nils-herrmann/clean_scirepeval huggingface dataset:
Tunable narrow linewidth 1.5 μm fiber laser using a π phase-shifted FBG

Here we demonstrate, to the best of our knowledge, a tunable 1.5 μm narrow linewidth fiber ring laser using a temperature tuning π-phase shifted fiber Bragg grating (π-PSFBG) for the first time. The π-PSFBG is employed as both a narrow band filter and a wavelength tuning component by a thermo electric cooler (TEC). The tuning range of the laser is 1.29 nm with the precision of 14 pm/°C, and the maximum output power is 1.1 W with a linewidth of 2.57pm, and the slope efficiency is about 12.2%.
New word identification in social network text based on time series information

Different from the languages widely used in western countries such as English or French, there are no spaces between words in Chinese language, and a segmentation of the texts is necessary before other superior processes. New word identification is an important problem in the segmentation process, especially when the segmentation targets are social network texts which have more abbreviated words or other non-standard representations. Several methods have been proposed to detect Chinese new words. Most of these methods take the corpus as a static set and they don't consider the time domain information. Different from these studies, we regard our social network corpus as a text series spreading along the time line and design a new kind of features named dynamic features which can reflect the temporal variety of the string's statistical features. The experimental results on the dataset crawled from the biggest microblogging application in China show that this method can significantly improve the effect of Chinese new word identification.
Dynamic symmetrical components analysis and compensation for unbalanced distribution systems

The short circuit fault and the unbalanced three-phase loads are the common reasons causing unbalanced voltage distortion (UVD) in the distribution system. In the past, the decline in electric motor performance and life cycle was a well-known fact caused by UVD. However, the UVD deserves more attention in recent years due to the rapid growth of the wind power penetration in the distribution system. The wind power system operated under the UVD suffers not only the pulsation torque but also penetrates unbalanced current and consequently deteriorates the UVD of the distribution system. This paper presents an approach to dynamically resolve the symmetrical components from the three-phase instantaneous voltages in order to clarify the degree of the UVD for compensation. The vector representation for the symmetrical components involves the information of angular frequency into the steady-state phasor, which can push much of the complexity down on the controller design for compensating the UVD. A dynamic voltage restorer (DVR) is employed as a voltage compensator in the series path of the feeder for refining the percent voltage unbalance of the UVD. The impact of the UVD reduction on a grid-tied 3-kW squirrel cage induction generator (SCIG) shows the effectiveness of the proposed approach.
Salience Preserving Multi-Focus Image Fusion

This paper proposes a novel multi-focus image fusion algorithm. Different from traditional decision map based methods, our algorithm is based on salience preserving gradient, which can better emphasize the structure details of sources while preserving the color consistency. We firstly measure the salience map of the gradient from each source, and then use their saliency to modulate their contributions in computing the global statistics. Gradients with high saliency are properly highlighted in the target gradient, and thereby salient features in the sources are well preserved. Furthermore we extend it to color domain by proposing an importance-weight based trigonometric average method to merge the color components. Extensive experiments on several datasets have demonstrated the effectiveness of our approach.
Isolator-Free Unidirectional Multiwavelength Tm-Doped Double-Clad Fiber Laser Based on Multimode Interference Effect

In this paper, an isolator-free unidirectional multiwavelength Tm-doped fiber laser is experimentally investigated. The isolator-free unidirectional operation of the laser is realized by the construction of a type of theta cavity. A single-mode-multimode-single-mode fiber structure served as a multimode interference filter is employed for implementing multiwavelength operation. Wavelength numbers from one to three can be obtained by increasing the pump power and properly adjusting the polarization controller. Besides the single wavelength emission at ∼2002.6 nm, dual-wavelength operations (∼1952.6 nm, ∼1954.7 and ∼1954.8 nm, ∼2002.7 nm) with signal-to-noise ratios larger than ∼46 dB and three-wavelength operation (∼1952.8, ∼1954.5, and ∼2002.9 nm) with signal-to-noise ratios larger than ∼50 dB have also been obtained, respectively. The maximum output power of ∼545 mW was obtained at pump power of ∼7.3 W, when the laser operates at three-wavelength oscillation and the slope efficiency is ∼10%. Our demonstrated fiber laser source represents a type of all-fiberized solution for the realization of high-power, unidirectional and controllable multiwavelength operations, simultaneously.
High-voltage thick layer SOI technology for PDP scan driver IC

