nils-herrmann/clean_scirepeval huggingface dataset:
Application studies of superconducting fault current limiters in electric power systems

In power systems, superconducting fault current limiters (SFCLs) can limit the prospective short-circuit currents to lower levels, so that the underrated switchgear can be operated safely. This paper presents a detailed theoretical analysis of improving power system stability by using SFCLs. Electromagnetic Transient Program (EMTP) simulation results based on a model system also show that SFCLs are effective for enhancing the transient stability of electric power systems by restraining the generator rotors from accelerating after faults and improving power quality. This paper outlines the basic application guidelines for using superconducting fault current limiters in different voltage-level power systems. In order to insure the safe and proper usage of SFCLs, some future considerations on the interaction between SFCLs and power systems are presented as well.
A Weak-Form Efficiency Testing of China's Stock Markets

The main purpose of this paper is to test the degree of development of China's stock market, as well as its stage. Utilizing the GARCH amended model and the AR-X-GARCH (1, 1) model; we analyzed whether the opportunity for excess returns in China's stock markets exists, and tested the randomness of the returns' series in China's stock market. Finally, we concluded that China's two stock markets in Shenzhen and Shanghai have not reached the significant excess rate of return opportunities, but the stock markets as a whole have not reached the level of the weak-form efficiency. At the same time, we can see that China's stock market is gradually maturing. This can provide a good guide to understand the degree of development of China's stock market, as well as its stage.
Example-based caricature generation with exaggeration

In this paper, we present a system that automatically generates caricatures from input face images. From example caricatures drawn by an artist, our caricature system learns how an artist draws caricatures. In our approach, we decouple the process of caricature generation into two parts, i.e., shape exaggeration and texture style transferring. The exaggeration of a caricature is accomplished by a prototype-based method that captures the artist's understanding of what are distinctive features of a face and the exaggeration style. Such prototypes are learnt by analyzing the correlation between the image caricature pairs using partial least-squares (PLS). Experimental results demonstrate the effectiveness of our system.
Time Domain Measurement System for Conducted EMI and CM/DM Noise Signal Separation

This article discusses how to use a digital oscilloscope to match the time-domain measurement computer-aided technique and Fourier transformation to measure the conducted EMI. Taking advantage of the multi-channel oscilloscope and the computer, it will deal with the Line and Neutral lines synchronization. It can mitigate the phase coherency problem to separate the common-mode (CM) and differential-mode (DM) EMI noise at the same time without any hardware implementations. Due to the time-domain measuring method, which allows the noise amplitude and phase in parallel processing for the whole spectrum range, measuring time is reduced. The computerized time-domain measurement environment is based upon the LabVIEW test and measurement system. The separation results are compared with the frequency-domain measurements. The proposed time-domain measured system can provide a user friendly and convincible approach for fast EMI measurement and countermeasures.
KPAT: A kernel and protocol analysis tool for embedded networking devices

Sniffer tools capture protocol data. Kernel-profiling tools track function calls and events occurring in the kernel. These two types of tools help us observe external and internal behaviors of networking protocols, respectively. We need both types of data for a comprehensive view of protocol behavior. However, none of existing tools performs these two tasks in an integrated way. We developed Kernel and Protocol Analysis Tool (KPAT). KPAT injects software probes into Linux kernel to track interested function calls and event occurrences in the kernel. Probe injection is done systematically and does not require recompiling the kernel. A module in KPAT finds the association between the tracked functions and protocol data captured by an independent sniffer. The result as an integrated log allows users to identify two-way relationship between protocol data and the execution sequence of network functions in the kernel. We successfully used KPAT to identity accurate latency of each handover phase in IEEE 802.11 wireless networks. Experimental results show that KPAT causes light overhead to the patched kernel.
Generating Multi-modality Virtual Samples with Soft DBSCAN for Small Data Set Learning

Owing to the factors of cost and time limit, the number of samples is usually small in the early stages of manufacturing systems. When the number of available data is small, traditional statistic techniques have difficulty to obtain robust analyses. Therefore, based on a uni-modality distribution assumption, many researchers have proposed virtual sample generation methods to expand the training sample size to enhance the performance of small data set learning. In practice, small data may be following a multi-modality distribution. Therefore, in order to solve multi-modal small data sets, this study proposes a new approach to create multi-modality Weibull virtual samples, where we use the maximal p-value to estimate parameters of the Weibull distribution. In addition, the soft DBSCAN method is used to identify a suitable number of modalities. One data set is employed to check the performance of the proposed method, and comparisons are made by the prediction on root mean square error. The results using a paired t-test show that the proposed method has a superior prediction performance than that of the mega-trend-diffusion method using a uni-modality triangular membership function.
Correntropy Induced L2 Graph for Robust Subspace Clustering

