Comparative analysis of different iterative model for exoatmospheric autonomous iterative guidance method

Space applications have raised the demand on autonomy, security and reliability for current transportation vehicle, which require guidance technology of vehicle must have strong robustness and adaptability. Therefore, it is needed to research exoatmospheric autonomous iterative guidance method with stronger adaptivity and higher accuracy. Based on preliminary research results, two new iterative models with performance index of maximum terminal energy for exoatmospheric autonomous iterative guidance method are proposed in this paper. Then comparative analysis between preliminary research iterative model and two new iterative models proposed is performed. The results demonstrate that the inner update iterative model proposed is the least sensitive to initial values and have the best convergence and performance in the three iterative models.

Iterative static modeling of channelized reservoirs using history-matched facies probability data and rejection of training image

Most inverse reservoir modeling techniques require many forward simulations, and the posterior models cannot preserve geological features of prior models. This study proposes an iterative static modeling approach that utilizes dynamic data for rejecting an unsuitable training image(TI) among a set of TI candidates and for synthesizing history-matched pseudo-soft data. The proposed method is applied to two cases of channelized reservoirs, which have uncertainty in channel geometry such as direction, amplitude, and width. Distance-based clustering is applied to the initial models in total to select the qualified models efficiently. The mean of the qualified models is employed as a history-matched facies probability map in the next iteration of static models. Also, the most plausible TI is determined among TI candidates by rejecting other TIs during the iteration. The posterior models of the proposed method outperform updated models of ensemble Kalman filter(EnKF) and ensemble smoother(ES) because they describe the true facies connectivity with bimodal distribution and predict oil and water production with a reasonable range of uncertainty. In terms of simulation time, it requires 30 times of forward simulation in history matching, while the EnKF and ES need 9000 times and 200 times, respectively.

Iterative regularization method for image denoising with adaptive scale parameter

In order to decrease the sensitivity of the constant scale parameter, adaptively optimize the scale parameter in the iteration regularization model （IRM） and attain a desirable level of applicability for image denoising, a novel IRM with the adaptive scale parameter is proposed. First, the classic regularization item is modified and the equation of the adaptive scale parameter is deduced. Then, the initial value of the varying scale parameter is obtained by the trend of the number of iterations and the scale parameter sequence vectors. Finally, the novel iterative regularization method is used for image denoising. Numerical experiments show that compared with the IRM with the constant scale parameter, the proposed method with the varying scale parameter can not only reduce the number of iterations when the scale parameter becomes smaller, but also efficiently remove noise when the scale parameter becomes bigger and well preserve the details of images.

An Iterative Clustering-Based Localization Algorithm for Wireless Sensor Networks

In wireless sensor networks,node localization is a fundamental middleware service.In this paper,a robust and accurate localization algorithm is proposed,which uses a novel iterative clustering model to obtain the most representative intersection points between every two circles and use them to estimate the position of unknown nodes.Simulation results demonstrate that the proposed algorithm outperforms other localization schemes (such as Min-Max,etc.) in accuracy,scalability and gross error tolerance.

Space station short-term mission planning using ontology modelling and time iteration

This paper studies the problem of the space station short-term mission planning, which aims to allocate the executing time of missions effectively, schedule the corresponding resources reasonably and arrange the time of the astronauts properly. A domain model is developed by using the ontology theory to describe the concepts, constraints and relations of the planning domain formally, abstractly and normatively. A method based on time iteration is adopted to solve the short-term planning problem. Meanwhile, the resolving strategies are proposed to resolve different kinds of conflicts induced by the constraints of power, heat, resource, astronaut and relationship. The proposed approach is evaluated in a test case with fifteen missions, thirteen resources and three astronauts. The results show that the developed domain ontology model is reasonable, and the time iteration method using the proposed resolving strategies can successfully obtain the plan satisfying all considered constraints.

