Unidade de Investigação - INESCC-DL – Instituto de Engenharia de Sistemas e Computadores de Coimbra [delegação Politécnico de Leiria]
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- Analysis of Power Quality Disturbances in Industry in the Centre Region of PortugalPublication . Moreira, Licinio; Leitão, Sérgio; Vale, Zita; Galvão, João; Franco Marques, Pedro JoséPower quality issues have taken a more prominent role in power systems over the last years. These issues are of major concern for energy customers, primarily for customers with a widespread use of electronic devices in their manufacturing processes. Even though the quality of service is increasing, customers are becoming more demanding of the energy provider. This research aims to provide some industrial managers the technical support in deciding of investments in the mitigation of power quality disturbances, such as the use of less sensitive devices or the use of interface devices (UPS, DVR...) In order to recommend an appropriate solution, the problem is characterized. The technical and economic influences of the PQ disturbances in the manufacturing processes are assessed resorting to power quality audits in the customer facilities. This research covered a significant number of facilities in several industrial activities.
- An Approach to Assess the Performance of Mobile Applications: A Case Study of Multiplatform Development FrameworksPublication . Mota, Dany; Martinho, RicardoComparative studies between software multiplatform development frameworks lack a proper approach that can be replicated in future performance assessments. Moreover, there is still a deficit in performance comparison tools. Also, performance comparisons realized between mobile applications developed under these multiplatform frameworks should be done with applications running in Release Mode, which ends up not happening in most studies. The objective of this paper is thus to create a whole comparative process as correct and stable as possible, so that we can use it to safely assess performance of mobile applications developed with these frameworks. As a case study, we compare the well-known Flutter and React Native frameworks, and present the obtained results under the proposed approach. With this work, developers can not only assess both these particular frameworks, but also use the approach for further comparisons.
- Architectural Challenges on the Integration of e-Commerce and ERP Systems: A Case StudyPublication . Santos, Fábio; Martinho, RicardoMany retail companies had to go online before their Enterprise Resource Planning (ERP)-type systems were ready to fulfill all business requirements. Their overall daily operation still heavily depends on these highly customized systems often mandatory because of legal obligations, which frequently come without e-commerce “off the shelf” integration. This paper identifies main challenges derived out of the architectural and integration requirements from a case study at an e-tailer company that operates via two sales channels: online store and third-party marketplaces. These challenges led to the definition of a system architecture and implementation considerations for this common integration scenario, which was validated through its implementation. Our proposed approach allows ERP-dependent organizations to start selling online with open-source technologies, avoiding extra ERP licensing and hidden maintenance costs.
- Assessing the influence of uncertainty in land cover mapping and digital elevation models on flood risk mappingPublication . Gonçalves, Luísa M.S.; Fonte, Cidália C.; Gomes, RicardoThis paper proposes an approach to assess the influence of the uncertainty present in the parameters dependent on the land cover and elevation data over the peak flow values and the subsequent delineation of flooded areas. The proposed approach was applied to produce vulnerability and risk maps that integrate uncertainty for the urban area of Leiria, Portugal. A SPOT-4 satellite image and DEMs of the region were used. The peak flow was computed using the Soil Conservation Service method and HECHMS, HEC-RAS, Matlab and ArcGIS software programs were used. The analysis of the results obtained for the presented case study enables the identification of the order of magnitude of uncertainty associated to the watershed peak flow value and the identification of the areas which are more susceptible to flood risk to be identified.
- Blind Guide: An Ultrasound Sensor-based Body Area Network for Guiding Blind PeoplePublication . Pereira, António; Nunes, Nelson; Vieira, Daniel; Costa, Nuno; Fernandes, Hugo; Barroso, JoãoWireless Sensor Networks, in particular Wireless Body Area Networks, is a technology suggested by the research community as allowing elderly people, or people with some kind of disability, to live in a safer, responsive and comfortable environment while at their homes. One of the most active threats to the autonomous life of blind people is the quantity and variety of obstacles they face while moving, whether they are obstacles in the footpath or obstacles coming out from the walls of buildings. Hence, it is necessary to develop a solution that helps or assists blind people while moving either in indoor or outdoor scenarios, simultaneously allowing the use of the use of white cane or the Seeing Eye dog. In this article, the authors propose the use of an ultra-sound based body area network for obstacle detection and warning as a complementary and effective solution for aiding blind people when moving from place to place. According to the cost estimates of the solution and to the negligible setup time, this could be a real effective complementary solution for blind people.
- Data Acquisition and Monitoring System for Legacy Injection MachinesPublication . Silva, Bruno; Sousa, João; Alenya, GuillemNowadays, companies must embrace the concept of Digitalization and Industry 4.0 to remain competitive in the market. The reality is that most of them do not have their industrial devices prepared to access their data on a real-time basis. As most companies do not have the possibility to renew all their legacy devices and because these devices are still very productive, a retrofit solution is of high interest. In this work, we propose an affordable procedure that allows data collection and monitoring of older injection machines, as a contribution towards legacy devices integration. The developed system neither requires additional proprietary modules, nor contractual annual fees for different devices, sharing the same interface across different machine manufacturers and also contributing to uniform data collection. Evaluation was carried out in a real shop floor, monitoring the injection parameters for different machine models, validating the effectiveness of the developed system.
