Speed limit effect during lane change in a two-lane lattice model under V2X environment

Speed limit measures are ubiquitous due to the complexity of the road environment,which can be supplied with the help of vehicle to everything(V2X)communication technology.Therefore,the influence of speed limit on traffic system will be investigated to construct a two-lane lattice model accounting for the speed limit effect during the lane change process under V2X environment.Accordingly,the stability condition and the mKdV equation are closely associated with the speed limit effect through theory analysis.Moreover,the evolution of density and hysteresis loop is simulated to demonstrate the positive role of the speed limit effect on traffic stability in the cases of strong reaction intensity and high limited speed.

Study on establishment of standardized load spectrum on bogie frames of high-speed trains

Establishing structural load spectrum under actual operating conditions is a major problem in structural fatigue life analysis. This study introduces the load measuring method for the bogie frame structure. The load-measuring frame based on quasi-static can measure different load systems synchronously. The t-test method is employed to evaluate the least test time of deducing the parent distribution. In order to fit the load spectrum distribution accurately, the kernel density estimation method is employed which is based on the sample characteristics. The expansion factor method is used to deduce the maximum load. The formula of standardized load spectrum derives from the deduced maximum load, the linear factor between operating condition length and cumulative frequency and the parent distribution of each load system. The damage consistency criterion is performed by solving the objective function with constraint conditions. The calibrated damage provides a suitable representation of the real damage under actual operating conditions. By processing and analyzing the load spectrum and stress spectrum data of the measured lines, it is verified that the standardized load spectrum established in this paper is superior to the European specification and the Japanese specification in evaluating the fatigue reliability of the structure.

Parallel solving model for quantified boolean formula based on machine learning

A new parallel architecture for quantified boolean formula(QBF)solving was proposed,and the prediction model based on machine learning technology was proposed for how sharing knowledge affects the solving performance in QBF parallel solving system,and the experimental evaluation scheme was also designed.It shows that the characterization factor of clause and cube influence the solving performance markedly in our experiment.At the same time,the heuristic machine learning algorithm was applied,support vector machine was chosen to predict the performance of QBF parallel solving system based on clause sharing and cube sharing.The relative error of accuracy for prediction can be controlled in a reasonable range of 20%30%.The results show the important and complex role that knowledge sharing plays in any modern parallel solver.It shows that the parallel solver with machine learning reduces the quantity of knowledge sharing about 30%and saving computational resource but does not reduce the performance of solving system.

A first-principles study on zigzag phosphorene nanoribbons terminated by transition metal atoms

We have investigated the electronic and magnetic properties of zigzag phosphorene nanoribbons(ZPNRs)with transition metal(TM)passivated atoms,it can be found that the ZPNRs with TM passivated atoms exhibit different magnetisms except for the Ni-passivated system.Meanwhile,the results show that the magnetic moments of ZPNRs with TM passivated atoms are larger than that of ZPNRs with other passivated non-metals/groups.Interestingly,it can be found that Fe-passivated ZPNR exhibits magnetic semiconducting character,which provides the possbility for the application of phosphorene in information storage.For Mn-passivated ZPNRs,it exhibits the half-metallicity.These results may be useful for potential applications of phosphorene in electronic and high-performance spintronic devices.

VAGUE MAPPINGS

In this paper, the concepts of (α,β) vague mappings, surjective (α,β) vague mappings, injective (α,β) vague mappings, bijective (α,β) vague mappings,(α,β) adjoin vague mappings, (α,β) vague monomorphism,(α,β) vague epimorphism,(α,β) vague isomorphism,(α,β) Vague par-tition are introduced through the so-called "(α,β) hierarchical divide vague relations ". We extend the results on fuzzy mappings, and obtain some of their properties.

Research Progress and Trends of Domestic Smart Learning Environment

In order to explore the main progress and current status of domestic research on smart learning environment, this paper takes 260 core and CSSCI journal papers included in the CNKI database as the research objects, and uses CiteSpace visual analysis software and uses bibliometrics and knowledge graph analysis as the main research methods, summarizes and analyzes the time distribution of the literature, the distribution of institutions and authors, co-occurrence and clustering of keywords, and research hotspots,etc.

