The cell cycle related apoptotic susceptibility to arsenic trioxide is associated with the level of reactive oxygen species

Double staining flow cytometry was performed using 7-amino actinomycin D and 6-carboxy-2',7'-dichlorodihydrofluorescein diacetate,to detect the level fluctuation of reactive oxygen species (ROS) during the cell cycle of normal NB4 cells. Our results showed that NB4 cells possessed higher level of ROS in G2/M phase than in G1 and S phases. Double staining flow cytometry,with TdT mediated dUTP nick end labeling (Tunel) and propidium iodide (PI),indicated that As2O3 (2 μM) could induce apoptosis in NB4 cells prevailingly from G2/M phase,and this efficacy was enhanced upon co-administration of 2,3-dimethoxy-1,4-naphthoquinone (DMNQ) (2.5 μM) which could produce the endogenous ROS. These results suggested that different ROS level in different cell cycle phases of NB4 cells might determin the selective induction of G2/M apoptosis and the cells' susceptibility to apoptosis by As2O3.

APPLYING PARTICLE SWARM OPTIMIZATION TO JOB-SHOPSCHEDULING PROBLEM

A new heuristic algorithm is proposed for the problem of finding the minimummakespan in the job-shop scheduling problem. The new algorithm is based on the principles ofparticle swarm optimization (PSO). PSO employs a collaborative population-based search, which isinspired by the social behavior of bird flocking. It combines local search (by self experience) andglobal search (by neighboring experience), possessing high search efficiency. Simulated annealing(SA) employs certain probability to avoid becoming trapped in a local optimum and the search processcan be controlled by the cooling schedule. By reasonably combining these two different searchalgorithms, a general, fast and easily implemented hybrid optimization algorithm, named HPSO, isdeveloped. The effectiveness and efficiency of the proposed PSO-based algorithm are demonstrated byapplying it to some benchmark job-shop scheduling problems and comparing results with otheralgorithms in literature. Comparing results indicate that PSO-based algorithm is a viable andeffective approach for the job-shop scheduling problem.

Nonlinear system PID-type multi-step predictive control

A compound neural network was constructed during the process of identification and multi-step prediction. Under the PID-type long-range predictive cost function, the control signal was calculated based on gradient algorithm. The nonlinear controller’s structure was similar to the conventional PID controller. The parameters of this controller were tuned by using a local recurrent neural network on-line. The controller has a better effect than the conventional PID controller. Simulation study shows the effectiveness and good performance.

A Simulation Optimization Algorithm for CTMDPs Based on Randomized Stationary Policies^1）

Based on the theory of Markov performance potentials and neuro-dynamic programming(NDP) methodology, we study simulation optimization algorithm for a class of continuous timeMarkov decision processes (CTMDPs) under randomized stationary policies. The proposed algo-rithm will estimate the gradient of average cost performance measure with respect to policy param-eters by transforming a continuous time Markov process into a uniform Markov chain and simula-ting a single sample path of the chain. The goal is to look for a suboptimal randomized stationarypolicy. The algorithm derived here can meet the needs of performance optimization of many diffi-cult systems with large-scale state space. Finally, a numerical example for a controlled Markovprocess is provided.

Temperature prediction control based on least squares support vector machines

A prediction control algorithm is presented based on least squares support vector machines (LS-SVM) model for a class of complex systems with strong nonlinearity. The nonlinear off-line model of the controlled plant is built by LS-SVM with radial basis function (RBF) kernel. In the process of system running, the off-line model is linearized at each sampling instant, and the generalized prediction control (GPC) algorithm is employed to implement the prediction control for the controlled plant. The obtained algorithm is applied to a boiler temperature control system with complicated nonlinearity and large time delay. The results of the experiment verify the effectiveness and merit of the algorithm.

SOFT SENSING MODEL BASED ON SUPPORT VECTOR MACHINE AND ITS APPLICATION

Soft sensor is widely used in industrial process control. It plays animportant role to improve the quality of product and assure safety in production. The core of softsensor is to construct soft sensing model. A new soft sensing modeling method based on supportvector machine (SVM) is proposed. SVM is a new machine learning method based on statistical learningtheory and is powerful for the problem characterized by small sample, nonlinearity, high dimensionand local minima. The proposed methods are applied to the estimation of frozen point of light dieseloil in distillation column. The estimated outputs of soft sensing model based on SVM match the realvalues of frozen point and follow varying trend of frozen point very well. Experiment results showthat SVM provides a new effective method for soft sensing modeling and has promising application inindustrial process applications.

