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Previsão da demanda de potência residencial utilizando redes neurais artificiais

The traffic volume of video streaming service has increased considerably in recent years due to the success of content distributors such as YouTube and Netflix. However, limitations in network capacity and instability, impact the user Quality of Experience (QoE). In this work, a quality assessment model for video streaming service is proposed and implemented in mobile devices. The proposed model considers the spatial degradations of the video frames and the temporal interruptions. The experimental results show the impact of degradation factors on the user's QoE; emphasizing that the proposed model achieved a high correlation with subjective tests. Furthermore, the implementation on the mobile device has a low consumption in both processing capacity and energy.

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Current implementations of 5G networks consider higher frequency range of operation than previous telecommunication networks, and it is possible to offer higher data rates for different applications. On the other hand, atmospheric phenomena could have a more negative impact on the transmission quality. Thus, the study of the transmitted signal quality at high frequencies is relevant to guaranty the user ́s quality of experience. In this research, the recommendations ITU-R P.838-3 and ITU-R P.676-11 are implemented in a network scenario, which are methodologies to estimate the signal degradations originated by rainfall and atmospheric gases, respectively. Thus, speech signals are encoded by the AMR-WB codec, transmitted and the perceptual speech quality is evaluated using the algorithm described in ITU-T Rec. P.863, mostly known as POLQA. The novelty of this work is to propose a non-intrusive speech quality classifier that considers atmospheric phenomena. This classifier is based on Deep Belief Networks (DBN) that uses Support Vector Machine (SVM) with radial basis function kernel (RBF-SVM) as classifier, to identify five predefined speech quality classes. Experimental Results show that the proposed speech quality classifier reached an accuracy between 92% and 95% for each quality class overcoming the results obtained by the sole non-intrusive standard described in ITU-T Recommendation P.563. Furthermore, subjective tests are carried out to validate the proposed classifier performance, and it reached an accuracy of 94.8%.

The aim of the present study is to evaluate the energy balance and energy efficiency of the silage maize crop in the Center for Research, Development and Technology Transfer of the Universidade Federal de Lavras (CDTT-UFLA). The crop was irrigated by center pivot and the stages of maize cultivation and energy inputs were monitored for the 1st and 2nd crops of the 2014/2015 harvest. Results from the energy analysis showed the crop had a total energy input of 45,643.85 MJ ha-1 and 47,303.60 MJ ha-1 for the 1st and 2nd crops and a significant predominance of direct energy type (about 92% of the matrix). Regarding direct energy inputs, the diesel oil was the most representative, contributing with approximately 38% of the total energy demand. Conversely, the irrigation system contribute with 3.92% e 5.97% in the 1st and 2nd crops, representing the largest indirect energy input. Nevertheless, irrigation and crop management allowed the system achieving high levels of productivity, resulting in an energy efficiency of 25.1 and 28.1 for the first and second crops respectively.