nils-herrmann/clean_scirepeval huggingface dataset:
Numerical Investigation on Interference and Absorption of Electromagnetic Waves in the Plasma-Covered Cavity Using FDTD Method

We present real-time status of the electromagnetic (EM) scattering by the plasma-covered metal cavity, using the 2-D finite-difference time-domain method. The interference and absorption of EM waves of such progresses were analyzed. The variations in their return loss as a function of incident angle, frequency of EM wave, electron density of plasma, and plasma collision frequency were also presented. The results show that when the EM wave hits the cavity at the front aspect angle, the signals propagate along the central axis of the cavity, and the returning waves converge to source for all incident frequencies. On the contrary, when the EM wave hits the cavity at the oblique aspect angle, the combining of reflected waves among the inner walls of the cavity will result in constructive or destructive interference, consequently the returning wave not converging to source any more. The values of peak return loss are extremely different between plasma-covered metal plate and plasma-covered metal cavity. The former is less than 4 dB, while the latter reach to as large as 25 dB. The appropriate parameter regimes of plasma can be chosen for more efficiency design.
A novel permutation algorithm in frequency-domain Blind Source Separation

This paper presents a novel permutation algorithm for solving the permutation ambiguity of frequency-domain BSS(Blind Source Separation). The proposed algorithm harnesses the characteristics that signals at adjacent frequency bins have a high similarity and utilizes Dynamic Time Warping (DTW) to compare this similarity in order to obtain an adjusting matrix at adjacent frequency bin. In real experimental environment, ASSNR(Average Segmental SNR) was utilized to evaluate the separation quality and the proposed method is found to improve the separation over the traditional criterions.
Multiple axes vibration energy harvester based on PTFE and metal balls

In this paper, a novel electrostatic vibration energy harvester based on PTFE electrets and metal ball cluster is presented, which is designed for multiple axes and wideband frequency vibration applications. The advancements of the energy harvester include: (1) converting both vertical and horizontal vibration energy into electric energy; (2) low and wide frequency response; (3) simple structure and fabrication processes. Experimental results show that the device produces more power in vertical than in horizontal direction. From 10Hz to 30Hz, there is no sharp drop of output power on the frequency response trace of the device. The maximum peak output power reaches 25μW with vibration condition of 5mm @ 30Hz. For a 0.5μF capacitance load, the maximum charging power is
Numerical integration algorithms with artificial damping for the PTA method applied to DC analysis of nonlinear circuits

In this paper, new numerical integration algorithms with artificial damping effect are proposed which can be applied to the constant, pure PTA method to find DC operating points of nonlinear circuits when the Newton-Raphson method fails. This method can effectively avoid the oscillation and the “time step too small” problems compared with conventional PTA algorithms. The mathematical description for this method is presented. The application of the proposed method is illustrated by solving practical CMOS nonlinear circuits.
Chinese Traditional Visual Cultural Symbols recognition based on Convolutional neural network

Chinese Traditional Visual Cultural Symbols(CT-VCSs) is the important component of Chinese ancient civilization, and it is the crystallization of Chinese culture with a history of several thousand years. So it has great significance to research CT-VCSs. In this paper, we mainly research the recognition and classification of CT-VCSs based on Convolutional neural network(CNN). We mainly use Caffenet and Alexnet in the Caffe framework, and fine-tune the existed Caffe models. Meanwhile, we also use GPU to speed up the process of training. Experimental results indicate that using CNN poses remarkable enhancement on the recognition task of CT-VCSs, and the recognition result of using Alexnet is the best.
Research on Organizational Learning Mode in the Life Cycle of ERP System: a Case Study of Westlake Electronics Group

Enterprise resource planning (ERP) is not only a software system, but also an advanced management idea. The goals of enterprises' implementing an ERP system are to make full use of enterprise's resource. Some enterprises benefit much from implementing ERP system. But some enterprises fail and even go bankrupt. Why are their results so different? This paper tries to explain it from organizational learning perspective. We argued that organizational learning competency is a determinant factor of implementing ERP system successfully. There are 4 stages in the life cycle of ERP, including introducing stage, implementing stage, crisis stage, and decline stage. We argue that there is a leading organizational learning mode in each stage of ERP's life cycle. With formal intra-organizational training and learning from others, an organization can accumulate knowledge and overcome knowledge barriers, which will help to implement ERP system successfully.
Derivative-based component global importance measure for phased mission systems

Importance measures for system reliability analysis are used to estimate the relative importance of components to the system reliability, and further to provide useful information to improve system performance. Components in phased mission systems (PMS) may have unequal structural importance working in different phases with different reliability logics, and their reliability parameters also may vary from phase to phase caused by different phase environment. Thus, traditional importance measure based on fixed component structural importance and component nominal reliability are not suitable to be applied in these situations. This paper presents a derivative-based global sensitivity measure to evaluate the component importance of PMS. The proposed method can consider component with different structural logic in different phase task profiles, and variational reliability parameter caused by different work environment. Finally, a PMS example is given for illustration the effectiveness of the provided measure. Additionally, compared with the Birnbaum structural importance, the results show that the proposed measure displays reasonable component importance by considering component various reliability parameters and structural logics.
The study of ANP-Based strategic decision model for green supply chain

