849 resultados para Applied artificial intelligence


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The control of the right application of medical protocols is a key issue in hospital environments. For the automated monitoring of medical protocols, we need a domain-independent language for their representation and a fully, or semi, autonomous system that understands the protocols and supervises their application. In this paper we describe a specification language and a multi-agent system architecture for monitoring medical protocols. We model medical services in hospital environments as specialized domain agents and interpret a medical protocol as a negotiation process between agents. A medical service can be involved in multiple medical protocols, and so specialized domain agents are independent of negotiation processes and autonomous system agents perform monitoring tasks. We present the detailed architecture of the system agents and of an important domain agent, the database broker agent, that is responsible of obtaining relevant information about the clinical history of patients. We also describe how we tackle the problems of privacy, integrity and authentication during the process of exchanging information between agents.

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Random problem distributions have played a key role in the study and design of algorithms for constraint satisfaction and Boolean satisfiability, as well as in ourunderstanding of problem hardness, beyond standard worst-case complexity. We consider random problem distributions from a highly structured problem domain that generalizes the Quasigroup Completion problem (QCP) and Quasigroup with Holes (QWH), a widely used domain that captures the structure underlying a range of real-world applications. Our problem domain is also a generalization of the well-known Sudoku puz- zle: we consider Sudoku instances of arbitrary order, with the additional generalization that the block regions can have rectangular shape, in addition to the standard square shape. We evaluate the computational hardness of Generalized Sudoku instances, for different parameter settings. Our experimental hardness results show that we can generate instances that are considerably harder than QCP/QWH instances of the same size. More interestingly, we show the impact of different balancing strategies on problem hardness. We also provide insights into backbone variables in Generalized Sudoku instances and how they correlate to problem hardness.

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Tractable cases of the binary CSP are mainly divided in two classes: constraint language restrictions and constraint graph restrictions. To better understand and identify the hardest binary CSPs, in this work we propose methods to increase their hardness by increasing the balance of both the constraint language and the constraint graph. The balance of a constraint is increased by maximizing the number of domain elements with the same number of occurrences. The balance of the graph is defined using the classical definition from graph the- ory. In this sense we present two graph models; a first graph model that increases the balance of a graph maximizing the number of vertices with the same degree, and a second one that additionally increases the girth of the graph, because a high girth implies a high treewidth, an important parameter for binary CSPs hardness. Our results show that our more balanced graph models and constraints result in harder instances when compared to typical random binary CSP instances, by several orders of magnitude. Also we detect, at least for sparse constraint graphs, a higher treewidth for our graph models.

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In this paper we provide a new method to generate hard k-SAT instances. We incrementally construct a high girth bipartite incidence graph of the k-SAT instance. Having high girth assures high expansion for the graph, and high expansion implies high resolution width. We have extended this approach to generate hard n-ary CSP instances and we have also adapted this idea to increase the expansion of the system of linear equations used to generate XORSAT instances, being able to produce harder satisfiable instances than former generators.

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Recently, edge matching puzzles, an NP-complete problem, have received, thanks to money-prized contests, considerable attention from wide audiences. We consider these competitions not only a challenge for SAT/CSP solving techniques but also as an opportunity to showcase the advances in the SAT/CSP community to a general audience. This paper studies the NP-complete problem of edge matching puzzles focusing on providing generation models of problem instances of variable hardness and on its resolution through the application of SAT and CSP techniques. From the generation side, we also identify the phase transition phenomena for each model. As solving methods, we employ both; SAT solvers through the translation to a SAT formula, and two ad-hoc CSP solvers we have developed, with different levels of consistency, employing several generic and specialized heuristics. Finally, we conducted an extensive experimental investigation to identify the hardest generation models and the best performing solving techniques.

