nils-herrmann/clean_scirepeval huggingface dataset:
Study on Extraction Technology of Hemicellulose from Pinus massoniana Waste Wood for Bioenergy

Much waste wood causing pollution and energy waste was produced in the processing of Pinus massoniana wood. However, hemicellulose extracted from Pinus massoniana waste can be transformed into bioenergy by means of pyrolysis, gasification, liquefaction, etc. Therefore, effects of deresination technology on extracting hemicellulose from Pinus massioniana were investigated by orthogonal and the whole factors test. The results show that (1) effects of deresinating temperature and time on quantity of dissolved resin is highly significant, but effects of deresinating pressure and volume fraction of degreasing agents on dissolved oleoresin volume are lightly significant. (2) Effects of deresinating temperature, time, pressure, volume fraction on removal volume of hemicellulose from Pinus massioniana are lightly significant, removal volume of hemicellulose from deresinated wood is larger than that from untreated wood, but both increase in response to the increase of quantity of dissolved resin.
Study on Inhibition Effects of Bioactive Substance in Leaves on Mould by Energy Saving Technology

Plantations have been planted in large area in South China now. A mass of leaves was naturally abandoned and degradable so that the resources were wasted. Therefore, in order to make full use of the leaves resources, the effect of bioactive substance in leaves on bamboo biomass moulding was studied based on full-factor test. The result is: (1) Tree species of leavies extractives had highly obvious influence on bamboo biomass moulding. Tree species of leavies was key to preparation of mould inhibitor. And treatment time had no influence on bamboo biomass moulding. And treatment time was adjusted based on the anti-mold effect of the leavies extractives on bamboo biomass moulding. (2) The optimal anti-mold effects of bamboo biomass was the leavies extractives of Cinnamomum camphora and Cornus controversa. The next was Cryptomeria japonic, Cupressus funebr, Masson pine, Pterocarya tonkinesis, and Idesia polycarpa. The worse were Metasequoia glyptostroboides. Bioactive Substance, Leaves Extractives, Mould, Bamboo Moulding, Natural Resources
Uncertain Graph Method Based on Triadic Closure Improving Privacy Preserving in Social Network

With the development and popularity of the Internet, various kinds of network services have gone deep into people's lives. Although the Internet brings convenience to people, it also brings us enormous information security problems. There may be a risk of privacy disclosure of individual privacy when the social network data is published. To mitigate these problems, we aim to improve privacy preserving in social networks by means of new methods. The purpose of this paper is to propose an efficient uncertain graph method based on the triadic closure, which includes adding edges, determining the triangle and injecting probability. In order to improve privacy preserving and data utility efficiently, we add nearby potential edges and keep the expected values unchanged. The experimental results show that the method not only improves the performance of privacy preserving in the social network, but also reduces the loss of data utility for the original graph.
Hardware design of image information processor based on ADSP-TS201 DSPs

This paper studies on the hardware design and implementation of a universal multi-DSP and FPGA image information processor in accordance with the PCI-E and CPCI specifications. The image information processor features two clusters of total four ADSP-TS201 TigerSHARC DSPs from ADI as the kernel processing unit, reconfigurable framework implemented by two Xilinx Virtex-5 FXT FPGA chips. Taking full advantage of the high performance, unprecedented I/O bandwidth of TigerSHARC DSP and Virtex-5 FXT FPGA, the processor can easily meet the huge image processing and computing work. The hardware design of high speed Linkport interconnection between TS201 DSPs and FPGA chips is described.
Perfluorinated polymer optical fiber for precision strain sensing based on novel SMS fiber structure

This paper presents an experimental investigation of a novel strain sensor based on a single-mode-multimode-single-mode (SMS) fiber structure using perfluorinated graded index polymer optical fiber as the multimode fiber section. The experimental results showed that the strain sensitivity is as high as 101 pm/με, which is 16 times higher than those obtained in silica multimode fiber based on SMS fiber structure.
Determination of Biomedicine Components of Dinochloa Puberula McClure by Py-GC/MS

