求简 求优 求实——例谈初中语文课堂有效教学的策略

随着新课程改革的深入推进,教师的教学思想和行为发生了很大的改变,观念得以更新,课程意识得到增强,但是,在改革过程中出现了一些华而不实的课堂,这些课堂表面上看起来热闹纷呈,学生却没有学到多少。对于语文教学而言,不管课程如何改革,有效教学永远是教师应当追求的目标。那么,教师怎样打造出有效课堂呢？笔者认为,这就需要求简、求优、求实。下面笔者就这三方面进行具体阐述。

数学建模教学模块:认识与示例

阐述了对数学建模教学模块的认识,给出了一个数学建模教学模块示例——卷轴的最佳直径.

界变量算法的改进

本文给出了一种求解变量带有上界约束的线性规划问题的改进算法。此方法的关键在于充分利用上界约束的特殊性,剔除了多余的约束,不必扩大系数矩阵,而且迭代程序较为简单。实现了最大限度的简化,几乎与没有上界限制的情形相同。

浅谈作文教学“三步曲”

实践证明作文教学大致可分为三步走:"求趣——求严——求优"。这是循序渐进的三步,在实际教学中"趣"字当头,它培养学生的兴趣;"趣"后求严,培养学生严格要求自己的习惯;前二者是手段,后者是通过作文训练,推广经验,以点促面,大面积提高写作水平。

NEW OPTIMIZATION METHOD WITH REGARD TO MACHINING PRECISION

An optimization method of variant step length based on precision and two new concepts “variant step length based on precision” and “one dimensional search based on the sorting” are presented.They make the optimum design closely combined with precision of every design variable.In addition,the authors propose that for a multi peaked one dimensional search problem its optimum solution can be obtained by ordinary optimization methods only when the convexity or concavity of the objective function matches the step length determining the one dimensional search region and the method of finding the optimum step length factor.

A Study of Power Sources Optimization in Guangdong

The main problem existing in Guangdong electric power sources is analyzed in this paper. Based on theanalysis on energy-supply features, power demand and the technical and economic performances of various powersources in Guangdong, the power sources construction scale and its structure are studied and analyzed in detail byusing Generation Expansion Software Package (GESP). The future development of Guangdong electric power sourcesunder the new situation of "Power from West to East" is studied as well.[

Application of LINGO to the Solution of the Water Supply System′s Optimal Operation Model

In this research, LINGO is used successfully to solve the water supply system′s optimal operation model. Firstly, the language of LINGO and the using method were studied intensively, on the basis of which the model was transformed to LINGO form and solved successfully. Secondly, the research on the interface between LINGO and the popular office software was made. The optimization software was developed, which had Excel as the workspace and LINGO as the core of computation. Through practice, this software was found stable, easy to use and suitable for the application to the water supply corporations.

Demand change detection and inventory costs minimization

To minimize the inventory costs of detecting demand change, an acceptance/rejection method （ threshold） is proposed. The proposed threshold can be identified by the newsvendor based on the excess cost, the shortage cost, the transitional probability of the demand change, and the magnitude of the demand change. Compared with the single exponential smoothing method, it is proved that the proposed method can save many more inventory costs when detecting a step change in demand. By analyzing the proposed method, it shows that as the magnitude of step change increases, the supply chain members turn to synchronously judge a step change, and as excess （ shortage） cost increases, a newsvendor tends to respond slowly （early） to an increase in demand and responds early （slowly） to a decrease in demand. Observations from this study suggest that supply chain members should pay careful attention to different profit-margin products and different magnitude demand changes in cooperating and sharing demand information with others.

Solving material distribution routing problem in mixed manufacturing systems with a hybrid multi-objective evolutionary algorithm

The material distribution routing problem in the manufacturing system is a complex combinatorial optimization problem and its main task is to deliver materials to the working stations with low cost and high efficiency. A multi-objective model was presented for the material distribution routing problem in mixed manufacturing systems, and it was solved by a hybrid multi-objective evolutionary algorithm (HMOEA). The characteristics of the HMOEA are as follows: 1) A route pool is employed to preserve the best routes for the population initiation; 2) A specialized best?worst route crossover (BWRC) mode is designed to perform the crossover operators for selecting the best route from Chromosomes 1 to exchange with the worst one in Chromosomes 2, so that the better genes are inherited to the offspring; 3) A route swap mode is used to perform the mutation for improving the convergence speed and preserving the better gene; 4) Local heuristics search methods are applied in this algorithm. Computational study of a practical case shows that the proposed algorithm can decrease the total travel distance by 51.66%, enhance the average vehicle load rate by 37.85%, cut down 15 routes and reduce a deliver vehicle. The convergence speed of HMOEA is faster than that of famous NSGA-II.

