Common understanding in a multi-agent system using ontology-guided learning

Traditionally, communication among agents has been established based on the group commitment to a common ontology which is unfortunately often too strong or unrealistic. In the real world of communicating agents, it is preferred to enable agents to exchange information while they keep their own individual ontology. While this assumption makes agents represent their knowledge more independently and give them more flexibility, it also adds to the complexity of communication. We believe that agents can overcome this complexity by using their learning capability. The agents can learn any concept they do not know but want to communicate about with other agents in the multi-agent system where they work in. Our goal in this paper is to present a general method for agents using ontologies to teach each other concepts to improve their communication, and therefore cooperation abilities. In our method, a particular agent that understands a concept only ambiguously intends to learn it by receiving positive and negative examples for that concept from the other agents. Then, utilizing one of the known concept learning methods, the agent learns the concept in question. In case of conflicts in the received set of examples, the learning agent asks other agents again to get involved in the learning process by taking votes. While this method allows agents not to share common ontologies, it enables agents to establish common grounds on the concepts known only by some of them if these common grounds are needed during cooperation. In fact, the learned concepts by an agent are compromised among the views of other agents the method improves the autonomy of agents using them significantly.     

Resource constrained project scheduling problem with discounted earliness–tardiness penalties: Mathematical modeling and solving procedure

In this paper, we consider the resource-constrained project scheduling problem with a due date for each activity. The objective is to minimize the net present value of the earliness–tardiness penalty costs. The problem is first mathematically modeled. Then, two meta-heuristics, genetic algorithm and simulated annealing are proposed to solve this strongly NP-hard problem. Design of experiments and response surface methodology are employed to fine-tune the meta-heuristics’ parameters. Finally, a comprehensive computational experiment is described, performed on a set of instances and the results are analyzed and discussed.     

A dynamic feedback control strategy for control loops with time-varying delay

Dynamic systems of nth order with time-varying delay in the control loop are examined in this paper. The infinite-dimensional pure delay problem is approximated using a jth-order Padé approximation. Although the approximation provides a well-matched finite-dimensional configuration, it poses a new challenge in terms of unstable internal dynamics for the resulted non-minimum phase system. Such a non-minimum phase characteristic limits the closed-loop system bandwidth and leads to an imperfect tracking performance. To circumvent this problem, the unstable internal dynamics of the system is captured and a new dynamic compensator is proposed to stabilise it in a systematic framework. A dynamic controller is developed, which provides the overall system stability against unmatched perturbation and meets the desired tracking error dynamics. The proposed approach is then applied to fuelling control in gasoline engines addressing the varying transport delay of the oxygen-sensor measurement in the exhaust. The developed methodology is finally validated on a Ford F-150 SI lean-burn engine model with large time-varying delay in the control loop.     

Efficient 2D viewpoint combination for human action recognition

The ability to recognize human actions using a single viewpoint is affected by phenomena such as self-occlusions or occlusions by other objects. Incorporating multiple cameras can help overcome these issues. However, the question remains how to efficiently use information from all viewpoints to increase performance. Researchers have reconstructed a 3D model from multiple views to reduce dependency on viewpoint, but this 3D approach is often computationally expensive. Moreover, the quality of each view influences the overall model and the reconstruction is limited to volumes where the views overlap. In this paper, we propose a novel method to efficiently combine 2D data from different viewpoints. Spatio-temporal features are extracted from each viewpoint and then used in a bag-of-words framework to form histograms. Two different sizes of codebook are exploited. The similarity between the obtained histograms is represented via the Histogram Intersection kernel as well as the RBF kernel with \(\chi ^2\) distance. Lastly, we combine all the basic kernels generated by selection of different viewpoints, feature types, codebook sizes and kernel types. The final kernel is a linear combination of basic kernels that are properly weighted based on an optimization process. For higher accuracy, the sets of kernel weights are computed separately for each binary SVM classifier. Our method not only combines the information from multiple viewpoints efficiently, but also improves the performance by mapping features into various kernel spaces. The efficiency of the proposed method is demonstrated by testing on two commonly used multi-view human action datasets. Moreover several experiments indicate the efficacy of each part of the method on the overall performance.     

Engineering the nonlinear phase shift with multistage autoregressive moving-average optical filters

We propose and demonstrate the application of concepts from digital filter design in order to optimize artificial optical resonant structures to produce a nearly ideal nonlinear phase shift response. Multistage autoregressive moving average (ARMA) optical filters (ring-resonator-based Mach-Zehnder interferometer lattices) are designed and studied. The filter group delay is used as a measure instead of finesse or quality factor to study the nonlinear sensitivity for multiple resonances. The nonlinearity of a four-stage ARMA filter is 17 times higher than that of the intrinsic material of the same group delay. We demonstrate that the nonlinear sensitivity can be increased within constant bandwidth by allocating more in-band phase or by using higher-order filter structures and that the nonlinear sensitivity enhancement improves with increasing group delay. We also investigate methods to precompensate the nonlinear response to reduce the occurrence of optical bistabilities. The effect of optical loss, including linear absorption and two-photon absorption, is discussed in postanalysis. In addition, we discuss how the improvement in nonlinear response scales with respect to various filter parameters.     

