nils-herrmann/clean_scirepeval huggingface dataset:
A Dynamic Parzen Window Approach Based on Error-entropy Minimization Algorithm for Supervised Training of Nonlinear Adaptive System

This paper presents a dynamic Parzen window estimator in the MEE approach for supervised training of nonlinear adaptive system. By adjusting the Parzen window width dynamically so that the overall information force (OIF) among error-samples of each step is as large as possible, the training speed is accelerated and the error is reduced. The simulation result has proved the effectiveness and robustness of this algorithm.
A novel wireless power and data transmission AC to DC converter for an implantable device

This article presents a novel AC to DC converter implemented by standard CMOS technology, applied for wireless power transmission. This circuit combines the functions of the rectifier and DC to DC converter, rather than using the rectifier to convert AC to DC and then supplying the required voltage with regulator as in the transitional method. This modification can reduce the power consumption and the area of the circuit. This circuit also transfers the loading condition back to the external circuit by the load shift keying(LSK), determining if the input power is not enough or excessive, which increases the efficiency of the total system. The AC to DC converter is fabricated with the TSMC 90nm CMOS process. The circuit area is 0.071mm2. The circuit can produce a 1V DC voltage with maximum output current of 10mA from an AC input ranging from 1.5V to 2V, at 1MHz to 10MHz.
A CMOS synchronous time amplifier

This paper presents a synchronous time amplifier (TA). The proposed TA has predictable amplification process delay and can be synchronous with a clock signal. The TA is implemented and simulated in 0.18 μm CMOS process. It works with 100 Ms/s under 1-V voltage. Simulation results show that the TA has 8ns output linear range and obtains a constant gain independent of PVT with 9.0% total gain error.
Digital Charge Balance Controller to Improve the Loading/Unloading Transient Response of Buck Converters

A linear/nonlinear digital controller is presented that allows a Buck converter to recover from a load transient event with near-optimal voltage deviation and recovery time. A novel digital double accumulator calculation block is used to calculate the appropriate pulse width modulation switching time instants. The proposed controller possesses many advantages not demonstrated by a single controller in the previous literature. For example, unlike many previously proposed time-optimal digital controllers, the proposed controller provides an excellent transient response as it is capable of reacting asynchronously to a load transient event. In addition, it is demonstrated that the proposed controller can operate without requiring information pertaining to the Buck converter's output inductor. Furthermore, the proposed controller can be extended to applications that require load-line regulation. Lastly, unlike all previous digital time-optimal controllers, the proposed controller does not require digital multiplier or divider blocks nor does it require 2-D lookup tables. Thus, the controller can be implemented through the use of low-cost field programmable gate arrays or complex programmable logic devices.
An interleaved LLC resonant converter operating at constant switching frequency

The interleaving and phase shedding techniques on LLC resonant converters will increase the load capacity, reduce the output current ripple and improve the efficiency. However, conventional frequency-controlled LLCs will lose regulation in individual phases if all phases are synchronized for interleaving, causing current imbalance. Existing load-sharing solutions for multiphase LLCs cannot achieve voltage regulation, phase shedding, and expandable load capacity all at the same time. In this paper, a switch-controlled capacitor (SCC) modulated LLC converter (SCC-LLC) is presented for multiphase paralleling. It features constant switching frequency, which results in a simple structure for paralleling and the ability for phase shedding to improve light-load efficiency. In the proposed structure, a SCC unit is used in each individual LLC stage to regulate the output gain, thereby inherently solves the conflict between the interleaving and the load sharing.
Design and Implementation of Traffic Signal Optimization Hardware-in-Loop Simulation System

A distributed regional Traffic Signal Optimization Hardware-in-loop Simulation (TSOHS) system is designed, which aims at improving the operating ability for post-graduate students. The main components of the TSOHS system include 1 Center Server, 5 local intersection controllers and 120 traffic lights. The traffic lights can be controlled by each local intersection controller in defined control strategies, the defined control strategies in local intersection controller will be adjusted in Center Server through dynamic optimization method online. The communication among local intersection controllers and Center Server adopts RS-485 and zigbee patterns. The optimization algorithm programmed in C language by students can be put in the Center Server, and running in TSOHS, so that the validity of the traffic optimization algorithms can be tested. Two fuzzy optimization algorithms are used to test TSOHS. Experimental results show TSOHS is effective to accomplish traffic signal optimization simulation.
A 2-D FDTD Model for Analysis of Plane Wave Propagation Through the Reentry Plasma Sheath

