Metalearner based on Dynamic Neural Network for Strategic Bidding in electricity Markets

The restructuring of electricity markets, conducted to increase the competition in this sector, and decrease the electricity prices, brought with it an enormous increase in the complexity of the considered mechanisms. The electricity market became a complex and unpredictable environment, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. Software tools became, therefore, essential to provide simulation and decision support capabilities, in order to potentiate the involved players’ actions. This paper presents the development of a metalearner, applied to the decision support of electricity markets’ negotiation entities. The proposed metalearner executes a dynamic artificial neural network to create its own output, taking advantage on several learning algorithms implemented in ALBidS, an adaptive learning system that provides decision support to electricity markets’ players. The proposed metalearner considers different weights for each strategy, depending on its individual quality of performance. The results of the proposed method are studied and analyzed in scenarios based on real electricity markets’ data, using MASCEM - a multi-agent electricity market simulator that simulates market players’ operation in the market.

A Data-mining-based Methodology to support MV Electricity Customers' Characterization

This paper presents an electricity medium voltage (MV) customer characterization framework supportedby knowledge discovery in database (KDD). The main idea is to identify typical load profiles (TLP) of MVconsumers and to develop a rule set for the automatic classification of new consumers. To achieve ourgoal a methodology is proposed consisting of several steps: data pre-processing; application of severalclustering algorithms to segment the daily load profiles; selection of the best partition, corresponding tothe best consumers’ segmentation, based on the assessments of several clustering validity indices; andfinally, a classification model is built based on the resulting clusters. To validate the proposed framework,a case study which includes a real database of MV consumers is performed.

Constraint-based modeling of minimum set covering: application to species differentation

Work presented in the context of the European Master in Computational Logics, as partial requisit for the graduation as Master in Computational Logics

An agent-based evolutionary approach for manufacturing system layout design

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Electrotécnica e de Computadores

The influence of task design on upper limb muscles fatigue during low-load repetitive work: A systematic review

Ergonomic interventions such as increased scheduled breaks or job rotation have been proposed to reduce upper limb muscle fatigue in repetitive low-load work. This review was performed to summarize and analyze the studies investigating the effect of job rotation and work-rest schemes, as well as, work pace, cycle time and duty cycle, on upper limb muscle fatigue. The effects of these work organization factors on subjective fatigue or discomfort were also analyzed. This review was based on relevant articles published in PubMed, Scopus and Web of Science. The studies included in this review were performed in humans and assessed muscle fatigue in upper limbs. 14 articles were included in the systematic review. Few studies were performed in a real work environment and the most common methods used to assess muscle fatigue were surface electromyography (EMG). No consistent results were found related to the effects of job rotation on muscle activity and subjective measurements of fatigue. Rest breaks had some positive effects, particularly in perceived discomfort. The increase in work pace reveals a higher muscular load in specific muscles. The duration of experiments and characteristics of participants appear to be the factors that most have influenced the results. Future research should be focused on the improvement of the experimental protocols and instrumentation, in order to the outcomes represent adequately the actual working conditions. Relevance to industry: Introducing more physical workload variation in low-load repetitive work is considered an effective ergonomic intervention against muscle fatigue and musculoskeletal disorders in industry. Results will be useful to identify the need of future research, which will eventually lead to the adoption of best industrial work practices according to the workers capabilities.

Intra-Task Device Scheduling for Real-Time Embedded Systems

An ever increasing need for extra functionality in a single embedded system demands for extra Input/Output (I/O) devices, which are usually connected externally and are expensive in terms of energy consumption. To reduce their energy consumption, these devices are equipped with power saving mechanisms. While I/O device scheduling for real-time (RT) systems with such power saving features has been studied in the past, the use of energy resources by these scheduling algorithms may be improved. Technology enhancements in the semiconductor industry have allowed the hardware vendors to reduce the device transition and energy overheads. The decrease in overhead of sleep transitions has opened new opportunities to further reduce the device energy consumption. In this research effort, we propose an intra-task device scheduling algorithm for real-time systems that wakes up a device on demand and reduces its active time while ensuring system schedulability. This intra-task device scheduling algorithm is extended for devices with multiple sleep states to further minimise the overall device energy consumption of the system. The proposed algorithms have less complexity when compared to the conservative inter-task device scheduling algorithms. The system model used relaxes some of the assumptions commonly made in the state-of-the-art that restrict their practical relevance. Apart from the aforementioned advantages, the proposed algorithms are shown to demonstrate the substantial energy savings.

