nils-herrmann/clean_scirepeval huggingface dataset:
Empirical study of robust combination of forecasts for short-term highway traffic flow forecast

In order to improve forecast accuracy and reliability of expressway traffic flows, the variance reciprocal weighting methods in linear combination of forecasts are compared numerically with the simple average. Ten individual methods for combination include the autoregression, exponential smoothing models, moving average models, and cybernetics method. Six variance estimators for the variance reciprocal weighting methods are the standard deviation, mean absolute deviation, median absolute deviation from median, fourth-spread, biweight estimator and Andrews wave M-estimator of scale. The empirical results show that the variance reciprocal weighting methods are usually better than the simple average, and they can be further improved by robust scale estimators.
Combining point and edge for satellite pose tracking under illumination varying

Pose tracking is a fundamental technology needs to be solved in on-orbit service. Visual pose tracking provides an inexpensive solution while it remains a large obstacle to track satellites under space illumination changing condition. This paper combines point feature with edge feature to track a model-known satellite. It makes use of the distinctiveness of point feature and robustness of edge feature, and treats them differently through a weight-self-tune procedure according to their residuals respectively. Simulation results show that the proposed combination approach improves the tracking accuracy and holds the robustness simultaneously.
Optimal Allocation of the Active Filters Based on the Tabu Algorithm in Distribution Network

It is an effective measure to reduce voltage distortion and guarantee power quality by allocating active filters in distribute network. The location and parameter of active filters are coded firstly, then the active filter's capacity is calculated through solution variable based on calculation of the harmonic power flow. This ensures the filter's safty operation constraint. The current optimal variables are founded in adjacent area with the evaluation function that the investment of active filters is smallest. The current optimal solution is judged by the constraints of voltage distortion standards to obtain wheather it satisfies the commands. The current optimal solution is updated through Tabu movement. Example shows that the location and parameter of active filters allocated by the Tabu algorithm can restrict harmonic voltage distortion in each node of distribution network effectively.
PCA and Kernel-based extreme learning machine for side-scan sonar image classification

As an important role of oceanographic survey, side-scan sonar image classification has attracted much attention in the past two decades. Due to the special properties of sonar image, traditional approaches are difficult to get good classification accuracy, so their implementation in real world is blocked. In this paper, a novel classification system based on kernel-based extreme learning machine (KELM) and principle component analysis (PCA) is proposed. Experimental results demonstrate that the proposed method can get better stability and higher classification accuracy than traditional approaches such as support vector machine (SVM).
Structural damage identification based on information fusion technology

In order to improve the accuracy of structural damage identification, information fusion technology is introduced into structural damage identification. Firstly, the stiffness matrix damage factor iterative method and modal strain energy method are used to identify the structural damage, and give the decision vectors respectively. Then, the decision vectors are imported into the D-S evidence theory formula or the Bayes theory formula, the damage probability of every element would be obtained and the damage location would be determined finally. Numerical simulation of plane truss and model experiments of offshore platform show that the results of structural damage identification are more accurate and reliable after the information fusion techniques is used.
Video Annotation Based on Kernel Linear Neighborhood Propagation

The insufficiency of labeled training data for representing the distribution of the entire dataset is a major obstacle in automatic semantic annotation of large-scale video database. Semi-supervised learning algorithms, which attempt to learn from both labeled and unlabeled data, are promising to solve this problem. In this paper, a novel graph-based semi-supervised learning method named kernel linear neighborhood propagation (KLNP) is proposed and applied to video annotation. This approach combines the consistency assumption, which is the basic assumption in semi-supervised learning, and the local linear embedding (LLE) method in a nonlinear kernel-mapped space. KLNP improves a recently proposed method linear neighborhood propagation (LNP) by tackling the limitation of its local linear assumption on the distribution of semantics. Experiments conducted on the TRECVID data set demonstrate that this approach outperforms other popular graph-based semi-supervised learning methods for video semantic annotation.
Fatigue Life Predicting of Lead-Free Soldered Joints of QFP Device

Anand model was used to establish the constitutive equation of SnAgCu solder, the stress-strain response of soldered joints was analyzed by finite element code. Results indicated that the stress concentrates on the sharp corner of interior part of outermost soldered joint, where is the weakest position at which cracks initiation take place easily and lead to failure owing to thermal fatigue finally. The Modified Coffin-Mason equation by Engel Maier was uti lized to predict the fatigue life of soldered joint, the value of strain and stress in the soldered joint of Cu/Ni/Au finishes was least, the value was middle in the joint of Cu/Ni and the value was largest in the joint of Cu, which will provide a theory guide for practical applications.
Research on public's perception of risk propagation process of nuclear power plant

