nils-herrmann/clean_scirepeval huggingface dataset:
Fault isolation for light rail vehicle suspension system based on multi-sensor information fusion

In this paper, fault isolation issue for Light Rail Vehicle suspension system based on multi-sensor information fusion is discussed. The faults considered are the vertical damper fault and the vertical spring fault in suspension systems. The Eros is applied for similarity measurement in the fault feature database, Fast Fourier Transformation for nine sensor outputs equipped on the vehicle. After the belief function assignments are obtained by using Eros method, the belief function assignments are fused by using the rules of D-S evidence combination and the faulty component is determined and isolated. The effectiveness of the proposed method is demonstrated by a case study.
Rational Harmonic Mode-Locking of a Fiber Optical Parametric Oscillator at 30 GHz

We demonstrate rational harmonic mode-locking of a fiber optical parametric oscillator (FOPO) in which the parametric gain serves as an all-optical mode-locker. A pulse train with a repetition rate from 10 to 30 GHz is generated using a 10-GHz optical clock signal. The generated pulses have a pulsewidth of ~ 9 ps, a root-mean-square (RMS) timing jitter of ~ 300 fs, and a wavelength tunable to more than 20 nm.
On-line rule generation for robotic behavior controller based on a neural-fuzzy inference network

Fuzzy logic method presents an efficient way for the control of behavior modules in robot navigation. However, it is difficult to construct and maintain correct and complete fuzzy rule base by a human expert. In this paper, considering the drawbacks of the existing neural-network-based approaches for rule generation, we present an on-line learning scheme for the adaptive and dynamic rule generation of a fuzzy controller based on a developed neural-fuzzy inference network. The proposed learning scheme consists of the rule construction and the rule examination. The former is used to locate the parameters of membership functions and create if-then rules simultaneously for each incoming training data. The latter is for the optimization of the existing rule base. The proposed scheme is applied to the wall-following control of an omnidirectional mobile robot equipped with an ultrasonic sensor ring. The simulation results show the designed fuzzy controller has a satisfying performance.
Data driven- Adaptive single neuron predictive controller based on Lyapunov approach

In this paper, a novel data driven-adaptive single neuron predictive controller is proposed. The self-tuning algorithm for the single neuron predictive controller is derived by a rigorous analysis based on the Lyapunov method such that the predicted tracking error convergences asymptotically. Simulation results are presented to illustrate the proposed adaptive predictive controller and a comparison with its conventional counterparts is made.
Information extracting and assessment of soil erosion based on remote sensing information in the upper basin of Guanting reservoir

Soil erosion of the upper basin of Guanting reservoir in Zhangjiakou was assessed based on Landsat TM5(2007-5-19) and DEM (1∶50000). <Standards for classification and gradation of soil erosion> (SL190-2007) and the multifactor comprehensive method were adopted, and land use, vegetal cover and slope were selected as assessing factors. The result shows that very low soil erosion covers 43.36%, and the intensive soil erosion patches are rather fragmentized and difficult to be treatment uniformly.
Performance degradation assessment of train rolling bearings based on SVM and segmented vote method

For solving the problem of performance degradation assessment of train rolling bearings, the degradation pattern recognition and assessment method based on segmentation vote and SVM was proposed. Firstly, in order to obtain effective features indexes of bearing performance degradation, with the collected vibration acceleration data, the data was decomposed by LMD and state features were extracted and dimensionality reduced by PCA. And then, on the basis of the feature indexes, the method that combined least squares SVM and segmented vote was developed. Compared with the traditional pattern recognition methods, the new method improves identification accuracy effectively. The experiment results shown that the identification accuracy rates of three stages (normal operation, the primary degradation and the severely degradation) are higher than 95%. Finally, by the precision degradation pattern recognition results, train rolling bearing performance degradation assessment was completed.
Analysis of the thermal resistance and performance degradation of 4H-SiC PiN power diode

The excellent physical and electronic properties of SiC recently takes itself to be the focused power device material for high temperature high power and high frequency application. In this paper, the model is established through simulation method Ansys14.0, studying the factors affecting the thermal resistance of PiN diode. Increasing the area or decreasing the thickness of the chip and the solder layer can reduce the thermal resistance effectively. At last we give a thermodynamic model of the device in Sentaurus TCAD software, modeling the temperature phenomenon and self-heating effects on performance degradation of 4H-SiC PiN diode, finally we analysis and forecast the failure mechanism of similar SiC devices. Results show that the higher the temperature, the higher heat flux generated by the section of the device will be to promote temperature, and the smaller the temperature gradient inside and outside the device which leads heat conducted, hence resulting in increased current degradation.
Global evapotranspiration derived by ETMonitor model based on earth observations

