Search results for "Data type"
showing 10 items of 1183 documents
Iterated greedy with variable neighborhood search for a multiobjective waste collection problem
2020
Abstract In the last few years, the application of decision making to logistic problems has become crucial for public and private organizations. Efficient decisions clearly contribute to improve operational aspects such as cost reduction or service improvement. The particular case of waste collection service considered in this paper involves a set of economic, labor and environmental issues that translate into difficult operational problems. They pose a challenge to nowadays optimization technologies since they have multiple constraints and multiple objectives that may be in conflict. We therefore need to resort to multiobjective approaches to model and solve this problem, providing efficie…
Networked Bio-Inspired Evolutionary Dynamics on a Multi-Population
2019
We consider a multi-population, represented by a network of groups of individuals. Every player of each group can choose between two options, and we study the problem of reaching consensus. The dynamics not only depend on the dynamics within the group, but they also depend on the topology of the network, so neighboring groups influence individuals as well. First, we develop a mathematical model of this networked bio-inspired evolutionary behavior and we study its steady-state. We look at the special case where the underlying network topology is a regular and unweighted graph and show that the steady-state is a consensus equilibrium. A sufficient condition for exponential stability is given.…
Graph-theoretical derivation of brain structural connectivity
2020
Brain connectivity at the single neuron level can provide fundamental insights into how information is integrated and propagated within and between brain regions. However, it is almost impossible to adequately study this problem experimentally and, despite intense efforts in the field, no mathematical description has been obtained so far. Here, we present a mathematical framework based on a graph-theoretical approach that, starting from experimental data obtained from a few small subsets of neurons, can quantitatively explain and predict the corresponding full network properties. This model also changes the paradigm with which large-scale model networks can be built, from using probabilisti…
Do Randomized Algorithms Improve the Efficiency of Minimal Learning Machine?
2020
Minimal Learning Machine (MLM) is a recently popularized supervised learning method, which is composed of distance-regression and multilateration steps. The computational complexity of MLM is dominated by the solution of an ordinary least-squares problem. Several different solvers can be applied to the resulting linear problem. In this paper, a thorough comparison of possible and recently proposed, especially randomized, algorithms is carried out for this problem with a representative set of regression datasets. In addition, we compare MLM with shallow and deep feedforward neural network models and study the effects of the number of observations and the number of features with a special dat…
Comparison of fully non-stationary artificial accelerogram generation methods in reproducing seismicity at a given site
2020
Abstract Seismic input modelling is a crucial step when Non-Linear Time-History Analyses (NLTHAs) are performed, the seismic response of structures being highly responsive to the input employed. When natural accelerograms able to represent local seismicity are not available, the use of generated accelerograms is an efficient solution for input modelling. The aim of the present paper is to compare four methods for generating fully non-stationary artificial accelerograms on the basis of a target spectrum, identified using seven recorded accelerograms registered in the neighbourhood of the construction site during a single event, assumed as target accelerograms. For each method, seven accelero…
A Novel Border Identification Algorithm Based on an “Anti-Bayesian” Paradigm
2013
Published version of a chapter in the book: Computer Analysis of Images and Patterns. Also available from the publisher at: http://dx.doi.org/10.1007/978-3-642-40261-6_23 Border Identification (BI) algorithms, a subset of Prototype Reduction Schemes (PRS) aim to reduce the number of training vectors so that the reduced set (the border set) contains only those patterns which lie near the border of the classes, and have sufficient information to perform a meaningful classification. However, one can see that the true border patterns (“near” border) are not able to perform the task independently as they are not able to always distinguish the testing samples. Thus, researchers have worked on thi…
Rough Set Theory for Optimization of Packet Management Mechanism in IP Routers
2020
Bandwidth and consequently optimum overall efficiency of network system relies greatly on mechanism of packet management in IP routers. Our research objective is to implement rough set theory to minimizing number of the network system attributes responsible for decision making in selection of those packets, which improve its transmission. Such an approach is called priority queuing system model, as we assign priority to the packets selected, following rough set theory. Regardless of the file format, for all the routers, packets are transmitted in sequence one-by-one. Nonetheless, quality of streaming data largely depends on how much the packet loss is minimized, or eliminated at all, if pos…
Combined column-and-row-generation for the optimal communication spanning tree problem
2018
Abstract This paper considers the exact solution of the optimal communication spanning tree problem (OCSTP), which can be described as follows: Given an undirected graph with transportation costs on every edge and communication requirements for all pairs of vertices, the OCSTP seeks for a spanning tree that minimizes the sum of the communication costs between all pairs of vertices, where the communication cost of a pair of vertices is defined as their communication requirement multiplied by the transportation cost of the unique tree path that connects the two vertices. Two types of compact formulations for OCSTP were presented in the literature. The first one is a four-index model based on …
The ELECTRE I method to support the FMECA
2018
Abstract In traditional Failure Modes, Effects and Criticality Analysis (FMECA), risk priorities of failure modes are determined through the Risk Priority Number (RPN), which is a function of the three risk parameters Occurrence (O), Severity (S), and Detection (D). In the present paper, an alternative approach to RPN is proposed for the criticality assessment of system failure modes. Particularly, the Multi-Criteria Decision Making (MCDM) method ELECTRE I is proposed to select the most critical failure mode in the set of the failure modes charactering a complex system. The method has been applied to a case study previously proposed by Zammori and Gabrielli (2012).
Constructing Antidictionaries of Long Texts in Output-Sensitive Space
2021
AbstractA wordxthat is absent from a wordyis calledminimalif all its proper factors occur iny. Given a collection ofkwordsy1, … ,ykover an alphabetΣ, we are asked to compute the set$\mathrm {M}^{\ell }_{\{y_1,\ldots ,y_k\}}$M{y1,…,yk}ℓof minimal absent words of length at mostℓof the collection {y1, … ,yk}. The set$\mathrm {M}^{\ell }_{\{y_1,\ldots ,y_k\}}$M{y1,…,yk}ℓcontains all the wordsxsuch thatxis absent from all the words of the collection while there existi,j, such that the maximal proper suffix ofxis a factor ofyiand the maximal proper prefix ofxis a factor ofyj. In data compression, this corresponds to computing the antidictionary ofkdocuments. In bioinformatics, it corresponds to c…