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Recent Advances in the Modelling and Analysis of Opinion Dynamics on Influence Networks
Brian D. O. Anderson, Mengbin Ye
2019, vol. 16, no. 2, pp. 129-149, doi: 10.1007/s11633-019-1169-8
Abstract SpringerLink
Abstract:
A fundamental aspect of society is the exchange and discussion of opinions between individuals, occurring in situations as varied as company boardrooms, elementary school classrooms and online social media. After a very brief introduction to the established results of the most fundamental opinion dynamics models, which seek to mathematically capture observed social phenomena, a brief discussion follows on several recent themes pursued by the authors building on the fundamental ideas. In the first theme, we study the way an individual′s self-confidence can develop through contributing to discussions on a sequence of topics, reaching a consensus in each case, where the consensus value to some degree reflects the contribution of that individual to the conclusion. During this process, the individuals in the network and the way they interact can change. The second theme introduces a novel discrete-time model of opinion dynamics to study how discrepancies between an individual′s expressed and private opinions can arise due to stubbornness and a pressure to conform to a social norm. It is also shown that a few extremists can create " pluralistic ignorance”, where people believe there is majority support for a position but in fact the position is privately rejected by the majority. Last, we consider a group of individuals discussing a collection of logically related topics. In particular, we identify that for topics whose logical interdependencies take on a cascade structure, disagreement in opinions can occur if individuals have competing and/or heterogeneous views on how the topics are related, i.e., the logical interdependence structure varies between individuals.
Research Article
Motion-force Transmissibility Characteristic Analysis of a Redundantly Actuated and Overconstrained Parallel Machine
Hai-Qiang Zhang, Hai-Rong Fang, Bing-Shan Jiang
2019, vol. 16, no. 2, pp. 150-162, doi: 10.1007/s11633-018-1156-5
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Abstract:
This paper presents a novel 1T2R three degrees of freedom redundantly actuated and overconstrained 2\begin{document}${\underline{\rm P}}$\end{document}RU-\begin{document}${\underline{\rm P}}$\end{document}R\begin{document}${\underline{\rm P}}$\end{document}S parallel machining head (\begin{document}${\underline{\rm P}}$\end{document} denotes the active prismatic joint), which can construct 5-axis hybrid machine to complete high speed freedom surface milling for large complex structural components in aerospace. Firstly, based on the screw theory, the mobility of the proposed parallel manipulator is briefly analysed. Secondly, the kinematic inverse position and the parasitic motion of the parallel manipulator are explicitly expressed. Furthermore, motion-force transmission performance evaluation indices are derived in detail via an alternative approach based on the screw theory. More importantly, a simple method for quickly solving the maximum virtual power coefficient is proposed, and the motion-force transmission performance evaluation index is greatly improved. To evaluate the kinematic performance, its workspace is calculated. With numerical examples, performance distribution atlases of the manipulator are depicted visually. The corresponding results illustrate that the proposed parallel manipulator has better orientation workspace and superior motion-force transmission performance than the 2PRU-PRS parallel manipulator, which proves the validity and applicability of applying this manipulator as a machining head.
Fuzzy Behavior-based Control of Three Wheeled Omnidirectional Mobile Robot
Nacer Hacene, Boubekeur Mendil
2019, vol. 16, no. 2, pp. 163-185, doi: 10.1007/s11633-018-1135-x
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Abstract:
In this paper, a fuzzy behavior-based approach for a three wheeled omnidirectional mobile robot (TWOMR) navigation has been proposed. The robot has to track either static or dynamic target while avoiding either static or dynamic obstacles along its path. A simple controller design is adopted, and to do so, two fuzzy behaviors " Track the Target” and " Avoid Obstacles and Wall Following” are considered based on reduced rule bases (six and five rules respectively). This strategy employs a system of five ultrasonic sensors which provide the necessary information about obstacles in the environment. Simulation platform was designed to demonstrate the effectiveness of the proposed approach.
