A robust adaptive pole‐placement controller without strictly positive real condition

This paper considers the adaptive pole‐placement control problem for system (1) with unmodelled dynamics η_n dominated by a small constant ε multiplied by a quantity independent of ε but tending to infinity as the past input, output, and noise grow. Using bounded external excitation and randomly varying truncation techniques, we give a design method of adaptive pole‐placement controller. It is shown that the closed‐loop system is globally stable, the estimation error for the parameter contained in the modelled part is of order ε, and the closed‐loop system under the adaptive pole‐placement control law is suboptimal in the sense of $$\mathop{\lim\sup}\limits_{{n\to\infty }}{1\over n}\mathop{\sum}\limits_{i=0}^n{\left({A^{*}(z)y_{n}‐L(z)C(z)w_{n}‐B(z)R(z)y_{n}^{*}}\right)^{2}{\leq}O({\varepsilon}^{2})+\gamma^{2}\mathop{\sum}\limits_{j=1}^q{b_{j}^{2}}}$$\nopagenumbers\end while the SPR condition used usually in other papers is replaced by a stability condition. Copyright © 2001 John Wiley & Sons, Ltd.     

Thermodynamic modeling of native point defects and dopants of GaN semiconductors

A thermodynamic modeling of GaN was carried out to describe the thermodynamic behavior of native defects, dopants, and carriers (free electrons and holes) in GaN semiconductors. The compound energy model (CEM) was used. An unintentionally doped GaN was taken as an example. Oxygen was introduced into the model as the unintentionally doped impurity, according to the practical experimental phenomena. The energies of component compounds in the model were defined based on the results of the ab initio calculations and adjusted to fit experimental data. The thermodynamic properties of the defects and the oxygen doped were calculated to show the facility of the model.     

Object-oriented DBMS requirements

The requirements on an object-oriented DBMS for management of information in a large, complex enterprise are presented. These requirements aid in the achievement of an environment characterized by data sharing, open architectures, application and data portability, and assurance of data integrity. They were defined from the point of view of a user of the DBMS; therefore they describe the expected functionality of the DBMS and do not specify the method of implementation to achieve this functionality. They encompass requirements on the data model, query and data manipulation languages, the system architecure, interfaces to the system, change management, and transaction management.     

Rate of ammonia oxidation in a synthetic saline wastewater by a nitrifying mixed‐culture

Most of the kinetic studies on nitrification have been performed in diluted salts medium. In this work, the ammonia oxidation rate (AOR) was determined by respirometry at different ammonia (0.01 and 33.5 mg N‐NH₃ L^?1), nitrite (0–450 mg N‐NO₂^? L^?1) and nitrate (0 and 275 mg N‐NO₃^? L^?1) concentrations in a saline medium at 30 °C and pH 7.5. Sodium azide was used to uncouple the ammonia and nitrite oxidation, so as to measure independently the AOR. It was determined that ammonia causes substrate inhibition and that nitrite and nitrate exhibit product inhibition upon the AOR. The effects of ammonia, nitrite and nitrate were represented by the Andrews equation (maximal ammonia oxidation rate, r_AOMAX, = 43.2 [mg N‐NH₃ (g VSS_AO h)^?1]; half saturation constant, K_SAO, = 0.11 mg N‐NH₃ L^?1; inhibition constant K_IAO, = 7.65 mg N‐NH₃ L^?1), by the non‐competitive inhibition model (inhibition constant, K_INI, = 176 mg N‐NO₂^? L^?1) and by the partially competitive inhibition model (inhibition constant, K_INA, = 3.3 mg N‐NO₃^? L^?1; α factor = 0.24), respectively. The r_AOMAX value is smaller, and the K_SAO value larger, than the values reported in diluted salts medium; the K_IAO value is comparable to those reported. Process simulations with the kinetic model in batch nitrifying reactors showed that the inhibitory effects of nitrite and nitrate are significant for initial ammonia concentrations larger than 100 mg N‐NH₄⁺ L^?1. Copyright © 2005 Society of Chemical Industry     

A Bayesian propensity score adjustment for latent variable modeling and MCMC algorithm

The estimation of the differences among groups in observational studies is frequently inaccurate owing to a bias caused by differences in the distributions of covariates. In order to estimate the average treatment effects when the treatment variable is binary, Rosenbaum and Rubin [1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70, 41-55] proposed an adjustment method for pre-treatment variables using propensity scores. Imbens [2000. The role of the propensity score in estimating dose-response functions. Biometrika 87, 706-710] extended the propensity score methodology for estimation of average treatment effects with multivalued treatments.However, these studies focused only on estimating the marginal mean structure. In many substantive sciences such as the biological and social sciences, a general estimation method is required to deal with more complex analyses other than regression, such as testing group differences on latent variables. For latent variable models, the EM algorithm or the traditional Monte Carlo methods are necessary. However, in propensity score adjustment, these methods cannot be used because the full distribution is not specified.In this paper, we propose a quasi-Bayesian estimation method for general parametric models that integrate out the distributions of covariates using propensity scores. Although the proposed Bayes estimates are shown to be consistent, they can be calculated by existing Markov chain Monte Carlo methods such as Gibbs sampler. The proposed method is useful to estimate parameters in latent variable models, while the previous methods were unable to provide valid estimates for complex models such as latent variable models.We also illustrated the procedure using the data obtained from the US National Longitudinal Survey of Children and Youth (NLSY1979-2002) for estimating the effect of maternal smoking during pregnancy on the development of the child's cognitive functioning.     

