nils-herrmann/clean_scirepeval huggingface dataset:
Investigation on surface flashover characteristics of the insulator with different transformer oil coating conditions in vacuum

The vacuum insulator surface flashover phenomenon unusually occurred in the pulsed power devices. We used the transformer oil to coat on the insulator surface for increasing the insulation strength in vacuum. The experiment results show that the transformer oil coating could significantly improve insulator flashover voltage level due to oil layer limited the development of the secondary electron avalanche. We have changed the transformer oil coated regions to investigate the insulator flashover strength. The experiments indicated that the coating region would influence the flashover voltage. The flashover strength would be improved by coating transformer oil on the cathode region. The design of grooved insulator would increase the flashover strength. The flashover strength of grooved insulator with coating oil is higher that of flat insulator with coating oil.
Spectroscopic investigation of Tm3+ doped tungsten-tellurite glasses sensitized by Yb3+

The Yb<sup>3+</sup>/Tm<sup>3+</sup> co-doped 60TeO<inf>2</inf>-30WO<inf>3</inf>-10La<inf>2</inf>O<inf>3</inf> glass with good thermal stability and efficient 1.8µm emission is reported. The energy transfer mechanism for both upconversion luminescence and the 1.8µm emission is discussed.
CC-fog: Toward content-centric fog networks for E-health

E-health is one of the domains which will need more and more IoT solutions in the future, as it requires short delays for life-dependency situations and safe local storage spaces for privacy matters. The emergency context demands efficient communication and computing capacities, and the cloud vision as well as traditional TCP/IP way of communication cannot really suit these requirements. New approaches are currently used : the fog computing paradigm, which describe the IoT environment in a more relevant way than the cloud vision, provides new performances as low latency, local storage opportunities, end-to-user services. E-health fogs have attracted a lot of attentions. To realize the efficient communication, E-Health systems also introduce Content-Centric Network (CCN) approach, whose characteristics include quick response, content distribution, IoT oriented technology. Nowadays, the existing E-health fog or CCN are seen as separate and autonomous networks, but many applications could require that those E-health fog computing systems exchange data, share processing results, or subscribe to each other information flux. In this paper we propose a solution for E-health systems to communicate by combining fog computing and CCN to provide communication efficiency and local storage opportunities. Finally we use simulation to evaluate the delay performances of our proposal.
The Optimization of the Hierarchical Storage System Based on the Hybrid SSD Technology

This paper introduces the hierarchical storage system (HSM) which is adopted in most computers in consideration of achieving a good balance between cost and performance. As the cost of Flash memory rapidly decreases, Flash-based Solid-State Disk (SSD) is used as a non-volatile cache between the DRAM and Hard Disk Drive (HDD) in the HSM. Although typical SSD can deliver higher performances than HDD can, SSD is currently not adopted in most systems due to its limited life and performance. This study introduces a Hybrid SSD approach that combines DRAM, Phase Changed Memory (PCM) and Flash. Even the Flash architectures has its own drawbacks, the Hybrid SSD could support full capabilities benefit by PCM and DRAM. The performance gap between HDD and DRAM will be narrowed by Hybrid SSD more than the pure Flash SSD while delivering a longer endurance life. So, the HSM is well optimized by lead in a Hybrid SSD between the DRAM and HDD.
A Low-Complexity 2-D Angle of Arrival Estimation in Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) systems are promising technology to greatly increase the spectral efficiency for the 5G cellular system. However, the implementation is practically a challenge due to the limitation of cost, space, and complexity. Though the millimeter-wave (mm-wave) transmission can greatly save the space for deploying numerous antennas, the demand on the numerous RF chains increases the implementation cost significantly. The hybrid structures of sub-array antennas are then developed to alleviate the cost, where the entire array is grouped into several sub-arrays. All antennas in a subarray share a common RF chain, which greatly reduces the complexity. Furthermore, the downlink channel state information (CSI) is crucial for several pre-processing technologies such as precoding. Nevertheless, the CSI estimation is difficult due to the large dimension of a channel matrix. Accordingly, CSI estimation by the structured channel matrix is attractive since only few unknown angle-of-arrivals (AoA) and deterministic signatures can model the CSI. Estimation of CSI is equivalent to estimating the AoA. In this paper, we propose a new AoA estimation by using estimating signal parameters via rotational invariance technique (ESPRIT) for the massive MIMO system with two kinds of hybrid subarrays, referred to side-by-side and interleave sub-arrays. Numerical results validate the proposed AoA estimation and show that the proposed AoA estimation with side-by-side sub-arrays can approach to the fully-digitized arrays while keeping a lower computational complexity.
Multi-document Summarization Based on Word Feature Mining