Bamboo has been used to heal some diseases in the Chinese Folk for a long time. In order to explore widely the potential as the biomedicine utilization, the determination of the chemical components from the five-year-old Dinochloa puberula McClure in South China was carried out by means of Py- GC/MS. The results indicated that the main chemical components in Dinochloa puberula were carbon dioxide(46.88%), acetic acid(12.59%), 1-propanol(12.09%), 2-butanamine, (S)-( 12.07%), 2-propanone, 1-hydroxy-(7.92%), benzofuran, 2,3- dihydro-(1.78%), l-hepten-4-ol(1.16%), cyclohexanone, 3- hydroxy-(0.82%), 1,6-anhydro-.beta.-D-glucopyranose(0.71%), butanoic acid, 3-cyano-3-hydroxy-(0.58%), 2,3,5,6- tetrafluoroanisole(0.45%), oxirane, (butoxymethyl)- (0.41%), 3(2H)-benzofuranone, 4,6-dimethoxy-(0.25%), 1-pentanol, 2- methyl-(0.16%), butanedial(0.11%) and so on. Therefore, based on the above-mentioned analysis, we can draw a conclusion that the pyrolysis byproducts on Dinochloa puberula may be used as raw materials for bioenergy and rare biomedicines.
Nonnegative Shifted Tensor Factorization in time frequency domain

In this paper, we proposed a Nonnegative Shifted Tensor Factorization (NSTF) model considering multiple component delays by time frequency analysis. Explicit mathematical representation for the delays is presented to recover the patterns from the original data. In order to explore multilinear shifted component in different modes, we use fast fourier transform (FFT) to transform the non-integer delays into frequency domain by gradients search. The ALS algorithm for NSTF is developed by alternating least square procedure to estimate the nonnegative factor matrices in each mode and enforce the sparsity of model. Simulation results indicate that ALS-NSTF algorithm can extract the shift-invariance sparse features and improve the recognition performance of robust speaker identification and structural magnetic resonance imaging (sMRI) diagnosis for Alzheimer's Disease.
Analysis and simulation of high power multi-phase BLDC motor

A trapezoidal waveform is usually used instead of the actual back EMF in numerous research literatures on simulation of brushless DC (BLDC) motors, which causes the errors to the simulation and calculation of the motor. This paper adopts the flux linkage changing rate matrixes of the permanent magnets to calculate the back EMF, establishes the mathematical model of a 30 kW six-phase BLDC motor based on it, simulates the dynamic performance of the motor, and verifies the validity by experimental results.
SVM based ASM for facial landmarks location

Finding a new position for each landmark is a crucial step in active shape model (ASM). Mahalanobis distance minimization is used for this finding, provided there are enough training data such that the grey-level profiles for each landmark follow a multivariate Gaussian distribution. However, this condition could not be satisfied in most cases. In this paper, a new method support vector machine (SVM) based ASM (SVMBASM) is proposed. It approaches the finding task as a small sample size classification problem, and uses SVM classifier to deal with this problem. Moreover, considering imbalanced dataset which contains more negative instances(incorrect candidates for new position) than positive instances (correct candidates for new position), a multi-class classification framework is adopted. Performance evaluation on SJTU face database show that the proposed SVMBASM outperforms the original ASM in terms of the average error as well as the average frequency of convergence.
A comparative study of face recognition algorithms on R1 face database

This paper introduces the development process of face recognition and analyses representative algorithms for each period. Considering different races and numbers of samples in database, and a wide variety of pose, shelter, illumination, and expressions, different algorithms are tested based on the application requirement. It adopts CAS-PEAL-R1 face database, composed entirely by Asian faces, while the previous face recognition test are almost all based on Europe and America face database. The main work is to get two indices (recognition rate and recognition time) when applying different algorithms on R1 face database and then analysis the advantages as well as disadvantages of each algorithm. According to the comparison of the indices for each algorithm, it showed that LBP algorithm achieves state-of-the-art performance in both recognition rate and time, so it meets the requirements for real-time recognition. In addition, although the SFD (Improved SIFT Algorithm) obtained the highest recognition rate in the comparison, it doesn't satisfy the requirements in real-time recognition system for its long recognition time. Contrast previous face recognition algorithms utilized on R1 face database, some more comprehensive algorithms are introduced and tested on R1 in this paper and it sure can gives a more comprehensive reference for later researchers.
Whole Brain fMRI Pattern Analysis Based on Tensor Neural Network

Functional magnetic resonance imaging (fMRI) has increasingly come to dominate brain mapping research, as it provides a dynamic view of brain matter. Feature selection or extraction methods play an important role in the successful application of machine learning techniques to classifying fMRI data by appropriately reducing the dimensionality of the data. While whole-brain fMRI data contains large numbers of voxels, the curse of dimensionality problem may limit the feature selection/extraction and classification performance of traditional methods. In this paper, we propose a novel framework based on a tensor neural network (TensorNet) to extract the essential and discriminative features from the whole-brain fMRI data. The tensor train model was employed to construct a simple and shallow neural network and compress a large number of network weight parameters. The proposed framework can avoid the curse of dimensionality problem, and allow us to extract effective patterns from the whole-brain fMRI data. Furthermore, it reveals a new perspective for analyzing complex fMRI data with a large numbers of voxels, through compressing the number of parameters in a neural network. Experimental results confirmed that our proposed classification framework based on TensorNet outperforms traditional methods based on an SVM classifier for multi-class fMRI data.
Fine-Grained and Semantic-Guided Visual Attention for Image Captioning

