nils-herrmann/clean_scirepeval huggingface dataset:
Automatic adaptation of a generic pedestrian detector to a specific traffic scene

In recent years significant progress has been made learning generic pedestrian detectors from manually labeled large scale training sets. However, when a generic pedestrian detector is applied to a specific scene where the testing data does not match with the training data because of variations of viewpoints, resolutions, illuminations and backgrounds, its accuracy may decrease greatly. In this paper, we propose a new framework of adapting a pre-trained generic pedestrian detector to a specific traffic scene by automatically selecting both confident positive and negative examples from the target scene to re-train the detector iteratively. An important feature of the proposed framework is to utilize unsupervisedly learned models of vehicle and pedestrian paths, together with multiple other cues such as locations, sizes, appearance and motions to select new training samples. The information of scene structures increases the reliability of selected samples and is complementary to the appearance-based detector. However, it was not well explored in previous studies. In order to further improve the reliability of selected samples, outliers are removed through multiple hierarchical clustering steps. The effectiveness of different cues and clustering steps is evaluated through experiments. The proposed approach significantly improves the accuracy of the generic pedestrian detector and also outperforms the scene specific detector retrained using background subtraction. Its results are comparable with the detector trained using a large number of manually labeled frames from the target scene.
Method for frequency measurement with ApFFT based on FPGA

The conventional method of frequency measurement based on traditional fast Fourier transform (FFT) can't provide to an ideal performance due to the fence effect and the leakage of frequency spectrum. As an improved algorithm of the traditional FFT, the all phase FFT (apFFT) spectrum analysis has a perfect performance on suppressing the spectrum leakage and the property of phase invariant. In this paper, a novel method for frequency measurement which takes the advantage of all phase FFT is proposed. This method utilizing two largest spectrum lines of the signal from time-domain provides a higher precision than traditional method. In the paper, a corresponding frequency measurement design based on Field Programmable Gate Array (FPGA) is implemented. The functional simulation results show that the design based on FPGA has better performance compared with the traditional method for frequency measurement.
A complex-baseband active-set approach for tone reservation PAR reduction in OFDM systems

In this paper, we present an active-set approach for peak-to-average power ratio (PAR) reduction via tone reservation in complex-baseband orthogonal frequency division multiplexing (OFDM) systems. In the complex-baseband model, the optimization problem of tone reservation turns out to be a quadratically constrained quadratic program (QCQP), which is computationally prohibitive for a practical implementation. To address this problem, we consider the complex-valued samples of an OFDM symbol as vectors in the complex plane and develop an iterative clipping algorithm. In our approach, PAR reduction is achieved by minimizing the radius of the circle that encloses all samples, thus minimizing the maximum power peak. Simulation results show that the proposed algorithm can achieve near-optimal performance with fast convergence and limited complexity.
Low sidelobe leaky-wave antenna based on spoof plasmonic waveguide

Spoof surface plasmon polaritons (SSPPs) are highly confined surface electromagnetic waves, which can be supported by subwavelength periodic metal structure. According to the theory of sinusoidally modulated reactance surface (SMRS), SSPPs could be transformed into directional leaky-wave radiation through wavenumber modulation. In this paper, we propose a method to suppress the sidelobe level of the plasmonic leaky-wave antenna by designing a non-uniform sinusoidally modulated plasmonic waveguide composed of double edge corrugated metallic strip. A low sidelobe plasmonic leaky-wave antenna has been designed by controlling the amplitude of SSPPs to be a taper distribution on the aperture of plasmonic waveguide. Furthermore, an unsymmetrical SSPPs waveguide is designed by staggering corrugated grooves on two sides with μ/4 offset to increase the radiation efficiency at broadside. Both numerical simulations and experimental results have good agreements, which show that this method is effective to reduce the sidelobe of leaky-wave radiation by using non-uniform sinusoidally modulation of the plasmonic waveguide.
An optimal back-off parameters study for multi-channel MAC protocols in VANETs

Delay and reliability sensitive safety messages and throughput sensitive non-safety messages are transmitted in VANETs. Throughput and delay are always opposed in wireless networks, so it is necessary to reach the optimum point between these two parameters in many cases. This paper presents an optimization method of the back-off parameters in multi-priority supported multi-channel MAC protocol in VANETs to solve this problem. This method calculates throughput and delay of the network, then determines the optimum transmitting probabilities of the two classes of messages by an optimization model. Then the optimum back-off parameters can be calculated according to the transmitting probabilities. Simulation results show that this method can achieve the aim and remain stable.
Neighborhood Discriminant Hashing for Large-Scale Image Retrieval

