Adaptive exception monitoring agents for management by exceptions

Management by exceptions (MBE) is an effective management strategy in many domains. It suggests that managers focus on important jobs (e.g., planning and decision-making) without being involved in the tedious monitoring of exceptions (e.g., a critical item whose current status violates some regulations). Once an exception is detected, the managers are notified to respond to the exception promptly. Therefore, exception monitoring is the key to realize the idea of MBE. An exception monitoring system should detect exceptions in a timely manner for the managers. It should also control the extra loading it incurs to related information servers (e.g., database management systems) and the Intranet, which are fundamental backbones for information processing in businesses. In this paper, a multiagent paradigm Adaptive Agents for Management by Exceptions (AAMBE) is proposed for exception monitoring. The agents adapt to the environment by learning to work together to achieve timely detection of exceptions. An experiment to investigate the performance of AAMBE is conducted by simulating real-world operations of financial management in merchandising trades. Empirical and theoretical analyses show that AAMBE may detect more exceptions in a timelier manner by incurring less extra loading to the related information servers and the Intranet.     

Reversible privacy preserving data mining: a combination of difference expansion and privacy preserving

Privacy Preserving Data Mining (PPDM) can prevent private data from disclosure in data mining. However, the current PPDM methods damaged the values of original data where knowledge from the mined data cannot be verified from the original data. In this paper, we combine the concept and technique based on the reversible data hiding to propose the reversible privacy preserving data mining scheme in order to solve the irrecoverable problem of PPDM. In the proposed privacy difference expansion (PDE) method, the original data is perturbed and embedded with a fragile watermark to accomplish privacy preserving and data integrity of mined data and to also recover the original data. Experimental tests are performed on classification accuracy, probabilistic information loss, and privacy disclosure risk used to evaluate the efficiency of PDE for privacy preserving and knowledge verification.     

Exponential stability of impulsive stochastic fuzzy cellular neural networks with mixed delays and reaction–diffusion terms

The problem of mean square exponential stability for a class of impulsive stochastic fuzzy cellular neural networks with distributed delays and reaction–diffusion terms is investigated in this paper. By using the properties of M-cone, eigenspace of the spectral radius of nonnegative matrices, Lyapunov functional, Itô’s formula and inequality techniques, several new sufficient conditions guaranteeing the mean square exponential stability of its equilibrium solution are obtained. The derived results are less conservative than the results recently presented in Wang and Xu (Chaos Solitons Fractals 42:2713–2721, 2009), Zhang and Li (Stability analysis of impulsive stochastic fuzzy cellular neural networks with time varying delays and reaction–diffusion terms. World Academy of Science, Engineering and Technology 2010), Huang (Chaos Solitons Fractals 31:658–664, 2007), and Wang (Chaos Solitons Fractals 38:878–885, 2008). In fact, the systems discussed in Wang and Xu (Chaos Solitons Fractals 42:2713–2721, 2009), Zhang and Li (Stability analysis of impulsive stochastic fuzzy cellular neural networks with time varying delays and reaction–diffusion terms. World Academy of Science, Engineering and Technology 2010), Huang (Chaos Solitons Fractals 31:658–664, 2007), and Wang (Chaos Solitons Fractals 38:878–885, 2008) are special cases of ours. Two examples are presented to illustrate the effectiveness and efficiency of the results.     