SENSITIVITY ANALYSIS FOR ROLLING PROCESS BASED ON SUPPORT VECTOR MACHINE

A method for the calculation of the sensitivity factors of the rollingprocess has been obtained by differentiating the roll force model based on support vector machine.It can eliminate the algebraic loop of the analytical model of the rolling process. The simulationsin the first stand of five stand cold tandem rolling mill indicate that the calculation forsensitivities by this proposed method can obtain a good accuracy, and an appropriate adjustment onthe control variables determined directly by the sensitivity has an excellent compensation accuracy.Moreover, the roll gap has larger effect on the exit thickness than both front tension and backtension, and it is more efficient to select the roll gap as the control variable of the thicknesscontrol system in the first stand.

Advanced control of walking-beam reheating furnace

Rehearing furnace is an important device with complex dynamic characteristicsin steel plants. The temperature tracing control of reheating furnace has great importance both tothe quality of slabs and energy saving. A model-based control strategy, multivariable constrainedcontrol (MCC) for the reheating furnace control is used. With this control method, the furnace istreated as a six-input-six-output general model with loops coupled in nature. Compared with thetraditional control, the proposed control strategy gets better temperature tracing accuracy andexhibits some energy saving feature. The simulation results show that the performance of the furnaceis greatly improved.

Digital implementation of fractional order PID controller and its application

A new discretization scheme is proposed for the design of a fractional order PID controller. In the design of a fractional order controller the interest is mainly focused on the s-domain, but there exists a difficult problem in the s-domain that needs to be solved, i.e. how to calculate fractional derivatives and integrals efficiently and quickly. Our scheme adopts the time domain that is well suited for Z-transform analysis and digital implementation. The main idea of the scheme is based on the definition of Grünwald-Letnicov fractional calculus. In this case some limited terms of the definition are taken so that it is much easier and faster to calculate fractional derivatives and integrals in the time domain or z-domain without loss much of the precision. Its effectiveness is illustrated by discretization of half-order fractional differential and integral operators compared with that of the analytical scheme. An example of designing fractional order digital controllers is included for illustration, in which different fractional order PID controllers are designed for the control of a nonlinear dynamic system containing one of the four different kinds of nonlinear blocks: saturation, deadzone, hysteresis, and relay.

Lubrication and tribology in seawater hydraulic piston pump

Water hydraulic systems have provoked major interest because of the human friendly and environmental safety aspects. Piston pump is one of the most frequently used hydraulic units in recent engineering technique. In water hydraulic piston pump, poor lubrication is more likely to happen than in oil hydraulic one because of difference in properties between water and oil. So there are some key problems such as corrosive wear and erosion, which are investigated briefly. Many new materials have been developed, which give longer life expectancies with water without corrosion and erosion. A new type of seawater hydraulic piston pumps with better suction characteristics had been developed at HUST. Much of this research has concentrated on new materials, structure and experiments, which are also specially introduced.

Optimization model of truck flow at open-pit mines and standards for feasibility test

In order to increase production efficiency at open-pit mines, on the basis oflinear programming, a practical mathematical model for optimizing truck flow was developed, whichimproved the traditional fixed manual schedule method. The model has advantages from linearprogramming and objective programming, makes most handling points working at full capacity and keepsan optimized ratio between trucks and excavators. For ensuring feasibility of the model inpractical production, four standards for feasibility test were proposed. The model satisfied all thestandards. The application in a large scale open-pit iron mine indicated that the model reduced thenumber of required trucks by 10 percent compared with the fixed manual schedule method.

Predictive functional control of integrating process based on impulse response

The predictive model is built according to the characteristics of the impulse response of integrating process. In order to eliminate the permanent offset between the setpoint and the process output in the presence of the load disturbance, a novel error compensation method is proposed. Then predictive functional control of integrating process is designed. The method given generates a simple control structure, which can significandy reduce online computation. Furthermore, the tuning of the controller is fairly straightforward. Simulation results indicate that the designed control system is relatively robust to the parameters variation of the process.

Establishing formal state space models via quantization forquantum control systems

Formal state space models of quantum control systems are deduced and a scheme to establish formal state space models via quantization could been obtained for quantum control systems is proposed. State evolution of quantum control systems must accord with Schrdinger equations, so it is foremost to obtain Hamiltonian operators of systems. There are corresponding relations between operators of quantum systems and corresponding physical quantities of classical systems, such as momentum, energy and Hamiltonian, so Schrdinger equation models of corresponding quantum control systems via quantization could been obtained from classical control systems, and then establish formal state space models through the suitable transformation from Schrdinger equations for these quantum control systems. This method provides a new kind of path for modeling in quantum control.

