Exploração de radar para reconhecimento de gestos

Communication disorders have a notable negative impact on people’s lives, leading to isolation, depression and loss of independence. Over the years, many different approaches to attenuate these problems were proposed, although most come with noticeable drawbacks. Lack of versatility, intrusive solutions or the need to carry a device around are some of the problems that these solutions encounter. Radars have seen an increase in use over the past few years and even spreading to different areas such as the automotive and health sectors. This technology is non-intrusive, not sensitive to changes in environmental conditions such as lighting, and does not intrude on the user’s privacy unlike cameras. In this dissertation and in the scope of the APH-ALARM project, the author tests the radar in a gesture recognition context to support communication in the bedroom scenario. In this scenario, the user is someone with communication problems, lying in their bed trying to communicate with a family member inside or outside the house. The use of gestures allows the user to have assistance communicating and helps express their wants or needs. To recognize the gestures executed by the user, it is necessary to capture the movement. To demonstrate the capabilities of the technology, a proof of concept system was implemented, which captures the data, filters and transforms it into images used as input for a gesture classification model. To evaluate the solution, we recorded ten repetitions of five arm gestures executed by four people. A subject independent solution proved to be more challenging when compared to a subject dependent solution, where all datasets but one achieved a median accuracy above 70% with most going over 90%.Os problemas de comunicação têm um efeito nocivo nas vidas das pessoas como isolamento, depressão e perda de independência. Ao longo dos anos, várias abordagens para atenuar estes problemas foram propostas, sendo que a maioria tem desvantagens. Falta de versatilidade, soluções intrusivas ou a necessidade de andar com um dispositivo são alguns dos problemas destas soluções. O uso de radares tem visto um aumento nos últimos anos, chegando até áreas variadas como o setor de saúde ou automóvel. Este tipo de solução é não intrusiva, não é sensível a mudanças das condições ambientais como luz e não invade a privacidade do utilizador como o uso de câmaras. Nesta dissertação e no âmbito do projeto APH-ALARM, testou-se um radar no contexto do reconhecimento de gestos para apoio à comunicação no cenário do quarto. Neste cenário, o utilizador é alguém com problemas de comunicação, que se encontra deitado na sua cama e precisa de comunicar com um familiar dentro ou fora de casa. O uso de gestos permite ao utilizador ter algum apoio durante a comunicação e ajuda o mesmo a expressar as suas necessidades. Para reconhecer os gestos feitos pelo utilizador, é necessário capturar o movimento humano. Para demonstrar as capacidades da tecnologia para este contexto, foi implementada uma prova de conceito de um sistema que captura os dados do radar e de seguida os filtra, converte-os em imagens e usa as mesmas como entrada de um modelo para classificação de gestos. Para avaliar a solução proposta, foram recolhidos dados de quatro pessoas enquanto realizavam dez repetições de cinco gestos diferentes com um dos braços. Uma solução independente do utilizador mostrou ser um caso mais desafiante quando comparada com uma solução dependente do utilizador, em que todos os datasets excepto um tem um acerto médio superior a 70% em que a maioria deles supera os 90%.Mestrado em Engenharia de Computadores e Telemátic

Consumption Management of Air Conditioning Devices for the Participation in Demand Response Programs

Demand Response has been taking over the years an extreme importance. There’s a lot of demand response programs, one of them proposed in this paper, using air conditioners that could increase the power quality and decrease the spent money in many ways like: infrastructures and customers energy bill reduction. This paper proposes a method and a study on how air conditioners could integrate demand response programs. The proposed method has been modelled as an energy resources management optimization problem. This paper presents two case studies, the first one with all costumers participating and second one with some of costumers. The results obtained for both case studies have been analyzed

Towards transactive energy systems: An analysis on current trends

This paper presents a comprehensive analysis on the latest advances in transactive energy systems. The main contribution of this work is centered on the definition of transactive energy concepts and how such systems can be implemented in the smart grid paradigm. The analyzed works have been categorized into three lines of research: (i) transactive network management; (ii) transactive control; and (iii) peer-to-peer markets. It has been found that most of the current approaches for transactive energy are available as a model, lacking the real implementation to have a complete validation. For that purpose, both scientific and practical aspects of transactive energy should be studied in parallel, implementing adequate simulation platforms and tools to scrutiny the results.This work has received funding from the European Union's Horizon 2020 research and innovation programme under project DOMINOES (grant agreement No. 771066) and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2019.info:eu-repo/semantics/publishedVersio

Hybrid-adaptive differential evolution with decay function (HyDE-DF) applied to the 100-digit challenge competition on single objective numerical optimization

In this paper, a hybrid-adaptive differential evolution with a decay function (HyDE-DF)1 is proposed for numerical function optimization. The proposed HyDE-DF is applied to the 100-Digit Challenge in a set of 10 benchmark functions. Results show that HyDE-DF can achieve a 93/100 score, proving its effectiveness for numerical optimization.This research has received funding from FEDER funds through the Operational Programme for Competitiveness and Internationalization (COMPETE 2020), under Project POCI-01-0145-FEDER-028983; by National Funds through the FCT Portuguese Foundation for Science and Technology, under Projects PTDC/EEI-EEE/28983/2017 (CENERGETIC).info:eu-repo/semantics/publishedVersio

A Statistical Analysis of Performance in the 2021 CEC-GECCO-PESGM Competition on Evolutionary Computation in the Energy Domain