With the proliferation of large-scale community-contributed images, hashing-based approximate nearest neighbor search in huge databases has aroused considerable interest from the fields of computer vision and multimedia in recent years because of its computational and memory efficiency. In this paper, we propose a novel hashing method named neighborhood discriminant hashing (NDH) (for short) to implement approximate similarity search. Different from the previous work, we propose to learn a discriminant hashing function by exploiting local discriminative information, i.e., the labels of a sample can be inherited from the neighbor samples it selects. The hashing function is expected to be orthogonal to avoid redundancy in the learned hashing bits as much as possible, while an information theoretic regularization is jointly exploited using maximum entropy principle. As a consequence, the learned hashing function is compact and nonredundant among bits, while each bit is highly informative. Extensive experiments are carried out on four publicly available data sets and the comparison results demonstrate the outperforming performance of the proposed NDH method over state-of-the-art hashing techniques.
Reactive power optimization considering the probability of voltage violation due to wind farm connection

A new reactive power optimization model involved the probability of voltage violation (PVV) and power losses to resolve the stochastic impact of wind power injection on voltage regulation. The derivation of PVV is given and applied to a variable-speed constant-frequency (VSCF) wind turbine firstly which is calculated by combining the probability of wind farm power output and the relationship between the power output of wind farm and nodal voltage of power grid. A case study of the proposed model for a regional power grid in China is presented. The simulation shows that the proposed model has the advantages of voltage control when the power output of wind farm is changed frequently. The novel model also has better adaptability of the reactive power optimization.
Design of the pressure control system on a device for simulating deep-sea environment

In order to simulate the high pressure environment in deep-sea, a set of tester which can simulate the deep-sea environment was designed. Based on the analysis of its control system, this paper proposed a closed-loop control strategy of pressure. Hardware circuit of the constant frequency variable speed control system was designed. Its main control circuit used Atema8L, a single-chip microcomputer, as the core. ECN30206 was used in the drive circuit as the core and Max7219 was used in the show circuit as the core. Then software was made. The various parts of the hardware circuit can be individually used to meet different needs. And they can also form a closed-loop control system. This system not only can improve the accuracy of pressure adjustment significantly, but also have energy-saving effect.
Impedance matching of pulsed power accelerator for megajoule-class dynamic-material-physics experiments

The pulsed-power accelerator uses its parallel branches triggered to shape the load-current pulse as required for a material physics experiment. We obtained the energy efficiencies with the source impedance and the load impedance in any branches triggered sequence. It is found that the energy efficiencies is only depend for the pulsed power accelerator impedance and the load impedance for any order of the branches triggered. And it is the highest when they are equaled.
Mid-long term optimal dispatching method of power system with large-scale wind-photovoltaic-hydro power generation

Wind power, photovoltaic power and hydro power have been used as clean energy power generation widely around the world for low generation costs and considerable environmental benefits. However, the strong random fluctuations of new energy make the pressure of system peaking and frequency modulation increase, and the safety and reliability of the system decline when large-scale wind-photovoltaic-hydro power integrated to the grid. It's even more difficult to predict wind-photovoltaic power output especially in the mid-long term optimal dispatching. This paper analyses the output characteristics of wind power, photovoltaic power and hydro power in mid-long periods, and generates power output curve of typical day in every month from historical data. Then we establish the mid-long term optimization model of power system with wind-photovoltaic-hydro power which aims to make the total cost of the system operation optimal. It can determine the optimal working position and capacity of each power plant in the daily load curve basis of taking the characteristics of various types of power plants into account and making full use of renewable energy resources. The result of the test system verifies the rationality and feasibility of this model, and gives guidance to the mid-long term dispatching of wind-photovoltaic-hydro power system.
A Unique “Nonnegative” Solution to an Underdetermined System: From Vectors to Matrices

This paper investigates the uniqueness of a nonnegative vector solution and the uniqueness of a positive semidefinite matrix solution to underdetermined linear systems. A vector solution is the unique solution to an underdetermined linear system only if the measurement matrix has a row-span intersecting the positive orthant. Focusing on two types of binary measurement matrices, Bernoulli 0-1 matrices and adjacency matrices of general expander graphs, we show that, in both cases, the support size of a unique nonnegative solution can grow linearly, namely O(n), with the problem dimension n . We also provide closed-form characterizations of the ratio of this support size to the signal dimension. For the matrix case, we show that under a necessary and sufficient condition for the linear compressed observations operator, there will be a unique positive semidefinite matrix solution to the compressed linear observations. We further show that a randomly generated Gaussian linear compressed observations operator will satisfy this condition with overwhelmingly high probability.
Peculiar work mode of GaAs PCSSs at high bias electric-field