Based on 11-μm-thick silicon layer and 1-μm-thick buried oxide layer, a novel high-voltage thick layer SOI technology has been developed for driving plasma display panels (PDP). HV pLDMOS, nLDMOS, nLIGBT and LV CMOS are compatible with deep trench isolation. The length T, Y for HV pLDMOS and TD for HV nLDMOS are optimized to reduce the device size and satisfy the off-state breakdown voltage simultaneously. Interdigitated N+&P+ and a deep P+ are adopted in the source region of HV nLDMOS and cathode region of HV nLIGBT to suppress parasitic NPN action and gain better on-state characteristics. A PDP scan driver IC using the developed high-voltage thick layer SOI technology shows that the rise and fall times of the output stages are about 17.6 ns and 16.6 ns respectively.
Sparse Error Correction From Nonlinear Measurements With Applications in Bad Data Detection for Power Networks

In this paper, we consider the problem of sparse error correction from general nonlinear measurements, which has applications in state estimation of electrical power networks, when bad data (outliers) are present. An iterative mixed l1 and l2 convex program is used to estimate the true state by locally linearizing the nonlinear measurements. In the special case when the measurements are linear, through using the almost Euclidean property for a linear subspace, we derive a new performance bound for the state estimation error under sparse bad data and additive observation noise. As a byproduct, in this paper we provide sharp bounds on the almost Euclidean property of a linear subspace, using the “escape-through-the-mesh” theorem from geometric functional analysis. When the measurements are nonlinear, we give conditions under which the solution of the iterative algorithm converges to the true state even though the locally linearized measurements may not be the actual nonlinear measurements. We are able to use a semidefinite program to verify the conditions for convergence of the proposed iterative sparse recovery algorithms from nonlinear measurements. We then numerically evaluate our iterative convex programming approach of performing bad data detections in nonlinear electrical power networks problems.
Missing Data Recovery by Exploiting Low-Dimensionality in Power System Synchrophasor Measurements

This paper presents a new framework of recovering missing synchrophasor measurements (erasures). Leveraging the approximate low-rank property of phasor measurement unit (PMU) data, we connect the problem of recovering PMU data erasures with recent advances in low-rank matrix completion methods. Since the existing analysis for matrix completion methods assumes an independent-erasure model that does not capture the correlations in PMU erasures, we propose two models to characterize the temporal and the channel correlations in PMU erasures and provide theoretical guarantees of a matrix completion method in recovering correlated erasures in both models. We also propose an online algorithm that can fill in the missing PMU measurements for real-time applications. Numerical experiments on actual PMU data are conducted to verify the effectiveness of the proposed methods.
An Efficient Tracking System by Orthogonalized Templates

Sparse representation (SR)-based tracking systems have become popular in the past recent years for its effectiveness. However, the underlying assumption of these tracking systems is that the target appearance can be linearly represented by a sparse approximation over a set of templates and a residual term, which usually needs to solve ℓ1 norm minimization for many times and brings a heavy computational cost. This paper introduces an efficient tracking system by discovering orthogonalized templates. By orthogonalizing templates from previous frames and removing their correlation, we show that the sparsity of template weights is not necessary in target appearance modeling and thus a least squares regularization can be employed. We also decompose the residual term into two components in observation model to take occlusion cases into consideration. We demonstrate that, in comparison with the SR-based tracking systems that use ℓ1 learning, our tracking system is much more computationally efficient while getting an even better performance. Experiments on a variety of challenging video sequences demonstrate both the effectiveness and efficiency of the system.
A Model For Photoresist-induced Charging Damage In Ultra-thin Gate Oxides

The role of photoresist (PR) in affecting the gate oxide damage during plasma processing was reexamined in this work. It was observed that, during the 0 2 plasma ashing, more severe antenna effect may occur in devices with PR covering. A model is proposed to explain this phenomenon. This model considers the ability of plasma current injection through the substrate contacts, which are hindered by PR covering, to adjust the potential difference between gate electrode and substrate. We found that the presence of PR, antenna ratio, and gate-oxide thickness are all important parameters for charging damage effect.
Crack Propagation Simulation and Residual Life Analysis of Coupler Yoke

The operation of 20,000-ton heavy haul freight cars on Datong-Qinhuangdao Railway has played a very important role on the economic development of China, and at the same time it caused serious damage to the couplers and coupler yokes of the freight cars, threatening the traffic safety. In this paper, taking cracks on the coupler yoke into account, the stress intensity factors (SIF) for different crack sizes are calculated using the approach of 1/4 node displacement and sub-model techniques. The commercial finite element code ABAQUS is used for modeling and 3-D prismatic quarter point 20-node isoparametric degenerate singular elements are adopted for the region near the crack tip. Models on fatigue crack growth and residual life prediction are applied with help of Paris equation. Finally, AAR load spectrum is provided and the relationship between crack propagation size and wagon operation mileage is obtained. The work provides fundamental data for setting up the inspection plan and assessing the replacement period of the coupler yoke.
Research on 3MO-based genetic algorithm for solving order planning