Presently, China has proved a remarkable progress in nuclear power, under which circumstance this paper is written to make a study about the consequences and influences of two well-known nuclear accidents in history by way of questionnaire. Having analyzed the public's cognition about the risks of nuclear industry, the author concludes that as the crisis spread, the information content will reduce or increase. Besides, a correction coefficient is also put forward to revise the risk formula frequently used in engineering. Finally, the crisis management is proposed for controlling coefficient in field of nuclear power.
Sensor fault diagnosis of autonomous underwater vehicle based on extreme learning machine

Autonomous underwater vehicles (AUVs) work in complex marine environments, and sensors play an important role in AUV systems. Therefore, research on sensor failure diagnosis technology is important for improving the reliability of AUV systems. In this paper, a new method combining phase space reconstruction and extreme learning machine (ELM) is proposed. This method is applied to predict sensor output to achieve sensor fault diagnosis for AUVs. The results of the simulation experiments based on sea trial data shown that the proposed method can diagnose sensor faults and recover the signal after faults occur in a period of time.
A synchronous and multi-domain feature extraction method of EEG and sEMG in power-assist rehabilitation robot

To propose a synchronous and multi-domain feature extraction method of electroencephalogram (EEG) and surface electromyogram (sEMG) signals is of great significance to power-assist rehabilitation robot control with humancomputer interface (HCI). In this paper, nonnegative Tucker decomposition which is one model of nonnegative tensor factorization (NTF) is used to fuse two kinds of bioelectricity signals (EEG and sEMG) and extract multi-domain features of EEG and sEMG signals for classification which contain time, frequency, and space domains. In the first step the EEG and sEMG data are transformed into multidimensional information using continuous wavelet transform and the 4-D EEG-sEMG tensor is established. Then the tensor is decomposed into four components (spatial components, spectral components, temporal components and category components) and the core tensor is the feature extracted. The feature after being eliminated and compressed are fed into KNN, LDA and SVM classifiers for pattern recognition, and a comparison is done in single EEG analysis, single sEMG analysis and both EEG and sEMG analysis. An experiment about 10 healthy participants' upper limb movements was carried out to verify the validity of this algorithm. The result implied that NTF is a meaningful and valuable synchronous and multi-domain feature extraction method which may be promising in power-assist rehabilitation robot control.
Formation and obstacle avoidance in the unknown environment of multi-robot system

This paper presents formation and obstacle avoidance in the unknown environment of multi-robot system. With the leader-following algorithm, multi-robot system can form and keep various formations and realize formation transformation. Through artificial potential field algorithm, multi-robot system can successfully avoid obstacle in the unknown environment while keeping formation. The proposed method not only can realize obstacle avoidance of multi-robot system with formation in the unknown environment, but also can be adopted in real time and applicable to many different situations. By means of Pioneer3 mobile robot platform, the simulation and experimental works, which considering various formations and unknown environment, demonstrate the effectiveness of the proposed method.
Using Information Content to Evaluate Semantic Similarity on HowNet

Evaluating Semantic similarity has a widely application areas range from Psychology, Linguistics, Cognitive Science to Artificial Intelligence. This paper proposes the merely use of HowNet to evaluate Information Content (IC) as the semantic similarity of two terms or word senses. While the conventional ways of measuring the IC of word senses must depend on both an ontology like WordNet and a large corpus, experiments of this paper prove that the semantic similarity measured in this method is easy to calculate and more approach to human judgments.
A hybrid energy storage system based on DSP for the ship

In this paper, we built an experimental platform for the hybrid energy storage system for the ship based on DSP28335. The design of bidirectional DC/DC controller is completed. The PI control algorithm to improve the stability of the control system, and two kinds of operating modes of the half bridge bi-directional DC/DC controller for boost and buck, respectively from the angle of voltage and current control for experimental testing. The two working modes are tested respectively from the voltage and current control. Through the research of this system, we can improve the utilization rate and service life of the battery energy, and provide the research foundation for the research of the green energy storage system.
An Analytic Model for Nanowire MOSFETs With Ge/Si Core/Shell Structure