Development Elements of VR Classrooms in China

VR classroom is an integrated teaching environment of content, hardware and platform. Its use is affected by the following three aspects, including price and performance of hardware equipment, cost and difficulty of courseware development, and quality of teaching by VR courseware. Therefore, the future development trend of in VR classroom should be the trinity of terminal+courseware+SaaS.

Image Edge Detection Method Based on Ant Colony Algorithm

Ant colony algorithm has good results in finding the optimal solution in a certain field;and image edge detection is an essential foundation for all kinds of image processing. How to improve image edge detection becomes a hot topic in image processing. In this paper, the ant colony algorithm is applied to image edge detection, and the ant colony algorithm’s discreteness, parallelism and positive feedback are fully utilized. Through repeated iteration, pheromone acquisition and pheromone matrix were continuously updated to search for images step by step. The experimental results show that the ant colony algorithm can effectively detect the edge of the image, and the detection effect of the algorithm is significantly improved compared with the Roberts algorithm.

Identifying Transportation Modes from Raw GPS Data

Raw Global Positioning System (GPS) data can provide rich context information for behaviour understanding and transport planning. However, they are not yet fully understood, and fine-grained identification of transportation mode is required. In this paper, we present a robust framework without geographic information, which can effectively and automatically identify transportation modes including car, bus, bike and walk. Firstly, a trajectory segmentation algorithm is designed to divide raw GPS trajectory into single mode segments. Secondly, several modern features are proposed which are more discriminating than traditional features. At last, an additional postprocessing procedure is adopted with considering the wholeness of trajectory. Based on Random Forest classifier, our framework can achieve a promising accuracy by distance of 82.85% for identifying transportation modes and especially 91.44% for car mode.

Deep Broad Learning for Emotion Classification in Textual Conversations

Emotion classification in textual conversations focuses on classifying the emotion of each utterance from textual conversations.It is becoming one of the most important tasks for natural language processing in recent years.However,it is a challenging task for machines to conduct emotion classification in textual conversations because emotions rely heavily on textual context.To address the challenge,we propose a method to classify emotion in textual conversations,by integrating the advantages of deep learning and broad learning,namely DBL.It aims to provide a more effective solution to capture local contextual information(i.e.,utterance-level)in an utterance,as well as global contextual information(i.e.,speaker-level)in a conversation,based on Convolutional Neural Network(CNN),Bidirectional Long Short-Term Memory(Bi-LSTM),and broad learning.Extensive experiments have been conducted on three public textual conversation datasets,which show that the context in both utterance-level and speaker-level is consistently beneficial to the performance of emotion classification.In addition,the results show that our proposed method outperforms the baseline methods on most of the testing datasets in weighted-average F1.

A Mixed-Integer Programming Formulation for a Simplified Model of the Double Row Layout Problem

The double row layout problem(DRLP)is to assign facilities on two rows in parallel so that the total cost of material handling among facilities is minimized.Since it is vital to save cost and enhance productivity,the DRLP plays an important role in many application fields.Nevertheless,it is very hard to handle the DRLP because of its complex model.In this paper,we consider a new simplified model for the DRLP(SM-DRLP)and provide a mixed integer programming(MIP)formulation for it.The continuous decision variables of the DRLP are divided into two parts:start points of double rows and adjustable clearances between adjacent facilities.The former one is considered in the new simplified model for the DRLP with the purpose of maintaining solution quality,while the latter one is not taken into account with the purpose of reducing computational time.To evaluate its performance,our SM-DRLP is compared with the model of a general DRLP and the model of another simplified DRLP.The experimental results show the efficiency of our proposed model.

Scoring the collective effects of SNPs: association of minor alleles with complex traits in model organisms

It has long been assumed that most parts of a genome and most genetic variations or SNPs are non-functional with regard to reproductive fitness.However,the collective effects of SNPs have yet to be examined by experimental science.We here developed a novel approach to examine the relationship between traits and the total amount of SNPs in panels of genetic reference populations.We identified the minor alleles(MAs)in each panel and the MA content(MAC)that each inbred strain carried for a set of SNPs with genotypes determined in these panels.MAC was nearly linearly linked to quantitative variations in numerous traits in model organisms,including life span,tumor susceptibility,learning and memory,sensitivity to alcohol and anti-psychotic drugs,and two correlated traits poor reproductive fitness and strong immunity.These results suggest that the collective effects of SNPs are functional and do affect reproductive fitness.

