Numerical analysis of sleeve monopole in parallel‐plate waveguide

A monopole with double sleeves, which consists of a resonant loading and a conventional sleeve monopole, is experimentally investigated. The loaded monopole is put vertically in a parallel‐plate waveguide and driven by a coaxial feeder. The new structure exhibits a remarkably broad impedance bandwidth. In this paper, a modal expansion technique is used to numerically evaluate the impedance characteristics of the monopole by modeling the fields between the plates using cylindrical harmonic functions. A Fourier least‐square integration is applied to finding the expansion coefficients by the boundary and continuity conditions. Prior to modeling the proposed sleeve monopole, the developed analysis scheme is examined for its convergence and accuracy. Calculated results are validated by the measurements. For the optimum design at 5.8 Ghz, we investigate the effects of the structure parameters on the impedance characteristics. © 2001 John Wiley & Sons, Inc. Int J RF and Microwave CAE 11: 86–98, 2001.     

Removal and recovery of nickel ion from wastewater of electroless plating by reduction crystallization

Wastewater treatment after electroless nickel plating runs has been known to be difficult and this has been a subject of worldwide concern. Discarding wastewater of electroless nickel plating into sea was prohibited under the London Dumping Treaty since 1996. We have studied on recovering nickel from wastewater by reduction crystallization. In this study, the characteristics of nickel ion removal and recovery were investigated in the process of reduction crystallization using a laboratory scale batch crystallizer (500 ml), in order to propose a new process for wastewater treatment from nickel plating. In the reduction crystallization, which can use hypophosphite ion as a reducing agent for nickel ion in the wastewater, nickel ion could be recovered as a form of nickel metal by seeding nickel powder having large specific surface area.     

Adaptability analysis of genetic network programming with reinforcement learning in dynamically changing environments

Genetic network programming (GNP) has been proposed as one of the evolutionary algorithms and extended with reinforcement learning (GNP-RL). The combination of evolution and learning can efficiently evolve programs and the fitness improvement has been confirmed in the simulations of tileworld problems, elevator group supervisory control systems, stock trading models and wall following behavior of Khepera robot. However, its adaptability in testing environments, where the situations dynamically change, has not been analyzed in detail yet. In this paper, the adaptation mechanism in the testing environment is introduced and it is confirmed that GNP-RL can adapt to the environmental changes using a robot simulator WEBOTS, especially when unexperienced sensor troubles suddenly occur. The simulation results show that GNP-RL works well in the testing even if wrong sensor information is given because GNP-RL has a function to automatically change programs using alternative actions. In addition, the analysis on the effects of the parameters of GNP-RL is carried out in both training and testing simulations.     

Secular degradation of crystalline photovoltaic modules

The performance of crystalline photovoltaic (PV) modules has been measured once a year in field exposure. Their maximum power output (P_max) decreases 4.8% for single-crystalline PV modules and 2.0% for the poly-crystalline case in comparison with their initial P_max at ex-work after five years' field exposure.This paper describes the secular degradation of crystalline PV modules.     

A holonic control system based on a universal learning network

A new control method is presented using the holonic concept on a universal learning network (ULN). The holonic concept was proposed by Arthur Koestler in 1905. Its aim is to harmonize entire systems with partial systems that have hierarchal structures. On the other hand, a ULN that models and controls large-scale complicated systems such as industrial plants and, economic, social, and life phenomena is proposed. In this paper, a holonic control system based on the holonic concept and ULN is presented. From simulation results from a nonlinear crane system, it has been proved that holonic control can harmonize the system rather than optimize it, which used to be the conventional method in control engineering.     

New Insights into Additive Structure Effect on Crystal Agglomeration of L‐Valine

Specific small amounts of amino acids caused agglomeration of L‐valine (L‐Val) crystals during evaporative crystallization from aqueous solutions. The agglomeration of L‐Val occurred only under acidic condition when guest amino acids satisfied several conditions. Only L‐form amino acids that have carboxylic acid groups and sufficiently long alkyl chain in the side‐chains could induce agglomeration of L‐Val. The length of alkyl chain in the side‐chains controls the degree of agglomeration. Data indicated only 0.5 wt % of L‐2‐aminoadipic acid, which has a similar chemical structure to L‐glutamic acid (L‐Glu), produced the large agglomerates > 1000 μm. The particle size was ～ 500 μm when using the same amount of L‐Glu. Based on the results from previous tests and this paper, the whole mechanism for the L‐Val agglomeration in the presence of specific guest amino acids has been revealed.     

Genetic relation algorithm with guided mutation for the large-scale portfolio optimization

The survey of the relevant literatures shows that there have been many studies for portfolio optimization problems and that the number of studies which have investigated the optimum portfolio using evolutionary computation is quite large. But, almost none of these studies deals with genetic relation algorithm (GRA), where GRA is one of the evolutionary methods with graph structure. This study presents an approach to large-scale portfolio optimization problems using GRA with a new operator, called guided mutation. In order to pick up the most efficient portfolio, GRA considers the correlation coefficient between stock brands as strength, which indicates the relation between nodes in each individual of GRA. Guided mutation generates offspring according to the average value of correlation coefficients in each individual, which means to enhance the exploitation ability of evolution of GRA. A genetic relation algorithm with guided mutation (GRA/G) for the portfolio optimization is proposed in this paper. Genetic network programming (GNP), which was proposed in our previous research, is used to validate the performance of the portfolio generated with GRA/G. The results show that GRA/G approach is successful in portfolio optimization.