nils-herrmann/clean_scirepeval huggingface dataset:
Design on four-quadrant DC/AC converter for wind power flow optimization system

A four-quadrant DC/AC converter for wind power flow optimization system is designed. Firstly, the basic idea of wind power flow optimization and control is analyzed. Secondly, working principle of the four-quadrant DC/AC converter is explained and its linear small signal mathematical model in the rotating reference frame is constructed. Then, based on the model, control unit consisting of active current PI close-loop regulation and reactive current one is designed. The high frequency component of the output power of wind power generation is separated by a one-order low pass filter, which is used to produce the active power command of the converter. In addition, the reactive power command is produced based on the deviation between the gird voltage in real time and the ideal one. The experimental results show that the converter can rapidly and accurately trace active power command and reactive power one of different direction and size.
Content Adaptive Subsampling for Stereo Interleaving Video Coding

Stereo interleaving video coding receives considerable attention due to its desirable property of being compatible with 2D video coding standards. The errors caused by sub sampling (causing distortion between subsampling interpolated image and the original full resolution one) and by quantization during compression lead to the final distortion in stereo interleaving video coding. In this paper, the rate and distortion analysis in stereo interleaving video coding is provided. It proves that appropriate sub sampling in stereo interleaving video coding is able to obtain good compression performance. Subsequently, a content adaptive sub sampling (CAS) is proposed. In CAS, the half resolution frames are generated by decimation, where the down sampling filter coefficients are calculated based on frame contents and the targeted interpolation coefficients. Experiment results demonstrate that the CAS is able to achieve high compression efficiency of stereo interleaving encoding scheme for stereoscopic videos.
Design and Implementation of Electro-Hydraulic Control System of Multi-station Press

Multi-station press is an advanced special forming mechanical equipment. The Centralized control mode has been adopted by PLC in the machine. The control system software construction is the modular program type. The control program of PLC fulfills real-time controlling and testing in the system’s runtime. And there are various protection function devices and alarm devices on the press. Equipped with Electro-hydraulic servo-driven hydraulic cylinder and sensor with the properties of high precision and good linearity, many procedures calendar of vehicles leaf spring such as rolling, scrap edge, pressure tank, stamping and so on could has been completed on the machine by Using multi-stage feeding mechanism (manipulators) which delivers work-pieces from one mold to be shaped, then to another one to formed.
A Multiscale Approach to Retinal Vessel Segmentation Using Gabor Filters and Scale Multiplication

This paper presents a new approach to automated retinal vessel segmentation based on multiscale analysis and adaptive thresholding. The accurate identification of the appearance of blood vessels in ocular fundus plays an important role in medical diagnosis of many diseases. In contrast to the existing methods for computer aided diagnosis which are either window-based or tracking based, we propose a novel scheme which combines multiscale analysis and adaptive thresholding to help eye care specialists to screen larger populations for vessel abnormalities under various conditions such as vessel size and local contrast. Our method includes a multiscale analytical scheme based on Gabor filters and scale multiplication, and adaptive thresholding. The experimental results demonstrate the feasibility and effectiveness of the proposed algorithms which are good for detecting large and small vessels concurrently with robustness to denoise and enhance the responses at low contrast.
A Deep Ensemble Learning Method for Monaural Speech Separation

Monaural speech separation is a fundamental problem in robust speech processing. Recently, deep neural network (DNN)-based speech separation methods, which predict either clean speech or an ideal time-frequency mask, have demonstrated remarkable performance improvement. However, a single DNN with a given window length does not leverage contextual information sufficiently, and the differences between the two optimization objectives are not well understood. In this paper, we propose a deep ensemble method, named multicontext networks, to address monaural speech separation. The first multicontext network averages the outputs of multiple DNNs whose inputs employ different window lengths. The second multicontext network is a stack of multiple DNNs. Each DNN in a module of the stack takes the concatenation of original acoustic features and expansion of the soft output of the lower module as its input, and predicts the ratio mask of the target speaker; the DNNs in the same module employ different contexts. We have conducted extensive experiments with three speech corpora. The results demonstrate the effectiveness of the proposed method. We have also compared the two optimization objectives systematically and found that predicting the ideal time-frequency mask is more efficient in utilizing clean training speech, while predicting clean speech is less sensitive to SNR variations.
A 66-to-76GHz E-band QPLL with amplifier feedback QVCO in 65-nm CMOS

