Loading...
11 results
Search Results
Now showing 1 - 10 of 11
- Artificial intelligence applications and innovations: day-to-day life impactPublication . Rodrigues, João; Cardoso, Pedro; Chinnici, MartaThe idea of an intelligent machine has fascinated humans for centuries. But what is intelligence? Some define it as the capacity for learning, reasoning, understanding or, from a different perspective, the aptitude to grasp truths, relationships, facts, or meanings. All these perspectives require the capacity to acquire data from the surrounding world and, possibly, act over that environment. In short, the building of more or less autonomous agents, served with sensors and actuators, capable of learning and producing educated answers has been long foreseen. New trends in intelligente systems comprise, among other aspects, pervasive robotization, ubiquitous online data access, empowered edge computing, smart spaces, and digital ethics. These trends build the research on “Artificial Intelligence Applications and Innovation”, impacting our day-to-day life, our cities, and even our free time. Nevertheless, artificial intelligence (AI) is still closely associated with some popular misconceptions that cause the public to either have unrealistic fears about it or to have unrealistic expectations about how it will change our workplace and life in general. It is important to show that such fears are unfounded and that new trends, innovations, technologies, and smart systems will be able to improve the way we live, benefiting society without replacing humans in their core activities.
- Anomaly detection of consumption in Hotel Units: A case study comparing isolation forest and variational autoencoder algorithmsPublication . Mendes, Tomás; Cardoso, Pedro; Monteiro, Jânio; Raposo, JoãoBuildings are responsible for a high percentage of global energy consumption, and thus, the improvement of their efficiency can positively impact not only the costs to the companies they house, but also at a global level. One way to reduce that impact is to constantly monitor the consumption levels of these buildings and to quickly act when unjustified levels are detected. Currently, a variety of sensor networks can be deployed to constantly monitor many variables associated with these buildings, including distinct types of meters, air temperature, solar radiation, etc. However, as consumption is highly dependent on occupancy and environmental variables, the identification of anomalous consumption levels is a challenging task. This study focuses on the implementation of an intelligent system, capable of performing the early detection of anomalous sequences of values in consumption time series applied to distinct hotel unit meters. The development of the system was performed in several steps, which resulted in the implementation of several modules. An initial (i) Exploratory Data Analysis (EDA) phase was made to analyze the data, including the consumption datasets of electricity, water, and gas, obtained over several years. The results of the EDA were used to implement a (ii) data correction module, capable of dealing with the transmission losses and erroneous values identified during the EDA’s phase. Then, a (iii) comparative study was performed between a machine learning (ML) algorithm and a deep learning (DL) one, respectively, the isolation forest (IF) and a variational autoencoder (VAE). The study was made, taking into consideration a (iv) proposed performance metric for anomaly detection algorithms in unsupervised time series, also considering computational requirements and adaptability to different types of data. (v) The results show that the IF algorithm is a better solution for the presented problem, since it is easily adaptable to different sources of data, to different combinations of features, and has lower computational complexity. This allows its deployment without major computational requirements, high knowledge, and data history, whilst also being less prone to problems with missing data. As a global outcome, an architecture of a platform is proposed that encompasses the mentioned modules. The platform represents a running system, performing continuous detection and quickly alerting hotel managers about possible anomalous consumption levels, allowing them to take more timely measures to investigate and solve the associated causes.
- A decision-support system to Analyse Customer Satisfaction Applied to a Tourism Transport ServicePublication . Ramos, Celia; Cardoso, Pedro; Fernandes, Hortênsio C. L.; Rodrigues, JoãoDue to the perishable nature of tourist products, which impacts supply and demand, the possibility of analysing the relationship between customers’ satisfaction and service quality can contribute to increased revenues. Machine learning techniques allow the analysis of how these services can be improved or developed and how to reach new markets, and look for the emergence of ideas to innovate and improve interaction with the customer. This paper presents a decision-support system for analysing consumer satisfaction, based on consumer feedback from the customer’s experience when transported by a transfer company, in the present case working in the Algarve region, Portugal. The results show how tourists perceive the service and which factors influence their level of satisfaction and sentiment. One of the results revealed that the first impression associated with good news is what creates the most value in the experience, i.e., “first impressions matter”..