This paper discusses an approach to multi-document summarization that builds on understanding word as feature deeply. We created 7 basic word features using the frequency, position information, event information and topic information. Then choose logistic regression model to compute words value. The summarizer gives a score of sentence by words value, and combines score and redundancy of sentence to produce summarization. The evaluation of summaries uses three parameters which are N-gram co-occurrence statistics, term word coverage and high frequency word coverage. The experiment results show the systempsilas has more effectiveness and feasibility.
Notice of RetractionStudy on geochemistry characteristic of heavy metal in soils and effects on fruit quality in Yanbian area

Heavy metal pollution in soil has become the focus issue globally. They could be phytotoxic once their concentrations exceed the limits of soil bearing capacity, however, a considerable part of heavy metals will be essential as trace elements for biology on condition that their contents are not beyond the safety threshold level. Based on this, this paper studied geochemistry characteristic of heavy metals in soils and effects on fruit quality by statistical analysis using 120 samples of surface soil and 40 apple-pear sample data in Yanbian area. The results showed that the accumulations of heavy metals in Yanbian area were regionally different and the spatial variability of 8 heavy metals were quiet different. In addition, the metal pollution in study area was at safe level, and As, Hg, Cu, Ni, Zn and Cd, within the safety threshold level of soil, had significant correlations with quality indicators of apple-pear.
A fast and efficient nearest neighbor method for protein secondary structure prediction

Using PSSM profiles, various machine learning methods have been successfully developed for protein secondary structure prediction. With the steady increase of protein structure data, the probability of having available homologous structural information of the protein in real prediction is now fairly high and will continue to increase. Therefore, how to effectively utilize the ever-growing protein structure data has become a huge challenge and opportunity. In this paper, we propose a novel nearest neighbor method, DPred, to use both homologous and non-homologous information for protein secondary structure prediction. On the dataset composed of new solved proteins, the method achieves the overall Q3 and SOV scores of 87.51% and 86.50%, which is comparable with Porter_H and better than PROTEUS and CDM.
Image Location Inference by Multisaliency Enhancement

Locations of images have been widely used in many application scenarios for large geotagged image corpora. As to images that are not geographically tagged, we estimate their locations with the help of the large geotagged image set by content-based image retrieval. Bag-of-words image representation has been utilized widely. However, the individual visual word-based image retrieval approach is not effective in expressing the salient relationships of image region. In this paper, we present an image location estimation approach by multisaliency enhancement. We first extract region-of-interests (ROIs) by mean-shift clustering on the visual words and salient map of the image based on which we further determine the importance of the ROI. Then, we describe each ROI by the spatial descriptors of visual words. Finally, region-based visual phrases are generated to further enhance the saliency in image location estimation. Experiments show the effectiveness of our proposed approach.
Cooperative Car-Following Control: Distributed Algorithm and Impact on Moving Jam Features

We design controllers and derive implementable algorithms for autonomous and cooperative car-following control (CFC) systems under a receding horizon control framework. An autonomous CFC system controls vehicle acceleration to optimize its own situation, whereas a cooperative CFC (C-CFC) system coordinates accelerations of cooperative vehicles to optimize the joint situation. To realize simultaneous control of many vehicles in a traffic system, decentralized and distributed algorithms are implemented in a microscopic traffic simulator for CFC and C-CFC controllers, respectively. The impacts of the proposed controllers on dynamic traffic flow features, particularly on formation and propagation of moving jams, are investigated through a simulation on a two-lane freeway with CFC/C-CFC vehicles randomly distributed. The simulation shows that the proposed decentralized CFC and distributed C-CFC algorithms are implementable in microscopic simulations, and the assessment reveals that CFC and C-CFC systems change moving jam characteristics substantially.
Potential impacts of ecological adaptive cruise control systems on traffic and environment

In this contribution, the authors put forward a modelling framework for generic Advanced Driver Assistance Systems (ADAS) based on rolling horizon optimal control and design control algorithms for an Ecological Adaptive Cruise Control (EcoACC) system under this framework. The accelerations of EcoACC vehicles are determined by minimizing some predicted cost, and the optimal control problem is solved using a dynamic programming approach. The proposed algorithm is applied on a single lane ring road to examine the impacts of the EcoACC system employing the Eco-driving strategy comparison with a system employing an Efficient-driving strategy. Simulation results show that the Eco-driving strategy results in smoother vehicle behaviour compared to the driving strategies that only consider travel efficiency (Efficient-driving strategy). At the macroscopic level, the Eco-driving strategy results in a lower speed and lower flow at free traffic conditions, but a higher speed and higher flow at moderate congested conditions compared to the Efficient-driving strategy. From an environment perspective, the Eco-driving strategy results in a lower spatial CO₂ emission rate. However, in the ring-road scenario where the demand is not fixed, the impact of the EcoACC system on total CO₂ emissions is negative at moderate congested conditions, due to the high flow it supports.
Study on the Electromagnetic Characteristics of the Consequent Pole In-Wheel Motor

