Nonlinear Model Algorithmic Control of a pH Neutralization Process

Control of pH neutralization processes is challenging in the chemical process industry because of their inherent strong nonlinearity. In this paper, the model algorithmic control (MAC) strategy is extended to nonlinear processes using Hammerstein model that consists of a static nonlinear polynomial function followed in series by a linear impulse response dynamic element. A new nonlinear Hammerstein MAC algorithm (named NLH-MAC) is presented in detail. The simulation control results of a pH neutralization process show that NLH-MAC gives better control performance than linear MAC and the commonly used industrial nonlinear propotional plus integral plus derivative (PID) controller. Further simulation experiment demonstrates that NLH-MAC not only gives good control response, but also possesses good stability and robustness even with large modeling errors.

Selection of regression models for predicting strength and deformability properties of rocks using GA

Recently,many regression models have been presented for prediction of mechanical parameters of rocks regarding to rock index properties.Although statistical analysis is a common method for developing regression models,but still selection of suitable transformation of the independent variables in a regression model is diffcult.In this paper,a genetic algorithm(GA)has been employed as a heuristic search method for selection of best transformation of the independent variables(some index properties of rocks)in regression models for prediction of uniaxial compressive strength(UCS)and modulus of elasticity(E).Firstly,multiple linear regression(MLR)analysis was performed on a data set to establish predictive models.Then,two GA models were developed in which root mean squared error(RMSE)was defned as ftness function.Results have shown that GA models are more precise than MLR models and are able to explain the relation between the intrinsic strength/elasticity properties and index properties of rocks by simple formulation and accepted accuracy.

On the E-Valuation of Certain E-Business Strategies on Firm Performance by Adaptive Algorithmic Modeling： An Alternative Strategic Managerial Approach

This paper describes an innovative adaptive algorithmic modeling approach, for solving a wide class of e-business and strategic management problems under uncertainty conditions. The proposed methodology is based on basic ideas and concepts of four key-field interrelated sciences, i.e., computing science, applied mathematics, management sciences and economic sciences. Furthermore, the fundamental scientific concepts of adaptability and uncertainty are shown to play a critical role of major importance for a （near） optimum solution of a class of complex e-business/services and strategic management problems. Two characteristic case studies, namely measuring e-business performance under certain environmental pressures and organizational constraints and describing the relationships between technology, innovation and firm performance, are considered as effective applications of the proposed adaptive algorithmic modeling approach. A theoretical time-dependent model for the evaluation of firm e-business performances is also proposed.

A Class of Generalized Approximate Inverse Solvers for Unsymmetric Linear Systems of Irregular Structure Based on Adaptive Algorithmic Modelling for Solving Complex Computational Problems in Three Space Dimensions

A class of general inverse matrix techniques based on adaptive algorithmic modelling methodologies is derived yielding iterative methods for solving unsymmetric linear systems of irregular structure arising in complex computational problems in three space dimensions. The proposed class of approximate inverse is chosen as the basis to yield systems on which classic and preconditioned iterative methods are explicitly applied. Optimized versions of the proposed approximate inverse are presented using special storage (k-sweep) techniques leading to economical forms of the approximate inverses. Application of the adaptive algorithmic methodologies on a characteristic nonlinear boundary value problem is discussed and numerical results are given.

Space-Time Chaos Filtering for the Incoherent Paradigm for 6G Wireless System Design from Theoretic Perspective

The following material is devoted to the generalization of the chaos modeling to random fields in communication channels and its application on the space-time filtering for the incoherent paradigm;that is the purpose of this research. The approach, presented hereafter, is based on the “Markovian” trend in modeling of random fields, and it is applied for the first time to the chaos field modeling through the well-known concept of the random “treatment” of deterministic dynamic systems, first presented by A. Kolmogorov, M. Born, etc. The material presents the generalized Stratonovich-Kushner Equations (SKE) for the optimum filtering of chaotic models of random fields and its simplified quasi-optimum solutions. In addition to this, the application of the multi-moment algorithms for quasi-optimum solutions is considered and, it is shown, that for scenarios, when the covariation interval of the input random field is less than the distance between the antenna elements, the gain of the space-time algorithms against their “time” analogies is significant. This is the general result presented in the following.

