nils-herrmann/clean_scirepeval huggingface dataset:
Numerical simulation of 2-D MT tipper response using finite element method

In the two-dimensional geo-electric model, the tipper response of magnetotelluric(MT) depends on the numerical simulation TE mode. Firstly, the boundary value problem and variational problem were shown, and the finite element method was used for element subdivision, interpolation, integration and general synthesis. Secondly, the electric field value of each node was obtained through solving the complex equation. Lastly, the partial derivatives along the horizontal and vertical electric field obtained using the difference method, and the tipper response of the two-dimensional MT can be calculated. By the numerical simulation of the two models, the tipper responses are obtained including real part, imaginary part and amplitude, and the results reflect the horizontal distribution of the body.
Performance of Space-Time Trellis coding for TH-UWB-IR Systems

In this paper, we discuss the design of STTC for TH-UWB-IR systems with different modulations. The performances of these STTC schemes are evaluated under various conditions such as multipath and multiuser through simulations. The results show that STTC-UWB with PPM can achieve much better performance than the schemes employing PAM and biorthogonal modulation. Furthermore, we find the performance degrades when the number of users increases
Binaural Sound Localization Based on Detection of Multi-band Zero-Crossing Points

A method of binaural sound localization based on detection of multi-band zero-crossing points generated from binaural filter-band outputs is presented. In the presented method, the ITD values are estimated by a histogram weighted by the estimated source noise rate(SNR) computed from the variances of ITD samples. As a result, the method provides an accurate estimate of sound source direction and is more robust to noise. To verify the validity of the method, simulations for sound localization are performed under the condition of white noise. The simulation results show that the method is able to provide an accurate estimate of sound source direction and is more robust to noise while offering significantly less computational complexity compared to the conventional cross-correlation based methods.
Optimal output tracking control for autonomous underwater vehicles

A nonlinear discrete-time system model is used for autonomous underwater vehicles (AUV) and the optimal output tracking control (OOTC) is designed. By introducing a sensitivity parameter and expanding around it, the original nonlinear two-point boundary value (TPBV) problem led by the OOTC problem is transformed into a series of linear TPBV problems containing known low-order nonlinear terms. Algorithm of nonlinear expanding terms is obtained and substituting them into the recursion formula of adjoint vector equations, the OOTC law consisting of linear terms and a nonlinear compensation term can be approximately obtained. A simulation example from AUV is employed to test the validity of the presented algorithm.
Distribution network reconfiguration for load balancing using binary particle swarm optimization

In this paper, a method based on modified binary particle swarm optimization (BPSO) is proposed for distribution network reconfiguration with the objective of load balancing. A novel model to simplify distribution network is presented. The feeder reconfiguration problem is formulated as a non-linear optimization problem, and BPSO is used to find the optimal solution. According to the characteristics of distribution network, some modifications are done to retain the radial structure and reduce searching requirement. Test results based on a sample network have shown that the proposed feeder reconfiguration method can effectively keep load balancing, and the BPSO technique is efficient in searching for the optimal solution.
An audio secret sharing system based on fractal encoding

Secrets sharing is an interesting topic and has studied for a period of time. This paper proposed a method to deal with the audio secret sharing based on fractal encoding. The proposed method has negligible constraints on secret audio and shared audios. Using fractal encoding makes the size of the secret reduces 40% in this study and the architecture is simple to practice. The fractal codes were divided into several parts. Through staggered way with least significant bit method, these codes were put in frequency domain of other audios. To rearrange these codes in order is not easy without the idea how they were put in carriers. Different persons can hold different parts of the fractal codes to share this secret audio. In the experiments, two secret audios were used and showed two experimental results. Signal to noise ratio (SNR) is applied as a criterion. The results of experiment showed the secret audios were restored successfully. The best value of SNR in this experiment is 29dB so far. To observe whether this audio has been exploited to hide the fractal codes in frequency and time domain is not easy. In the future, this system could be used for voice controlled locks or one's last will and testament.
Notice of RetractionResearch on GA-ANN Based WLAN Indoor Location Method

Neural network optimized by genetic algorithm (GA) based WLAN indoor location method is proposed. GA based artificial neural network (GA-ANN) method can effectively reduce the storage cost, enhance real-time ability, and greatly improves the accuracy of indoor location. By analyzing the inherent shortage in neural network when applying in indoor environment, make use of genetic algorithm to encode structure parameters of neural network, and thereby increase its convergence rate, optimize its learning algorithm and enhance its generalization ability. Finally, through the simulation comparison with the neural network method, the feasibility and effectiveness of neural network optimized by genetic algorithm based indoor location method is verified, with the location accuracy 2.64 m.
MRF-Based Intensity Invariant Elastic Registration of Cardiac Perfusion Images Using Saliency Information

