Prediction of the dynamic effective properties of particle-reinforced composite materials

The prediction behaviors of some coherent plane wave equations for the effective velocities and attenuations of the coherent plane waves propagating through a composite material and for the effective elastic moduli of the composites are studied. The numerical results obtained by Waterman & Truell's, Twersky's and Gubernatis's equations for Glass-Epoxy composites with various volume fractions are compared. It is found that the predictions by both Twersky's and Gubernatis's equations underestimate the effective velocities and the effective elastic moduli when compare with the predictions by Waterman & Truell's equation. Furthermore, the deviations are more evident for the shear wave than that for the longitudinal wave. But these deviations decrease gradually with the increase of the frequency and increase gradually with the increase of the volume fraction.

A Novel User Behavior Prediction Model Based on Automatic Annotated Behavior Recognition in Smart Home Systems

User behavior prediction has become a core element to Internet of Things(IoT)and received promising attention in the related fields.Many existing IoT systems(e.g.smart home systems)have been deployed various sensors and the user’s behavior can be predicted through the sensor data.However,most of the existing sensor-based systems use the annotated behavior data which requires human intervention to achieve the behavior prediction.Therefore,it is a challenge to provide an automatic behavior prediction model based on the original sensor data.To solve the problem,this paper proposed a novel automatic annotated user behavior prediction(AAUBP)model.The proposed AAUBP model combined the Discontinuous Solving Order Sequence Mining(DVSM)behavior recognition model and behavior prediction model based on the Long Short Term Memory(LSTM)network.To evaluate the model,we performed several experiments on a real-world dataset tuning the parameters.The results showed that the AAUBP model can effectively recognize behaviors and had a good performance for behavior prediction.

ICS-SVM:A user retweet prediction method for hot topics based on improved SVM

In social networks,many complex factors affect the prediction of user forwarding behavior.This paper proposes an improved SVM prediction method for user forwarding behavior of hot topics to improve prediction accuracy.Firstly,we consider that the improved Cuckoo Search algorithm can select the optimal penalty parameters and kernel function parameters to optimize the SVM and thus predict the user's forwarding behavior.Secondly,this paper considers the factors that affect the user forwarding behavior comprehensively from the user's own factors and external factors.Finally,based on the characteristics of the user's forwarding behavior changing over time,the time-slicing method is used to predict the trend of hot topics.Experiments show that the method can accurately predict the user's forwarding behavior and can sense the trend of hot topics.

Numerical simulation and experimental verification of bubble size distribution in an air dense medium fluidized bed

Bubble size distribution is the basic apparent performance and obvious characteristics in the air dense medium fluidized bed (ADMFB). The approaches of numerical simulation and experimental verification were combined to conduct the further research on the bubble generation and movement behavior. The results show that ADMFB could display favorable expanded characteristics after steady fluidization. With different particle size distributions of magnetite powder as medium solids, we selected an appropriate prediction model for the mean bubble diameter in ADMFB. The comparison results indicate that the mean bubble diameters along the bed heights are 35 mm < D b < 66 mm and 40 mm < D b < 69 mm with the magnetite powder of 0.3 mm+0.15mm and 0.15mm+0.074mm, respectively. The prediction model provides good agreements with the experimental and simulation data. Based on the optimal operating gas velocity distribution, the mixture of magnetite powder and <1mm fine coal as medium solids were utilized to carry out the separation experiment on 6-50mm raw coal. The results show that an optimal separation density d P of 1.73g/cm 3 with a probable error E of 0.07g/cm 3 and a recovery efficiency of 99.97% is achieved, which indicates good separation performance by applying ADMFB.

On the Chaotic Behavior and Predictability of the Real Atmosphere

In this paper the concept of Chaos and its applications to the study of predictability theory is introduced. The author's attempt is to give a general overview of ideas and methods involved in this problem to scientists,who are interested in the problem of predictability but not familiar with the theory of chaos. The problem is discussed in 4 sections. In the first section, the concept of chaos and the study methods are outlined briefly; in the second section, the methods of quantitatively measuring the main characteristics of chaos which are the basis for the predictability theory are introduced; the third section discusses the time series analysis for directly studying chaotic phenomena in practical problems; and the last section presents some research results on the chaotic characteristics and the predictability of the real atmosphere.

Energy Balance-related Behaviors Are Related to Cardiometabolic Parameters and Predict Adiposity in 8-14-year-old Overweight Chinese Children One Year Later

To identify target energy balance-related behaviors（ERBs）,baseline data from 141overweight or obese schoolchildren（aged 8-14years old）was used to predict adiposity[body mass index（BMI）and fat percentage]one year later.The ERBs included a modified Dietary Approach to Stop Hypertension diet score（DASH score）,leisure-time physical activity（PA,days/week）,and leisure screen time（minutes/day）.Several cardiometabolic variables were measured in the fasting state, including systolic blood pressure （SBP）, diastolic blood pressure （DBP）, blood glucose （GLU）, total cholesterol （TC）, triglycerides （TG）, low-density lipoprotein （LDL-C）, and high-density lipoprotein （HDL-C）.