Soft-attention is regarded as one of the representative methods for image captioning. Based on the end-to-end CNN-LSTM framework, it tries to link the relevant visual information on the image with the semantic representation in the text (i.e. captioning) for the first time. In recent years, there are several state-of-the-art methods published, which are motivated by this approach and include more elegant fine-tune operation. However, due to the constraints of CNN architecture, the given image is only segmented to fixed-resolution grid at a coarse level. The overall visual feature created for each grid cell indiscriminately fuses all inside objects and/or their portions. There is no semantic link among grid cells, although an object may be segmented into different grid cells. In addition, the large-area stuff (e.g. sky and beach) cannot be represented in the current methods. To tackle the problems above, this paper proposes a new model based on the FCN-LSTM framework which can segment the input image into a fine-grained grid. Moreover, the visual feature representing each grid cell is contributed only by the principal object or its portion in the corresponding cell. By adopting the pixel-wise labels (i.e. semantic segmentation), the visual representations of different grid cells are correlated to each other. In this way, a mechanism of fine-grained and semantic-guided visual attention is created, which can better link the relevant visual information with each semantic meaning inside the text through LSTM. Without using the elegant fine-tune, the comprehensive experiments show promising performance consistently across different evaluation metrics.
Adaptive tracking control of mobile manipulator with holonomic and affine constraints

The adaptive trajectory and force tracking control is considered for mobile manipulator with holonomic constraints and affine constraints in this paper. A new state transformation is proposed to deal with the affine constraints, and then based on a suitable reduced dynamic model, adaptive controller is presented to ensure that the states of closed-loop system asymptotically track to desired trajectories while the constraint force remains bounded by tuning design parameters. Detailed simulation results confirm the effectiveness of the control strategy.
Low complexity texture mode decision method for 3D-HEVC

High efficiency video coding (HEVC) based 3D video coding (3D-HEVC) is the latest 3D video coding standard under development. In 3D-HEVC, most of coding units (CU) select merge modes as their best modes. However, they still have to check a large number of irrelevant candidate modes in mode decision process, which brings significant complexity. In this paper, we propose a low complexity texture mode decision method for dependent texture views. First, Coding modes of inter-view reference group and upper-depth CU are used to obtain a coarse combination of merge mode coded CUs in the current frame. Then, skip mode coded CUs are distinguished from merge mode coded CUs in the combination. CUs in the combination only check limited modes in mode decision process according to their determined modes. Experimental results demonstrate that the proposed method can achieve average 11.2% of encoding time saving and 69.4% of complexity reduction with negligible rate distortion performance loss.
Simultaneous measurement of both magnetic field strength and temperature with a microfiber coupler based fiber laser sensor

In this paper we propose and investigate a novel magnetic field sensor based of a ring erbium-doped fiber laser combined with a fiber Bragg grating and a Sagnac loop containing a microfiber coupler and magnetic fluid. In addition to the magnetic field sensing capability, the proposed structure can simultaneously provide temperature information. Thanks to the dual-ring structure of the MFC-Sagnac loop and the FBG-assisted resonant cavity, the output has two distinct laser peaks. Experimentally demonstrated magnetic field sensitivity of one of the laser peaks is 15 pm/mT in the magnetic field range from 0 to 100 mT. The spectral position of the second laser peak is independent on the magnetic field but shifts towards long wavelengths with a sensitivity of 13 pm/°C.
Real-time analog global optimization with constraints: application to the direction of arrival estimation problem

An analog technique for real-time, multivariate, global optimization with constraints is presented. The basic structure is a simple gradient descent loop, where the gradients are computed using an analog neural network. Constraints are implemented using a variation of an idea, where neural networks are also used to implement the required constraint functions. It is shown that the system converges to a stable equilibrium point, which satisfies the Kuhn-Tucker conditions for a constrained minimum. Global optimization is achieved by introducing a diffusion process into the governing differential equation. This procedure is a continuous-time analog of the simulated annealing algorithm. Even though the proposed method is applicable to a wide range of engineering problems, the real-time, global and other capabilities of this method are demonstrated specifically with an optimization problem from array signal processing-the maximum likelihood direction of arrival estimator. The satisfactory performance of all aspects of this proposed optimization technique is demonstrated by simulations.
A semi-blind multiuser projection receiver based on conjugate gradient and subspace tracking