In this paper, we propose a Markov random field based method that uses saliency and gradient information for elastic registration of dynamic contrast enhanced (DCE) magnetic resonance (MR) images of the heart. DCE-MR images are characterized by rapid intensity changes over time, thus posing challenges for conventional intensity-based registration methods. Saliency information contributes to a contrast invariant metric to identify similar regions in spite of contrast enhancement. Its robustness and accuracy are attributed to a close adherence to a neurobiological model of the human visual system (HVS). The HVS has a remarkable ability to match images in the face of intensity changes and noise. This ability motivated us to explore the efficacy of such a model for registering DCE-MR images. The data penalty is a combination of saliency and gradient information. The smoothness cost depends upon the relative displacement and saliency difference of neigh boring pixels. Saliency is also used in a modified narrow band graph cut framework to identify relevant pixels for registration, thus reducing the number of graph nodes and computation time. Experimental results on real patient images demonstrate superior registration accuracy for a combination of saliency and gradient information over other similarity metrics.
BTP Prediction Model Based on ANN and Regression Analysis

In this paper, data mining technology is adopted to find correlations from massive production data to predict Burning Through Point (BTP) of sintering process. A hybrid BTP prediction model is presented which is based on artificial neural network and multi-linear regression error compensation algorithm. In this model, the final prediction result is calculated based on both the prediction value from ANN model and the compensation value from linear regression model. Compared with the pure ANN model, the maximal prediction error is reduced from 3.85 meter to 1.3 meter and the prediction errors are kept in the range of  1 meter with the probability of 98.89%. Finally, the prediction values and correlated variables are sent back to L1 system through BTP control model to realize a closed-loop control system.
The effect of Gramicidin on the membrane potential of neurons in the CNS of L. stagnalis

Gramicidin is a pore-forming peptide which exhibits lethal properties against a large spectrum of cells. It forms monovalent cation-specific channel in the lipid bilayer of a cellular membrane with limited permeability to anions or polyvalent cations. Both ions and water move through the pore which is formed by the peptide backbone. We detected formation of pores induced by the dimerization of gramicidin molecules by monitoring changes in the membrane and action potentials of neurons in the central nervous system of Lymnaea stagnalis. This methodology could be used for the study of peptide interactions with neuronal cellular membranes.
Automated delineation of pulmonary artery cross-sections in CT scans

The purpose of this project was to develop a software tool for studying the dynamic changes of the cross-sections of the pulmonary arteries from computer-tomography (CT) scans. The software for analyzing CT images in the DICOM format was developed using Cocoa, the Apple's object-oriented C++ application programming interface (API) for the Mac OS X operating system, specifically using Mac OS X Lion. The software was developed as a plugin for OsiriX, which is an open-source, widely used DICOM viewer. The main algorithm for detecting the artery border first establishes a seed point within the cross-sectional area of the pulmonary artery, based off of an initial user click. Then the artery border is detected in an automated way by launching 24 radial lines from this seed point. In a similar fashion the border detection continues onto the subsequent frames.
Light activated position sensing array for persons with disabilities

The light activated position sensing array will bring great independence to many persons with disabilities. Enabling those that have limited to no use of their limbs, this product will allow its user to perform many day to day activities that may have not otherwise been possible without outside assistance. The system is a hands-free electric wheelchair control that will also function as a powerful environmental control tool. Guided by a light emitter worn on the user's head, it will replace orally activated (sip and puff) switches, as well as any other switches that may be mounted on or near the user's head.
Combined beamforming and space-time block coding for wireless communications

This paper presents un approach for beamforming based on third-order cyclic statistics. The approach has fairly well accuracy. Further, a new scheme joint beamforming and space-time block coding is suggested that multiple antenna arrays transmit at base station and multi-antenna elements receive at mobile terminal. This scheme improves the receiving error performance of mobile terminal and increases the capacity of the entire wireless communication system. The computer simulation validates that the aforementioned scheme is superior and effective.
Recursive Tracking Of Vascular Trees In Angiograms Using A Detection-deletion Scheme

A computer algorithm was developed to automatically identify the vascular tree in an angiogram. This was accomplished by recursively implementing a vessel tracking and a branch-point detection algorithm. To successfully segment overlapped vessels and to prevent reentry of the tracking path, a detection-deletion scheme was employed. Once a vessel segment was identified, it was filled with the background pixel values and thus deleted from the angiogram. It was demonstrated that the algorithm provides satisfactory segmentations of vascular trees in both digital subtraction angiograms and unprocessed angiograms.
Research on Financing Environment for Building Energy Efficient Service Market in China Based on Dynamic Actor Network Analysis

