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In the paper, there is presented the theory of logical consequence operators indexed with taboo functions. It describes the mechanisms of logical inference in the environment of forbidden sentences. This kind of processes take place in ideological discourses within which their participants create various narrative worlds (mental worlds). A peculiar feature of ideological discourses is their association with taboo structures of deduction which penalize speech acts. The development of discourse involves, among others, transforming its deduction structure towards the proliferation of consequence operators and modifying penalty functions. The presented theory enables to define various processes of these transformations in the precise way. It may be used in analyses of conflicts between competing elm experts acting within a discourse.

and Robotics (MMAR 2003), Mi˛edzyzdroje, Poland, pp. 1213-1216 . Janiak, A. and Winczaszek, M. (2004). Scheduling problem with nonlinear earliness-tardiness penalty functions, in Z. Bubnicki, O. Hryniewicz and J. Weglarz (Eds.), Operation and System Research 2004 , Academic Publishing House EXIT, Warsaw, pp. 261-270, (in Polish). Janiak, A. and Winczaszek, M. (2005a). Optimal algorithm for parallel processor scheduling problem with due window assignment, Proceedings of the 11th IEEE International Conference on Methods and Models in Automation and Robotics, Mi


In this paper, combination of the cross-Wigner distribution (XWD) and the Viterbi algorithm (VA) for the instantaneous frequency (IF) estimation of frequency modulated (FM) signals in high noise environments is proposed. The favourable properties of the VA, the IF reconstruction based on minimization of the path penalty functions, and the XWD, iterative accuracy improvement of the IF estimation, give hybrid IF estimator with improved accuracy for high noise environments


This text describes a mathematical model of a strut finite element for isotropic incompressible hyperelastic materials. The invariants of the Right Cauchy-Green deformation tensor are written in terms of nodal displacements. The equilibrium problem is formulated as an unconstrained nonlinear programming problem, where the objective function is the total potential energy of the structure and the nodal displacements are the unknowns. The constraint for incompressibility is satisfied exactly, thereby eliminating the need for a penalty function. The results of the examples calculated by the proposed mathematical model show five significant digits in agreement when compared with commercial finite element analysis software.

A method of planning sub-optimal trajectory for a mobile manipulator working in the environment including obstacles is presented. The path of the end-effector is defined as a curve that can be parameterized by any scaling parameter, the reference trajectory of a mobile platform is not needed. Constraints connected with the existence of mechanical limits for a given manipulator configuration, collision avoidance conditions and control constraints are considered. The motion of the mobile manipulator is planned in order to maximize the manipulability measure, thus to avoid manipulator singularities. The method is based on a penalty function approach and a redundancy resolution at the acceleration level. A computer example involving a mobile manipulator consisting of a nonholonomic platform and a SCARA type holonomic manipulator operating in a two-dimensional task space is also presented.


In Multi-Channel Multi-Radio Wireless Mesh Networks (MCMR-WMN), finding the optimal routing by satisfying the Quality of Service (QoS) constraints is an ambitious task. Multiple paths are available from the source node to the gateway for reliability, and sometimes it is necessary to deal with failures of the link in WMN. A major challenge in a MCMR-WMN is finding the routing with QoS satisfied and an interference free path from the redundant paths, in order to transmit the packets through this path. The Particle Swarm Optimization (PSO) is an optimization technique to find the candidate solution in the search space optimally, and it applies artificial intelligence to solve the routing problem. On the other hand, the Genetic Algorithm (GA) is a population based meta-heuristic optimization algorithm inspired by the natural evolution, such as selection, mutation and crossover. PSO can easily fall into a local optimal solution, at the same time GA is not suitable for dynamic data due to the underlying dynamic network. In this paper we propose an optimal intelligent routing, using a Hybrid PSO-GA, which also meets the QoS constraints. Moreover, it integrates the strength of PSO and GA. The QoS constraints, such as bandwidth, delay, jitter and interference are transformed into penalty functions. The simulation results show that the hybrid approach outperforms PSO and GA individually, and it takes less convergence time comparatively, keeping away from converging prematurely.


Lack of up-to-date software documentation hinders the software evolution and maintenance processes, as simply the outdated software structure and code could be easily misunderstood. One approach to overcoming such problems is using software modularization, in which the software architecture is extracted from the available source code; such that developers can assess the reconstructed architecture against the required changes. Unfortunately, existing software modularization approaches are not accurate, as they ignore polymorphic calls among system modules. Furthermore, they are tightly coupled to the used programming language. To overcome such problems, this paper proposes the E-CDGM approach. E-CDGM decouples the extracted call dependency graph from the programming language by using the proposed intermediate code language (known as mCode). It also takes into consideration the polymorphic calls during the call dependency graph generation. It uses a new evolutionary optimization approach to find the best modularization option; adopting reward and penalty functions. Finally, it uses statistical analysis to build a final consolidated modularization model using different generated modularization solutions. Experimental results show that the proposed E-CDGM approach provides more accurate results when compared against existing well-known modularization approaches.

Annual Conference on Decision and Control (CDC), Maui, HI, USA, pp. 1166-1171. Gurobi Optimization (2013). Gurobi optimizer reference manual, Hoff, A., Andersson, H., Christiansen, M., Hasle, G. and Lkketangen, A. (2010). Industrial aspects and literature survey: Fleet composition and routing, Computers & Operations Research 37(12): 2041 - 2061. ILOG (2007). 11.0 users manual, ILOG CPLEX Division, Incline Village, NV. Kerrigan, E. and Maciejowski, J. (2000). Soft constraints and exact penalty functions in model predictive control, Control 2000

References 1. Dempe, S. Foundation of Bi-Level Programming. London, Kluwer Academic Publishers, 2002. 2. Stoilova, K., T. Stoilov, V. Ivanov. Practical Bi-Level Optimization as a Tool for Implementation of Intelligent Transportation Systems. – Cybernetics and Information Technologies, Vol. 17 , 2017, No 2, pp. 97-105. 3. Lv, Y., Z. Chen, Z. Wan. A Penalty Function Method Based on Bi-Level Programming for Solving Inverse Optimal Value Problems. – Applied Mathematics Letters, Vol. 23 , 2010, pp. 170-175. 4. Yang, H., M. G. H. Bell. Transport Bi-Level Programming

Wall Centre Hotel, Vancouver, BC, Canada. Takasu, T. (2011) RTKLIB: An Open Source Program Package for GNSS Positioning. Wu, C., Chou, H., and Su, W. (2007) A Genetic Approach for Coordinate Transformation Test of GPS Positioning. IEEE, Geoscience And Remote Sensing Letters , 4(2), pp.297-301. Xu, J., Arslan, T., Wang, Q., and Wan, D. (2002) An EHW Architecture for Real-Time GPS Attitude Determination Based on Parallel Genetic Algorithm. Proceedings of the 2002 NASA/DOD Conference on Evolvable Hardware , VA, USA. Yeniay, Ö. (2005) Penalty