nils-herrmann/clean_scirepeval huggingface dataset:
Convective weather forecast accuracy analysis at center and sector levels

This paper presents a detailed convective forecast accuracy analysis at center and sector levels. The study is aimed to provide more meaningful forecast verification measures to aviation community, as well as to obtain useful information leading to the improvements in the weather translation capacity models. In general, the vast majority of forecast verification efforts over past decades have been on the calculation of traditional standard verification measure scores over forecast and observation data analyses onto grids. These verification measures based on the binary classification have been applied in quality assurance of weather forecast products at the national level for many years. Our research focuses on the forecast at the center and sector levels. We calculate the standard forecast verification measure scores for en-route air traffic centers and sectors first, followed by conducting the forecast validation analysis and related verification measures for weather intensities and locations at centers and sectors levels. An approach to improve the prediction of sector weather coverage by multiple sector forecasts is then developed. The weather severe intensity assessment was carried out by using the correlations between forecast and actual weather observation airspace coverage. The weather forecast accuracy on horizontal location was assessed by examining the forecast errors. The improvement in prediction of weather coverage was determined by the correlation between actual sector weather coverage and prediction. The analysis was accomplished by using observed and forecasted Convective Weather Avoidance Model (CWAM) data collected from June to September in 2007. CWAM zero-minute forecast data with aircraft avoidance probability of 60% and 80% are used as the actual weather observation. All forecast measurements are based on 30-minute, 60-minute, 90-minute, and 120-minute forecasts with the same avoidance probabilities. The forecast accuracy analysis for times under one-hour showed that the errors in intensity and location for center forecast are relatively low. For example, 1-hour forecast intensity and horizontal location errors for ZDC center were about 0.12 and 0.13. However, the correlation between sector 1-hour forecast and actual weather coverage was weak, for sector ZDC32, about 32% of the total variation of observation weather intensity was unexplained by forecast; the sector horizontal location error was about 0.10. The paper also introduces an approach to estimate the sector three-dimensional actual weather coverage by using multiple sector forecasts, which turned out to produce better predictions. Using Multiple Linear Regression (MLR) model for this approach, the correlations between actual observation and the multiple sector forecast model prediction improved by several percents at 95% confidence level in comparison with single sector forecast.
Driving Stability of Closed Loop Tractor-Semitrailer Vehicle System with Optimal Driver Model

A quasi-linear driver model was developed, and a closed loop tractor-semitrailer vehicle system composed of the driver model and a tractor-semitrailer vehicle model of 3-degrees-of-freedom were proposed. The stability of closed loop vehicle system was studied by simulating the system with different parameter values. A roots-locating numerical method was used to identify critical speed of the system. Some simulation of closed loop vehicle system were made in Matlab and the results shown that driver's inherent time delay, a semitrailer's load and driving speed make important role in stability of closed loop tractor-semi trailer.
Numerical simulation applications of aluminum extrusion engineering based on rigid-plastic finite element method

Computer-Aided Engineering(CAE) methods are becoming more and more important for the modern industry. However, the applications in the extrusion industry are still limited because of the contradition between the slow simulation speed and the fast die delivery requirement. This paper presents a new fast simplified method for analyzing the exit velocity field of aluminum extrusion. Rigid-plastic finite element method is employed with zero displacement boundary conditions and friction boundary conditions to meet the specific requirement of the aluminum extrusion die industry. The computational time of the proposed method is only a few hours. In the applications, the simulated results fit the experiments and the theoretical solution very well, which demonstrates the efficiency and versatility of the method.
Fast Node Merging With Don't Cares Using Logic Implications

Node merging is a popular and effective logic restructuring technique that has recently been applied to minimize logic circuits. However, in the previous satisfiability (SAT)-based methods, the search for node mergers required trial-and-error validity checking of a potentially large set of candidate mergers. Here, we propose a new method, which directly identifies node mergers using logic implications without any SAT solving calls. Although the efficiency benefits of the method come at the expense of quality, we further engage the redundancy removal and the wire replacement techniques to enhance its quality. The experimental results show that the proposed optimization method achieves approximately 46 times the speedup while possessing a competitive capability of circuit minimization compared to the state-of-the-art method.
Design and analysis of an outer rotor flux switching permanent magnet machine for electric vehicle

This study presents the design and analysis of a 188 W 120 rpm outer rotor flux switching permanent magnet machine for electric vehicle applications. Theoretical analysis and finite element method are used to determine the kind of combination of stator slots and rotor poles that is best selection for the machine. It can be found that the reluctance torque has a very small contribution to the electromagnetic torque when compared with the permanent magnet torque.
Study of Erase Band and Write Performance for Shingled Magnetic Recording With FePt-Based Exchanged Coupled Composite Media