Chinese organizations have paid much more attention to the green supply chain (GSC) than ever before, and much of them have put it into practice. Certain decision-making problems in GSC's strategic plan stage were discussed, key factors and their relationship during GSC's operation were analyzed, and an ANP-Based strategic decision model was put forward. An illustrative case was given to explain the entire process of working out the model by the analytic network process (ANP). Finally, simple sensitivity analysis with pairwise comparison evaluations was illustrated. The conclusion is helpful to guide the construction and implementation of GSC.
Cross-Talk Calibrating Based on Correlation Analysis in High-Throughput Genome Sequencing

The images, in high-throughput genome sequencing of sequencing by synthesize, consist of A, C, G, T base fluorescence images, and the fluorophore emission spectra partially overlap, which causes cross-talk problem. This paper presents a method of correlation analysis, construct the calibration matrix to eliminate interference of the fluorophore. The experiment result shows the method provided in this article is effective to correct cross-talk influence, and get the deconvolution of the clusters signal.
Effective components sourcing management; perspective of export processing in China

Numerous global manufacturing firms use China as their manufacturing platform and are engaged in processing trade for global markets. They are engaged in export processing operation through the form of processing with supplied materials(PSM) and processing with purchased materials(PPM). The major goal of this paper is to explore, from the perspective of export processing in China, the key characteristics of PSM and PPM mode supply chain, and investigate the driving factors influencing foreign MNCs to choose appropriate processing types for effective components sourcing management. Through case study this paper suggests that components sourcing capability, products characteristics, overall operational capability and business environment in China may influence the choice of PSM or PPM.
Fault detection for networked control system with time-delay and data packet dropout

Focusing on the networked control system with network-induced time-delay and transmitted data packet dropout, based on assumptions that the rate of the data packet dropout is fixed and the network-induced time-delay is less than one sampling period, problems of observer-based fault detection of the system are studied. According to conditions of data arrival of the controller, the state observers of the system are designed to detect faults when they occur in the system. When the system is normal, the observer system is modeled as an asynchronous dynamic system with rate constraints on events. Based on the model, stability of the whole system is given. When conditions are satisfied, the system is exponentially stable. When a fault occurs, the observer residual can change rapidly to detect the fault. A numerical example shows the effectiveness of the proposed method.
Fuzzy support vector machines based on collaborative representation

The design of fuzzy membership functions is the key to fuzzy support vector machines. The fuzzy membership functions of traditional fuzzy support vector machines are based on the distances between the samples and the class center. However, it is easily affected by the sample points distribution. To alleviate this problem, in this paper, we proposed a new design of fuzzy membership function. The new method calculated fuzzy membership values based on the collaborative representation coefficients among the sample points. Experimental results show that the new method achieves higher accuracy which verifies its effectiveness.
Pyroelectric performances of rhombohedral 0.42Pb(In1/2Nb1/2)O3-0.3Pb(Mg1/3Nb2/3)O3-0.28PbTiO3 single crystals

Relaxor-based ferroelectric single crystals xPb(In<sub>1/2</sub>Nb<sub>1/2</sub>)O<sub>3-y</sub>Pb(Mg1/3Nb2/3)O3-(100x100y)PbTiO<sub>3</sub> {PIMNT [100x/100y/100(100x100y)]} have been grown by a modified Bridgman technique. The as-grown PIMNT (42/30/28) crystal with rhombohedral perovskite-type structure shows higher Curie temperature TC ~ 187°C, and higher ferroelectric rhombohedral-to-tetragonal phase transition temperature TRT ~ 152°C, both about 50°C higher than those found for 0.71Pb(Mg<sub>1/3</sub>Nb<sub>2/3</sub>)O<sub>3</sub>-0.29PbTiO<sub>3</sub> crystals. Moreover, as a core parameter of pyroelectric material, the detectivity figures-of-merit of PIMNT (42/30/28) crystal are higher than other typical relaxor-based ferroelectric crystals, which primarily stems from the lower dielectric loss, making it promising candidate for infrared detector applications.
SRFW: a simple, fast and effective text classification algorithm

