HYBRID MULTI-OBJECTIVE GRADIENT ALGORITHM FOR INVERSE PLANNING OF IMRT

The intelligent optimization of a multi-objective evolutionary algorithm is combined with a gradient algorithm. The hybrid multi-objective gradient algorithm is framed by the real number. Test functions are used to analyze the efficiency of the algorithm. In the simulation case of the water phantom, the algorithm is applied to an inverse planning process of intensity modulated radiation treatment （IMRT）. The objective functions of planning target volume （PTV） and normal tissue （NT） are based on the average dose distribution. The obtained intensity profile shows that the hybrid multi-objective gradient algorithm saves the computational time and has good accuracy, thus meeting the requirements of practical applications.

小学英语复习课的若干思考

小学英语复习课是按照特定的教学程序对小学生学习英语进行的定期性规律性安排,是语言课程教育的一个重要分支,它保持着特有的课程特性,在学生教育上发挥着重要作用,对小学生英语教学具有一定的促进作用,在新型教育形势下,小学英语复习课作为一门特殊的又普遍有效的课程在前进中不断发展,在此,我仅对当前小学英语复习课中所存在的问题和需要改进的地方进行思考,并提出相应的意见和建议。

Hybrid FEM-BEM for simulation of electromagnetic response

A finite element method with boundary element method (FEM-BEM) is presented for computing electromagnetic induction. The features of an edge element method including the volume and surface edge element method are investigated in depth. Surface basis functions of edge elements to an arbitrary shape of target are derived according to the geometrical property of basis functions and applied to discretize the surface integral equation for 3-D general targets. The proposed model is presented to compute resonant frequencies and surface current of underground unexplored ordnance (UXO), and then the electromagnetic responses of single target with different frequencies and positions of sensor are simulated and results are validated by experiments.

Method for Underwater Target Tracking Based on an Interacting Multiple Model

According to the requirements of real-time performance and reliability in underwater maneuvering target tracking as well as clarifying motion features of the underwater target, an interacting multiple model algorithm based on fuzzy logic inference （FIMM） is proposed. Maneuvering patterns of the target are represented by model sets, including the constant velocity model （CA）, the Singer mode~, and the nearly constant speed horizontal-turn model （HT） in FIMM technology. The simulation results show that compared to conventional IMM, the reliability and real-time performance of underwater target tracking can be improved by FIMM algorithm.

Modeling and Multi-objective Optimization of Refinery Hydrogen Network

The demand of hydrogen in oil refinery is increasing as market forces and environmental legislation, so hydrogen network management is becoming increasingly important in refineries. Most studies focused on single-objective optimization problem for the hydrogen network, but few account for the multi-objective optimization problem. This paper presents a novel approach for modeling and multi-objective optimization for hydrogen network in refineries. An improved multi-objective optimization model is proposed based on the concept of superstructure. The optimization includes minimization of operating cost and minimization of investment cost of equipment. The proposed methodology for the multi-objective optimization of hydrogen network takes into account flow rate constraints, pressure constraints, purity constraints, impurity constraints, payback period, etc. The method considers all the feasible connections and subjects this to mixed-integer nonlinear programming （MINLP）. A deterministic optimization method is applied to solve this multi-objective optimization problem. Finally, a real case study is intro-duced to illustrate the applicability of the approach.

An improved method for multiple targets tracking

The difficulty of multiple targets tracking is how to quickly fulfill the target matching from one flame image to another and fix the position of the target. In order to accurately choose target feature information for reliable matching, simplify operations under the reliable precondition, and realize precise moving objects tracking, an approach based on Kalman prediction and feature matching was proposed. The position of the target in next frame image was predicted by Kalman, and then the moving objects of two adjacent frames were matched by the centroid and area methods. When occlusion occurs, the best matching result was found to realize tracking by matching matrix algorithm. The simulation results show that the proposed method can achieve multiple targets tracking accurately and in real-time under complicated motion movements.