In this paper, we study the robust subspace clustering problem, which aims to cluster the given possibly noisy data points into their underlying subspaces. A large pool of previous subspace clustering methods focus on the graph construction by different regularization of the representation coefficient. We instead focus on the robustness of the model to non-Gaussian noises. We propose a new robust clustering method by using the correntropy induced metric, which is robust for handling the non-Gaussian and impulsive noises. Also we further extend the method for handling the data with outlier rows/features. The multiplicative form of half-quadratic optimization is used to optimize the non-convex correntropy objective function of the proposed models. Extensive experiments on face datasets well demonstrate that the proposed methods are more robust to corruptions and occlusions.
A service-layer diagnostic approach for the OSGi framework

The open service gateway initiative (OSGi) framework is to provide an open environment for home services, which can be deployed by different service providers. However, currently there is no total solution for the OSGi framework to diagnose and recover from unexpected faults, especially those generated by resource conflicts between services, which would require user intervention. This paper summarizes the requirements for diagnosis in the open service platform and proposes an integrated approach to detect service conflict faults, handle general exceptions, and diagnose service errors on OSGi platforms. By providing user friendly diagnosis messages, and suggestions to fix exceptions, users can operate home gateways without a heavy learning burden, and service providers can reduce the overhead associated with user requests for technical support. The implementation is packaged as a service bundle running on the service layer of the OSGi platform. This is the first attempt to diagnose and handle the service conflicts on the OSGi platform during runtime.
Human Parsing with Contextualized Convolutional Neural Network

In this work, we address the human parsing task with a novel Contextualized Convolutional Neural Network (Co-CNN) architecture, which well integrates the cross-layer context, global image-level context, semantic edge context, within-super-pixel context and cross-super-pixel neighborhood context into a unified network. Given an input human image, Co-CNN produces the pixel-wise categorization in an end-to-end way. First, the cross-layer context is captured by our basic local-to-global-to-local structure, which hierarchically combines the global semantic information and the local fine details across different convolutional layers. Second, the global image-level label prediction is used as an auxiliary objective in the intermediate layer of the Co-CNN, and its outputs are further used for guiding the feature learning in subsequent convolutional layers to leverage the global image-level context. Third, semantic edge context is further incorporated into Co-CNN, where the high-level semantic boundaries are leveraged to guide pixel-wise labeling. Finally, to further utilize the local super-pixel contexts, the within-super-pixel smoothing and cross-super-pixel neighbourhood voting are formulated as natural sub-components of the Co-CNN to achieve the local label consistency in both training and testing process. Comprehensive evaluations on two public datasets well demonstrate the significant superiority of our Co-CNN over other state-of-the-arts for human parsing. In particular, the F-1 score on the large dataset <xref ref-type="bibr" rid="ref1">[1]</xref> reaches <inline-formula><tex-math notation="LaTeX">$81.72\,\text{percent}$</tex-math><alternatives> <inline-graphic xlink:href="lin-ieq1-2537339.gif"/></alternatives></inline-formula> by Co-CNN, significantly higher than <inline-formula><tex-math notation="LaTeX">$62.81\,\text{percent}$</tex-math><alternatives> <inline-graphic xlink:href="lin-ieq2-2537339.gif"/></alternatives></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$64.38\,\text{percent}$</tex-math><alternatives> <inline-graphic xlink:href="lin-ieq3-2537339.gif"/></alternatives></inline-formula> by the state-of-the-art algorithms, M-CNN <xref ref-type="bibr" rid="ref2">[2]</xref> and ATR <xref ref-type="bibr" rid="ref1">[1]</xref> , respectively. By utilizing our newly collected large dataset for training, our Co-CNN can achieve <inline-formula> <tex-math notation="LaTeX">$85.36\,\text{percent}$</tex-math><alternatives> <inline-graphic xlink:href="lin-ieq4-2537339.gif"/></alternatives></inline-formula> in F-1 score.
Hierarchical 3D perception from a single image

Inspirited by the human vision mechanism, this paper discusses a hierarchical grammar model for 3D inference of man-made object from a single image. This model decomposes an object with two layers: (i) 3D parts (primitives) with 3D spatial relationship and (ii) 2D aspects with prediction (production) rules. Thus each object is represented by a set of co-related 3D primitives that are generated by a set of 2D aspects. The 3D relationships can be learned for each object category specifically by a discriminative boosting method, and the 2D production rules are defined according to the human visual experience. With this representation, the inference follows a data-driven Markov Chain Monte Carlo computing method in the Bayesian framework. In the experiments, we demonstrate the 3D inference results on 8 object categories and also propose a psychology analysis to evaluate our work.
Accurate Face Alignment using Shape Constrained Markov Network