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Työn tavoitteena oli uudentyyppisen hiontaprosessin ohjausjärjestelmän kehittäminen ja testaaminen UPM-Kymmene Kaukaan hiomossa. Uuden ohjausjärjestelmän perusajatuksena oli pitää yksittäistä hiomakiveä jatkuvasti optimaalisessa toimintapisteessä, ja hiomon kaikilla koneilla pyrittiin samaan kivenalusmassan laatuun. Uutta ohjaustapaa kutsuttiin Optimum operating point -strategiaksi (OOPS). Hiomakiven pitäminen optimaalisessa toimintapisteessä tapahtui pääosin vesiteräyskäsittelyllä, jonka intensiteettiä ohjasi asiantuntijajärjestelmä (AI-järjestelmä). Lisäksi testattiin ohjelmoidun anturan nopeussäädön vaikutusta hiomakoneen resurssien käyttöön. AI-järjestelmä päätteli vesiteräyskäsittelyn tarpeellisuuden CSF-mallin ja hiomakoneen resurssien perusteella. Seurannasta saatujen tulosten perusteella AI-järjestelmän käyttöönotto vesiteräyskäsittelyssä paransi tuotannon ja massan laadun tasaisuutta. Hiomakoneiden resurssien havaittiin pienenevän puunsyöttölinjan mukaisesti. Paksummat pöllit kerääntyvät linjan päähän, jolloin varsinkin hydrauliikkapaineiden tarve lisääntyy linjan päässä olevilla hiomakoneilla. Hiomakoneen resurssit saatiin paremmin käyttöön kuormittamalla konetta ohjelmoidulla anturan nopeussäädöllä (ONS) kuin vakioidulla anturan nopeussäädöllä (VNS). Kuitenkin hydraulipaineresurssien puutteellisuus rajoitti koeajon aikana ONS:n toimintaa. Resursseja ei optimoitu koeajon aikana, koska kiven pinnan haluttiin pysyvän mahdollisimman stabiilina. Kivelle ei suoritettu mekaanista käsittelyä, vaikka kivenpinnan massan kuljetuskapasiteetin havaittiin olevan huono. AI-järjestelmä otettiin ohjaamaan vesiteräyskäsittelyä vasta ONS - VNS -koeajon jälkeen. Mekaanisen rullateräyksen jälkeen massan ominaisuudet muuttuivat, koska kiven pinnan terävät särmät katkoivat kuituja alentaen massan sitoutumiskykyä. Heti rullakäsittelyn jälkeen mitattu CSF saattoi jopa alentua huomattavasti, mutta AI-järjestelmän laskema CSF nousi selvästi indikoiden energian ominaiskulutuksen (EOK) laskua. Muutaman päivän hionnan jälkeen mitattu ja laskettu CSF saavuttivat saman tason.

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The purpose of the research is to define practical profit which can be achieved using neural network methods as a prediction instrument. The thesis investigates the ability of neural networks to forecast future events. This capability is checked on the example of price prediction during intraday trading on stock market. The executed experiments show predictions of average 1, 2, 5 and 10 minutes’ prices based on data of one day and made by two different types of forecasting systems. These systems are based on the recurrent neural networks and back propagation neural nets. The precision of the predictions is controlled by the absolute error and the error of market direction. The economical effectiveness is estimated by a special trading system. In conclusion, the best structures of neural nets are tested with data of 31 days’ interval. The best results of the average percent of profit from one transaction (buying + selling) are 0.06668654, 0.188299453, 0.349854787 and 0.453178626, they were achieved for prediction periods 1, 2, 5 and 10 minutes. The investigation can be interesting for the investors who have access to a fast information channel with a possibility of every-minute data refreshment.

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We tested and compared performances of Roach formula, Partin tables and of three Machine Learning (ML) based algorithms based on decision trees in identifying N+ prostate cancer (PC). 1,555 cN0 and 50 cN+ PC were analyzed. Results were also verified on an independent population of 204 operated cN0 patients, with a known pN status (187 pN0, 17 pN1 patients). ML performed better, also when tested on the surgical population, with accuracy, specificity, and sensitivity ranging between 48-86%, 35-91%, and 17-79%, respectively. ML potentially allows better prediction of the nodal status of PC, potentially allowing a better tailoring of pelvic irradiation.

