A dual‐observer approach for global output feedback stabilization of planar nonlinear systems with output‐dependent growth rates

In this paper, we investigate the problem of global output feedback stabilization for a class of planar nonlinear systems under a more general growth condition, which encompasses both lower‐order and higher‐order state growths with output‐dependent rates. For more accurate estimation, two new observers with nonlinear gains are constructed to estimate the states on the lower‐order and higher‐order scales, respectively. The estimates produced from the dual‐observer are used delicately in the output feedback control law with both lower‐order and higher‐order modes. The overall stability of the system is guaranteed by rigorously choosing these nonlinear gains in the control law and the dual‐observer.     

Assessing a cross-border logistics policy using a performance measurement system framework: the case of Hong Kong and the Pearl River Delta region

For the most rapidly growing economic entity in the world, China, a new logistics operation called the indirect cross-border supply chain model has recently emerged. The primary idea of this model is to reduce logistics costs by storing goods at a bonded warehouse with low storage cost in certain Chinese regions, such as the Pearl River Delta (PRD). This research proposes a performance measurement system (PMS) framework to assess the direct and indirect cross-border supply chain models. The PMS covers four categories including cost, time, quality and flexibility in the assessment of the performance of direct and indirect models. Furthermore, a survey was conducted to investigate the logistics performance of third party logistics (3PLs) at the PRD regions, including Guangzhou, Shenzhen and Hong Kong. The significance of the proposed PMS framework allows 3PLs accurately pinpoint the weakness and strengths of it current operations policy at four major performance measurement categories. Hence, this helps 3PLs further enhance the competitiveness and operations efficiency through better resources allocation at the area of warehousing and transportation.     

A simulation modeling and analysis for RFID-enabled mixed-product loading strategy for outbound logistics: A case study

Loading of trucks in a plant can be a problem when different kinds of products are to be loaded on the same truck, but these goods are not ready. Therefore, the truck has to wait at the loading bay for an unbearably long time, blocking other trucks also in the process. In this paper we address this problem and present several alternative strategies for drawing loading schedules in such situations, using information on status of products obtained with radio-frequency identification (RFID) technology, and compare their performance with traditional strategies, without RFID. A case study on a giant printing and paper bag manufacturer is conducted. Simulation models under these strategies are built for comparisons with respect to three performance measures: average operating time in system, throughput of trucks and percentage of tardy performance trucks. Results demonstrate that these strategies have different performances with respect to different criteria.     

Reduced‐cost surrogate modeling of input characteristics and design optimization of dual‐band antennas using response features

In this article, a procedure for low‐cost surrogate modeling of input characteristics of dual‐band antennas has been discussed. The number of training data required for construction of an accurate model has been reduced by representing the antenna reflection response to the level of suitably defined feature points. The points are allocated to capture the critical features of the reflection characteristic, such as the frequencies and the levels of the resonances, and supplemented by the additions (infill) points, which is necessary to provide sufficient data that allows restoring the entire response through interpolation. Because the coordinates of the feature points exhibit less nonlinear behavior (as a function of antenna geometry parameters) compared to S‐parameters as a function of frequency, surrogate model construction can be realized with a smaller number of data points. The presented modeling approach is demonstrated using an example of a planar dipole antenna. Also, the feature‐based method is favorably compared to direct modeling of reflection characteristics using kriging. The relevance of the technique is further verified by its application for design optimization.     

Design of a dual‐polarized omnidirectional antenna for broadband applications

A broadband dual‐polarized omnidirectional antenna is presented. The proposed antenna consists of two parts, an asymmetric biconical antenna and a cylindrical multilayer polarizer. To have an almost perfect omnidirectional radiation pattern in the horizontal plane and the main radiating beam position at around , in the elevation plane, the asymmetric biconical antenna is used. Moreover, to provide dual polarization performance over the 2–18 GHz operational bandwidth, a multilayer polarizer is designed and optimized. Numerous simulations via Ansoft HFSS and CST microwave Studio CAD tools have been made to optimize the radiation pattern, gain, polarization, and the reflection coefficient of the antenna. Simulation results show that the radiation characteristics of the proposed antenna are extremely sensitive to the configuration and dimensional parameters of the multilayer polarizer. The designed antenna was fabricated with high mechanical accuracy and measured. Satisfactory agreement of computer simulations and experimental results was obtained. The main feature that distinguishes this antenna from the previous designs is the ability to provide the omnidirectional radiation pattern with small ripples, dual polarizations performance, and the wide bandwidth simultaneously. Based on these characteristics, the proposed antenna can be useful for broadband communication applications. © 2015 Wiley Periodicals, Inc. Int J RF and Microwave CAE 25:591–600, 2015.     