In this paper, we present a shape constrained Markov network for accurate face alignment. The global face shape is defined as a set of weighted shape samples which are integrated into the Markov network optimization. These weighted samples provide structural constraints to make the Markov network more robust to local image noise. We propose a hierarchical Condensation algorithm to draw the shape samples efficiently. Specifically, a proposal density incorporating the local face shape is designed to generate more samples close to the image features for accurate alignment, based on a local Markov network search. A constrained regularization algorithm is also developed to weigh favorably those points that are already accurately aligned. Extensive experiments demonstrate the accuracy and effectiveness of our proposed approach.
High precision marking-cutting of shipbuilding profiled bars robot flexible manufacturing system

A novel high precision flexible manufacturing system, which is an automatic marking and cutting manufacturing line of shipbuilding-profiled bars, is developed. The system integrates the robot technology, control and measure technology, signal processing technology, plasma arc cutting and marking technology, computer software and communication technology. The two key technologies of the system are: off-line program of robot based on PC net, and multi-sensor online measurement.
Non-parametric Statistical Assistance in Virtual Sample Selection for Small Data Set Prediction

Science learned models based on limited data are usually fragile, researchers suggest the adoption of virtual samples to improve the prediction model. In this study, nonparametric statistical tool, Kolmogorov-Smirnov test, is introduced to examine the distribution of virtual samples without any assumption about the underlying population. The examination procedure would help control the quality of the generated virtual samples, such that the prediction model can be more robust with the basis of high quality virtual samples. Experimental results show that the prediction model with statistical test procedure performs better than the original one, with more stable and improved accuracies, and the examination procedure can effectively lower the prediction error.
Performance Characterization of Circular Silicon Transformers

We have characterized performance of on-chip circular silicon transformers. The circular transformers were fabricated by the standard 0.18 mum CMOS process. We used measured two-port S-parameters, a de-embedding procedure, and simulated S-parameters derived from a lumped-element circuit model. We examined performance indicators of transformers such as quality factor (Q), maximum available gain (G max), coupling coefficient (kappa), and minimum noise figure (NF min) for various cases. We characterized all the parasitic capacitive and inductive effects. The results obtained are useful in the global optimization of high-performance transformers used in the design of certain radio-frequency integrated circuits.
Improvement in the Light Output Power of GaN-Based Light-Emitting Diodes by Natural-Cluster Silicon Dioxide Nanoparticles as the Current-Blocking Layer

In this study, the fabrication and characterization of InGaN-GaN multiple-quantum-well light-emitting diodes (LEDs) with silicon dioxide (SiO2) nanoparticles as the current-blocking layer (CBL) are described. The performance was improved by introducing SiO2 nanoparticles, not deposited by commonly used plasma-enhanced chemical vapor deposition, as the CBL beneath the p-pad. The injected current was forced to spread outside instead of flowing directly downward. At 20 mA, the light output power of the LED with a CBL was increased 15.7% as compared to that of the conventional LED. The forward voltage of the LED with the SiO2 nanoparticle CBL was 3.34 V at 20 mA, which was slightly higher than that of the conventional LED (3.29 V). The increase in the light output power can be attributed to the injection of additional current into the light-emitting active layer of the LED by the SiO2 nanoparticles CBL and thus a reduction in optical absorption at the p-pad.
Content-based vehicle retrieval using 3D model and part information

Content-based vehicle retrieval in unconstrained environment plays an important role in surveillance system. However, due to large variations in viewing angle/position, illumination, and background, traditional vehicle retrieval is extremely challenging. We approach this problem in a different way by rectifying vehicles from disparate views into the same reference view and searching the vehicles based on informative parts such as grille, lamp, and wheel. To extract those parts, we fit 3D vehicle models to a 2D image using active shape model (ASM). In the experiments, we compare different 3D model fitting approaches and verify that the impact of part rectification on the content-based vehicle retrieval performance is significant. We propose a model fitting approach with weighted jacobian system which leverages the prior knowledge of part information and shows better results. We compute mean average precision of vehicle retrieval with L1 distance on NetCarShow300 dataset, a new challenging dataset we construct. We conclude that it benefits more from the fusion of informative rectified parts (e.g., grille, lamp, wheel) than a whole vehicle image described by SIFT feature for content-based vehicle retrieval.
Basic Particle Swarm Optimization Based on Reasonable Full Share Information Mechanism

In order to prevent particle swarm optimization from being trapped in local optima, a new vector called the weighted individual best position of the other particles is introduced. On the one hand, the behavior of each particle is not only influenced by its own best position and the global best position but also by the individual best positions of the others in the swarm. On the other hand, fitness value is used, i.e., each particle weighs the contributions of the others according to their fitness values. So the modified algorithm strengthens cooperation and competition among the particles by making each particle share more useful information of the others. Six benchmark functions are tested and results show that the modified algorithm is more effective than basic particle swarm optimization.
Orthogonal optimization design for structural parameters of SiC reversely switched dynistor