Age of acquisition: Its neural and computational mechanisms.

The acquisition of new skills over a life span is a remarkable human ability. This ability, however, is constrained by age of acquisition (AoA); that is, the age at which learning occurs significantly affects the outcome. This is most clearly reflected in domains such as language, music, and athletics. This article provides a perspective on the neural and computational mechanisms underlying AoA in language acquisition. The authors show how AoA modulates both monolingual lexical processing and bilingual language acquisition. They consider the conditions under which syntactic processing and semantic processing may be differentially sensitive to AoA effects in second-language acquisition. The authors conclude that AoA effects are pervasive and that the neural and computational mechanisms underlying learning and sensorimotor integration provide a general account of these effects. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Measuring consistency in TTL-based caches

We propose a general modeling framework to evaluate the performance of cache consistency algorithms. In addition to the usual hit rate, we introduce the hit^* rate as a consistency measure, which captures the fraction of non-stale downloads from the cache. We apply these ideas to the analysis of the fixed TTL consistency algorithm in the presence of network delays. The hit and hit^* rates are evaluated when requests and updates are modeled by renewal processes. Classical results on the renewal function lead to various bounds.     

Performance modeling and evaluation of data/voice services in wireless networks

Application-level performance is a key to the adoption and success of the CDMA 2000. To predict this performance in advance, a detailed end-to-end simulation model of a CDMA network is built to include application traffic characteristics, network architecture, network element details using the proposed simulation methodology. We assess the user-perceived application performance when a RAN and a CN adopt different transport architectures such as ATM and IP. To evaluate the user-perceived quality of voice service, we compare the end-to-end packet delay for different vocoder schemes such as G.711, G.726 (PCM), G.726 (ADPCM), and vocoder bypass scheme. By the simulation results, the vocoder bypass scenario shows 30% performance improvement over the others. We also compare the quality of voice service with and without DPS scheduling scheme. We know that DPS scheme keep the voice delay bound even if the service traffic is high. For data packet performance, HTTP v.1.1 shows better performance than that of HTTP v.1.0 due to the pipelining and TCP persistent connection. We may conclude that IP transport technology is better solution for higher FER environment since the packet overhead of IP is smaller than that of ATM for web browsing data traffic, while it shows opposite effect to the small size voice packet in RAN architecture. We show that the 3G-1X EV-DO system gives much better packet delay performance than 3G-1X RTT. The main conclusion is that end-to-end application-level performance is affected by various elements and layers of the network and thus it must be considered in all phases of the development process. Jae-Hyun Kim He received the B.S., M.S., and Ph.D. degrees, all in computer science and engineering, from Hanyang University, Ansan, Korea, in 1991, 1993, and 1996 respectively. In 1996, he was with the Communication Research Laboratory, Tokyo, Japan, as a Visiting Scholar. From April 1997 to October 1998, he was a post-doctoral fellow at the department of electrical engineering, University of California, Los Angeles. From November 1998 to February 2003, he worked as a member of technical staff in Performance Modeling and QoS management department, Bell laboratories, Lucent Technologies, Holmdel, NJ. He has been with the department of electrical engineering, Ajou University, Suwon, Korea, as an assistant professor since 2003. His research interests include QoS issues and cross layer optimization for high-speed wireless communication. Dr. Kim was the recipient of the LGIC Thesis Prize and Samsung Human-Tech Thesis Prize in 1993 and 1997, respectively. He is a member of the Korean Institute of Communication Sciences (KICS), Korea Institute of Telematics and Electronis (KITE), Korea Information Science Society (KISS), and IEEE. Hyun-Jin Lee received the B.S. degree in electrical engineering from Ajou University, Suwon, Korea, in 2004, and is working toward the M.S. degree and Ph. D. degree in electrical engineering at Ajou University. He has been awarded Samsung Human-Tech Thesis Prize in 2004. His research interests QoS, especially network optimization and wireless packet scheduling. He is a member of the KICS. Sung-Min Oh received the B.S. and M. S. degrees in electrical engineering form Ajou University, Suwon, Korea, in 2004, and is working toward the Ph. D. degree in electrical engineering at Ajou University. His research interests QoS performance analysis and 4G network. He is a member of the KICS. Sung-Hyun Cho received his B.S., M.S., and Ph.D. in computer science and engineering from Hanyang University, Korea, in 1995, 1997, and 2001, respectively. From 2001 to 2005, he has been with Samsung Advanced Institute of Technology, where he has been engaged in the design and standardization of MAC and upper layers of B3G, IEEE 802.16e, and WiBro systems. He is currently a MAC part leader in the telecommunication R&D center of Samsung Electronics. His research interests include 4G air interface design, radio resource management, cross layer design, and handoff in wireless systems.     

Logistic Regression Analysis of Customer Satisfaction Data

Variation exists in all processes. Significant work has been done to identify and remove sources of variation in manufacturing processes resulting in large returns for companies. However, business process optimization is an area that has a large potential return for a company. Business processes can be difficult to optimize due to the nature of the output variables associated with them. Business processes tend to have output variables that are binary, nominal or ordinal. Examples of these types of output include whether a particular event occurred, a customer's color preference for a new product and survey questions that assess the extent of the survey respondent's agreement with a particular statement. Output variables that are binary, nominal or ordinal cannot be modeled using ordinary least‐squares regression. Logistic regression is a method used to model data where the output is binary, nominal or ordinal. This article provides a review of logistic regression and demonstrates its use in modeling data from a business process involving customer feedback. Copyright © 2006 John Wiley & Sons, Ltd.