nils-herrmann/clean_scirepeval huggingface dataset:
A current control strategy of three-phase grid-connected inverter with LCL filter based on one-cycle control

A digital one-cycle control strategy combined with the Quasi Proportional Resonant (QPR) controller is applied to the three-phase grid-connected inverter with LCL filter in this paper. The control system contains dual loops with inner loop of capacitive current and outer loop of net current. Instead of conventional SPWM modulation method, one-cycle control for the inner loop has functions of control and modulation, and then the bipolar control algorithm of one-cycle controller with capacitor current is derived to improve anti-interference performance of the system and to shorten the tuning process, the QPR controller in stationary reference frame is adopted to the outer-loop, which elides complicated coordinates transformation. Considering the high-order feature of the system, the parameters of controllers are designed by using the high-order poles assignment. Finally, a 3kW three-phase grid-connected inverter simulation is performed and the results verify feasibility of the scheme.
An improved tone mapping algorithm for High Dynamic Range images

Real world scenes contain a large range of light intensities. To adapt to display device, High Dynamic Range (HDR) image should be converted into Low Dynamic Range (LDR) image. A common task of tone mapping algorithms is to reproduce high dynamic range images on low dynamic range display devices. In this paper, a new tone mapping algorithm is proposed for high dynamic range images. Based on the probabilistic model is proposed for high dynamic image's tone reproduction, the proposed method uses a logarithmic normal distribution instead of normal distribution. Therefore, the algorithm can preserve visibility and contrast impression of high dynamic range scenes in the common display devices. Experimental results show the superior performance of the app roach in terms of visual quality.
Adaptive robust fuzzy control for a class of nonlinear systems based on backstepping method

An adaptive robust fuzzy control approach is developed in this paper for uncertain nonlinear strict-feedback systems with unknown control input gain functions. The prominent features of the developed approach are: 1) the controller singularity problem is solved perfectly, 2) it improves the robust performance of the system, 3) compared with most of results in adaptive fuzzy or neural networks control for nonlinear strict-feedback systems, the developed approach alleviates on-line computation burden. The overall control system guarantees that all signals are uniformly ultimately bounded and that the tracking error variables exponentially converge to an arbitrarily small neighborhood around zero by suitably choosing the design parameters. A simulation example demonstrates the feasibility of the developed control approach.
A Novel High Speed/High Precision Displacement Measurement Method Using Double Grating Scales for a Macro/Micro Driven System: Principle Design and Experimental Verification

High speed and high precision displacement measurement is hard to be achieved because of the restriction of displacement sensors: the maximum allowable moving speed is in contradiction to the measuring accuracy. In order to enable the high precision displacement measurement of the ending to achieve high speed moving course, a high speed and high precision displacement measurement method based on double grating scales is presented. Based on the analysis of measuring theory of grating scale, the method of signal's switch and merge of double grating scales is discussed. By using the analysis of the subdivision error of Moire fringe, the error of the count impulse's switch process is decreased. The signal process circuit is designed by using the programmable logic device-FPGA. And through the logical and timing simulation by the design software of FPGA, the feasibility of method is proved. The experimental results verify that the method is effectively used to control a macro/micro driven system.
Analysis of Topic Evolution Based on Subtopic Similarity

In the area of topic tracking, topic is developing with time. Actually speaking, traditional topic tracking approaches could track the relevant stories. However, the relation between events occurred during the topic development process could not be learned with traditional approaches. Furthermore, the whole history of the topic tracking could not be acquired either. Based on these disadvantages in traditional approaches, we propose anew method based on the calculation of the subtopic similarity.This method utilizes the idea of time partition so as to analyze the developing history of the topic. Our method concerned the time characteristic of a event as well as the location characteristic. Moreover, we use it to manipulate the information. In the experiment applying the practical data, it shows our method works more efficiently .
A novel equivalent circuit thermal model for integrated power modules

This paper proposes a new analytical thermal resistance model which provides a specific temperature estimation for integrated power modules. Unlike the conventional two-resistor compact model, two case nodes are introduced into the proposed model to reflect the temperatures of junction and inductor. Multiple heat sources are also included in the model. FEA simulation is applied to evaluate the thermal model and verify that the accuracy of junction temperature estimation is significantly improved. It is noted that reducing thermal resistance between the junction and IC top case node can significantly enhance the cooling performance, which enables flexible methods to modify the thermal performance.
An Empirical Study of Throughput Prediction in Mobile Data Networks