Fitting Generalized Additive Logistic Regression Model with GAM Procedure

In dealing with nonparametric regression the GAM procedure is the most versatile of several new procedures. The terminology behind this procedure is more flexible than traditional parametric modeling tools. It relaxes the usual assumptions of parametric model and enables us to uncover structure to establish the relationship between independent variables and dependent variable in exponential family that may not be obvious otherwise. In this paper, we discussed two methods of fitting generalized additive logistic regression model, one based on Newton Raphson method and another based on iterative weighted least square method for first and second order Taylor series expansion. The use of the GAM procedure with the specified set of weights, using local scoring algorithm, was applied to real life data sets. The cubic spline smoother is applied to the independent variables. Based on nonparametric regression and smoothing techniques, this procedure provides powerful tools for data analysis.

Mode shift and stability control of a current mode controlled buck-boost converter operating in discontinuous conduction mode with ramp compensation

By establishing the discrete iterative mapping model of a current mode controlled buck-boost converter, this paper studies the mechanism of mode shift and stability control of the buck-boost converter operating in discontinuous conduction mode with a ramp compensation current. With the bifurcation diagrazn, Lyapunov exponent spectrum, time- domain waveform and parameter space map, the performance of the buck-boost converter circuit utilizing a compensating ramp current has been analysed. The obtained results indicate that the system trajectory is weakly chaotic and strongly intermittent under discontinuous conduction mode. By using ramp compensation, the buck-boost converter can shift from discontinuous conduction mode to continuous conduction mode, and effectively operates in the stable period-one region.

Dynamics and stabilization of peak current-mode controlled buck converter with constant current load

The discrete iterative map model of peak current-mode controlled buck converter with constant current load（CCL）,containing the output voltage feedback and ramp compensation, is established in this paper. Based on this model the complex dynamics of this converter is investigated by analyzing bifurcation diagrams and the Lyapunov exponent spectrum. The effects of ramp compensation and output voltage feedback on the stability of the converter are investigated. Experimental results verify the simulation and theoretical analysis. The stability boundary and chaos boundary are obtained under the theoretical conditions of period-doubling bifurcation and border collision. It is found that there are four operation regions in the peak current-mode controlled buck converter with CCL due to period-doubling bifurcation and border-collision bifurcation. Research results indicate that ramp compensation can extend the stable operation range and transfer the operating mode, and output voltage feedback can eventually eliminate the coexisting fast-slow scale instability.

Joint electricity and carbon market for Northeast Asia energy interconnection

Decarbonization of the electricity sector is crucial to mitigate the impacts of climate change and global warming over the coming decades.The key challenges for achieving this goal are carbon emission trading and electricity sector regulation,which are also the major components of the carbon and electricity markets,respectively.In this paper,a joint electricity and carbon market model is proposed to investigate the relationships between electricity price,carbon price,and electricity generation capacity,thereby identifying pathways toward a renewable energy transition under the transactional energy interconnection framework.The proposed model is a dynamically iterative optimization model consisting of upper-level and lower-level models.The upper-level model optimizes power generation and obtains the electricity price,which drives the lower-level model to update the carbon price and electricity generation capacity.The proposed model is verified using the Northeast Asia power grid.The results show that increasing carbon price will result in increased electricity price,along with further increases in renewable energy generation capacity in the following period.This increase in renewable energy generation will reduce reliance on carbon-emitting energy sources,and hence the carbon price will decline.Moreover,the interconnection among zones in the Northeast Asia power grid will enable reasonable allocation of zonal power generation.Carbon capture and storage (CCS) will be an effective technology to reduce the carbon emissions and further realize the emission reduction targets in 2030-2050.It eases the stress of realizing the energy transition because of the less urgency to install additional renewable energy capacity.

Symmetrical dynamics of peak current-mode and valley current-mode controlled switching dc-dc converters with ramp compensation

The discrete iterative map models of peak current-mode （PCM） and valley current-mode （VCM） controlled buck converters, boost converters, and buck-boost converters with ramp compensation are established and their dynamical behaviours are investigated by using the operation region, parameter space map, bifurcation diagram, and Lyapunov exponent spectrum. The research results indicate that ramp compensation extends the stable operation range of the PCM controlled switching dc-dc converter to D 〉 0.5 and that of the VCM controlled switching dc-dc converter to D 〈 0.5. Compared with PCM controlled switching dc-dc converters with ramp compensation, VCM controlled switching dc-dc converters with ramp compensation exhibit interesting symmetrical dynamics. Experimental results are given to verify the analysis results in this paper.