- Cultural heritage visits supported on visitors' preferences and mobile devicesPublication . Cardoso, Pedro; Rodrigues, Joao; Pereira, Joao; Nogin, Sergey; Lessa, Joana; Ramos, Celia; Bajireanu, Roman; Gomes, Miguel; Bica, PauloMonuments, museums and cities are great places to feel and experience neat and interesting things. But cultural heritage is experienced differently by different visitors. The more erudite may know beforehand what they intend to explore, while the least literate usually know and are capable of expressing some of their preferences but do not exactly realize what to see and explore. This paper proposes the use of a mobile application to set an itinerary where you can move at your own pace and, at the same time, have all the complementary information you need about each of the points of interest. The application is designed in face of an adaptive user interface where the routing and augmented reality are connected to acknowledge the needs of different user categories, such as elders, kids, experts or general users
- A digital twin of charging stations for fleets of electric vehiclesPublication . Francisco, André; Monteiro, Jânio; Cardoso, PedroThe increasing concern over the environmental impact of fossil fuels and associated CO2 emissions created a growing interest on the use of electric vehicles (EVs) and green energy utilization. In this context, the widespread adoption of EVs should be accompanied by the introduction of generation from renewable energy sources (RES). That insertion, at the distribution level, presents challenges that result from their intermittent nature, requiring demand-response measures that can be addressed by adjusting the charging processes to match the available power. In the framework of EVs renting companies, it is essential to have an efficient charging management that allows achieving high levels of self-consumption and self-sufficiency, lower operational costs and lower payback periods for the investments made. The utilization of digital twins (DTs) can be key to achieve those goals, providing accurate simulations and predictions. Their use in the context of EV charging can offer valuable insights into optimizing charging scheduling and predicting energy demands, taking into consideration distinct scenarios. This paper presents the work done to implement DTs of a set of charging stations (CSs) and EVs, which allow the modeling and improved management of the charging processes of EV fleets, for a set of CSs, integrating RES. In this charging context, experimental results using the DT were applied considering a predicted mobility. The applied scenarios supported an effective and optimized managing performance, reaching low paybacks and high self-sufficiency values. The obtained results show that this method is a viable and cost-effective solution for companies renting EVs.
- Hybrid neural network based models for evapotranspiration prediction over limited weather parametersPublication . Vaz, Pedro J.; Schutz, G.; Guerrero, Carlos; Cardoso, PedroEvapotranspiration can be used to estimate the amount of water required by agriculture projects and green spaces, playing a key role in water management policies that combat the hydrological drought, which assumes a structural character in many countries. In this context, this work presents a study on reference evapotranspiration (ETo) estimation models, having as input a limited set of meteorological parameters, namely: temperature, humidity, and wind. Since solar radiation (SR) is an important parameter in the determination of ETo, SR estimation models are also developed. These ETo and SR estimation models compare the use of Artificial Neural Networks (ANN), Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), and hybrid neural network models such as LSTM-ANN, RNN-ANN, and GRU-ANN. Two main approaches were taken for ET(o )estimation: (i) directly use those algorithms to estimate ETo, and (ii) estimate solar radiation first and then use that estimation together with other meteorological parameters in a method that predicts ETo. For the latter case, two variants were implemented: the use of the estimated solar radiation as (ii.1) a feature of the neural network regressors, and (ii.2) the use of the Penman-Monteith method (a.k.a. FAO-56PM method, adopted by the United Nations Food and Agriculture Organization) to compute ETo, which has solar radiation as one of the input parameters. Using experimental data collected from a weather station (WS) located in Vale do Lobo (Portugal), the later approach achieved the best result with a coefficient of determination (R-2) of 0.977. The developed model was then applied to data from eleven stations located in Colorado (USA), with very distinct climatic conditions, showing similar results to the ones for which the models were initially designed ((R2) > 0.95), proving a good generalization. As a final notice, the reduced-set features were carefully selected so that they are compatible with free online weather forecast services.
- A study on the prediction of evapotranspiration using freely available meteorological dataPublication . J. Vaz, Pedro; Schütz, Gabriela; Guerrero, Carlos; Cardoso, PedroDue to climate change, the hydrological drought is assuming a structural character with a tendency to worsen in many countries. The frequency and intensity of droughts is predicted to increase, particularly in the Mediterranean region and in Southern Africa. Since a fraction of the fresh water that is consumed is used to irrigate urban fabric green spaces, which are typically made up of gardens, lanes and roundabouts, it is urgent to implement water waste prevention policies. Evapotranspiration (ETO) is a measurement that can be used to estimate the amount of water being taken up or used by plants, allowing a better management of the watering volumes but, the exact computation of the evapotranspiration volume is not possible without using complex and expensive sensor systems. In this study, several machine learning models were developed to estimate reference evapotranspiration and solar radiation from a reducedfeature dataset, such has temperature, humidity, and wind. Two main approaches were taken: (i) directly estimate ETO, or (ii) previously estimate solar radiation and then inject it into a function or method that computes ETO. For the later case, two variants were implemented, namely the use of the estimated solar radiation as (ii.1) a feature of the machine learning regressors and (ii.2) the use of FAO-56PM method to compute ETO, which has solar radiation as one of the input parameters. Using experimental data collected from a weather station located in Vale do Lobo, south Portugal, the later approach achieved the best result with a coefficient of determination (R 2 ) of 0.975 over the test dataset. As a final notice, the reduced-set features were carefully selected so that they are compatible with online freely available weather forecast services.
