We give a linear-time recognition algorithm for circular-arc graphs based on the algorithm of Eschen and Spinrad (Proceedings of the Fourth Annual ACM-SIAM Symposium on Discrete algorithms (SODA), pp. 128-137, 1993) a...
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We give a linear-time recognition algorithm for circular-arc graphs based on the algorithm of Eschen and Spinrad (Proceedings of the Fourth Annual ACM-SIAM Symposium on Discrete algorithms (SODA), pp. 128-137, 1993) and Eschen (PhD thesis, 1997). Our algorithm both improves the time bound of Eschen and Spinrad, and fixes some flaws in it. Our algorithm is simpler than the earlier linear-time recognition algorithm of McConnell (algorithmica 37(2):93-147, 2003), which is the only linear time recognition algorithm previously known.
With the acceleration of global warming and energy shortages, the smart grid has become the goal of power grid development, which makes intelligent household appliance control systems essential. To promote the integra...
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With the acceleration of global warming and energy shortages, the smart grid has become the goal of power grid development, which makes intelligent household appliance control systems essential. To promote the integration of traditional household appliances into a new electrical system, this study focused on an appliance recognition algorithm applicable to traditional and typical household appliances. Using sequential appliance power consumption data from intelligent power sockets, this study generalized and extracted the characteristics of occupant behavior and power consumption of typical household appliances. A new recognition algorithm for household appliances, based on a Bayes classification model, is presented in this paper. Seven types of household appliances (refrigerator, electric cooker, air conditioner, television, laptop computer, washing machine, and water dispenser) were analyzed in 15 Beijing households. The proposed algorithm was proven to be applicable for appliance recognition.
The method of Grimson, Lozano Pérez and others for the recognition of polyhedral objects with sparse data, has been developed and implemented on a distributed array processor, the AMT DAP 500, which operates in s...
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The method of Grimson, Lozano Pérez and others for the recognition of polyhedral objects with sparse data, has been developed and implemented on a distributed array processor, the AMT DAP 500, which operates in single instruction-multiple data (SIMD) mode. Measurements involving the location vectors and the surface normals at m data points, considered in pairs, are compared with the corresponding maximum and minimum values associated with n × n pairs of object model faces in a process that exploits n × n parallelism. The overall processing time is essentiallv proportional to m × (m−1) 2 to explore the interpretation tree to its full depth. This paper discusses the nature of the comparisons between object models and data, together with the need to make these comparisons in a particular sequence, and results of test runs with a variety of object models and different geometric constraints are presented herein. Comparison is made with the corresponding sequential process, and with the more costly method of Flynn and Harris, in which n m processing elements are required to acheive, at best a processing time of the same order of magnitude.
We consider the class A of graphs that contain no odd hole, no antihole of length at least 5, and no prism ( a graph consisting of two disjoint triangles with three disjoint paths between them) and the class A' of...
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We consider the class A of graphs that contain no odd hole, no antihole of length at least 5, and no prism ( a graph consisting of two disjoint triangles with three disjoint paths between them) and the class A' of graphs that contain no odd hole, no antihole of length at least 5, and no odd prism ( prism whose three paths are odd). These two classes were introduced by Everett and Reed and are relevant to the study of perfect graphs. We give polynomial-time recognition algorithms for these two classes. In contrast we prove that determining if a general graph contains a prism ( or an even prism, or an odd prism) is NP-complete.
The fault classification of medium-voltage transmission lines consisting of wind farms connected to the high-voltage (HV) transmission networks is carried out using the power-spectrum-based hyperbolic S-transform (HST...
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The fault classification of medium-voltage transmission lines consisting of wind farms connected to the high-voltage (HV) transmission networks is carried out using the power-spectrum-based hyperbolic S-transform (HST) powerful technique for analysing non-linear and non-stationary fault signals through the non-intrusive monitoring systems. The HST technique extracts the useful features in the time-frequency domain from measuring fault current waveforms of the HV utility side to discriminate the fault types. Parseval's theorem is applied to each HST coefficient to quantify the energy distribution of various fault types for reducing the size of inputs for recognition algorithms. Next, multiclass support vector machines achieve identification. The results have proved that the proposed classification technique is independent of fault resistance, source impedance, and fault inception angles. Extensive simulations are conducted using the electromagnetic transients program to show that the recognition accuracy of the fault classification for all types is up to 96.84%.
