To reduce side lobe level of slotted array antennas using nonuniform waveguides

In this article, a method is presented to reduce the side lobe level of slotted waveguide array antennas while the gain to be constant. In this method, the H‐plane dimension of the waveguide is considered as the variable rather than constant. The nonuniformity of waveguide walls obviates the need for offset of slots and thereby reduces the side lobe level of radiation pattern. A slotted nonuniform waveguide is designed at frequency 10 GHz and then fabricated and tested. © 2015 The Authors International Journal of RF and Microwave Computer‐Aided Engineering Published by Wiley Periodicals, Inc. Int J RF and Microwave CAE 26:42–46, 2016.     

Rigid formation control of double-integrator systems

In this paper, we study rigid formation control systems modelled by double integrators. Two kinds of double-integrator formation systems are considered, namely formation stabilisation systems and flocking control systems. Novel observations on the measurement requirement, the null space and eigenvalues of the system Jacobian matrix will be provided, which reveal important properties of system dynamics and the associated convergence results. We also establish some new links between single-integrator formation systems and double-integrator formation systems via a parameterised Hamiltonian system, which, in addition, provide novel stability criteria for different equilibria in double-integrator formation systems by using available results in single-integrator formation systems.     

A GMM/HMM model for reconstruction of missing speech spectral components for continuous speech recognition

This paper presents a method for reconstructing unreliable spectral components of speech signals using the statistical distributions of the clean components. Our goal is to model the temporal patterns in speech signal and take advantage of correlations between speech features in both time and frequency domain simultaneously. In this approach, a hidden Markov model (HMM) is first trained on clean speech data to model the temporal patterns which appear in the sequences of the spectral components. Using this model and according to the probabilities of occurring noisy spectral component at each states, a probability distributions for noisy components are estimated. Then, by applying maximum a posteriori (MAP) estimation on the mentioned distributions, the final estimations of the unreliable spectral components are obtained. The proposed method is compared to a common missing feature method which is based on the probabilistic clustering of the feature vectors and also to a state of the art method based on sparse reconstruction. The experimental results exhibits significant improvement in recognition accuracy over a noise polluted Persian corpus.     

A novel method for the classification of Alzheimer's disease from normal controls using magnetic resonance imaging

Alzheimer's disease (AD) is the most prevalent form of dementia. Although fewer people, who suffer from AD are correctly and promptly diagnosed, due to a lack of knowledge of its cause and unavailability of treatment, AD is more manageable if the symptoms of mild cognitive impairment (MCI) are in an early stage. In recent years, computer‐aided diagnosis has been widely used for the diagnosis of AD. The main motive of this paper is to improve the classification and prediction accuracy of AD. In this paper, a novel approach is developed to classify MCI, normal control (NC), and AD using structural magnetic resonance imaging (sMRI) from the Alzheimer's disease Neuroimaging Initiative (ADNI) dataset (50 AD, 50 NC, 50 MCI subjects). FreeSurfer is used to process these MRI data and obtain cortical features such as volume, surface area, thickness, white matter (WM), and intrinsic curvature of the brain regions. These features are modified by normalizing each cortical region's features using the absolute maximum value of that region's features from all subjects in each group of MCI, NC, and AD independently. A total of 420 features are obtained. To address the curse of dimensionality, the obtained features are reduced to 30 features using a sequential feature selection technique. Three classifiers, namely the twin support vector machine (TSVM), least squares TSVM (LSTSVM), and robust energy‐based least squares TSVM (RELS‐TSVM), are used to evaluate the classification accuracy from the obtained features. Five‐fold and 10‐fold cross‐validation are used to validate the proposed method. Experimental results show an accuracy of 100% for the studied database. The proposed approach is innovative due to its higher classification accuracy compared to methods in the existing literature.     

Structural controllability of multi-agent networks: Robustness against simultaneous failures

In this paper, structural controllability of a leader–follower multi-agent system with multiple leaders is studied from a graph-theoretic point of view. The problem of preservation of structural controllability under simultaneous failures in both the communication links and the agents is investigated. The effects of the loss of agents and communication links on the controllability of an information flow graph are previously studied. In this work, the corresponding results are exploited to introduce some useful indices and importance measures that help characterize and quantify the role of individual links and agents in the controllability of the overall network. Existing results are then extended by considering the effects of losses in both links and agents at the same time. To this end, the concepts of joint (r,s)$(r,s)$-controllability and joint t$t$-controllability are introduced as quantitative measures of reliability for a multi-agent system, and their important properties are investigated. Lastly, the class of jointly critical digraphs is introduced, and it is stated that if a digraph is jointly critical, then joint t$t$-controllability is a necessary and sufficient condition for remaining controllable following the failure of any set of links and agents, with cardinality less than t$t$. Various examples are exploited throughout the paper to elaborate on the analytical findings.     

Production planning and worker training in dynamic manufacturing systems

Production planning is a vital activity in any manufacturing system, and naturally implies assigning the available resources to the required operations. This paper develops and analyzes a comprehensive mathematical model for dynamic manufacturing systems. The proposed model integrates production planning and worker training considering machine and worker time availability, operation sequence and multi-period planning horizon. The objective is to minimize machine maintenance and overhead, system reconfiguration, backorder and inventory holding, training and salary of worker costs. Computational results are presented to verify the proposed model.     

Presenting a hybrid model in social networks recommendation system architecture development

There are many studies conducted on recommendation systems, most of which are focused on recommending items to users and vice versa. Nowadays, social networks are complicated due to carrying vast arrays of data about individuals and organizations. In today’s competitive environment, companies face two significant problems: supplying resources and attracting new customers. Even the concept of supply-chain management in a virtual environment is changed. In this article, we propose a new and innovative combination approach to recommend organizational people in social networks based on organizational communication and SCM. The proposed approach uses a hybrid strategy that combines basic collaborative filtering and demographic recommendation systems, using data mining, artificial neural networks, and fuzzy techniques. The results of experiments and evaluations based on a real dataset collected from the LinkedIn social network showed that the hybrid recommendation system has higher accuracy and speed than other essential methods, even substantially has eliminated the fundamental problems with such systems, such as cold start, scalability, diversity, and serendipity.

     

Smart Innovation Engineering: Toward Intelligent Industries of the Future

ABSTRACT

Knowledge-based engineering systems are founded upon integration of knowledge into computer systems and are one of the core requirements for the future Industry 4.0. This paper presents a system called smart innovation engineering (SIE) capable of facilitating product innovation process semi-automatically. It enhances decision-making processes using the explicit knowledge of formal decision events. The SIE system carries the promise to support the innovation processes of manufactured products in a quick and efficient way. It stores and reuses past decisional events or sets of experiences related to innovation issues, which significantly enhances innovation progression. The analysis of basic concepts and implementation method proves that SIE system is an advanced form of cyber physical systems. It is flexible, systematic, fast, and supports customization. It can play a vital role toward Industry 4.0 development.     

Notes on integral performance indices in fractional-order control systems

Integral performance indices as quantitative measures of the performance of a system are commonly used to evaluate the performance of designed control systems. In this paper, it is pointed out that due to existence of non-exponential modes in the step response of a fractional-order control system having zero steady state error, integral performance indices of such a system may be infinite. According to this point, some simple conditions are derived to guarantee the finiteness of different integral performance indices in a class of fractional-order control systems. Finally, some numerical examples are presented to show the applicability of the analytical achievements of the paper.