Performance Evaluation and Improvement of Chipset Assembly & Test Production Line Based on Variability
Chang-Jun Li, Zong-Shi Xie, Xin-Ran Peng, Bo Li
2019, vol. 16, no. 2, pp. 186-198, doi: 10.1007/s11633-018-1129-8
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Abstract:
" Factory physics principles” provided a method to evaluate the performance of a simple production line, whose fundamental parameters are known or given. However, it is difficult to obtain the exact and reasonable parameters in actual manufacturing environment, especially for the complex chipset assembly & test production line (CATPL). Besides, research in this field tends to focus on evaluation and improvement of CATPL without considering performance interval and status with variability level. A developed internal benchmark method is proposed, which established three-parameter method based on the Little′s law. It integrates the variability factors, such as processing time, random failure time, and random repair time, to meet performance evaluation and improvement. A case study in a chipset assembly and test factory for the performance of CATPL is implemented. The results demonstrate the potential of the proposed method to meet performance evaluation and emphasise its relevance for practical applications.
An Approach to Reducing Input Parameter Volume for Fault Classifiers
Ann Smith, Fengshou Gu, Andrew D. Ball
2019, vol. 16, no. 2, pp. 199-212, doi: 10.1007/s11633-018-1162-7
Abstract SpringerLink
Abstract:
As condition monitoring of systems continues to grow in both complexity and application, an overabundance of data is amassed. Computational capabilities are unable to keep abreast of the subsequent processing requirements. Thus, a means of establishing computable prognostic models to accurately reflect process condition, whilst alleviating computational burdens, is essential. This is achievable by restricting the amount of information input that is redundant to modelling algorithms. In this paper, a variable clustering approach is investigated to reorganise the harmonics of common diagnostic features in rotating machinery into a smaller number of heterogeneous groups that reflect conditions of the machine with minimal information redundancy. Naïve Bayes classifiers established using a reduced number of highly sensitive input parameters realised superior classification powers over higher dimensional classifiers, demonstrating the effectiveness of the proposed approach. Furthermore, generic parameter capabilities were evidenced through confirmatory factor analysis. Parameters with superior deterministic power were identified alongside complimentary, uncorrelated, variables. Particularly, variables with little explanatory capacity could be eliminated and lead to further variable reductions. Their information sustainability is also evaluated with Naïve Bayes classifiers, showing that successive classification rates are sufficiently high when the first few harmonics are used. Further gains were illustrated on compression of chosen envelope harmonic features. A Naïve Bayes classification model incorporating just two compressed input variables realised an 83.3% success rate, both an increase in classification rate and an immense improvement volume-wise on the former ten parameter model.
Robust Neural Control of Discrete Time Uncertain Nonlinear Systems Using Sliding Mode Backpropagation Training Algorithm
Imen Zaidi, Mohamed Chtourou, Mohamed Djemel
2019, vol. 16, no. 2, pp. 213-225, doi: 10.1007/s11633-017-1062-2
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Abstract:
This work deals with robust inverse neural control strategy for a class of single-input single-output (SISO) discrete-time nonlinear system afiected by parametric uncertainties. According to the control scheme, in the flrst step, a direct neural model (DNM) is used to learn the behavior of the system, then, an inverse neural model (INM) is synthesized using a specialized learning technique and cascaded to the uncertain system as a controller. In previous works, the neural models are trained classically by backpropagation (BP) algorithm. In this work, the sliding mode-backpropagation (SM-BP) algorithm, presenting some important properties such as robustness and speedy learning, is investigated. Moreover, four combinations using classical BP and SM-BP are tested to determine the best conflguration for the robust control of uncertain nonlinear systems. Two simulation examples are treated to illustrate the efiectiveness of the proposed control strategy.
Contribution of the FPGAs for Complex Control Algorithms: Sensorless DTFC with an EKF of an Induction Motor
Saber Krim, Souflen Gdaim, Abdellatif Mtibaa, Mohamed Faouzi Mimouni
2019, vol. 16, no. 2, pp. 226-237, doi: 10.1007/s11633-016-1017-z
