运用四大策略,以学程优化赋能素养生成

当前的课堂教学应凸显学生学习的主体地位,注重个性化、多样化学习和发展的需求,这就要求教师重新审视“教”与“学”的关系,适当转换自身角色,重视“学程”。为此,教师可运用四种策略,让学程充满情感温度、发展韧性并焕发出新活力,从而促进学生能力发展、素养生成:情境创设,让学程更有趣味;问题切入,让学程更有指向;多向互动,让学程更加深入;任务驱动,让学程更加明晰。

高中语文革命传统作品的学程优化——以《长征胜利万岁》《大战中的插曲》联读为例

高中语文革命传统作品的学习目标要着眼于学程,要指向学生语文学科核心素养的提升。为了优化高中语文革命传统作品的学程,教师可从以下四个方面着手:聚焦核心素养,确定学程目标;立足学情反馈,创设学程情境;构建学习任务,连缀学程链条;融入信息技术,赋能学程活动。

借助政史课程表达 优化主题融合学习

现行高中思政和历史课程诸多内容相同,且这些相同内容成为各自课程的课题。基于政史课程相同课题下两科都有事实(史实)和思想,但“历史偏重史实、思政偏重思想”的差异,可以就政史两科相同内容开展主题教学。对于思政学科而言,开展主题教学有助于厚植史实根基,有助于借助历史阐释佐证课程思想,更好地实现对学生的价值引领;对于历史学科而言,实施主题学习有助于史实基础上的拓展延伸,让厚重的史料迸发时代光辉。由此,政史两科应借助各自的课程表达,优化主题融合学习,让课程素养落地落实。

优化设计学程:助力学生深度学习真实发生——以人教版“认识东、南、西、北”教学为例

依据学生的认知特点以及班级学情优化设计学程,能很好地激活学生的思维因子,让学生带着疑惑和学习兴趣进入学境。学生经过思维的"跌打滚爬"获得学习体验,产生新的学习认知,教师再发挥自己"四两拨千斤"的教学指导策略,由此才能真正助力学生深度学习的发生。

基于优化学程的课堂教学行为改变

改变讲授式为主的教学行为，创设学生乐于参与的课堂氛围，需要教师关注和优化学生的学习过程，以“学”定“教”，变“教”为“导”。更需要教学管理者关注课堂，不断调查、研究，寻求促动教师改变课堂行为的有效方法，并使之成为教师的实践行为。

略论“教程”与“学程”的科学规划

教师实施教程的真谛就在于理解并参与学生的学程,使学生将学习资源更好地内化和发展。数学课堂教学中可以从学生学力的发展、学生思维过程的暴露、尊重学生的认知规律及实现有意义学习等方面规划教程与学程。

Modified Augmented Lagrange Multiplier Methods for Large-Scale Chemical Process Optimization

Chemical process optimization can be described as large-scale nonlinear constrained minimization. The modified augmented Lagrange multiplier methods (MALMM) for large-scale nonlinear constrained minimization are studied in this paper. The Lagrange function contains the penalty terms on equality and inequality constraints and the methods can be applied to solve a series of bound constrained sub-problems instead of a series of unconstrained sub-problems. The steps of the methods are examined in full detail. Numerical experiments are made for a variety of problems, from small to very large-scale, which show the stability and effectiveness of the methods in large-scale problems.

An Optimum and Effective Drawing Teaching System in Engineering

In this study, the authors shall illustrate the difficulties, pros and cons of ＂Technical Drawing＂ courses in engineering curriculums. The authors also show the efficiency, effectiveness of students＇ grasping the engineering drawing concepts in depth. The authors have conducted a survey on students and their instructors for teaching the course. Whether the course should be taught with manual handling and then computerized or starting with computer aid and finishing the content of the course, the authors shall also go in detail of syllabus how the drawing rooms and aid tools will be. This part of study shall look into the ergonomics, safety and conditions of classrooms. Responses of 300 students will be shared in this presentation. Statistical analysis will detect the effects of different factors involved in the questionnaire.

