Combined hybrid energy storage system and transmission grid model for peak shaving based on time series operation simulation

This study proposes a combined hybrid energy storage system(HESS) and transmission grid(TG) model, and a corresponding time series operation simulation(TSOS) model is established to relieve the peak-shaving pressure of power systems under the integration of renewable energy. First, a linear model for the optimal operation of the HESS is established, which considers the different power-efficiency characteristics of the pumped storage system, electrochemical storage system, and a new type of liquid compressed air energy storage. Second, a TSOS simulation model for peak shaving is built to maximize the power entering the grid from the wind farms and HESS. Based on the proposed model, this study considers the transmission capacity of a TG. By adding the power-flow constraints of the TG, a TSOS-based HESS and TG combination model for peak shaving is established. Finally, the improved IEEE-39 and IEEE-118 bus systems were considered as examples to verify the effectiveness and feasibility of the proposed model.

Combined model of trip mode and destination

This paper analyzes the characteristics of the destination distribution of trips and proposes a stratified sampling strategy for travel mode choice.The stratified sampling strategy can reduce the size of the alternative set;thus,the computation burden of simulation is decreased.Using the stratified sampling strategy,a combined choice model of the trip mode and destination is developed based on the Bayesian theory.Simulations are carried out to verify the proposed model.The results show that the combined choice model of the trip mode and destination can efficiently simulate travelers＇ choice behaviors.Furthermore,the forecasting accuracy of the combined choice model is higher than the one of the gravity model.Therefore,the proposed model is a powerful tool with which to analyze travelers＇ behaviors in selecting the trip mode.

Forecasting China’s natural gas consumption based on a combination model

Ensuring a sufficient energy supply is essential to a country. Natural gas constitutes a vital part in energy supply and therefore forecasting natural gas consumption reliably and accurately is an essential part of a country＇s energy policy. Over the years, studies have shown that a combinative model gives better projected results compared to a single model. In this study, we used Polynomial Curve and Moving Average Combination Projection （PCMACP） model to estimate the future natural gas consumption in China from 2009 to 2015. The new proposed PCMACP model shows more reliable and accurate results： its Mean Absolute Percentage Error （MAPE） is less than those of any previous models within the investigated range. According to the PCMACP model, the average annual growth rate will increase for the next 7 years and the amount of natural gas consumption will reach 171600 million cubic meters in 2015 in China.

Micro Mechanical Model of 3D Woven Composites

A combined beam model representing the periodicity of the microstructure and micro deformation of 3D woven composites is developed for predicting mechanical properties. The model considers the effects of off axial tension/compression and bending/shearing couplings as well as the mutual reactions of fiber yarns. The method determining microstructure by using woven parameters is described for a typical 3D woven composite material. An analytical cell, constructed by a minimum periodic section of yarn and interlayer matrix, is adopted. Micro stresses in the cell under in-plane tensile loading are obtained by using the proposed beam model and macro modulus is then obtained by the averaging method. Material tests and a 2D micro FEM analysis are made to evaluate this model. Analyses reveal that micro stress caused by tensile/bending coupling effect is not negligible in the stress analysis.

Combined model based on optimized multi-variable grey model and multiple linear regression

The construction method of background value is improved in the original multi-variable grey model （MGM（1,m）） from its source of construction errors. The MGM（1,m） with optimized background value is used to eliminate the random fluctuations or errors of the observational data of all variables, and the combined prediction model together with the multiple linear regression is established in order to improve the simulation and prediction accuracy of the combined model. Finally, a combined model of the MGM（1,2） with optimized background value and the binary linear regression is constructed by an example. The results show that the model has good effects for simulation and prediction.

A Novel Approach for QoS Prediction Based on Bayesian Combinational Model

As an important factor in evaluating service,QoS(Quality of Service) has drawn more and more concerns with the rapid increasing of Web services. However,due to the great volatility of services in Mobile Internet environments,such as internet of vehicles,Web services often do not work as announced and thus cause unacceptable problems. QoS prediction can avoid failure before it takes place,which is considered a more effective way to assure quality. However,Current QoS prediction approaches neither consider the highly dynamic of Web services,nor maintain good prediction performance all the time. Consequently we propose a novel Bayesian combinational model to predict QoS by continuously adjusting credit values of the basic models so as to keep good prediction accuracy. QoS attributes such as response time,throughput and reliability are used to validate the proposed model. Experimental results show that the model can provide stable prediction results in Mobile Internet environments.