This paper proposes a novel method for recognizing a set of signals with linearly and nonlinearly included parameters from a given ensemble of signals under essential a priori uncertainty. Due to this uncertainty, wel...
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This paper proposes a novel method for recognizing a set of signals with linearly and nonlinearly included parameters from a given ensemble of signals under essential a priori uncertainty. Due to this uncertainty, well-known statistical methods become inapplicable. Signals may be present in an additive mixture containing an observation noise and a singular interference;the distribution law of the noise is unknown, and only its correlation matrix is specified. The novel method is invariant to this interference, does not require traditional state-space expansion, and ensures the decomposition and parallelization of the computational procedure. The signals and interference are represented using conventional linear spectral decompositions with unknown coefficients and given basis functions. Random and methodological errors, as well as the resulting computational effect, are analyzed. An illustrative example is provided.
Overlap graphs occur in computational biology and computer science, and have applications in genome sequencing, string compression, and machine scheduling. Given two strings and , their overlap string is defined as th...
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Overlap graphs occur in computational biology and computer science, and have applications in genome sequencing, string compression, and machine scheduling. Given two strings and , their overlap string is defined as the longest string such that and , for some non empty strings , and its length is called the overlap between these two strings. A weighted directed graph is an overlap graph if there exists a set of strings with one-to-one correspondence to the vertices of the graph, such that each arc weight in the graph equals the overlap between the corresponding strings. In this paper, we characterize the class of overlap graphs, and we present a polynomial time recognition algorithm as a direct consequence. Given a weighted directed graph , the algorithm constructs a set of strings that has as its overlap graph, or decides that this is not possible.
A matroid M has all-graphic cocircuits if M\Y is a graphic matroid for every cocircuit Y is an element of C*(M). In Papalamprou and Pitsoulis (2013) it has been shown that signed-graphic matroids that are representabl...
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A matroid M has all-graphic cocircuits if M\Y is a graphic matroid for every cocircuit Y is an element of C*(M). In Papalamprou and Pitsoulis (2013) it has been shown that signed-graphic matroids that are representable in GF(2) can be decomposed into graphic matroids and matroids with all-graphic cocircuits. In this paper we study this class of signed-graphic matroids with all-graphic cocircuits and provide the set of regular excluded minors as well as a complete characterization when the matroid is cographic, which in turn results in a simple recognition algorithm. (C) 2017 Elsevier B.V. All rights reserved.
The graph sandwich problem for property Pi: is defined as follows: Given two graphs G(1) = (V, E(1)) and G(2) = (V, E(2)) such that E(1) subset of or equal to E(2), is there a graph G = (V, E) such that E(1) subset of...
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The graph sandwich problem for property Pi: is defined as follows: Given two graphs G(1) = (V, E(1)) and G(2) = (V, E(2)) such that E(1) subset of or equal to E(2), is there a graph G = (V, E) such that E(1) subset of or equal to E subset of or equal to E(2) which satisfies property Pi? Such problems generalize recognition problems and arise in various applications. Concentrating mainly on properties characterizing subfamilies of perfect graphs, we give polynomial algorithms for several properties and prove the NP-completeness of others. We describe polynomial time algorithms for threshold graphs, split graphs, and cographs. For the sandwich problem for threshold graphs, the only case in which a previous algorithm existed, we obtain a faster algorithm. NP-completeness proofs are given for comparability graphs, permutation graphs, and several other families. For Eulerian graphs;one Version of the problem is polynomial and another is NP-complete. (C) 1995 Academic Press, Inc.
Truemper configurations are four types of graphs (namely thetas, wheels, prisms and pyramids) that play an important role in the proof of several decomposition theorems for hereditary graph classes. In this paper, we ...
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Truemper configurations are four types of graphs (namely thetas, wheels, prisms and pyramids) that play an important role in the proof of several decomposition theorems for hereditary graph classes. In this paper, we prove two structure theorems: one for graphs with no thetas, wheels and prisms as induced subgraphs, and one for graphs with no thetas, wheels and pyramids as induced subgraphs. A consequence is a polynomial time recognition algorithms for these two classes. In Part II of this series we generalize these results to graphs with no thetas and wheels as induced subgraphs, and in Parts III and IV, using the obtained structure, we solve several optimization problems for these graphs. (C) 2018 Elsevier Inc. All rights reserved.
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