锦囊遗计人难料 却叫成功在马前——谈新闻写作中的巧设悬念

蜀汉建兴七年夏四月,孔明领兵在祁山与司马懿对阵。一日,孔明探得魏兵半路而歇,便召集众将,分拨人马,一一授计,分别去攻打魏营。王平、张翼受计领兵而去之后: 孔明又唤姜维、廖化吩咐曰:“与汝二人一个锦囊。引三千精兵,偃旗息鼓,伏于前山之上。

教学设计要有“制高点”——几个教例的自我剖析

赋诗作文,立意须高;教学设计,其理亦然。大至一个完整的教学过程的构思,小至一个教学环节的处理,都必须有一个“制高点”才能居高临下,统览全局;执教时也才能够胸有主见,导有方向。“以学生为主体,以教师为主导,以训练为主线”是我的语文教学的教学论基础,我的每一个教学设计,无不以“三主”为“制高点”。下面我的几个教例,都是“三主”的体现。本文的意图只在于指出;教学设计不能没有“制高点”;不然,教学就会缺少“主心骨”,设计也就成为只是体教法上弄巧求新的“雕虫小技”。

Design Method for Optimizing the Interactive Interface of Live Broadcasting Platform for the Elderly Users

In the era of network live broadcasting for everyone,the development of live broadcasting platforms is also more intelligent and diversified.However,in the face of a large group of elderly users,the interface interaction design mode used is still mainly based on the interaction mode for young groups,and is not designed for elderly users.Therefore,a design method for optimizing the interaction interface of live broadcasting platform for elderly users was proposed in this study.Firstly,the case study method and Delphi expert survey method were used to determine the design needs of elderly users and the design mode was analysed.Secondly,the orthogonal design principle was used to design a test sample of the interactive interface of live broadcasting platform applicable for the elderly users,and then a user evaluation system was established to calculate the weights of the design elements using hierarchical analysis,and then the predictive relationship between the design mode of the interactive interface of live broadcasting platform and the elderly users was established by Quantitative Theory I.Finally,Genetic Algorithm was applied to generate the optimized design scheme.The results showed that the design method based on the Genetic Algorithm and the combination of Quantitative Theory can scientifically and effectively optimize the design of the interactive interface of the live broadcasting platform for the elderly users,and improve the experience of the elderly users.

古代民族智慧与现代领导意识的重合——《三国演义》用人谋略分析

用人谋略之一:趁时擢用,扬其朝气 三国战争画卷充满了年轻化色彩。在三国的风云际会中,纵横驰骋、叱咤风云的是大批年轻的武将文臣。孔明任刘备军师时27岁,周瑜任东吴都督,指挥赤壁大战时26岁,曹操高级谋士郭嘉“遗计定辽东”时38岁。使用小字辈需要胆识和魄力。“黄口孺子”们因为浅短的资历、阅历常被以不成熟为由喝退一边。即使在今天,论资排辈仍是人事管理中的一大弊端。有战略头脑的领导者却独具慧眼,看到年轻人背后所蕴藏的巨大效益,看到活力(战斗力)与年轻化之间的内在联系。以胆识魄力识英雄于未遇时。三国须导人深谙使用年轻人对于事业成功的重要意义。排除各种阻力,放手启用年轻人。孔明高卧隆中,“

APPROXIMATION TECHNIQUES FOR APPLICATION OF GENETIC ALGORITHMS TO STRUCTURAL OPTIMIZATION

Although the genetic algorithm (GA) has very powerful robustness and fitness, it needs a large size of population and a large number of iterations to reach the optimum result. Especially when GA is used in complex structural optimization problems, if the structural reanalysis technique is not adopted, the more the number of finite element analysis (FEA) is, the more the consuming time is. In the conventional structural optimization the number of FEA can be reduced by the structural reanalysis technique based on the approximation techniques and sensitivity analysis. With these techniques, this paper provides a new approximation model-segment approximation model, adopted for the GA application. This segment approximation model can decrease the number of FEA and increase the convergence rate of GA. So it can apparently decrease the computation time of GA. Two examples demonstrate the availability of the new segment approximation model.