With more attentions to environment and energy, building energy efficiency (BEE) becomes the consequential selection to protect global environment and realize sustainable development. BEE service is the important part of BEE market and it can directly affect the healthy development of total market. Nowadays, the market is in the initial stages and capital is scare. The research is little and centralized on qualitative analysis and index appraisement. Therefore, this study mainly analyzes financing environment for BEE service market adopting the research method combining with qualitative and quantitative analysis, identify the main influence factors for BEE service market through questionnaires, make concept model for participants of BEE service market by strategic thought and dynamic actor network analysis (DANA) software platform. Construct the relevant relationships between different agents based on attribute and behavior. Finally, improve the analysis model on financing model for BEE service market and explore the effective ways to improve financing environment. The research result proves that government promulgating economic incentive policy, establishing building energy efficient supervision management system, improving building energy efficient service content and strengthening knowledge diffusion mode can promote BEE service market development.
Modification design of high-precision above ground marking system

Above ground marking system is one of the three main components of Magnetic Flux Leakage Detector, which is widely used in safety and integrity assessment of long-range transmission pipeline. After analyzing the advantages and disadvantages of three types of former above ground marking systems, a high-precision above ground marking system with smart appearance, intelligent communication and low cost is designed to eliminate the accumulated errors caused by mechanical construction, turning or slippage of the miles round of the magnetic Flux Leakage Detector. Hardware design of the new above ground system, including above ground marker and master clock system, is investigated in detail in this paper and reliable experiments and traction tests were performed to test the stability and dependability of the system.
An Improved Stability Condition for T-S Fuzzy System

Stability conditions for T-S fuzzy system based on a common positive definite matrix or a set of positive definite matrices have been proposed in lots of literature. This paper tries to improve the conservatism of those stability conditions. By employing the concept of the efficient mutual non-neighboring rule group (EMNNRG), a sufficient condition is proposed to guarantee quadratic stability of the fuzzy model globally. This condition only requires finding a local common positive definite matrix in each EMNNRG. Moreover, the proposed stability condition includes previous results as special cases. An example is illustrated to show the effectiveness of the proposed stability condition
Adaptive lane departure warning system on Android smartphone

This study proposes an adaptive Lane Departure Warning system (LDW) on smartphone, to deal with flexible warning capability for different road environment and driver behavior. The proposed system is migrated the preliminary developed LDW algorithm and utilizing mobile camera built-in Android smartphone to capture images of road environment. Through "Java Call C" approach, the LDW kernel is processing the captured images to obtain line-marking of road. When the vehicle departs from its lane, the departure alarm is active. An adaptive mechanism could adjust the parameters of alarm box according to different driving situations without any extra manual setting and yields user-friendly manipulation.
A neuron emulator for single-electrode settings

This paper describes the development of an electronic neuron emulator that has both passive and active electrical properties of a neuron as seen from a single electrode. The main utility of this device is for testing electrophysiological equipment such as a single-electrode voltage clamp or a patch-clamp amplifier. Each spike of the action potential is generated by a limited amount of charge stored on a capacitor. This design results in a more realistic simulation of the action potential generation by ionic currents in a live neuron compared to using a current or voltage source. An embedded microprocessor is used to control the firing of the action potential. Preliminary results from a prototype show that the neuron emulator meets the design specifications and is capable of performing rate responsive current clamp.
Nonlinear Function Approximation Based on Least Wilcoxon Takagi-Sugeno Fuzzy Model

The purpose of this paper is based on radial basis function neural network (RBFN) to develop a self-constructing least Wilcoxon-generalized RBFN fuzzy inference system (LW-GRBFNFIS) and applied to nonlinear function approximation and chaotic time series prediction. As is well known in statistics, the resulting linear function by using the rank-based least Wilcoxon (LW) norm approximate to linear function problems are usually robust against (or insensitive to) outliers. In addition, the Takagi-Sugeno fuzzy model and RBFN techniques have been proved the functional equivalence and successfully applied to modeling nonlinear function approximation problems. This paper introduce proposed method to improve traditional least square GRBFNFIS for nonlinear function approximation and overcome outliers' problem. Nonlinear function approximation and chaotic time series prediction problems used to verify the proposed method. The experiments results show the proposed method can effectively solve outliers' problems.
Segmentation of High-Resolution Remote Sensing Image Based on Marker-Based Watershed Algorithm

