Training control centers' operators in incident diagnosis and power restoration using intelligent tutoring systems

The activity of Control Center operators is important to guarantee the effective performance of Power Systems. Operators’ actions are crucial to deal with incidents, especially severe faults like blackouts. In this paper, we present an Intelligent Tutoring approach for training Portuguese Control Center operators in tasks like incident analysis and diagnosis, and service restoration of Power Systems. Intelligent Tutoring System (ITS) approach is used in the training of the operators, having into account context awareness and the unobtrusive integration in the working environment. Several Artificial Intelligence techniques were criteriously used and combined together to obtain an effective Intelligent Tutoring environment, namely Multiagent Systems, Neural Networks, Constraint-based Modeling, Intelligent Planning, Knowledge Representation, Expert Systems, User Modeling, and Intelligent User Interfaces.

E-Learning as a shared service in shared services centers

Book Subtitle International Conference, CENTERIS 2010, Viana do Castelo, Portugal, October 20-22, 2010, Proceedings, Part II

Characterization of middle-ear condition of Oporto daycare children up-to 3 years-old: A cross sectional study

Objective The aim of this study was to determine tympanometric values of children who attend Oporto daycare centers and further analyze any relations with host and environmental factors. Methods Cross sectional study in a randomly selected sample of 117 daycare children up-to 3-years old from Oporto. Tympanometric measures were collected. Results Children presented in left ear (LE) a mean peak pressure (PP) of −156.53 daPa and a mean compliance of 0.16 cm3. Right ear (RE) revealed a PP of −145.61 daPa and a compliance of 0.19 cm3. Normal tympanograms (type A) had a lower frequency than abnormal tympanograms (type B and type C). There was a positive association between age and compliance (LE: p = 0.016; RE: p = 0.013) and between the presence of rhinorrhea and PP (LE: p = 0.002; RE: p < 0.05). Abnormal tympanograms were more frequent in Spring (RE: p = 0.009), in younger children (LE: p = 0.03) and in children that had rhinorrhea (LE: p = 0.002; RE: p = 0.044). Healthy children had a mean PP of −125.19 daPa and a mean compliance of 0.21 cm3 in LE and a mean PP of −144.27 daPa and a mean compliance of 0.22 cm3 in RE. Conclusion Tympanometric measures presented in this paper may be applicable to Oporto daycare children up-to 3 years-old. Most of daycare children revealed abnormal tympanograms. Age, rhinorrhea and season influenced children's middle-ear condition.

Deteção e extinção de incêndios em Data Centers

São muitas as organizações que por todo o mundo possuem instalações deste tipo, em Portugal temos o exemplo da Portugal Telecom que recentemente inaugurou o seu Data Center na Covilhã. O desenvolvimento de um Data Center exige assim um projeto muito cuidado, o qual entre outros aspetos deverá garantir a segurança da informação e das próprias instalações, nomeadamente no que se refere à segurança contra incêndio.

A microscope for the data center

Nowadays, data centers are large energy consumers and the trend for next years is expected to increase further, considering the growth in the order of cloud services. A large portion of this power consumption is due to the control of physical parameters of the data center (such as temperature and humidity). However, these physical parameters are tightly coupled with computations, and even more so in upcoming data centers, where the location of workloads can vary substantially due, for example, to workloads being moved in the cloud infrastructure hosted in the data center. Therefore, managing the physical and compute infrastructure of a large data center is an embodiment of a Cyber-Physical System (CPS). In this paper, we describe a data collection and distribution architecture that enables gathering physical parameters of a large data center at a very high temporal and spatial resolution of the sensor measurements. We think this is an important characteristic to enable more accurate heat-flow models of the data center and with them, find opportunities to optimize energy consumptions. Having a high-resolution picture of the data center conditions, also enables minimizing local hot-spots, perform more accurate predictive maintenance (failures in all infrastructure equipments can be more promptly detected) and more accurate billing. We detail this architecture and define the structure of the underlying messaging system that is used to collect and distribute the data. Finally, we show the results of a preliminary study of a typical data center radio environment.

PIASA: A power and interference aware resource management strategy for heterogeneous workloads in cloud data centers

Cloud data centers have been progressively adopted in different scenarios, as reflected in the execution of heterogeneous applications with diverse workloads and diverse quality of service (QoS) requirements. Virtual machine (VM) technology eases resource management in physical servers and helps cloud providers achieve goals such as optimization of energy consumption. However, the performance of an application running inside a VM is not guaranteed due to the interference among co-hosted workloads sharing the same physical resources. Moreover, the different types of co-hosted applications with diverse QoS requirements as well as the dynamic behavior of the cloud makes efficient provisioning of resources even more difficult and a challenging problem in cloud data centers. In this paper, we address the problem of resource allocation within a data center that runs different types of application workloads, particularly CPU- and network-intensive applications. To address these challenges, we propose an interference- and power-aware management mechanism that combines a performance deviation estimator and a scheduling algorithm to guide the resource allocation in virtualized environments. We conduct simulations by injecting synthetic workloads whose characteristics follow the last version of the Google Cloud tracelogs. The results indicate that our performance-enforcing strategy is able to fulfill contracted SLAs of real-world environments while reducing energy costs by as much as 21%.