The shingled magnetic recording (SMR) with <inline-formula> <tex-math notation="LaTeX">$\text{L}1_{0}$ </tex-math></inline-formula>-FePt-based exchange coupled composite (ECC) media is a promising way to achieve ultrahigh recording density. During the writing process, the insensitive angular dependence of switching field provided by ECC media is found to play an important role in mitigating transition noise and adjacent track erasure, furthermore the investigation indicates that the skew angle (SA) and corner angle (CA) of wide main pole inevitably affect the erase band noise in SMR. Hence, this paper studies an improved calculation method of erase bandwidth (EBW) to evaluate the erase band noise more accurately, and investigates the impact of SA and CA on write performance. Additionally, by investigating the bit error rate of adjacent track’s bits caused by the erasure, the results show that the high angular tolerance of switching field provided by <inline-formula> <tex-math notation="LaTeX">$L1_{0}$ </tex-math></inline-formula>-FePt(6 nm)/[Co/Ni]<sub>5</sub>(4 nm) ECC media can effectively reduce EBW. Finally, the analyses demonstrate that an optimum combination of writer CA, tapered angle, and suitable range of SA is helpful to reduce erase band noise and improve the write performance.
Classification TV programs based on audio information using hidden Markov model

This paper describes a technique for classifying TV broadcast video using a hidden Markov model (HMM). Here we consider the problem of discriminating five types of TV programs, namely commercials, basketball games, football games, news reports, and weather forecasts. Eight frame-based audio features are used to characterize the low-level audio properties, and fourteen clip-based audio features are extracted based on these frame-based features to characterize the high-level audio properties. For each type of these five TV programs, we build an ergodic HMM using the clip-based features as observation vectors. The maximum likelihood method is then used for classifying testing data using the trained models.
Screen content image segmentation using sparse-smooth decomposition

Sparse decomposition has been extensively used for different applications including signal compression and denoising and document analysis. In this paper, sparse decomposition is used for image segmentation. The proposed algorithm separates the background and foreground using a sparse-smooth decomposition technique such that the smooth and sparse components correspond to the background and foreground respectively. This algorithm is tested on several test images from HEVC test sequences and is shown to have superior performance over other methods, such as the hierarchical k-means clustering in DjVu. This segmentation algorithm can also be used for text extraction, video compression and medical image segmentation.
Mean, median and tri-mean based statistical detection methods for differential gene expression in microarray data

The detection of differential gene expression in microarray data can recognize genes with significant alteration of expression level with regard to varying experimental environment. Traditional differential gene expression detecting methods work on the assumption that all cancer samples are over-expressed compared with normal samples and need to define the key criterion with the mean of sample data. In recent proposed methods, one often considers the situation that only a subgroup of cancer samples are over-expressed and only the key criterion with the median and median absolute deviation is required. We proposed a detecting method for over-expressed cancer subgroup by defining the key criterion with tri-mean and tri-mad. Numerical experiments on public microarray data indicate that the improved method outperforms the compared methods.
Logic optimization with considering boolean relations

Boolean Relation (BR) is a many-to-many mapping between two domains. Logic optimization considering BR can exploit the potential flexibility existed in logic networks to minimize the circuits. In this paper, we present a logic optimization approach considering BR. The approach identifies a proper sub-circuit and locally changes its functionality by solving the corresponding BR in the sub-circuit without altering the overall functionality of the circuit. We conducted experiments on a set of MCNC benchmarks that cannot be further optimized by resyn2 script in ABC. The experimental results show that the node counts of these benchmarks can be further reduced. Additionally, when we apply our approach followed by the resyn2 script repeatedly, we can obtain 6.11% improvements in average.
Study on GA-based Training Algorithm for Extreme Learning Machine

In view of the prediction accuracy of Extreme Learning Machine's (ELM) is affected by its input weights and hidden layer neurons thresholds, an improved training method for ELM with Genetic Algorithms (GA-ELM) is proposed in this paper. In GA-ELM, after selection, crossover and mutation of Genetic Algorithm (GA), we will get the optimal weights and thresholds, in initial which are randomly obtained by ELM, then to enhance the generalization performance of ELM. The simulation results show that, compared with other algorithms, the GA-ELM has better prediction accuracy.
Analysis of influencing factors using GRAPPA algorithm for image reconstruction

Magnetic resonance imaging (MRI) is one of the major imaging modalities in medicine and biology with advantages in imaging soft tissues and great flexibility for various applications. However, its relatively long imaging time has obstructed its further development. To accelerate the imaging process, parallel MRI (pMRI) was proposed which applies multiple receiving coils operating in parallel to acquire k-space signals. Unfortunately, because of constraints of image reconstruction algorithms, pMRI cannot accelerate the imaging process as much as expected. So in order to accelerate the speed, it is necessary to analyze the influencing factors. There are many factors affecting the image quality of reconstruction. In this paper, it discusses three influencing factors in the GRAPPA reconstruction algorithm, there are block number, ACS lines and acceleration factor. It is found by experiments that all the factors that should be maintained within a range, in order to take into account the speed and quality of imaging.
Fiber Optic Hydrogen Sensor Based on Fabry–Perot Interferometer Coated With Sol-Gel Pt/WO 3 Coating

