Cost allocation model for distribution networks considering high penetration of distributed energy resources

The high penetration of distributed energy resources (DER) in distribution networks and the competitive environment of electricity markets impose the use of new approaches in several domains. The network cost allocation, traditionally used in transmission networks, should be adapted and used in the distribution networks considering the specifications of the connected resources. The main goal is to develop a fairer methodology trying to distribute the distribution network use costs to all players which are using the network in each period. In this paper, a model considering different type of costs (fixed, losses, and congestion costs) is proposed comprising the use of a large set of DER, namely distributed generation (DG), demand response (DR) of direct load control type, energy storage systems (ESS), and electric vehicles with capability of discharging energy to the network, which is known as vehicle-to-grid (V2G). The proposed model includes three distinct phases of operation. The first phase of the model consists in an economic dispatch based on an AC optimal power flow (AC-OPF); in the second phase Kirschen's and Bialek's tracing algorithms are used and compared to evaluate the impact of each resource in the network. Finally, the MW-mile method is used in the third phase of the proposed model. A distribution network of 33 buses with large penetration of DER is used to illustrate the application of the proposed model.

Trace Elements in Ambient Air at Porto Metropolitan Area—Checking for Compliance with New European Union (Eu) Air Quality Standards

Because of the scientific evidence showing that arsenic (As), cadmium (Cd), and nickel (Ni) are human genotoxic carcinogens, the European Union (EU) recently set target values for metal concentration in ambient air (As: 6 ng/m3, Cd: 5 ng/m3, Ni: 20 ng/m3). The aim of our study was to determine the concentration levels of these trace elements in Porto Metropolitan Area (PMA) in order to assess whether compliance was occurring with these new EU air quality standards. Fine (PM2.5) and inhalable (PM10) air particles were collected from October 2011 to July 2012 at two different (urban and suburban) locations in PMA. Samples were analyzed for trace elements content by inductively coupled plasma–mass spectrometry (ICP-MS). The study focused on determination of differences in trace elements concentration between the two sites, and between PM2.5 and PM10, in order to gather information regarding emission sources. Except for chromium (Cr), the concentration of all trace elements was higher at the urban site. However, results for As, Cd, Ni, and lead (Pb) were well below the EU limit/target values (As: 1.49 ± 0.71 ng/m3; Cd: 1.67 ± 0.92 ng/m3; Ni: 3.43 ± 3.23 ng/m3; Pb: 17.1 ± 10.1 ng/m3) in the worst-case scenario. Arsenic, Cd, Ni, Pb, antimony (Sb), selenium (Se), vanadium (V), and zinc (Zn) were predominantly associated to PM2.5, indicating that anthropogenic sources such as industry and road traffic are the main source of these elements. High enrichment factors (EF > 100) were obtained for As, Cd, Pb, Sb, Se, and Zn, further confirming their anthropogenic origin.

Adaptive modeling and high quality spectral estimation for speech enhancement

In this work an adaptive modeling and spectral estimation scheme based on a dual Discrete Kalman Filtering (DKF) is proposed for speech enhancement. Both speech and noise signals are modeled by an autoregressive structure which provides an underlying time frame dependency and improves time-frequency resolution. The model parameters are arranged to obtain a combined state-space model and are also used to calculate instantaneous power spectral density estimates. The speech enhancement is performed by a dual discrete Kalman filter that simultaneously gives estimates for the models and the signals. This approach is particularly useful as a pre-processing module for parametric based speech recognition systems that rely on spectral time dependent models. The system performance has been evaluated by a set of human listeners and by spectral distances. In both cases the use of this pre-processing module has led to improved results.

Adaptive filtering for high quality HMM based speech synthesis

In this work an adaptive filtering scheme based on a dual Discrete Kalman Filtering (DKF) is proposed for Hidden Markov Model (HMM) based speech synthesis quality enhancement. The objective is to improve signal smoothness across HMMs and their related states and to reduce artifacts due to acoustic model's limitations. Both speech and artifacts are modelled by an autoregressive structure which provides an underlying time frame dependency and improves time-frequency resolution. Themodel parameters are arranged to obtain a combined state-space model and are also used to calculate instantaneous power spectral density estimates. The quality enhancement is performed by a dual discrete Kalman filter that simultaneously gives estimates for the models and the signals. The system's performance has been evaluated using mean opinion score tests and the proposed technique has led to improved results.