PCA weight and Johnson transformation based alarm threshold optimization in chemical processes

To alleviate the heavy load of massive alarm on operators, alarm threshold in chemical processes was optimized with principal component analysis(PCA) weight and Johnson transformation in this paper. First, few variables that have high PCA weight factors are chosen as key variables. Given a total alarm frequency to these variables initially, the allowed alarm number for each variable is determined according to their sampling time and weight factors. Their alarm threshold and then control limit percentage are determined successively. The control limit percentage of non-key variables is determined with 3σ method alternatively. Second, raw data are transformed into normal distribution data with Johnson function for all variables before updating their alarm thresholds via inverse transformation of obtained control limit percentage. Alarm thresholds are optimized by iterating this process until the calculated alarm frequency reaches standard level(normally one alarm per minute). Finally,variables and their alarm thresholds are visualized in parallel coordinate to depict their variation trends concisely and clearly. Case studies on a simulated industrial atmospheric-vacuum crude distillation demonstrate that the proposed alarm threshold optimization strategy can effectively reduce false alarm rate in chemical processes.

Synthesis of Multi-component Mass-exchange Networks

This paper presents a superstructure-based formulation for the synthesis of mass-exchange networks (MENs) considering multiple components. The superstructure is simplified by directly using the mass separation agents (MSA) from their sources, and therefore the automatic synthesis of the multi-component system involved in the MENs can be achieved without choosing a 'key-component' either for the whole process or the mass exchangers. A mathematical model is proposed to carry out the optimization process. The concentrations, flow rates, matches and unit operation displayed in the obtained network constitute the exact representation of the mass exchange process in terms of all species in the system. An example is used to illustrate and demonstrate the application of the proposed method.

The Information Modeling and Intelligent Optimization Method for Logistics Vehicle Routing and Scheduling with Multi-objective and Multi-constraint

The vehicle routing and scheduling (VRS) problem with multi-objective and multi-constraint is analyzed, considering the complexity of the modern logistics in city economy and daily life based on the system engineering. The objective and constraint includes loading, the dispatch and arrival time, transportation conditions,total cost,etc. An information model and a mathematical model are built,and a method based on knowledge and biologic immunity is put forward for optimizing and evaluating the programs dimensions in vehicle routing and scheduling with multi-objective and multi-constraints. The proposed model and method are illustrated in a case study concerning a transport network, and the result shows that more optimization solutions can be easily obtained and the method is efficient and feasible. Comparing with the standard GA and the standard GA without time constraint,the computational time of the algorithm is less in this paper. And the probability of gaining optimal solution is bigger and the result is better under the condition of multi-constraint.

A Novel Real-time Optimization Methodology for Chemical Plants

In this paper, a novel approach termed process goose queue (PGQ) is suggested to deal with real-time optimization (RTO) of chemical plants. Taking advantage of the ad-hoc structure of PGQ which imitates biologic nature of flying wild geese, a chemical plant optimization problem can be re-formulated as a combination of a multi-layer PGQ and a PGQ-Objective according to the relationship among process variables involved in the objective and constraints. Subsequently, chemical plant RTO solutions are converted into coordination issues among PGQs which could be dealt with in a novel way. Accordingly, theoretical definitions, adjustment rule and implementing procedures associated with the approach are explicitly introduced together with corresponding enabling algorithms. Finally, an exemplary chemical plant is employed to demonstrate the feasibility and validity of the contribution.

Multiple objectives application approach to waste minimization

Besides economics and controllability, waste minimization has now become an objective in designing chemical processes, and usually leads to high costs of investment and operation. An attempt was made to minimize waste discharged from chemical reaction processes during the design and modification process while the operation conditions were also optimized to meet the requirements of technology and economics. Multiobjectives decision nonlinear programming (NLP) was employed to optimize the operation conditions of a chemical reaction process and reduce waste. A modeling language package-SPEEDUP was used to simulate the process. This paper presents a case study of the benzene production process. The flowsheet factors affecting the economics and waste generation were examined. Constraints were imposed to reduce the number of objectives and carry out optimal calculations easily. After comparisons of all possible solutions, best-compromise approach was applied to meet technological requirements and minimize waste.