Design and development of safety evaluation system of buildings on a seismic field based on the network platform

This paper describes a set of on-site earthquake safety evaluation systems for buildings, which were developed based on a network platform. The system embedded into the quantitative research results which were completed in accordance with the provisions from Post-earthquake Field Works, Part 2： Safety Assessment of Buildings, GB18208.2 -2001, and was further developed into an easy-to-use software platform. The system is aimed at allowing engineering professionals, civil engineeing technicists or earthquake-affected victims on site to assess damaged buildings through a network after earthquakes. The authors studied the function structure, process design of the safety evaluation module, and hierarchical analysis algorithm module of the system in depth, and developed the general architecture design, development technology and database design of the system. Technologies such as hierarchical architecture design and Java EE were used in the system development, and MySQL5 was adopted in the database development. The result is a complete evaluation process of information collection, safety evaluation, and output of damage and safety degrees, as well as query and statistical analysis of identified buildings. The system can play a positive role in sharing expert post-earthquake experience and promoting safety evaluation of buildings on a seismic field.

Supply-based optimal scheduling of oil product pipelines

Oil product pipelines have features such as transporting multiple materials, ever-changing operating conditions, and synchronism between the oil input plan and the oil offloading plan. In this paper, an optimal model was established for a single-source multi-distribution oil pro- duct pipeline, and scheduling plans were made based on supply. In the model, time node constraints, oil offloading plan constraints, and migration of batch constraints were taken into consideration. The minimum deviation between the demanded oil volumes and the actual offloading volumes was chosen as the objective function, and a linear programming model was established on the basis of known time nodes＇ sequence. The ant colony optimization algo- rithm and simplex method were used to solve the model. The model was applied to a real pipeline and it performed well.

Adaptive learning algorithm based on mixture Gaussian background

The key problem of the adaptive mixture background model is that the parameters can adaptively change according to the input data. To address the problem, a new method is proposed. Firstly, the recursive equations are inferred based on the maximum likelihood rule. Secondly, the forgetting factor and learning rate factor are redefined, and their still more general formulations are obtained by analyzing their practical functions. Lastly, the convergence of the proposed algorithm is proved to enable the estimation converge to a local maximum of the data likelihood function according to the stochastic approximation theory. The experiments show that the proposed learning algorithm excels the formers both in converging rate and accuracy.

Model algorithm control using neural networks for input delayed nonlinear control system

The performance of the model algorithm control method is partially based on the accuracy of the system＇s model. It is difficult to obtain a good model of a nonlinear system, especially when the nonlinearity is high. Neural networks have the ability to ＂learn＂the characteristics of a system through nonlinear mapping to represent nonlinear functions as well as their inverse functions. This paper presents a model algorithm control method using neural networks for nonlinear time delay systems. Two neural networks are used in the control scheme. One neural network is trained as the model of the nonlinear time delay system, and the other one produces the control inputs. The neural networks are combined with the model algorithm control method to control the nonlinear time delay systems. Three examples are used to illustrate the proposed control method. The simulation results show that the proposed control method has a good control performance for nonlinear time delay systems.

Multiple model tracking algorithms based on neural network and multiple process noise soft switching

A multiple model tracking algorithm based on neural network and multiple-process noise soft-switching for maneuvering targets is presented.In this algorithm, the＂current＂statistical model and neural network are running in parallel.The neural network algorithm is used to modify the adaptive noise filtering algorithm based on the mean value and variance of the＂current＂statistical model for maneuvering targets, and then the multiple model tracking algorithm of the multiple processing switch is used to improve the precision of tracking maneuvering targets.The modified algorithm is proved to be effective by simulation.

Comprehensive Analysis and Artificial Intelligent Simulation of Land Subsidence of Beijing, China

Mechanism and modeling of the land subsidence are complex because of the complicate geological background in Beijing, China. This paper analyzed the spatial relationship between land subsidence and three factors, including the change of groundwater level, the thickness of compressible sediments and the building area by using remote sensing and GIS tools in the upper-middle part of alluvial-proluvial plain fan of the Chaobai River in Beijing. Based on the spatial analysis of the land subsidence and three factors, there exist significant non-linear relationship between the vertical displacement and three factors. The Back Propagation Neural Network (BPN) model combined with Genetic Algorithm (GA) was used to simulate regional distribution of the land subsidence. Results showed that at field scale, the groundwater level and land subsidence showed a significant linear relationship. However, at regional scale, the spatial distribution of groundwater depletion funnel did not overlap with the land subsidence funnel. As to the factor of compressible strata, the places with the biggest compressible strata thickness did not have the largest vertical displacement. The distributions of building area and land subsidence have no obvious spatial relationships. The BPN-GA model simulation results illustrated that the accuracy of the trained model during fifty years is acceptable with an error of 51% of verification data less than 20 mm and the average of the absolute error about 32 mm. The BPN model could be utilized to simulate the general distribution of land subsidence in the study area. Overall, this work contributes to better understand the complex relationship between the land subsidence and three influencing factors. And the distribution of the land subsidence can be simulated by the trained BPN-GA model with the limited available dada and acceptable accuracy.

