A CLOSED‐LOOP SUBSPACE IDENTIFICATION METHOD FOR CONTINUOUS‐TIME SYSTEMS BASED ON δ‐OPERATOR MODEL

This paper is concerned with a subspace identification of a continuous‐time plant operating in closed‐loop in the framework of the joint input‐output approach. The main procedure consists of two steps. Firstly, the dual‐Youla parametrization of the plant is used for obtaining an equivalent open‐loop problem to the original closed‐loop identification problem. Then, a δ‐operator based IV‐MOESP type subspace identification algorithm is developed to estimate the state space model for the joint input‐output process, whereby a higher‐order state space model of the plant is obtained by an algebraic operation. Subsequently, a model reduction procedure is employed to derive a lower‐order plant model removing irrelevant modes from the higher order model. Simulation results by using numerical and chemical plant models demonstrate the feasibility of the proposed method.     

基于隐式参数化的白车身建模方法

针对利用传统方法进行新车型概念设计时,需要在大量待选方案的基础上分别建立CAD模型和CAE模型,耗费大量人力和时间的问题,介绍车身隐式参数化建模的基本构成元素和高级构成元素、多层面建模技术和映射连接技术,以及基于隐式参数化的车身建模思路;对某车型前部白车身进行隐式参数化建模;对参数化模型与原有限元模型进行碰撞仿真分析和对比,结果表明用隐式参数化建模方法进行车型概念开发可行,能实现早期开发中车辆结构性能的快速评估.     

Behavior‐Based Propagation of Trust in Social Networks with Restricted and Anonymous Participation

Increasing interactions and engagements in social networks through monetary and material incentives is not always feasible. Some social networks, specifically those that are built on the basis of fairness, cannot incentivize members using tangible things and thus require an intangible way to do so. In such networks, a personalized recommender could provide an incentive for members to interact with other members in the community. Behavior‐based trust models that generally compute social trust values using the interactions of a member with other members in the community have proven to be good for this. These models, however, largely ignore the interactions of those members with whom a member has interacted, referred to as “friendship effects.” Results from social studies and behavioral science show that friends have a significant influence on the behavior of the members in the community. Following the famous Spanish proverb on friendship “Tell Me Your Friends and I Will Tell You Who You Are,” we extend our behavior‐based trust model by incorporating the “friendship effect” with the aim of improving the accuracy of the recommender system. In this article, we describe a trust propagation model based on associations that combines the behavior of both individual members and their friends. The propagation of trust in our model depends on three key factors: the density of interactions, the degree of separation, and the decay of friendship effect. We evaluate our model using a real data set and make observations on what happens in a social network with and without trust propagation to understand the expected impact of trust propagation on the ranking of the members in the recommended list. We present the model and the results of its evaluation. This work is in the context of moderated networks for which participation is by invitation only and in which members are anonymous and do not know each other outside the community. Copyright © 2014 John Wiley & Sons, Ltd.     

Investigating Modern‐Day Talaria: Mobile Phones and the Mobility‐Impaired in Singapore

Revisiting the medical and social models of disability, this study adopted the integrated biopsychosocial approach to examine experiences of 25 mobility‐impaired respondents in Singapore with using mobile phones. We found that mobile phones provided respondents a greater degree of mobility, a sense of control, and opportunities to escape the stigma of disability, thus challenging the boundaries between the able‐bodied and the disabled. Mobile phone appropriation allowed the management of personal identities and social networks, leading to a sense of empowerment. However, mobile phone usage might act as a double‐edged sword for disabled people, creating mobile dependencies and a spatial narrowing of social connections. Theoretical and practical implications are discussed.     

Forward‐adaptive method for context‐based compression of large binary images

A method for compressing large binary images is proposed for applications where spatial access to the image is required. The proposed method is a two‐stage combination of forward‐adaptive modeling and backward‐adaptive context based compression with re‐initialization of statistics. The method improves compression performance significantly in comparison to a straightforward combination of JBIG and tiling. Only minor modifications to the QM‐coder are required, and therefore existing software implementations can be easily utilized. Technical details of the modifications are provided. Copyright © 1999 John Wiley & Sons, Ltd.     

