Artificial bee colony based algorithm for maximum power point tracking (MPPT) for PV systems operating under partial shaded conditions

Artificial bee colony (ABC) algorithm has several characteristics that make it more attractive than other bio-inspired methods. Particularly, it is simple, it uses fewer control parameters and its convergence is independent of the initial conditions. In this paper, a novel artificial bee colony based maximum power point tracking algorithm (MPPT) is proposed. The developed algorithm, does not allow only overcoming the common drawback of the conventional MPPT methods, but it gives a simple and a robust MPPT scheme. A co-simulation methodology, combining Matlab/Simulink™ and Cadence/Pspice™, is used to verify the effectiveness of the proposed method and compare its performance, under dynamic weather conditions, with that of the Particle Swarm Optimization (PSO) based MPPT algorithm. Moreover, a laboratory setup has been realized and used to experimentally validate the proposed ABC-based MPPT algorithm. Simulation and experimental results have shown the satisfactory performance of the proposed approach.     

一种可替代漂珠的低密度材料

漂珠低密度水泥浆由于密度低、强度高、体系稳定而在油田低压易漏层及低压油气层长封固段固井作业中获得了广泛的应用。但近年来,由于漂珠的产量越来越少,油田低成本要求急需开发代替漂珠的新型减轻材料,以满足配制低密度水泥浆的需求。开发了BCL-10S低密度混合减轻材料,其物化性能与漂珠相当,用它作减轻剂配制的低密度水泥浆性能与用漂珠作减轻剂配制的低密度水泥浆性能相媲美,并已经在长庆油田获得了应用。     

人工神经网络预测煤的发热量

应用人工神经网络处理部分广东地区煤质分析数据,建立网络模型,预测出煤的发热量,经实测值与预测值比较,在一定的煤种范围内预测值与实测值比较接近。     

人工智能的原理及应用

介绍了人工智能的发展，并对改进的神经网络专家系统的优越性做了介绍，指出了神经网络专家系统广阔的应用前景及实现。     

Integration framework for intelligent manufacturing processes

In this article, we describe a new approach to applying distributed artificial intelligence techniques to manufacturing processes. The construction of intelligent systems is one of the most important techniques among artificial intelligence research. Our goal is to develop an integrated intelligent system for real time manufacturing processes. An integrated intelligent system is a large knowledge integration environment that consists of several symbolic reasoning systems (expert systems) and numerical computation packages. These software programs are controlled by a meta-system which manages the selection, operation and communication of these programs. A meta-system can be implemented in different language environments and applied to many disciplines. This new architecture can serve as a universal configuration to develop high performance intelligent systems for many complicated industrial applications in real world domains.To whom all correspondence should be addressed.     

海上油气田的废弃处置

废弃处置是油气田开发的最终环节 ,海上油气田的废弃处置在国内外都是一个新的领域。在对国内外相关文献分析的基础上 ,结合开发工程方面的经验 ,介绍海上油气田废弃处置的标准 ,相关的领域以及废弃处置的过程和方法     

15000 m^3／h制氧机组同步电动机起动技术研究

介绍液态软起动技术在济钢 15000 m3/h制氧机配套的 6000 kW同步电动机上的应用情况,阐述了系统的原理、特点、仿真计算及实际应用后达到的效果.     

Experimental Study on Polymer Artificial Bone with HA Coated and Fiber Liners

1Introduction HA(hydroxyapatite)wasakindofbioactiveceram ics,whichhadexcellentbiocompatibilityandtissueaffin ityinthatitscomponentsweresimilartothoseofhuman bone[1].Soitwasthebestknownhumanbonesubstitute,andunprecedentedeffecthadbeenharvestedinrecenttwo d…     

RSFM网络及其在信号滤波中的应用

介绍了一种适用于模式识别的新型神经网络模型--局部有监督特征映射（Reglonal Supervised Feature Mapping,RSFM）网络,描述了该网络的拓扑结构和学习算法,研究了网络的基本性能,最后将其应用到了信号滤波中.理论研究和仿真实验表明,该网络结构简单、算法简洁,收敛速度快、识别精度高,适用于需要大样本训练、随机干扰严重的复杂模式的分类与识别.     

Ant colony optimization approach to estimate energy demand of Turkey

This paper attempts to shed light on the determinants of energy demand in Turkey. Energy demand model is first proposed using the ant colony optimization (ACO) approach. It is multi-agent systems in which the behavior of each ant is inspired by the foraging behavior of real ants to solve optimization problem. ACO energy demand estimation (ACOEDE) model is developed using population, gross domestic product (GDP), import and export. All equations proposed here are linear and quadratic. Quadratic_ACOEDE provided better-fit solution due to fluctuations of the economic indicators. The ACOEDE model plans the energy demand of Turkey until 2025 according to three scenarios. The relative estimation errors of the ACOEDE model are the lowest when they are compared with the Ministry of Energy and Natural Resources (MENR) projection.