Nonlinear model predictive control based on support vector machine and genetic algorithm

This paper presents a nonlinear model predictive control(NMPC) approach based on support vector machine(SVM) and genetic algorithm(GA) for multiple-input multiple-output(MIMO) nonlinear systems.Individual SVM is used to approximate each output of the controlled plant Then the model is used in MPC control scheme to predict the outputs of the controlled plant.The optimal control sequence is calculated using GA with elite preserve strategy.Simulation results of a typical MIMO nonlinear system show that this method has a good ability of set points tracking and disturbance rejection.

Fast Parallel Algorithm for Slicing STL Based on Pipeline

In Additive Manufacturing field, the current researches of data processing mainly focus on a slicing process of large STL files or complicated CAD models. To improve the efficiency and reduce the slicing time, a parallel algorithm has great advantages. However, traditional algorithms can＇t make full use of multi-core CPU hardware resources. In the paper, a fast parallel algorithm is presented to speed up data processing. A pipeline mode is adopted to design the parallel algorithm. And the complexity of the pipeline algorithm is analyzed theoretically. To evaluate the performance of the new algorithm, effects of threads number and layers number are investigated by a serial of experiments. The experimental results show that the threads number and layers number are two remarkable factors to the speedup ratio. The tendency of speedup versus threads number reveals a positive relationship which greatly agrees with the Amdahl＇s law, and the tendency of speedup versus layers number also keeps a positive relationship agreeing with Gustafson＇s law. The new algorithm uses topological information to compute contours with a parallel method of speedup. Another parallel algorithm based on data parallel is used in experiments to show that pipeline parallel mode is more efficient. A case study at last shows a suspending performance of the new parallel algorithm. Compared with the serial slicing algorithm, the new pipeline parallel algorithm can make full use of the multi-core CPU hardware, accelerate the slicing process, and compared with the data parallel slicing algorithm, the new slicing algorithm in this paper adopts a pipeline parallel model, and a much higher speedup ratio and efficiency is achieved.

RECONFIGURABLE PRODUCTION LINE MODELING AND SCHEDULING USING PETRI NETS AND GENETIC ALGORITHM

In response to the production capacity and functionality variations, a genetic algorithm （GA） embedded with deterministic timed Petri nets（DTPN） for reconfigurable production line（RPL） is proposed to solve its scheduling problem. The basic DTPN modules are presented to model the corresponding variable structures in RPL, and then the scheduling model of the whole RPL is constructed. And in the scheduling algorithm, firing sequences of the Petri nets model are used as chromosomes, thus the selection, crossover, and mutation operator do not deal with the elements in the problem space, but the elements of Petri nets model. Accordingly, all the algorithms for GA operations embedded with Petri nets model are proposed. Moreover, the new weighted single-objective optimization based on reconfiguration cost and E/T is used. The results of a DC motor RPL scheduling suggest that the presented DTPN-GA scheduling algorithm has a significant impact on RPL scheduling, and provide obvious improvements over the conventional scheduling method in practice that meets duedate, minimizes reconfiguration cost, and enhances cost effectivity.

MODELING, VALIDATION AND OPTIMAL DESIGN OF THE CLAMPING FORCE CONTROL VALVE USED IN CONTINUOUSLY VARIABLE TRANSMISSION

Associated dynamic performance of the clamping force control valve used in continuously variable transmission （CVT） is optimized. Firstly, the structure and working principle of the valve are analyzed, and then a dynamic model is set up by means of mechanism analysis. For the purpose of checking the validity of the modeling method, a prototype workpiece of the valve is manufactured for comparison test, and its simulation result follows the experimental result quite well. An associated performance index is founded considering the response time, overshoot and saving energy, and five structural parameters are selected to adjust for deriving the optimal associated performance index. The optimization problem is solved by the genetic algorithm （GA） with necessary constraints. Finally, the properties of the optimized valve are compared with those of the prototype workpiece, and the results prove that the dynamic performance indexes of the optimized valve are much better than those of the prototype workpiece.