Cooperative task allocation for heterogeneous multi-UAV using multi-objective optimization algorithm

The application of multiple UAVs in complicated tasks has been widely explored in recent years.Due to the advantages of flexibility,cheapness and consistence,the performance of heterogeneous multi-UAVs with proper cooperative task allocation is superior to over the single UAV.Accordingly,several constraints should be satisfied to realize the efficient cooperation,such as special time-window,variant equipment,specified execution sequence.Hence,a proper task allocation in UAVs is the crucial point for the final success.The task allocation problem of the heterogeneous UAVs can be formulated as a multi-objective optimization problem coupled with the UAV dynamics.To this end,a multi-layer encoding strategy and a constraint scheduling method are designed to handle the critical logical and physical constraints.In addition,four optimization objectives:completion time,target reward,UAV damage,and total range,are introduced to evaluate various allocation plans.Subsequently,to efficiently solve the multi-objective optimization problem,an improved multi-objective quantum-behaved particle swarm optimization(IMOQPSO)algorithm is proposed.During this algorithm,a modified solution evaluation method is designed to guide algorithmic evolution;both the convergence and distribution of particles are considered comprehensively;and boundary solutions which may produce some special allocation plans are preserved.Moreover,adaptive parameter control and mixed update mechanism are also introduced in this algorithm.Finally,both the proposed model and algorithm are verified by simulation experiments.

Predictive performance of'Diprifusor'TCI system in patients during upper abdominal surgery under propofol/fentanyl anesthesia

Objective:To evaluate the predictive performance of‘Diprifusor’TCI(target-controlled infusion)system for its betterapplication in clinical anesthesia.Methods:The predictive performance of a‘Diprifusor’TCI system was investigated in 27Chinese patients(16 males and 11 females)during upper abdominal surgery under total intravenous anesthesia(TIVA)withpropofol/fentanyl.Measnred arterial propofol concentrations were compared with the values predicted by the TCI infusion system.Performance was determined by the median performance error(MDPE),the median absolute performance error(MDAPE),thedivergence(the percentage change of the absolute PE with time),and the wobble(the median absolute deviation of each PE fromthe MDPE).Results:The median(range)values of 14.9%(-21.6%～42.9%)for MDPE,23.3%(6.9%～62.5%)for MDAPE,-1.9%h^(-1)(-32.7%～23.0% h^(-1))for divergence,and 18.9%(4.2%～59.6%)for wobble were obtained from 227 samples from all patients.For the studied population,the PE did not increase with time but with increasing target propofol concentration,particularly fol-lowing induction.Conclusions:The control of depth of anaesthesia was good in all patients undergoing upper abdominal surgicaloperation and the predictive performance of the‘Diprifusor’target controlled mthsion system was considered acceptable forclinical purposes.But the relatively bigger wobble showed that the pharmacokinetic model is not so suitable and requires im-provement.

Reliability-based robust multi-obj ective optimization of a 5-DOF vehicle vibration model subjected to random road profiles

Ride and handling are two paramount factors in design and development of vehicle suspension systems. Conflicting trends in ride and handling characteristics propel engineers toward employing multi-objective optimization methods capable of providing the best trade-off designs compromising both criteria simultaneously. Although many studies have been performed on multi-objective optimization of vehicle suspension system, only a few of them have used probabilistic approaches considering effects of uncertainties in the design. However, it has been proved that optimum point obtained from deterministic optimization without taking into account the effects of uncertainties may lead to high-risk points instead of optimum ones. In this work, reliability-based robust multi-objective optimization of a 5 degree of freedom （5-DOF） vehicle suspension system is performed using method of non-dominated sorting genetic algorithm-II （NSGA-II） in conjunction with Monte Carlo simulation （MCS） to obtain best designs considering both comfort and handling. Road profile is modeled as a random function using power spectral density （PSD） which is in better accordance with reality. To accommodate the robust approach, the variance of all objective functions is also considered to be minimized. Also, to take into account the reliability criterion, a reliability-based constraint is considered in the optimization. A deterministic optimization has also been performed to compare the results with probabilistic study and some other deterministic studies in the literature. In addition, sensitivity analysis has been performed to reveal the effects of different design variables on objective functions. To introduce the best trade-off points from the obtained Pareto fronts, TOPSIS method has been employed. Results show that optimum design point obtained from probabilistic optimization in this work provides better performance while demonstrating very good reliability and robustness. However, other optimum points from deterministic optimizations violate the regarded constraints in the presence of uncertainties.