Q‐learning for continuous‐time graphical games on large networks with completely unknown linear system dynamics

In this paper, we consider the problem of leader synchronization in systems with interacting agents in large networks while simultaneously satisfying energy‐related user‐defined distributed optimization criteria. But modeling in large networks is very difficult, and for that reason, we derive a model‐free formulation that is based on a separate distributed Q‐learning function for every agent. Every Q‐function is a parametrization of each agent's control, of the neighborhood controls, and of the neighborhood tracking error. It is also evident that none of the agents has any information on where the leader is connected to and from where she spreads the desired information. The proposed algorithm uses an integral reinforcement learning approach with a separate distributed actor/critic network for each agent: a critic approximator to approximate each value function and an actor approximator to approximate each optimal control law. The derived tuning laws for each actor and critic approximators are designed appropriately by using gradient descent laws. We provide rigorous stability and convergence proofs to show that the closed‐loop system has an asymptotically stable equilibrium point and that the control policies form a graphical Nash equilibrium. We demonstrate the effectiveness of the proposed method on a network consisting of 10 agents. Copyright © 2016 John Wiley & Sons, Ltd.     

Extending UML to model Web 2.0‐based context‐aware applications

Web 2.0 Mashups offer entirely new opportunities for context‐aware application (CAA) developers by integrating Web 2.0 technologies to facilitate interoperability among heterogeneous context‐aware systems. From a software engineering perspective, a visualized approach for Web 2.0‐based CAA modeling is crucial. Current CAA development, however, cannot provide a conceptual model for Web 2.0‐based CAA. Therefore, the development efficiency and potential for reuse are decreased. The UML is a general purpose modeling language with potential for use in many application domains. However, UML often lacks elements needed to model concepts in specific domains, such as Web 2.0‐based CAA modeling. To address the above issues, this study presents the Web 2.0‐based CAA UML profile, a UML profile for modeling Web 2.0‐based CAA. Copyright © 2011 John Wiley & Sons, Ltd.     

COIVA: context‐aware and ontology‐powered information visualization architecture

This paper exploits semantics to apply context in run‐time adaptation, particularly for services in a user‐centered smart environment. Context‐sensitive services are usually focused on their own information without interoperation pretensions. It is necessary to enable common context models and systems in order to make context‐aware applications interoperable. Moreover, context management systems need to implement mechanisms to support the dynamic behavior of the users and their surroundings, including techniques to adapt the model to their future needs, to maintain the context information at run‐time and to be interoperable with external context models. By adapting web semantic technologies we can enable smarter and more proactive operation of context management systems. Copyright © 2010 John Wiley & Sons, Ltd.     

Parametric modeling of microwave filter using combined MLS‐SVR and pole‐residue‐based transfer functions

In this article, an advanced technique is developed to combine multi‐output least‐squares support vector regression (MLS‐SVR) and pole‐residue‐based transfer function models for microwave filter parametric modeling. MLS‐SVR is trained to learn the relationship between the length of tuning screws and the pole/residues of the transfer function, where MLS‐SVR is an effective method to cope with the multi‐output case unlike the traditional approach. Traditional approach treats the different outputs separately in the multi‐output case and it cannot model the relation between different outputs. Another important element for modeling is feature parameters. Extracted feature parameters have an important influence on the accuracy of modeling. For the purpose of establishing a more accurate model, the complex system poles and residues from Y‐parameters are chosen as the outputs of modeling, which are obtained by vector fitting (VF). Then we give a solution to obtaining pole/residues extracted by VF when the filter is in high detuned. After the proposed modeling process, trained model can be used to provide an accurate and fast prediction of the behavior of microwave filter with the length of tuning screws as variables, and model the electromagnetic simulation (or actual) microwave filter tuning. The methodology is applied to a narrow band coaxial‐resonator filter modeling, and more accurate results are achieved compared with the other methods. An example is presented to illustrate the efficiency of the proposed method.     

An Area‐Preserving Parametrization for Spherical Rectangles

We present an area‐preserving parametrization for spherical rectangles which is an analytical function with domain in the unit rectangle [0, 1]² and range in a region included in the unit‐radius sphere. The parametrization preserves areas up to a constant factor and is thus very useful in the context of rendering as it allows to map random sample point sets in [0, 1]² onto the spherical rectangle. This allows for easily incorporating stratified, quasi‐Monte Carlo or other sampling strategies in algorithms that compute scattering from planar rectangular emitters.     

Finding Diachronic Like‐Minded Users

User communities in social networks are usually identified by considering explicit structural social connections between users. While such communities can reveal important information about their members such as family or friendship ties and geographical proximity, just to name a few, they do not necessarily succeed at pulling like‐minded users that share the same interests together. Therefore, researchers have explored the topical similarity of social content to build like‐minded communities of users. In this article, following the topic‐based approaches, we are interested in identifying communities of users that share similar topical interests with similar temporal behavior. More specifically, we tackle the problem of identifying temporal (diachronic) topic‐based communities, i.e., communities of users who have a similar temporal inclination toward emerging topics. To do so, we utilize multivariate time series analysis to model the contributions of each user toward emerging topics. Further, our modeling is completely agnostic to the underlying topic detection method. We extract topics of interest by employing seminal topic detection methods; one graph‐based and two latent Dirichlet allocation‐based methods. Through our experiments on Twitter data, we demonstrate the effectiveness of our proposed temporal topic‐based community detection method in the context of news recommendation, user prediction, and document timestamp prediction applications, compared with the nontemporal as well as the state‐of‐the‐art temporal approaches.     