A fuzzy rule based genetic algorithm and its application in FMS

Most of the FMS (flexible manufacturing systems) problems belong to NP-hard (non-polynomial hard) problems. The facility layout problem and job-shop schedule problem are such examples. GA (genetic algorithm) is applied to get an optimal solution. However, traditional GAs are usually of low efficiency because of their early convergence. In order to overcome the shortcoming of the GA a fuzzy rule based GA is proposed, in which a fuzzy logical controller is introduced to adjust the value of crossover probability, mutation probability and crossover length. The HGA (hybrid genetic algorithm), which is integrated with a fuzzy logic controller, can avoid premature convergence, and improve the efficiency greatly. Finally, simulation results of the facility layout problem and job-shop schedule problem are given. The results show that the new genetic algorithm integrated with fuzzy logic controller is excellent in searching efficiency.

DYNAMIC RELOCATION OF PLANT/WAREHOUSE FACILITIES: A FAST COMPACT GENETIC ALGORITHM APPROACH

The problem of dynamic relocation and phase-out of combined manufacturingplant and warehousing facilities in the supply chain are concerned. A multiple time/multipleobjective model is proposed to maximize total profit during the time horizon, minimize total accesstime from the plant/warehouse facilities to its suppliers and customers and maximize aggregatedlocal incentives during the time horizon. The relocation problem keeps the feature of NP-hard andwith the traditional method the optimal result cannot be got easily. So a compact genetic algorithm(CGA) is introduced to solve the problem. In order to accelerate the convergence speed of the CGA,the least square approach is introduced and a fast compact genetic algorithm (fCGA) is proposed.Finally, simulation results with the fCGA are compared with the CGA and classical integerprogramming (IP). The results show that the fCGA proposed is of high efficiency for Paretooptimality problem.

Robust H_∞ control for discrete-time polytopic uncertain systems with linear fractional vertices

The robust H∞ control problem for discrete-time uncertain systems is investigated in this paper. The uncertain systems are modelled as a polytopic type with linear fractional uncertainty in the vertices. A new linear matrix inequality (LMI) characterization of the H∞ performance for discrete systems is given by introducing a matrix slack variable which decouples the matrix of a Lyapunov function candidate and the parametric matrices of the system. This feature enables one to derive sufficient conditions for discrete uncertain systems by using parameter-dependent Lyapunov functions with less conservativeness. Based on the result, H∞ performance analysis and controller design are carried out. A numerical example is included to demonstrate the effectiveness of the proposed results.

Genetic programming-based chaotic time series modeling

This paper proposes a Genetic Programming-Based Modeling (GPM) algorithm on chaotic time series. GP is used here to search for appropriate model structures in function space, and the Particle Swarm Optimization (PSO) algorithm is used for Nonlinear Parameter Estimation (NPE) of dynamic model structures. In addition, GPM integrates the results of Nonlinear Time Series Analysis (NTSA) to adjust the parameters and takes them as the criteria of established models. Experiments showed the effectiveness of such improvements on chaotic time series modeling.

Delay Margin for Predictive PI Control System

The variation of plant dead-time deeply a?ects the stability of the predictive PI controlsystem. It is important for designing and applying the PPI controller to calculate the delay margin.A criterion of stability for the PPI system and the quantitive relationship among the delay margin,the time constant of the closed-loop system, and the dead-time of the model are given. A graphicalgorithm to compute the delay margin is also developed. The phenomenon that there exist morethan one stability delay zones is discussed. The algorithm is shown to be precise by some simulations.

Hybrid Estimation of State and Input for Linear Continuous Time-varying Systems:A Game Theory Approach

The H∞ hybrid estimation problem for linear continuous time-varying systems is in-vestigated in this paper, where estimated signals are linear combination of state and input. Designobjective requires the worst-case energy gain from disturbance to estimation error be less than a pre-scribed level. Optimal solution of the hybrid estimation problem is the saddle point of a two-playerzero sum di?erential game. Based on the di?erential game approach, necessary and su?cient solvableconditions for the hybrid estimation problem are provided in terms of solutions to a Riccati di?e-rential equation. Moreover, one possible estimator is proposed if the solvable conditions are satisfied.The estimator is characterized by a gain matrix and an output mapping matrix that re?ects theinternal relations between the unknown input and output estimation error. Both state and unknowninputs estimation are realized by the proposed estimator. Thus, the results in this paper are alsocapable of dealing with fault diagnosis problems of linear time-varying systems. At last, a numericalexample is provided to illustrate the proposed approach.

Robust H_∞ control for fuzzy descriptor systems with state delay

The problem of robust H ∞ fuzzy state feedback control for uncertain fuzzy descriptor systems with state delay is solved. In the case that time-varying uncertainties are in all parameter matrices, sufficient conditions for the existence of fuzzy state feedback controller are presented in terms of linear matrix inequality (LMI). The proposed robust H ∞ control laws guarantee that the resulting closed-loop system is regular, impulse free, and stable with prescribed H ∞ norm bounded constraint for all admissible uncertainties. Finally, a numerical example is provided to demonstrate the validity of the proposed method.