In order to optimize the turn-on characteristics of the SiC reversely switched dynistor (RSD), by the orthogonal experimental design method, the optimum structural parameters for the voltage rate of 4kV are proposed in this paper, with full consideration of the influences of the several key parameters of SiC RSD. The influences of the single factors that contain the main structural parameters of the doping concentration, the width and length, the carrier lifetime, etc., on the quasi-static voltage drop are discussed, separately. According to the orthogonal table L18(37) of 7 factors and 3 levels, the turn-on voltage of the 18 groups is simulated and the optimized structure is acquired. The blocking voltage of 750V and 2340V are acquired in experiment for two different specifications of epitaxy wafers, with bevel edge termination.
Turn-on Condition and Characteristics of High-power Semiconductor Switch RSD

RSD (reversely switched dynistor), a high-power semiconductor switch, is similar in design to thyristor with pnpn-regions. Instead of a conventional gate structure, RSD's anode consists of alternating n+ and p+ sections. In this paper, the RSD turn-on condition is systematically investigated. Analyses and simulations reveal that the trigger charge must be sufficient to assure RSD normal turn-on. To explore the potential application of RSD, several turn-on characteristics are experimentally evaluated. Results indicate that RSD has advantages in fast voltage-falling, great capability of handling high current and high di/dt
Application of neural network method for process synthesis

Process synthesis is a top-down design methodology and can effectively reduce the process design time. In the paper the general method and the neural network (NN) package used for process synthesis are discussed. Then the characteristics of synthesizing some key process modules, including ion implantation and well formation, are analyzed, based on which the training set used to build the NN is generated. After that the NN model used for process modules synthesis is constructed and trained. Test results show that this model can fulfill the process modules synthesis.
High-performance Ni/SiO2/Si programmable metallization cell

Resistive switching in Ni/SiO2/Si programmable metallization cells was investigated. The proposed switching mechanism is the formation and dissolution of Ni filaments. Under positive bias, Ni cations migrate through SiO2 and are reduced at the cathode forming filaments. The filaments are dissolved by Joule-heating effect under positive or negative voltages. Promising resistive-switching characteristics, such as a large resistance ratio of ∼400, excellent data retention, and good immunity to read disturbance, are also revealed.
Learning Object Interactions and Descriptions for Semantic Image Segmentation

Recent advanced deep convolutional networks (CNNs) achieved great successes in many computer vision tasks, because of their compelling learning complexity and the presences of large-scale labeled data. However, as obtaining per-pixel annotations is expensive, performances of CNNs in semantic image segmentation are not fully exploited. This work significantly increases segmentation accuracy of CNNs by learning from an Image Descriptions in the Wild (IDW) dataset. Unlike previous image captioning datasets, where captions were manually and densely annotated, images and their descriptions in IDW are automatically downloaded from Internet without any manual cleaning and refinement. An IDW-CNN is proposed to jointly train IDW and existing image segmentation dataset such as Pascal VOC 2012 (VOC). It has two appealing properties. First, knowledge from different datasets can be fully explored and transferred from each other to improve performance. Second, segmentation accuracy in VOC can be constantly increased when selecting more data from IDW. Extensive experiments demonstrate the effectiveness and scalability of IDW-CNN, which outperforms existing best-performing system by 12% on VOC12 test set.
Piezoresistive cantilever for measuring mass of airborne particles

This paper introduces a piezoresistive cantilever that is designed for measuring the mass of airborne particles. The principle of mass measurement is based on measuring the frequency shift in the mechanical resonant frequency due to an additional mass deposited on the cantilever. To detect the resonant frequency, piezoresistors are fabricated on the surface of the cantilever and utilized as a strain gauge. Excitation of the cantilever is provided by an external PZT stack. Preliminary results on sensor characterization and discussion are also given in the paper.
Spectral Shaping of Dithered Quantization Errors in Sigma–Delta Modulators

A well-known problem in sigma-delta (SigmaDelta) modulators is limit cycle oscillations, which can cause unwanted tones of significant power to appear in the quantization error. This problem becomes more severe as the amplitude of an input signal is small. The unwanted tones can be eliminated from the quantization error by employing a properly chosen random sequence to break up the limit cycle oscillations. However, the injected random sequence inevitably raises the noise floor in the signal band of interest, and thus degrades the signal-to-quantization-noise ratio (SQNR). This paper first presents a spectrally shaped noise generator and the theoretical basis for the behavior of the proposed generator is explained. Then we introduce a class of pseudorandom noise sequences with least significant bit dithering. The effectiveness of this class of dither sequences is demonstrated through a SigmaDelta modulator. Simulation results show that the proposed dither sequences improve the SQNR by more than 10 dB compared to the case wherein a white pseudorandom binary sequence is applied. Meanwhile, it is shown that the proposed sequences successfully break up the limit cycle oscillations for small input signals