Two-Dimensional Entropy Method Based on Genetic Algorithm

Two dimensional(2 D) entropy method has to pay the price of time when applied to image segmentation. So the genetic algorithm is introduced to improve the computational efficiency of the 2 D entropy method. The proposed method uses both the gray value of a pixel and the local average gray value of an image. At the same time, the simple genetic algorithm is improved by using better reproduction and crossover operators. Thus the proposed method makes up the 2 D entropy method’s drawback of being time consuming, and yields satisfactory segmentation results. Experimental results show that the proposed method can save computational time when it provides good quality segmentation.

GENETIC OPTIMIZATION OF HOT FILAMENT PARAMETERS IN HFCVD SYSTEM

In HFCVD system the substrate temperature is a key factor which deeply affects the quality of diamond films. Th e magnitude and the variation of the substrate temperature must be limited in a suitable range to deposit diamond films of uniform thickness over large areas. In this paper, the hot filament parameters are investigated on the basi s of GAs to realize a good substrate temperature profile. Computer simulations d emonstrate that on parameters optimized by GAs a uniform substrate temperatur e field can be formed over a relatively large circle area with R s=10 cm.

Optimal design of pressure vessel using an improved genetic algorithm

As the idea of simulated annealing (SA) is introduced into the fitness function, an improved genetic algorithm (GA) is proposed to perform the optimal design of a pressure vessel which aims to attain the minimum weight under burst pressure con- straint. The actual burst pressure is calculated using the arc-length and restart analysis in finite element analysis (FEA). A penalty function in the fitness function is proposed to deal with the constrained problem. The effects of the population size and the number of generations in the GA on the weight and burst pressure of the vessel are explored. The optimization results using the proposed GA are also compared with those using the simple GA and the conventional Monte Carlo method.

Optimal operation of water supply systems with tanks based on genetic algorithm

In view of the poor water supply system’s network properties, the system’s complicated network hydraulic equations were replaced by macroscopic nodal pressure model and the model of relationship between supply flow and water source head. By using pump-station pressure head and initial tank water levels as decision variables, the model of optimal allocation of water supply between pump-sources was developed. Genetic algorithm was introduced to deal with the model of optimal allocation of water supply. Methods for handling each constraint condition were put forward, and overcome the shortcoming such as premature convergence of genetic algorithm; a solving method was brought forward in which genetic algorithm was combined with simulated annealing technology and self-adaptive crossover and mutation probabilities were adopted. An application example showed the feasibility of this algorithm.

RESEARCH OF QUANTUM GENETIC ALGORITH AND ITS APPLICATION IN BLIND SOURCE SEPARATION

This letter proposes two algorithms: a novel Quantum Genetic Algorithm (QGA)based on the improvement of Han's Genetic Quantum Algorithm (GQA) and a new Blind Source Separation (BSS) method based on QGA and Independent Component Analysis (ICA). The simulation result shows that the efficiency of the new BSS method is obviously higher than that of the Conventional Genetic Algorithm (CGA).

Multi-Criterion Optimal Design of Automotive Door Based on Metamodeling Technique and Genetic Algorithm

A method for optimizing automotive doors under multiple criteria involving the side impact, stiffness, natural frequency, and structure weight is presented. Metamodeling technique is employed to construct approximations to replace the high computational simulation models. The approximating functions for stiffness and natural frequency are constructed using Taylor series approximation. Three popular approximation techniques,i.e.polynomial response surface (PRS), stepwise regression (SR), and Kriging are studied on their accuracy in the construction of side impact functions. Uniform design is employed to sample the design space of the door impact analysis. The optimization problem is solved by a multi-objective genetic algorithm. It is found that SR technique is superior to PRS and Kriging techniques in terms of accuracy in this study. The numerical results demonstrate that the method successfully generates a well-spread Pareto optimal set. From this Pareto optimal set, decision makers can select the most suitable design according to the vehicle program and its application.

The Effective Clustering Partition Algorithm Based on the Genetic Evolution

To the problem that it is hard to determine the clustering number and the abnormal points by using the clustering validity function, an effective clustering partition model based on the genetic algorithm is built in this paper. The solution to the problem is formed by the combination of the clustering partition and the encoding samples, and the fitness function is defined by the distances among and within clusters. The clustering number and the samples in each cluster are determined and the abnormal points are distinguished by implementing the triple random crossover operator and the mutation. Based on the known sample data, the results of the novel method and the clustering validity function are compared. Numerical experiments are given and the results show that the novel method is more effective.

