Problem solving through digital game design: A quantitative content analysis

Project Tech engages secondary students (ages 14 to 17) in the process of digital game design in a variety of in-school, after-school, and secure settings. The goal of Project Tech is to leverage students’ interests in games and design to foster their problem-solving in a supportive environment where they learn to create games about a social issue they have experienced personally. The present study compares the in-school special needs version of Project Tech (n = 11) to examine problem solving. Students enrolled in Project Tech were guided in the process of designing digital games aimed at teaching younger students (ages 12–15) about social issues facing teenagers. A quantitative content analysis was conducted on 35 iterations of a directed design game and 35 iterations of a free design game created by special needs young people and director notes. The purpose of the study was to draw from the game iterations a list of empirically grounded problem solving attributes that are associated with digital game design in a special needs classroom. The findings of the study resulted in the understanding of problem solving with special needs young people in four areas: representative characteristics, planning characteristics, executing characteristics and evaluation characteristics.     

PID controller for automatic voltage regulator using teaching–learning based optimization technique

The present work presents teaching–learning based optimization (TLBO) algorithm as an optimization technique in the area of tuning of the classical controller installed in automatic voltage regulator (AVR). The proposed TLBO algorithm is applied with an aim to find out the optimum value of proportional integral derivative (PID) controller gains with first order low pass filter installed in the AVR. The voltage response of the AVR system, as obtained by using the proposed TLBO based PID controller with first order low pass filter, is compared to those offered by the other algorithms reported in the recent state-of-the-art literatures. The advantage of using this control strategy may be noted by providing good dynamic responses over a wide range of system parametric variations. For on-line, off-nominal operating conditions, fast acting Sugeno fuzzy logic technique is applied to obtain the on-line dynamic responses of the studied model. Furthermore, robustness analysis is also carried out to check the performance of the designed TLBO based PID controller. An analysis, based on voltage response profile, has been investigated with the variations of the model parameters. The simulation results show that the proposed TLBO based PID controller is a significant optimization tool in the subject area of the AVR system. The essence of the present work signifies that the proposed TLBO technique maybe, successfully, applied for the AVR of power system.     

Adaptive modulation recognition based on the evolutionary algorithms

Nowadays, the development of classification algorithms gives the ability to improve Automatic Modulation Recognition (AMR) effectively. This paper presents a novel modulation recognition algorithm based on clustering approach. Generally, we aim to distinguish multicarrier modulation OFDM from single-carrier modulations. In this regard, two statistics of the amplitude of the received signal are calculated at the output of a quadrature mixer as key features. The extracted features of training data points are submitted to the clustering algorithm, then, centroids for single-carrier and multicarrier modulations are assessed. Afterwards, each point of testing dataset is dedicated to its nearest centroid based on Euclidian distance and the recognition is accomplished. Simulation results demonstrate that the algorithm is beneficial in a wide range from low to high SNRs.     

A few bad ideas on the way to the triumph of parallel computing

Parallelism has become mainstream, in the multicore chip, the GPU, and the internet datacenter running MapReduce. In my field, large-scale scientific computing, parallelism now reigns triumphant.     

Iron Ore Sintering: Environment,Automatic, and Control Techniques

Sintering is a process of agglomeration of iron ore fines, fluxes, recycled products, slag-forming elements, and coke with the purpose of achieving an agglomerated product with the suitable composition, quality and granulometry to be used as burden material in the blast furnace. However, the sintering process accounts for an important percentage of the ironmaking and steelmaking emissions, so the minimization of them will promote environmental and health improvements as well as economical profitability. Automatic and control systems contribute to all these objectives.     

成庄矿洗选厂斜轮自动放矸装置的改造应用

针对成庄矿洗选厂原斜轮分选机放矸系统的设计缺陷及运行中存在的问题,如工人劳动强度大、系统运行方式不合理、技术相对落后等,通过对斜轮分选机放矸装置进行技术改造,形成了完善的自动放矸系统。该系统改造主要包括:放矸阀门自动关联互锁控制、介质高效回收、矸石自动排放等,实现了斜轮分选机的定时自动放矸。经过使用验证,此项革新解决了斜轮分选机的放矸问题,年经济效益达到800万元以上,并有效降低了工人的劳动强度。     

Grammar-based generation of stochastic local search heuristics through automatic algorithm configuration tools

Several grammar-based genetic programming algorithms have been proposed in the literature to automatically generate heuristics for hard optimization problems. These approaches specify the algorithmic building blocks and the way in which they can be combined in a grammar; the best heuristic for the problem being tackled is found by an evolutionary algorithm that searches in the algorithm design space defined by the grammar.In this work, we propose a novel representation of the grammar by a sequence of categorical, integer, and real-valued parameters. We then use a tool for automatic algorithm configuration to search for the best algorithm for the problem at hand. Our experimental evaluation on the one-dimensional bin packing problem and the permutation flowshop problem with weighted tardiness objective shows that the proposed approach produces better algorithms than grammatical evolution, a well-established variant of grammar-based genetic programming. The reasons behind such improvement lie both in the representation proposed and in the method used to search the algorithm design space.     

Towards fully automatic reliable 3D acquisition: From designing imaging network to a complete and accurate point cloud

This paper describes a novel system for accurate 3D digitization of complex objects. Its main novelties can be seen in the new approach, which brings together different systems and tools in a unique platform capable of automatically generating an accurate and complete model for an object of interest. This is performed through generating an approximate model of the object, designing a stereo imaging network for the object with this model and capturing the images at the designed postures through exploiting an inverse kinematics method for a non-standard six degree of freedom robot. The images are then used for accurate and dense 3D reconstruction using photogrammetric multi-view stereo method in two modes, including resolving scale with baseline and with control points. The results confirm the feasibility of using Particle Swarm Optimization in solving inverse kinematics for this non-standard robot. The system provides this opportunity to test the effect of incidence angle on imaging network design and shows that the matching algorithms work effectively for incidence angle of 10°. The accuracy of the final point cloud generated with the system was tested in two modes through a comparison with a dataset generated with a close range 3D colour laser scanner.     

DCS系统在鼓风机控制中的应用

本文主要介绍了DCS控制系统在莱钢焦化厂鼓风机改造中的应用，重点介绍了鼓风机改造的技术要点，并对鼓风机改造前的现状和改造后的效果进行了简要阐述。     

Viscosity virtual sensor to control combustion in fossil fuel power plants

Thermo-electrical power plants utilize fossil fuel oil to transform the calorific power of fuel into electric power. An optimal combustion in the boiler requires the fuel oil to be in its best conditions. One of fuel's most important properties to consider is viscosity. Viscosity has influence on the optimal combustion between fuel and air. Hardware viscosity meters for fuel oils are expensive and unreliable to operate in power plant conditions. Chemical laboratory measures viscosity accurately with special apparatus, but they cannot be used in a real time process. This paper describes the development of a virtual sensor that estimates fuel oil viscosity in the combustion process of a power plant. A virtual sensor or soft sensor is a computer program that estimates the value of a certain variable based on related measurements and a model of the process where the variable participates. In this project, a probabilistic model is constructed using automatic learning algorithms with historical data and experts' advice. The learning and validation experiments are described and discussed. The virtual sensor is installed in the Tuxpan Power Plant in Veracruz, Mexico.