unlike most conventional active contour models that drive the initial contours to match the object boundaries, we propose an “active image” segmentation method that deforms the image to match the initial contours, and hence can simultaneously segment multiple objects. The deformation field is modeled by B-spline free form deformations (FFD). Penalizing the bending energy of B-spline FFD enables us to preserve both the local shape and the topology of objects of interest. Preliminary results on both synthetic and real world images show that the proposed method is able to overcome low contrast, occlusion, and other defects by using simple segmentation criteria.
Inverse kinematics equation of 6-DOF robot based on geometry projection and simulation

There has been the method of neural networks, optimization algorithms and so on to solving inverse kinematics problems in recent years. In this paper, the kinematics equations of the ABB IRB-1400 spot welding robot with 6-degree of freedom which found on the D-H matrix method have been set up. Based on the method of geometry projection principle, the inverse kinematics problem of robotic manipulators is proposed through the giving position and posture of manipulator. In addition, by using the software ADAMS the validity of inverse kinematics equations based on geometric and the trajectory between two points in the simulation of welding process are verified, which provide the method for the next step of dynamics analysis and the control of trace planning.
A novel calibration method based on heuristic B-spline model for fish-eye lenses

Distortion model vary from lens to lens even are same type camera because some unavoidable imperfections during manufacturing. Therefore we propose a novel calibration method based on heuristic B-spline model to identify the distortion model. Unlike common distortion models, proposed heuristic B-spline model can get better calibrated result. In order to evaluate the result of calibration, a test image is considered to compare proposed method with other distortion models. The experiment is shown that the proposed method has less mean square error (MSE) than others.
State-space analysis of ventricular/vascular coupling

The state-space analysis technique is applied to characterizing the time-varying interaction between the left ventricle and systematic vascular bed. An analog model of the circulatory system is developed. Pressure and flow waveforms in the system are computed by numerically integrating the five simultaneous state equations that represent the system dynamics. The model-predicted waveforms agree well with the psychological responses regarding the changes in peripheral resistance, vascular capacitance, viscoelastic loss, aortic valve stenosis, and intraaortic balloon pumping.<<ETX>>
Distributed big-data optimization via block-iterative convexification and averaging

In this paper, we study distributed big-data non-convex optimization in multi-agent networks. We consider the (constrained) minimization of the sum of a smooth (possibly) nonconvex function, i.e., the agents' sum-utility, plus a convex (possibly) nonsmooth regularizer. Our interest is in big-data problems wherein there is a large number of variables to optimize. If treated by means of standard distributed optimization algorithms, these large-scale problems may be intractable, due to the prohibitive local computation and communication burden at each node. We propose a novel distributed solution method whereby at each iteration agents optimize and then communicate (in an uncoordinated fashion) only a subset of their decision variables. To deal with non-convexity of the cost function, the novel scheme hinges on Successive Convex Approximation (SCA) techniques coupled with i) a tracking mechanism instrumental to locally estimate gradient averages; and ii) a novel block-wise consensus-based protocol to perform local block-averaging operations and gradient tacking. Asymptotic convergence to stationary solutions of the nonconvex problem is established. Finally, numerical results show the effectiveness of the proposed algorithm and highlight how the block dimension impacts on the communication overhead and practical convergence speed.
X-ray sensor based on pixelated scintillation film on photodiode arrays

A X-ray sensor based on pixelated CsI:Tl scintillation directly deposited on 6 ×6 P+/Nwell/Psub photodiode arrays was proposed in this paper. The photodiode arrays fabricated through high temperature diffusion and photolithography technology has sensitivity about 10nA/lux under visible light and leak current about 1.2nA. The pixelation of scintillation was achieved by on-chip netlike SU-8 photoresist. The SU-8 photoresist is 81.6um in height and 25um in width.
Existence of concave positive solutions for fractional Sturm-Liouville boundary value problems with p-Laplacian

In this paper, by using fixed point theory, we investigate existence and multiplicity of concave positive solutions for the following Sturm-Liouville boundary value problems with p-Laplacian operator {D<sub>0+</sub><sup>α</sup>(φ<sub>p</sub>(D<sub>0</sub><sup>ρ</sup>+v(t))) + g(t, v(t), D<sub>0+</sub><sup>γ</sup>v(t)) = 0 av(0)-bv'(0) = 0, cv(1) + dv'(1) = 0, v"(0) = 0, D<sub>0+</sub><sup>ρ</sup>+v(t)|<sub>t=0</sub> = 0, where φ<sub>p</sub>(s) = |s|<sup>p-2</sup> s, > 1. As an application, an example is given to demonstrate the main result.
Adaptive neural network controller for the flush material Belt Weigh Feeder

