Model-constrained and data-driven double-supervision acoustic impedance inversion

Seismic impedance inversion is an important technique for structure identification and reservoir prediction.Model-based and data-driven impedance inversion are the commonly used inversion methods.In practice,the geophysical inversion problem is essentially an ill-posedness problem,which means that there are many solutions corresponding to the same seismic data.Therefore,regularization schemes,which can provide stable and unique inversion results to some extent,have been introduced into the objective function as constrain terms.Among them,given a low-frequency initial impedance model is the most commonly used regularization method,which can provide a smooth and stable solution.However,this model-based inversion method relies heavily on the initial model and the inversion result is band limited to the effective frequency bandwidth of seismic data,which cannot effectively improve the seismic vertical resolution and is difficult to be applied to complex structural regions.Therefore,we propose a data-driven approach for high-resolution impedance inversion based on the bidirectional long short-term memory recurrent neural network,which regards seismic data as time-series rather than image-like patches.Compared with the model-based inversion method,the data-driven approach provides higher resolution inversion results,which demonstrates the effectiveness of the data-driven method for recovering the high-frequency components.However,judging from the inversion results for characterization the spatial distribution of thin-layer sands,the accuracy of high-frequency components is difficult to guarantee.Therefore,we add the model constraint to the objective function to overcome the shortages of relying only on the data-driven schemes.First,constructing the supervisor1 based on the bidirectional long short-term memory recurrent neural network,which provides the predicted impedance with higher resolution.Then,convolution constraint as supervisor2 is introduced into the objective function to guarantee the reliability and accuracy of the inversion results,which makes the synthetic seismic data obtained from the inversion result consistent with the input data.Finally,we test the proposed scheme based on the synthetic and field seismic data.Compared to model-based and purely data-driven impedance inversion methods,the proposed approach provides more accurate and reliable inversion results while with higher vertical resolution and better spatial continuity.The inversion results accurately characterize the spatial distribution relationship of thin sands.The model tests demonstrate that the model-constrained and data-driven impedance inversion scheme can effectively improve the thin-layer structure characterization based on the seismic data.Moreover,tests on the oil field data indicate the practicality and adaptability of the proposed method.

Anomaly detection method based on kinematics model and nonholonomic constraint of vehicle

A method used to detect anomaly and estimate the state of vehicle in driving was proposed.The kinematics model of the vehicle was constructed and nonholonomic constraint conditions were added,which refer to that once the vehicle encounters the faults that could not be controlled,the constraint conditions are violated.Estimation equations of the velocity errors of the vehicle were given out to estimate the velocity errors of side and forward.So the stability of the whole vehicle could be judged by the velocity errors of the vehicle.Conclusions were validated through the vehicle experiment.This method is based on GPS/INS integrated navigation system,and can provide foundation for fault detections in unmanned autonomous vehicles.

Multimodel-based flight control system reconfiguration control in the presence of input constraints

In this paper,an active fault accommodate strategy is proposed for the plant in the presence of actuator fault and input constraints,which is a combination of a direct adaptive control algorithm with multiple model switching.The μ-modification is introduced in the model reference architecture to construct the adaptive controller.The proof of stability is based on the candidate Lyapunov function,while appropriate switching of multiple models guarantees asymptotic tracking of the system states and the boundedness of all signals.Simulation results illustrate the efficiency of the proposed method.

Mathematical Constraints in Multiscale Subgrid-Scale Modeling of Nonlinear Systems

To shed light on the subgrid-seale （SGS） modeling methodology of nonlinear systems such as the Navier-Stokes turbulence, we define the concepts of assumption and restriction in the modeling procedure, which are shown by generalized derivation of three general mathematical constraints for different combinations of restrictions. These constraints are verified numerically in a one-dimensional nonlinear advection equation. This study is expected to inspire future research on the SGS modeling methodology of nonlinear systems.