Evapotranspiration (ET) is an important ecohydrological process especially in arid and semi-arid regions. In current study, a process-based model named ETMonitor was developed to estimate the ET, based mainly on the biophysical and hydrological parameters retrieved from satellite earth observations. And global daily ET from 2008 to 2012 with a spatial resolution of 1 km was estimated based on multi-source earth observations datasets. The estimated ET agreed well with the in situ observations at field scale, with R2 = 0.74, Bias = -0.05 mm d-1, RMSE = 0.87 mm d-1. The spatial patterns of estimated ET also agree well with the current available global ET products such as MOD16 and GLEAM. The ET products provide critical information on global terrestrial water and energy cycles and environmental change.
CFD-based thermal characterization of board-level microelectronic devices under natural convection cooling

In the present paper, a systematic investigation on steady-state heat dissipation of a board-level microelectronic device under natural convection cooling is performed. For solving the conjugate solid- fluid heat transfer problems, a three-dimensional computational fluid dynamics (CFD) model is developed. The effectiveness of the proposed CFD model is extensively verified through experimental validations: an existing thermal test die experiment based on joint electron device engineering council (JEDEC) specification under a natural convection environment and a PBGA typed package is considered as test vehicle. The package is assembled to a thermal test board, which is a four-layer printed circuit board (PCB). For best controlling of the heating powers and furthermore, conducting chip junction temperature measurement, the specifically designed thermal test chip are used instead of real dice. The thermal test die measurement can derive the chip junction temperature of the assembly under various chip power dissipation rates. Based on the validated CFD model, a evaluation of empirical heat transfer correlation model is proceeded under various chip power dissipation rate. In addition, the effect of PCB structure on the thermal performance of the device under natural convection is also addressed.
Despeckling structural loss(DSL): A new metric for measuring structure-preserving capability of despeckling algorithms

In this paper, a new metric called despeckling structural loss(DSL) is proposed for performance assessment of despeckling algorithms with a focus on the preservation of structural information. By taking into account characteristics of the best and worst structure preservation in despeckling, the DSL metric examines the presence of image structures in ratio images by using local correlations between the ratio image and the noise-free reference image at edge points, leading an objective and quantitative measure of the structure-preserving capability of despeckling algorithms. The DSL metric has been tested on despeckling results of a simulated SAR image using three types of algorithms and efficiency of the DSL has been demonstrated. In comparison, the other five commonly used despeckling metrics fail to keep a consistency with the structural loss shown in despeckling results as well as ratio images.
Intelligent Object Locality Naming Model in an Object-Based Distributed System for Engineering Applications

Complex engineering applications demand powerful computing resources. An object based distributed system is suitable for such applications due to the inherent parallelism nature in the object based computing model. As the model is based on message passing, which in turns relies on location transparent object names, an efficient name translation system is therefore required to map the object names to the corresponding physical addresses dynamically. In this paper, we propose a heuristic distributed search algorithm, the hierarchy random walker (HRW), to take advantage of the locality of object communications thereby increasing the overall system performance. Analysis of the simulation results from a number of benchmark programs have demonstrated that the benefits of the proposed algorithm are in terms of flexibility and efficient to locate the most used objects. The approach will be applied to intelligent techniques for engineering problems.
High reliable multi-hop transmission for sensor data in mine tunnels

Wireless sensor network (WSN) and wireless mesh network (WMN) suffer frequent disruptions and limited capacity for delivering sensor data in mine tunnels. A Braided Cooperative Reliable Transport (BCRT) algorithm which maintains one-hop reliability to insure end-to-end reliability is proposed. BCRT is not sensitive to route breaks, since it optimizes the data rate on every forwarding node and lost packets are retransmitted locally. The key of BCRT is a cooperative rate control method, in which every three contiguous nodes along a routing path exchange their information about link-occupied conditions. Then they adjust their data rate respectively based on that information to maximize the three-hop throughput before the path breaks. The comparison of BCRT with TCP and Vegas shows that BCRT improves end-to-end throughput in mine tunnels.
Research on Disaster Information Mapping Templates of the Secondary Disasters of Earthquake

The disaster information mapping templates developed according to the distribution of the earthquake and the secondary disasters can improve the thematic mapping efficiency. It can also provide accurate disaster information of disaster and affected body quickly. First, Disaster special subject sign database should be designed, including disaster category symbols, loss assessment category symbols, disaster risk category symbols and disaster implication environment symbols. Then what should be designed are Geographic-basic-map symbol database for base map templates and Image database based on the research of the disaster element selection, symbolization and expression of thematic information. The development of disaster information mapping templates can provide various templates for the fast mapping and improve the mapping efficiency.
A collaborative filtering recommendation algorithm based on user clustering and Slope One scheme

