36,155 research outputs found
Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants
Within the field of soft computing, intelligent optimization modelling techniques include
various major techniques in artificial intelligence. These techniques pretend to generate new business
knowledge transforming sets of "raw data" into business value. One of the principal applications of
these techniques is related to the design of predictive analytics for the improvement of advanced
CBM (condition-based maintenance) strategies and energy production forecasting. These advanced
techniques can be used to transform control system data, operational data and maintenance event data
to failure diagnostic and prognostic knowledge and, ultimately, to derive expected energy generation.
One of the systems where these techniques can be applied with massive potential impact are the
legacy monitoring systems existing in solar PV energy generation plants. These systems produce a
great amount of data over time, while at the same time they demand an important e ort in order to
increase their performance through the use of more accurate predictive analytics to reduce production
losses having a direct impact on ROI. How to choose the most suitable techniques to apply is one of
the problems to address. This paper presents a review and a comparative analysis of six intelligent
optimization modelling techniques, which have been applied on a PV plant case study, using the
energy production forecast as the decision variable. The methodology proposed not only pretends
to elicit the most accurate solution but also validates the results, in comparison with the di erent
outputs for the di erent techniques
Sensor networks security based on sensitive robots agents. A conceptual model
Multi-agent systems are currently applied to solve complex problems. The
security of networks is an eloquent example of a complex and difficult problem.
A new model-concept Hybrid Sensitive Robot Metaheuristic for Intrusion
Detection is introduced in the current paper. The proposed technique could be
used with machine learning based intrusion detection techniques. The new model
uses the reaction of virtual sensitive robots to different stigmergic variables
in order to keep the tracks of the intruders when securing a sensor network.Comment: 5 page
Natural language processing
Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems
Using Information Filtering in Web Data Mining Process
Web service-oriented Grid is becoming a standard for achieving loosely coupled distributed computing. Grid services could easily be specified with web-service based interfaces. In this paper we first envisage a realistic Grid market with players such as end-users, brokers and service providers participating co-operatively with an aim to meet requirements and earn profit. End-users wish to use functionality of Grid services by paying the minimum possible price or price confined within a specified budget, brokers aim to maximise profit whilst establishing a SLA (Service Level Agreement) and satisfying end-user needs and at the same time resisting the volatility of service execution time and availability. Service providers aim to develop price models based on end-user or broker demands that will maximise their profit. In this paper we focus on developing stochastic approaches to end-user workflow scheduling that provides QoS guarantees by establishing a SLA. We also develop a novel 2-stage stochastic programming technique that aims at establishing a SLA with end-users regarding satisfying their workflow QoS requirements. We develop a scheduling (workload allocation) technique based on linear programming that embeds the negotiated workflow QoS into the program and model Grid services as generalised queues. This technique is shown to outperform existing scheduling techniques that don't rely on real-time performance information
Constraint-based Sequential Pattern Mining with Decision Diagrams
Constrained sequential pattern mining aims at identifying frequent patterns
on a sequential database of items while observing constraints defined over the
item attributes. We introduce novel techniques for constraint-based sequential
pattern mining that rely on a multi-valued decision diagram representation of
the database. Specifically, our representation can accommodate multiple item
attributes and various constraint types, including a number of non-monotone
constraints. To evaluate the applicability of our approach, we develop an
MDD-based prefix-projection algorithm and compare its performance against a
typical generate-and-check variant, as well as a state-of-the-art
constraint-based sequential pattern mining algorithm. Results show that our
approach is competitive with or superior to these other methods in terms of
scalability and efficiency.Comment: AAAI201
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