A 2-D finite-difference time domain model was developed to analyze the wideband plane wave propagation characteristics in the reentry plasma sheath at the receiving antenna position. Because of the variation of the flow field, various inhomogeneous structures exist in the plasma sheath, such as electron density irregularities, large-scale ablation particles, and vortex electron density structures, which play important roles in refracting and distorting the electromagnetic wave signals. The proposed model directly included the effects of these inhomogeneous structures. The model used a  $\pi $ -shaped total-field/scattered-field (TF/SF) boundary to introduce the plane wave source to the simulation domain. Based on the current density convolution (JEC) method and the phase matching principle, six 1-D auxiliary propagators were generated to obtain field values at the TF/SF boundary. The convolutional perfectly matched layer was implemented as the absorbing boundary condition of the auxiliary propagator. The efficiency and accuracy of the proposed method were validated by numerical examples.
A novel parameter-independent digital optimal control algorithm for DC-DC Buck converters based on parabolic curve fitting

Recently, optimal control on Buck converter for powering the latest computer central processor units (CPUs) has attracted more and more attention. In this paper, a novel digital control algorithm is presented to achieve the time-optimal response for dc-dc Buck converters without relying on any knowledge of converter design parameters such as output inductance, capacitance and even ESR value. This algorithm is based on the parabolic curve fitting analysis for deriving the algorithm formulas under the step load transients. Furthermore, this algorithm can be extended to adaptive voltage positioning (AVP) applications with simple modifications for a low ESR designed Buck converter. Also, it delivers a practical and cost-effective interface to AVP schemes due to the parameter-independent and current-sensorless detecting mechanism. Finally, simulations and experimental results of a 12 V-1.5 V Buck converter prototype are provided to validate the proposed schemes using digital signal processor (DSP) implementation.
Compartmental sparse feature selection method for Alzheimer's disease identification.

For high-dimensional magnetic resonance imaging (MRI) data, many feature selection methods have been proposed to reduce feature dimension in the study of computer-aided Alzheimer's disease (AD) diagnosis. This paper presents a compartmental sparse feature selection method used for AD identification. Based on the derived atlas-based regions-of-interest (ROIs) of brain, the proposed method partitioned the T1-weighted MRI data into several compartments. It performs feature selection and classification compartmentally according to the local feature dimension estimation and local feature selection using sparse principal component analysis (SPCA) method followed with elastic-net logistic regression (ENLR) classifier. Experimental results showed that the proposed method improves the classification performance for small ROIs with high computational efficiency.
Comprehensive evaluation of listed companies in power industry on the operation performance based on fuzzy borda combination method

The modeling technology of combination evaluation drops the imperfectness of information in complex system. However, the fuzzy combination evaluation method unifying the Fuzzy Theory and combination makes the evaluation of the complex system more realistic. Firstly, adopting the TOPSIS evaluation method, Principal Component Analysis and Factor Analysis, deliberating the data of the Chinese listed companies' financial statements in the first quarter of 2011, this paper conducts the operating performance on the comprehensive evaluation and rank, which is from the 53 listed companies whose main business is power system in the Shanghai and Shenzhen Stock Market. Secondly, the article proves the consistency of the three evaluation methods through the nonparametric Spearman correlation coefficient test. Finally, establishing and applying the Fuzzy Borda Combination Evaluation Method, the article processes the operating performance afresh. The result makes the comprehensive evaluation and rank of the 53 enterprises more scientific and rational.
All-Fiber Light Intensity Detector Based on an Ionic-Liquid-Adorned Microstructured Optical Fiber

A compact optical fiber light intensity detector based on an ionic-liquid-adorned microstructured optical fiber (MOF) is proposed. The light intensity detector is implemented by splicing an index-guiding MOF with two air holes in the innermost layer infused with ionic liquid (IL) between two segments of single-mode fibers. The transmission spectral characteristics of the proposed intensity detector under different power densities have been investigated from experimental as well as theoretical perspectives. Owing to modal interference effect and intrinsic absorption of the IL, the power density sensitivity reaches 1.5291 dB/(mW•mm–2) within a relatively linear range. The high sensitivity and feasibility of our proposed intensity detector is anticipated to serve as the key element, such as optical switch, in future all-optical networks.
A Novel Non-Isolated Full Bridge Topology for VRM Applications