Bandwidth-sensitive applications such as adaptive video streaming rely on accurate prediction of future network throughput to enable them to react to and compensate for the rapidly fluctuating bandwidth often found in mobile networks. Researchers have developed various prediction algorithms in the literature of which many have been employed in real-world applications. However, there is a lack of systematic study on the comparative performance of the existing prediction algorithms in the context of mobile networks. This work addresses this void by conducting a systematic performance comparison of 7 prediction algorithms, and analyzes their characteristics when applied to the prediction of TCP throughput in mobile networks. The performance results are obtained from extensive trace-driven simulations where the throughput trace data were captured in production 3G/HSPA mobile networks in 3 locations over a period of 9 months and hence offer a good representation of the prediction algorithms' real- world performance. Furthermore, we applied the theory of differential entropy in information theory to obtain an estimated lower bound on throughput prediction errors which, for the first time, enables one to evaluate the absolute performance of these prediction algorithms. The results revealed that more complex algorithms are not necessarily better, and there exists a specific range of operating parameters where predictions are generally more accurate.
A continuous time sigma-delta modulator using time-domain quantizer and feedback element

This paper discusses a continuous time sigma-delta modulator architecture that uses efficient time-domain quantizer and corresponding feedback element. The time-domain quantizer takes advantage of all quantization time slots to realize multi-bit quantization. Thus, the performance is enhanced while the power consumption of the quantizer is reduced. The feedback element generates feedback pulse, and outputs to 1-bit DAC, which is inherently linear. A 20 MHz bandwidth sigma-delta modulator is designed. The evaluation indicates that the 3-order modulator achieves 75.1dB peak SNDR, 81 dB peak SNR and 82 dB DR.
A Probabilistic Flooding Algorithm Using Weighting Factor

In this paper, we firstly study the connectivity of ad hoc networks and establish the correlations of connectivity with average node degree. Then, based on the simulation result of connectivity, we present a probabilistic flooding algorithm that can tradeoff the efficiency and reliability by using weighting factor. Thus, we treat the well-known contradictions in our new algorithm. Our simulation results show that, with the choice of an optimal flooding probability by using weighting factor, we can get a great deal of overhead gain with a little loss of packet delivery ratio.
Adaptive Robust Fuzzy Control for a Class of Uncertain Nonlinear Systems in Pure-Feedback Form

This paper studies an adaptive fuzzy control problem for a class of uncertain nonlinear systems in the pure-feedback form. Compared with the existing approaches, the two main advantages are that the proposed algorithm can be implemented by utilizing only one fuzzy logic systems (FLS) approximator and the robustness of the closed-loop system is improved. The proposed adaptive fuzzy control algorithm can guarantee that all the signals of the closed-loop system are uniformly bounded and the tracking errors converge to an arbitrary small neighborhood around zero. The feasibility of the proposed algorithm is obtained by a simulation example.
Editorial: Special Issue on Power Supply on Chip, 2013

The thirty papers in this special issue discuss the research challenges currently being addressed globally, in academia and industry, in the evolution to delivering the vision of power supply on chip technology, or PwrSoC. In most cases, two key target applications are considered. At one hand, there is the idea of replacing the ubiquitous low cost, but low efficiency Low Drop Out linear regulator with a fully integrated monolithic dc-dc switching converter with a similar footprint and cost, but with much higher efficiency. At the other end of the spectrum, there is the concept of monolithically integrating a complete power management system architecture with a complex system on chip.
A Naive Bayes and Maximum Entropy approach to sentiment analysis: Capturing domain-specific data in Weibo

Since social media have become very popular during the past few years, researchers have been focusing on being able to automatically process and extract sentiments information from large volume of social media data. This paper contributes to the topic, by focusing on sentiment analysis for Chinese social media. In this paper, we propose to rely on Part of Speech (POS) tags in order to extract unigrams and bigrams features. Bigrams are generated according to the grammatical relation between consecutive words. With those features, we have shown that focusing on a specific topic allows to reach higher estimation accuracy.
A Dynamic Growing Neural Network for Supervised or Unsupervised Learning

A dynamic growing neural network (DGNN) for supervised learning of pattern recognition or unsupervised learning of clustering is presented. The main ideas included in DGNN are growing, resonance, and post-prune. DGNN is called dynamic growing because it is based on the Hebbian learning rule and adds new neurons under certain conditions. When DGNN performs supervised learning, resonance will happen if the winner can't match the training example; this rule combines the ART/ARTMAP neural network and WTA learning rule. When DGNN performs unsupervised learning, post-prune is carried out to prevent over fitting the training data just like decision tree learning. DGNN's prune rule is based on the distance threshold. DGNN has some advantages: learning not only is stable because it grows under certain conditions; but also it is faster than back-propagation rules and favorable learned predictive accuracy in small, noisy, online or offline data sets. Three classes of simulations are performed on the primary benchmarks: circle-in-the-square and two-spirals-apart benchmarks are used to check DGNN's supervised learning and compare it with ARTMAP and BP neural networks; DGNN's unsupervised learning ability is checked on UCI Machine Learning Archive's Synthetic Control Chart Time Series data set
Anti-Tumor Effect and Its Mechanism of Vaccaria segetalis on Mouse Inoculated H22 Solid Carcinoma