Settlement Prediction of Dredger Fill with the Optimal Combination Model

Post-construction settlement has gained increasing attention because it frequently causes engineering problems. A combined model is a commonly used prediction model that overcomes the difficulty of a single model( i. e., cannot reflect various regulations of settlement at some stages or the entire process). In this study,the correlation coefficient,maximum error values,and other values were obtained according to the fitting and predicted results of a single model. The coefficient of variation was then introduced to determine the weight of each model forming the combination. The proposed model was used to fit and predict for settlement and overcome the issue of utilizing a single model while determining the weight. The fitting predictive effect was also analyzed using the settlement fitting precision results. The fitting precision of optimizing the combination model is high. The predicted data of the post-construction settlement are closer to the calculated value of the settlement monitoring data. Moreover,the proposed model has good practicability,does not require the interval data of settlement,and restricts the model number. Thus,this model can be applied in the engineering field.

Traffic flow prediction of urban road network based on LSTM-RF model

Traffic flow prediction,as the basis of signal coordination and travel time prediction,has become a research point in the field of transportation.For traffic flow prediction,researchers have proposed a variety of methods,but most of these methods only use the time domain information of traffic flow data to predict the traffic flow,ignoring the impact of spatial correlation on the prediction of target road segment flow,which leads to poor prediction accuracy.In this paper,a traffic flow prediction model called as long short time memory and random forest(LSTM-RF)was proposed based on the combination model.In the process of traffic flow prediction,the long short time memory(LSTM)model was used to extract the time sequence features of the predicted target road segment.Then,the predicted value of LSTM and the collected information of adjacent upstream and downstream sections were simultaneously used as the input features of the random forest model to analyze the spatial-temporal correlation of traffic flow,so as to obtain the final prediction results.The traffic flow data of 132 urban road sections collected by the license plate recognition system in Guiyang City were tested and verified.The results show that the method is better than the single model in prediction accuracy,and the prediction error is obviously reduced compared with the single model.

A combined numerical tidal model for the Hangzhou Bay and Qiantang River

-In order to avoid prescribing open boundary condition on the upstream side of the Hangzhou Bay, in numerical simulation of the tides and residual currents of the Bay, a 1-D model for the Qiantang River is connected to the 2-D model for the Hangzhou Bay. The harmonic constants of diurnal constituent [ (K1+O1)/2],semidiurnal constituent (M2) and shallow water constituent (M4) are obtained. The results produced by the combined model are in better agreement with the observed ones than those produced solely by the original 2-D model. The combined model gives much more reliable results for tide-induced residual water level and current.

Study of establishing disease-syndrome combined with animal model for immune thrombocytopenic purpura without additional conditions

Objective:To explore the feasibility of establishing the disease-syndrome combined animal model for immune thrombocytopenic purpura(ITP)without additional conditions.Methods:Three batches of data related to the ITP model mice obtained by replication at different time were analyzed,and whether the APS-injected model mice replicated through the passive immune modeling method could simulate the pathogenesis and clinical characteristics of human ITP was evaluated according to the differentiation criteria for diseasesyndrome combined model.Results:The APS-injected replicated ITP model mice possessed the following traits:(1)Compared with the normal group,the platelet count was significantly decreased,and coagulation time was significantly increased in the model group(P<.01).(2)Compared with the normal group,the medullary thrombocytogenous megakaryocytes were significantly decreased(P<.05,.01,.001).(3)The APS-injected sites and other parts of the model mice had spontaneous hemorrhage.(4)Behavioral changing signs were observed 1 week after the modeling(i.e.low activity,delayed activity,poor appetite,skin petechia/hemorrhage and spontaneous hemorrhage at the injected sites or other parts),and were getting more and more severe.Conclusion:According to the syndrome differentiation criteria for disease-syndrome combined model of ITP,the APS-injected animal model of ITP replicated through the passive immune modeling method without additional conditions possesses the characteristics of disease-syndrome combined model.It provides an ideal tool for the development of traditional Chinese medicine pharmacology experiment.

Grey series time-delay predicting model in state estimation for power distribution networks

A new combined model is proposed to obtain predictive data value applied in state estimation for radial power distribution networks. The time delay part of the model is calculated by a recursive least squares algorithm of system identification, which can gradually forget past information. The grey series part of the model uses an equal dimension new information model (EDNIM) and it applies 3 points smoothing method to preprocess the original data and modify remnant difference by GM(1,1). Through the optimization of the coefficient of the model, we are able to minimize the error variance of predictive data. A case study shows that the proposed method achieved high calculation precision and speed and it can be used to obtain the predictive value in real time state estimation of power distribution networks.