- Data Integration in the Brazilian Public Health System for Tuberculosis: Use of the Semantic Web to Establish InteroperabilityPublication . Pellison, Felipe Carvalho; Rijo, Rui Pedro Charters Lopes; Lima, Vinicius Costa; Crepaldi, Nathalia Yukie; Bernardi, Filipe Andrade; Galliez, Rafael Mello; Kritski, Afrânio; Abhishek, Kumar; Alves, DomingosBackground: Interoperability of health information systems is a challenge due to the heterogeneity of existing systems at both the technological and semantic levels of their data. The lack of existing data about interoperability disrupts intra-unit and inter-unit medical operations as well as creates challenges in conducting studies on existing data. The goal is to exchange data while providing the same meaning for data from different sources. Objective: To find ways to solve this challenge, this research paper proposes an interoperability solution for the tuberculosis treatment and follow-up scenario in Brazil using Semantic Web technology supported by an ontology. Methods: The entities of the ontology were allocated under the definitions of Basic Formal Ontology. Brazilian tuberculosis applications were tagged with entities from the resulting ontology. Results: An interoperability layer was developed to retrieve data with the same meaning and in a structured way enabling semantic and functional interoperability. Conclusions: Health professionals could use the data gathered from several data sources to enhance the effectiveness of their actions and decisions, as shown in a practical use case to integrate tuberculosis data in the State of São Paulo.
- A Data-Driven Approach to Forecasting Heating and Cooling Energy Demand in an Office Building as an Alternative to Multi-Zone Dynamic SimulationPublication . Godinho, Xavier; Bernardo, Hermano; Sousa, João C. de; Oliveira, Filipe T.Nowadays, as more data is now available from an increasing number of installed sensors, load forecasting applied to buildings is being increasingly explored. The amount and quality of resulting information can provide inputs for smarter decisions when managing and operating office buildings. In this article, the authors use two data-driven methods (artificial neural networks and support vector machines) to predict the heating and cooling energy demand in an office building located in Lisbon, Portugal. In the present case-study, these methods prove to be an accurate and appealing alternative to the use of accurate but time-consuming multi-zone dynamic simulation tools, which strongly depend on several parameters to be inserted and user expertise to calibrate the model. Artificial neural networks and support vector machines were developed and parametrized using historical data and different sets of exogenous variables to encounter the best performance combinations for both the heating and cooling periods of a year. In the case of support vector regression, a variation introduced simulated annealing to guide the search for different combinations of hyperparameters. After a feature selection stage for each individual method, the results for the different methods were compared, based on error metrics and distributions. The outputs of the study include the most suitable methodology for each season, and also the features (historical load records, but also exogenous features such as outdoor temperature, relative humidity or occupancy profile) that led to the most accurate models. Results clearly show there is a potential for faster, yet accurate machine-learning based forecasting methods to replace well-established, very accurate but time-consuming multi-zone dynamic simulation tools to forecast building energy consumption.
- Decision Support System to Diagnosis and Classification of Epilepsy in ChildrenPublication . Rijo, Rui; Silva, Catarina; Pereira, Luis; Gonçalves, Dulce; Agostinho, MargaridaClinical decision support systems play an important role in organizations. They have a tight relation with the information systems. Our goal is to develop a system to support the diagnosis and the classification of epilepsy in children. Around 50 million people in the world have epilepsy. Epilepsy diagnosis can be an extremely complex process, demanding considerable time and effort from physicians and healthcare infrastructures. Exams such as electroencephalograms and magnetic resonances are often used to create a more accurate diagnosis in a short amount of time. After the diagnosis process, physicians classify epilepsy according to the International Classification of Diseases, ninth revision (ICD-9). Physicians need to classify each specific type of epilepsy based on different data, e.g., types of seizures, events and exams' results. The classification process is time consuming and, in some cases, demands for complementary exams. This work presents a text mining approach to support medical decisions relating to epilepsy diagnosis and ICD-9-based classification in children. We put forward a text mining approach using electronically processed medical records, and apply the K-Nearest Neighbor technique as a white-box multiclass classifier approach to classify each instance, mapping it to the corresponding ICD-9-based standard code. Results on real medical records suggest that the proposed framework shows good performance and clear interpretations, albeit the reduced volume of available training data. To overcome this hurdle, in this work we also propose and explore ways of expanding the dataset.
- Development of CART model for prediction of tuberculosis treatment loss to follow up in the state of São Paulo, Brazil: A case–control studyPublication . Yamaguti, Verena Hokino; Alves, Domingos; Rijo, Rui Pedro Charters Lopes; Miyoshi, Newton Shydeo Brandão; Ruffino-Netto, AntônioBackground: Tuberculosis is the leading cause of infectious disease-related death, surpassing even the immunodeficiency virus. Treatment loss to follow up and irregular medication use contribute to persistent morbidity and mortality. This increases bacillus drug resistance and has a negative impact on disease control. Objective: This study aims to develop a computational model that predicts the loss to follow up treatment in tuberculosis patients, thereby increasing treatment adherence and cure, reducing efforts regarding treatment relapses and decreasing disease spread. Methods: This is a case-controlled study. Included in the data set were 103,846 tuberculosis cases from the state of São Paulo. They were collected using the TBWEB, an information system used as a tuberculosis treatment monitor, containing samples from 2006 to 2016. This set was later resampled into 6 segments with a 1-1 ratio. This ratio was used to avoid any bias during the model construction. Results: The Classification and Regression Trees were used as the prediction model. Training and test sets accounted for 70% in the former and 30% in the latter of the tuberculosis cases. The model displayed an accuracy of 0.76, F-measure of 0.77, sensitivity of 0.80 and specificity of 0.71. The model emphasizes the relationship between several variables that had been identified in previous studies as related to patient cure or loss to follow up treatment in tuberculosis patients. Conclusion: It was possible to construct a predictive model for loss to follow up treatment in tuberculosis patients using Classification and Regression Trees. Although the fact that the ideal predictive ability was not achieved, it seems reasonable to propose the use of Classification and Regression Trees models to predict likelihood of treatment follow up to support healthcare professionals in minimising the loss to follow up.
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