Influence of dual-task on sit-to-stand-to-sit postural control in Parkinson's disease

Postural control deficits are the most disabling aspects of Parkinson's disease (PD), resulting in decreased mobility and functional independence. The aim of this study was to assess the postural control stability, revealed by variables based on the centre of pressure (CoP), in individuals with PD while performing a sit-to-stand-to-sit sequence under single- and dual-task conditions. An observational, analytical and cross-sectional study was performed. The sample consisted of 9 individuals with PD and 9 healthy controls. A force platform was used to measure the CoP displacement and velocity during the sit-to-stand-to-sit sequence. The results were statistically analysed. Individuals with PD required greater durations for the sit-to-stand-to-sit sequence than the controls (p < 0.05). The anteroposterior and mediolateral CoP displacement were higher in the individuals with PD (p < 0.05). However, only the anteroposterior CoP velocity in the stand-to-sit phase (p = 0.006) was lower in the same individuals. Comparing the single- and dual-task conditions in both groups, the duration, the anteroposterior CoP displacement and velocity were higher in the dual-task condition (p < 0.05). The individuals with PD presented reduced postural control stability during the sit-to-stand-to-sit sequence, especially when under the dual-task condition. These individuals have deficits not only in motor performance, but also in cognitive performance when performing the sit-to-stand-to-sit sequence in their daily life tasks. Moreover, both deficits tend to be intensified when two tasks are performed simultaneously.

Standing balance in individuals with Parkinson's disease during single and dual-task conditions

This study aimed to examine the differences in standing balance between individuals with Parkinson's disease (PD) and subjects without PD (control group), under single and dual-task conditions. A cross-sectional study was designed using a non-probabilistic sample of 110 individuals (50 participants with PD and 60 controls) aged 50 years old and over. The individuals with PD were in the early or middle stages of the disease (characterized by Hoehn and Yahr as stages 1-3). The standing balance was assessed by measuring the centre of pressure (CoP) displacement in single-task (eyes-open/eyes-closed) and dual-task (while performing two different verbal fluency tasks). No significant differences were found between the groups regarding sociodemographic variables. In general, the standing balance of the individuals with PD was worse than the controls, as the CoP displacement across tasks was significantly higher for the individuals with PD (p<0.01), both in anteroposterior and mediolateral directions. Moreover, there were significant differences in the CoP displacement based parameters between the conditions, mainly between the eyes-open condition and the remaining conditions. However, there was no significant interaction found between group and condition, which suggests that changes in the CoP displacement between tasks were not influenced by having PD. In conclusion, this study shows that, although individuals with PD had a worse overall standing balance than individuals without the disease, the impact of performing an additional task on the CoP displacement is similar for both groups.

Manoeuvre Based Mission Control System for an Autonomous Surface Vehicle

In this work the mission control and supervision system developed for the ROAZ Autonomous Surface Vehicle is presented. Complexity in mission requirements coupled with flexibility lead to the design of a modular hierarchical mission control system based on hybrid systems control. Monitoring and supervision control for a vehicle such as ROAZ mission is not an easy task using tools with low complexity and yet powerful enough. A set of tools were developed to perform both on board mission control and remote planning and supervision. “ROAZ- Mission Control” was developed to be used in support to bathymetric and security missions performed in river and at seas.

Task partitioning and priority assignment for distributed hard real-time systems

In this paper, we propose the Distributed using Optimal Priority Assignment (DOPA) heuristic that finds a feasible partitioning and priority assignment for distributed applications based on the linear transactional model. DOPA partitions the tasks and messages in the distributed system, and makes use of the Optimal Priority Assignment (OPA) algorithm known as Audsley’s algorithm, to find the priorities for that partition. The experimental results show how the use of the OPA algorithm increases in average the number of schedulable tasks and messages in a distributed system when compared to the use of Deadline Monotonic (DM) usually favoured in other works. Afterwards, we extend these results to the assignment of Parallel/Distributed applications and present a second heuristic named Parallel-DOPA (P-DOPA). In that case, we show how the partitioning process can be simplified by using the Distributed Stretch Transformation (DST), a parallel transaction transformation algorithm introduced in [1].