Model and algorithm of optimizing alternate traffic restriction scheme in urban traffic network

An optimization model and its solution algorithm for alternate traffic restriction(ATR) schemes were introduced in terms of both the restriction districts and the proportion of restricted automobiles. A bi-level programming model was proposed to model the ATR scheme optimization problem by aiming at consumer surplus maximization and overload flow minimization at the upper-level model. At the lower-level model, elastic demand, mode choice and multi-class user equilibrium assignment were synthetically optimized. A genetic algorithm involving prolonging codes was constructed, demonstrating high computing efficiency in that it dynamically includes newly-appearing overload links in the codes so as to reduce the subsequent searching range. Moreover,practical processing approaches were suggested, which may improve the operability of the model-based solutions.

Constitution of Hamiltonian Function and Solution of Optimal Control of a Sort of Performance Index

Hamiltonian function is firstly constituted for solving optimal control. When character of controlled object is known,Hamiltonian function is determined by performance index. In this paper,by constituting Hamiltonian function and solving optimal control of a sort of performance index,we can understand optimal control theory and apply optimal control method better.

Research on a randomized real-valued negative selection algorithm

A real-valued negative selection algorithm with good mathematical foundation is presented to solve some of the drawbacks of previous approach. Specifically, it can produce a good estimate of the optimal number of detectors needed to cover the non-self space, and the maximization of the non-self coverage is done through an optimization algorithm with proven convergence properties. Experiments are performed to validate the assumptions made while designing the algorithm and to evaluate its performance.

A novel hybrid estimation of distribution algorithm for solving hybrid flowshop scheduling problem with unrelated parallel machine

The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this work, a novel mathematic model for the hybrid flow shop scheduling problem with unrelated parallel machine(HFSPUPM) was proposed. Additionally, an effective hybrid estimation of distribution algorithm was proposed to solve the HFSPUPM, taking advantage of the features in the mathematic model. In the optimization algorithm, a new individual representation method was adopted. The(EDA) structure was used for global search while the teaching learning based optimization(TLBO) strategy was used for local search. Based on the structure of the HFSPUPM, this work presents a series of discrete operations. Simulation results show the effectiveness of the proposed hybrid algorithm compared with other algorithms.

Hybrid particle swarm optimization with chaotic search for solving integer and mixed integer programming problems

A novel chaotic search method is proposed,and a hybrid algorithm combining particle swarm optimization(PSO) with this new method,called CLSPSO,is put forward to solve 14 integer and mixed integer programming problems.The performances of CLSPSO are compared with those of other five hybrid algorithms combining PSO with chaotic search methods.Experimental results indicate that in terms of robustness and final convergence speed,CLSPSO is better than other five algorithms in solving many of these problems.Furthermore,CLSPSO exhibits good performance in solving two high-dimensional problems,and it finds better solutions than the known ones.A performance index(PI) is introduced to fairly compare the above six algorithms,and the obtained values of(PI) in three cases demonstrate that CLSPSO is superior to all the other five algorithms under the same conditions.

Development of Cubic Bezier Curve and Curve-Plane Intersection Method for Parametric Submarine Hull Form Design to Optimize Hull Resistance Using CFD

Optimization analysis and computational fluid dynamics （CFDs） have been applied simultaneously, in which a parametric model plays an important role in finding the optimal solution. However, it is difficult to create a parametric model for a complex shape with irregular curves, such as a submarine hull form. In this study, the cubic Bezier curve and curve-plane intersection method are used to generate a solid model of a parametric submarine hull form taking three input parameters into account： nose radius, tail radius, and length-height hull ratio （L/H）. Application program interface （API） scripting is also used to write code in the ANSYS DesignModeler. The results show that the submarine shape can be generated with some variation of the input parameters. An example is given that shows how the proposed method can be applied successfully to a hull resistance optimization case. The parametric design of the middle submarine type was chosen to be modified. First, the original submarine model was analyzed, in advance, using CFD. Then, using the response surface graph, some candidate optimal designs with a minimum hull resistance coefficient were obtained. Further, the optimization method in goal-driven optimization （GDO） was implemented to find the submarine hull form with the minimum hull resistance coefficient （Ct）. The minimum C, was obtained. The calculated difference in （7, values between the initial submarine and the optimum submarine is around 0.26%, with the C, of the initial submarine and the optimum submarine being 0.001 508 26 and 0.001 504 29, respectively. The results show that the optimum submarine hull form shows a higher nose radius （rn） and higher L/H than those of the initial submarine shape, while the radius of the tail （r1） is smaller than that of the initial shape.