A fully integrated 66-76GHz E-band low phase noise quadrature phase-locked loop (QPLL) is proposed. The quadrature voltage-controlled oscillator (QVCO) with a symmetrical coupling network formed by diode-connected transistors is used to reduce the phase error. In order to improve the oscillation frequency and reduced the phase noise, a feedback network composed of buffer amplifiers and capacitors is introduced to the QVCO. The wide locking-range divider chain of PLL consists of an injection-locked frequency divider (ILFD) with a 3-bit binary-weighted switch-capacitor bank, current mode logic (CML) dividers, and a multiple modulus divider (MMD). The proposed circuit was designed in a 65-nm CMOS process, and the QVCO achieves a tuning range of 16.4% from 66GHz to 76GHz with a phase noise of -97dBc/Hz at 1MHz offset while consuming only 12.5mW. The QPLL achieves an excellent phase noise of -93.2 dBc/Hz at 1MHz offset, consuming 37.1mW of power and with a FoM of -174.4dBc/Hz.
CFRP damage identification system by using FBG sensor and RBF neural network

A damage identification system of carbon fiber reinforced plastics (CFRP) structures was studied using the damage detection network, which was constituted by fiber Bragg grating (FBG) sensors, and radial basis function (RBF) neural network. First, FBG sensors were used to detect the structural dynamic response signals, which were excited by active excitation method. Then, the damage characteristic was extracted by Fourier transform from the signal. In addition, the RBF neural network was designed to identify the type of damage, with the damage characteristic as the input and the damage state as the output. At last, the system of CFRP by using FBG sensors was verified by experimental method. The results showed that, in the 160mm*160mm experimental area of CFRP plate, the damage state was investigated with accurate identification. Briefly, the system provided an effective method for CFRP structural damage identification.
Deep Convolutional Neural Models for Picture-Quality Prediction: Challenges and Solutions to Data-Driven Image Quality Assessment

Convolutional neural networks (CNNs) have been shown to deliver standout performance on a wide variety of visual information processing applications. However, this rapidly developing technology has only recently been applied with systematic energy to the problem of picture-quality prediction, primarily because of limitations imposed by a lack of adequate ground-truth human subjective data. This situation has begun to change with the development of promising data-gathering methods that are driving new approaches to deep-learning-based perceptual picture-quality prediction. Here, we assay progress in this rapidly evolving field, focusing, in particular, on new ways to collect large quantities of ground-truth data and on recent CNN-based picture-quality prediction models that deliver excellent results in a large, real-world, picture-quality database.
Comparison Research on the Wisdom City Regional Development in Yangtze River Delta Region-based on Data from "Two Provinces One City"

The wisdom city construction meets the needs of the age of information technology in scientific development process of Chinese cities, with the significance of the economic, social, service value. This paper focuses on a comparative study of the overall development status of the Yangtze River Delta region, and makes some meaningful exploration on formation of the overall development path in the region.
Application of Monte Carlo sampling and Latin Hypercube sampling methods in pumping schedule design during establishing surrogate model

For creating surrogate model of the groundwater numerical simulation model of Jinquan Industrial Park in Inner Mongolia, the application of Monte Carlo sampling method and Latin Hypercube Sampling method in pumping test design is studied. Firstly make sure the pumping load of each pumping wells obeys uniform distribution, then generate Monte Carlo samples and Latin Hypercube samples according to their own methods. Analyses these two results comparing with each other, it suggests that in small sample size, Monte Carlo sampling method has a low sampling efficiency, low coverage of the sampling value to population and needs a large amount of calculation, while Latin Hypercube Sampling method relatively improves the sampling efficiency, coverage of the sampling value to population, and reduce the workload. Latin Hypercube Sampling method has better practicability in this job.
A typhoon simulation test with assimilated Doppler radar data

Doppler radar reflectivity and radial velocity data are added directly each hour to the mesoscale model ARPS (The Advanced Regional Prediction System) in numerical simulation by its data processing system ADAS (ARPS Data Analysis System). By simulating the Typhoon Chanthu (1003) process, we analyzed the impact of improving the initial field and forecasting results which brought about by radar data added. Two numerical contrast experiments included: the control test with no radar data assimilated and the test which radar data added only to the initial field. Experiments prove that continuous assimilation of radar data test showed advantage in simulating rain and some physical quantity such as qc and qv, moreover the reported location and radar observations of precipitation are much close, however, precipitation bias still exists.
CRALA: Towards a domain specific language of architecture-centric Cloud robotics

Cloud robotic system is a mono- or multi- robot system that profits one or more services of Cloud Computing. In a few short years, Cloud robotics as a newly emerged field has already received much research and industrial attention. The use of the Cloud for robotics and automation brings some potential benefits largely ameliorating the performance of robotic systems. However, there are also some challenges. First of all, from the viewpoint of architecture, how to model and describe the architectures of Cloud robotic systems? How to deploy these architectures in Clouds? Merely a language could explicitly describe or model the architecture of Cloud robotic systems. In this paper, we present an architecture approach to support design and implementation of Cloud robotic system.
An Energy Efficient Cooperation Relay Scheduling Scheme in Multi-hop Cognitive Vehicular Networks