A miniaturized fiber tip Fabry-Perot interferometer (FPI) with Pt/WO3 coating was demonstrated as a hydrogen sensor. The air-cavity of FPI was formed by inserting a single mode fiber into a partially polymer-filled glass capillary, which was highly sensitive to temperature. A layer of Pt/WO3 was coated on the glass capillary of the FPI serving as a reaction heater upon hydrogen exposure. The heat locally raised the FPI temperature, which led to the wavelength shift of interference spectrum. The average wavelength shift of sensor's two interference dips was -20.3 nm upon exposure to ~20000-ppm hydrogen concentration. The hydrogen sensitivity of the sensor was estimated to be better than 1 pm/ppm.
Performance analysis of sparse traffic grooming in WDM mesh networks

Sparse traffic grooming is a practical problem to be addressed in heterogeneous multi-vendor optical WDM networks where only some of the optical cross-connects (OXCs) have grooming capabilities. Such a network is called as a sparse grooming network. The sparse grooming problem under dynamic traffic in optical WDM mesh networks is a relatively unexplored problem. In this work, we propose the maximize-lightpath-sharing multi-hop (MLS-MH) grooming algorithm to support dynamic traffic grooming in sparse grooming networks. We also present an analytical model to evaluate the blocking performance of the MLS-MH algorithm. Simulation results show that MLS-MH outperforms an existing grooming algorithm, the shortest-path single-hop (SPSH) algorithm. The numerical results from analysis show that it matches closely with the simulation. The effect of the number of grooming nodes in the network on the blocking performance is also analyzed.
A DEP-assisted single-cell electroporation chip with low operation voltage

This paper reports an insulator-based dielectrophoresis (iDEP)-assisted single-cell electroporation chip that can locally electroporate cells with an operation voltage of 1V. The electroporation chip consists of two metal electrodes and a sandwiched dielectric polymer layer with microwells. The dielectric microwell arrays create non-uniform electric fields to generate positive DEP forces to capture a single-cell at each microwell. The following DC pulse voltage imposes a large electric field across the membranes of the captured cells and electroporates the cells. Human lymphoma cells in an 8 × 11 array are successfully electroporated.
A Novel Sparsity Measure for Tensor Recovery

In this paper, we propose a new sparsity regularizer for measuring the low-rank structure underneath a tensor. The proposed sparsity measure has a natural physical meaning which is intrinsically the size of the fundamental Kronecker basis to express the tensor. By embedding the sparsity measure into the tensor completion and tensor robust PCA frameworks, we formulate new models to enhance their capability in tensor recovery. Through introducing relaxation forms of the proposed sparsity measure, we also adopt the alternating direction method of multipliers (ADMM) for solving the proposed models. Experiments implemented on synthetic and multispectral image data sets substantiate the effectiveness of the proposed methods.
Low-Rank Matrix Factorization under General Mixture Noise Distributions

Many computer vision problems can be posed as learning a low-dimensional subspace from high dimensional data. The low rank matrix factorization (LRMF) represents a commonly utilized subspace learning strategy. Most of the current LRMF techniques are constructed on the optimization problem using L_1 norm and L_2 norm, which mainly deal with Laplacian and Gaussian noise, respectively. To make LRMF capable of adapting more complex noise, this paper proposes a new LRMF model by assuming noise as Mixture of Exponential Power (MoEP) distributions and proposes a penalized MoEP model by combining the penalized likelihood method with MoEP distributions. Such setting facilitates the learned LRMF model capable of automatically fitting the real noise through MoEP distributions. Each component in this mixture is adapted from a series of preliminary super-or sub-Gaussian candidates. An Expectation Maximization (EM) algorithm is also designed to infer the parameters involved in the proposed PMoEP model. The advantage of our method is demonstrated by extensive experiments on synthetic data, face modeling and hyperspectral image restoration.
A novel method for RNA secondary structure prediction