The photocurrent waveform from the semi-insulating GaAs Photoconductive semiconductor switch (PCSS) when using different trigger laser energy and electric field has been tested. The peak value of current is in direct proportion to the work voltage at higher laser energy and lower storage capacitor shown different switch characterizes from the existing two work modes. A photograph of the switch has been taken using a high speed camera when it discharged and no filament was founded. The results were discussed from the generating and recombination process of the carrier and a peculiar work mode called quasi-linear in this letter of high power GaAs PCSSs was mentioned. Tests have been fulfilled to learn the requirement of laser energy to achieve this work mode at different terms. Because there is no filament in the switch quasi-linear mode means long lifetime. It has been shown in a typical example of the quasi-linear mode in which a hold-off voltage of 11.0 kV, current of 2.5 kA, FWHM of ~3 ns, repetition of 50 Hz and continues test time of more than two hours has been achieved.
Coaxial–Conical Transition in Magnetically Insulated Transmission Line

Coaxial magnetically insulated transmission line (MITL) was widely used in high-current pulsed power facilities. It is also an important option in future high-current accelerators based on the linear transformer driver (LTD) technology, in which the output line is usually coaxial to fit the component layout. In one of the schemes for high-current LTD accelerator, tens of coaxial MITLs are connected to conical disk lines in parallel. As a special structure, the coaxial–conical transition is a key component of the system. The feasibility of efficient current transmission under magnetic insulation condition is the precondition for building such a pulsed power facility. In this paper, we report the results of coaxial–conical MITL transition experiment which were conducted on the primary test stand (PTS). At the inner MITL of PTS, four coaxial MITLs were connected, gradually changed to mini-triplate and finally connected two conical MITLs. The total current at the entrance of coaxial lines (input) and at the conical MITL (output) were recorded. It showed that an output–to-input current efficiency of above 96% and of 87% across the transition was achieved with short load connection and open load connection, respectively. The 3-D transient electromagnetic simulation and 3-D particle-in-cell simulations were conducted to check the transmission efficiency, which confirmed very tiny loss when the load was short connected.
Consistent Iterative Multi-view Transfer Learning for Person Re-identification

Inconsistent data distributions among multiple views is one of the most crucial aspects of person re-identification. To solve the problem, this paper presents a novel strategy called consistent iterative multi-view transfer learning model. The proposed model captures seven groups of multi-view visual words (MvVW) through an unsupervised cluster method (K-means) from human body. For each group of MvVW, a multi-view discriminative common subspace can be obtained by the fusion of transfer learning and discriminative analysis. In these common subspaces, the original samples can be reconstructed based on MvVW under the low-rank and sparse constraints. Then, we solve it via the inexact augmented Lagrange multiplier method. The proposed strategy is performed on three different challenging person re-identification databases (i.e., VIPeR, CUHK01 and PRID450S), which shows that our model outperforms several state-of-the-art models with improving of 6.36%, 7.7% and 4.0% respectively.
Trajectory similarity metric based on vector fields on manifold and multi-scale space partition

For trajectory model to study mining, using Vector Fields on Manifold instead of the Euclidean distance to metric similarity between trajectories, multi scale transform method is used to optimize the mapping in the Vector Fields on Manifold trajectory distance calculation and use Som algorithm for training a classification model. This method will be the trajectory shape features to measure the similarity between trajectories, multiscale partition reduces the Vector Fields on Manifold similarity measurement noise, Som, and solve the artificially set the number of clusters by clustering is not accurate. By calculating the Euclidean distance and trajectory point minimum distance respectively using Kmeans and Som as a comparative experiment. The results show that the method in the measurement of trajectory similarity accuracy is higher than that of the contrast experiment of the method using 1000 hurricane trajectory data.
Real-Time Dynamic Voltage Loop Scheduling for Multi-Core Embedded Systems

In this brief, we propose a novel real-time loop-scheduling technique to minimize energy consumption via dynamic voltage scaling (DVS) for applications with loops considering transition overhead. One algorithm, dynamic voltage loop scheduling (DVLS), is designed integrating with DVS. In DVLS, we repeatedly regroup a loop based on rotation scheduling and decrease the energy by DVS as much as possible within a timing constraint. We conduct the experiments on a set of digital signal processing benchmarks. The experimental results show that DVLS achieves big energy saving compared with the traditional time-performance-oriented scheduling algorithm
The research on simulation and control method of direct-operated electro-hydraulic proportional-reducing valve