Groundtruth system for underwater benchmarking

Oceans - San Diego, 2013

Non-preemptive and SRP-based fullypreemptive scheduling of real-time Software Transactional Memory

Recent embedded processor architectures containing multiple heterogeneous cores and non-coherent caches renewed attention to the use of Software Transactional Memory (STM) as a building block for developing parallel applications. STM promises to ease concurrent and parallel software development, but relies on the possibility of abort conflicting transactions to maintain data consistency, which in turns affects the execution time of tasks carrying transactions. Because of this fact the timing behaviour of the task set may not be predictable, thus it is crucial to limit the execution time overheads resulting from aborts. In this paper we formalise a FIFO-based algorithm to order the sequence of commits of concurrent transactions. Then, we propose and evaluate two non-preemptive and one SRP-based fully-preemptive scheduling strategies, in order to avoid transaction starvation.

A Clinical Support System Based on Quality of Life Estimation

Quality of life is a concept influenced by social, economic, psychological, spiritual or medical state factors. More specifically, the perceived quality of an individual's daily life is an assessment of their well-being or lack of it. In this context, information technologies may help on the management of services for healthcare of chronic patients such as estimating the patient quality of life and helping the medical staff to take appropriate measures to increase each patient quality of life. This paper describes a Quality of Life estimation system developed using information technologies and the application of data mining algorithms to access the information of clinical data of patients with cancer from Otorhinolaryngology and Head and Neck services of an oncology institution. The system was evaluated with a sample composed of 3013 patients. The results achieved show that there are variables that may be significant predictors for the Quality of Life of the patient: years of smoking (p value 0.049) and size of the tumor (p value < 0.001). In order to assign the variables to the classification of the quality of life the best accuracy was obtained by applying the John Platt's sequential minimal optimization algorithm for training a support vector classifier. In conclusion data mining techniques allow having access to patients additional information helping the physicians to be able to know the quality of life and produce a well-informed clinical decision.

Tuning metadata for better movie content-based recommendation systems

The increasing number of television channels, on-demand services and online content, is expected to contribute to a better quality of experience for a costumer of such a service. However, the lack of efficient methods for finding the right content, adapted to personal interests, may lead to a progressive loss of clients. In such a scenario, recommendation systems are seen as a tool that can fill this gap and contribute to the loyalty of users. Multimedia content, namely films and television programmes are usually described using a set of metadata elements that include the title, a genre, the date of production, and the list of directors and actors. This paper provides a deep study on how the use of different metadata elements can contribute to increase the quality of the recommendations suggested. The analysis is conducted using Netflix and Movielens datasets and aspects such as the granularity of the descriptions, the accuracy metric used and the sparsity of the data are taken into account. Comparisons with collaborative approaches are also presented.

SHõWA: A Self-healing Framework for Web-based Applications

The complexity of systems is considered an obstacle to the progress of the IT industry. Autonomic computing is presented as the alternative to cope with the growing complexity. It is a holistic approach, in which the systems are able to configure, heal, optimize, and protect by themselves. Web-based applications are an example of systems where the complexity is high. The number of components, their interoperability, and workload variations are factors that may lead to performance failures or unavailability scenarios. The occurrence of these scenarios affects the revenue and reputation of businesses that rely on these types of applications. In this article, we present a self-healing framework for Web-based applications (SHõWA). SHõWA is composed by several modules, which monitor the application, analyze the data to detect and pinpoint anomalies, and execute recovery actions autonomously. The monitoring is done by a small aspect-oriented programming agent. This agent does not require changes to the application source code and includes adaptive and selective algorithms to regulate the level of monitoring. The anomalies are detected and pinpointed by means of statistical correlation. The data analysis detects changes in the server response time and analyzes if those changes are correlated with the workload or are due to a performance anomaly. In the presence of per- formance anomalies, the data analysis pinpoints the anomaly. Upon the pinpointing of anomalies, SHõWA executes a recovery procedure. We also present a study about the detection and localization of anomalies, the accuracy of the data analysis, and the performance impact induced by SHõWA. Two benchmarking applications, exercised through dynamic workloads, and different types of anomaly were considered in the study. The results reveal that (1) the capacity of SHõWA to detect and pinpoint anomalies while the number of end users affected is low; (2) SHõWA was able to detect anomalies without raising any false alarm; and (3) SHõWA does not induce a significant performance overhead (throughput was affected in less than 1%, and the response time delay was no more than 2 milliseconds).