Simulation-based multi-objective optimization for roll shifting strategy in hot strip mill

A simulation-based multi-objective optimization approach for roll shifting strategy in hot strip mills was presented. Firstly, the effect of roll shifting strategy on wear contour was investigated by mtmerical simulation, and two evaluation indexes including edge smoothness and body smoothness of wear contours were introduced. Secondly, the edge smoothness average and body smoothness average of all the strips in a rolling campaign were selected as objective functions, and shifting control parameters as decision variables, the multi-objective method of MODE/D as the optimizer, and then a simulation-based multi-objective optimization model for roll shifting strategy was built. The experimental result shows that MODE/D can obtain a good Pareto-optimal front, which suggests a series of alternative solutions to roll shifting strategy. Moreover, the conflicting relationship between two objectives can also be found, which indicates another advantage of multi-objective optimization. Finally, industrial test confirms the feasibility of the multi-objective approach for roll shifting strategy, and it can improve strip profile and extend same width rolling miles of a rolling campaign from 35 km to 70 km.

Digital Model of Human Lower Extremity Musculoskeletal System Based on CT & MRI

A new method to reconstruct a comparatively complete muscle model of the human lower limb from CT and MRI data is presented. Topological structure of more than fourteen muscles is built and coordinates of origin and insertion points are given. Based on this model, straight-line model and centroid-line muscle model are acquired. Muscle force prediction is discussed according to the model reconstructed, and a multi-objective optimization method is put forward for evaluating muscle forces of the human lower extremity.

让“心”香馥郁——浅析思想品德课与心理健康课的区别

随着素质教育的深入开展,加强对学生的心理健康教育,提高学生的心理素质,已越来越为各级各类学校所重视。但由于各种主客观原因在心健课教学过程中存在类似思品课的问题,应如何避免呢?

Research on goal-setting method of rock drivage based on the balance among period, cost and quality

On the basis of the analysis on the disadvantages of the original goal-setting about rock drivage, this paper defined the ＂life cycle quality＂. With project management theory and the Cobb-Douglas function, ＂quality-cost and quality-price curve model＂ and the ＂total cost-period prediction model＂ were built. Then the goal-setting method of the balance among quality, cost and period of rock drivage was constructed by finding ＂life cycle cost＂ through ＂life cycle quality＂ using ＂quality-cost and quality-price curve model＂ and ensuring period through ＂life cycle cost＂ using ＂total cost-period prediction model＂ （hereinafter referred to as the ＂three goals balance method＂）. ＂Value contribution＂ which is the value of the contribution to a mine because of rock drivage, was found in the process of constructing the ＂quality-cost and quality-price curve model＂. An industrial test was done in coal mine A with the research results, staff footage efficiency improved by 24.24%, the period shortened by 14.3%, the ＂life cycle cost＂ dropped by 2.09%, the ＂life cycle quality price＂ improved by 3.29%, and value contribution increased by 25.3%. The result shows that the new goal method setting on the basis of coal mine profit maximization can ensure construc- tion period. At the same time, it can realize cost and quality objectives and the optimization and balance of relationship among them; rewarding excavation teams by ＂value contribution＂ can combine organizational goal with personal goal, it significantly raise the employee＇s work efficiency.

Multi-objective Evolutionary Algorithms for MILP and MINLP in Process Synthesis

Steady-state non-dominated sorting genetic algorithm (SNSGA), a new form of multi-objective genetic algorithm, is implemented by combining the steady-state idea in steady-state genetic algorithms (SSGA) and the fitness assignment strategy of non-dominated sorting genetic algorithm (NSGA). The fitness assignment strategy is improved and a new self-adjustment scheme of is proposed. This algorithm is proved to be very efficient both computationally and in terms of the quality of the Pareto fronts produced with five test problems including GA difficult problem and GA deceptive one. Finally, SNSGA is introduced to solve multi-objective mixed integer linear programming (MILP) and mixed integer non-linear programming (MINLP) problems in process synthesis.

Multi-objective nonlinear model predictive control through switching cost functions and its applications to chemical processes

This paper proposes a switching multi-objective model predictive control(MOMPC) algorithm for constrained nonlinear continuous-time process systems.Different cost functions to be minimized in MPC are switched to satisfy different performance criteria imposed at different sampling times.In order to ensure recursive feasibility of the switching MOMPC and stability of the resulted closed-loop system,the dual-mode control method is used to design the switching MOMPC controller.In this method,a local control law with some free-parameters is constructed using the control Lyapunov function technique to enlarge the terminal state set of MOMPC.The correction term is computed if the states are out of the terminal set and the free-parameters of the local control law are computed if the states are in the terminal set.The recursive feasibility of the MOMPC and stability of the resulted closed-loop system are established in the presence of constraints and arbitrary switches between cost functions.Finally,implementation of the switching MOMPC controller is demonstrated with a chemical process example for the continuous stirred tank reactor.