An analytic model for the nanowire MOSFETs (NWFETs) with Ge/Si core/shell structure is developed in this paper. The analytical expressions of electrostatic potential and charges of this device are derived from classical device physics under the gradual channel approximation. Then, a drift-diffusion (DD) mechanism-based drain current model is obtained and verified by comparisons with the numerical simulation results. By modifying the intrinsic carrier concentration under 2-D confinement, quantum-mechanical effect is also taken into account approximately, and then, a ballistic current model is developed to study the impact of quantum-mechanical effect on the device characteristics. The performances of Ge/Si core/shell NWFETs are analyzed, and significant characteristics are demonstrated, in detail, by the proposed model. The presented analytic model may provide a base for device scientists and circuit engineers to understand the device physics and further develop a compact model of the NWFETs with Ge/Si core/shell heterostructure for circuit design and simulation.
Segmentation of Sidescan Sonar Imagery Using Markov Random Fields and Extreme Learning Machine

As a widely used segmentation scheme, Markov random field (MRF) utilizes $k$-means clustering to calculate the initial model for sidescan sonar image segmentation. However, for the noise and intensity inhomogeneity nature of the sidescan sonar images, the segmentation results of $k$-means clustering have low accuracy, motivating us to use machine learning methods to initialize MRF. Meanwhile, an extreme learning machine (ELM), a supervised learning algorithm derived from the single-hidden-layer feedforward neural networks, learns faster than randomly generated hidden-layer parameters and is superior to a support vector machine (SVM). Therefore, in this paper, we proposed a novel method for sidescan sonar image segmentation based on MRF and ELM. The proposed method segments sidescan sonar images in object-highlight, object-shadow, and sea-bottom reverberation areas. Specifically, we intend to use an ELM to get an initial model for MRF. Moreover, to improve the stability of an ELM, a simple ensemble ELM (SE-ELM) based on an ensemble algorithm is utilized to obtain the prediction model. In an SE-ELM, we use an ensemble of ELMs and majority votes to determine the prediction of testing data sets. Then, the classification results of the SE-ELM are utilized to initialize MRF, termed as SE-ELM-MRF. With features consisting of pixels of small image patches, our experiments on real sonar data indicate that the SE-ELM performs better than other machine learning methods such as ELM, kernel-based extreme learning machine, SVM, and convolutional neural networks. Moreover, using SE-ELM as the initial method in the proposed SE-ELM-MRF, the segmentation results are smoother and the segmentation process converges faster than the traditional MRF.
Controlling the Business Growth Speed from Financial Angle of View: Using Cash Flow System Dyanmics Model

Every business wants to grow fast, but how fast can a business afford to grow? Organizational growth needs many factors' support, such as industry development, strategy and top management. Among all the determinants, abounded cash is indispensable. Maintaining a company's normal operation needs amount of cash, growing a company will need more. Cash is a key factor, which determines the business growth rate. This paper describes a System Dynamics model for capturing cash flow and business growth dynamics, and provides a method to control the speed of business growth. We find that system dynamics model is useful to integrate the cash flow with many factors affecting cash flow to produce a financial decision support system.
Consistent target tracking via multiple underwater cameras

Multi-targets tracking has shown great prospects in ocean investigations. In this paper, we apply the adaptive background mixture models to extracts the moving target from the image sequences, and propose a new method which is made use of the EKF and SURF-RANSAC to realize multi-targets tracking via multiple underwater cameras. In this method the centroid coordinate homographic mapping and the Speeded Up Robust Features (SURF) technique have been taken to capture and match the same target from the multiple cameras. Simulation experiments have shown that the proposed method could perform robustly and stably to perform the multi-targets tracking.
Special software's development of spiral chute for design and expression based on the redevelopment of solidworks

The spiral chute installed coal storehouse is an advanced equipment that can keep lump coal from being stricken into pieces. The aim can be achieved that to increases effectively the lump coal rate by to limit the coal flow to move spirally through the chute instead of free-fall motion. The purposes of the entire design are the speed of impact point is as low as possible while it runs smoothly in the whole process and ensuring that no half-way down coal until the coal supply ends. In addition no coal accumulates in the chute if there is no more coal to transport. Because the bottom of the whole chute is an undevelopable surface, and all of the parameters including width, spiral angle and tilt angle are continuously variable for the purpose of controlling speed and direction in one segment. It is so complicated that it is almost impossible to get all of the reasonable parameters in every point, or to get its view or to draw the developing drawing by traditional drawing method. For this reason, we developed a special software commissioned by HanDan Design Engineering China Do., LTD. The software can give these important parameters of parts, such as the width, spiral angle, tilt angle, acceleration, speed, and the variation of each parameter and so on according to coal situation (different coals, different humidity, different grain size), and coal storage conditions (diameter, height). In this article, we first study the movement rules of coal in chute, create a mechanical model and introduce of coal flow in the chute in the run-time kinematic equation. Secondly according to the requirement to make sure the coal flow moves through the chute at a constant speed we calculate the parameters of the standard segment; thirdly we could have the parameters of the non-standard segment based on the speed and the state of the coal. So a series of the boundary equations for not only the standard and non-standard but also the interior and exterior panels of the spiral chute are derived. Finally 3D solid models, views and developing drawings based on redevelopment of Solidworks are come into being. All above are not only automatic, precise and parameterized, moreover, involves more technological information. So it has an important significance on its design, expression and processing.
Study on voting safe function block and safety system