Text classification is a powerful technique for automating assignment of documents to topic hierarchies. Although there are a number of text classification algorithms, most of them are either inefficient or too complex. We present a linear text classification algorithm called SRFW, which is fast, effective and easily used. SRFW obtains relevance factors. For new unlabelled documents, SRFW adopts sum of weights based on relevance factors to obtain the probability that these documents belong to each category and assigns them to categories that have the biggest probability. We have evaluated our algorithm on a subset of Reuters-21578 and 20-newsgroups text collections and compared it against k-NN and SVM. Experimental results show that SRFW is competitive with k nearest neighbor (k-NN) and support vector machines (SVM), while SRFW is much simpler and faster than them.
A contextual method for electroencephalogram recognition

The contextual method is used to classify electroencephalogram data. The experimental results show that this method has an advantage in recognition rate over conventional statistical methods and the fuzzy method. By experience, five important testing markers are extracted from an electroencephalogram, and each marker is divided into several quantization levels. The five markers are tested at four positions on the patient's head. Each marker is represented by its upper and lower bounds. Thus 76 test results can be obtained, then converted into a 136-dimensional eigenvector according to discretization in quantization levels.<<ETX>>
Algorithm of Scattered Data Reduction for Surface Reconstruction using Radial Basis Function

This paper concerns a method to reduce centers progressively for radial basis function surface reconstruction from scattered point set to decrease the computational complexity, which belongs to the category of data filtering. In the procedure of the algorithm, we use kd tree to reduce the computational complexity and make it practical for large number of points, then use the radial basis function to approximate, the good performance of the proposed filtering scheme is finally shown by some experimental examples
Dynamically self-adaptive load current maximization technique for maximum power point tracking in photovoltaic arrays

A new technique is presented for tracking the maximum power point (MPP) of PV arrays, whose hardware is highly simple with all analog circuits, less sensors and no analog multipliers. A dynamically self-adaptive modulation for Iref is proposed to eliminate inflexibility over characteristics variations of PV arrays, where only converter's output current is sensed for control purpose. The boost converter is regulated in peak-current-programmed DCM conditions by three-loop PWM control, reducing EMI and switching loss. The system is designed with the VIS 0.50µm 5V mixed mode 2P3M process. Results of simulation verify good tracking precision and fast response in both steady and dynamic states, and conversion efficiency is about 86% averagely, and 92% as peak value.
Single-inductor dual-output converter with dynamic power distribution

A single-inductor dual-output switching converter was presented in this paper. This converter can be applied to solar-cell portable devices as battery power management system. Considering the limited power supplied by solar cells, two output channels, battery charging and power regulating, were designed for this converter. Controlled by it, the output electric energy could be dynamically distributed between two channels. The state-machine is used to decide the amount of energy delivered to each channel. The converter chip is designed in a 1.5µm BCD (Bipolar-CMOS-DMOS) technology. Simulation results showed that all main targets were well achieved1.
Potential Absorptive Capacity, Network Embeddedness and Knowledge Acquisition in Global Manufacturing Network

Global manufacturing network (GMN) is a new manufacturing system, which provides great opportunities for local firms in developing countries to acquire knowledge and upgrade through collaborations in GMN. This paper hypothesizes that local firm's potential absorptive capacity has a positive impact on its knowledge acquisition in GMN, and the association between them is moderated by network embeddedness. Using data of manufacturing firms, this paper confirm the hypothesis that local firm's potential absorptive capacity can contribute to its knowledge acquisition in GMN, and the hypothesis of the moderating effect of network embeddedness is partially supported by results for the reason that only information sharing and joint problem solving (components of network embeddedness) have moderating effect on the association between potential absorptive capacity and local firm's knowledge acquisition
An improved area-efficient design method for interpolation filter of Sigma-Delta audio DAC

This paper presents an improved area-efficient design method to construct interpolation filter of Sigma-Delta audio digital-to-analog converter (DAC). Several architectures and implementation techniques are adopted to reduce the complexity of the system. The Matlab simulation, FPGA verification and digital synthesis are performed to support the feasibility of the presented method. The cell area of the interpolation filter built by this method is 0.33mm2 in TSMC 0.18µm COMS process.
Evaluation of Dense 3D Reconstruction from 2D Face Images in the Wild

This paper investigates the evaluation of dense 3D face reconstruction from a single 2D image in the wild. To this end, we organise a competition that provides a new benchmark dataset that contains 2000 2D facial images of 135 subjects as well as their 3D ground truth face scans. In contrast to previous competitions or challenges, the aim of this new benchmark dataset is to evaluate the accuracy of a 3D dense face reconstruction algorithm using real, accurate and high-resolution 3D ground truth face scans. In addition to the dataset, we provide a standard protocol as well as a Python script for the evaluation. Last, we report the results obtained by three state-of-the-art 3D face reconstruction systems on the new benchmark dataset. The competition is organised along with the 2018 13th IEEE Conference on Automatic Face & Gesture Recognition.
Fish-bone resonator with new coupling structure and its applications to wideband bandpass filters

