Distributed learning consensus control based on neural networks for heterogeneous nonlinear multiagent systems

This paper considers a novel distributed iterative learning consensus control algorithm based on neural networks for the control of heterogeneous nonlinear multiagent systems. The system's unknown nonlinear function is approximated by suitable neural networks; the approximation error is countered by a robust term in the control. Two types of control algorithms, both of which utilize distributed learning laws, are provided to achieve consensus. In the provided control algorithms, the desired reference is considered to be an unknown factor and then estimated using the associated learning laws. The consensus convergence is proven by the composite energy function method. A numerical simulation is ultimately presented to demonstrate the efficacy of the proposed control schemes.     

An airlight estimation method for image dehazing based on gray projection

Multimedia Tools and Applications - As the key parameter of dehazing algorithms, airlight value directly affect the calculation accuracy of sky region, and any deviation will lead to the chromatic...     

Reoptimization framework and policy analysis for maritime inventory routing under uncertainty

We study a maritime inventory routing problem, in which shipments between production and consumption nodes are carried out by a fleet of vessels. The vessels have specific capacities and can be chartered under different agreements. The inventory levels of all consumption nodes and some production nodes should be maintained within specified bounds; for the remaining production nodes, orders should be picked up within pre-defined time windows. We propose a discrete-time mixed-integer programming model. In the face of new information and uncertainty, this optimization model has to be re-solved, as the horizon is rolled forward. We discuss how to account for different sources of uncertainty. We present a rolling-horizon reoptimization framework that allows us to study different policies that impact the quality of the implemented solution, so we can identify the optimal set of policies.     

TUD-DNS3脱硝助剂在催化裂化装置的工业应用

TUD-DNS3脱硝助剂在中韩（武汉）石油化工有限公司1号催化裂化装置上进行了工业应用。结果表明：针对两段再生工艺，当系统中助剂占总催化剂藏量的质量分数为2.6%时，烟气脱硫装置外排污水中的氨氮质量浓度由加剂前的100 mg/L下降至40 mg/L以内；助剂的加入降低了CO焚烧炉的炉膛温度，有利于烟气的合格排放，并对装置操作、产品分布及汽油、柴油产品质量无不良影响。     

An Analysis of Citation Counts of ETRI‐Invented US Patents

From its foundation until 2004, ETRI has registered over 1,000 US patents. This letter analyzes the characteristics of these patents and addresses the explanatory factors affecting their citation counts. For explanatory variables, research team related variables, invention specific variables, and geographical domain related variables are suggested. Zero‐altered count data models are used to test the impact of independent variables. A key finding is that technological cumulativeness, the scale of invention, outputs in the electronic field, and the degree of dependence on the US technology domain positively affect the citation counts of ETRI‐invented US patents. The magnitude of international presence appears to negatively affect the citation counts of ETRI‐invented US patents.     

连续波雷达测距中的解模糊技术

研究连续波雷达测距中的距离模糊问题，分析几种常用解模糊方法的性能，并讨论参差比的选取对测距范围的影响。     

关于我国钢铁工业发展战略的思考

21世纪初是世界经济发展的重大战略机遇期,如何抓住机遇加快发展,是中国钢铁行业应认真思考的问题。十一五我国钢铁工业的发展,靠什么来拉动,还能靠大规模的投资吗?目前我们还不能生产的双高产品怎么办,靠花钱能买来技术吗?靠市场能换来核心产品吗?值得深思。当今,我们思考的不仅应考虑个别企业的生存问题,而更应考虑的是钢铁行业整体的长远发展问题,即     

了解您的管理跨度--科研项目管理方法探索

概要介绍了科学的管理技术——项目管理方法，讨论了管理跨度在项目管理中的重要性，并推荐了如何了解管理跨度的方法。