Publications

In settings wherein discussion topics are not statically assigned, such as in microblogs, a need exists for identifying and separating topics of a given event. We approach the problem by using a novel type of similarity, calculated between the major terms used in posts. The occurrences of such terms are periodically sampled from the posts stream. The generated temporal series are processed by using marker-based stigmergy, i.e., a biologically-inspired mechanism performing scalar and temporal information aggregation. More precisely, each sample of the series generates a functional structure, called mark, associated with some concentration. The concentrations disperse in a scalar space and evaporate over time. Multiple deposits, when samples are close in terms of instants of time and values, aggregate in a trail and then persist longer than an isolated mark. To measure similarity between time series, the Jaccard’s similarity coefficient between trails is calculated. Discussion topics are generated by such similarity measure in a clustering process using Self-Organizing Maps, and are represented via a colored term cloud. Structural parameters are correctly tuned via an adaptation mechanism based on Differential Evolution. Experiments are completed for a real-world scenario, and the resulting similarity is compared with Dynamic Time Warping (DTW) similarity.

In this paper, we present a block of adaptive weight change (AWC) mechanism for analog current-mode Kohonen's Neural Network (KNN) implemented in CMOS 0.18 μm technology. As some other essential building blocks of KNNs dealing with the calculations of the Euclidean distance, formation of a conscience mechanism and a determination of the winner-takes-all (WTA) circuits have been already developed, the AWC forms another essential step towards the realization of the network. We show that the proposed network works with small values of analog signals thus resulting in low power dissipation and chip area when compared with digital realizations of KNNs. Each neuron occupies chip area equal to about 1000 μm2 and dissipates 20 μW of power for 20 MHz input data rate.

Energy supply is one of the most critical challenges of wireless sensor networks (WSNs) and industrial wireless sensor networks (IWSNs). While research on coverage optimization problem (COP) centers on the network’s monitoring coverage, this research focuses on the power banks’ energy supply coverage. The study of 2-D and 3-D spaces is typical in IWSN, with the realistic environment being more complex with obstacles (i.e., machines). A 3-D surface is the field of interest (FOI) in this work with the established hybrid power bank deployment model for the energy supply COP optimization of IWSN. The hybrid power bank deployment model is highly adaptive and flexible for new or existing plants already using the IWSN system. The model improves the power supply to a more considerable extent with the least number of power bank deployments. The main innovation in this work is the utilization of a more practical surface model with obstacles and training while improving the convergence speed and quality of the heuristic algorithm. An overall probabilistic coverage rate analysis of every point on the FOI is provided, not limiting the scope to target points or areas. Bresenham’s algorithm is extended from 2-D to 3-D surface to enhance the probabilistic covering model for coverage measurement. A dynamic search strategy (DSS) is proposed to modify the artificial bee colony (ABC) and balance the exploration and exploitation ability for better convergence toward eliminating NP-hard deployment problems. Further, the cellular automata (CA) is utilized to enhance the convergence speed. The case study based on two typical FOI in the IWSN shows that the CA scheme effectively speeds up the optimization process. Comparative experiments are conducted on four benchmark functions to validate the effectiveness of the proposed method. The experimental results show that the proposed algorithm outperforms the ABC and gbest-guided ABC (GABC) algorithms. The results show that the proposed energy coverage optimization method based on the hybrid power bank deployment model generates more accurate results than the results obtained by similar algorithms (i.e., ABC, GABC). The proposed model is, therefore, effective and efficient for optimization in the IWSN.

The paper deals with some problems of decision‐making by the use of fuzzy set theory. The application of different operators from a wide class of intersection connectives makes it possible to modelize the process of decision‐making in a very flexible manner. The method of calculation of final non‐fuzzy decision from fuzzy set of decision D is discussed in detail and the problem of the choice of a threshold level presented as appropriate to the intersection operator applied before. The notion of sensitivity of any of intersection operators discussed is introduced and considered in detail.

The relative importance of alternatives expressed in terms of interval numbers in the fuzzy analytic hierarchy process aims to capture the uncertainty experienced by decision makers (DMs) when making a series of comparisons. Under the assumption of full rationality, the judgements of DMs in the typical analytic hierarchy process could be consistent. However, since the uncertainty in articulating the opinions of DMs is unavoidable, the interval number judgements are associated with the limited rationality. In this paper, we investigate the concept of limited rationality by introducing interval multiplicative reciprocal comparison matrices. By analyzing the consistency of interval multiplicative reciprocal comparison matrices, it is observed that the interval number judgements are inconsistent. By considering the permutations of alternatives, the concepts of approximation-consistency and acceptable approximation-consistency of interval multiplicative reciprocal comparison matrices are proposed. The exchange method is designed to generate all the permutations. A novel method of determining the interval weight vector is proposed under the consideration of randomness in comparing alternatives, and a vector of interval weights is determined. A new algorithm of solving decision making problems with interval multiplicative reciprocal preference relations is provided. Two numerical examples are carried out to illustrate the proposed approach and offer a comparison with the methods available in the literature.