In this paper, a new non-isolated full bridge (NFB) topology is introduced to solve the narrow duty cycle and hard switching problems of the buck converter in low output voltage, high output current applications. In comparison to the Buck converter, it operates at a significantly wider duty cycle and can achieve zero voltage switching for the high side MOSFETs. The NFB significantly reduces the input peak current and transfers a portion of the primary side energy directly to the load thereby reducing the stress on the synchronous rectifiers and filter inductors. Using self-driven synchronous rectifiers, the body diode conduction loss is reduced since no dead time is required between the primary side MOSFETs and the synchronous rectifiers. Given these significant advantages, the NFB can achieve higher efficiency than a two and three phase interleaved buck at the same power level. The efficiency gain enables the NFB to operate at a high switching frequency thereby enabling smaller output inductors to be used to achieve improved dynamic performance. Experimental results and analysis demonstrate that the new NFB can significantly improve the performance of a voltage regulator. The prototype built operated at 500 kHz, 12 V input, 1 V output, up to 30 A load and achieved an efficiency of 84.4% at 30 A load. A 1 MHz prototype achieved a full load efficiency of 82.1%. In comparison, the efficiency of a two and three phase Buck prototype was 79.7% and 82.6% at 30 A load and at 500kHz switching frequency.
Customized Benchmark Generation Using MDA

This paper describes an approach for generating customized benchmark applications from a software architecture description using a Model Driven Architecture (MDA) approach. The benchmark generation and performance data capture tool implementation is based on widely used open source MDA frameworks. The business logic of the benchmark application is modeled in UML and generated by taking advantage of the existing generation "cartridges" so that the current component technology can be exploited in the benchmark. This greatly reduces the effort and expertise needed for benchmarking with complex component technology. We have also extended the MDA framework to model and generate a load testing suite and automatic performance measurement infrastructure. The approach complements current model-based performance prediction and analysis methods by generating the benchmark application from the same application architecture that the performance models are derived from. This provides the potential for tightly integrating runtime performance measurement with model-based prediction either for model validation or improving model prediction accuracy. We illustrate the approach using a case study based on EJB component technology.
A novel fast phase unwrapping method for large interferometric datasets

Phase unwrapping, a process of recovering the real phase from ambiguous one, is one of key techniques for interferometric synthetic aperture radar (InSAR) data processing. The larger interferograms make the phase unwrapping to be a more challenging task. A novel fast phase unwrapping method is proposed in this paper which is very effective in dealing with the large scale datasets. Several sub-networks consisting of residues are constructed in the method. Therefore, smaller networks make the execution more efficient using minimum cost flows(MCF) algorithm. In addition, the method takes an effective strategy to balance all the sub-networks in order to ensure a feasible solution to each sub-network. Experiment with real data demonstrates the effectiveness of the proposed method.
A family of contextual measures of similarity between distributions with application to image retrieval

We introduce a novel family of contextual measures of similarity between distributions: the similarity between two distributions q and p is measured in the context of a third distribution u. In our framework any traditional measure of similarity / dissimilarity has its contextual counterpart. We show that for two important families of divergences (Bregman and Csisz'ar), the contextual similarity computation consists in solving a convex optimization problem. We focus on the case of multinomials and explain how to compute in practice the similarity for several well-known measures. These contextual measures are then applied to the image retrieval problem. In such a case, the context u is estimated from the neighbors of a query q. One of the main benefits of our approach lies in the fact that using different contexts, and especially contexts at multiple scales (i.e. broad and narrow contexts), provides different views on the same problem. Combining the different views can improve retrieval accuracy. We will show on two very different datasets (one of photographs, the other of document images) that the proposed measures have a relatively small positive impact on macro Average Precision (which measures purely ranking) and a large positive impact on micro Average Precision (which measures both ranking and consistency of the scores across multiple queries).
Band structure calculations of Ge-Si core-shell nanowires

We have modeled and calculated the band structure of Ge-Si core-shell nanowire, with both subband interactions between Ge core and Si shell and the inhomogeneous strain effects taken into account. Our results show that the effective masses of subbands, the densities of states and quantum conductance will undergo significant changes and converge to saturated values, as the Si shell turns thicker and provides more band modulations.
Data-driven modeling method for analyzing grade crossing safety

A grade crossing is defined as an intersection between a roadway and a railway at the same elevation or grade. Multiple new prevention measures have been implemented to reduce the number of train-vehicle collisions; however, crossing safety is still a major issue as accidents still frequently occur. The push for data-driven models to evaluate risks at grade crossings has also increased to keep up with the changing technologies. There are many different protection types (gates with bells, cross-buck, stop-sign, mirrors and etc.) that serve to warn or stop oncoming traffic. Many attributes have an inherent impact on accident frequency; including the protection type, train speed, traffic volume and e.t.c. To address which factors are most important, we propose a data-driven modeling method to effectively analyze the impact of multiple factors that affect crossing safety and subsequently provide scientific insight for key factors for enhancing crossing safety. In this work, the Canadian crossing accident database for the years of 2004 - 2013 was used with additional generated features to enhance the scope of the study. These include features that were computed using GIS and sightline measurements. Data-driven modeling using RandomForests were used to rank and analyze 21 attributes for each protection type. From the analysis results it is possible to identify which key factors have the highest influence on improving safety and collision prediction at grade crossings.
A Pornographic Videos Detection Method Based on Optical Flow Direction's Statistical Histogram

