SVD-based Preisach hysteresis identification and composite control of piezo actuators

In this paper, the singular value decomposition (SVD) based identification and compensation of the hysteretic phenomenon in piezo actuators are addressed using a Preisach model. First, this paper presents an SVD-based least squares algorithm and a revision approach of the identification through updating the SVD. With the identified parameters and a log of the memory curve, a Preisach-based inversion compensator is constructed which is complemented with a feedback controller to address the inevitable and residual modeling errors. Experimental results are furnished for both the identification and compensation approaches. The Preisach-based feedforward controller significantly improves the tracking performance and reduces the root-mean-square (RMS) tracking error of a PID controller by 76.7% and 89% at 1 Hz and 25 Hz, respectively. With the proposed composite controller, the percent-RMS errors at 1 Hz and 25 Hz are reduced to 0.035% and 0.31%, respectively.     

Biofouling control by quorum sensing inhibition and its dependence on membrane surface

Biofouling control by quorum sensing (QS) inhibition and the influence of membrane surface characteristics on biofilm formation and QS inhibition were investigated. Pseudomonas putida isolated from the bio-fouled reverse osmosis (RO) membranes in a real plant was used. Acylase was chosen as a model QS inhibitor. Bacteria on the membrane coupons were quantified with the heterotrophic plate count method. Cell distribution was imaged by a confocal laser scanning microscope. Results showed that biofilm formation on the membrane was reduced by acylase as it inhibits the activity of N-acylhomoserine lactone (AHL) which is a signal molecule of QS. It was also shown that membrane surface characteristics were influential factors affecting bacterial adhesion, biofilm formation, and QS inhibition.     

Proactive Human Search for the Designated Person with Prior Context Information in an Undiscovered Environments

This paper describes a scheme for proactive human search for a designated person in an undiscovered indoor environment without human operation or intervention. In designing and developing human identification with prior information a new approach that is robust to illumination and distance variations in the indoor environment is proposed. In addition, a substantial exploration method with an octree structure, suitable for path planning in an office configuration, is employed. All these functionalities are integrated in a message- and component-based architecture for the efficient integration and control of the system. This approach is demonstrated by succeeding human search in the challenging robot mission of the 2009 Robot Grand Challenge Contest.     

Effects of user age and target-expansion methods on target-acquisition tasks using a mouse

Target expansion, i.e., the increase of target size according to cursor movement, can be a practical scheme to improve the usability of target-selection tasks using a mouse. This study examined the effects of different user age groups and target-expansion methods on target-acquisition tasks with grouped icons. Twenty-eight subjects performed acquisition tasks under eight experimental conditions: combinations of four expansion areas (no, one-icon, fish-eye, and group expansion) and two expansion techniques (occlusion and push). Older users took longer to acquire targets than younger users; however, they showed no significant difference in accuracy. Target expansion did not substantially improve performance speed compared to the static condition. However, the error rate was lowest when group area was expanded with the push technique, and both age groups were most satisfied with one-icon area expansion with the occlusion technique. We suggest alternative guidelines in designing target-expansion schemes.     

Uniformly subsampled ensemble (USE) for churn management: Theory and implementation

The present paper explores the possible application of a new ensemble model. The model, which is based on multiple SVM classifiers, is employed to address churner identification problems in the mobile telecommunication industry, a sector in which the role of customer retention program becomes increasingly important due to its very competitive business environment. In particular, the current study introduces a uniformly subsampled ensemble (USE) model of SVM classifiers, not only to reduce the computational complexity of large-scale data, but also to boost the reliability and accuracy of calibrated models on data sets with highly skewed class distributions. According to our experiments, the performance of the USE SVM model is superior compared to all single and ensemble models. It is more scalable than well-known ensemble models as well.     

Integrated framework of risk evaluation and risk allocation with bounded data

This paper presents an integrated framework for risk evaluation and risk allocation with bounded data in a critical risk management. A risk evaluation framework using the Imprecise Data Envelopment Analysis (IDEA) method is proposed to be applied to operations of Korean Army helicopters. The risks pertaining to pilots, missions and helicopters are evaluated based on bounded data, and pilots are appropriately allocated to missions and helicopters using goal programming with bounded risk scores. Using bounded data, two risk allocation models are developed to be used with the expected value and lower/upper limit values, resulting in improved reliability of the solutions. Numerical experiments show reasonable solutions and valuable information for risk management.     

Forecasting trends of high-frequency KOSPI200 index data using learning classifiers

Recently many statistical learning techniques have been applied to the prediction of financial variables. The aim of this paper is to conduct a comprehensive study of the applications of statistical learning techniques to predict the trend of the return of high-frequency Korea composite stock price index (KOSPI) 200 index data using the information from the one-minute time series of spot index, futures index, and foreign exchange rate. Through experiments, it is observed that the spot index change is better predictable with high-frequency time series data and the futures index information significantly improves the prediction accuracy of the return trends of the spot index for high-frequency index data, while the information of exchange rate does not. Also, dimension reduction process before training helps to increase the accuracy and dramatically for some classifiers. In addition, the trained classifiers with which a virtual trading strategy is applied to, noticeable better profits can be achieved than just a buy-and-hold-like strategy.     

Grafting of enzymes on collagen films using Woodward''s reagent "K" and a water-soluble carbodiimide derivative

Two new methods of activation were developed to graft enzymes on collegen films. They involved chemical modifications of surface groups of collagen either by Woodward's reagent "K" or by EDC, a water-soluble derivative of carbodiimide. EDC was a better coupling agent and a detailed study was conducted with this agent. It could be used either in a global method of activation and coupling, or in a two-step procedure of activation of collagen, followed by spontaneous coupling of enzyme. All enzymes tested were successfully bound: malate dehydrogenase, lactate dehydrogenase, aspartate aminotransferase, urease, creatine kinase, hexokinase. The influence on the yield of grafted enzyme, of pretreatment of films, time and temperature of EDC activation, concentration of EDC and enzyme, protecting agents was studied. Stability of enzyme activity on storage was greatly increased after grafting. A co-grafted dual system creatine kinase/heoxkinase, was achieved which exhibited a good efficiency. A striking renaturing process at 0-4degreesC after thermal denaturation, was observed with hexokinase.