Reduced‐cost microwave filter modeling using a two‐stage Gaussian process regression approach

A technique for the reduced‐cost modeling of microwave filters is presented. Our approach exploits variable‐fidelity electromagnetic (EM) simulations, and Gaussian process regression (GPR) carried out in two stages. In the first stage of the modeling process, a mapping between EM simulation filter models of low and high fidelity is established. The mapping is subsequently used in the second stage, making it possible for the final surrogate model to be constructed from training data obtained using only a fraction of the number of high‐fidelity simulations normally required. As demonstrated using three examples of microstrip filters, the proposed technique allows us to reduce substantially (by up to 80%) the central processing unit (CPU) cost of the filter model setup, as compared to conventional (single‐stage) GPR—the benchmark modeling method in this study. This is achieved without degrading the model generalization capability. The reliability of the two‐stage modeling method is demonstrated through the successful application of the surrogates to surrogate‐based filter design optimization. © 2014 Wiley Periodicals, Inc. Int J RF and Microwave CAE 25:453–462, 2015.     

CLUSTERED BASED TAKAGI‐SUGENO NEURO‐FUZZY MODELING OF A MULTIVARIABLE NONLINEAR DYNAMIC SYSTEM

This research frame work investigates the application of a clustered based Neuro‐fuzzy system to nonlinear dynamic system modeling from a set of input‐output training patterns. It is concentrated on the modeling via Takagi‐Sugeno (T‐S) modeling technique and the employment of fuzzy clustering to generate suitable initial membership functions. Hence, such created initial memberships are then employed to construct suitable T‐S sub‐models. Furthermore, the T‐S fuzzy models have been validated and checked through the use of some standard model validation techniques (like the correlation functions). Compared to other well‐known approximation techniques such as artificial neural networks, fuzzy systems provide a more transparent representation of the system under study, which is mainly due to the possible linguistic interpretation in the form of rules. Such intelligent modeling scheme is very useful once making complicated systems linguistically transparent in terms of fuzzy if‐then rules. The developed T‐S Fuzzy modeling system has been then applied to model a nonlinear antenna dynamic system with two coupled inputs and outputs. Validation results have resulted in a very close antenna sub‐models of the original nonlinear antenna system. The suggested technique is very useful for development transparent linear control systems even for highly nonlinear dynamic systems.     

Bayesian inference‐based small‐signal modeling technique for GaN HEMTs

A new modeling methodology for gallium nitride (GaN) high‐electron‐mobility transistors (HEMTs) based on Bayesian inference theory, a core method of machine learning, is presented in this article. Gaussian distribution kernel functions are utilized for the Bayesian‐based modeling technique. A new small‐signal model of a GaN HEMT device is proposed based on combining a machine learning technique with a conventional equivalent circuit model topology. This new modeling approach takes advantage of machine learning methods while retaining the physical interpretation inherent in the equivalent circuit topology. The new small‐signal model is tested and validated in this article, and excellent agreement is obtained between the extracted model and the experimental data in the form of dc I–V curves and S‐parameters. This verification is carried out on an 8 × 125 μm GaN HEMT with a 0.25 μm gate feature size, over a wide range of operating conditions. The dc I–V curves from an artificial neural network (ANN) model are also provided and compared with the proposed new model, with the latter displaying a more accurate prediction benefiting, in particular, from the absence of overfitting that may be observed in the ANN‐derived I–V curves.     

Neural‐space mapping‐based large‐signal modeling for MOSFET

In this article, a large‐signal modeling approach based on the combination of equivalent circuit and neuro‐space mapping modeling techniques is proposed for MOSFET. In order to account for the dispersion effects, two neuro‐space (S) mapping based models are used to model the drain current at DC and RF conditions, respectively. Corresponding training process in our approach is also presented. Good agreement is obtained between the model and data of the DC, S parameter, and harmonic performance for a 0.13 μm channel length, 5 μm channel width per finger and 20 fingers MOSFET over a wide range of bias points, demonstrating the proposed model is valid for DC, small‐signal and nonlinear operation. Comparison of DC, S‐parameter, and harmonic performance between proposed model and empirical model further reveals the better accuracy of the proposed model. © 2011 Wiley Periodicals, Inc. Int J RF and Microwave CAE , 2011.     