An efficient approach for shadow detection based on Gaussian mixture model

An efficient approach was proposed for discriminating shadows from moving objects. In the background subtraction stage, moving objects were extracted. Then, the initial classification for moving shadow pixels and foreground object pixels was performed by using color invariant features. In the shadow model learning stage, instead of a single Gaussian distribution, it was assumed that the density function computed on the values of chromaticity difference or bright difference, can be modeled as a mixture of Gaussian consisting of two density functions. Meanwhile, the Gaussian parameter estimation was performed by using EM algorithm. The estimates were used to obtain shadow mask according to two constraints. Finally, experiments were carried out. The visual experiment results confirm the effectiveness of proposed method. Quantitative results in terms of the shadow detection rate and the shadow discrimination rate(the maximum values are 85.79% and 97.56%, respectively) show that the proposed approach achieves a satisfying result with post-processing step.

Dynamic airspace sectorization via improved genetic algorithm

This paper deals with dynamic airspace sectorization （DAS） problem by an improved genetic algorithm （iGA）. A graph model is first constructed that represents the airspace static structure. Then the DAS problem is formulated as a graph-partitioning problem to balance the sector workload under the premise of ensuring safety. In the iGA, multiple populations and hybrid coding are applied to determine the optimal sector number and airspace sectorization. The sector constraints are well satisfied by the improved genetic operators and protect zones. This method is validated by being applied to the airspace of North China in terms of three indexes, which are sector balancing index, coordination workload index and sector average flight time index. The improvement is obvious, as the sector balancing index is reduced by 16.5 %, the coordination workload index is reduced by 11.2 %, and the sector average flight time index is increased by 11.4 % during the peak-hour traffic.

Optimization of the catch bench design using a genetic algorithm

Rockfalls are one of the hazards that may be associated with open pit mining. The majority of rockfalls occur due to the existing conditions of slopes, such as back break, fractures and joints. Constructing a berm on the catch bench is a popular method for the mitigation of rockfall hazards in open pit mining.The width of the catch bench and the height of the berm play a major role in the open pit bench design.However, there is no systematic method currently available to optimize the size of these parameters. This study proposes a novel methodology which calculates the optimum catch bench width by integrating the rockfall simulation model and genetic algorithm into a Simulation-Optimization Model. The proposed methodology is useful when used to determine the minimum catch bench width, or the maximum overall slope angle, insuring that a sufficient factor of safety of the slope is included while maximizing the overall profitability of the open pit mine.

Multi-objective planning model for simultaneous reconfiguration of power distribution network and allocation of renewable energy resources and capacitors with considering uncertainties

This research develops a comprehensive method to solve a combinatorial problem consisting of distribution system reconfiguration, capacitor allocation, and renewable energy resources sizing and siting simultaneously and to improve power system's accountability and system performance parameters. Due to finding solution which is closer to realistic characteristics, load forecasting, market price errors and the uncertainties related to the variable output power of wind based DG units are put in consideration. This work employs NSGA-II accompanied by the fuzzy set theory to solve the aforementioned multi-objective problem. The proposed scheme finally leads to a solution with a minimum voltage deviation, a maximum voltage stability, lower amount of pollutant and lower cost. The cost includes the installation costs of new equipment, reconfiguration costs, power loss cost, reliability cost, cost of energy purchased from power market, upgrade costs of lines and operation and maintenance costs of DGs. Therefore, the proposed methodology improves power quality, reliability and security in lower costs besides its preserve, with the operational indices of power distribution networks in acceptable level. To validate the proposed methodology's usefulness, it was applied on the IEEE 33-bus distribution system then the outcomes were compared with initial configuration.

Construction of a 3D meso-structure and analysis of mechanical properties for deposit body medium

For deposit body medium, the internal structural properties may be the controlling factors for the strength of the material and the mechanical response. Based on the results of soil-rock meso-statistics using digital imaging, a simulated annealing algorithm is adopted to expand the meso-structural features of deposit bodies in 3D. The construction of the 3D meso-structure of a deposit body is achieved, and then the particle flow analysis program PFC3 D is used to simulate the mechanical properties of the deposit body. It is shown that with a combination of the simulated annealing algorithm and the statistical feature functions, the randomness and heterogeneity of the rock distribution in the 3D inner structure of deposit body medium can be realized, and the reconstructed structural features of the deposit medium can match the features of the digital images well. The spatial utilizations and the compacting effects of the body-centered cubic, hexagonal close and face-centered packing models are high, so these structures can be applied in the simulations of the deposit structures. However, the shear features of the deposit medium vary depending on the different model constructive modes. Rocks, which are the backbone of the deposit, are the factors that determine the shear strength and deformation modulus of the deposit body. The modeling method proposed is useful for the construction of 3D meso-scope models from 2D meso-scope statistics and can be used for studying the mechanical properties of mixed media, such as deposit bodies.