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Aquest projecte s'ha desenvolupat dins de l'àrea de visió per computadors, mitjançant el reconeixement d'un patró podem definir tres eixos que conformen un espai tridimensional on hem implementat un videojoc de combats entre robots a sobre d'un entorn real.

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L'Hexadom és un joc de taula, de creació pròpia, inspirat en el dòmino tradicional, amb el que hi comparteix l'objectiu: jugar totes les fitxes (o hexàgons en el cas de l'Hexadom). Però hi ha una diferència substancial: la seva complexitat; ja que a diferència del joc original -on s'uneixen els extrems de dues fitxes que tinguin el mateix número- a l'Hexadom s'han d'unir hexàgons entre si, com a mínim per dos dels seus costats, mantenint la coherència entre els colors.

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One of the major interests in soil analysis is the evaluation of its chemical, physical and biological parameters, which are indicators of soil quality (the most important is the organic matter). Besides there is a great interest in the study of humic substances and on the assessment of pollutants, such as pesticides and heavy metals, in soils. Chemometrics is a powerful tool to deal with these problems and can help soil researchers to extract much more information from their data. In spite of this, the presence of these kinds of strategies in the literature has obtained projection only recently. The utilization of chemometric methods in soil analysis is evaluated in this article. The applications will be divided in four parts (with emphasis in the first two): (i) descriptive and exploratory methods based on Principal Component Analysis (PCA); (ii) multivariate calibration methods (MLR, PCR and PLS); (iii) methods such as Evolving Factor Analysis and SIMPLISMA; and (iv) artificial intelligence methods, such as Artificial Neural Networks.

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L'objectiu principal d'aquest projecte és dissenyar un algorisme de hashing que ens permeti, donat un conjunt d'hiperplans separadors, reduïr-ne el nombre, minimitzant la pèrdua d'eficiència en la classificació.

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The development of new tools for chemoinformatics, allied to the use of different algorithms and computer programmes for structure elucidation of organic compounds, is growing fast worldwide. Massive efforts in research and development are currently being pursued both by academia and the so-called chemistry software development companies. The demystification of this environment provoked by the availability of software packages and a vast array of publications exert a positive impact on chemistry. In this work, an overview concerning the more classical approaches as well as new strategies on computer-based tools for structure elucidation of organic compounds is presented. Historical background is also taken into account since these techniques began to develop around four decades ago. Attention will be paid to companies which develop, distribute or commercialize software as well as web-based and open access tools which are currently available to chemists.

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Des dels inicis dels ordinadors com a màquines programables, l’home ha intentat dotar-los de certa intel•ligència per tal de pensar o raonar el més semblant possible als humans. Un d’aquests intents ha sigut fer que la màquina sigui capaç de pensar de tal manera que estudiï jugades i guanyi partides d’escacs. En l’actualitat amb els actuals sistemes multi tasca, orientat a objectes i accés a memòria i gràcies al potent hardware del que disposem, comptem amb una gran varietat de programes que es dediquen a jugar a escacs. Però no hi ha només programes petits, hi ha fins i tot màquines senceres dedicades a calcular i estudiar jugades per tal de guanyar als millors jugadors del món. L’objectiu del meu treball és dur a terme un estudi i implementació d’un d’aquests programes, per això es divideix en dues parts. La part teòrica o de l’estudi, consta d’un estudi dels sistemes d’intel•ligència artificial que es dediquen a jugar a escacs, estudi i cerca d’una funció d’avaluació vàlida i estudi dels algorismes de cerca. La part pràctica del treball es basa en la implementació d’un sistema intel•ligent capaç de jugar a escacs amb certa lògica. Aquesta implementació es porta a terme amb l’ajuda de les llibreries SDL, utilitzant l’algorisme minimax amb poda alfa-beta i codi c++. Com a conclusió del projecte m’agradaria remarcar que l’estudi realitzat m’ha deixat veure que crear un joc d’escacs no era tan fàcil com jo pensava però m’ha aportat la satisfacció d’aplicar tot el que he après durant la carrera i de descobrir moltes altres coses noves.