2019
Authors
Faia R.; Pinto T.; Vale Z.; Corchado J.M.;
Publication
Energy Informatics
Abstract
In many large-scale and time-consuming problems, the application of metaheuristics becomes essential, since these methods enable achieving very close solutions to the exact one in a much shorter time. In this work, we address the problem of portfolio optimization applied to electricity markets negotiation. As in a market environment, decision-making is carried out in very short times, the application of the metaheuristics is necessary. This work proposes a Hybrid model, combining a simplified exact resolution of the method, as a means to obtain the initial solution for a Particle Swarm Optimization (PSO) approach. Results show that the presented approach is able to obtain better results in the metaheuristic search process.
2019
Authors
Nobre, R; Reis, L; Cardoso, JMP;
Publication
EURO-PAR 2018: PARALLEL PROCESSING WORKSHOPS
Abstract
Iterative compilation focused on specialized phase orders (i.e., custom selections of compiler passes and orderings for each program or function) can significantly improve the performance of compiled code. However, phase ordering specialization typically needs to deal with large solution space. A previous approach, evaluated by targeting an x86 CPU, mitigates this issue by first using a training phase on reference codes to produce a small set of high-quality reusable phase orders. This approach then uses these phase orders to compile new codes, without any code analysis. In this paper, we evaluate the viability of using this approach to optimize the GPU execution performance of OpenCL kernels. In addition, we propose and evaluate the use of a heuristic to further reduce the number of evaluated phase orders, by comparing the speedups of the resulting binaries with those of the training phase for each phase order. This information is used to predict which untested phase order is most likely to produce good results (e.g., highest speedup). We performed our measurements using the PolyBench/GPU OpenCL benchmark suite on an NVIDIA Pascal GPU. Without heuristics, we can achieve a geomean execution speedup of 1.64x, using cross-validation, with 5 non-standard phase orders. With the heuristic, we can achieve the same speedup with only 3 non-standard phase orders. This is close to the geomean speedup achieved in our iterative compilation experiments exploring thousands of phase orders. Given the significant reduction in exploration time and other advantages of this approach, we believe that it is suitable for a wide range of compiler users concerned with performance.
2019
Authors
Ganesan, K; Saraiva, JT; Bessa, RJ;
Publication
ENERGIES
Abstract
Providing a price tariff that matches the randomized behavior of residential consumers is one of the major barriers to demand response (DR) implementation. The current trend of DR products provided by aggregators or retailers are not consumer-specific, which poses additional barriers for the engagement of consumers in these programs. In order to address this issue, this paper describes a methodology based on causality inference between DR tariffs and observed residential electricity consumption to estimate consumers' consumption elasticity. It determines the flexibility of each client under the considered DR program and identifies whether the tariffs offered by the DR program affect the consumers' usual consumption or not. The aim of this approach is to aid aggregators and retailers to better tune DR offers to consumer needs and so to enlarge the response rate to their DR programs. We identify a set of critical clients who actively participate in DR events along with the most responsive and least responsive clients for the considered DR program. We find that the percentage of DR consumers who actively participate seem to be much less than expected by retailers, indicating that not all consumers' elasticity is effectively utilized.
2019
Authors
Silva, W; Castro, E; Cardoso, MJ; Fitzal, F; Cardoso, JS;
Publication
2019 IEEE 16TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2019)
Abstract
Breast cancer high survival rate led to an increased interest in the quality of life after treatment, particularly regarding the aesthetic outcome. Currently used aesthetic assessment methods are subjective, which make reproducibility and impartiality impossible. To create an objective method capable of being selected as the gold standard, it is fundamental to detect, in a completely automatic manner, keypoints in photographs of women's torso after being subjected to breast cancer surgeries. This paper proposes a deep and a hybrid model to detect keypoints with high accuracy. Our methods are tested on two datasets, one composed of images with a clean and consistent background and a second one that contains photographs taken under poor lighting and background conditions. The proposed methods represent an improvement in the detection of endpoints, nipples and breast contour for both datasets in terms of average error distance when compared with the current state-of-the-art.
2019
Authors
Fujiwara, E; Hayashi, JG; Delfino, TD; Jorge, PAS; de Barros Cordeiro, CMD;
Publication
IEEE SENSORS JOURNAL
Abstract
An optical fiber anemometer based on a flexible multi-FBG curvature sensor is reported. The probe is comprised of a structured polymer shell with embedded single-mode fibers with written fiber Bragg gratings. When the sensor is bent, the different spectral shift of the Bragg wavelengths allows the determination of the mechanical stimulus. Moreover, the probe was also used as a cantilever sensor for assessing the airflow speed in a wind tunnel. The sensor presented sensitivities of 0.8 nm/m(-1) and 1.05 pm/(m/s) for curvature and square speed measurements, respectively, and the sensing characteristics can be improved by simply changing the material and the geometry of the bulk polymer shell, providing a versatile and feasible probe for the mechanical and flow measurements.
2019
Authors
Amorim, VA; Maia, JM; Viveiros, D; Marques, PVS;
Publication
EPJ Web of Conferences
Abstract
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