MODELING COMPLIANT NON-PENETRATION CONSTRAINT FOR VP MOTION SIMULATION

A unilateral non-penetration constraint dynamical simulation model withfriction is constructed based on compliant model for mechanical system VP (virtual prototyping)simulation. This model combines computer graphics with multi-body system dynamics. It avoidshandling multiplicity of solution, such as cases of no solution, multi-solution brought about byfriction during traditional construction of non-penetration constraint based on rigid model. At thesame time, the realism of VE (virtual environment) is improved in process of simulation.Furthermore, the valid condition of rolling and sliding unilateral contact is constituted based onsingular perturbation and linear complementary theory. Finally, the compliant method is verified byan interaction between a multi-legged robot and VE.

Multi-model predictive control with local constraints based on model switching

Because model switching system is a typical form of Takagi-Sugeno（T-S） model which is an universal approximator of continuous nonlinear systems, we describe the model switching system as mixed logical dynamical （MLD） system and use it in model predictive control （MPC） in this paper. Considering that each local model is only valid in each local region,we add local constraints to local models. The stability of proposed multi-model predictive control （MMPC） algorithm is analyzed, and the performance of MMPC is also demonstrated on an inulti-multi-output（MIMO） simulated pH neutralization process.

Establishment of Output Models and Constraints Analysis for Independent Wind/PV/Storage System

In remote areas far from the grid, wind/PV/storage generating system is relatively a good choice, whatever in resource configuration, performance or prices. For the independent hybrid power system, the output models of wind turbines, photovoltaic arrays and batteries are built in this paper, and based on the objectives of the capacity configuration optimal model, constraints used in the process of capacity configuration are analyzed. These provide convenient conditions and theoretical basis for the optimal capacity configuration of independent wind/PV/storage system.

CONSTRAINT MODELLING IN AN OBJECT-ORIENTED DATA MODEL FOR THE CAPP

The Object-Attribute-Relationship(OAR)model provides a single model tocapture data structure,behavior properties and knowledge rules in the CAPP application.This paper focuses on how to integrate a variety of different knowledge rules into the OARmodel and how to form transactions against the data structure with these rules in order toimplement intelligent process planning for designed components.The mapping from theOAR to Smalltalk can be directly carried out.It makes the implementation an entirely Ob-ject-Oriented approach.

Cosmic Constraints to the wCDM Model from Strong Gravitational Lensing

We study the cosmic constraint to the wCDM （cold dark matter with a constant equation of state w） model via 118 strong gravitational lensing systems which are compiled from SLA CS, BELLS, LSD and SL2S surveys, where the ratio between two angular diameter distances Dobs =DA（Zl, Zs ） / D A （ O, Zs ） is taken as a cosmic observable. To obtain this ratio, we adopt two strong tensing models： one is the singular isothermal sphere model （SIS） and the other one is the power-law density profile （PLP） model. Via the Markov chain Monte Carlo method, the posterior distribution of the cosmological model parameters space is obtained. The results show that the cosmological model parameters are not sensitive to the parameterized forms of the power-law index γ. Furthermore, the PLP model gives a relatively tighter constraint to the cosmological parameters than that of the SIS model. The predicted value of Ωm = 0.31＋0.44 -0.24 by the SIS model is compatible with that obtained by P1anck2015： Ωm = 0.313 ± 0.013. However, the value of Ωm =0.15＋0.13 -0.11 based on the PLP model is smaller and has 1.25σ tension with that obtained by Planck2015.

New model reference adaptive control with input constraints

A new scheme of adaptive control is proposed for a class of linear time-invariant（ LTI） dynamical systems,especially in aerospace,with matched parametric uncertainties and input constraints. Based on a typical and conventional direct model reference adaptive control scheme,various modifications have been employed to achieve the goal. ＂C omposite model reference adaptive control＂of higher performance is seam-lessly combined with ＂positive μ-mod＂,which consequently results in a smooth tracking trajectory despite of the input constraints. In addition,bounded-gain forgetting is utilized to facilitate faster convergence of parameter estimates. The stability of the closed-loop systemcan be guaranteed by using Lyapunov theory.The merits and effectiveness of the proposed method are illustrated by a numerical example of the longitudinal dynamical systems of a fixed-wing airplane.