Optimal Power Allocation for Multiuser Underlay Cognitive Radio Networks under QoS and Interference Temperature Constraints

Power allocation is an important issue for Cognitive Radio Networks(CRNs),since it needs to consider the Quality of Service(QoS) for Secondary Users(SUs) while maintaining the interference power to Primary User(PU) below the Interference Temperature(IT) threshold. In this paper, based on Euclidean projection, we propose a distributed power control algorithm with QoS requirements to minimise the total power consumption of SUs under the time-varying channel scenario. Considering the maximum transmit power constraints and the minimum signal to interference plus noise constraints for each SU, together with the IT constraints for each PU, the power allocation problem is transformed into a convex optimization problem without auxiliary variables, and is solved by the Lagrangian dual method with less information exchange.Simulation results demonstrate that the proposed scheme is superior to the Iterative Water-Filling Algorithm(IWFA).

A GA approach to vehicle routing problem with time windows considering loading constraints

As a new variant of vehicle routing problem( VRP),a finished vehicle routing problem with time windows in finished vehicle logistics( FVRPTW) is modeled and solved. An optimization model for FVRPTW is presented with the objective of scheduling multiple transport routes considering loading constraints along with time penalty function to minimize the total cost. Then a genetic algorithm( GA) is developed. The specific encoding and genetic operators for FVRPTW are devised.Especially,in order to accelerate its convergence,an improved termination condition is given. Finally,a case study is used to evaluate the effectiveness of the proposed algorithm and a series of experiments are conducted over a set of finished vehicle routing problems. The results demonstrate that the proposed approach has superior performance and satisfies users in practice. Contributions of the study are the modeling and solving of a complex FVRPTW in logistics industry.

Optimal production lot sizing model in a supply chain with periodically fixed demand considering learning effect

This paper presents an optimal production model for manufacturer in a supply chain with a fixed demand at a fixed interval with respect to the learning effect on production capacity. An algorithm is employed to find the optimal delay time for production and production time sequentially. It is found that the optimal delay time for production and the production time are not static, but dynamic and variant with time. It is important for a manufacturer to schedule the production so as to prevent facilities and workers from idling.

A Composite Model Predictive Control Strategy for Furnaces

Tube furnaces are essential and primary energy intensive facilities in petrochemical plants. Operational optimization of furnaces could not only help to improve product quality but also benefit to reduce energy consumption and exhaust emission. Inspired by this idea, this paper presents a composite model predictive control(CMPC)strategy, which, taking advantage of distributed model predictive control architectures, combines tracking nonlinear model predictive control and economic nonlinear model predictive control metrics to keep process running smoothly and optimize operational conditions. The controllers connected with two kinds of communication networks are easy to organize and maintain, and stable to process interferences. A fast solution algorithm combining interior point solvers and Newton's method is accommodated to the CMPC realization, with reasonable CPU computing time and suitable online applications. Simulation for industrial case demonstrates that the proposed approach can ensure stable operations of furnaces, improve heat efficiency, and reduce the emission effectively.

Peer priority based data scheduling algorithm in P2P streaming system

In order to solve the problem that the existing data scheduling algorithm cannot make full use of neighbors' bandwidth resources when allocating data request among several senders in the multisender based P2P streaming system,a peer priority based scheduling algorithm is proposed.The algorithm calculates neighbors' priority based on peers' historical service evaluation as well as how many wanted data that the neighbor has.The data request allocated to each neighbor is adjusted dynamically according to the priority when scheduling.Peers with high priority are preferred to allocate more data request.Experiment shows the algorithm can make full use of neighbors' bandwidth resources to transmit data to reduce server pressure effectively and improve system scalability.