Base-type languages: Program design by upper semantic approximation

Translated from Kibernetika i Sistemnyi Analiz, No. 4, pp. 140–155, July–August 1994.     

Nonlocal quasi-Newton method for solving convex variational inequalities

Conclusion In the optimization problem [f ₀(x)│h_i(x)<-0,i=1,…,l] relaxation of the functionf ₀(x)+Nh⁺(x) does not produce, as we know [6, 7], α_k=1 in Newton's method with the auxiliary problem (5), (6), whereF(x)=f _0′(x). For this reason, Newton type methods based on relaxation off ₀(x)+Nh⁺(x) are not superlinearly convergent (so-called Maratos effect). The results of this article indicate that if (F(x)=f _0′(x), then replacement of the initial optimization problem with a larger equivalent problem (7) eliminates the Maratos effect in the proposed quasi-Newton method. This result is mainly of theoretical interest, because Newton type optimization methods in the space of the variablesx ∈R ⁿ are less complex. However to the best of our knowledge, the difficulties with nonlocal convergence arising in these methods (choice of parameters, etc.) have not been fully resolved [10, 11]. The discussion of these difficulties and comparison with the proposed method fall outside the scope of the present article, which focuses on solution of variational inequalities (1), (2) for the general caseF′(x)≠F′ ^T(x). Translated from Kibernetika i Sistemnyi Analiz, No. 6, pp. 78–91, November–December, 1994.     

Fault trees—Problems and the modern state of investigations

Published in Kibernetika i Sistemnyi Analiz, No. 3, pp. 128–150, May–June, 1994.     

First break refraction event picking using fuzzy logic systems

First break picking is a pattern recognition problem in seismic signal processing, one that requires much human effort and is difficult to automate. The authors' goal is to reduce the manual effort in the picking process and accurately perform the picking. Feedforward neural network first break pickers have been developed using backpropagation training algorithms applied either to an encoded version of the raw data or to derived seismic attributes which are extracted from the raw data. The authors summarize a study in which they applied a backpropagation fuzzy logic system (BPFLS) to first break picking. The authors use derived seismic attributes as features, and take lateral variations into account by using the distance to a piecewise linear guiding function as a new feature. Experimental results indicate that the BPFLS achieves about the same picking accuracy as a feedforward neural network that is also trained using a backpropagation algorithm; however, the BPFLS is trained in a much shorter time, because there is a systematic way in which the initial parameters of the BPFLS can be chosen, versus the random way in which the weights of the neural network are chosen     

Statistical synchronization among participants in real-time multimedia conferencing

In this paper, an algorithm to determine the set of packets generated continuously and periodically from different participants that are arriving at a node either for mixing at the master of a conference, or for simply playing back at a regular participant of a conference, is proposed. The essence of the algorithm is to estimate the expected packet arrival time (or reference time) for each participant. With the reference time at hand, the maximum jitter and the optimum waiting time for a mixer to wait packets from all participants can be determined. An enhancement to improve synchronization which deals with the estimation of the time offsets between the individual periods of the sources and the period of the receiver is also presented. The error of the proposed algorithm is enumerated by the Chernoff bound and demonstrated by simulation and is shown to be acceptable in practical application. The algorithm can also be employed when traffic sources operate with different periods.     

Qualitative representation of spatial knowledge in two-dimensional space

Various relation-based systems, concerned with the qualitative representation and processing of spatial knowledge, have been developed in numerous application domains. In this article, we identify the common concepts underlying qualitative spatial knowledge representation, we compare the representational properties of the different systems, and we outline the computational tasks involved in relation-based spatial information processing. We also describesymbolic spatial indexes, relation-based structures that combine several ideas in spatial knowledge representation. A symbolic spatial index is an array that preserves only a set of spatial relations among distinct objects in an image, called the modeling space; the index array discards information, such as shape and size of objects, and irrelevant spatial relations. The construction of a symbolic spatial index from an input image can be thought of as a transformation that keeps only a set of representative points needed to define the relations of the modeling space. By keeping the relative arrangements of the representative points in symbolic spatial indexes and discarding all other points, we maintain enough information to answer queries regarding the spatial relations of the modeling space without the need to access the initial image or an object database. Symbolic spatial indexes can be used to solve problems involving route planning, composition of spatial relations, and update operations.