Energy Conditions and Constraints on the Generalized Non-Local Gravity Model

We study and derive the energy conditions in generalized non-local gravity, which is the modified theory of general relativity obtained by adding a term m2n-2R□-nRto the Einstein-Hilbert action. Moreover, to obtain some insight on the meaning of the energy conditions, we illustrate the evolutions of four energy conditions with the model parameter ε for different n. By analysis we give the constraints on the model parameters ε.

Feasibility analysis and online adjustment of constraints in model predictive control integrated with soft sensor

Feasibility analysis of soft constraints for input and output variables is critical for model predictive control(MPC).When encountering the infeasible situation, some way should be found to adjust the constraints to guarantee that the optimal control law exists. For MPC integrated with soft sensor, considering the soft constraints for critical variables additionally makes it more complicated and difficult for feasibility analysis and constraint adjustment. Therefore, the main contributions are that a linear programming approach is proposed for feasibility analysis, and the corresponding constraint adjustment method and procedure are given as well. The feasibility analysis gives considerations to the manipulated, secondary and critical variables, and the increment of manipulated variables as well. The feasibility analysis and the constraint adjustment are conducted in the entire control process and guarantee the existence of optimal control. In final, a simulation case confirms the contributions in this paper.

Rheological Behaviour for Polymer Melts and Concentrated Solutions Part Ⅰ:A New Multiple Reptation Model to Predict the Nonlinear Visco-elasticity with Nagai Chain Constraints in Entangled Polymer Melts

An approach of stochastically statistical mechanics and a unified molecular theory of nonlinear viscoelasticity with constraints of Nagai chain entanglement for polymer melts have been proposed. A multimode model structure for a single polymer chain with n tail segments and N reversible entanglement sites on the test polymer chain is developed. Based on the above model structure and the mechanism of molecular flow by the dynamical reorganization of entanglement sites, the probability distribution function of the end-to-end vectr for a single polymer chain at entangled state and the viscoelastic free energy of deformation for polymer melts are calculated by using the method of the stochastically statistical mechanics. The four types of stress-strain relation and the memory function are derived from this thery. The above theoretical relations are verified by the experimentaf data for various polymer melts. These relations are found to be in good agreement with the experimental results

The Information Modeling and Intelligent Optimization Method for Logistics Vehicle Routing and Scheduling with Multi-objective and Multi-constraint

The vehicle routing and scheduling (VRS) problem with multi-objective and multi-constraint is analyzed, considering the complexity of the modern logistics in city economy and daily life based on the system engineering. The objective and constraint includes loading, the dispatch and arrival time, transportation conditions,total cost,etc. An information model and a mathematical model are built,and a method based on knowledge and biologic immunity is put forward for optimizing and evaluating the programs dimensions in vehicle routing and scheduling with multi-objective and multi-constraints. The proposed model and method are illustrated in a case study concerning a transport network, and the result shows that more optimization solutions can be easily obtained and the method is efficient and feasible. Comparing with the standard GA and the standard GA without time constraint,the computational time of the algorithm is less in this paper. And the probability of gaining optimal solution is bigger and the result is better under the condition of multi-constraint.

Multiple-Constraint-Aware RWA Algorithms Based on a Comprehensive Evaluation Model:Use in Wavelength-Switched Optical Networks

Because of explosive growth in Internet traffic and high complexity of heterogeneous networks, improving the routing and wavelength assignment （RWA） algorithm in underlying optical networks has become very important. Where there are multiple links between different the node pairs, a traditional wavelength-assignment algorithm may be invalid for a wavelength-switched optical networks （WSON） that has directional blocking constraints. Also, impairments in network nodes and subsequent degradation of optical signals may cause modulation failure in the optical network. In this paper, we propose an RWA algorithm based on a novel evaluation model for a WSQN that has multiple constraints. The algorithm includes comprehensive evaluation model （CEM） and directional blocking constraint RWA based on CEM （DB-RWA）. Diverse constraints are abstracted into various constraint conditions in order to better assign routing and wavelength. We propose using the novel CEM to optimize routing according to an assessed value of constraints on transmission performance. This eliminates the effects of physical transmission impairments in a WSON. DB-RWA based on CEM abstracts directional blocking conditions in multiple links between network nodes into directional blocking constraints. It also satisfies rigorous network specifications and provides flexibility, scalability, and first-fit rate for the backbone, especially in multiple links between WSON nodes.