Aimed at that there is often a video paragraph of human body's reciprocating motion in the pornographic videos, a novel method of pornographic videos detection is proposed. On the basis of calculating the optical flow field, we extract the characteristics points of the moving target, and classify optical flow direction of these points and get the statistics of them. Then we establish the optical flow direction's statistical histogram. According to the changes in the direction of moving target and the changes in the frequency of it, we can determine there is pornographic information or not. The experimental results show that the method is simple and convenient, fast and efficient, high in the recognition rate and well in the robustness.
A New Measure of Significance of Condition Attribute and Its Use in Attribute Reduction

In Pawlak's rough set system, approximation quality can be used to measure the classification capability of a condition attribute and can be used to define the significance of condition attributes. But the measure only gives us the determinate classification capability, and does not give us the uncertain classification capability. The information entropy is a mean value in terms of probability that embodies the whole information of an attribute, measuring the mean classification capability. The definition of mutual information is not intuitionistic and not easily understood; the calculation of the mutual information is somewhat complicated too. In this paper, we give a definition of probability equivalence between condition and decision attributes, and based on the probability equivalence we give a measure of significance of a condition attribute. An Illustrative example shows that with it the optimal attribute reduction can be gotten
A New Technique for Eliminating Data Migration in Logistic Evolution Storage System

In order to change the status that distributed storage system can't adapt to dynamic workloads, we introduce a new concept of evolution redundancy in logistic evolution storage system. It associates the redundancy level that characterizes the storage organization to sets of data and not to storage areas while eliminating data migration for reconfiguration. Inside storage node, the storage space is divided into some sequential region that can automatically adjust data layout on the basis of workload without data migration. Then evolution redundancy makes the map between logic block address and physical block address become very simply so as to avoid bottleneck incurred by complex map. For saving storage space during transforming between various RAID levels, we propose independent disks group by which our system not only implement evolution redundancy but also achieve well performance/cost. Finally, we carry out a large number of simulations to validate transformation threshold between different RAID levels
A Wireless Intrusion Detection Method Based on Dynamic Growing Neural Network

In this paper an intrusion detection method based on dynamic growing neural network (DGNN) for wireless networking is presented. DGNN 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 overfitting the training data just like decision tree learning. The intrusion detection method is an anomaly detection method and the feature is selected from the packets. In the experiments, we first check the ability of the neural network and then use it to perform detection in a WLAN. The results show that it can detect new intrusion behavior and some improving methods are presented in the conclusions
A self-adaptive integrated particle swarm optimization

The particle swarm optimization (PSO) algorithm is a new and efficient intelligent search algorithm. However, most of the existing improved PSO algorithm in the late search efficiency is low, and easy fall into local minima, In view of this situation, a kind of new strategy of inertia weight adaptive changes been proposed in this paper, which enable the algorithm in the interim iterations can quick access into the local search, conductive to facilitate the search for the best value. And in order to prevent the algorithm from falling into local extreme value, we joined the disturbance factor and the improved acceleration factor. Using five kinds of classic test functions for performance test of the improved algorithm, the test results indicate that the improved algorithm can get better value and can avoid falling into local minimum too early.
A study on a new drafting transmission system of rubbed roving frame

Rubbed roving frames are main textile machines, and the drafting transmission system is the key one which constitutes rubbed roving frame, whose studies have absorbed some people at home and abroad who are in this field. A new type of rubbed roving frame drafting transmission system is presented in this paper, which consists of the main drawing part and the fine-tuning parts of drawing tension. The main drawing part is mostly composed of a pair of pulley speed change mechanism which can meet the range requirements of drawing multiple. The fine-tuning parts of drawing tension consist of nine-link corrected gear and slipping gear. Tension adjustment range and the difference in the rate of transformation can be met through the gearing mesh of the same diameter and different modification coefficient. Drawing multiple design method of these two forms of coupling drawing is proposed, and nine-link corrected gear coefficient theory formulas and slipping gear design formulas are derived. Twin-wheel design method is given based on series portfolio theory. The problem that the same drawing system can meet different fiber drawing effects having differences is solved. As a result, a new design method of drafting transmission system of rubbed roving frame is established.