ND2A 2020 Abstracts

Full Papers
Full Papers
Paper Nr: 2
Title:

Condition Monitoring of Elevator Systems using Deep Neural Network
Authors:

Krishna M. Mishra and Kalevi Huhtala
Abstract: In this research, we propose a generic deep autoencoder model for automatic calculation of highly informative deep features from the elevator data. Random forest algorithm is used for fault detection based on extracted deep features. Maintenance actions recorded are used to label the sensor data into healthy or faulty. In our research, we have included all fault types present for each elevator. The rest of the healthy data is used for validation of the model to prove its efficacy in terms of avoiding false positives. New extracted deep features provide 100% accuracy in fault detection along with avoiding false positives, which is better than statistical features. Random forest was also used to detect faults based on statistical features to compare results. New deep features extracted from the dataset with deep autoencoder random forest outperform the statistical features. Good classification and robustness against overfitting are key characteristics of our model. This research will help to reduce unnecessary visits of service technicians to installation sites by detecting false alarms in various predictive maintenance systems.
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Paper Nr: 5
Title:

An Improvement of Genetic Algorithm based on Dynamic Operators Rates Controlled by the Population Performance
Authors:

Beatriz F. Azevedo, Ana I. Pereira and Glaucia M. Bressan
Abstract: This work presents a hybrid approach of genetic algorithm with dynamic operators rates that adapt to the phases of the evolutionary process. The operator’s rates are controlled by the amplitude variation and standard deviation of the objective function. Besides, a new stopping criterion is presented to be used in conjunction with the proposed algorithm. The developed approach is tested with six optimization benchmark functions from the literature. The results are compared to the genetic algorithm with constant rates in terms of the number of function evaluations, the number of iterations, execution time and optimum solution analysis.
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Paper Nr: 6
Title:

Automatic Nurse Allocation based on a Population Algorithm for Home Health Care
Authors:

Filipe Alves, Ana C. Rocha, Ana I. Pereira and Paulo Leitão
Abstract: The provision of home health care services is becoming an important research area, mainly because in Portugal the population is ageing and it is necessary to perform home care services. Home care visits are organized taking into account the medical treatments and general support that elder/sick people need at home. This health service can be provided by nurses teams from Health Units, requiring some logistics for this purpose. Usually, the visits are manually planned and without computational support. The main goal of this work is to carry out the automatic nurse’s allocation of home care visits, of one Bragança Health Unit, in order to minimize the nurse’s workload balancing, spent time in all home care visits and, consequently, reduce the costs involved. The developed methodology was coded in MatLab Software and the problems were efficiently solved by the particle swarm optimization method. The nurse’s allocation solution of home care visits for the presented case study shows a significant improvement and reduction in the maximum time, in the nurse workload balancing, as well as the patients waiting time.
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Paper Nr: 7
Title:

Experiments of a New Generation Points Strategy in a Multilocal Method
Authors:

Amulya Baniya, Rui Fernandes and Florbela P. Fernandes
Abstract: Nonlinear programming problems appear frequently in industrial/real problems. It is important to obtain its solution in the lowest time possible, since the company could benefit from this. Taking this into account, a derivative free method (MCSFilter method) is addressed with a different strategy to generate the initial points to start each local search. The idea is to spread more the points so that the code execution will require a shortest amount of time when compared with the MCSFilter method execution. Some experiments were performed with simple bounds problems, equality and inequality constraint problems, chosen from a set of well known nonlinear problems. The results obtained were encouraging and with the new strategy the method needs less time to obtain the global solution.
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Paper Nr: 8
Title:

Optimal Sensors Positioning to Detect Forest Fire Ignitions
Authors:

Thadeu Brito, Ana I. Pereira, José Lima, João P. Castro and António Valente
Abstract: Forests have been harassed by fire in recent years. Whether by human action or for other reasons, the burned area has increased harming fauna and flora. It is fundamental to detect an ignition early in order to firefighters fight the fire minimizing the fire impacts. The proposed Forest Monitoring System aims to improve the nature monitoring and to enhance the existing surveillance systems. A set of innovative operations is proposed that will allow to identify a forest ignition and also will monitor the fauna. For that, a set of sensors are being developed and placed in the forest to transmit data and identify forest fire ignition. This paper addresses a methodology that identifies the optimal positions to place the developed sensors in order to minimize the fire hazard. Some preliminary results are shown by a stochastic algorithm that spread points to position the sensor modules in areas with a high risk of fire hazard.
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