A Novel Cultural Crowd Model Toward Cognitive Artificial Intelligence

Existing literature shows cultural crowd management has unforeseen issues due to four dynamic elements;time,capacity,speed,and culture.Crosscultural variations are increasing the complexity level because each mass and event have different characteristics and challenges.However,no prior study has employed the six Hofstede Cultural Dimensions(HCD)for predicting crowd behaviors.This study aims to develop the Cultural Crowd-Artificial Neural Network(CC-ANN)learning model that considers crowd’s HCD to predict their physical(distance and speed)and social(collectivity and cohesion)characteristics.The model was developed towards a cognitive intelligent decision support tool where the predicted characteristics affect the estimated regulation plan’s time and capacity.We designed the experiments as four groups to analyze the proposed model’s outcomes and extract the interrelations between the HCD of crowd’s grouped individuals and their physical and social characteristics.Furthermore,the extracted interrelations were verified with the dataset’s statistical correlation analysis with a P-value<0.05.Results demonstrate that the predicted crowd’s characteristics were positively/negatively affected by their considered cultural features.Similarly,analyzing outcomes identified the most influential HCD for predicting crowd behavior.The results also show that the CC-ANN model improves the prediction and learning performance for the crowd behavior because the achieved accepted level of accuracy does not exceed 10 to 20 epochs in most cases.Moreover,the performance improved by 90%,93%respectively in some cases.Finally,all prediction best cases were related to one or more cultural features with a low error of 0.048,0.117,0.010,and 0.014 mean squared error,indicating a novel cultural learning model.

Fractal and chaotic laws on seismic dissipated energy in an energy system of enginering structures

Fractal and chaotic laws of engineering structures are discussed in this paper, it means that the intrinsic essences and laws on dynamic systems which are made from seismic dissipated energy intensity E d and intensity of seismic dissipated energy moment I e are analyzed. Based on the intrinsic characters of chaotic and fractal dynamic system of E d and I e, three kinds of approximate dynamic models are rebuilt one by one: index autoregressive model, threshold autoregressive model and local-approximate autoregressive model. The innate laws, essences and systematic error of evolutional behavior I e are explained over all, the short-term behavior predictability and long-term behavior probability of which are analyzed in the end. That may be valuable for earthquake-resistant theory and analysis method in practical engineering structures.

cffdrs: an R package for the Canadian Forest Fire Danger Rating System

Introduction:The Canadian Forest Fire Danger Rating System(CFFDRS)is a globally known wildland fire risk assessment system,and two major components,the fire weather index system and the fire behavior prediction system,have been extensively used both nationally and internationally to aid operational wildland fire decision making.Methods:In this paper,we present an overview of an R package cffdrs,which is developed to calculate components of the CFFDRS,and highlight some of its functionality.In particular,we demonstrate how these functions could be used for large data analysis.Results and Discussion:With this cffdrs package,we provide a portal for not only a collection of R functions dealing with all available components in CFFDRS but also a platform for various additional developments that are useful for the understanding of fire occurrence and behavior.This is the first time that all relevant CFFDRS methods are incorporated into the same platform,which can be accessed by both the management and research communities.

Experimental and simulated characterization of creep behavior of P92 steel with prior cyclic loading damage

The effect of prior cyclic loading on creep behavior of P92 steel was investigated. Creep tests on prior cyclic loading exposure specimens were performed at 650？C and 130 MPa. In order to clarify the influence of prior cyclic loading on creep behavior, optical microscope, scanning electron microscope and transmission electron microscope were used. Experimental results indicate that the prior cyclic loading degrades the creep strength significantly. However, the degradation tends to be saturated with further increase in prior cyclic loading. From the view of microstructural evolution, the recovery of martensite laths takes place during prior cyclic loading exposure. This facilitates the dislocation movement during the following creep process. Therefore, premature rupture of creep test occurs. Additionally, saturated behavior of degradation can be attributed to the near completed recovery of martensite laths. Based on the effect of prior cyclic loading, a newly modified Hayhurst creep damage model was proposed to consider the prior cyclic loading damage. The main advantage of the proposed model lies in its ability to directly predict creep behavior with different levels of prior cyclic loading damage. Comparison of the predicted and experimental results shows that the proposed model can give a reasonable prediction for creep behavior of P92 steel with different level of prior cyclic loading damage.

A novel spatio-temporal trajectory data-driven development approach for autonomous vehicles

Nowadays,autonomous driving has been attracted widespread attention from academia and industry.As we all know,deep learning is effective and essential for the development of AI components of Autonomous Vehicles(AVs).However,it is challenging to adopt multi-source heterogenous data in deep learning.Therefore,we propose a novel data-driven approach for the delivery of high-quality Spatio-Temporal Trajectory Data(STTD)to AVs,which can be deployed to assist the development of AI components with deep learning.The novelty of our work is that the meta-model of STTD is constructed based on the domain knowledge of autonomous driving.Our approach,including collection,preprocessing,storage and modeling of STTD as well as the training of AI components,helps to process and utilize huge amount of STTD efficiently.To further demonstrate the usability of our approach,a case study of vehicle behavior prediction using Long Short-Term Memory(LSTM)networks is discussed.Experimental results show that our approach facilitates the training process of AI components with the STTD.

On-Line Predicting Behaviors of Jobs in Dynamic Load Balancing

A key issue of dynamic load balancing in a loosely coupled distributed systemis selecting appropriate jobs to transfer. In this paper, a job selection policybased on on-line predicting behaviors of jobs is proposed. Thacing is used atthe beginning of execution of a job to predict the approkimate execution timeand resource requirements of the job so as to make a correct decision aboutwhether transferring the job is worthwhile. A dynamic load balancer using thejob selection policy has been implemelited. Experimelital measurement resultsshow that the policy proposed is able to improve mean response time of jobsand resource utilization of systems substantially.