High-Content Analysis of Breast Cancer Using Single-Cell Deep Transfer Learning

High-content analysis has revolutionized cancer drug discovery by identifying substances that alter the phenotype of a cell, which prevents tumor growth and metastasis. The high-resolution biofluorescence images from assays allow precise quantitative measures enabling the distinction of small molecules of a host cell from a tumor. In this work, we are particularly interested in the application of deep neural networks (DNNs), a cutting-edge machine learning method, to the classification of compounds in chemical mechanisms of action (MOAs). Compound classification has been performed using image-based profiling methods sometimes combined with feature reduction methods such as principal component analysis or factor analysis. In this article, we map the input features of each cell to a particular MOA class without using any treatment-level profiles or feature reduction methods. To the best of our knowledge, this is the first application of DNN in this domain, leveraging single-cell information. Furthermore, we use deep transfer learning (DTL) to alleviate the intensive and computational demanding effort of searching the huge parameter's space of a DNN. Results show that using this approach, we obtain a 30% speedup and a 2% accuracy improvement.

Proposta de solução de videovigilância para locais remotos

Desde tempos remotos que homens faziam a vigilância de bens e mercadorias e mais recentemente também de pessoas com o intuito de dissuadir roubos, atos de vandalismo e de violência. Nos últimos anos, com a evolução das novas tecnologias verificou-se a sua adoção para auxílio da vigilância. Os atos de terrorismo que têm acontecido um pouco por todo o mundo trouxeram um clima de insegurança à população mundial. Este fenómeno, juntamente com o elevado número de roubos e atos de violência levou à expansão de utilização dos meios de videovigilância de forma a dissuadir estes tipos de crime podendo mesmo, nalguns casos servir como prova para punir os autores dos mesmos. Em Portugal tem-se verificado uma escalada de crimes nas zonas mais rurais não só de bens como as alfaias agrícolas mas também de frutos e mesmo de animais. Estes crimes predominam em locais rurais, relativamente distantes das povoações e em locais onde não existem (ou são praticamente inexistentes) infraestruturas necessárias para implementar meios de videovigilância como a falta de rede elétrica e internet o que torna quase inviável a existência de sistemas de videovigilância nesses locais. Dotar esses locais das infra estruturas necessárias poderia tornar-se demasiado dispendioso e os vigilantes humanos poderiam correr riscos no meio dos montes ou noutros locais remotos para além dos seus elevados custos. Para além do problema dos roubos, existe um outro flagelo relacionado com os incêndios na floresta portuguesa, que todos os anos é dizimada pelo fogo devido a incêndios que surgem na sua maioria causados pelo homem sendo uma parte significativa os de origem criminosa. Para dar resposta a estes problemas e no sentido de vigiar e dissuadir estes tipos de crimes, iniciamos um estudo que pretende propor um protótipo de um sistema de videovigilância para locais remotos (SVR - Sistema de Videovigilância Remota) de baixo custo de forma a diminuir o número de crimes e assim minimizar os prejuízos económico e sociais causados pelos mesmos. Pretendemos estudar o problema e analisar tecnologias com potencial para propor uma solução que possa auxiliar a vigilância nesse tipo de locais com o pressuposto de poder vir a contribuir para a diminuição deste tipo de crimes devido ao seu efeito dissuasor pelo facto de se poder divulgar que estes locais já têm uma solução de vigilância oculta. A solução proposta contempla um sistema de videovigilância com uma camara construída com base num Raspberry Pi onde o vídeo é transmitido em streaming via Web através de comunicações móveis. A alimentação do sistema nestes espaços sem energia elétrica é feita através de um painel fotovoltaico. É proporcionado ao utilizador uma interface para visualizar o vídeo transmitido e um mecanismo de notificações por email. É ainda possível a visualização de imagens gravadas num cartão de memória relativas a ocorrências de deteção de movimentos. Foram realizados inúmeros testes ao protótipo SVR sendo os resultados obtidos aqui descritos.