A semi-blind multiuser projection receiver is proposed. Using interference cancellation of the known users' signal and subspace tracking for the unknown users' signal subspace, the orthogonal projection matrix of the unknown users' signal subspace is obtained. Then, using this matrix, we transform the detection problem of known users' data into a minimization problem of a quadratic convex function in K variables and solve it using a conjugate gradient (CG) algorithm. The initial estimation of the known users' data also serves as the initial value of the CG to speed convergence.
Finite time sliding mode control for time delay systems

In this paper, a terminal sliding mode control is proposed for the finite time convergence of general input-delay systems. The proposed sliding surface is given recursively by the state variables of a predictor which compensates for the input delay of the systems. The global stability of the closed-loop system is guaranteed and finite time convergence of all the signals in the system, including the control law, is proved. Although the proposed control strategy is sliding mode based, the control signal is continuous except at a single discontinuous point. The chattering phenomenon commonly associated with sliding mode control does not occur.
Coordinated, distributed, formal energy management of chip multiprocessors

Designers are moving toward chip-multiprocessors (CMPs) to leverage application parallelism for higher performance while keeping design complexity under control. However, to date, no power management techniques have been proposed for coordinated power control of multiple processor cores. In this paper, we illustrate how the use of local, per-tile dynamic voltage and frequency scaling (DVFS) techniques can result in tiles counteracting each others' power management policies, significantly hurting chip power-performance. We then propose a coordinated DVFS scheme for CMPs, which eliminates the oscillations and ensures efficient and resilient DVFS control. Specifically, our proposed technique incorporates thread information collected at runtime across the chip. In addition, by extending a control-theoretic local DVFS control technique toward DVFS for chip-multiprocessors, our technique prescribes DVFS settings formally at each tile, thus ensuring stable, distributed, coordinated DVFS control of a CMP. Experimental results show that our technique achieves a 15.5% improvement in energy-delay product over a CMP with no DVFS control, and a 1% improvement in energy-delay product against the latest state-of-the-art local DVFS scheme.
Fuzzy controller and observer design for nonlinear system-LMI method

A fuzzy controller and observer design for a nonlinear system are studied based on LMI. The global asymptotically stable fuzzy controller and observer is gained, using the T-S fuzzy dynamic model approaching a nonlinear system and using a parallel distributed compensation method. The parameters of the fuzzy controller and fuzzy observer are obtained by the convex optimal method of LMI. The simulation of an inverted pendulum tested the efficiency of the design method.
Effects of finger length on finger joint process of glued laminated timber

In this paper, the finger joint of larch were taken four kind of finger length, used Larix Gmelini as materials and resorcinol resin as adhesive. This paper made a systematic study on the influence law of end pressure and finger joint strength by finger length. The research results showed that the optimal the end pressure of finger joint decreased with the increase of finger length and the finger joint strength of glued laminated timber rise with the increase of finger length. The better finger length of finger joint was 13mm.
An Adaptive H-ARQ Strategy Based on the Types of Business in Aerospace Information Network

This paper deals with the application of H-ARQ techniques to reliable end-to-end data communications with adaptive adjustment of parameters according to the type of business in aerospace information network, whose applications have to cope with accurate Quality-of-Service (QoS) requirements. In the adaptive H-ARQ strategy, we adopt cell-level RS code as Forward Error Correction (FEC) coding scheme. Retransmissions of the corrupted data block are combined at the receiver with the previously received sequences of the same data block for decoding. Particularly, adaptive adjustment of FEC coding parameters and ARQ retransmission times by the type of business joint the state of network. As a result, it has been possible to allow each business a better QoS, in terms of error rate, throughput, and delivery delay.
Explicit Edge Inconsistency Evaluation Model for Color-Guided Depth Map Enhancement

Color-guided depth enhancement is used to refine depth maps according to the assumption that the depth edges and the color edges at the corresponding locations are consistent. In methods on such low-level vision tasks, the Markov random field (MRF), including its variants, is one of the major approaches that have dominated this area for several years. However, the assumption above is not always true. To tackle the problem, the state-of-the-art solutions are to adjust the weighting coefficient inside the smoothness term of the MRF model. These methods lack an explicit evaluation model to quantitatively measure the inconsistency between the depth edge map and the color edge map, so they cannot adaptively control the efforts of the guidance from the color image for depth enhancement, leading to various defects such as texture-copy artifacts and blurring depth edges. In this paper, we propose a quantitative measurement on such inconsistency and explicitly embed it into the smoothness term. The proposed method demonstrates promising experimental results compared with the benchmark and state-of-the-art methods on the Middlebury ToF-Mark, and NYU data sets.