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Abstract:
In a conventional direct torque control (CDTC) of the induction motor drive, the electromagnetic torque and the stator flux are characterized by high ripples. In order to reduce the undesired ripples, several methods are used in the literature. Nevertheless, these methods increase the algorithm complexity and dependency on the machine parameters such as the space vector modulation (SVM). The fuzzy logic control method is utilized in this work to decrease these ripples. Moreover, to eliminate the mechanical sensor the extended kalman filter (EKF) is used, in order to reduce the cost of the system and the rate of maintenance. Furthermore, in the domain of controlling the real-time induction motor drives, two principal digital devices are used such as the hardware (FPGA) and the digital signal processing (DSP). The latter is a software solution featured by a sequential processing that increases the execution time. However, the FPGA is featured by a high processing speed because of its parallel processing. Therefore, using the FPGA it is possible to implement complex algorithms with low execution time and to enhance the control bandwidth. The large bandwidth is the key issue to increase the system performances. This paper presents the interest of utilizing the FPGAs to implement complex control algorithms of electrical systems in real time. The suggested sensorless direct torque control using the fuzzy logic (DTFC) of an induction motor is successfully designed and implemented on an FPGA Virtex 5 using xilinx system generator. The simulation and implementation results show proposed approach's performances in terms of ripples, stator current harmonic waves, execution time, and short design time.
Function Projective Lag Synchronization of Chaotic Systems with Certain Parameters via Adaptive-impulsive Control
Xiu-Li Chai, Zhi-Hua Gan
2019, vol. 16, no. 2, pp. 238-247, doi: 10.1007/s11633-016-1020-4
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Abstract:
A new method is presented to study the function projective lag synchronization (FPLS) of chaotic systems via adaptive-impulsive control. To achieve synchronization, suitable nonlinear adaptive-impulsive controllers are designed. Based on the Lyapunov stability theory and the impulsive control technology, some effective sufficient conditions are derived to ensure the drive system and the response system can be rapidly lag synchronized up to the given scaling function matrix. Numerical simulations are presented to verify the effectiveness and the feasibility of the analytical results.
A Creative Approach to Reducing Ambiguity in Scenario-based Software Architecture Analysis
Xi-Wen Wu, Chen Li, Xuan Wang, Hong-Ji Yang
2019, vol. 16, no. 2, pp. 248-260, doi: 10.1007/s11633-017-1102-y
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Abstract:
In software engineering, a scenario describes an anticipated usage of a software system. As scenarios are useful to understand the requirements and functionalities of a software system, the scenario-based analysis is widely used in various tasks, especially in the design stage of software architectures. Although researchers have proposed various scenario-based approaches to analyse software architecture, there are still limitations in this research field, and a key limitation is that scenarios are typically not formally defined and thus may contain ambiguities. As these ambiguities may lead to defects, it is desirable to reduce them as many as possible. In order to reduce ambiguity in scenario-based software architecture analysis, this paper introduces a creative computing approach to scenario-based software requirements analysis. Our work expands this idea in three directions. Firstly, we extend an architecture description language (ADL)-based language – Breeze/ADL to model the software architecture. Secondly, we use a creative rule – combinational rule (CR) to combine the vector clock algorithm for reducing the ambiguities in modelling scenarios. Then, another creative rule – transformational rule (TR) is employed to help to transform our Breeze/ADL model to a popular model – unified modelling language (UML) model. We implement our approach as a plugin of Breeze, and illustrate a running example of modelling a poetry to music system in our case study. Our results show the proposed creative approach is able to reduce ambiguities of the software architecture in practice.
Current Issue

2019 Vol.16 No.2

Table of Contents

ISSN 1476-8186

E-ISSN 1751-8520

CN 11-5350/TP

Editors-in-chief
Tieniu TAN, Chinese Academy of Sciences Guoping LIU, University of South Wales Huosheng HU, University of Essex
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