The flush material Belt Weigh Feeder (BWF) is used in many material handling plants. The stability and the performance of the layer control system will affect the quality of the production. In general, the behavior of the flush material on the BWF is non-linear, time-lag, and disturbance character. The layer of the flush material on the belt is hard to be stably controlled especially the occurrence of unstable situation in flush material or the Prefeeder feeding rate. This paper adopted two adaptive neural network (ANN) controller in the general PID control system to improve the performance. The proposed ANN controllers are utilized instead of the original PID controllers to deal with the problems of the unstable pheomena. This paper offers the simulation result of the material character (coarse size, moisture content) or the Prefeeder feeding rate stable and unstable condition (involve the Interference Signal) for the original and the proposed control system. The simulation results are distinct better than the original control system. The variations of the material character (coarse size, moisture content) or the Prefeeder feeding rate can be successfully predicted by the ANN controllers and adjusted the Proportional Actuator immediately. Finally the proposed control system remains convergence with smooth and stable. Therefore, the accuracy, stability and the performance of the control system are improved.
Design and implementation of ATM simulation system based on MVC pattern

The Model-View-Controller (MVC) pattern style separates software into models representing core functionality, views which display the models to the user, and controllers which let the user change the models. Although more sophisticated architectures have since been developed, MVC is interesting to explore because its simplicity makes it more acceptable to practitioners and it is beginning to be come well-known in industry. This paper presents teaching reform and practice of the course - “software design and development”, using the methods of case analysis and group discussion. In the teaching reform, the teaching case, ATM simulation system based MVC design pattern was illustrated. The usage of MVC pattern was illustrated. Moreover, the ways to deal with business rules variation were also described and compared with each other. At last, the problem of permission management was explained based on role-based access control principle. Students are very familiar with the domain of this system and so teaching effect can be improved.
Improved anomaly detection using multi-scale PLS and generalized likelihood ratio test

Process monitoring has a central role in the process industry to enhance productivity, efficiency, and safety, and to avoid expensive maintenance. In this paper, a statistical approach that exploit the advantages of multiscale PLS models (MSPLS) and those of a generalized likelihood ratio (GLR) test to better detect anomalies is proposed. Specifically, to consider the multivariate and multi-scale nature of process dynamics, a MSPLS algorithm combining PLS and wavelet analysis is used as modeling framework. Then, GLR hypothesis testing is applied using the uncorrelated residuals obtained from MSPLS model to improve the anomaly detection abilities of these latent variable based fault detection methods even further. Applications to a simulated distillation column data are used to evaluate the proposed MSPLS-GLR algorithm.
Parameter Estimation in the Stenosed Coronary Circulatory System

In a stenosed coronary circulation, the following parameters were estimated from pressure measurements: coronary vascular resistances, transmural blood flow, and endocardial to epicardial (ENDO: EPI) flow ratio. This was done by modeling the hemodynamics of the coronary system with an analogous electrical circuit. The circuit topology consists of a nonlinear stenosis resistance and an endocardial branch in parallel with an epicardial branch. The unknown, linear endocardial and epicardial resistances were determined by a least-squares estimation method with three inputs: the left ventricular pressure (LVP), the aortic pressure (AP), and the distal coronary arterial pressure (DP). In this paper, computer simulations also show that the input DP may be replaced by a phasic arterial coronary flow synthesized from its mean systolic and diastolic values. In animal studies, DP may be measured directly while such pressure measurements are difficult to obtain clinically since it requires passing a catheter distal to a stenosis. However, mean systolic and diastolic coronary arterial flow have been obtained in humans via video densitometry. Thus, this method may potentially be useful in clinical situations. Validity of the method was tested with 23 animal studies using swine under a variety of physiological conditions. Linear regression analyses were made between the ENDO: EPI flow ratios estimated by the model and those measured by use of the radiolabeled microsphere technique.
Single-trial event-related potential emotional classification based on compressed sensing

In this study, a robust classification method for emotional speech single-trial event-related potential (ERP) signal was developed. The classification method based on compression sensing (CS) theory. First, we use CS theory to reduce the dimensionality of the ERP signal. Second, the ERP signal was reconstructed by using K-SVD method to construct the over-complete redundant dictionary. Finally, the ERP signal was classified by calculating the residuals between the reconstructed samples and the test samples. The experimental results show that the proposed algorithm can effectively classify the noisy ERP signal and avoid the feature extraction process in the signal recognition.
Time-varying left ventricular elastance determined by a finite element model