Efficient resource discovery in self‐organized unstructured peer‐to‐peer networks

In unstructured peer‐to‐peer (P2P) networks, two autonomous peer nodes can be connected if users in those nodes are interested in each other's data. Owing to the similarity between P2P networks and social networks, where peer nodes can be regarded as people and connections can be regarded as relationships, social strategies are useful for improving the performance of resource discovery by self‐organizing autonomous peers on unstructured P2P networks. In this paper, we present an efficient social‐like peer‐to‐peer (ESLP) method for resource discovery by mimicking different human behaviours in social networks. ESLP has been simulated in a dynamic environment with a growing number of peer nodes. From the simulation results and analysis, ESLP achieved better performance than current methods. Copyright © 2008 John Wiley & Sons, Ltd.     

Context‐Centric Speech‐Based Human–Computer Interaction

This paper describes research that addresses the problem of dialog management from a strong, context‐centric approach. We further present a quantitative method of measuring the importance of contextual cues when dealing with speech‐based human–computer interactions. It is generally accepted that using context in conjunction with a human input, such as spoken speech, enhances a machine's understanding of the user's intent as a means to pinpoint an adequate reaction. For this work, however, we present a context‐centric approach in which the use of context is the primary basis for understanding and not merely an auxiliary process. We employ an embodied conversation agent that facilitates the seamless engagement of a speech‐based information‐deployment entity by its human end user. This dialog manager emphasizes the use of context to drive its mixed‐initiative discourse model. A typical, modern automatic speech recognizer (ASR) was incorporated to handle the speech‐to‐text translations. As is the nature of these ASR systems, the recognition rate is consistently less than perfect, thus emphasizing the need for contextual assistance. The dialog system was encapsulated into a speech‐based embodied conversation agent platform for prototyping and testing purposes. Experiments were performed to evaluate the robustness of its performance, namely through measures of naturalness and usefulness, with respect to the emphasized use of context. The contribution of this work is to provide empirical evidence of the importance of conversational context in speech‐based human–computer interaction using a field‐tested context‐centric dialog manager.     

Analysis of modeling approaches for on‐chip spiral inductors

The recent modeling approaches for on‐chip spiral inductors are extensively investigated and compared. The key features of the models are detailedly analyzed. By actual implementation of each model's parameter extraction procedure, the pros and cons of equivalent circuit topologies, parameter extraction techniques, and fitting capacity of models are provided, including 1‐π, 2‐π, and T‐models. 1‐π models and T‐models are proved to be adequate for modeling distributed effect. 2‐π models are more convenient in fitting asymmetric inductors. The transfer function of the spiral inductor is employed in the singularity analysis of the models. The proposals are verified by measured the S‐parameters of 10 fabricated CMOS spiral inductors up to 40 GHz. © 2012 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2012.     

Understanding the continuance use of social network sites: a computer self-efficacy perspective

Based on social cognitive theory and the balanced thinking–feelings model, this study proposes a research model to examine the different roles of general computer self-efficacy (CSE) and specific CSE. The research model was tested with a survey of university students in Singapore in the Facebook context. It is found that while general CSE affects continuance intention through both cognition and affection, specific CSE mainly affects continuance intention through cognition. It is also found that general computer experience affects general CSE only and specific computer experience affects specific CSE only.     

Advanced system‐level simulation paradigm for ultra‐wideband systems using SCERNE platform

In this article, a progressive system‐level simulation framework is developed based on Simulation de Chaînes d'Emission/Réception Nouvelle gEnération (SCERNE) platform to simulate an ultra‐wideband (UWB) impulse radar transmitter and accurately predict its performance. With the purpose of demonstrating the usefulness of the SCERNE ability in system‐level modeling, we present and simulate a simplified structure of UWB impulse radar transmitter. First, after simulation each component in different circuit‐level tools such as ADS, CST, and HFSS, each part has been modeled by using different modeling methods to transfer their data into MATLAB environment. Then, we duplicate the transmitter structure in SCERNE toolbox to validate the results. The advantage conferred by the proposed SCERNE toolbox is that fast and accurate bilateral modeling method is available at multi‐medium structures in contrast with conventional unilateral modeling, and so a lower memory and higher accuracy of the behavioral model is achieved. It can also be beneficial when the user is looking for system‐level, as the increased components amounts can help as a surrogate model. The system model can be easily extended to other UWB radar systems by simply changing the input pulse shape, UWB channel environment, transceiver topology, etc. Various effects such as signal quality, and pulse shape that can easily investigate and re‐optimize for high performance are using the developed model. To validate the practicality of the proposed paradigm, the simulations and predictions through model results are being outlined.