Vaccaria segetalis was reported anti-tumor action in vitro. In this study, the effect and mechanism of inhibiting tumor growth and improving the quality of life of Vaccaria segetalis on mouse inoculated H22 solid carcinoma were investigated.  The solid carcinoma model of mouse was made by H22 cells subcutaneous injection and they were divided into four groups including model control group, 1mg/kg treatment dose, 2.5mg/kg treatment dose and 5mg/kg treatment dose groups of Vaccaria segetalis.After ig treatment for 10 days,the mice were killed,and the inhibited rate of tumor growing was calculated. The pathological morhpologic change of tumor was observed by HE staining.The apoptosis number of tumor was observed by TUNEL method. The expression of CD31 of tumor endothelial cells was measured by immunohistochemical method. Vaccaria segetalis can improve the quality of life on mice invased by tumor, inhibite the growth of tumor.The effect of Vaccaria segetalis on the tumor morphology showed nuclear shrinkage, necrosis, and inflammatory cell infiltration on the periphery, etc. And it inhibited the expression of CD31, induced apoptosis of tumor cells and vascular endothelial cells. Vaccaria segetalis can improve the the quality of life on mice invased by tumor, and inhibit the growth of tumor. The mechanisms were likely due to inhibiting the angiogenesis, and inducing the apoptosis of tumor cells and vascular endothelial cells.
A speed based active queue management scheme

This paper presents a speed based active queue management scheme. This scheme predicts the incoming and outgoing speed of every active flow in the next sample intervals according to the history data. And then with the queue length of every flow at the end of last a few intervals, predicted incoming and outgoing rate and expected queue length, drop probability in this interval is calculated. The goal of this scheme is to achieve both stabilized queue length and per-flow fairness. Through ns-2 simulation, we compare SBAQM against FRED and SRED and obtain satisfying results.
3D Face Reconstruction Based on Geometric Transformation

Most of current 3D face reconstruction methods based on 2D image are complex. In this paper, a novel and efficient method on the basis of geometric transformation is presented. The method is composed of two aspects, pose estimation and 3D model reconstruction. For the first part, an improved Active Shape Model(ASM) algorithm is used to detect the feature points of the face in the image, and the frontal face information is got based on the symmetry of human face. For the second part, first, establish a correspondence between the key facial feature points and the generic model points, then divide the face model into different regions and adjust them separately based on affine transformation. Experimental results show that the proposed algorithm provides satisfactory results.
Bayesian reliability prediction of a medical device system

High product quality and reliability are critical in the medical device industry. Accurate reliability prediction during the product development stage provides inputs for the design strategy and boosts understanding and confidence in product reliability before products are released to the market. A Bayesian framework provides a straightforward solution for product system reliability prediction with uncertainty quantified as confidence intervals. This is achieved by propagating the uncertainty of model parameters and component-level reliability to the system level. In this work, Bayesian models are applied in the Design for Reliability process to assess medical device system reliability with confidence. In cases where a single data source is insufficient due to sample size limitations, Bayesian models are flexible to aggregate various sources of data to reduce uncertainty.
Contrasting CFD for electronic systems modeling with that for aerospace

With aerospace industries wishing to take maximum advantage of computational modeling CFD (computational fluid dynamics) has found increasing use at off-design conditions. These are characterized by significant areas of separated flow and the need for the resolution of small scale complex geometrical features. This new aerospace CFD direction has increased the common links between electronics CFD needs. This link is explored. As part of this, it is demonstrated how for typical non-separated aerospace flows with traditional turbulence models (RANS) CFD yields high solution accuracy (integrated stresses to within 1%). More separated aerospace flows, and the modern eddy resolving approaches now used to solve these are then considered. Despite the relatively simple level of geometrical complexity (in relation to electronics) and aerodynamic nature of geometries a significant reduction in solution accuracy is shown. However, even so the predictive accuracy could be better than 10 %. The same model types are then applied, by the same research group, with high accuracy well validated CFD codes (again with aerospace Rolls Royce - origins) to a non-isothermal ribbed channel flow, cube flow and complex geometry CPU system flow. With increasing geometrical complexity solution accuracy is shown to rapidly deteriorate. For the most complex geometry, turbulence models are shown to give a vast range of heat transfer levels deviating from measurements by around /spl plusmn/100 %. The important question of solution uniqueness is considered (for both aerospace and electronic geometry flows) and the matter of how different solvers can give qualitatively different flow solutions even for very simple multiple boundary isothermal flows noted.
Core talent quality assessment in enterprises based on multiple attribute grey clustering