This paper proposes a mixed integer-programming model for order planning of iron-steel enterprise. Because this problem is a kind NP-hard problem and the size of this problem is bigger, the genetic algorithm based on repeatable natural scale code and 3-mutation operator is presented to solve the model. For ensuring the quality of copy strategy of the genetic algorithm, this paper develops an improving selection function for copying. Finally, this paper uses the order planning of Shanghai Baoshan iron-steel enterprise as an example to test the model and the algorithm. The numerical analysis shows that the model comes up to the production process, the solutions obtained by this algorithm are superior to those obtained by the human-machine system. So, the model and the algorithm are valid.
Gabor filtering-based scale and rotation invariance feature for 2d barcode region detection

2D barcode region detection is a non trivial problem for barcode revognition and decoding especially in complex backgrounds. Currently, morphological processing has been widely applied to extract potential regions of Data Matrix barcodes due to its low computation complexity. However, this method leads to two problems, adaptive selection of morphological structuring element and high false accept rate. To solve these problems, this paper proposes an innovative method for 2D barcode region detection based on Gabor filtering and BP neural network. The contributions are two folds: 1) we propose a texture feature formulation independent of scale and rotation; 2) BP neural network can avoid the difficulty in morphological structure construction. Large scale experiments show the accuracy and robustness of the proposed method over the traditional morphological method.
Learning Deep CNN Denoiser Prior for Image Restoration

Model-based optimization methods and discriminative learning methods have been the two dominant strategies for solving various inverse problems in low-level vision. Typically, those two kinds of methods have their respective merits and drawbacks, e.g., model-based optimization methods are flexible for handling different inverse problems but are usually time-consuming with sophisticated priors for the purpose of good performance, in the meanwhile, discriminative learning methods have fast testing speed but their application range is greatly restricted by the specialized task. Recent works have revealed that, with the aid of variable splitting techniques, denoiser prior can be plugged in as a modular part of model-based optimization methods to solve other inverse problems (e.g., deblurring). Such an integration induces considerable advantage when the denoiser is obtained via discriminative learning. However, the study of integration with fast discriminative denoiser prior is still lacking. To this end, this paper aims to train a set of fast and effective CNN (convolutional neural network) denoisers and integrate them into model-based optimization method to solve other inverse problems. Experimental results demonstrate that the learned set of denoisers can not only achieve promising Gaussian denoising results but also can be used as prior to deliver good performance for various low-level vision applications.
Theory and Application of Format-Management in Highway Projects

Format-management in highway projects is a brand new concept and concrete implementation of elaborative management theory, which holds significance for improving domestic construction management. This paper reveals the connotation and characteristics of format-management, constructs the theory system of format-management, and expounds the principles, requirements and basis of format design in format-management. The main contents of highway project format-management contain owner business format-management, supervision business format-management and construction business format-management. The successful use of format-management in Huangpu Bridge above Zhujiang River in Guangzhou City should be well worth attention and study by the managers of highway projects.
Event Driven Web Video Summarization by Tag Localization and Key-Shot Identification

With the explosive growth of web videos on the Internet, it becomes challenging to efficiently browse hundreds or even thousands of videos. When searching an event query, users are often bewildered by the vast quantity of web videos returned by search engines. Exploring such results will be time consuming and it will also degrade user experience. In this paper, we present an approach for event driven web video summarization by tag localization and key-shot mining. We first localize the tags that are associated with each video into its shots. Then, we estimate the relevance of the shots with respect to the event query by matching the shot-level tags with the query. After that, we identify a set of key-shots from the shots that have high relevance scores by exploring the repeated occurrence characteristic of key sub-events. Following the scheme in  and [22], we provide two types of summaries, i.e., threaded video skimming and visual-textual storyboard. Experiments are conducted on a corpus that contains 60 queries and more than 10 000 web videos. The evaluation demonstrates the effectiveness of the proposed approach.
An extension neural network based incremental MPPT method for a PV system

In this paper, a novel incremental conductance (INC) maximum power point tracking (MPPT) method based on extension neural network (ENN) is developed to make full use of photovoltaic (PV) array output power. The proposed method can adjust the step size to track the PV array's maximum power point (MPP) automatically. Compared with the conventional fixed step size INC and variable step size INC methods, the presented approach is able to effectively improve the dynamic response and steady state performance of a PV system simultaneously. A theoretical analysis and the design principle of the proposed method are described in detail. Some simulation results are performed to verify the effectiveness of the proposed ENN MPPT method.
The study of intelligent analysis technology for command information systems based Petri net