In this paper, a novel fish-bone resonator is presented by cascading multiple cross-shaped resonators. Characterized by transmission line model analysis, it is found that the resonances of the proposed fish-bone resonator can be conveniently controlled, which results in a compact size. On the other hand, in order to constructed wideband bandpass filter, a novel coupling structure is studied in connection with the special configuration of the fish-bone resonator. Besides, transmission zeros are generated by the proposed fish-bone resonator, thus an ultra-wide stopband is achieved. To illustrate the concept, a wideband bandpass filter with high performance is constructed and measured by two-stage fish-bone resonators. The measured results are in good agreement with the full-wave simulation results. Both simulated and measured results are in good agreement to verify the predicted results.
Multi-model predictive control of post-combustion CO2 capture plants

Mono-ethanol-amine (MEA) based post combustion CO2 capture is considered as one of the most promising technology to reduce the CO2 emissions from fossil-fired power plants. With increasing intermittent renewable energy into the electricity grid, the fossil-fired power plants need to operate in a more flexible way. However the CO2 capture plant is a nonlinear multivariable system with large time delay, advance control technics must be applied to the capture process to obtain a satisfactory control performance. To achieve this goal, the gCCS software based on gPROMS simulation platform is firstly used to model the CO2 capture process with MEA solvent. Then this paper uses subspace identification methods to identify the local linear models at different operating points using the input and output data collected from the simulation platform. With the identified model, the multi-model MPC control system is developed to control the capture rate and reboiler temperature. Compared with conventional MPC controller, the closed-loop simulation results show that multi-model MPC controller can track the set point of capture rate quickly and smoothly without any oscillation while keeping the reboiler temperature to its desired operating range, which demonstrates that the multimodel MPC has high control accuracy and better robustness. Thus the multi-model MPC can realize the flexible operation of large-scale CO2 capture rate change.
Optimal Market Entry Modes for Multinationals: Export Trade, Greenfield Investment, M&A or Joint Venture

The purpose of this paper is to formalize the choice of market entry modes for an individual multinational enterprise among export trade, Greenfield investment, M&A and a joint venture. We assume that for M&A and a joint venture to yield synergies the participating firms had to make additional investments. We have found that there exists a strong interdependence among the different modes of FDI. Specially, even if Greenfield investment is a viable option, the multinational may prefer a joint venture to M&A, and M&A to Greenfield investment, provided that M&A and joint venture both involve sufficiently low fixed costs. The reason is that the profitability of Greenfield investment both reduces the acquisition price in the case of M&A, and gives local firms an incentive to agree to a joint venture.
An Improved SLM Scheme with Superimposed Training Sequence for PAPR Reduction in OFDM System

Orthogonal Frequency Division Multiplexing (OFDM) technology has high spectrum efficiency and good anti-multipath interference capacity, but it has an inherent shortcoming that high peak-average power ratio (PAPR). Select Mapping (SLM) method is an effective algorithm to reduce Peak to Average Power Ratio (PAPR) of OFDM (Orthogonal Frequency Division Multiplexing) signal, and does not bring about any distortion of the signal. However, the biggest disadvantage of SLM method is that computational complexity is very large. In this paper, we propose a kind of improved SLM scheme which combines superimposed training sequence method, which using the a pre-set threshold method to select sub-optimization phase sequence as the phase factor in the procession of SLM method on signals. The simulation results show that the proposed scheme can not only effectively reduce the PAPR of system, not only reduce the computational complexity of the system, at the same time, there is no significant effect on system performance.
Fault Diagnosis of Ship Main Power System Based on Multi-Layer Fuzzy Neural Network

Artificial neural network (ANN) has been successfully applied to fault diagnosis systems in real-world applications. But only single network is used for diagnosis, which is not good at handling expert knowledge. Multiple faults in complex systems occur commonly in practice. When the single network is used to deal with complicated problems of fault diagnosis, it'll be so gigantic that a series of difficulties will be brought to network training. Based on the analysis of hierarchical classified diagnostic model, a multi-layer fuzzy neural network (MFNN) is presented for fault diagnosis of ship main power system. The diagnostic result indicates that the model is feasible and valid. With good generalization performance, the network has significantly improved the diagnostic precision
A High Precision Channel Estimation Method for OFDM System

The traditional channel estimation for orthogonal frequency division multiplexing (OFDM) systems over fast-varying fading channels is usually carried out in two steps. Firstly obtain the least-square (LS) estimate over the pilot sub-carriers, and then interpolate it over the entire frequency-domain. In this paper, we propose a high precision channel estimation method by adding an intermediate step, which is based on the strong correlation in each path of the channel during the coherent time and can distinguish valid path taps and noise taps effectively, to improve the accuracy of the preliminary estimate over the pilot sub-carriers. The simulation results in the frequency band of 2.4 GHz show that the proposed method can obtain refined channel functions more efficiently and achieve a good bit error rate (BER) performance close to the theoretical bound of ideal channel estimation.