Design of a novel metasurface for dual‐band Fabry‐Pérot cavity antenna

A novel reflective metasurface which presents different reflection phases to different polarization waves is designed in this article. The metasurface is used as the ground plane of a Fabry‐Pérot cavity (FPC) antenna. The radiator of the FPC antenna is a dual‐band patch antenna which has different polarizations in different bands, so by tuning the reflection phase of the metasurface correctly, the FPC antenna can work in two frequency bands. A prototype antenna is fabricated and measured. The measured results show that the antenna can operate at 8.35‐8.56 GHz (2.5%) in x‐polarization and 9.5‐10.1 GHz (6.1%) in y‐polarization. The maximum realized gains of the antenna in two bands are 17.6 dB at 8.75 GHz and 19 dB at 9.8 GHz. The measured results agree well with the simulated results which confirm the correctness of the design.     

A two-stage fuzzy optimization model for scarce drugs supply and ration planning under uncertainty: A case study

The scarce drugs rationing is a serious challenge in the public health system. This study proposes a two-stage linear programming model for the scarce drug rationing problem which reflects the conflicts of interest between supply chain members. The proposed model uses a rolling horizon planning mechanism along with an iterative procedure for supplying and rationing the scarce drugs. The maximization of minimum suppliers’ profit and the minimization of maximum shortage are considered as the first and second stage models’ objective functions, respectively. Furthermore, two need-based indicators including disability-adjusted life years (DALYs) and population are utilized to enhance the performance of the proposed model. Besides, human capital method is applied for calculating the shortage costs of demand zones. Due to the inherent epistemic uncertainty in some critical input parameters such as the exchange rate and demand, a credibility-based robust possibilistic programming approach is used to deal with imprecise parameters. Finally, the applicability and usefulness of the proposed approach are investigated by a real case study from Iranian food and drug administration. The computational results demonstrate the superiority of robust programming model against deterministic one.     

The impact of consumer behavior on preannounced pricing for a dual‐channel supply chain

This study discusses a dual‐channel supply chain in which a manufacturer sells a regular‐priced product through dual channels in the normal sales period and only sells overstocked products through the direct channel in the discounted sales period in the presence of strategic consumers. The manufacturer acts as a Stackelberg leader to adopt a preannounced pricing policy. This study first proposes demand functions for a two‐period dual‐channel model by incorporating consumer utility functions. Based on the demand functions, optimal pricing strategies for both manufacturer and retailer are established. The results show that the manufacturer prefers to raise prices in both periods for consumers with a short delivery lead time. However, counterintuitively, the selling prices set by the manufacturer do not decrease as the degree of consumer patience increases. Finally, there is a Pareto zone under a certain condition where both the manufacturer and the retailer in the two‐period dual‐channel model outperform their counterparts in terms of profit in the single‐period dual‐channel model.     

A dual‐tree complex wavelet transform‐based approach for recognition of power system transients

The present paper proposes a dual‐tree complex wavelet transform (DTCWT) based approach for recognition of power system transients. Several researchers, all over the world, have so far attempted to solve the problems of recognition of power system transients, hybridizing transform‐based techniques with popular computational intelligence based tools, for example, using wavelet transform and S‐transform for feature extraction, followed by artificial neural networks (ANN) or fuzzy logic‐based classifiers. The proposed method of hybridizing DTCWT‐based feature extraction with ANN‐based classification could efficiently detect several commonly occurring power quality (PQ) disturbance events. The PQ disturbance events considered include four different transient conditions, namely transients due to capacitor switching, transformer inrush currents, transients due to motor switching and transients due to short circuit faults. A detailed performance comparison with several contemporary, competing methods existing in the literatures for similar problems aptly demonstrates the suitability of the proposed method.     

Global output‐feedback stabilization for stochastic nonlinear systems: A double‐domination approach

This paper is concerned with global stabilization via output feedback for a class of stochastic nonlinear systems with time‐varying continuous output function. Under linear growth conditions, a new double‐domination method is proposed for the first time to construct an output‐feedback stabilizing controller. Different from the related results, the design of the observer is performed without using the information on the output function and nonlinearities. This paper also provides a viewpoint at the feedback stabilization to eliminate the continuous measurement error originating from inaccurate detection of system state. A simulation example is presented to demonstrate the effectiveness of control strategy.     