A multi-stage learning framework for intelligent system

As information technologies advance and user-friendly interfaces develop, the interaction between humans and computers, information devices, and new consumer electronics is increasingly gaining attention. One example that most people can relate to is Apple’s innovation in human–computer interaction which has been used on many products such as iPod and iPhone. Siri, the intelligent personal assistant, is a typical application of machine-learning human–computer interaction.Algorithms in machine learning have been employed in many disciplines, including gesture recognition, speaker recognition, and product recommendation systems. While the existing learning algorithms compute and learn from a large quantity of data, this study proposes an improved learning to rank algorithm named MultiStageBoost. In addition to ranking data through multiple stages, the MultiStageBoost algorithm significantly improves the existing algorithms in two ways. Firstly, it classifies and filters data to small quantities and applies the Boosting algorithm to achieve faster ranking performance. Secondly, it enhances the original binary classification by using the reciprocal of fuzzily weighted membership as the ranking distance.The importance of data is revealed in their ranked positions. Usually data ranked in the front are given more attention than those ranked in the middle. For example, after ranking 10,000 pieces of data, the top 10, or at most 100, are the most important and relevant. Whether the data after the top ones are ranked precisely does not really matter. Due to this reason, this study has made improvement on the conventional methods of the pair-wise ranking approach. Not only are data classified and ranked binarily, they are also given different weights depending on whether they are concordant or discordant. Incorporating the concept of weighting into the ranking distance allows us to increase the precision of ranking. Results from experiments demonstrate that our proposed algorithm outperforms the conventional methods in three evaluation measures: P@n, MAP, and NDCG. MultiStageBoost was then applied to speech recognition. However, we do not aim to improve the technology of speech recognition, but simply hope to provide evidences that MultiStageBoost can be used in the classification and ranking in speech recognition. Experiments show that the recognition optimization procedures established by this study are able to increase the recognition rate to over 95% in the personal computing device and industrial personal computer. It is expected that in the future this voice management system will accurately and effectively identify speakers answering the voice response questionnaire and will successfully carry out the functions in the choice of answers, paying the way for the formation of a virtual customer service person.     

The oxidation behavior of an Fe61B15Zr8Mo7Co5Y2Cr2 bulk metallic glass at 650 °C in various oxygen-containing environments

The oxidation behavior of an Fe₆₁B₁₅Zr₈Mo₇Co₅Y₂Cr₂ bulk metallic glass (Fe7-BMG) was investigated in three oxygen-containing atmospheres over the oxygen partial pressure range of 10³–10⁵ Pa at 650 °C. The oxidation kinetics of the Fe7-BMG followed the parabolic rate law in all cases, indicating that solid-state diffusion is the rate-controlling step during oxidation. The oxidation rate constants (k_p values) increased with increasing oxygen partial pressure, implying a typical scaling behavior with a p-type semiconductivity. Duplex scales formed on the Fe7-BMG consisted of an outer layer of mostly iron oxides (Fe₂O₃/Fe₃O₄) and minor amounts of B₂O₃, and a heterophasic inner layer of mostly tetragonal-ZrO₂ (t-ZrO₂) intermixed with non-oxidized intermetallic phases.     

Time-Multiplexed Analog Transmission of Three Broadcast-Quality Television Channels Through One Satellite Transponder

This paper describes a TV signal compression scheme which may become a common practice for analog video transmission and broadcast via satellite by the year 2000. The technique uses twodimensional interfield processing to reduce the video bandwidth with minimum degradation in subjective quality, and time multiplexed with analog multiplexing technique for transmission in component format. Computer simulations of the processing technique on the test slides, and analysis of subjective quality andS/Nexpected over a typical satellite link, are reported. A proof-of-concept prototype being developed at COMSAT Laboratories is also outlined.     

Bounds for Throughput in Production/Inventory Systems in Series with Deterministic Processing Times

A production/inventory system consisting of work stations in series and a finished product warehouse is introduced. Bounds for the throughput are developed through the analysis of the reversed system of tandem queues. Examples are provided to show the bounds.     

Effects of Ni Additions on the Growth of Cu<Subscript>3</Subscript>Sn in High-Lead Solders

In reactions between solders and Cu, additions of minor alloying elements, such as Fe, Co or Ni, to solders often reduce the Cu₃Sn growth rate. Nevertheless, the mechanism for this effect remains unresolved. To provide more experimental observations that are essential for uncovering this mechanism, growth of Cu₃Sn in the reaction between Cu and high-lead solders with or without Ni additions has been studied. The solders used for this study were 10Sn-90Pb and 5Sn-95Pb doped with 0 wt.%, 0.03 wt.%, 0.06 wt.%, 0.1 wt.% or 0.2 wt.% Ni. Reaction conditions included one reflow at 350°C for 2 min and solid-state aging at 160°C for up to 2000 h. The effect of Ni on the growth of Cu₃Sn is discussed in detail based on the experimental results.     

Global exponential stability analysis for cellular neural networks with variable coefficients and delays

Some sufficient conditions for the global exponential stability of cellular neural networks with variable coefficients and time-varying delays are obtained by a method based on a delayed differential inequality. The method, which does not make use of Lyapunov functionals, is simple and effective for the stability analysis of cellular neural networks with variable coefficients and time-varying delays. Some previous results in the literature are shown to be special cases of our results.