In cognitive vehicular networks, cooperation transmission has many advantages to access licensed spectrum under interference constraints, by exploring space diversity of vehicles. And in the urban city, there are many candidate vehicles can be a cooperation relay especially when traffic congestion. Recently many papers concern about cognitive multi-hop network, but most of them only analyzed the performance like outage probability, capacity and so on, which ignored the performance of energy efficiency. Moreover, the selection of cooperation relay closely related with the performance, and in vehicular environment it must consider the geographic distribution of vehicles. So, in this paper we analyze the performance of multi-hop cognitive vehicular networks with considering the vehicle distribution, then we optimize the energy-efficient problem and propose the cooperation relay scheduling scheme (CRSS) to improve the energy efficiency. Simulation results show that, by using CRSS the performance of energy efficiency and outage probability in multi-hop cognitive vehicular have been improved.
A case study on the MPC for ozone dosing process based on SVM

Ozone is an important disinfectant in drinking water treatment. The ideal ozone dosage should be a good trade-off between the requirement of disinfection and the restriction of bromate formation. Additionally, the ozone dosing process should cope with the changes of raw water quality and maintain the treated water stable. However, ozone dosing process is very difficult to be controlled because of its complicated nonlinear behaviour. In this paper, model predictive control (MPC) scheme based on support vector machine (SVM) model is designed for the ozone dosing process. With the proposed control scheme, a SVM model is established which could be used for MPC. The control strategy of maintaining a constant ozone exposure is attempted for the requirement of more effective disinfection. The plant-scale experimental studies demonstrate the efficiency and effectiveness of proposed control scheme.
Adaptive backstepping control algorithm for a rotorcraft disturbed by different wind

In order to keep the attitude stability of the rotorcraft under external wind disturbance, the mathematical model of the rotorcraft under wind disturbance is established. Integral terms are added into the conventional backstepping method to resist the continuing wind disturbance which has a low amplitude. Adaptive control approach is used to compensate for the model error of rotorcraft and the sudden gust. The disadvantage of the conventional backstepping method in resisting the wind disturbance is improved. The simulation results show that the controllers designed by this method have better disturbance rejection performance and tracking performance.
A Study of Shelterbelt Transpiration and Cropland Evapotranspiration in an Irrigated Area in the Middle Reaches of the Heihe River in Northwestern China

The transpiration from shelterbelts and the evapotranspiration (ET) from cropland (maize and vegetables) and orchards (apple) in an irrigated area in the middle reaches of the Heihe River, China, were estimated using a modified Penman-Monteith (P-M) formula and airborne remote sensing data. The results were compared to the shelter transpiration results obtained from measurements of sup flow in tree trunks made with thermal dissipation probes and the latent heat fluxes observed by the eddy covariance technique at flux towers in croplands. The modified P-M formula was found to be an effective means to estimate not only the cropland and orchard ET but also the shelter transpiration. The seasonal variation of shelterbelt transpiration was smaller than those of cropland and orchard ET. Estimates of ET made using the P-M formula along with the remote sensing data showed that 9.9%, 3.1%, and 87.0% of the total ET were allotted to shelterbelts, apple orchards, and cropland, respectively.
Digital predistortion of RF power amplifiers with Zhu's general sampling theorem

In this paper, the wideband digital predistortion (DPD) and the Zhu's general sampling theorem (ZGST) are applied to compensate the nonlinear characteristics of a wideband RF power amplifier with memory effects. In order to utilize the ZGST, the DPD test bench is carefully designed after analysis with the ZGST. The power amplifiers are excited by a long term evolution advanced signal with up to 100 MHz bandwidth and the memory polynomial model is chosen as the DPD mode after some modifications. The mode parameters are extracted by the standard least square method. Experimental results show that adjacent channel leakage ratio of the PA reaches about -55 dBc with over 10-dB improvement by DPD and the ZGST.
Accurate vehicle monitoring based on wireless sensor network

Wireless sensor networks have been proven to be attractive in vehicle monitoring. To apply wireless sensor network in vehicle monitoring, the fundamental problems, vehicle detection should be explored. To address this problem, challenges like strong noise disturbance, severely limited node resource, should be overcome. In this paper, we propose a CBNP algorithm to accurately detection vehicle presence. To apply cross-correlation in vehicle detection, we build a vehicle response model for cross-correlation calculation. Simulation indicates that the detection accuracy of the CBNP algorithm is as high as 90%.
A BP Neural Network Model Based on Genetic Algorithm for Comprehensive Evaluation

BP algorithm can be applied in comprehensive evaluation. A hybrid neural network based on the combination of GA and BP algorithms is proposed. The algorithm made fully use of GA's global searching to improve the learning ability of BP neural network. Then, the method is used in comprehensive evaluation, which the genetic algorithm can improve the weights of the neural network and enhance the training precision of the neural network. The experimental results show that the method is valid and feasible.
A five-order butterworth low-pass filter with DC offset cancellation for WLAN applications