Core talent quality assessment in enterprises is very important in keeping core competence for enterprises. By grey system theory, a method based on multiple attribute grey clustering was raised to assess core talent quality in enterprises. The assessment steps of this method were analyzed and an example of this method was given. This method has the advantages of being fit for multiple attribute decision-making of core talent quality assessment in enterprises, making core talent quality assessment in enterprises more flexible and being easy to operate for enterprises to assess core talent quality, so it's a scientific and valid method with broad application prospects for core talent quality assessment in enterprises.
Design of 600 bps Speech Coding Algorithm Based on MELPE

This paper describes a new 600 bps speech coding algorithm based on the enhanced mixed excitation linear prediction model (MELPe), which is the new NATO standard STANAG 4591. The MELPe speech coders are robust in difficult background noise environments and intended mostly for military communications. Multi-frame joint vector quantification algorithm is used, which takes advantage of inherent inter-frame redundancy. The predicted multi-stage vector quantization algorithm is designed to quantize the line spectrum frequency (LSF) parameters. To handle channel errors and without requiring extra overhead, simulated annealing (SA) algorithm is used to perform a judicious assignment of binary codes to index the vectors in the vector quantification codebook. Simulation results have proven that the proposed coder is found better than that of LPClOe and approximately near the 2.4 kbps MELP standard.
An Algorithm of Decision-Tree Generating Automatically Based on Classification

Decision tree classification techniques have been used for a wide range of classification problems and becoming an increasingly important tool for classification of remotely sensed data. These techniques have substantial advantages for land use classification problems because of the reflexibility, nonparametric nature, and ability to handle non-linear relations between features and classes. In this paper, an algorithm, by which a decision-tree of classification is generated automatically according to the distribution of targets in feature spaces, is proposed. The method of selecting threshold, data structures and flow of the algorithm are introduced. An example is showed and compared with man-made tree, both of them are generated on the same condition.
A novel discretization scheme combined entropy with rough set theory for transient stability assessment based on ANN

Transient stability assessment (TSA) is of great importance to power system operation and control. In recent years, a large amount of research work has been devoted to the TSA approach based on artificial neural networks (TSAANN). The TSAANN has shown much promise. Noticeably, existing research has focused mainly on the ANN construction and training algorithm design. However, the data pretreatment, as an inevitable and important step of TSAANN, has not attracted much attention for a long time. In this paper, the data discretization, the kernel of the data pretreatment of TSAANN is studied. A new discretization scheme combined entropy with rough sets theory (RST) for TSAANN is proposed. Simulation results indicate the validity of the proposed scheme for TSAANN.
Relative Position Estimation Using Airport Image from a Camera of Missile-borne

In vision aided terminal inertial navigation system, the camera always attaches rigidly, or straps down, to the body of missile. Because the pitch angle of the missile usually is not constant and varies with time, the optical axis of the camera is not vertical. Then the rectangular border of the runway stripe in horizontal land plane is projected as trapezoid in the image plane. The weak perspective camera model does not destroy the parallel structure of the runway. Using the parallel structure of the runway, weak perspective image of the runway can be achieved from the perspective image of the missile-borne camera. When the length of the runway is known, based on the error position of the feature points of the runway in perspective image and weak perspective image, an algorithm estimating the relative position of missile to the runway is presented in this paper. Theoretical deduction and the results of simulation experiments demonstrate that the method proposed is feasible and performs well.
Design and Implementation of Network Management System for Large-Scale Wireless Sensor Networks

Wireless sensor networks consist of a large number of low-cost and micro sensor nodes, which form the networks in wireless and ad hoc way to achieve the tasks that contain gathering, processing and transferring data in the deployment region. Sensor network management is to monitor and control the operational status of sensor networks, enabling them to provide effective, reliable, safe and economical services. With the increase of WSNs applications. WSNs management has become more and more important. A network management system for large-scale wireless sensor networks (WSNMS) is presented and implemented in this paper, which is designed and implemented with the characteristics of WSNs. The functions which WSNMS has implemented include four categories: configuration management, performance management, fault management and accounting management. According to the results of actual experiments which consist of 215 nodes as managed objects and a gateway, WSNMS is able to undertake the large-scale network management functions and has good performance.
Data process and signal analysis based on LabVIEW

Virtual instrument technology is a kind of way for data acquisition and signal analysis; it consists of relevant hardware, software and its function library. The system of data acquisition and signal analysis with Virtual instrument technology includes data collection, instrument control, and data process and network server. This paper presents the principle about it, and gives out an example for data acquisition and signal analysis. The result shows that it works well about data exchange in remote worksite.