Optimization of assembly line balancing using genetic algorithm

In a manufacturing industry, mixed model assembly line(MMAL) is preferred in order to meet the variety in product demand. MMAL balancing helps in assembling products with similar characteristics in a random fashion. The objective of this work aims in reducing the number of workstations, work load index between stations and within each station. As manual contribution of workers in final assembly line is more, ergonomics is taken as an additional objective function. Ergonomic risk level of a workstation is evaluated using a parameter called accumulated risk posture(ARP), which is calculated using rapid upper limb assessment(RULA) check sheet. This work is based on the case study of an MMAL problem in Rane(Madras) Ltd.(India), in which a problem based genetic algorithm(GA) has been proposed to minimize the mentioned objectives. The working of the genetic operators such as selection, crossover and mutation has been modified with respect to the addressed MMAL problem. The results show that there is a significant impact over productivity and the process time of the final assembled product, i.e., the rate of production is increased by 39.5% and the assembly time for one particular model is reduced to 13 min from existing 18 min. Also, the space required using the proposed assembly line is only 200 m2 against existing 350 m2. Further, the algorithm helps in reducing workers fatigue(i.e., ergonomic friendly).

Deep-water Riser Fatigue Monitoring Systems Based on Acoustic Telemetry

Marine risers play a key role in the deep and ultra-deep water oil and gas production. The vortex-induced vibration （VIV） of marine risers constitutes an important problem in deep water oil exploration and production. VIV will result in high rates of structural failure of marine riser due to fatigue damage accumulation and diminishes the riser fatigue life. In-service monitoring or full scale testing is essential to improve our understanding of V1V response and enhance our ability to predict fatigue damage. One ma- rine riser fatigue acoustic telemetry scheme is proposed and an engineering prototype machine has been developed to monitor deep and ultra-deep water risers＇ fatigue and failure that can diminish the riser fatigue life and lead to economic losses and eco-catastrophe. Many breakthroughs and innovation have been achieved in the process of developing an engineering prototype machine. Sea trials were done on the 6th generation deep-water drilling platform HYSY-981 in the South China Sea. The inclination monitoring results show that the marine riser fatigue acoustic telemetry scheme is feasible and reliable and the engineering prototype machine meets the design criterion and can match the requirements of deep and ultra-deep water riser fatigue monitoring. The rich experience and field data gained in the sea trial which provide much technical support for optimization in the engineering prototype machine in the future.

A motion retargeting algorithm based on model simplification

A new motion retargeting algorithm is presented, which adapts me motion capture data to a new character. To make the resulting motion realistic, the physically-based optimization method is adopted. However, the optimization process is difficult to converge to the optimal value because of high complexity of the physical human model. In order to address this problem, an appropriate simplified model automatically determined by a motion analysis technique is utilized, and then motion retargeting with this simplified model as an intermediate agent is implemented. The entire motion retargeting algorithm involves three steps of nonlinearly constrained optimization： forward retargeting, motion scaling and inverse retargeting. Experimental results show the validity of this algorithm.

An efficient latent variable optimization approach with stochastic constraints for complex industrial process

For complex chemical processes,process optimization is usually performed on causal models from first principle models.When the mechanism models cannot be obtained easily,restricted model built by process data is used for dynamic process optimization.A new strategy is proposed for complex process optimization,in which latent variables are used as decision variables and statistics is used to describe constraints.As the constraint condition will be more complex by projecting the original variable to latent space,Hotelling T^2 statistics is introduced for constraint formulation in latent space.In this way,the constraint is simplified when the optimization is solved in low-dimensional space of latent variable.The validity of the methodology is illustrated in pH-level optimal control process and practical polypropylene grade transition process.

Multi-domain modeling and simulation of proportional solenoid valve

A multi-domain nonlinear dynamic model of a proportional solenoid valve was presented.The electro-magnetic,mechanical and fluid subsystems of the valve were investigated,including their interactions.Governing equations of the valve were derived in the form of nonlinear state equations.By comparing the simulated and measured data,the simulation model is validated with a deviation less than 15%,which can be used for the structural design and control algorithm optimization of proportional solenoid valves.

The Application and Optimization of Modular Hierarchical Teaching in Computer Courses of Universities and Colleges

The current status of university computer teaching, taking fully into account the differences between students, competency-based education philosophy as a guide, this paper discusses the application of modular and hierarchical optimization problems in teaching computer courses in colleges and universities, and in-depth analyzes the basic content and implementation strategies of module level teaching.

Analysis on the Optimization of the P.E. Course Teaching Plan in Colleges and Universities

In the new period, the physical education in colleges and universities is of greater and greater significance for the enhancement of students＇ learning efficiency. In this paper, the current situation of the current physical education is analyzed, and also related suggestions on the optimization of the P.E. course teaching plan are proposed.