A novel consequent pole in-wheel motor (CPM) with surface-mounted permanent magnet (PM) for electric vehicles is investigated in this paper. The finite- element analysis (FEA) is carried out to study the electromagnetic characteristics of this kind of motor, also the structural features and design rules of the motor are discussed through the analysis of the air gap flux density and leakage flux. The influence of the structural parameters to the leakage flux is compared between the conventional PM in-wheel motor and CPM. The simulation results show that CPM has some interesting electromagnetic characteristics different from the conventional PM in-wheel motor, which might be beneficial for its further development.
Analysis of Wind-Turbine Steel Tower by Transfer Matrix

A new method is put forward for analysis bending deflection of the wind-turbine steel tower based on transfer matrix method. The steel tower of wind-turbine structure is simplified as a beam-column with varying diameter involve in bending and torsion. The transfer matrix is obtained by solution of the differential equations of beam-column subject to an axial force and other complex loads in 2-dimension. One exist steel tower is analyzed by this method for example, and the on-plane deformation was calculated by the present method. The results show that transfer matrix method is reliability and accuracy, which can meet the requirement of wind turbine engineering structure, and it is effective to solve the problems of the wind-turbine tower, especially for the case with additional bending moments caused by compressive force.
Study on electro-thermal coupling optimal power flow model and its simplification

A new optimal power flow (OPF) model accounts for electro-thermal coupling relationship is established based on electro-thermal coordination (ETC) concept in this paper This new OPF model organically combines the traditional OPF with heat balance equation (HBE) which reflects thermal dynamic process of transmission component. It considers the temperature limit of transmission component as its security constraint in power system operating dispatch. The model not only takes into account the electro-thermal coupling dynamic process, but also reflects the coordination of the dynamic course of current and temperature of transmission component, and in operation dispatch, it can exploit potential transfer capability of electric power grid by means of the thermal inertia characteristic of transmission component. Further, a simplified expression is derived from the model, which reduces the solution complexity without changing the essential idea. Finally, a simulation is carried out to demonstrate the validity of the proposed model and its simplified expression.
Template Based Stereo Matching Using Graph-cut

Stereo matching is a challenging issue in multicamera scene reconstruction, and recently many methods have been proposed in different contexts. This paper presents a template based stereo matching algorithm using graph-cut. First, a set of non-overlapping templates are derived from the reference image (left or right image) to represent the scene structure. Each template is assigned to a corresponding disparity value in order to obtain an energy function which is applied to construct a graph. The minimum cut on the graph by using graph-cut also minimizes the energy function. Experimental results on the Middlebury test set show that the proposed algorithm can obtain satisfactory stereo matching results and is competitive with the state-of-the-art algorithms.
Energy saving modification planning for distribution systems

Under the hierarchical partition management mode of regional distribution systems, the multi measures combined with energy saving modification (ESM) investment planning model which is constrained by the total investment are developed. In this model, the modification investment amount of each measure is set as a variable, and the core parameters, such as gray connection weights, indicator inferior degrees, as well as energy-saving efficiency of transformation, are obtained by gray relational analysis, sampling statistics and contrasts of sample transformation. Considering investment constraints and weight coefficients, the problem of the whole network energy saving investment allocation under the circumstance of limited funds is solved. The example shows that the proposed method is helpful for understanding the loss reduction space and modification demand of distribution systems, and improves the efficiency of investment for ESM.
Biomedical Image Processing: A Cross-Discipline Course for Undergraduate Computer Science and Technology Major

Biomedical image processing is a relatively new but rapidly growing interdisciplinary area in both computer science and biology fields. This paper provides a systematic view and design method of the biomedical image processing course for undergraduate computer science major. The cross-discipline pedagogies that provide the guiding principles for course system design are presented, along with specific issues related to class room teaching and student practice. The paper also presents four improvements for the future course development.
Online Feature Selection with Group Structure Analysis