Voting mechanism based safe function block compatible with IEC 61499 is proposed. The voting safe function block can vote the discrete or continuous parameters and get a reliable value. A new safe communication approach is proposed for not only error sensing but also error correcting. The approach use a triple data field copies to address error correcting problem, and more safety is achieved in communication by this way. By using voting safe function block, a more safe system based on common control devices is designed. Furthermore, a markov model is established to describe safety and reliability of the voting safe function block based safe system. At the end, simulation under given parameters is done, which is about PFD, PFS and MTTF factors.
Multi-UAV cooperative reconnaissance decision-making based on Lingo

In this paper, the decision-making problem of multi-UAVs cooperative reconnaissance under uncertainty environment is considered. Since multi-UAV cooperative reconnaissance task decision making problem is not only a complicated multi-objective optimization and decision problem with multi-constraint, but also a large-scale coordination control problem. We construct the mathematical model according to the maximization of reconnaissance gains of all task areas for multi-UAVs. By balancing the reconnaissance gains among the multi-task areas in order to generate a plan that is efficient for the reconnaissance mission, the Lingo software is adopted to settle this scheduling problem. The numerical result shows that we can obtain the optimal solution for the multi-UAV cooperative reconnaissance decision-making problem by Lingo software efficiently.
Lazy Learning Based Efficient Video Annotation

Eager learning methods, such as SVM, are widely applied in video annotation task for their substantial performance. However, their computational costs are usually prohibitive when a large dataset is faced, especially when annotating a large lexicon of semantic concepts. This paper proposes a video annotation scheme based on lazy learning, and shows that this scheme is much more computationally efficient and flexible. Based on a recently proposed improved Parzen window method, we provide a lazy learning based video annotation scheme. After building the pairwise relationships in dataset, the annotation can be finished rapidly for each concept. Experiments show that the proposed method is much more efficient than SVM while retaining comparable performance.
Analysis of response abilities of time-varying fuzzy systems

The design and response abilities of time-varying fuzzy systems are discussed. Firstly, based on non-singleton fuzzifier and dynamic fuzzy inference method, the response function of time-varying fuzzy system is obtained. Then, a sufficient and necessary condition for time-varying fuzzy system possessing interpolation property is given. It is proved that time-varying fuzzy systems determined by t-norm or regular implication operators can be represented as some interpolation functions. Further, two sufficient conditions for time-varying fuzzy systems satisfying fitted properties are deduced. At last, a simulation result shows the effectiveness of the conclusion.
Does joint decoding really outperform cascade processing in English-to-Chinese transliteration generation? The role of syllabification

Transliteration is a challengeable task aimed at converting a proper name into another language with phonetic equivalence. Since the conversion relates to the phonetic aspect of a text, syllabification is considered a major factor affecting the performance of a transliteration system. In grapheme-based approaches, there are two routines to transliterate, one is to perform in a pipeline of separate syllabification and other components in generation process step by step, the other is to synchronously segment syllables and generating transliteration options. Usually, joint decoding outperforms the cascade processing in many natural language processing missions, however, syllabification is a special component in transliteration task. Thus in this paper, we investigate the two routines with a systematic analysis and compare their results to illustrate the strength of syllabification. A phrase-based statistical machine translation framework for joint decoding and a conditional random field syllabification system are used in this work for our investigation, which shows a different scenario on the issue of joint decoding versus cascade processing in transliteration.
A complete surface potential-based current-voltage and capacitance-voltage core model for undoped surrounding-gate MOSFETs

A complete surface potential-based current-voltage and capacitance-voltage core model for cylindrical undoped surrounding-gate (SRG) MOSFETs is presented in this paper. This model allows the current-voltage (IV) and capacitance-voltage (CV) characteristics to be adequately described by a single set of the equations in terms of the surface potential. The model is valid for all operation regions and predicted SRG-MOSFET¿s characteristics by the 3-D numerical simulation.