Design and implementation of a dual‐band concurrent fully‐integrated LNA for WLAN IEEE 802.11a/b/g applications

This article presents a dual‐band concurrent fully‐integrated low‐noise amplifier (LNA) targeted to WLAN IEEE 802.11a/b/g standards. The use of a concurrent topology enables saving die area and power consumption compared with the parallel solution that employs two separated LNAs. An original design methodology that helps in the selection of input/output matching network element values is also presented. The LNA die area is 1.0 × 0.9 mm² and it consumes 9 mW (5 mA at 1.8 V). © 2008 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2009.     

Global adaptive finite‐time stabilization for a class of p‐normal nonlinear systems via an event‐triggered strategy

This article addresses the problem of global adaptive finite‐time control for a class of p‐normal nonlinear systems via an event‐triggered strategy. A state feedback controller is first designed for the nominal system by adding a power integrator method. Then, by the skillful design of adaptive dynamic gain mechanism, a novel event‐triggered controller is constructed for uncertain nonlinear system without homogeneous growth condition. It is proved that the global finite‐time stabilization of p‐normal nonlinear systems is guaranteed and the Zeno phenomenon is excluded. Finally, two examples are presented to indicate the effectiveness of the proposed control scheme.     

Consensus strategy for a class of multi‐agents with discrete second‐order dynamics

This paper investigates consensus strategies for a group of agents with discrete second‐order dynamics under directed communication topology. Consensus analysis for both the fixed topology and time‐varying topology cases is systematically performed by employing a novel graph theoretic methodology as well as the classical nonnegative matrix theory. Furthermore, it is shown that the necessary and sufficient condition for the agents under fixed communication topology to reach consensus is that the communication topology has a spanning tree; and sufficient conditions for the agents to reach consensus when allowing for the dynamically changing communication topologies are also given. Finally, an illustrative example is provided to demonstrate the effectiveness of the proposed results. Copyright © 2011 John Wiley & Sons, Ltd.     

A resilient consensus strategy of near‐optimal control for state‐saturated multiagent systems with round‐robin protocol

In this paper, the resilient consensus strategy design problem is investigated for the time‐varying state‐saturated multiagent systems (MASs). A round‐robin protocol is adopted to schedule the communication network among the MASs for the purpose of preventing the data from collision. In presence of the state‐saturation and gain perturbation phenomena, it is literally impossible to obtain the accurate value of the associate cost function, which describes the consensus performance. As an alternative, an upper bound is derived for the cost function to quantify the consensus performance. Then, the resilient consensus strategy is designed such that this upper bound can be minimized in an iterative manner. The sufficient condition is also provided to guarantee that the upper bound of the cost function exists as time goes to infinity. Finally, a numerical example is provided to illustrate the validity of the proposed methodology.     

Modified sequential k‐means clustering by utilizing response: A case study for fashion products

Modified sequential k‐means clustering concerns a k‐means clustering problem in which the clustering machine utilizes output similarity in addition. While conventional clustering methods commonly recognize similar instances at features‐level modified sequential clustering takes advantage of response, too. To this end, the approach we pursue is to enhance the quality of clustering by using some proper information. The information enables the clustering machine to detect more patterns and dependencies that may be relevant. This allows one to determine, for instance, which fashion products exhibit similar behaviour in terms of sales. Unfortunately, conventional clustering methods cannot tackle such cases, because they handle attributes solely at the feature level without considering any response. In this study, we introduce a novel approach underlying minimum conditional entropy clustering and show its advantages in terms of data analytics. In particular, we achieve this by modifying the conventional sequential k‐means algorithm. This modified clustering approach has the ability to reflect the response effect in a consistent manner. To verify the feasibility and the performance of this approach, we conducted several experiments based on real data from the apparel industry.     

Letter: Driving waveforms based on powder charge for a quick‐response liquid‐powder display

Abstract— A driving waveform based on the powder charge of a quick‐response liquid‐powder display (QR‐LPD®) was investigated. The charging mechanisms for contact charging and triboelectric charging determine the powder charge of a QR‐LPD®. In this letter, driving waveforms containing both charging mechanisms are used for driving a QR‐LPD®. As the experimental results indicate, the charge of the powder accumulates according to the image refresh times. The waveform of a pixel voltage with a slight frictional charge achieves a better contrast ratio for a QR‐LPD®.     