In this paper, we propose PSOfold, a particle swarm optimization for RNA secondary structure prediction. PSOfold is based on the recently published IPSO. We present two strategies to improve the performance of IPSO. Firstly, in order to boost the competence in searching an optimal solution, fuzzy logic control is used to adaptively adjust the parameters in PSO. Accordingly, three fuzzy logic controls are designed by which the inertia weight, learning factors and the number of ants are tuned respectively. Secondly, to further settle the stem permutation problem, we put forward a solution conversion strategy (SCS), which can transform discrete values of stems into an ordered stem combination, thereby supplying an enhanced solution to evaluation of objective function. An evaluation of the performance of PSOfold in terms of prediction accuracy is made via comparison with one dynamic programming algorithm mfold and four metaheuristics, IPSO, ACRNA, RnaPredict, SARNA-Predict and mfold for ten individual known structures. PSOfold is able to predict structures with higher prediction accuracy than the other metaheuristic based methods on certain sequences, and has comparable performance compared with mfold.
Analysis of electromechanical problems using the dual-primal finite element tearing and interconnecting method

The dual-primal finite element tearing and interconnecting (FETI-DP) method is combined with the tree-cotree splitting (TCS) method to expand the capability and improve the efficiency of the finite-element analysis of electromechanical devices. With the FETI-DP method, an original large-scale problem is decomposed into smaller subdomain problems and parallel computing schemes are then employed to reduce the computation time significantly. The TCS method is adopted to deal with the low-frequency breakdown problem, which often accompanies the finite-element analysis of electromechanical problems. The proposed technique is applied to solve a multi-connected eddy-current problem and compute force for a magnetostatic problem. The results show that the proposed method can achieve an excellent accuracy and a good parallel efficiency.
A universal asynchronous receiver transmitter design

In this paper, a UART design is proposed. The design has an auto-tuning baud rate generator. For achieving the speed matching of the processor and UART interface, it takes asynchronous FIFOs as buffers to realize data exchange between UART and external devices. The whole design is functionally verified using ModelSim SE 6.0, and synthesized and optimized by Synplicity's Synplify Premier 9.6.2.
Streaming layered encoded video using peers

Peer-to-peer video streaming has emerged as an important means to transport stored video. The peers are less costly and more scalable than an infrastructure-based video streaming network which deploys a dedicated set of servers to store and distribute videos to clients. In this paper, we investigate streaming layered encoded video using peers. Each video is encoded into hierarchical layers which are stored on different peers. The system serves a client request by streaming multiple layers of the requested video from separate peers. The system provides unequal error protection for different layers by varying the number of copies stored for each layer according to its importance. We evaluate the performance of our proposed system with different copy number allocation schemes through extensive simulations. Finally, we compare the performance of layered coding with multiple description coding.
The FTCSMA/CD network

This paper presents a new network model, FTCSMA/CD to provide fault-tolerant capability on IEEE 802.3 (CSMA/CD). This model inserts a sublayer between the LLC (logical link control) sublayer and MAC (medium access control) sublayer, in order to provide the fault tolerance function. That insertion sublayer is called ILM (Interface between LLC and MAC). The new network model adopts two channels as the communication path, but only one channel is permitted to work at a time. Though the network bandwidth of this network model is not expanded, the network model still reached the goal of network fault tolerance. Further, the network model design is based on compatibility with the existent ethernet, so that the original software in the existent network could be used in the fault-tolerant model.
Acoustic scene classification using self-determination convolutional neural network

A practical acoustic scene classification (ASC) system identifies the scene of environment by analyzing the properties of audio data that characterize the surroundings. Proposing a robust ASC system is challenging as the sound from natural environment compromises various audio sources and the microphones are not arranged in a controlled condition. Furthermore, not all sounds from long-duration audio data are relevant for identifying scene label. Some sounds may contribute to the noises or the mix of various scene labels. In this work, the problem of mixed information from various sources in natural environment is addressed by proposing a classification system that adapts to the data for each frames of long-duration audio data. The proposed ASC system is designed based on deep learning based approach, and is motivated by the researches in computer vision field. Our method is evaluated on the TUT acoustic scenes 2016 dataset. Several ASC systems have been implemented and discussed in the experiments. The results show the superiority of proposed system versus another systems that have been discussed in this work.
Modeling of transmission-loss-induced distortion in decoded video

This paper analyzes the distortion in decoded video caused by random packet losses in the underlying transmission network. A recursion model is derived that relates the average channel-induced distortion in successive P-frames. The model is applicable to all video encoders using the block-based motion-compensated prediction framework (including the H.261/263/264 and MPEG1/2/4 video coding standards) and allows for any motion-compensated temporal concealment method at the decoder. The model explicitly considers the interpolation operation invoked for motion-compensated temporal prediction and concealment with sub-pel motion vectors. The model also takes into account the two new features of the H.264/AVC standard, namely intraprediction and inloop deblocking filtering. A comparison with simulation data shows that the model is very accurate over a large range of packet loss rates and encoder intrablock rates. The model is further adapted to characterize the channel distortion in subsequent received frames after a single lost frame. This allows one to easily evaluate the impact of a single frame loss.