Establishment and verification of prediction models for evaluating the physical and chemical properties of soilless substrates

In soilless culture,a suitable mixed substrate that provides a balanced and stable rhizosphere environment is vital for promoting plant growth.The present study was undertaken to establish seven prediction models of physical and chemical properties,including bulk density(DB),total porosity(TP),water-holding porosity(WHP),air porosity(AP),WHP/AP,electrical conductivity(EC)and cation exchange capacity(CEC)of mixed substrate based on regression equations of measured values from 76 substrate combinations.These seven models were verified using the measured values of 12 mixed substrates,and the average relative prediction errors(REs)were all less than 10%.A comprehensive property prediction model was established by weighted summation of the seven models of physical and chemical properties.According to the set values of DB,TP,WHP,AP,WHP/AP,EC and CEC,the comprehensive property model predicted the six mixture proportions of mixed-substrate,as verified using the measured values.This study is the first to establish prediction models for the physical and chemical properties of mixed substrates.The comprehensive property model could be used to evaluate the physical and chemical properties of commercial mixed substrates,and to provide the optimal mixture substrate formulations according to the setting property value of production requirement.

A Binary Particle Swarm Optimization for the Minimum Weight Dominating Set Problem

The minimum weight dominating set problem （MWDSP） is an NP-hard problem with a lot of real-world applications. Several heuristic algorithms have been presented to produce good quality solutions. However, the solution time of them grows very quickly as the size of the instance increases. In this paper, we propose a binary particle swarm optimization （FBPSO） for solving the MWDSP approximately. Based on the characteristic of MWDSP, this approach designs a new position updating rule to guide the search to a promising area. An iterated greedy tabu search is used to enhance the solution quality quickly. In addition, several stochastic strategies are employed to diversify the search and prevent premature convergence. These methods maintain a good balance between the exploration and the exploitation. Experimental studies on 106 groups of 1 060 instances show that FBPSO is able to identify near optimal solutions in a short running time. The average deviation between the solutions obtained by FBPSO and the best known solutions is 0.441%. Moreover, the average solution time of FBPSO is much less than that of other existing algorithms. In particular, with the increasing of instance size, the solution time of FBPSO grows much more slowly than that of other existing algorithms.

Broadband coplane metamaterial filter based on two nested split-ring-resonators

Split ring resonators （SRRs）-based broadband metamaterial filters have attracted considerable attention due to their great prospect of practical applications. These filters had been usually obtained by stacking multiple different-sized metallic patterns, making their fabrication quite troublesome. Herein, we presented a simple design of broadband filter composed of two nested SRRs. The resonance bandwidth of the metamatefial filter gradually increased with the decrease of the ann length of the inner SRR. The increase in the resonance bandwidth was attributed to the increase in the radiation of the entire structure. Moreover, the bandwidth of the metamaterial can be further broadened by decreasing the period of the structure. The proposed filter provides a meaningful way toward expanding the bandwidth operating range from narrowband to broadband in an effective way.

The distribution characteristics of the flows in the near-Earth region: TC-1 observational results

TC-1 observational results clearly indicate that the velocity of the flows in the near-Earth region is de- pendent on the satellite location. The flow speed decreases while satellite moves close to the Earth. The plasma flow in the region close to the Earth tends to drift into the midnight region from the dawn and dusk region while the flow in the region away from the Earth shows an opposite drift. The obser- vational results also show that the tailward flows are mainly located in the plasma sheet boundary while the earthward flow becomes dominant in the plasma sheet. It is found that both the strong tailward and earthward flows are distributed in the region around X= -11Re, which coincides with the trigger region of the substorm onset. Hence, it may suggest that the flows are related with the trigger of the substorm onset. In addition, the BBFs coming from the mid-tail maily distributed in the region where X<-9RE and |Z|<3RE that differs from the convection.