Search results for "computer.software_genre"
showing 10 items of 3858 documents
Hybrid Genetic Algorithms in Data Mining Applications
2009
Genetic algorithms (GAs) are a class of problem solving techniques which have been successfully applied to a wide variety of hard problems (Goldberg, 1989). In spite of conventional GAs are interesting approaches to several problems, in which they are able to obtain very good solutions, there exist cases in which the application of a conventional GA has shown poor results. Poor performance of GAs completely depends on the problem. In general, problems severely constrained or problems with difficult objective functions are hard to be optimized using GAs. Regarding the difficulty of a problem for a GA there is a well established theory. Traditionally, this has been studied for binary encoded …
Predicting Heuristic Search Performance with PageRank Centrality in Local Optima Networks
2015
Previous studies have used statistical analysis of fitness landscapes such as ruggedness and deceptiveness in order to predict the expected quality of heuristic search methods. Novel approaches for predicting the performance of heuristic search are based on the analysis of local optima networks (LONs). A LON is a compressed stochastic model of a fitness landscape's basin transitions. Recent literature has suggested using various LON network measurements as predictors for local search performance.In this study, we suggest PageRank centrality as a new measure for predicting the performance of heuristic search methods using local search. PageRank centrality is a variant of Eigenvector centrali…
Intelligent Cloud Storage Management for Layered Tiers
2018
Today, the cloud offers a large array of possibilities for storage, with this flexibility comes also complexity. This complexity stems from the variety of storage mediums, such as, blob storage or NoSQL tables, and also from the different cost tiers within these systems. A strategic thinking to navigate this complex cloud storage landscape is important, not only for cost saving but also for prioritizing information, this prioritization has wider implications in other domains such as the Big Data realm, especially for governance and efficiency. In this paper we propose a strategy centered around probabilistic graphical model (PGM), this heuristic oriented management and organizational strate…
Replacing radiative transfer models by surrogate approximations through machine learning
2015
Physically-based radiative transfer models (RTMs) help in understanding the processes occurring on the Earth's surface and their interactions with vegetation and atmosphere. However, advanced RTMs can take a long computational time, which makes them unfeasible in many real applications. To overcome this problem, it has been proposed to substitute RTMs through so-called emulators. Emulators are statistical models that approximate the functioning of RTMs. They are advantageous in real practice because of the computational efficiency and excellent accuracy and flexibility for extrapolation. We here present an ‘Emulator toolbox’ that enables analyzing three multi-output machine learning regress…
DEMAND Project: An algorithm for the assessment of the prosumers’ flexibility
2020
Demand side aggregation represents an important opportunity for ancillary services provision due to the potential that the coordinated management of distributed resources has on affecting power systems' operation. In this framework, the Aggregator has a main role and takes on different relationships with the other actors of the power system, usually becoming a mediator between the prosumers and the distribution system operator. The DEMAND project introduces a new point of view in demand side aggregation by proposing a new framework where the Aggregator is no more needed and prosumers can share and combine their flexibility in a new aggregation platform called Virtual Aggregation Environment…
Factorial graphical models for dynamic networks
2015
AbstractDynamic network models describe many important scientific processes, from cell biology and epidemiology to sociology and finance. Estimating dynamic networks from noisy time series data is a difficult task since the number of components involved in the system is very large. As a result, the number of parameters to be estimated is typically larger than the number of observations. However, a characteristic of many real life networks is that they are sparse. For example, the molecular structure of genes make interactions with other components a highly-structured and, therefore, a sparse process. Until now, the literature has focused on static networks, which lack specific temporal inte…
DeCyMo: Decentralized Cyber-physical System for Monitoring and Controlling Industries and Homes
2018
The recent revolution of the Internet of Things has given the birth of a series of new technologies and cyber-physical systems to be used in industrial and home scenarios. Cyber- physical systems include physical and software components for providing smart monitoring and control with flexibility and adaptability to the operating context. The IoT paradigm enables the intertwined use of physical and software components through the interconnection of devices that exchange data with each other without direct human interaction in several fields, especially in industrial and home environments. We propose DeCyMo, a decentralized architecture that aims at solving common IoT issues and vulnerabiliti…
Obstacle Detection in an Unstructured Industrial Robotic System: Comparison of Hidden Markov Model and Expert System
2012
Abstract This paper presents a comparison of two approaches for detecting unknown obstacles inside the workspace of an industrial robot using a laser rangefinder for 2-D measurements. The two approaches are based on Expert System (ES) and Hidden Markov Model (HMM). The results presented in the paper demonstrate that both approaches are able to correctly detect and classify unknown objects. The ES is characterised by low computational requirements and an easy setup when relatively few known objects are to be included inside the workspace. HMMs are characterised by a higher flexibility and the ability to handle a larger amount of known objects inside the workspace. Another significant benefit…
SmartResource Platform and Semantic Agent Programming Language (S-APL)
2007
Although the flexibility of agent interactions has many advantages when it comes to engineering a complex system, the downside is that it leads to certain unpredictability of the run-time system. Literature sketches two major directions for search for a solution: social-level characterization of agent systems and ontological approaches to inter-agent coordination. Especially the latter direction is not yet studied much by the scientific community. This paper describes our vision and the present state of the SmartResource Platform. The main distinctive features of the platform are externalization of behavior prescriptions, i.e. agents access them from organizational repositories, and utiliza…
A methodology to generate a synergetic land-cover map by fusion of different land-cover products
2012
Abstract The main goal of this study is to develop a general framework for building a hybrid land-cover map by the synergistic combination of a number of land-cover classifications with different legends and spatial resolutions. The proposed approach assesses class-specific accuracies of datasets and establishes affinity between thematic legends using a common land-cover language such as the UN Land-Cover Classification System (LCCS). The approach is illustrated over a large region in Europe using four land-cover datasets (CORINE, GLC2000, MODIS and GlobCover), but it can be applied to any set of existing products. The multi-classification map is expected to improve the performance of indiv…