Online selection of dynamic features has attracted intensive interest in recent years. However, existing online feature selection methods evaluate features individually and ignore the underlying structure of a feature stream. For instance, in image analysis, features are generated in groups which represent color, texture, and other visual information. Simply breaking the group structure in feature selection may degrade performance. Motivated by this observation, we formulate the problem as an online group feature selection. The problem assumes that features are generated individually but there are group structures in the feature stream. To the best of our knowledge, this is the first time that the correlation among streaming features has been considered in the online feature selection process. To solve this problem, we develop a novel online group feature selection method named OGFS. Our proposed approach consists of two stages: online intra-group selection and online inter-group selection. In the intra-group selection, we design a criterion based on spectral analysis to select discriminative features in each group. In the inter-group selection, we utilize a linear regression model to select an optimal subset. This two-stage procedure continues until there are no more features arriving or some predefined stopping conditions are met. Finally, we apply our method to multiple tasks including image classification and face verification. Extensive empirical studies performed on real-world and benchmark data sets demonstrate that our method outperforms other state-of-the-art online feature selection methods.
An integral sliding-mode control approach for series elastic actuator torque control

In this paper, an integral sliding-mode control(ISMC) approach for the SEA torque control is presented. The controller is efficient even in the presence of external disturbances. In particular, the general dynamics of SEA system is described and some model transformation operations are performed, and then an integral sliding-mode controller is designed, on the basis of which some theoretical analysis is implemented to show the stability and boundedness of the closed-loop signals. Both simulation and experimental results are included to demonstrate the designed controller achieves improved performance than existing method and shows strong robustness with respect to extraneous disturbances.
Research on Fuzzy Smith Control Method for Time Delay Systems

To overcome the shortcomings that the control effect of conventional Smith predictor will be worse when the parameters of time delay systems change, an adaptive fuzzy Smith control method is presented in this paper. It adds a fuzzy controller in the combination of Smith predictor and PID controller. The control parameters are adjusted online by the fuzzy controller to adapt the control requirement. And the simulation study had been done for a first-order time-delay system. The results show that this method offer good static and dynamic characteristics, at the same time its disturbance-rejection and robustness are better.
The limits of error correction with lp decoding

An unknown vector f in R<sup>n</sup> can be recovered from corrupted measurements y = Af + e where A<sup>m×n</sup>(m ≥ n) is the coding matrix if the unknown error vector e is sparse. We investigate the relationship of the fraction of errors and the recovering ability of l<inf>p</inf>-minimization (0 < p ≤ 1) which returns a vector x that minimizes the “l<inf>p</inf>-norm” of y-Ax. We give sharp thresholds of the fraction of errors that is recoverable. If e is an arbitrary unknown vector, the threshold strictly decreases from 0.5 to 0.239 as p increases from 0 to 1. If e has fixed support and fixed signs on the support, the threshold is ⅔ for all p in (0, 1), and 1 for p = 1.
MedFDTD: A Parallel and Open-Source Cross-Platform Framework for Bioelectromagnetics Field Simulation

MedFDTD, a new parallel and open-source cross-platform framework for bioelectromagnetics research, has been developed by solving Maxwell’s equations using the finite-difference time-domain method. This framework implements the complex-frequency-shifted perfectly matched layer, supports the import of antenna and bioelectromagnetic models through media with non-dispersive/dispersive properties, and can calculate the antenna output power and specific absorption rate of biological tissues, thereby making it favorable for bioelectromagnetics studies. In addition, MedFDTD can be implemented on multiple CPUs or a variety of chip-based GPUs to accelerate all parallel computing tasks.
The urban expansion monitoring of Wuhan city using multitemporal remote sensing images

City is a huge and complicated system. People around the world are paying strong attention to the development of cities. This paper uses Wuhan as the study area and uses Landsat TM images acquired in 2002, 2005 and 2010 respectively as the basic data. Based on object-oriented image analysis approach, the land use information is extracted firstly, and then the urban expansion of Wuhan city during last decade is analyzed with the urban expansion intensity index, the quadrants position analysis and the overall pattern analysis. From the result we can see that the pattern of urban expansion of Wuhan is a combination of ribbon expansion and centralized concentric expansion. And the scale of the expansion is under control.
An approach on the automatic generation of concept weights for test items

This paper proposes a structure that automatically analyzes the parameters of Chinese test items. This structure utilizes latent semantic analysis (LSA) to analyze the relationships of keywords among all test items in an item bank. It also uses the similarity measure to calculate the similarity degree of keywords. We also propose an algorithm, which is similar to the graph-theoretic algorithm, for keyword clustering. The concept weights of each test item can be generated automatically with the proposed automatic generate concept weights.
Face Recognition Using a Multilinear Framework

Appearance-based method classifies images by their appearances, using statistical knowledge learned from training data. This paper presents a framework of multilinear algebra-based face recognition, which proposes the use of tensor projector. We will discuss the choice of distance function, which is not usually paid attention to in most research on this area. In fact, a properly chosen distance function is nontrivial to the recognition rate, even for the linear Tensorface method. This implies that if we apply a more advanced method such as ICA, an even better performance can be achieved. At the end of this paper, we present our experiment result based on the framework, which was performed on the Freiburg face database.