Because of the particularity of high resolution remote sensing image such as Quickbird, the segmentation methods used in common video or image are unsuitable for high-resolution remote sensing image. In order to overcome the shortcomings of over-segmentation existing in watershed algorithm, we bring in a new method which is an improvement to the marker-based watershed algorithm. Firstly, the median filtering is carried out to the high-resolution remote sensing image to eliminate random noise in the image, and then gradients are obtained with mathematical morphology; finally, the maker-based watershed algorithm is applied to the gradients to segment the image. Segmentation experiments carried out for the Quickbird images with different kinds of land cover types, show that the method is efficient and it can avoid over-segmentation when the image texture is homogeneous, but it also has some limitations when the image texture is relatively complex.
Impedance Control of Thyristor Controlled Series Capacitor to Improve the Transfer Capability of Remote Wind Farms

In this paper, we put forward a new impedance control method of thyristor controlled series capacitor (TCSC) by modifying variable structure PID feedback of firing angle, aiming at improving the transfer capability of remote wind farms. TCSC can get the best results with nonlinear closed-loop control method, but it is very difficult, if not impossible, to be implemented because a huge amount of data is required. The impedance controller with variable structure PID feedback of firing angle is designed as follows: the coefficient of its integral part is equal to zero before or around the moment that measuring impedance reaches the desired impedance first time, meanwhile it's called PD controller; then add the integral part and it becomes PID controller. When TCSC steps from different impedance values to the same, the value of error accumulator changes a little, and so a better approximate control effect is obtained. Digital simulation and dynamic experiment results show that this control method can improve the transfer capability with good dynamic and static performance.
Motion Estimation Via Cluster Matching

A new method for estimating displacements in computer imagery through cluster matching is presented. Without reliance on any object model, the algorithm clusters two successive frames of an image sequence based on position and intensity. After clustering, displacement estimates are obtained by matching the cluster centers between the two frames using cluster features such as position, intensity, shape and average gray-scale difference. The performance of the algorithm was compared to that of a gradient method and a block matching method. The cluster matching approach showed the best performance over a broad range of motion, illumination change and object deformation.
Study on the Refrigerant Natural Circulation System Performance in Calefaction Condition

Because of the mutual influence of the parameter between each condenser,the operation characteristic of the refrigerant natural circulating system became comparative complex. In order to analyze the operation characteristic of refrigerant natural circulating system in calefaction working circumstance, the mathematical model based on the refrigerant natural circulating system of gas and liquid phase is built.Through analyzing and computing of the model,results exhibit the influence regularity from the parameters of water temperature at the inlet of condenser,the flow rates of cooling water,the indoor temperature,etc.The simulation results indicate , when the air volume of indoor heat transfer and the indoor temperature keep stable, the heating capacity of condenser increased with the electric flow valve position increased, the degree of subcooling in the condenser decreased with the electric flow valve position increased.
An Adaptive Algorithm For Coronary Artery Identification In Cineangiograms

The problem of identification of the coronal y arteiy from the angiogram is acldresscd. An adaptive system for the classification of the arterial structure is presented. The system is based on a parallel distributed network, and an adaptive segmentation algorithm. It is a computationally efficient system, since most of the calculation are preformed prior to the iteration stage. The segmentation results on a human coronary angiogram are illustrated.
A data-driven approach to prior extraction for segmentation of left ventricle in cardiac MR images

In this paper, we propose a data-driven approach that extracts prior information for segmentation of the left ventricle in cardiac MR images of transplanted rat hearts. In our approach, probabilistic priors are generated from prominent features, i.e., corner points and scale-invariant edges, for both endo- and epi-cardium segmentation. We adopt a level set formulation that integrates probabilistic priors with intensity, texture, and edge information for segmentation. Our experimental results show that with minimal user input, representative priors are correctly extracted from the data itself, and the proposed method is effective and robust for segmentation of the left ventricle myocardium even in images with very low contrast. More importantly, it avoids inter- and intra- observer variations and makes accurate quantitative analysis of low-quality cardiac MR images possible.
Move like humans: End-to-end Gaussian process regression based target tracking control for mobile robots

In this paper, we address the problem of target tracking control for mobile robots with limited sensing range. An end-to-end Gaussian process regression learning control method is proposed to transfer the human control experiences to the controller. The end-to-end learning architecture directly learns the control mapping from the original sensing input space to the final control output space in an human-like manner. The non-parametric Gaussian process regression accurately transfers the complex human control experiences to the control model. In addition, realistic training data set are collected from human operators for the end-to-end control learning. The performance of the proposed target tracking control method is extensively evaluated on various real-world scenarios, experimental results have demonstrated the robustness, accuracy, and effectiveness of the proposed method.