Point-of-Interest Recommendation in LocationBased Social Networks with Personalized Geo-Social Influence

Point-of-interest(POI) recommendation is a popular topic on location-based social networks(LBSNs).Geographical proximity,known as a unique feature of LBSNs,significantly affects user check-in behavior.However,most of prior studies characterize the geographical influence based on a universal or personalized distribution of geographic distance,leading to unsatisfactory recommendation results.In this paper,the personalized geographical influence in a two-dimensional geographical space is modeled using the data field method,and we propose a semi-supervised probabilistic model based on a factor graph model to integrate different factors such as the geographical influence.Moreover,a distributed learning algorithm is used to scale up our method to large-scale data sets.Experimental results based on the data sets from Foursquare and Gowalla show that our method outperforms other competing POI recommendation techniques.

The Discovery of Burst Topic and Its Intermittent Evolution in Our Real World

Nowadays, a considerably large number of documents are available over many online news sites (e.g., CNN and NYT). Therefore, the utilization of these online documents, for example, the discovery of a burst topic and its evolution, is a significant challenge. In this paper, a novel topic model, called intermittent Evolution LDA (iELDA) is proposed. In iELDA, the time-evolving documents are divided into many small epochs. iELDA utilizes the detected global topics as priors to guide the detection of an emerging topic and keep track of its evolution over different epochs. As a natural extension of the traditional Latent Dirichlet Allocation (LDA) and Dynamic Topic Model (DTM), iELDA has an advantage: it can discover the intermittent recurring pattern of a burst topic. We apply iELDA to real-world data from NYT; the results demonstrate that the proposed iELDA can appropriately capture a burst topic and track its intermittent evolution as well as produce a better predictive ability than other related topic models.

Cavity-Mode Properties of Semiconductor Lasers Operating in Strong-Feedback Regime

This paper investigates the modal properties of semiconductor lasers operating in the strong-feedback regime. Analytical expressions are developed based on an iterative travelling-wave model, which enable a complete and quantitative description of a compound cavity mode in its steady state. Additional information is provided about the physical inside into a compound laser system, such as a bifurcation diagram of the compound cavity modes for full variation range （from 0 to 1） of the external reflection coefficient and a more general shape for the diagram of photon density versus mode phase - this latter will reduce to the classical ＂ellipse＂ in the weak-feedback regime. It is shown that in the strong-feedback regime, a feedback laser is characterized by a small mode number and a high density of photons. This behavior confirms previous experimental observations, showing that beyond the coherence-collapse regime, the compound laser system could be re-stabilized, and that as a result power-enhanced low-noise stable laser operation with quasi-uniform pulsation is possible with external-mirror reflectivity close to 1. Moreover, it is also shown that for a compound system operating in the strong-feedback regime, an anti-reflection treatment of a laser can significantly reduce its current threshold, and that in the absence of this treatment excitation of a minimum-linewidth mode with higher output power would be possible inside such a system. Finally, it is shown that in the weak-feedback regime except for a phase shift the iterative travelling-wave model will reduce to the Lang-Kobayashi model in cases where the product of the feedback rate and the internal round-trip time is much less than unity （that would mean in situations of as-cleaved lasers）.

Methodology for Obtaining Optimal Sleeve Friction and Friction Ratio Estimates from CPT Data