Recommendation system has been widely used in electronic commerce, news, web2.0, E-Iearning and other fields. Collaborative filtering is one of the most important algorithms. But as scale of recommendation system continues to expand, more and more problems appear. Data sparsity and poor prediction are main problems that recommendation system has to face. To improve the quality and performance, a new collaborative filtering recommendation algorithm combining user-clustering and Slope One algorithm is proposed. In our algorithm, users were clustered into several classes based on users' rating on items; therefore the useless information was filtered. Then the slope-one scheme was applied to predict the object rating. The experiments were applied to the MovieLens dataset to exploit the benefits of our detector and the experiment results show that the accuracy of our algorithm is in advance of previous research.
Online Tracking Based on Multiple Appearances Model

Tracking target in a long-term is still a big challenge in computer vision. In recent research, many researchers pay much attention on updating current appearance of tracking target to build one online appearance model. However, one appearance model is always not enough to describe historical appearance information especially for long-term tracking task. In this paper, we propose an online multiple appearances model based on Dirichlet Process Mixture Model (DPMM), which can make different appearance representations of the tracking target grouped dynamically and in an unsupervised way. Since DPMM's appealing properties are characterized by Gibbs sampling and Gibbs sampling costs too much, we proposed an online Bayesian learning algorithm instead of Gibbs sampling to reliably and efficiently learn a DPMM from scratch through sequential approximation in a streaming fashion to adapt new tracking targets. Experiments on multiple challenging benchmark public dataset demonstrate the proposed tracking algorithm performs favorably against the state-of-the-art.
Low-Complexity Energy Detection for Spectrum Sensing With Random Arrivals of Primary Users

Due to random arrivals of primary-user signals, the timing misalignment issue should be considered for spectrum sensing in cognitive radio (CR) systems, such as CR-based femtocell networks. To deal with this issue, two approaches were recommended in the literature, including Bayesian and generalized likelihood ratio test (GLRT) detectors. However, the Bayesian test requires perfect knowledge of the distribution of unknown parameters. Therefore, it is impractical due to its implementation complexity. To design a low-complexity energy detector (ED), this work proposes an ED scheme based on the GLRT algorithm. As a result, maximum-likelihood estimation for the timing misalignment is devised, and the performance of the proposed scheme is analyzed. The results show that the proposed GLRT detector features a low-complexity and satisfactory performance.
A miniaturized antipodal vivaldi antenna for microwave imaging

A miniaturized antipodal Vivaldi antenna (AVA) with bandwidth of 1–5GHz is proposed in this letter. The miniaturization of this end-fire directional antenna was realized by employing elliptically shaped conductors with two pairs of elliptically opened slots on them and one pair of symmetrically loaded structures. Additional reflection structures was designed to ensure the antenna's directivity at low frequency part within the bandwidth. The antenna was fabricated and measured through experiment, agreeing well with that of computer CST® simulations. The designed antenna can be applied to medical microwave imaging system.
Application of information fusion based on RBF neural networks and fuzzy control to ball mill pulverizing system

Ball mill pulverizing system is a typical multi-input and multi-output (MIMO) system with the characteristics as strong coupling, nonlinearity, large delay and time-varying. The running conditions and the precise mathematic model of the system can not be obtained easily for its complex characteristics, so the conventional control strategy is not effective. An information fusion based method including multi-sensors, data preprocessing, RBF neural networks, fuzzy controller is put forward in this article. By combining multiple sensorspsila information, the RBF neural networks extract features and estimate the running conditions, and then the adjustments of outputs are decided by fuzzy control. By using the fusion method, the running conditionspsila measurements are more accurate and reliable than that using single sensor method and the control system can adapt to the time-varying characteristic of the ball mill pulverizing system. The practical application indicates that the information fusion based control system runs stably and efficiently.
A Space Mapping Deformation Technology for Dental Surface Restoration

Restoration of the occlusal teeth surface is an important aspect in dental CAD system. The designed surface shape must fit the existing jaw articulation and keep the anatomy features of the generic teeth. In this paper we presented a new method for teeth surface restoration based on space mapping deformation technology. The main focus on this method is the dental surface global and local mapping deformation using radial basis function (RBF) according to the corresponding feature points of the standard tooth and the preparation. The space mapping deformation method has been successfully applied on some preparation, which can guarantee the surface matching smoothness and keep the main anatomical features of teeth.
Analysis of generalised orthogonal matching pursuit using restricted isometry constant