SOLVING MINIMUM SPANNING TREE PROBLEM WITH DNA COMPUTING

Molecular programming is applied to minimum spanning problem whose solution requires encoding of real values in DNA strands. A new encoding scheme is proposed for real values that is biologically plausible and has a fixed code length. According to the characteristics of the problem, a DNA algorithm solving the minimum spanning tree problem is given. The effectiveness of the proposed method is verified by simulation. The advantages and disadvantages of this algorithm are discussed.

Optimum Design of Outrigger and Belt Truss Systems Using Genetic Algorithm

There are many structural lateral systems used in tall buildings： rigid frames, braced frames, shear walls, tubular structures and core structures. The outrigger and belt truss systems are efficient structures for drift control and base moment reduction in tall buildings where the core alone is not rigid enough to resist lateral loads. Perimeter columns are mobilized for increasing the effective width of the structure, and they developed tension in the windward columns and compression in the leeward columns. Optimum locations for the outriggers have been studied because of the influence on the top displacement and base moment in the core. It was analyzed the optimal position for two to seven outriggers and belt trusses, aiming to achieve minimum bending moment and minimum drift.

Robust design and optimization for autonomous PV-wind hybrid power systems

This study presents a robust design method for autonomous photovoltaic (PV)-wind hybrid power systems to obtain an optimum system configuration insensitive to design variable variations. This issue has been formulated as a constraint multi-objective optimization problem, which is solved by a multi-objective genetic algorithm, NSGA-II. Monte Carlo Simulation (MCS) method, combined with Latin Hypercube Sampling (LHS), is applied to evaluate the stochastic system performance. The potential of the proposed method has been demonstrated by a conceptual system design. A comparative study between the proposed robust method and the deterministic method presented in literature has been conducted. The results indicate that the proposed method can find a large mount of Pareto optimal system configurations with better compromising performance than the deterministic method. The trade-off information may be derived by a systematical comparison of these configurations. The proposed robust design method should be useful for hybrid power systems that require both optimality and robustness.

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.

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.

Optimization of project payment schedules with Nash equilibrium model and genetic algorithm

To minimize the deviations of the net present values of project payment for both the owner and the client and optimize project payment schedules, a Nash equilibrium model based on game theory was set up and a genetic algorithm was developed to work out the Nash equilibrium solution with a two-stage backward inductive approach that requires the client responds to the owner’s payment schedule with an activity schedule so as to maximize the client’s net present value of cash flows. A case study demonstrated that a payment schedule at the Nash equilibrium position enables both the owner and the client to gain their desirable interests, thus is a win-win solution for both parties. Despite the computation time of the proposed algrithm in need of improving, combining Nash equilibrium and genetic algorithm into a complete-information dynamic-game model is a promising method for project management optimization.

Genetic Algorithms to the Nesting Problem in the Leather Manufacturing Industry

The nesting problem in the leather manufacturing is the problem of placing a set of irregularly shaped pieces (called stencils) on a set of irregularly shaped surfaces (called leathers sheets). This paper presents a novel and promising processing approach. After the profile of leather sheets and stencils is obtained with digitizer, the discretization makes the processing independent of the specific geometrical information. The constraints of profile are regarded thoroughly. A heuristic bottom-left placement strategy is employed to sequentially locate stencils on sheets. The optimal placement sequence and rotation are deterimined by genetic algorithms (GA). A natural concise encoding method is developed to satisfy all the possible requirements of the leather nesting problem. The experimental results show that the proposed algorithm can not only be applied to the normal two-dimensional nesting problem, but also especially suitable for the placement of multiple two-dimensional irregular stencils on multiple two-dimensional irregular sheets.

A neurofuzzy system based on rough set theory and genetic algorithm

This paper presents a hybrid soft computing modeling approach for a neurofuzzy system based on rough set theory and the genetic algorithms (NFRSGA). The fundamental problem of a neurofuzzy system is that when the input dimension increases, the fuzzy rule base increases exponentially. This leads to a huge infrastructure network which results in slow convergence. To solve this problem, rough set theory is used to obtain the reductive rules, which are used as fuzzy rules of the fuzzy system. The number of rules decrease, and each rule does not need all the conditional attribute values. This results in a reduced, or not fully connected, neural network. The structure of the neural network is relatively small and thus the weights to be trained decrease. The genetic algorithm is used to search the optimal discretization of the continuous attributes. The NFRSGA approach has been applied in the practical application of building a soft sensor model for estimating the freezing point of the light diesel fuel in a Fluid Catalytic Cracking Unit (FCCU), and satisfying results are obtained.