Origin and Evolution of Life Constraints on the Solar Model

Life arose as a non-equilibrium thermodynamic process to dissipate the photon potential generated by the hot Sun and cold outer space. Evidence from the geochemical record of the evolutionary history of life on Earth suggests that life originated in a hot aqueous environment dissipating UV light and evolved later to dissipate visible light. This evidence places constraints on models of solar origin and evolution. The standard solar model seems less compatible with the data than does the pulsar centered solar model.

SEMANTIC CONSTRAINT MODELER FOR 2D AND 3D GEOMETRY

Design changes for 2D & 3D geometry are the most important features in the process of product design.Constraint modeling for variationl geometry based on geometric reasoning is one of the best approaches for this goal.However,it is difficult for the proposed systems to maintain or handle the consistency and completeness of the constraint model of the design objects.To change this situation,a semantic model and its control approach are presented,aiming at the integration of the data,knowledge and method related to design objects.Aconstraint definition system for in- teractively defining the semantic model and a prototype modeler based on the semantic model are also implemented to examine the idea which is extended to 3D geometric design too.

Explicit Correlation Model of Multi-source Constraints for Re-design Parts with Complex Curved Surface

In precision machining of complex curved surface parts with high performance, geometry accuracy is not the only constraint, but the performance should also be met. Performance of this kind of parts is closely related to the geometrical and physical parameters, so the final actual size and shape are affected by multiple source constraints, such as geometry, physics, and performance. These parts are rather difficult to be manufactured and new manufacturing method according to performance requirement is urgently needed. Based on performance and manufacturing requirements for complex curved surface parts, a new classification method is proposed, which divided the complex curved surface parts into two categories： surface re-design complex curved surface parts with multi-source constraints（PRCS） and surface unique complex curved surface parts with pure geometric constraints（PUCS）. A correlation model is constructed between the performance and multi-source constraints for PRCS, which reveals the correlation between the performance and multi-source constraints. A re-design method is also developed. Through solving the correlation model of the typical paws performance-associated surface, the mapping relation between the performance-associated surface and the related removal amount is obtained. The explicit correlation model and the method for the corresponding related removal amount of the performance-associated surface are built based on the classification of surface re-design complex curved surface parts with multi-source constraints. Research results have been used in the actual processing of the typical parts such as radome, common bottom components, nozzle, et al., which shows improved efficiency and accuracy of the precision machining for the surface re-design parts with complex curved surface.

面向短文本的增强上下文神经主题模型

目前的主题模型大多数基于自身文本的词共现信息进行建模,并没有引入主题的稀疏约束来提升模型的主题抽取能力,此外短文本本身存在词共现稀疏的问题,该问题严重影响了短文本主题建模的准确性。针对以上问题,提出了一种增强上下文神经主题模型(enhanced context neural topic model,ECNTM)。ECNTM基于主题控制器对主题进行稀疏性约束,过滤掉不相关的主题,同时模型的输入变成BOW向量和SBERT句子嵌入的拼接,在高斯解码器中,通过在嵌入空间中将单词上的主题分布处理为多元高斯分布或高斯混合分布,显式地丰富了短文本有限的上下文信息,解决了短文本词共现特征稀疏问题。在WS、Reuters、KOS、20 NewsGroups四个公开数据集上的实验结果表明,该模型在困惑度、主题一致性以及文本分类准确率上相较基准模型均有明显提升,证明了引入主题稀疏约束特性以及丰富的上下文信息到短文本主题建模的有效性。