Prediction of seismic slope stability through combination of particle swarm optimization and neural network

One of the main concerns in geotechnical engineering is slope stability prediction during the earthquake. In this study, two intelligent systems namely artificial neural network (ANN) and particle swarm optimization (PSO)–ANN models were developed to predict factor of safety (FOS) of homogeneous slopes. Geostudio program based on limit equilibrium method was utilized to obtain 699 FOS values with different conditions. The most influential factors on FOS such as slope height, gradient, cohesion, friction angle and peak ground acceleration were considered as model inputs in the present study. A series of sensitivity analyses were performed in modeling procedures of both intelligent systems. All 699 datasets were randomly selected to 5 different datasets based on training and testing. Considering some model performance indices, i.e., root mean square error, coefficient of determination (R ²) and value account for (VAF) and using simple ranking method, the best ANN and PSO–ANN models were selected. It was found that the PSO–ANN technique can predict FOS with higher performance capacities compared to ANN. R ² values of testing datasets equal to 0.915 and 0.986 for ANN and PSO–ANN techniques, respectively, suggest the superiority of the PSO–ANN technique.     

Applying inherent capabilities of quantum-dot cellular automata to design: D flip-flop case study

Nowadays, quantum cellular automata (QCA) has been considered as the pioneer technology in next generation computer designs. QCA provides the computer computations at nano level using molecular components as computation units. Although the QCA technology provides smaller chip area and eliminates the spatial constraints than earlier CMOS technology, but different characteristics and design limitations of QCA architectures have led to essential attentions in replacement of traditional structures with QCA ones. Inherent information flow control, limited wire length, and consumed area are of such features and restrictions. In this paper, D flip-flop structure has been considered and we have proposed two new D flip-flop structures which employ the inherent capabilities of QCA in timing and data flow control, rather than ordinary replacement of CMOS elements with equivalent QCA ones. The introduced structures involve small number of cells in contrast to earlier proposed ones in presence of the same or even lower input to output delay. The proposed structures are simulated using the QCADesigner and the validity of them has been proved.     

Estimating and optimizing safety factors of retaining wall through neural network and bee colony techniques

Engineering with Computers - An important task of geotechnical engineering is a suitable design of safety factor (SF) of retaining wall under both static and dynamic conditions. This paper presents...     

Development of AZ91/SiC surface composite by FSP: effect of vibration and process parameters on microstructure and mechanical characteristics

A surface composite layer enhances the mechanical characteristics of a surface while retaining the properties of the base material. Friction stir processing (FSP) is a method for forming surface metal matrix composites (SMMCs) that reinforce a surface with particles. In the current study, a new method entitled friction stir vibration processing (FSVP) was applied to form SMMCs on the surface of AZ91 magnesium alloy with SiC particles as the reinforcing particles. Contrary to FSP, in FSVP, the workpiece was vibrated normal to the processing line while the tool rotated and traversed. The microstructure and mechanical properties of friction stir (FS) and friction stir vibration (FSV) processed specimens were evaluated. Additionally, the effects of vibration frequency and process parameters on different characteristics of FS and FSV processed specimens were studied. The results showed that the stir zone grains for FSV processed specimens were finer than those for FS processed specimens, and the second phase particles (SiC particles) had a more homogenous distribution in the former specimens than in the latter specimens. This was related to the effect of workpiece vibration during FSVP, which increased the material deformation and led to enhanced dynamic recrystallization and the breakdown of agglomerated SiC particles. The results indicated that the stir zone grain size decreased, and the distribution homogeneity of the SiC particles increased as vibration frequency increased. It was also observed that the stir zone grain size increased, and the mechanical properties of the processed specimens decreased as tool rotation speed increased.The full text can be downloaded at https://link.springer.com/content/pdf/10.1007%2Fs40436-019-00288-9.pdf     

To improve the quality of cluster ensembles by selecting a subset of base clusters

Conventional clustering ensemble algorithms employ a set of primary results; each result includes a set of clusters which are emerged from data. Given a large number of available clusters, one is faced with the following questions: (a) can we obtain the same quality of results with a smaller number of clusters instead of full ensemble? (b) If so, which subset of clusters is more efficient to be used in the ensemble? In this paper, these two questions are going to be answered. We explore a clustering ensemble approach combined with a cluster stability criterion as well as a dataset simplicity criterion to discover the finest subset of base clusters for each kind of datasets. Also, a novel method is proposed in order to accumulate the selected clusters and to extract final partitioning. Although it is expected that by reducing the size of ensemble the performance decreases, our experimental results show that our selecting mechanism generally lead to superior results.