Cone penetration testing (CPT) is a cost effective and popular tool for geotechnical site characterization. CPT consists of pushing at a constant rate an electronic penetrometer into penetrable soils and recording cone bearing (qc), sleeve friction (fc) and dynamic pore pressure (u) with depth. The measured qc, fs and u values are utilized to estimate soil type and associated soil properties. A popular method to estimate soil type from CPT measurements is the Soil Behavior Type (SBT) chart. The SBT plots cone resistance vs friction ratio, Rf [where: Rf = (fs/qc)100%]. There are distortions in the CPT measurements which can result in erroneous SBT plots. Cone bearing measurements at a specific depth are blurred or averaged due to qc values being strongly influenced by soils within 10 to 30 cone diameters from the cone tip. The qcHMM algorithm was developed to address the qc blurring/averaging limitation. This paper describes the distortions which occur when obtaining sleeve friction measurements which can in association with qc blurring result in significant errors in the calculated Rf values. This paper outlines a novel and highly effective algorithm for obtaining accurate sleeve friction and friction ratio estimates. The fc optimal filter estimation technique is referred to as the OSFE-IFM algorithm. The mathematical details of the OSFE-IFM algorithm are outlined in this paper along with the results from a challenging test bed simulation. The test bed simulation demonstrates that the OSFE-IFM algorithm derives accurate estimates of sleeve friction from measured values. Optimal estimates of cone bearing and sleeve friction result in accurate Rf values and subsequent accurate estimates of soil behavior type.

Cone Bearing Estimation Utilizing a Hybrid HMM and IFM Smoother Filter Formulation

Cone penetration testing (CPT) is a widely used geotechnical engineering in-situ test for mapping soil profiles and assessing soil properties. In CPT, a cone on the end of a series of rods is pushed into the ground at a constant rate and resistance to the cone tip is measured (qm). The qm values are utilized to characterize the soil profile. Unfortunately, the measured cone tip resistance is blurred and/or averaged which can result in the distortion of the soil profile characterization and the inability to identify thin layers. This paper outlines a novel and highly effective algorithm for obtaining cone bearing estimates qt from averaged or smoothed qm measurements. This qt optimal filter estimation technique is referred to as the qtHMM-IFM algorithm and it implements a hybrid hidden Markov model and iterative forward modelling technique. The mathematical details of the qtHMM-IFM algorithm are outlined in this paper along with the results from challenging test bed. The test bed simulations have demonstrated that the qtHMM-IFM algorithm can derive accurate qt values from challenging averaged qm profiles. This allows for greater soil resolution and the identification and quantification of thin layers in a soil profile.

Technique for Estimating the Cone Bearing Smoothing Parameters

Cone penetration testing (CPT) is an extensively utilized and cost effective tool for geotechnical site characterization. CPT consists of pushing at a constant rate an electronic cone into penetrable soils and recording the resistance to the cone tip (qc value). The measured qc values (after correction for the pore water pressure) are utilized to estimate soil type and associated soil properties based predominantly on empirical correlations. The most common cone tips have associated areas of 10 cm2 and 15 cm2. Investigators also utilized significantly larger cone tips (33 cm2 and 40 cm2) so that gravelly soils can be penetrated. Small cone tips (2 cm2 and 5 cm2) are utilized for shallow soil investigations. The cone tip resistance measured at a particular depth is affected by the values above and below the depth of interest which results in a smoothing or blurring of the true bearing values. Extensive work has been carried out in mathematically modelling the smoothing function which results in the blurred cone bearing measurements. This paper outlines a technique which facilitates estimating the dominant parameters of the cone smoothing function from processing real cone bearing data sets. This cone calibration technique is referred to as the so-called CPSPE algorithm. The mathematical details of the CPSPE algorithm are outlined in this paper along with the results from a challenging test bed simulation.

IMIMMS──An Interactive Modelling and Intelligent Model Management Systems

This paper describes an Interactive Modelling and Intelligent Model Management System─IMIMMS.In the system, like-natural English stances can be understood,then they are interpreted into the WFF(Well-Formed Formula) as the goals of inference on modelling knowledge.The model is represented as the Predicates and Relational Framework (PRF).The IMIMMS is a problem -oriented system. Therefore, the model frame of solving problem can be generated during the iterative process. The IMIMMS implements intelligent model management and traces the environment change in a decision domain. Finally, the paper shows the IMIMMS framework and an example of iterative modelling.

MODELS OF ASYNCHRONOUS MRALLEL NONLINEAR MULTISPLITTING RELAXED ITERATIONS

In the sense of the nonlinear multisplitting and based on the principle of suffi-ciently using the delayed information, we propose models of asynchronous parallelaccelerated overrelaxation iteration methods for solving large scale system of non-linear equations. Under proper conditions, we set up the local convergence theoriesof these new method models.