Quantitative analysis of glutamate compounds in the swine brain following central analgesics nasal spray using proton magnetic resonance spectroscopy and linear combination model

BACKGROUND： In localized brain proton magnetic resonance spectroscopy （^1H-MRS）, metabolite levels are often expressed as ratios, rather than absolute concentrations. Frequently, the denominator is creatine, which is assumed to be stable in normal, as well as many pathological, states. However, in vivo creatine levels do not remain constant. Therefore, absolute metabolite measurements, which provide the precise concentrations of certain chemical compounds, are superior to metabolite ratios for determining pathological and evolutional changes. OBJECTIVE： To investigate the feasibility of quantification analysis of brain metabolite changes caused by central analgesics nasal spray using the ^1H-MRS and linear combination model （LCModel） methods. DESIGN, TIME AND SETTING： This neuroimaging, observational, animal study was performed at the Laboratory of the Department of Medical Imaging, Second Affiliated Hospital, Medical College, Shantou University, China from July to December 2007. MATERIALS： Butorphanol tartrate nasal spray, as a mixed agonist-antagonist opioid analgesic, was purchased from Shanghai Hengrui Pharmacy, China. A General Electric Signa 1.5T System （General Electric Medical Systems, Milwaukee, WI, USA） and LCModel software （Stephen Provencher, Oakville, Ontario, Canada） were used in this study. METHODS： MRS images were acquired in ten healthy swine aged 2 weeks using single-voxel point-resolved spectroscopic sequence. A region of interest （2 cm × 2 cm × 2 cm） was placed in the image centers of maximum brain parenchyma. Repeated MRS scanning was performed 15-20 minutes after intranasal administration of 1 mg of butorphanol tartrate. Three settings of repetition time/echo time were selected before and after nasal spray administration 3 000 ms/30 ms,1 500 ms/30 ms, and 3 000 ms/50 ms. Metabolite concentrations were estimated by LCModel software. MAIN OUTCOME MEASURES： ^1H-MRS spectra was obtained using various repetition time/echo time settings. Concentrations of glutamate compounds （glutamate ＋ glutamine）, N-acetyl aspartate, and choline were detected in swine brain prior to and following nasal spray treatment. RESULTS： The glutamate compounds curve was consistent with original spectra, when a repetition time/echo time of 3 000 ms/30 ms was adopted. Concentrations of glutamate compounds, N-acetyl aspartate, and choline decreased following administration. The most significant reduction was observed in glutamate compound concentrations from （9.28 ± 0.54） mmol/kg to （7.28 ± 0.54） mmol/kg （P 〈 0.05）. CONCLUSION： ^1H-MRS and LCModel software were effectively utilized to quantitatively analyze and measure brain metabolites. Glutamate compounds might be an important neurotransmitter in central analgesia.

A COMBINED MODEL OF WIND, WAVE, TIDE AND STORM SURGES

A combined numerical model of wind, wave, tide, and storm surges was built on the basis of the “wind field model in limited sea surface areas”. When used to forecast the sea surface wind, wave height and water level, it can describe them very well.

Education Knowledge System Combination Model Based on the Components

Resources are the base and core of education information, but current web education resources have no structure and it is still difficult to reuse them and make them can be self assembled and developed continually. According to the knowledge structure of course and text, the relation among knowledge points, knowledge units from three levels of media material, we can build education resource components, and build TKCM （Teaching Knowledge Combination Model） based on resource components. Builders can build and assemble knowledge system structure and make knowledge units can be self assembled, thus we can develop and consummate them continually. Users can make knowledge units can be self assembled and renewed, and build education knowledge system to satisfy users＇ demand under the form of education knowledge system.