Evolutionary algorithms (EAs) have emerged as an efficient alternative to deal with real-world applications with high complexity. However, due to the stochastic nature of the results obtained using EAs, the design of benchmarks and competitions where such approaches can be evaluated and compared is attracting attention in the field. In the energy domain, the “2021 CEC-GECCO-PESGM Competition on Evolutionary Computation in the Energy Domain: Smart Grid Applications” provides a platform to test and compare new EAs to solve complex problems in the field. However, the metric used to rank the algorithms is based solely on the mean fitness value (related to the objective function value only), which does not give statistical significance to the performance of the algorithms. Thus, this paper presents a statistical analysis using the Wilcoxon pair-wise comparison to study the performance of algorithms with statistical grounds. Results suggest that, for track 1 of the competition, only the winner approach (first place) is significantly different and superior to the other algorithms; in contrast, the second place is already statistically comparable to some other contestants. For track 2, all the winner approaches (first, second, and third) are statistically different from each other and the rest of the contestants. This type of analysis is important to have a deeper understanding of the stochastic performance of algorithms.This research has received funding from FEDER funds through the Operational Programme for Competitiveness and Internationalization (COMPETE 2020), under Project POCI01-0145-FEDER-028983; by National Funds through the FCT Portuguese Foundation for Science and Technology, under Projects PTDC/EEI-EEE/28983/2017(CENERGETIC),CEECIND/02814/2017, and UIDB/000760/2020.info:eu-repo/semantics/publishedVersio

Robust Energy Resource Management Incorporating Risk Analysis Using Conditional Value-at-Risk

The energy resource management (ERM) problem in today’s energy systems is complex and challenging due to the increasing penetration of distributed energy resources with uncertain behavior. Despite the improvement of forecasting tools, and the development of strategies to deal with this uncertainty (for instance, considering Monte Carlo simulation to generate a set of different possible scenarios), the risk associated with such variable resources cannot be neglected and deserves proper attention to guarantee the correct functioning of the entire system. This paper proposes a risk-based optimization approach for the centralized day-ahead ERM taking into account extreme events. Risk-neutral and risk-averse methodologies are implemented, where the risk-averse strategy considers the worst scenario costs through the conditional value-at-risk ( CVaR ) method. The model is formulated from the perspective of an aggregator that manages multiple technologies such as distributed generation, demand response, energy storage systems, among others. The case study analysis the aggregator’s management inserted in a 13-bus distribution network in the smart grid context with high penetration of renewable energy and electric vehicles. Results show an increase of nearly 4% in the day-ahead operational costs comparing the risk-neutral to the risk-averse strategy, but a reduction of up to 14% in the worst-case scenario cost. Thus, the proposed model can provide safer and more robust solutions incorporating the CVaR tool into the day-ahead management.This work was supported in part by the European Regional Development Fund (FEDER) through the Operational Program for Competitiveness and Internationalization (COMPETE 2020), under Project POCI-01-0145-FEDER-028983; and in part by the National Funds through the Fundação para a Ciância e Tecnologia (FCT) Portuguese Foundation for Science and Technology, under Project PTDC/EEI-EEE/28983/2017(CENERGETIC), Project CEECIND/02814/2017, Project UIDB/000760/2020, and Project UIDP/00760/2020.info:eu-repo/semantics/publishedVersio

Optimal strategy of electricity and natural gas aggregators in the energy and balance markets

This paper presents a stochastic two-stage model for energy aggregators (EAs) in the energy and balancing markets to supply electricity and natural gas to end-users equipped with combined heat and power (CHP) units. The suggested model takes into account the battery energy storage (BES) as a self-generating unit of EA. The upper and lower subproblems determine the optimal energy supply strategy of EA and consumption of consumers, respectively. In the lower subproblem, the McCormick relaxation is used to linearize the cost function of the CHP unit. To solve the proposed model, the two-stage problem is transformed into a linear single-stage problem using the KKT conditions of the lower subproblem, the Big M method, and the strong duality theory. The performance and efficiency of the proposed model are evaluated using a case study and three scenarios. According to the simulation results, adding CHP units to the energy-scheduling problem of BES-owned aggregators increases the profit of EA by 5.96% and decreases the cost of consumers by 1.57%.This work has received funding from the EU Horizon 2020 research and innovation program under project TradeRES (grant agreement No 864276). Pedro Faria is supported by FCT, grant CEECIND/01423/2021. The authors acknowledge the work facilities and equipment provided by GECAD research center (UIDB/00760/ 2020) to the project team.info:eu-repo/semantics/publishedVersio

Flexibility management model of home appliances to support DSO requests in smart grids

Several initiates have been taken promoting clean energy and the use of local flexibility towards a more sustainable and green economy. From a residential point of view, flexibility can be provided to operators using home-appliances with the ability to modify their consumption profiles. These actions are part of demand response programs and can be utilized to avoid problems, such as balancing/congestion, in distribution networks. In this paper, we propose a model for aggregators flexibility provision in distribution networks. The model takes advantage of load flexibility resources allowing the re-schedule of shifting/real-time home-appliances to provision a request from a distribution system operator (DSO) or a balance responsible party (BRP). Due to the complex nature of the problem, evolutionary computation is evoked and different algorithms are implemented for solving the formulation efficiently. A case study considering 20 residential houses equipped each with seven types of home-appliances is used to test and compare the performance of evolutionary algorithms solving the proposed model. Results show that the aggregator can fulfill a flexibility request from the DSO/BRP by re-scheduling the home-appliances loads for the next 24-h horizon while minimizing the costs associated with the remuneration given to end-users.The present work has been developed under the EUREKA – ITEA2 Project M2MGrids (ITEA-13011), Project SIMOCE (ANI—P2020 17690), and has received funding from FEDER Funds through COMPETE program and from National Funds through FCT under the project UIDB/00760/2020. Joao Soares is supported by FCT under CEECIND/02814/2017 grant.info:eu-repo/semantics/publishedVersio