To research the application of Petri net's theoretical method in the concurrent asynchronous communications control, find out the detect arithmetic of system's deadlock, and analyze the system's liveness.  A detect method of deadlock for information net based Petri net is proposed in this paper. The main method is as follows: After standardize the information systems and extract its mathematical model used the Petri net theory, and then standardize this model. On the basis of model's analysis, find out the loca where system is deadness.  After the statistical analysis of application result, this method can be effectively applied in the information systems' plan, it will reduce the times of deadlock number above 89%.  This method can be applied in the system which have the transmission of concurrent asynchronous information, provide the theory basis which have important reference value for finding the fault and repair system. The main problem is the search ergodic process of Petri net's data structure is quite time consuming, so it can be combined with experience and man-machine interactive in application, to improve the efficiency.
Diagnosis of cardiac arrhythmia using kernel difference weighted KNN classifier

In this paper, we proposed a kernel difference-weighted k-nearest neighbor classifier (KDF-WKNN) for the diagnosis of cardiac arrhythmia based on the standard 12 lead ECG recordings. Different from classical KNN, KDF-WKNN defines the weighted KNN rule as the constrained least-squares optimization of sample reconstruction from its neighborhood, and then uses the Lagrangian multiplier method to compute the weights of different nearest neighbors by introducing the kernel Gram matrix G. In arrhythmia analysis, it is unavoidable that some attribute values of a person would be missing. Thus, this paper further introduces a modified PCA method to address this problem. To evaluate the performance of KDF-WKNN, Experimental results on the UCI cardiac arrhythmia database indicate that, KDFWKNN is superior to the nearest neighbor classifier, and is very competitive while compared with several state-of-the-art methods in terms of classification accuracy.
Design considerations for narrow Vdc based power delivery architecture in mobile computing system

Narrow Vdc power architecture is one of initiatives to extend battery life(EBL) in mobile computing system. Advantages of it is analyzed in detail. All design considerations for such possible concerns as charger controller selection, pre-charge issue, thermal stress, Energy Star compliant related, is illustrated and solutions to some concerns are also provided in this paper. Finally, test is conducted in a typical Notebook system to verify all theoretical analysis and proposed solutions to those concerns.
Detection of tropospheric scatter array signal based on the truncation of multipath fading time and frequency domain characteristics

Microwave scattered by the troposphere can propagate Over-The-Horizon(OTH), how to effectively detect the received signal using its signal characteristics is the most important issue in troposcatter target reconnaissance. The received signal behaves with low signal to noise ratio (SNR), multipath fading. In order to effectively detect, the SNR needs to be enhanced utilizing the signal characteristics. This paper analyzes the signal multipath fading and finds energy diffusion characteristics affected by multipath fading in the time and frequency domain. Based on this, an appropriate signal truncation strategy is designed respectively in time, frequency domain to obtain the energy-concentrated part to maximize truncation gain. Next, the signal detection is carried out after the paper accumulates signal energy of the truncated part. The algorithm does not need any prior information which is effective in the non-cooperative circumstance. Finally, the simulation results validate the detection performance of the proposed algorithm is better than the traditional energy detection. The proposed one can satisfy the detection need of the troposcatter signal.
Multi-sensor image fusion with ICA bases and region rule

Images captured from multi-sensor contain abundant information. It is available to combine them by image fusion schemes such as wavelet or Laplacian pyramid. Recently schemes based on independent component analysis (ICA) bases were proposed. These schemes transform images to ICA domain and then fuse them by fusion rules. Accordingly, the region based fusion rule segments images, selects features in transform domain and combines them. In this paper, we trained ICA bases which are suitable for image fusion and disposed of low frequency energy properly. To effectively make use of the relationship of input images, a novel region based fusion rule was proposed. In contrast to traditional schemes, the performance is improved and shown by both objective and subjective evaluations.
Zero-Shot Learning via Attribute Regression and Class Prototype Rectification.

Zero-shot learning (ZSL) aims at classifying examples for unseen classes (with no training examples) given some other seen classes (with training examples). Most existing approaches exploit intermedia-level information (e.g., attributes) to transfer knowledge from seen classes to unseen classes. A common practice is to first learn projections from samples to attributes on seen classes via a regression method, and then apply such projections to unseen classes directly. However, it turns out that such a manner of learning strategy easily causes projection domain shift problem and hubness problem, which hinder the performance of ZSL task. In this paper, we also formulate ZSL as an attribute regression problem. However, different from general regression-based solutions, the proposed approach is novel in three aspects. First, a class prototype rectification method is proposed to connect the unseen classes to the seen classes. Here, a class prototype refers to a vector representation of a class, and it is also known as a class center, class signature, or class exemplar. Second, an alternating learning scheme is proposed for jointly performing attribute regression and rectifying the class prototypes. Finally, a new objective function which takes into consideration both the attribute regression accuracy and the class prototype discrimination is proposed. By introducing such a solution, domain shift problem and hubness problem can be mitigated. Experimental results on three public datasets (i.e., CUB200-2011, SUN Attribute, and aPaY) well demonstrate the effectiveness of our approach.