Robust elastic impedance inversion using L1-norm misfit function and constraint regularization

The classical elastic impedance （EI） inversion method, however, is based on the L2-norm misfit function and considerably sensitive to outliers, assuming the noise of the seismic data to be the Guassian-distribution. So we have developed a more robust elastic impedance inversion based on the Ll-norm misfit function, and the noise is assumed to be non-Gaussian. Meanwhile, some regularization methods including the sparse constraint regularization and elastic impedance point constraint regularization are incorporated to improve the ill-posed characteristics of the seismic inversion problem. Firstly, we create the Ll-norm misfit objective function of pre-stack inversion problem based on the Bayesian scheme within the sparse constraint regularization and elastic impedance point constraint regularization. And then, we obtain more robust elastic impedances of different angles which are less sensitive to outliers in seismic data by using the IRLS strategy. Finally, we extract the P-wave and S-wave velocity and density by using the more stable parameter extraction method. Tests on synthetic data show that the P-wave and S-wave velocity and density parameters are still estimated reasonable with moderate noise. A test on the real data set shows that compared to the results of the classical elastic impedance inversion method, the estimated results using the proposed method can get better lateral continuity and more distinct show of the gas, verifying the feasibility and stability of the method.

Questions to be reconsidered in teaching pronunciation

After systematically examining the recent development of research on teaching pronunciation in China, this paper has addressed several issues to be reconsidered in the context of pronunciation teaching and learning, such as the value, teaching objectives, ways of teaching, teaching emphasis and learners＇ affective needs. Based on these different aspects of teaching pronunciation, relevant suggestions are also provided.

Research on weak signal extraction and noise removal for GPR data based on principal component analysis

The ground penetrating radar （GPR） detection data is a wide band signal, always disturbed by some noise, such as ambient random noise and muhiple refleetion waves. The noise affects the target identification of underground medium seriously. A method based on principal component analysis （PCA） was proposed to ex- tract the target signal and remove the uncorrelated noise. According to the correlation of signal, the authors get the eigenvalues and corresponding eigenvectors by decomposing the covariance matrix of GPR data and make linear transformation for the GPR data to get the principal components （PCs）. The lower-order PCs stand h^r the strong correlated target signals of the raw data, and the higher-order ones present the uneorrelated noise. Thus the authors can extract the target signal and filter uncorrelated noise effectively by the PCA. This method was demonstrated on real ultra-wideband through-wall radar data and simulated GPR data. Both of the results show that the PCA method can effectively extract the GPR target signal and remove the uncorrelated noise.

Virtual local target method for avoiding local minimum in potential field based robot navigation

A novel robot navigation algorithm with global path generation capability is presented. Local minimum is a most intractable but is an encountered frequently problem in potential field based robot navigation.Through appointing appropriately some virtual local targets on the journey, it can be solved effectively. The key concept employed in this algorithm are the rules that govern when and how to appoint these virtual local targets. When the robot finds itself in danger of local minimum, a virtual local target is appointed to replace the global goal temporarily according to the rules. After the virtual target is reached, the robot continues on its journey by heading towards the global goal. The algorithm prevents the robot from running into local minima anymore. Simulation results showed that it is very effective in complex obstacle environments.

Fuzzy adaptive Kalman filter for indoor mobile target positioning with INS/WSN integrated method

Pure inertial navigation system(INS) has divergent localization errors after a long time. In order to compensate the disadvantage, wireless sensor network(WSN) associated with the INS was applied to estimate the mobile target positioning. Taking traditional Kalman filter(KF) as the framework, the system equation of KF was established by the INS and the observation equation of position errors was built by the WSN. Meanwhile, the observation equation of velocity errors was established by the velocity difference between the INS and WSN, then the covariance matrix of Kalman filter measurement noise was adjusted with fuzzy inference system(FIS), and the fuzzy adaptive Kalman filter(FAKF) based on the INS/WSN was proposed. The simulation results show that the FAKF method has better accuracy and robustness than KF and EKF methods and shows good adaptive capacity with time-varying system noise. Finally, experimental results further prove that FAKF has the fast convergence error, in comparison with KF and EKF methods.