This paper presents a five-order Butterworth low-pass filter with DC offset cancellation (DCOC) for WLAN Applications in a 65-nm CMOS process. The low-pass filter employs transconductance cell instead of conventional two-stage amplifiers to avoid internal poles which may otherwise cause the reduction of GBW. In order to achieve an appropriate 1-dB bandwidth of the integral filter, extra parasitic capacitances have been carefully considered. The proposed LPF achieves a maximal 1-dB bandwidth up to 80MHz with tunable gain and bandwidth. The LPF consume 4.2mA of current from a 1.2V power supply. A superior 1dB compression point of -2.57dBm is also achieved while with a noise figure of 27.55dB at 20MHz as a result of a good balance between linearity and noise.
Comparative researches on scattering properties of random surface

The different parameter characteristics of two types of surfaces with Gaussian distribution and fractal characteristics are discussed, and the outline of two different surface models are given in this paper. By the KA, Gaussian rough surface scattering coefficient is deduced, and the strength of electromagnetic scattering from fractal surfaces is calculated. Two scattering characteristic curves of different rough surface simulation models are compared. The simulation results show, for the Gaussian rough surface, the scattering characteristic is close to the specular scattering properties when the rms height δ is small enough and correlation length l is large enough. While, for nearly smooth fractal surfaces, with the increase of the fractal dimension D, the scattering showed a more pronounced angular distribution characteristics.
CancerVis: An interactive exploratory tool for cancer biomarker analysis

A biomarker is a measurable substance that can be used as an indicator of a particular disease. In the research field of cancer biomarker analysis, data visualization has generally been utilized as a technique for presenting results, with very little application of integrating data visualization as a tool for biological data exploration or pattern discovery. To address this issue, we have developed a visualization platform, CancerVis, for visual and interactive analysis of cancer data and pattern discovery. CancerVis provides multiple interactive views from different prospective of a dataset. The views are synchronized so that users can easily link the same data entry in multiple views. CancerVis's visualization technologies also supports common data mining techniques for pattern discovery in cancer biomarker research, such as visualization of optimal cutoff point and its variability over time. The usability of CancerVis is verified by a clinical dataset.
Binary Code Ranking with Weighted Hamming Distance

Binary hashing has been widely used for efficient similarity search due to its query and storage efficiency. In most existing binary hashing methods, the high-dimensional data are embedded into Hamming space and the distance or similarity of two points are approximated by the Hamming distance between their binary codes. The Hamming distance calculation is efficient, however, in practice, there are often lots of results sharing the same Hamming distance to a query, which makes this distance measure ambiguous and poses a critical issue for similarity search where ranking is important. In this paper, we propose a weighted Hamming distance ranking algorithm (WhRank) to rank the binary codes of hashing methods. By assigning different bit-level weights to different hash bits, the returned binary codes are ranked at a finer-grained binary code level. We give an algorithm to learn the data-adaptive and query-sensitive weight for each hash bit. Evaluations on two large-scale image data sets demonstrate the efficacy of our weighted Hamming distance for binary code ranking.
Cooperative wireless relay networks using partial rateless codes

Cooperative communications enhance throughput, link reliability, power consumption, and coverage in wireless relay networks, where many relay nodes in the network work together in order to transmit information from a source to a destination [1], . Many researches have been done to design and analyze the routing algorithms and protocols , . However, frequent feedback is required for transmitter adjusting the phases of the transmit signal to optimize the receive signal . Thus, the use of rateless codes has been introduced into cooperative relay networks [6], which are attractive because the realized data transmission rate is automatically a function of the instantaneous channel state, without the transmitting source or relays knowing it. Rateless codes generate a potentially infinite number of encoded symbols, which are transmitted until an acknowledgement is received from the recipient.
A Mandatory Access Control Model Based on Concept Lattice

Confidentiality and integrity of information are basic attributes of information security, but the existing access control models cannot unify the both well. A mandatory access control model based on access control concept lattice is proposed in this paper. This model integrates many safe sensitivity labels such as integrity, confidentiality, security category. Thus, mandatory constraints of the access of subject to the object can be realized, and Hasse diagram of lattice can be created automatically, such that security levels of the subject and object in the system can be displayed clearly.
A Periodic Observers Synthesis Approach for LDP Systems Based on Iteration

The problem of state observer design for the linear discrete-time periodic (LDP) system and its robust consideration are discussed in this paper. Applying the lifting technique and algebraic operations based on the well-known CG-algorithm, an iterative algorithm for periodic observer gain can be generated. By optimizing the free parameter matrix in the proposed algorithm, an algorithm on the minimum norm and robust observer design for the LDP systems is presented. One numerical example is worked out to illustrate the effect of the proposed approaches.