In compressive sensing, the generalised orthogonal matching pursuit (gOMP) algorithm is one kind of sparse signal recovery algorithm, which generalises the OMP algorithm by selecting a fixed number of atoms at each iteration. Restricted isometry constant-based sufficient conditions to guarantee the correct support identification and the successful recovery of a sparse signal using the gOMP algorithm in a noiseless case are proposed. The proposed sufficient bounds are more relaxed compared with the existing ones.
Improved Overall Performances of a Tribo-Aero-Electrostatic Separator for Granular Plastics From Waste Electric and Electronic Equipment

The two-conveyor-electrode tribo-aero-electrostatic separator patented in 2010 was designed to sort granular mixtures of insulating materials in a fluidized bed subjected to an electric field perpendicular to the direction of the fluidizing air. The prototype has proven its efficiency for the separation of insulating granules ≤ 2 mm, attaining excellent recovery rates. Its performances were less satisfactory for larger particles (2-5 mm). The objective of this work is to evaluate three modifications of the construction of this device, in order to increase the hourly throughput and improve the purity of the products, by controlling the charge acquired by the granules and the electric field intensity. Numerical simulations enabled the optimization of the electric field distribution in the active zone of the separator, and an experimental study was carried out, in order to identify the best conditions for granule charging by triboelectric effect. The tests were carried out for four configurations of the electrode system, by using a mixture of acrylonitrile butadiene styrene (ABS) and high-impact polystyrene (HIPS) originated from grinding out-of-use computer cases. The typical granule size was 2-5 mm. The experimental approach validated a change in the position of the fluidized bed, with respect to the conveyor electrodes, to increase the residence time of the granular materials and hence the amount of electric charge acquired by each particle. In order to intensify the electrical field, two solutions were tested and validated: 1) add a grounding electrode in the proximity of the high-voltage electrodes; 2) dispose vertically the high-voltage electrodes. For the tested materials, the vertical arrangement of the electrodes gave the best hourly throughput rates and purity of the separated products.
Forecast of Systemic Parameter Drift Based on Monte Carlo Simulation

This essay mainly concerns on the degree of systemic parameter drift in process of product life cycle, by establishing the function of system structure and using simulation software to imitate the process of drift. It not only gets the degree of systemic parameter drift and the result of approximate distributions of system output, but also gets the sensitivity of each component part contributes to systemic parameter drift. Particularly, the reliability of systemic performance (Rp) is used to measure the degree of systemic parameter drift.
A plasmonic refractive index sensor based on a MIM waveguide with a side-coupled nanodisk resonator

Based on a metal-insulator-metal (MIM) waveguide with a side-coupled nanodisk cavity, the sensor using the surface plasmon polaritons (SPPs) refractive index is investigated and studied numerically. The finite-difference time-domain (FDTD) method is used to simulate the performance of the sensor. The numerical simulation result indicates that all the resonance wavelengths in the transmission characteristic of the structure have a linear relationship with the refractive index of the cavity. Furthermore, the sensitivities of the sensor in this paper for the refractive index can be achieved as high as 1320 nm RIU for the mode1, 812.5 nm RIU for the mode2, 600 nm RIU for the mode3, respectively. Besides, the influences of the structural parameters on the transmission characteristic and the sensing characteristic are also studied in detail by the FDTD method. The sensor with compact and simple structure not only can be used to measure the temperature based on the linear relation between the refractive index and temperature, but also has many potential applications in optical networks on chip and On-chip sensor networks.
A Sectional Encryption Algorithm of Network Information Based on Cyclic Shift Key Code

Traditional chaotic encryption scheme of network information does not consider the data compression of synchronization and encryption, and the cyclic code encryption of network information cannot be achieved, resulting in the predictability of cipher text is not good, which cannot meet the requirements of secret communication. The ergodic properties and sensitivity to the system parameters of chaotic sequences is used, an improved coding and encryption scheme for source coding is designed based on Hoffman coding, and the cyclic shift technology is used, a sectional encryption algorithm of network information is proposed based on cyclic shift key code. The sectional linear chaotic map and the Logistic map have larger parameter space and phase space, the sectional linear chaotic map is applied in the arithmetic coding. The synchronization arithmetic coding and network information coding method are fused. Simulation result shows that the new network information coding method has a stable compression ratio output, and the average change rate of the cipher text sequence is between 47.652%~49.926%, which is close to the ideal value of 50%, it can effectively resist searching attack with brute force, the unpredictability of cipher text is improved, which ensures the network communication security.
Research on neural networks based modelling and control of electrohydraulic system

The electrohydraulic servo system of a certain type of mines weeping plough is a complex and nonlinear system. It is difficult to construct its accurate model by first principle method and to achieve satisfactory control performance by traditional PID controller. In this paper, the radial basis function neural network with orthogonal least square learning algorithm is used to model the electrohydraulic system and the neural network based direct inverse is adopted to control the system. The experimental results and comparisons with other techniques clearly show the validity of the proposed methods.