A finite element model of the left ventricle (LV) with an axisymmetric geometry has been developed to determine LV pressures and volumes. Regional myocardial contractions defined by the finite elements are then related to the time-varying LV elastance (instantaneous pressure over volume ratio). LV geometry is modeled as a truncated ellipsoid with user-defined parameters such as wall thickness, short axis length, and long axis length. The stress-strain relationship for each element is assumed to be linear but with a time-varying Young's modulus. Empirical values of the Young's modulus are assigned to finite elements for simulating normal and ischemic myocardium. The mesh generation is implemented in C++ and the stress-strain computation in Matlab. The finite element model produces reasonable geometry for normal and ischemic LV walls. More importantly, the LV ejection fraction and pressure and volume curves are consistent with typical clinical observations. The simplified model developed in this study is an effective and computationally efficient way to relate regional myocardial impairments to global LV functions and to generate LV elastance curves over an entire cardiac cycle.
The static stress analysis of supercritical dyeing equipment

Dyeing synthetic fibers with supercritical carbon dioxide has achieved excellent fruit on laboratory scale. Self-developed supercritical C02 fluid dyeing equipment with computer control system was introduced in this paper. In order to confirm the equipment operational safety, the finite element analysis software ANSYS is adopted to analyze static stress of major parts. As a key component of the supercritical dyeing equipment, the coloring matter kettle related tightly with the dyeing process. In our research, the model of the coloring matter kettle was established, and the analysis result of the coloring matter kettle was obtained. These models not only realize the simulation of dyeing process, but also provide the methods for improving the efficiency of dyeing equipment and offer related parameters.
Improved Adaptive backstepping Sliding Mode SVC controller design for multi-machine power system

For the two area interconnected power system with SVC, it is equivalent to the two machine system. A new stability controller for SVC which is designed to use the improved adaptive backstepping method and adaptive sliding mode control. The derivative of the virtual control is viewed as bounded uncertainty. In order to avoid the problem of “explosion of terms” of the backstepping method, the continuous sliding mode term is adopted to compensate the uncertainty. In this paper, the mathematical model of the system does not use the linearization method, so that the nonlinear of the system can be kept. The simulation results show that the controller can improve the transient stability of interconnected power system.
Notice of Violation of IEEE Publication PrinciplesDesign and Implementation of Wireless Computational Grid based on JavaSpace

Wireless devices have many limitations: limited processing power, energy constrain due to small batteries and small secondary storage. With the diminishing prices and increasing usage of mobile devices, the grid concept is now extended into the wireless domain. This paper focus on the design and implementation of a grid-based problem solving environment for wireless computational grid using JavaSpace .This experimental system is developed with the primary purpose to allow wireless devices to distribute the computational load among other available wireless devices and to solve problems that they would not be able to solve individually. The paper describes the system framework, operation and also presents an experimental analysis of the wireless computational grid.
Binary orthogonal code design for MIMO radar systems

Multiple Input Multiple Output (MIMO) radar can effectively improve radar performance by transmitting specially designed orthogonal signals. A novel algorithm is presented to design binary orthogonal code for MIMO radar system. The proposed algorithm employs the Walsh function to maintain the orthogonal property of signals. The binary orthogonal code set is obtained by executing synchronal random exchange in column and random selection from rows to coding template, with using Genetic Algorithm (GA) for optimization. The simulation results show that the algorithm is feasible.
A Genetic Algorithm Based Approach For Mixed-Media Advertising Budgeting

Mixed-media advertising which has become more effective and popular attracts the attention of advertising agencies, media planners and advertisers. How to decide and allocate the budget for mixed-media advertising becomes a fundamental part of a company's advertising campaign.In this paper, an optimization approach based on Genetic Algorithm is proposed to deal with this advertising budget problem which is formulated as a constrained nonlinear programming problem. Then an experiment example is given to explain the process of our approach. The result of this example supports the feasibility and efficiency of our method.
Cost allocation model and its solution about inter-organizational cooperation based on game theory

In Supply Chain Environment, the boundary of cost management is not limited in one enterprise, but extend to inter-organizational cooperation, which bring on the inter-organizational cost management. Traditional single-factor cost allocation method and activity-based costing could not satisfied the need of inter-organizational cost allocation, while cooperative game theory is always applied to solve the allocation problem of alliance profits or total cost. This paper firstly discusses the applicability of cooperative game theory in inter-organizational cost allocation, and then a cost allocation model is founded based on cooperative game theory. Kinds of solutions for the models are analyzed and then the Shapley value method is commended among the solutions. Finally, the disadvantages of Shapley value method are discussed.