A pseudo‐stochastic approach for optimal decision making under limited information: a case of an aggregate production system

In this study, which is both analytical and numerical, we compute the effective information horizon (EIH), i.e., the minimal time interval over which future information is relevant for optimal control and for measuring the performance of a single part‐type production system. Optimal control modeling and process solving, which consider aspects of decision making with limited forecast, are exemplified by a single part‐type production system. Specifically, the analysis reveals practical situations in which there is both a performance loss as well as feasibility violation when only information expected within the planning horizon is considered. The analysis is carried out by developing a pseudo‐stochastic model. We follow previous “pseudo‐stochastic” approaches that solve stochastic control problems by using deterministic, optimal control methods. However, we model the expected influences of all future events, including those that are beyond the planning horizon, as encapsulated by their density functions and not only by their mean values.     

Correspondence of projected 3‐D points and lines using a continuous GRASP

The field of computer vision has experienced rapid growth over the past 50 years. Many computer vision problems have been solved using theory and ideas from algebraic projective geometry. In this paper, we look at a previously unsolved problem from object recognition, namely object recognition when the correspondences between the object and image data are not known a priori. We formulate this problem as a mixed‐integer non‐linear optimization problem in terms of the unknown projection relating the object and image, as well as the unknown assignments of object points and lines to those in the image. The global optimum of this problem recovers the relationship between the object points and lines with those in the image. When certain assumptions are enforced on the allowable projections mapping the object into the image, a proof is provided which permits one to solve the optimization problem via a simple decomposition. We illustrate this decomposition approach on some example scenarios.     

A General Probabilistic Framework for uncertainty and global sensitivity analysis of deterministic models: A hydrological case study

The present study proposes a General Probabilistic Framework (GPF) for uncertainty and global sensitivity analysis of deterministic models in which, in addition to scalar inputs, non-scalar and correlated inputs can be considered as well. The analysis is conducted with the variance-based approach of Sobol/Saltelli where first and total sensitivity indices are estimated. The results of the framework can be used in a loop for model improvement, parameter estimation or model simplification. The framework is applied to SWAP, a 1D hydrological model for the transport of water, solutes and heat in unsaturated and saturated soils. The sources of uncertainty are grouped in five main classes: model structure (soil discretization), input (weather data), time-varying (crop) parameters, scalar parameters (soil properties) and observations (measured soil moisture). For each source of uncertainty, different realizations are created based on direct monitoring activities. Uncertainty of evapotranspiration, soil moisture in the root zone and bottom fluxes below the root zone are considered in the analysis. The results show that the sources of uncertainty are different for each output considered and it is necessary to consider multiple output variables for a proper assessment of the model. Improvements on the performance of the model can be achieved reducing the uncertainty in the observations, in the soil parameters and in the weather data. Overall, the study shows the capability of the GPF to quantify the relative contribution of the different sources of uncertainty and to identify the priorities required to improve the performance of the model. The proposed framework can be extended to a wide variety of modelling applications, also when direct measurements of model output are not available.     

Belief‐rule‐based expert systems for evaluation of e‐government: a case study

Little knowledge exists on the impact and results associated with e‐government projects in many specific‐use domains. Therefore, it is necessary to evaluate the efficiency and effectiveness of e‐government systems. Because the development of e‐government is a continuous process of improvement, it requires continuous evaluation of the overall e‐government system as well as evaluation of its various dimensions such as determinants, characteristics and results. E‐government development is often complex, with multiple stakeholders, large user bases and complex goals. Consequently, even experts have difficulties in evaluating these systems, especially in an integrated and comprehensive way, as well as on an aggregate level. Expert systems are a candidate solution to evaluate such complex e‐government systems. However, it is difficult for expert systems to cope with uncertain evaluation data that are vague, inconsistent, highly subjective or in other ways, challenging to formalize. This paper presents an approach that can handle uncertainty in e‐government evaluation: the combination of Belief Rule Base knowledge representation and Evidential Reasoning. This approach is illustrated with a concrete prototype, known as the Belief Rule Based Expert System (BRBES) and implemented in the local e‐government of Bangladesh. The results have been compared with a recently developed method of evaluating e‐government, and it is demonstrated that the results of the BRBES are more accurate and reliable. The BRBES can be used to identify the factors that need to be improved to achieve the overall aim of an e‐government project. In addition, various ‘what if’ scenarios can be generated, and developers and managers can obtain a foretaste of the outcomes. Thus, the system can be used to facilitate decision‐making processes under uncertainty.