Combined bottleneck effect of on-ramp and bus stop in a cellular automaton model

The combined bottleneck effect is investigated by modeling traffic systems with an on-ramp and a nearby bus stop in a two-lane cellular automaton model. Two cases, i.e. the bus stop locates in the downstream section of the on-ramp and the bus stop locates in the upstream section of the on-ramp, are considered separately. The upstream flux and downstream flux of the main road, as well as the on-ramp flux are analysed in detail, with respect to the entering probabilities and the distance between the on-ramp and the bus stop. It is found that the combination of the two bottlenecks causes the capacity to drop off, because the vehicles entering the main road from the on-ramp would interweave with the stopping （pulling-out） buses in the downstream （upstream） case. The traffic conflict in the former case is much heavier than that in the latter, causing the downstream main road to be utilized inefficiently. This suggests that the bus stop should be set in the upstream section of the on-ramp to enhance the capacity. The fluxes both on the main road and on the on-ramp vary with the distance between the two bottlenecks in both cases. However, the effects of distance disappear gradually at large distances. These findings might give some guidance to traffic optimization and management.

ARMA-GM combined forewarning model for the quality control

Three forecasting models are set up: the auto\|regressive moving average model, the grey forecasting model for the rate of qualified products P t, and the grey forecasting model for time intervals of the quality catastrophes. Then a combined forewarning system for the quality of products is established, which contains three models, judgment rules and forewarning state illustration. Finally with an example of the practical production, this modeling system is proved fairly effective.

Prediction of a maximum pull-out load of anchor bolts using an optimal combination model

The mixed model of improved exponential and power function and unequal interval gray GM(1,1)model have poor accuracy in predicting the maximum pull-out load of anchor bolts.An optimal combination model was derived using the optimally weighted combination theory and the minimum sum of logarithmic squared errors as the objective function.Two typical anchor bolt pull-out engineering cases were selected to compare the performance of the proposed model with those of existing ones.Results showed that the optimal combination model was suitable not only for the slow P-s curve but also for the steep P-s curve.Its accuracy and stable reliability,as well as its prediction capability classification,were better than those of the other prediction models.Therefore,the optimal combination model is an effective processing method for predicting the maximum pull-out load of anchor bolts according to measured data.

Application of BIM Technology in Rapid Modeling of Residential Building Design

Combining with engineering examples,this paper introduces BIM into architectural design model and adopts Revit rapid modeling.It focuses on realizing rapid combination modeling of similar components through secondary development,improving information conversion efficiency between engineering CAD and Revit building structure modeling,and quickly generating BIM structure construction drawings.

Combined refraction and diffraction models for sea waves over complicated topography and with currents in shallow water

-Combined refraction and diffraction models in the form of linear parabolic approximation are derived through smallparameter method. More strictly theoretical basis and more accuracy in the models than Lozano's (1980) are obtained. Some theoretical defects in Liu's model (1985) with consideration of current are not only found but also eliminated. More strict and accurate models are, therefore, presented in this paper.The calculation results and analysis in applying the models to actual wave field with consideration of bottom friction will be given in the following paper.

Designing a High-Performance Deep Learning Theoretical Model for Biomedical Image Segmentation by Using Key Elements of the Latest U-Net-Based Architectures

Deep learning (DL) has experienced an exponential development in recent years, with major impact in many medical fields, especially in the field of medical image and, respectively, as a specific task, in the segmentation of the medical image. We aim to create a computer assisted diagnostic method, optimized by the use of deep learning (DL) and validated by a randomized controlled clinical trial, is a highly automated tool for diagnosing and staging precancerous and cervical cancer and thyroid cancers. We aim to design a high-performance deep learning model, combined from convolutional neural network (U-Net)-based architectures, for segmentation of the medical image that is independent of the type of organs/tissues, dimensions or type of image (2D/3D) and to validate the DL model in a randomized, controlled clinical trial. We used as a methodology primarily the analysis of U-Net-based architectures to identify the key elements that we considered important in the design and optimization of the combined DL model, from the U-Net-based architectures, imagined by us. Secondly, we will validate the performance of the DL model through a randomized controlled clinical trial. The DL model designed by us will be a highly automated tool for diagnosing and staging precancers and cervical cancer and thyroid cancers. The combined model we designed takes into account the key features of each of the architectures Overcomplete Convolutional Network Kite-Net (Kite-Net), Attention gate mechanism is an improvement added on convolutional network architecture for fast and precise segmentation of images (Attention U-Net), Harmony Densely Connected Network-Medical image Segmentation (HarDNet-MSEG). In this regard, we will create a comprehensive computer assisted diagnostic methodology validated by a randomized controlled clinical trial. The model will be a highly automated tool for diagnosing and staging precancers and cervical cancer and thyroid cancers. This would help drastically minimize the time and effort that specialists put into analyzing medical images, help to achieve a better therapeutic plan, and can provide a “second opinion” of computer assisted diagnosis.