To acquire higher performance of direct-operated electro-hydraulic proportional-reducing valve, driving circuit and controller was developed. Simulation software, AMESim, was used to develop the model of the test bench according to construction and working principles presented in this paper. The results of simulation and experimental test, including pressure control characteristics and frequency response characteristics, are consistent, which proves the model is available. Based on the analysis of simulation, a control method, named dual closed loop consisting of current feed-forward and pressure feedforward, is developed. Both the dynamic characteristics and static characteristics are very good.
Overhead-Aware System-Level Joint Energy and Performance Optimization for Streaming Applications on Multiprocessor Systems-on-Chip

In this paper, we focus on joint energy and performance optimization for streaming applications on multiprocessor systems-on-chip by combining task-level coarse-grained software pipelining with DVS (dynamic voltage scaling)and DPM (dynamic power management) techniques with the considerations of transition overhead, inter-processor communication and discrete voltage levels. We propose a two-phase approach to solve the problem. In the first phase, we propose a coarse-grained task parallelization algorithm called RDAG to transform a periodic dependent task graph into a set of independent tasks based on the retiming technique[19]. In the second phase, we propose a novel scheduling algorithm called SpringS that works like a spring by iteratively adjusting task scheduling and voltage selection by combining DVS and DPM. We conduct experiments with a set of benchmarks from E3S  and TGFF [27]. The experimental results show that our technique can achieve 49:8% energy saving on average compared with the approach in [20], that applied DVS and DPM without software pipelining. In addition, given a tight timing constraint, our technique can obtain a feasible solution while the approach in [20] cannot.
Driver assistance systems modeling by model predictive control

Recently, an optimal control framework has been put forward to model human driver behavior and Advanced Driver Assistance Systems (ADAS). Although the models are shown to be face valid, applications are limited to the openloop optimal control problem and assume that a driver expects a certain dynamic speed profile of his predecessor. While human drivers are assumed to be good in anticipating traffic, ADAS are poor at predicting the dynamics of other vehicles, due to limited looking-ahead range and errors of sensors and actuators. This contribution furthers the optimal control framework to model automatic ADAS vehicles by relaxing the assumption that ADAS vehicles can predict the dynamics of other vehicles and solving the optimal control problem in a receding horizon way. This work assumes that within a prediction horizon, other vehicles are driving at stationary conditions (zero accelerations). An ADAS vehicle predicts system dynamics based on this assumption and makes control decisions to optimize its cost. The modeling framework is applied to design ACC systems and EcoACC systems, where multiple control objectives including safety, efficiency and sustainability are taken into account. The prediction horizon is tuned by face validating the behavior of a controlled vehicle. Simulation comparisons show that EcoACC systems result in higher fuel efficiency and a smoother vehicular behavior compared to ACC systems, although this depends on the exact formulation of the objectives.
Design of a Fault Diagnosis System for PEM Fuel Cells

This paper develops a fuel cell fault diagnosis system for Proton Exchange Membrane Fuel Cells (PEMFCs). First, it uses a set of ZigBee wireless sensors with a Modbus interface to accomplish PEMFC condition monitoring. The sensor modules are used to construct the data for PEMFC voltage, current, temperature and supplied gas pressure. The fault diagnosis system divides 7 fault types and uses 13 output characteristics to construct the matter-element models. Then, using an extension neural network (ENN) based method, builds the fault diagnosis mathematical platform. The hardware for the fault forecasting system is constructed by a Lab VIEW man-machine interface. To demonstrate the effectiveness of the proposed method, comparative studies using a multi layer neural network (MNN) and k-means algorithm are conducted on 400 sets of PEMFC field test patterns with rather encouraging results.
Islanding detection method for grid connected photovoltaic systems

The detection of islanding effect is one of the important issues for photovoltaic (PV) power system since islanding is dangerous to utility equipment and workers, and result in severe injuries and death. The novelty of this study is combining extension neural networks and Chaos synchronisation (CS) on islanding detection of grid connected PV systems based on non-autonomous Chua's circuit. Combining CS and extension neural network type-2 (ENN-2), this research is to propose a novel detection method at detecting and distinguishing the occurrence of islanding effect based on non-autonomous Chua's circuit. Simulation and experimental designs through powersim (PSIM) were applied to mimic PV power system to demonstrate the effectiveness of the proposed method. Results show that the accuracy of ENN-2 achieves 98.4% on detecting the islanding effect for PV power system.
Adaptive Prediction PID Control Based on RBFNN

To system which is of long time-delay in the industrial control fields, a control method of adaptive predictive PID control based on RBF neural network (RBFNN) is presented. The dynamic behavior of control object in the future time is implemented more accurately by the description of Predictive model based on RBFNN trained by clustering and the self-tuning of controller parameters was implemented by the RBFNN controller. The simulation result indicates that the system possesses the advantages of high precision, quick response speed and is of great adaptability and robustness.