- Modular dynamic neural network: a continual learning architecturePublication . Turner, Daniel; Cardoso, Pedro; Rodrigues, JoãoLearning to recognize a new object after having learned to recognize other objects may be a simple task for a human, but not for machines. The present go-to approaches for teaching a machine to recognize a set of objects are based on the use of deep neural networks (DNN). So, intuitively, the solution for teaching new objects on the fly to a machine should be DNN. The problem is that the trained DNN weights used to classify the initial set of objects are extremely fragile, meaning that any change to those weights can severely damage the capacity to perform the initial recognitions; this phenomenon is known as catastrophic forgetting (CF). This paper presents a new (DNN) continual learning (CL) architecture that can deal with CF, the modular dynamic neural network (MDNN). The presented architecture consists of two main components: (a) the ResNet50-based feature extraction component as the backbone; and (b) the modular dynamic classification component, which consists of multiple sub-networks and progressively builds itself up in a tree-like structure that rearranges itself as it learns over time in such a way that each sub-network can function independently. The main contribution of the paper is a new architecture that is strongly based on its modular dynamic training feature. This modular structure allows for new classes to be added while only altering specific sub-networks in such a way that previously known classes are not forgotten. Tests on the CORe50 dataset showed results above the state of the art for CL architectures.
- Development and implementation of a smart charging system for electric vehicles based on the ISO 15118 standardPublication . Santos, Joni; Francisco, André; Cabrita, Cristiano Lourenço; Monteiro, Jânio; Pacheco, André; Cardoso, PedroThere is currently exponential growth in the electric vehicle market, which will require an increase in the electrical grid capacity to meet the associated charging demand. If, on the one hand, the introduction of energy generation from renewable energy sources can be used to meet that requirement, the intermittent nature of some of these sources will challenge the mandatory real-time equilibrium between generation and consumption. In order to use most of the energy generated via these sources, mechanisms are required to manage the charging of batteries in electric vehicles, according to the levels of generation. An effective smart charging process requires communication and/or control mechanisms between the supply equipment and the electric vehicle, enabling the adjustment of the energy transfer according to the generation levels. At this level, the ISO 15118 standard supports high-level communication mechanisms, far beyond the basic control solutions offered through the IEC 61851-1 specification. It is, thus, relevant to evaluate it in smart charging scenarios. In this context, this paper presents the development of a charge emulation system using the ISO 15118 communication protocol, and it discusses its application for demand response purposes. The system comprises several modules developed at both ends, supply equipment and electric vehicles, and allows the exchange of data during an emulated charging process. The system also includes human interfaces to facilitate interactions with users at both ends. Tests performed using the implemented system have shown that it supports a demand response when integrated with a photovoltaic renewable energy source. The dynamic adjustment to charging parameters, based on real-time energy availability, ensures efficient and sustainable charging processes, reducing the reliance on the grid and promoting the use of renewable energy.
- Harnessing AI and NLP tools for innovating brand name generation and evaluation: a comprehensive reviewPublication . Lemos, Marco; Cardoso, Pedro; Rodrigues, JoaoThe traditional approach of single-word brand names faces constraints due to trademarks, prompting a shift towards fusing two or more words to craft unique and memorable brands, exemplified by brands such as SalesForce (c) or SnapChat (c). Furthermore, brands such as Kodak (c), Xerox (c), Google (c), H & auml;agen-Dazs (c), and Twitter (c) have become everyday names although they are not real words, underscoring the importance of brandability in the naming process. However, manual evaluation of the vast number of possible combinations poses challenges. Artificial intelligence (AI), particularly natural language processing (NLP), is emerging as a promising solution to address this complexity. Existing online brand name generators often lack the sophistication to comprehensively analyze meaning, sentiment, and semantics, creating an opportunity for AI-driven models to fill this void. In this context, the present document reviews AI, NLP, and text-to-speech tools that might be useful in innovating the brand name generation and evaluation process. A systematic search on Google Scholar, IEEE Xplore, and ScienceDirect was conducted to identify works that could assist in generating and evaluating brand names. This review explores techniques and datasets used to train AI models as well as strategies for leveraging objective data to validate the brandability of generated names. Emotional and semantic aspects of brand names, which are often overlooked in traditional approaches, are discussed as well. A list with more than 75 pivotal datasets is presented. As a result, this review provides an understanding of the potential applications of AI, NLP, and affective computing in brand name generation and evaluation, offering valuable insights for entrepreneurs and researchers alike.